An intelligent dynamic configuration management system and method

Abstract

The application relates to the technical field of software configuration management, in particular to an intelligent dynamic configuration management system and method, which comprises a configuration center server, a monitoring module, a data processing module, an intelligent decision module, a configuration pushing module and a client integration module; the configuration center server is used for responding to a registration request of a client and generating an initial basic configuration; the monitoring module is used for collecting node port data of each network node based on a data collection mechanism; the data processing module is used for determining a business peak period, a business load pressure characteristic value, a potential performance bottleneck and a user behavior mode; the intelligent decision module is used for determining a target update configuration and generating a configuration adjustment instruction; the configuration pushing module is used for pushing the target update configuration; and the client integration module is used for receiving the target update configuration and performing local update based on the target update configuration. The application can dynamically adjust system configuration parameters in real time according to multi-dimensional factors such as business demand, running environment and user behavior, and realizes accurate, flexible and efficient configuration management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of software configuration management, and particularly relates to an intelligent dynamic configuration management system and method. BACKGROUND

[0002] In the current digital era, software systems face many challenges such as rapid iteration, changing business requirements, complex operating environments, etc. The traditional configuration management method gradually exposes the following problems:

[0003] Complicated configuration update: modifying configuration usually requires manual editing of configuration files, and then restarting the system or service. The whole process is time-consuming and laborious, and restarting during the peak period of business may cause service interruption, seriously affecting user experience.

[0004] Difficult multi-environment adaptation: a set of systems often need to run in development, testing, pre-release, production and other different environments. The configurations of different environments are quite different, and manual maintenance of multiple sets of configuration files is prone to errors, and it is difficult to ensure the consistency and accuracy of the configuration.

[0005] Lagging response to business changes: frequent changes in business requirements require system configuration to be adjusted in a timely manner, but the traditional configuration management method is difficult to respond quickly, resulting in the system failing to adapt to new business rules and market changes in a timely manner.

[0006] Lack of intelligent adjustment capability: traditional configuration management systems cannot automatically adjust configurations based on real-time data and user behavior during system operation, and cannot achieve fine-grained performance optimization and resource allocation.

[0007] In the field of software development and operation, configuration management is a key link to ensure the efficient and stable operation of the system. Therefore, there is an urgent need for a method that can dynamically adjust system configuration parameters in real time based on business requirements, operating environments, user behavior and other multi-dimensional factors, to achieve precise, flexible and efficient configuration management. SUMMARY

[0008] To this end, the present application provides an intelligent dynamic configuration management system and method to overcome the problems of difficulty in adapting to multi-dimensional environmental factors, slow configuration update, and inability to respond to business changes in the prior art.

[0009] To achieve the above purpose, on the one hand, the present application provides an intelligent dynamic configuration management system, comprising:

[0010] a configuration center server, configured to respond to a registration request of a client and receive registration information, and generate an initial basic configuration based on the registration information and a preset configuration rule library;

[0011] a monitoring module connected with the configuration center server, configured to determine a data collection mechanism based on the registration information, and collect node port data of each network node based on the data collection mechanism, wherein the node port data comprises node service data, node operation data and node user behavior data, and the data collection mechanism comprises a periodic collection mechanism and an event-triggered mechanism;

[0012] a data processing module connected with the monitoring module, configured to determine a service peak period and a service load pressure representation value based on node service data of each network node in a target time period, determine a potential performance bottleneck based on node operation data of each network node, and determine a user behavior mode based on node user behavior data of each network node;

[0013] an intelligent decision module connected with the configuration center server and the data processing module respectively, configured to determine a service development trend based on the service peak period and the service load pressure representation value, determine a resource configuration requirement based on the potential performance bottleneck and the user behavior mode, update the initial basic configuration based on the preset configuration rule library, the service development trend and the resource configuration requirement to determine a target updated configuration, and generate a configuration adjustment instruction;

[0014] a configuration pushing module connected with the intelligent decision module, configured to determine a configuration pushing mode based on the configuration adjustment instruction, and push the target updated configuration based on the configuration adjustment instruction and the configuration pushing mode;

[0015] a client integration module connected with the configuration pushing module and the configuration center server respectively, configured to receive the target updated configuration, determine whether the target updated configuration meets a preset standard, perform local update based on the target updated configuration, and send an update state to the configuration center server.

[0016] Further, the monitoring module comprises a monitoring analysis submodule and a plurality of monitoring submodules, wherein,

[0017] the monitoring analysis module is connected with the configuration center server, configured to determine a client network node corresponding to a client based on the registration information, and determine a data collection mechanism based on the client network node;

[0018] each monitoring submodule is connected with the monitoring analysis module respectively, and collects node port data of each network node based on the data collection mechanism.

[0019] Further, the data processing module comprises:

[0020] a preprocessing submodule connected with the monitoring module, configured to preprocess the node port data to obtain key node port data, including key service data, key operation data and key user behavior data;

[0021] a service data analysis submodule connected with the preprocessing submodule, configured to determine a service peak period based on the key service data of each network node in a target time period, and determine a service load pressure representation value based on a distribution of the service peak period;

[0022] an operation data analysis submodule connected with the preprocessing submodule, configured to determine a plurality of associated nodes based on the node operation data of each network node, and perform aggregated analysis based on the node operation data of each associated node to determine a potential performance bottleneck;

[0023] a behavior data analysis submodule connected with the preprocessing submodule, configured to construct a user behavior analysis model based on historical user behavior data of each network node, and determine a user behavior pattern based on the node user behavior data and the user behavior analysis model.

[0024] Further, the intelligent decision module comprises:

[0025] a service development analysis submodule connected with the data processing module, configured to determine a service development trend based on the node service data of each network node in the service peak period and the service load pressure representation value;

[0026] a resource configuration analysis submodule connected with the data processing module, configured to determine a user configuration requirement based on the user behavior pattern, and determine a resource configuration requirement based on the user configuration requirement and the potential performance bottleneck;

[0027] a configuration update submodule connected with the configuration center server, the service development analysis submodule and the resource configuration analysis submodule, configured to determine an adjustment configuration rule based on the service development trend and the resource configuration requirement, and update the initial basic configuration based on the adjustment configuration rule and the preset configuration rule library to determine a target update configuration;

[0028] an adjustment instruction generation submodule connected with the configuration update submodule, configured to generate a configuration adjustment instruction based on the target update configuration.

[0029] Further, the configuration pushing module comprises:

[0030] a pushing mode determination submodule connected with the intelligent decision module, configured to determine a configuration pushing mode based on the key node port data corresponding to the client network node.

[0031] a pushing sub-module, connected with the pushing mode determining sub-module, configured to push the target update configuration based on the configuration adjustment instruction and the configuration pushing mode.

[0032] Further, the client integration module comprises:

[0033] a configuration receiving sub-module, connected with the configuration pushing module, configured to receive the target update configuration;

[0034] a configuration determining sub-module, connected with the configuration receiving sub-module, configured to determine a check representation value based on the target update configuration, and determine whether the target update configuration meets a preset standard based on a comparison result of the check representation value and a preset representation value;

[0035] an updating sub-module, connected with the configuration determining sub-module, configured to perform local updating based on the target update configuration under a first determining condition, and send an update status to the configuration center server, wherein the first determining condition is that the target update configuration meets the preset standard.

[0036] Further, the service data analysis sub-module divides the target time period into a plurality of time sub-periods, determines a service index threshold based on historical service data, and determines a service peak period based on a comparison result of key service data of each network node in each time sub-period and the service index threshold.

[0037] Further, the behavior data analysis sub-module clusters historical user behavior data of each network node to obtain a plurality of cluster groups, performs correlation analysis on historical user behavior data in the same cluster group to determine a plurality of associated user behavior data, generates a corresponding training sample set based on each associated user behavior data, trains a behavior analysis model based on the training sample set, and constructs a user behavior analysis model.

[0038] Further, the service development analysis sub-module determines a service peak representation value based on node service data of each network node in the service peak period, and determines a service development trend based on the service peak representation value and the service load pressure representation value.

[0039] In another aspect, the present application also provides an intelligent dynamic configuration management method, comprising:

[0040] generating an initial basic configuration based on the registration information and a preset configuration rule library in response to the configuration center server receiving a registration request and registration information sent by the client;

[0041] determine a data collection mechanism based on the registration information, and collect node port data of each network node based on the data collection mechanism, wherein the node port data comprises node service data, node operation data and node user behavior data, and the data collection mechanism comprises a periodic collection mechanism and an event triggering mechanism;

[0042] determine a service peak period and a service load pressure representation value based on the node service data of each network node in a target time period, determine a potential performance bottleneck based on the node operation data of each network node, and determine a user behavior mode based on the node user behavior data of each network node;

[0043] determine a service development trend based on the service peak period and the service load pressure representation value, and determine a resource configuration requirement based on the potential performance bottleneck and the user behavior mode;

[0044] update the initial basic configuration based on the preset configuration rule library, the service development trend and the resource configuration requirement to determine a target updated configuration, and generate a configuration adjustment instruction;

[0045] determine a configuration pushing mode based on the configuration adjustment instruction, push the target updated configuration based on the configuration adjustment instruction and the configuration pushing mode, and determine whether the target updated configuration meets a preset standard;

[0046] if the preset standard is met, perform local updating based on the target updated configuration, and send an updating state to the configuration center server.

[0047] Compared with the prior art, the application has the beneficial effects that: by setting the configuration center server to centrally manage the registration information and initial basic configuration of the client, the application facilitates unified management and maintenance, and reduces the complexity of configuration management; based on the registration information and the preset configuration rule library, the initial basic configuration can be generated according to the needs of different clients to generate personalized configuration, thereby improving the flexibility and adaptability of the configuration; as the center node of the system, the configuration rule library can be updated and expanded subsequently, without the need to modify the configuration of each client individually, thereby reducing the maintenance cost of the system; by setting the monitoring module to collect the node port data of each network node in real time, the periodic collection mechanism and the event triggering mechanism are supported, the appropriate collection mode can be selected according to different data types and business needs, and the efficiency and accuracy of data collection are improved; by setting the data processing module, based on the node business data in the target time period, the business peak period and the business load pressure representation value can be accurately determined, thereby providing data support for the prediction of the business development trend; by analyzing the node running data, potential performance bottlenecks can be quickly located, thereby improving the reliability of the system; based on the node user behavior data, the user behavior mode is determined, which helps to deeply understand the user needs and behavior habits, and provides data support for intelligent decision-making; by setting the intelligent decision-making module, based on the business peak period and the business load pressure representation value, the business development trend can be accurately predicted, which helps to avoid performance bottlenecks during the business peak period; in combination with the potential performance bottlenecks and the user behavior mode, the resource configuration demand can be reasonably determined to ensure efficient use of system resources, while avoiding resource waste and excessive configuration; based on the preset configuration rule library, the business development trend and the resource configuration demand, the initial basic configuration is dynamically updated to generate a target update configuration, and a configuration adjustment instruction is generated, thereby realizing the automated management and dynamic adjustment of the configuration, and improving the flexibility and adaptability of the system; by setting the configuration pushing module, the configuration pushing mode can be determined according to the configuration adjustment instruction, and the target update configuration can be quickly and accurately pushed to the client, thereby ensuring that the client can timely obtain the latest configuration information, and improving the response speed and efficiency of the system; a variety of configuration pushing modes are supported, and the appropriate pushing mode can be selected according to different business needs and network environments, thereby improving the flexibility and reliability of the configuration pushing; the configuration pushing process is automatically completed, the possibility of manual intervention and operation errors is reduced, and the stability and reliability of the system are improved; by setting the client integration module, after receiving the target update configuration, it can be determined whether the configuration meets the preset standard, and the update status is sent to the configuration center server, thereby realizing the closed-loop management of the configuration update, and ensuring the accuracy and effectiveness of the configuration; based on the target update configuration, local update is performed, which can quickly adapt to changes in system configuration, improve the flexibility and adaptability of the client, and also ensure the consistency of the client and server configurations.

[0048] Further, the monitoring module of the present application sets a monitoring analysis module to determine the client network node corresponding to the client based on the registration information, ensures the pertinence and accuracy of data collection, avoids resource waste and data redundancy, flexibly determines the data collection mechanism according to the characteristics and business needs of the network node, supports periodic collection and event-triggered collection, and meets the monitoring needs in different scenarios. Each monitoring submodule is responsible for data collection of different network nodes, realizes distributed collection, improves the efficiency and reliability of data collection, minimizes the occupation of network node resources during the collection process, ensures the normal operation of the network node, and ensures the timeliness and accuracy of the monitoring data. The cooperative work of the monitoring analysis submodule and each monitoring submodule realizes accurate positioning, flexible mechanism selection and efficient execution of data collection, improves the overall performance, reliability and user experience of the monitoring module, and reduces the maintenance cost and management complexity.

[0049] Further, the data processing module of the present application sets a preprocessing submodule, which performs preprocessing processes such as cleaning, conversion and aggregation on node port data, removes noise data and invalid data, and uniformly converts data of different sources and formats into a format suitable for analysis, so as to obtain key node port data, which can ensure the quality and consistency of the data and facilitate subsequent data processing. By setting a business data analysis submodule, based on the key business data in the target time period, the business peak period can be accurately identified, which provides a basis for resource planning and performance optimization; based on the distribution of the business peak period, the business load pressure representation value is determined, which can accurately quantify and evaluate the load capacity of the system in the peak period, and realize accurate prediction of business load. By running the data analysis submodule, based on the running data of each network node, the associated nodes can be accurately identified, which provides a basis for the positioning of performance bottlenecks; by aggregating and analyzing the running data of the associated nodes, the potential performance bottlenecks can be accurately determined. By setting a behavior data analysis submodule, a user behavior analysis model is constructed based on historical user behavior data, which provides a basis for identifying user behavior patterns; through the user behavior analysis model, the user behavior pattern of the user can be accurately identified, thereby providing accurate decision basis.

[0050] Further, the intelligent decision module of the application can accurately predict the business development trend by setting a business development analysis submodule based on the node business data and business load pressure representation value in the peak period. By setting a resource configuration analysis submodule, the user configuration demand is accurately determined based on the user behavior pattern, and a personalized configuration scheme is provided for the user. In combination with the user configuration demand and potential performance bottleneck, the resource configuration demand is reasonably determined, the resource configuration is dynamically adjusted, and the flexibility and adaptability of the system are improved. By setting a configuration update submodule, the configuration rules are dynamically adjusted based on the business development trend and resource configuration demand, the real-time and adaptability of the system configuration are ensured, the system configuration after the configuration update is consistent with the business demand and performance requirement, configuration conflicts and errors are avoided, the adjustment configuration rules are quickly generated, and the configuration update is completed based on the preset configuration rule library, thereby reducing the update time. By setting an adjustment instruction generation submodule, accurate configuration adjustment instructions are generated based on the target update configuration, the accurate execution of the configuration update is ensured, the generated configuration adjustment instructions are optimized, the conciseness and efficiency of the instructions are ensured, the execution time is reduced, and the response speed of the system is improved.

[0051] Further, the configuration push module of the application can dynamically adjust the push mode based on the configuration adjustment instructions and the key node port data corresponding to the client network node by setting a push mode determination submodule, thereby ensuring the efficiency and reliability of the configuration push.

[0052] Further, the client integration module of the application sets a configuration receiving submodule to receive the target update configuration sent from the configuration push module, thereby ensuring the complete reception of the configuration data. By setting a configuration judgment submodule, a check representation value is generated based on the target update configuration, the generated check representation value is compared with the preset representation value, it is judged whether the target update configuration meets the preset standard, and the integrity and accuracy of the configuration data are ensured. By setting an update submodule, the local update is performed based on the target update configuration in the case that the target update configuration meets the preset standard, the accuracy and consistency of the system configuration are ensured, the update state is sent to the configuration center server, the closed-loop management is completed, and the configuration center server can real-time understand the update situation of the client. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The structure block diagram of the intelligent dynamic configuration management system of the embodiment of the application is shown in the figure;

[0054] Figure 2 The structure block diagram of the data processing module of the embodiment of the application is shown in the figure;

[0055] Figure 3 The structure block diagram of the intelligent decision module of the embodiment of the application is shown in the figure;

[0056] Figure 4The structural block diagram of the push module configured for the embodiment of the present application is shown in the figure;

[0057] Figure 5 The structural block diagram of the client integration module of the embodiment of the present application is shown in the figure;

[0058] Figure 6 The flowchart of the intelligent dynamic configuration management method of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0059] In order to make the objects and advantages of the present application clearer, the present application will be further described in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0060] The preferred embodiments of the present application are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that the embodiments are only used to explain the technical principles of the present application and are not used to limit the protection scope of the present application.

[0061] It should be noted that, in the description of the present application, the terms of direction or position relationship such as "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or position relationship shown in the drawings, which is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present application.

[0062] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through an intermediate medium, or the communication inside two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0063] Please refer to Figures 1-5 shown, Figure 1 The structural block diagram of the intelligent dynamic configuration management system of the embodiment of the present application is shown in the figure; Figure 2 The structural block diagram of the data processing module of the embodiment of the present application is shown in the figure; Figure 3 The structural block diagram of the intelligent decision module of the embodiment of the present application is shown in the figure; Figure 4 The structural block diagram of the push module configured for the embodiment of the present application is shown in the figure; Figure 5 The structural block diagram of the client integration module of the embodiment of the present application is shown in the figure. The embodiment of the present application provides an intelligent dynamic configuration management system, which comprises:

[0064] a configuration center server, configured to respond to a registration request of a client and receive registration information, and generate an initial basic configuration based on the registration information and a preset configuration rule library;

[0065] In implementation, the configuration center server serves as a control core of the entire system, responsible for storing and managing all configuration information, including but not limited to basic configuration parameters (such as database connection information, service port, etc.), business rule configuration (such as promotion activity rule, permission control strategy, etc.), and environment-specific configuration (differential configuration for different deployment environments). The configuration center server adopts a high-availability architecture, ensuring the security and reliability of configuration data through data backup, multi-network node redundancy, and other mechanisms, and has strong data retrieval and update capabilities, capable of quickly responding to configuration requests and update operations of the client.

[0066] In a specific embodiment, the configuration center server adopts a distributed storage architecture, storing configuration data in multiple network nodes, and ensuring data consistency among network nodes through a data consistency protocol, while having an automatic fault recovery function, capable of automatically switching to a backup node when a network node fails, ensuring uninterrupted operation of the configuration service.

[0067] It can be understood that when the system is started for the first time or the client accesses for the first time, the client sends a registration request and registration information to the configuration center server through the client integration module, and the registration information includes a unique identifier of the client, running environment information, a list of required business modules, etc. The configuration center server matches the corresponding initial configuration template in the preset configuration rule library according to the registration information provided by the client, generates the corresponding initial basic configuration, and pushes the configuration data to the client integration module for storage.

[0068] a monitoring module connected to the configuration center server, configured to determine a data collection mechanism based on the registration information, and collect node port data of each network node based on the data collection mechanism, wherein the node port data includes node business data, node running data, and node user behavior data, and the data collection mechanism includes a periodic collection mechanism and an event-triggered mechanism;

[0069] Specifically, the monitoring module includes a monitoring analysis submodule and a plurality of monitoring submodules, wherein,

[0070] the monitoring analysis module is connected to the configuration center server, configured to determine a client network node corresponding to the client based on the registration information, and determine a data collection mechanism based on the client network node;

[0071] Each of the monitoring sub-modules is connected to the monitoring analysis module, and collects node port data of each network node based on the data collection mechanism.

[0072] In implementation, the client network node can be any of the network nodes, and the registration information includes the unique identifier of the client, and the client network node corresponding to the client can be determined according to the unique identifier of the client. The actual implementer can set the data collection mechanism corresponding to each network node based on the actual situation or the actual running situation of each network node in historical data. For the periodic collection mechanism, the collection period can be set based on the traffic of the client. The sampling period is negatively correlated with the traffic of the client, that is, the more the traffic of the client, the smaller the sampling period. For the event-triggered mechanism, the actual implementer can set the trigger event based on the actual situation, for example, a special time point, a specific environment, etc. It can be understood that the data collection mechanism can also set polling collection, log analysis collection, etc., which can adapt to different data sources and business scenarios.

[0073] It can be understood that each network node is set with a unique corresponding monitoring sub-module, and each monitoring sub-module collects node port data based on the determined data collection mechanism. The node business data includes order quantity, user access quantity, etc., the node running data includes CPU usage, memory occupation, network delay, etc., and the node user behavior data includes user operation path, dwell time, etc.

[0074] The monitoring module sets the monitoring analysis module to determine the client network node corresponding to the client based on the registration information, ensures the pertinence and accuracy of data collection, avoids resource waste and data redundancy, flexibly determines the data collection mechanism according to the characteristics and business needs of the network node, supports periodic collection and event-triggered collection, and meets the monitoring needs in different scenarios. Each monitoring sub-module independently collects data of different network nodes, realizes distributed collection, improves the efficiency and reliability of data collection, reduces the occupation of network node resources as much as possible during the collection process, ensures the normal operation of the network node, and ensures the timeliness and accuracy of the monitoring data. The cooperative work of the monitoring analysis sub-module and each monitoring sub-module realizes accurate positioning, flexible mechanism selection and efficient execution of data collection, improves the overall performance, reliability and user experience of the monitoring module, and reduces the maintenance cost and management complexity.

[0075] The data processing module is connected to the monitoring module, and is used to determine a business peak period and a business load pressure representation value based on node business data of each network node in a target time period, determine a potential performance bottleneck based on node running data of each network node, and determine a user behavior mode based on node user behavior data of each network node.

[0076] Specifically, the data processing module comprises:

[0077] a preprocessing submodule connected with the monitoring module, configured to preprocess the node port data to obtain key node port data, including key service data, key operation data and key user behavior data;

[0078] In implementation, preprocessing includes cleaning, conversion, aggregation and other operations on received data, removing noise data and invalid data, and uniformly converting data of different sources and different formats into a format suitable for analysis, which is prior art and will not be described in detail.

[0079] It can be understood that the data processing module can adopt a hybrid computing mode combining stream computing and batch processing, real-time processing of data with high real-time requirements (such as system alarm data) using stream computing, and deep analysis and mining of massive historical data (such as user behavior logs) using batch processing, to ensure the timeliness and accuracy of data analysis.

[0080] a service data analysis submodule connected with the preprocessing submodule, configured to determine a service peak period based on the key service data of each network node in a target time period, and determine a service load pressure representation value based on the distribution of the service peak period;

[0081] Specifically, the service data analysis submodule divides the target time period into a plurality of time sub-periods, determines a service index threshold based on historical service data, and determines a service peak period based on the comparison result of the key service data of each network node in each time sub-period with the service index threshold.

[0082] In implementation, the service index threshold is a service peak threshold corresponding to each service data, and the value range of the service index threshold is 80% to 90% of the highest value in the historical service data. For example, the service index threshold corresponding to the order quantity is 85% of the highest value in the historical order quantity data, and the service index threshold corresponding to the user access quantity is 90% of the highest value in the historical user access quantity data.

[0083] It can be understood that if the key service data of any network node in any time sub-period is greater than the service index, the time sub-period is determined as the service peak period. Actual implementers can set the target time period based on actual conditions, and preferably, the value range of the target time period is 3 min to 5 min, and the value range of the number of time sub-periods is 50 to 80.

[0084] It can be understood that the ratio of the total duration of the service peak period to the target duration corresponding to the target time period is determined as the service load pressure representation value.

[0085] a running data analysis submodule connected with the preprocessing submodule, configured to determine a plurality of associated nodes based on the node running data of each network node, and perform aggregated analysis based on the node running data of each associated node to determine potential performance bottlenecks;

[0086] In implementation, the node running data of each network node is subjected to correlation analysis to determine the correlation coefficient corresponding to each network node, and any two network nodes with a correlation coefficient greater than a preset correlation coefficient are determined as associated nodes, and the aggregated analysis includes calculating the average value, maximum value, minimum value and standard deviation of the node running data of each associated node, for example, network nodes A, B, C, D, F, wherein the correlation coefficient corresponding to A and B is greater than the preset correlation coefficient (the actual implementation personnel can set the preset correlation coefficient based on the actual situation, and preferably, the preset correlation coefficient is set to a value range of 0.8-0.9, and those skilled in the art know that any method capable of performing correlation analysis to determine the correlation coefficient in the prior art falls within the protection scope of the present application, and will not be described here. ), and the correlation coefficient corresponding to D and F is greater than the preset correlation coefficient, then the network nodes A and B are determined as a group of associated nodes, and the network nodes D and F are determined as a group of associated nodes, the node running data of the network nodes A and B is subjected to aggregated analysis to determine the average value, maximum value, minimum value and standard deviation of the node running data of the network nodes A and B, and the node running data of the network nodes D and F is subjected to aggregated analysis to determine the average value, maximum value, minimum value and standard deviation of the node running data of the network nodes D and F, if the standard deviation of any group of associated nodes is less than a preset standard deviation, then the group of associated nodes is determined as a key associated node, and the difference between the maximum value and the minimum value corresponding to the node running data of the key associated node and the ratio of the average value are determined as an adjustment coefficient, and the product of the adjustment coefficient and the maximum value is determined as the potential performance bottleneck corresponding to the node running data, for example, the average value AM AB , the maximum value BM AB , the minimum value CM AB and the standard deviation of the CPU usage rate of the network nodes A and B, wherein the standard deviation is less than the preset standard deviation, then the first adjustment coefficient M AB of the CPU usage rate = (BM AB -CM AB ) / AM AB , then the potential performance bottleneck XM AB of the CPU usage rate of the network nodes A and B = M AB ×BM AB ; the average value AM DF , the maximum value BM DF , the minimum value CM DFThe standard deviation is less than the preset standard deviation, and the second adjustment factor M corresponding to the CPU utilization rate is... DF =(BM DF -CM DF ) / AM DF The potential performance bottleneck XM corresponding to the CPU utilization of network nodes D and F is... DF =M DF ×BM DF Therefore, the potential performance bottleneck is ultimately determined to be the average of the potential performance bottlenecks corresponding to the CPU utilization rates of network nodes A and B and the potential performance bottlenecks corresponding to the CPU utilization rates of network nodes D and F. Understandably, implementers can set a preset standard deviation based on actual conditions.

[0087] The behavior data analysis submodule, which is connected to the preprocessing submodule, is used to construct a user behavior analysis model based on the historical user behavior data of each network node, and to determine user behavior patterns based on the node user behavior data and the user behavior analysis model.

[0088] Specifically, the behavior data analysis submodule clusters the historical user behavior data of each network node to obtain several cluster groups, performs correlation analysis on the historical user behavior data in the same cluster group to determine several related user behavior data, generates a corresponding training sample set based on each related user behavior data, and trains the behavior analysis model based on the training sample set to construct a user behavior analysis model.

[0089] In implementation, each cluster group includes several historical user behavior data. Correlation analysis is performed on the historical user behavior data in the same cluster group to determine the behavioral correlation coefficient between any two historical user behavior data. The mean of the behavioral correlation coefficients in the cluster group is determined as the cluster correlation coefficient corresponding to that cluster group. If the cluster correlation coefficient corresponding to any cluster group is greater than the preset comprehensive correlation coefficient (the actual implementer can set the preset correlation coefficient based on the actual situation. Preferably, the preset correlation coefficient is set to a value range of 0.85 to 0.95. The larger the preset correlation coefficient value, the higher the requirement for the correlation between historical user behavior data in the cluster group), then the historical user behavior data in that cluster group is determined as related user behavior data.

[0090] It is understood that those skilled in the art will recognize that any existing model capable of analyzing user behavior patterns falls within the scope of protection of this invention, such as neural network models, support vector machines, decision trees, etc., which will not be elaborated upon here.

[0091] Understandably, data analysis reports can be generated based on peak business periods, business load pressure indicators, potential performance bottlenecks, and user behavior patterns.

[0092] This invention's data processing module includes a preprocessing submodule. This submodule cleans, transforms, and aggregates node port data to remove noise and invalid data, unifying data from different sources and formats into a format suitable for analysis. This yields key node port data, ensuring data quality and consistency and facilitating subsequent data processing. A business data analysis submodule accurately identifies peak business periods based on key business data within a target timeframe, providing a basis for resource planning and performance optimization. Based on the distribution of peak business periods, it determines the business load pressure characterization value, accurately quantifying and assessing the system's load capacity during peak periods, enabling precise business load prediction. A runtime data analysis submodule accurately identifies related nodes based on the operational data of each network node, providing a foundation for locating performance bottlenecks. Aggregating and analyzing the operational data of related nodes accurately identifies potential performance bottlenecks. A behavioral data analysis submodule builds a user behavior analysis model based on historical user behavior data, providing a foundation for identifying user behavior patterns. This model accurately identifies user behavior patterns, providing accurate decision-making support.

[0093] The intelligent decision-making module is connected to the configuration center server and the data processing module respectively. It is used to determine the business development trend based on the business peak period and the business load pressure characterization value, determine the resource configuration requirements based on the potential performance bottleneck and the user behavior pattern, and update the initial basic configuration based on the preset configuration rule base, the business development trend and the resource configuration requirements to determine the target update configuration and generate configuration adjustment instructions.

[0094] Specifically, the intelligent decision-making module includes:

[0095] The business development analysis submodule, which is connected to the data processing module, is used to determine the business development trend based on the node business data of each network node during the peak business period and the business load pressure characterization value.

[0096] Specifically, the business development analysis submodule determines the business peak characterization value based on the node business data of each network node during the business peak period, and determines the business development trend based on the business peak characterization value and the business load pressure characterization value.

[0097] In implementation, the average ratio of the minimum to the maximum value of business at each node during peak periods is determined as the peak business characteristic value. For example, the ratio of the minimum to the maximum order volume during peak periods is determined as the order characteristic value, and the ratio of the minimum to the maximum user visits during peak periods is determined as the user visit characteristic value. The average of the order characteristic value and the user visit characteristic value is then determined as the peak business characteristic value. The ratio of the peak business characteristic value to the business load pressure characteristic value is determined as the business ratio. If the business ratio is greater than 1, it indicates that the business change is significantly greater than the increase during peak periods, and the business development trend is a general trend. If the business ratio is greater than 0.8 and less than or equal to 1, it indicates that the business change is consistent with the increase during peak periods. In this case, the business change is small and the increase during peak periods is small, or the business change is large and the increase during peak periods is large, indicating an abnormal business change, and the business development trend is an abnormal trend. If the business ratio is less than or equal to 0.8, it indicates that the business change is significantly less than the increase during peak periods, and the business development trend is an upward trend.

[0098] The resource configuration analysis submodule, which is connected to the data processing module, is used to determine user configuration requirements based on the user behavior pattern, and to determine resource configuration requirements based on the user configuration requirements and the potential performance bottlenecks.

[0099] In implementation, implementers can set user configuration requirements corresponding to each user behavior pattern based on the actual situation. Each user behavior pattern has a unique corresponding user configuration requirement. The user configuration requirement is compared with the potential performance bottleneck. If the user configuration requirement does not exceed the potential performance bottleneck, the user configuration requirement is determined as the resource configuration requirement. If the user configuration requirement exceeds the potential performance bottleneck, the resource configuration requirement is determined as 80% to 90% of the potential performance bottleneck.

[0100] The configuration update submodule is connected to the configuration center server, the business development analysis submodule, and the resource configuration analysis submodule, respectively. It is used to determine the adjustment configuration rules based on the business development trend and the resource configuration requirements, and to update the initial basic configuration based on the adjustment configuration rules and the preset configuration rule library to determine the target update configuration.

[0101] In implementation, personnel can construct a training dataset based on historical data of business development trends, resource allocation requirements, and configuration rules that have passed qualification checks. A pre-defined machine learning model is then trained on this dataset to obtain the target machine learning model. The business development trends and resource allocation requirements are then input into the target machine learning model to obtain the adjustment configuration rules output by the model. It is understood that the pre-defined configuration rule library includes several configuration rules, each with a corresponding configuration. The adjustment configuration rules are compared one-to-one with the configuration rules in the pre-defined configuration rule library to obtain the target updated configuration.

[0102] Understandably, the pre-defined configuration rule library supports visual editing and management. Operations personnel can flexibly define and modify configuration rules based on business needs and experience. Rule types include, but are not limited to, conditional judgment rules, mathematical model rules, and script rules. Machine learning algorithm models support online learning and model updates, enabling them to continuously adapt to business changes and the evolution of data characteristics.

[0103] An adjustment instruction generation submodule, which is connected to the configuration update submodule, is used to generate configuration adjustment instructions based on the target update configuration.

[0104] This invention's intelligent decision-making module, through a business development analysis submodule, accurately predicts business development trends based on node business data and business load pressure characteristics during peak business periods. Through a resource configuration analysis submodule, it precisely determines user configuration needs based on user behavior patterns, providing personalized configuration solutions. Combining user configuration needs with potential performance bottlenecks, it rationally determines resource configuration requirements and dynamically adjusts resource configuration, improving system flexibility and adaptability. Through a configuration update submodule, it dynamically adjusts configuration rules based on business development trends and resource configuration needs, ensuring the real-time nature and adaptability of system configuration. This ensures that the updated system configuration remains consistent with business and performance requirements, avoiding configuration conflicts and errors. It also quickly generates configuration adjustment rules and completes configuration updates based on a pre-set configuration rule library, reducing update time. Through an adjustment instruction generation submodule, it generates accurate configuration adjustment instructions based on the target configuration update, ensuring accurate execution of configuration updates. It optimizes the generated configuration adjustment instructions, ensuring their simplicity and efficiency, reducing execution time, and improving system response speed.

[0105] A configuration push module, which is connected to the intelligent decision module, is used to determine the configuration push mode based on the configuration adjustment instruction, and push the target updated configuration based on the configuration adjustment instruction and the configuration push mode;

[0106] Specifically, the configuration push module includes:

[0107] The push mode determination submodule is connected to the intelligent decision module and is used to determine the configuration push mode based on the key node port data corresponding to the client network node.

[0108] The push submodule, which is connected to the push mode determination submodule, is used to push the target update configuration based on the configuration adjustment instruction and the configuration push mode.

[0109] In implementation, the urgency of configuration updates is determined based on the key node port data corresponding to the client network node, and the configuration adjustment instructions with the highest urgency are pushed first.

[0110] Understandably, the configuration push module supports breakpoint resume functionality. When the network is interrupted or the push fails, it can record the progress of the push and continue pushing the remaining configuration update content from the breakpoint after the network is restored, ensuring the integrity of the configuration update. It also supports version management of configuration updates, which can trace historical version configuration information, making it convenient for troubleshooting and rollback operations.

[0111] The configuration push module of this invention determines the sub-module by setting the push mode. Based on the configuration adjustment command and the key node port data corresponding to the client network node, it can dynamically adjust the push mode to ensure the efficiency and reliability of configuration push.

[0112] The client integration module is connected to both the configuration push module and the configuration center server. It receives the target update configuration, determines whether the target update configuration meets a preset standard, performs a local update based on the target update configuration, and sends the update status to the configuration center server.

[0113] Specifically, the client integration module includes:

[0114] A configuration receiving submodule, which is connected to the configuration push module, is used to receive the target updated configuration;

[0115] A configuration determination submodule, which is connected to the configuration receiving submodule, is used to determine a verification characterization value based on the target update configuration, and to determine whether the target update configuration meets the preset standard based on the comparison result between the verification characterization value and the preset characterization value.

[0116] An update submodule, which is connected to the configuration determination submodule, is used to perform a local update based on the target update configuration under a first determination condition, and send the update status to the configuration center server, wherein the first determination condition is that the target update configuration meets a preset standard.

[0117] In implementation, the target update configuration is subjected to legality and integrity verification to determine the verification characterization value. If the legality verification passes, the verification characterization value is assigned the value of 1. If the integrity verification passes, the verification characterization value is assigned the value of 1. Based on the comparison result between the verification characterization value and the preset characterization value, it is determined whether the target update configuration meets the preset standard. If the verification characterization value is greater than the preset characterization value, it is determined that the target update configuration meets the preset standard, and the preset characterization value is 1.

[0118] Understandably, if the target configuration update meets the preset standards, the update submodule triggers the configuration hot loading mechanism to dynamically load the new configuration into the application runtime environment, making the configuration update take effect immediately, and sends the update status, including the update progress and update result (success or failure), to the configuration center server so that the configuration center server can track the execution of the configuration update and promptly retry or take other fault handling measures for clients whose updates fail.

[0119] Understandably, the client integration module provides a unified configuration management interface, supports the integration of multiple programming languages ​​and development frameworks, and has a configuration change callback mechanism. When the configuration update takes effect, it can automatically notify the relevant modules of the client application, trigger the corresponding business logic to be reloaded and executed, and achieve seamless connection between configuration updates and business functions.

[0120] This invention includes a client integration module with a configuration receiving submodule that receives target updated configurations sent from the configuration push module, ensuring complete reception of configuration data. A configuration judgment submodule generates a verification characteristic value based on the target updated configuration, compares this value with a preset characteristic value to determine if the target updated configuration meets a preset standard, ensuring the integrity and accuracy of the configuration data. An update submodule performs a local update based on the target updated configuration if it meets the preset standard, ensuring the accuracy and consistency of the system configuration. It then sends the update status to the configuration center server, completing closed-loop management and ensuring the configuration center server can monitor the client's update status in real time.

[0121] This invention establishes a configuration center server to centrally manage client registration information and initial basic configurations, facilitating unified management and maintenance and reducing the complexity of configuration management. Based on registration information and a pre-defined configuration rule base, it generates initial basic configurations that can be customized to meet the needs of different clients, improving configuration flexibility and adaptability. As the central node of the system, it facilitates subsequent updates and expansions of the configuration rule base without requiring individual configuration modifications for each client, reducing system maintenance costs. A monitoring module collects real-time port data from each network node, supporting both periodic and event-triggered mechanisms. It allows for selection of appropriate collection methods based on different data types and business requirements, improving data collection efficiency and accuracy. A data processing module accurately identifies peak business periods and business load pressure based on node business data within a target time period, providing data support for predicting business development trends. Analysis of node operational data quickly identifies potential performance bottlenecks, improving system reliability. Determining user behavior patterns based on node user behavior data helps in understanding user needs and habits, providing data support for intelligent decision-making. By setting up an intelligent decision-making module, based on peak business periods and business load pressure indicators, it can accurately predict business development trends, helping to avoid performance bottlenecks during peak periods. Combining potential performance bottlenecks and user behavior patterns, it can rationally determine resource allocation requirements, ensuring efficient utilization of system resources while avoiding resource waste and over-configuration. Based on a preset configuration rule base, business development trends, and resource allocation requirements, it dynamically updates the initial basic configuration, generates target update configurations, and generates configuration adjustment instructions, achieving automated configuration management and dynamic adjustment, improving system flexibility and adaptability. By setting up a configuration push module, it can determine the configuration push mode based on configuration adjustment instructions and quickly and accurately push the target update configuration to clients, ensuring clients can obtain the latest configuration information in a timely manner, improving system response speed and efficiency. It supports multiple configuration push modes, allowing selection of appropriate push methods based on different business needs and network environments, improving the flexibility and reliability of configuration push. Automating the configuration push process reduces the possibility of manual intervention and operational errors, improving system stability and reliability. By setting up a client integration module, after receiving the target updated configuration, it can determine whether the configuration meets the preset standards and send the update status to the configuration center server, thus realizing closed-loop management of configuration updates and ensuring the accuracy and effectiveness of the configuration. Local updates based on the target updated configuration can quickly adapt to changes in system configuration, improving the flexibility and adaptability of the client, while also ensuring the consistency of configuration between the client and the server.

[0122] Please see Figure 6As shown, it is a flowchart illustrating the intelligent dynamic configuration management method according to an embodiment of the present invention; the present invention also provides an intelligent dynamic configuration management method, including:

[0123] In response to the configuration center server receiving the registration request and registration information sent by the client, an initial basic configuration is generated based on the registration information and the preset configuration rule base;

[0124] A data collection mechanism is determined based on the registration information, and node port data of each network node is collected based on the data collection mechanism. The node port data includes node service data, node operation data, and node user behavior data. The data collection mechanism includes a periodic collection mechanism and an event triggering mechanism.

[0125] Based on the node service data of each network node within the target time period, determine the peak service period and the service load pressure characterization value; based on the node operation data of each network node, determine the potential performance bottleneck; and based on the node user behavior data of each network node, determine the user behavior pattern.

[0126] The business development trend is determined based on the peak business period and the business load pressure characteristics, and the resource allocation requirements are determined based on the potential performance bottlenecks and the user behavior patterns.

[0127] Based on the preset configuration rule base, the business development trend, and the resource configuration requirements, the initial basic configuration is updated to determine the target update configuration and a configuration adjustment instruction is generated.

[0128] Based on the configuration adjustment instruction, a configuration push mode is determined, and the target update configuration is pushed based on the configuration adjustment instruction and the configuration push mode, and it is determined whether the target update configuration meets the preset standard.

[0129] If the preset criteria are met, a local update is performed based on the target update configuration, and the update status is sent to the configuration center server.

[0130] Specifically, the intelligent dynamic configuration management method provided in this embodiment of the invention can be applied to the above-mentioned intelligent dynamic configuration management system to achieve the same technical effect, which will not be elaborated here.

[0131] Example 1:

[0132] Scenario Description: During large-scale e-commerce promotional events (such as "Double Eleven"), e-commerce business systems need to frequently adjust configuration parameters such as product prices, inventory display, and discount rules to adapt to the rapidly changing promotional pace and the pressure of massive user access.

[0133] Initialization Phase: Before the event begins, the e-commerce application server (client) connects to the intelligent dynamic configuration management system. Through the client integration module, it sends a registration request to the configuration center server, carrying its application identifier, operating environment (e.g., production environment), and required business modules (e.g., product module, order module, promotion module, etc.). Based on this information, the configuration center server matches an initial configuration template suitable for large-scale promotional events from the configuration rule base, generating initial basic configurations including product display page layout, initial discount rules, and database read / write separation strategies, and pushes this information to the client integration module for storage. Simultaneously, monitoring modules deployed on network nodes such as the application server, database server, and user terminals begin collecting node port data, including node business data (e.g., product views, add-to-cart counts, order counts), node operational data (e.g., server CPU utilization, memory usage, database connection counts, etc.), and node user behavior data (e.g., user browsing paths, dwell time, click behavior, etc.).

[0134] Real-time monitoring and data processing phase: The monitoring module collects data from each node port in real time according to a preset millisecond-level collection frequency and sends the data to the data processing module through an efficient message queue. The data processing module cleans the massive amount of data, removing noise data such as duplicate clicks and invalid requests, and then transforms and aggregates the data according to business modules and indicator types. For example, it calculates the growth rate of pageviews for various types of products per minute and the average server response time. Through in-depth analysis of this data, a data analysis report is generated. The report indicates that the pageviews of several popular product categories are showing an explosive growth trend, predicting that the order volume will increase by 300% within the next 10 minutes. It also identifies a certain risk of latency in database read / write operations and notes that some users spend a considerable amount of time on the discount rules page, potentially leading to difficulties in understanding the rules.

[0135] Intelligent Decision-Making and Configuration Adjustment Phase: After receiving the data analysis report, the intelligent decision-making module compares and matches it with the promotional activity configuration rules in the preset configuration rule library. Based on the rule defined in the rule library, "when the product pageview growth rate exceeds 200% and the predicted order volume growth exceeds 200%, automatically expand the server resources of the product service," and the business development trend predicted by the machine learning model, the intelligent decision-making module generates configuration adjustment rules for the product module. These rules include increasing the number of server instances for the product service, adjusting the database connection pool size, and optimizing the caching strategy for the product details page. Simultaneously, addressing the issue of long user dwell times on the discount rules page, the intelligent decision-making module adjusts the discount rule display configuration, optimizes the rule description text, adds example explanations, and pushes the new discount rule configuration to the front-end application client. After the configuration adjustment rules are generated, the intelligent decision-making module sends the configuration adjustment instructions to the configuration push module.

[0136] Configuration Push and Update Phase: After receiving the configuration adjustment command, the configuration push module, based on the network status (such as bandwidth and latency) and real-time load (such as the number of requests currently being processed) of each e-commerce application server, uses the HTTP long-connection push protocol to prioritize pushing the target updated configuration to the server instances handling popular product services. Upon receiving the target updated configuration, the client integration module first performs a validity check, verifying whether the number of server instances is within a reasonable range and whether the database connection pool configuration meets preset standards. If the check passes, it triggers the local application's configuration hot-reloading mechanism, and the target updated configuration takes effect immediately without requiring a service restart. Based on the target updated configuration, the application server automatically expands product service resources and optimizes database connection strategies, resulting in a 30% increase in product detail page loading speed and a 50% decrease in user complaint rates on the discount rules page. This successfully handles the peak business pressure of promotional activities and ensures a smooth user shopping experience.

[0137] Example 2:

[0138] Scenario Description: In a smart home system, various smart devices (such as smart lights, smart thermostats, smart curtains, etc.) need to dynamically adjust their parameters based on the user's lifestyle and environmental changes (such as light intensity, temperature, time, etc.) to achieve intelligent home environment control.

[0139] Initialization Phase: After the user completes the initial configuration (such as connecting to Wi-Fi and binding the user account), the smart light device (client) sends a registration request to the configuration center server through the integrated client module, carrying registration information such as the device's unique identifier (e.g., MAC address), the associated user account, and the user's geographical location. Based on the device type and the user's geographical location (time zone, climate zone, etc.), the configuration center server matches a suitable smart home initial configuration template from a preset configuration rule library. This generates initial basic configurations including default on / off time rules (e.g., turning on at 7 AM and turning off at 10 PM), brightness adjustment strategies (automatically adjusting based on light sensor default values), and linkage scene configurations (e.g., linkage rules with smart curtains), and pushes these configurations to the smart light device's storage. Simultaneously, monitoring modules deployed on network nodes such as the smart light device and environmental sensors (e.g., light sensors, human infrared sensors) begin collecting device operation data (e.g., device online status, number of on / off cycles, energy consumption), environmental data (e.g., light intensity, ambient temperature), and user operation data (e.g., the time and frequency of users manually turning the lights on and off).

[0140] Real-time monitoring and data processing phase: The monitoring module collects data from each node at a frequency of minutes and sends the data to the data processing module through the local gateway. The data processing module cleans the data, removing noise such as sensor false alarms and abnormal device status data, and then transforms and aggregates the data according to device type and indicator category. For example, it statistically analyzes the light intensity variation curves at different times of the day and calculates indicators such as the frequency and time distribution of users manually turning lights on and off. Through the analysis of this data, a data analysis report is generated. The report finds that users spend more time outdoors in the summer evenings, return home 2-3 hours later than the default setting, and frequently manually turn off the lights even when the light intensity in the living room is high, which may indicate a personalized preference for brightness perception. At the same time, ambient temperature sensor data shows that the energy consumption of smart lights increases significantly under certain high-temperature weather conditions.

[0141] Intelligent Decision-Making and Configuration Adjustment Phase: After acquiring the data analysis report, the intelligent decision-making module combines the smart home device configuration rules in the preset configuration rule library with predictions of user behavior patterns from machine learning models. Based on the rule library's rule that "when a user manually turns on the lights more than 80% of the time during a specific period (e.g., 10 PM to 12 AM) for two consecutive weeks, automatically adjust the default lighting rule for that period," and the model's prediction of future changes in user brightness preferences, the intelligent decision-making module generates configuration adjustment rules for the smart light devices. These rules include delaying the default lighting time of the smart lights to 12 AM in summer evenings, adjusting the brightness adjustment strategy to reduce brightness to the user's preferred level in high-light environments (learned from user manual dimming records), and optimizing the energy management configuration of the smart lights in hot weather, such as reducing the light's brightness level and enabling energy-saving modes. After the configuration adjustment rules are generated, the intelligent decision-making module sends the adjustment instructions to the configuration push module.

[0142] Configuration Push and Update Phase: After receiving the configuration adjustment command, the configuration push module pushes the configuration using the MQTT protocol suitable for IoT devices, based on the smart light device's network connection status (e.g., Wi-Fi signal strength) and current operating status (e.g., whether it is in standby mode). Upon receiving the configuration update, the client integration module first verifies whether the target updated configuration conforms to preset standards, ensuring that parameters such as brightness adjustment range and on-time are set correctly. After successful verification, the smart light device's configuration hot-loading mechanism is triggered, and the target updated configuration takes effect immediately. Based on the target updated configuration, the smart light device automatically turns on at user-preferred times during summer evenings, with brightness adjustment more closely matching user perception and effectively consuming energy.

[0143] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An intelligent dynamic configuration management system, characterized in that, The application relates to a network configuration system, comprising: a configuration center server configured to respond to a registration request from a client and receive registration information, and generate an initial basic configuration based on the registration information and a preset configuration rule library; a monitoring module connected to the configuration center server and configured to determine a data collection mechanism based on the registration information, and collect node port data of each network node based on the data collection mechanism, wherein the node port data comprises node service data, node operation data and node user behavior data, and the data collection mechanism comprises a periodic collection mechanism and an event-triggered mechanism; a data processing module connected to the monitoring module and configured to determine a service peak period and a service load pressure representation value based on node service data of each network node within a target time period, determine a potential performance bottleneck based on node operation data of each network node, and determine a user behavior mode based on node user behavior data of each network node; an intelligent decision module connected to the configuration center server and the data processing module respectively, and configured to determine a service development trend based on the service peak period and the service load pressure representation value, determine a resource configuration requirement based on the potential performance bottleneck and the user behavior mode, update the initial basic configuration based on the preset configuration rule library, the service development trend and the resource configuration requirement to determine a target updated configuration, and generate a configuration adjustment instruction; a configuration pushing module connected to the intelligent decision module and configured to determine a configuration pushing mode based on the configuration adjustment instruction, and push the target updated configuration based on the configuration adjustment instruction and the configuration pushing mode; a client integration module connected to the configuration pushing module and the configuration center server respectively, and configured to receive the target updated configuration, determine whether the target updated configuration meets a preset standard, update locally based on the target updated configuration, and send an update state to the configuration center server; the data processing module comprises: a preprocessing submodule connected to the monitoring module and configured to preprocess the node port data to obtain key node port data, including key service data, key operation data and key user behavior data; a service data analysis submodule connected to the preprocessing submodule and configured to determine a service peak period based on key service data of each network node within a target time period, and determine a service load pressure representation value based on a distribution of the service peak period; an operation data analysis submodule connected to the preprocessing submodule and configured to determine a plurality of associated nodes based on node operation data of each network node, and determine a potential performance bottleneck based on aggregated analysis of node operation data of the associated nodes; a behavior data analysis submodule connected to the preprocessing submodule and configured to construct a user behavior analysis model based on historical user behavior data of each network node, and determine a user behavior mode based on node user behavior data and the user behavior analysis model. The business data analysis submodule divides the target time period into several time sub-periods, determines a business index threshold based on historical business data, determines a business peak period based on a comparison result of key business data of each network node in each time sub-period and the business index threshold, and determines a business load pressure representation value based on a ratio of a total duration of the business peak period to a target duration corresponding to the target time period. The intelligent decision module includes: A business development analysis submodule connected to the data processing module to determine a business development trend based on node business data of each network node in the business peak period and the business load pressure representation value; A resource configuration analysis submodule connected to the data processing module to determine user configuration requirements based on the user behavior pattern and determine resource configuration requirements based on the user configuration requirements and the potential performance bottleneck; A configuration update submodule connected to the configuration center server, the business development analysis submodule, and the resource configuration analysis submodule to determine adjustment configuration rules based on the business development trend and the resource configuration requirements, and update the initial basic configuration based on the adjustment configuration rules and the preset configuration rule library to determine a target update configuration; An adjustment instruction generation submodule connected to the configuration update submodule to generate configuration adjustment instructions based on the target update configuration; The business development analysis submodule determines a business peak representation value based on node business data of each network node in the business peak period, determines a business ratio based on the business peak representation value and the business load pressure representation value, and determines a business development trend based on a comparison result of the business ratio and a preset threshold.

2. The intelligent dynamic configuration management system of claim 1, wherein, The monitoring module includes a monitoring analysis submodule and several monitoring submodules, wherein, The monitoring analysis module is connected to the configuration center server to determine a client network node corresponding to a client based on the registration information and determine a data collection mechanism based on the client network node; Each monitoring submodule is connected to the monitoring analysis module and collects node port data of each network node based on the data collection mechanism.

3. The intelligent dynamic configuration management system of claim 2, wherein, The configuration pushing module includes: A pushing mode determination submodule connected to the intelligent decision module to determine a configuration pushing mode based on key node port data corresponding to the client network node; A pushing submodule connected to the pushing mode determination submodule to push the target update configuration based on the configuration adjustment instructions and the configuration pushing mode.

4. The intelligent dynamic configuration management system of claim 3, wherein, The client integration module includes: A configuration receiving submodule connected to the configuration pushing module to receive the target update configuration; A configuration judgment submodule connected to the configuration receiving submodule to determine a verification representation value based on the target update configuration and determine whether the target update configuration meets a preset standard based on a comparison result of the verification representation value and a preset representation value; An updating submodule, connected with the configuration determining submodule, is configured to perform local updating based on the target updating configuration under a first determining condition, and send an updating state to the configuration center server, wherein the first determining condition is that the target updating configuration meets preset standards.

5. The intelligent dynamic configuration management system of claim 4, wherein, The behavior data analysis submodule clusters historical user behavior data of each network node to obtain a plurality of cluster groups, performs correlation analysis on historical user behavior data in the same cluster group to determine a plurality of associated user behavior data, generates a corresponding training sample set based on each associated user behavior data, trains a behavior analysis model based on the training sample set, and constructs a user behavior analysis model.

6. A method for intelligent dynamic configuration management applied to the intelligent dynamic configuration management system of any one of claims 1-5, characterized in that, Comprise: In response to the configuration center server receiving a registration request and registration information sent by the client, an initial basic configuration is generated based on the registration information and a preset configuration rule library; A data collection mechanism is determined based on the registration information, and node port data of each network node is collected based on the data collection mechanism, wherein the node port data includes node service data, node running data, and node user behavior data, and the data collection mechanism includes a periodic collection mechanism and an event triggering mechanism; A business peak period and a business load pressure representation value are determined based on node service data of each network node in a target time period, a potential performance bottleneck is determined based on node running data of each network node, and a user behavior mode is determined based on node user behavior data of each network node; A business development trend is determined based on the business peak period and the business load pressure representation value, and a resource configuration requirement is determined based on the potential performance bottleneck and the user behavior mode; The initial basic configuration is updated based on the preset configuration rule library, the business development trend, and the resource configuration requirement to determine a target updating configuration, and a configuration adjustment instruction is generated; A configuration push mode is determined based on the configuration adjustment instruction, and the target updating configuration is pushed based on the configuration adjustment instruction and the configuration push mode, and it is determined whether the target updating configuration meets preset standards; If the preset standards are met, local updating is performed based on the target updating configuration, and an updating state is sent to the configuration center server; The node port data is preprocessed to obtain key node port data, including key business data, key running data, and key user behavior data; The target time period is divided into a plurality of time sub-periods, a business index threshold is determined based on historical business data, a business peak period is determined based on a comparison result of key business data of each network node in each time sub-period and the business index threshold, and a business load pressure representation value is determined based on a ratio of a total duration of the business peak period to a target duration corresponding to the target time period; A plurality of associated nodes are determined based on node running data of each network node, and an aggregate analysis is performed on node running data of each associated node to determine a potential performance bottleneck. constructing a user behavior analysis model based on historical user behavior data of each of the network nodes, and determining a user behavior pattern based on the node user behavior data and the user behavior analysis model; determining a business peak representation value based on node business data of each of the network nodes during the business peak period, and determining a business ratio based on the business peak representation value and the business load pressure representation value, and determining a business development trend based on a comparison result of the business ratio and a preset threshold value; determining a user configuration requirement based on the user behavior pattern, and determining a resource configuration requirement based on the user configuration requirement and the potential performance bottleneck; determining an adjustment configuration rule based on the business development trend and the resource configuration requirement, and updating the initial basic configuration based on the adjustment configuration rule and the preset configuration rule library to determine a target updated configuration; generating a configuration adjustment instruction based on the target updated configuration.

Images