Configuration system and method for dynamically adjusting business process

Abstract

The invention discloses a configuration system and method for dynamically adjusting a business process, particularly relates to the technical field of business adjustment, and aims to dynamically group users accurately according to the degree of dependence of the users on different network services so as to enable resource allocation to be more accurate, and dynamically adjust a bucket dividing strategy according to the change of dependence of the users on the services so as to improve the efficiency of business process adjustment. According to the method, changes of markets and user behaviors can be quickly responded, and a network service provider can more accurately adjust priorities of the user bucket groups by comprehensively analyzing the hidden danger degree of abnormal user traffic distribution and network resource demands in peak periods; the preventive analysis and adjustment method not only can effectively predict and relieve the potential service interruption risk, but also optimizes the resource allocation, and through the dynamic and comprehensive adjustment strategy, the network operator can improve the service reliability and the user satisfaction, and reduce the service quality problem caused by improper resource allocation at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of business adjustment, and more specifically, to a configuration system and method for dynamically adjusting business processes. Background Art

[0002] In the dynamic adjustment of business processes for network service failures in urban areas, it is crucial to effectively handle and prevent network failures. User bucketing is the process of grouping users according to certain preset criteria. Users in each bucket have similar characteristics or needs. User bucket groups refer to user groups formed after the execution of specific bucketing strategies. Users in each bucket group receive similar levels of service priority and resource allocation, which facilitates more effective management and optimization of network service responses.

[0003] With the increasing complexity of network services and the diversity of user needs, the traditional one-size-fits-all fault response and resource allocation strategy can no longer meet the needs of efficient operations. Existing technologies usually rely on static resource management and fault response mechanisms, which not only have slow response speeds, but also have difficulty accurately meeting the specific needs of different users, resulting in resource waste and reduced service quality. If users cannot be accurately and flexibly divided, network resources cannot be reasonably allocated according to users' actual needs and usage patterns, which may lead to uneven resource allocation, reduced service quality, slow response speeds, and reduced user satisfaction.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a system and method for dynamically adjusting the configuration of a business process to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for dynamically adjusting a business process configuration includes the following steps: Bucketize users according to preset rules and assign users to corresponding user bucket groups; Evaluate the degree of user dependence on different network services; by analyzing the degree of change in the degree of user dependence on different network services, determine whether it is necessary to immediately trigger the adjustment of user buckets; Analyze the entropy changes of user network traffic and evaluate the potential risks of abnormal user traffic distribution. Use user traffic data and network resource consumption to simulate network pressure through a fluid dynamics model to evaluate user demand for network resources and service failure risks during peak hours. When there is no need to immediately trigger the adjustment of user buckets, a comprehensive analysis is conducted on the potential risk of abnormal user traffic distribution and the user's demand for network resources and service failures during peak hours, and the priority of the user's corresponding user bucket group is adjusted based on the comprehensive analysis results; Define response and processing strategies for each user bucket group, and implement AB testing to optimize the fault handling process.

[0007] In a preferred implementation, users are bucketed according to preset rules and assigned to corresponding user bucket groups, specifically: Collect multi-dimensional data related to users, including but not limited to geographic location, service level, historical fault records and network usage behavior; Define preset rules for user bucketing, including service type, usage, and impact; Users are divided into buckets based on their service type, usage, and impact.

[0008] In a preferred embodiment, evaluating the degree of dependence of users on different network services is specifically as follows: Obtain the frequency and duration of users’ visits to the Services; Define each service type as a node in the graph; define each user as a node in the graph; Create a directed graph; calculate the user center dependency index based on Katz centrality.

[0009] In a preferred embodiment, by analyzing the change range of the user's dependence on different network services, it is determined whether it is necessary to immediately trigger the adjustment of the user bucket, specifically: Obtain the user-centered dependency index within the statistical period, and calculate the average value of the user-centered dependency index within the statistical period; The ratio of the absolute value of the difference between the average value of the user-centered dependency index in the statistical period and the average value of the user-centered dependency index in the previous statistical period to the average value of the user-centered dependency index in the previous statistical period is marked as the dependency change value; Set the dependency change threshold and compare the dependency change value with the dependency change threshold: When the dependency change value is greater than the dependency change threshold, it is determined that a signal for adjusting the user bucket group is generated, and the adjustment of the user bucket group is triggered; When the dependency change value is less than or equal to the dependency change threshold, it is determined that a continue analysis signal is generated, and there is no need to immediately trigger the adjustment of the user bucket.

[0010] In a preferred implementation, entropy value change analysis is performed on user network traffic to assess the potential risk of abnormal user traffic distribution, specifically: Extract traffic data for each service; services include video streaming, online gaming, social media, file downloads, and general browsing; Calculate the traffic ratio of each service; Calculate network traffic entropy, the expression is: ;in, is the network traffic entropy, It is The traffic ratio of the service is the number of services, is the service number; Set the entropy monitoring interval; analyze the change of network traffic entropy within the entropy monitoring interval, and calculate the traffic entropy variation coefficient, which is expressed as: ;in, is the coefficient of variation of flow entropy, are the number of network traffic entropies monitored within the entropy monitoring interval and the serial number of network traffic entropies monitored within the entropy monitoring interval. is the first Network traffic entropy, is the first Network traffic entropy.

[0011] In a preferred embodiment, the network pressure is simulated by a fluid dynamics model using user traffic data and network resource consumption to assess the user's demand for network resources and the potential risk of service failure during peak hours, specifically: Obtain user traffic data; collect resource usage in different time periods; The propagation and pressure distribution of data flow in the network are simulated through fluid dynamics model; Simulate network traffic and resource consumption in different time periods; Mapping real network traffic data into the fluid dynamics model; Calculate the network service pressure index, the expression is: ;in, is the network service pressure index, is the number of time periods in the statistical period, is the number of the time period within the statistical period. Is the user in the time period The flow rate within It is the time period Length, For users in time period The amount of resources used within It is a specific time The total amount of network resources within.

[0012] In a preferred embodiment, when there is no need to immediately trigger the adjustment of user buckets, a comprehensive analysis is performed on the potential risk of abnormal user traffic distribution and the potential risk of network resource demand and service failure during peak hours. Based on the comprehensive analysis results and the user bucketing conditions of preset rules, the priority of the user's corresponding user bucket group is adjusted, specifically: When a continue analysis signal is generated, the traffic entropy variation coefficient and the network service pressure index are normalized, and after the normalized traffic entropy variation coefficient and the network service pressure index are assigned preset proportional coefficients respectively, the bucket priority evaluation coefficient is calculated; Based on the preset rules, the user bucket groups and bucket priority evaluation coefficients assigned to the user are re-determined to prioritize the user bucket groups: All users are sorted according to the calculated bucket priority evaluation coefficient; the higher the bucket priority evaluation coefficient, the more likely they should be assigned to a higher priority user bucket group.

[0013] On the other hand, the present invention provides a configuration system for dynamically adjusting business processes, including a preset grouping module, an adjustment trigger judgment module, a traffic entropy value analysis module, a peak risk analysis module, a bucket priority adjustment module, and a strategy generation optimization module; The preset grouping module buckets users according to preset rules and assigns users to corresponding user bucket groups; The adjustment trigger judgment module evaluates the user's dependence on different network services; by analyzing the change in the user's dependence on different network services, it determines whether it is necessary to immediately trigger the adjustment of user bucketing; The traffic entropy analysis module analyzes the entropy changes of user network traffic and assesses the potential risks of abnormal user traffic distribution. The peak risk analysis module uses user traffic data and network resource consumption to simulate network pressure through a fluid dynamics model to assess user demand for network resources and the degree of service failure risk during peak hours. When there is no need to immediately trigger the adjustment of user buckets, the bucket priority adjustment module will conduct a comprehensive analysis of the potential risk of abnormal user traffic distribution and the user's demand for network resources and service failures during peak hours, and adjust the priority of the user's corresponding user bucket group based on the comprehensive analysis results; The strategy generation and optimization module defines response and processing strategies for each user bucket group and implements AB testing to optimize the fault handling process.

[0014] The technical effects and advantages of the system and method for dynamically adjusting the configuration of business processes of the present invention are as follows: 1. By dynamically grouping users based on their degree of dependence on different network services, resource allocation is more accurate, avoiding the common resource waste and service mismatch problems in traditional static resource management mechanisms. In addition, dynamically adjusting the bucketing strategy based on changes in user dependence on services can quickly respond to changes in the market and user behavior, especially in emergencies or when demand increases sharply, and can quickly reconfigure resources to ensure the continuity and service quality of key businesses, significantly improving the efficiency of network operations and user satisfaction, while reducing the risk of user churn due to untimely response.

[0015] 2. Through comprehensive analysis of the potential risks of abnormal user traffic distribution and network resource demand during peak hours, network service providers can more accurately adjust the priority of user bucket groups. This preventive analysis and adjustment method can not only effectively predict and mitigate potential service interruption risks, but also optimize resource allocation to ensure that network resources are reasonably allocated to those users with the most urgent needs during peak resource demand periods. Through this dynamic and comprehensive adjustment strategy, network operators can respond to possible challenges in advance, improve service reliability and user satisfaction, and reduce service quality issues caused by improper resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a configuration method for dynamically adjusting a business process according to the present invention; Figure 2 The present invention is a structural diagram of a configuration system for dynamically adjusting business processes. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: Figure 1 The present invention provides a configuration method for dynamically adjusting a business process, which comprises the following steps: Users are bucketed according to preset rules and assigned to corresponding user bucket groups.

[0019] Evaluate the degree of user dependence on different network services; by analyzing the degree of change in the degree of user dependence on different network services, determine whether it is necessary to immediately trigger the adjustment of user buckets.

[0020] Analyze the changes in entropy values ​​of user network traffic and evaluate the potential risks of abnormal user traffic distribution. Use user traffic data and network resource consumption to simulate network pressure through a fluid dynamics model to evaluate user demand for network resources and the potential risks of service failures during peak hours.

[0021] When there is no need to immediately trigger the adjustment of user buckets, a comprehensive analysis is conducted on the potential risk of abnormal user traffic distribution and the potential risk of network resource demand and service failure during peak hours, and the priority of the user's corresponding user bucket group is adjusted based on the comprehensive analysis results.

[0022] Define response and processing strategies for each user bucket group, and implement AB testing to optimize the fault handling process.

[0023] The present invention adopts different response strategies for different degrees of network service hidden dangers, and optimizes resource allocation and response efficiency. When the user dependence changes significantly and it is necessary to immediately trigger the adjustment of buckets, priority is given to adjusting according to the user's dependence on different network services, which can quickly respond and ensure the service quality of key users, and prevent major service interruptions. In the case where it is not necessary to trigger the adjustment immediately, by comprehensively analyzing the hidden danger level of abnormal user traffic distribution and the network pressure during peak hours, the user's potential needs and network usage behavior can be more comprehensively evaluated and understood. The comprehensive analysis method allows for more detailed and global optimization of the user's bucket group, ensuring the reasonable allocation of resources among different users, and improving the stability of the overall network and user experience. Such a phased and hierarchical processing strategy can respond quickly in an emergency, while maintaining the efficient operation and service quality of the network under normal circumstances, taking into account the needs of rapid response and long-term optimization. Through this dual strategy, network service providers can manage resources and users more flexibly and accurately, and improve service reliability and user satisfaction.

[0024] Users are bucketed according to preset rules and assigned to corresponding user bucket groups, specifically: When network services fail in urban areas, it is critical to quickly and efficiently handle the failure and restore services. By dynamically adjusting business processes, especially by bucketing users, optimal resource allocation can be achieved, ensuring that customers with the greatest impact on the business are addressed first, thereby improving customer satisfaction and operational efficiency.

[0025] Collect multi-dimensional data related to users, including but not limited to geographic location, service level, historical fault records, network usage behavior, etc.

[0026] Clean and preprocess the data to ensure its accuracy and consistency.

[0027] Geographic location data: Use geographic information systems to obtain the user's specific location, including urban division information.

[0028] Service level data: Extract user subscription packages, contract types, service agreements and other information from the customer relationship management system.

[0029] Historical fault records: Extract users' past fault reports, resolution times, and customer feedback from the network operation and maintenance system.

[0030] Network usage behavior: Through deep packet inspection technology and network analysis tools, we can obtain user traffic usage, access frequency, service type and other behavioral data.

[0031] Define preset rules for user bucketing, including: Service type: such as home users, enterprise users, high-priority users, etc.

[0032] Usage: Users are divided into buckets based on their network usage. Users with high network usage may need to be given priority.

[0033] Impact degree: Users are divided into buckets according to the impact of the fault on them, and users with greater impact are processed first.

[0034] The process of bucketing users based on their service type, usage, and impact is as follows: The service types are divided into the following categories: Home users: generally have lower network requirements and higher tolerance for short-term failures.

[0035] Enterprise users: They have high demands on network and stability, especially those enterprises that rely on the network for business operations.

[0036] High-priority users: including hospitals, financial institutions, government agencies, etc., have the highest requirements for network services, and the impact of failures may lead to serious consequences.

[0037] Usage is divided into the following categories: High-volume users: Users with high network traffic demands, such as large and medium-sized enterprises, data centers, and video streaming users.

[0038] Medium usage users: general corporate users and heavy Internet home users.

[0039] Low-volume users: Average home users and small business users.

[0040] The degree of impact is divided into the following categories: High impact: The failure causes the user's key business to be interrupted, directly affecting the user's normal operation.

[0041] Moderate impact: The failure affects some functions or services of users but does not cause a complete interruption.

[0042] Low impact: The fault has little impact on the user's daily use, and the user can temporarily resolve it through other means.

[0043] Considering the service type, usage and impact, users are prioritized, for example: Highest priority: High priority users + High usage + High impact.

[0044] Higher priority: Enterprise users + High usage + High impact, or High priority users + Medium usage + High impact.

[0045] Medium priority: Enterprise users + medium usage + medium impact, or high priority users + low usage + medium impact.

[0046] Lower priority: Home user + High usage + Low impact, or Business user + Low usage + Low impact.

[0047] Lowest priority: Home users + Low usage + Low impact.

[0048] Create user bucket groups: Assign users to corresponding user bucket groups based on priority: High Priority Group: Includes highest priority and higher priority users.

[0049] Medium priority group: includes medium priority users.

[0050] Low Priority Group: Includes lower priority and lowest priority users.

[0051] Assess the degree of user dependence on different network services, specifically: Katz centrality considers all reachable paths from other nodes and gives decreasing weight to nodes that are reached via longer paths.

[0052] The frequency and duration of users’ visits to various services (e.g., video streaming, social media, online gaming, etc.) are obtained from web logs and service usage records.

[0053] Define each service type as a node in the graph. Define each user as a node in the graph.

[0054] The edge from the user node to the service node represents the user's access to the service. The weight of the edge is determined by the user's access frequency and duration. The weight of the edge increases with the increase of access frequency and the increase of duration.

[0055] Create a directed graph , ;in, is a collection of nodes (including user nodes and service nodes), It is a set of directed edges (representing the use of services by users).

[0056] based on Centrality calculates the user center dependency index, which is expressed as: ;in, Is a node The user-centered dependency index, is the index of the current node, is the index of the adjacent node, is the total number of nodes in the graph (this means the total number of nodes that need to be considered during the summation), is an element in the adjacency matrix of the graph, Is a node of Centrality, is the attenuation factor, is the baseline centrality.

[0057] : A positive constant that controls how fast the centrality value decreases when a path reaches a node. This value must be less than the inverse of the maximum eigenvalue of the graph to ensure convergence of the formula.

[0058] : Usually set to 1, this is the basic centrality value of each node, ensuring that even nodes with no incoming edges have non-zero centrality.

[0059] The larger the user's central dependence index is, the more dependent the user is on different network services. This usually means that the user not only directly and frequently accesses multiple services, but also indirectly depends on more services in the network through other users or services. This high centrality indicates that the user has a strong comprehensive dependence on network services.

[0060] By analyzing the changes in the degree of user dependence on different network services, it is determined whether it is necessary to immediately trigger the adjustment of user buckets. Specifically: Obtain the user-centered dependency index within the statistical period, and calculate the average value of the user-centered dependency index within the statistical period.

[0061] The ratio of the absolute value of the difference between the average value of the user-centered dependence index in the statistical period and the average value of the user-centered dependence index in the previous statistical period to the average value of the user-centered dependence index in the previous statistical period is marked as the dependence change value.

[0062] The larger the dependency change value, the greater the change in the user's dependency on different network services, and the more it is necessary to trigger the adjustment of the user's user bucket. The increase in the dependency change value reflects a major shift in user behavior or demand, which may be caused by factors such as the use of new services, the increase or decrease in demand for existing services, etc. When the dependency change value increases significantly, it means that the user's network usage pattern and demand have changed, which may affect their demand and priority for network resources. Therefore, network service providers need to adjust user buckets based on this change to ensure that resource allocation matches the user's current needs, optimize the network's operational efficiency, improve user satisfaction, and reduce the possibility of service interruptions. By dynamically adjusting user buckets, service providers can respond more flexibly to changes in the market and user behavior, provide more personalized and efficient services, thereby maintaining competitive advantage and enhancing customer loyalty.

[0063] The length of the statistical cycle is set based on the actual monitoring requirements for user bucket adjustment and other actual conditions, and will not be elaborated here.

[0064] Set the dependency change threshold and compare the dependency change value with the dependency change threshold: When the dependency change value is greater than the dependency change threshold, a signal for adjusting the user bucket group is generated, and the adjustment of the user bucket group is triggered. This indicates that the user's dependence on the network service has changed significantly, which is sufficient to affect its service quality or resource allocation needs. Therefore, the adjustment of the user bucket group is triggered to ensure that the user continues to receive a service level that suits his current needs.

[0065] Triggering adjustment of user bucket groups: The greater the user center dependency index, the higher the priority of the user bucket group should be adjusted. Specifically, it can be: For example, a number of fixed thresholds are set, and when the user center dependency index exceeds the threshold, the priority of the user bucket group corresponding to the threshold is increased.

[0066] According to the linear relationship between the user-centered dependency index and the initial priority, the new priority is directly calculated.

[0067] An exponential function or a logarithmic function is used to calculate the adjustment range according to the user-centered dependency index, making the priority adjustment more flexible.

[0068] The specific implementation formula or algorithm will not be described in detail here.

[0069] When the dependency change value is less than or equal to the dependency change threshold, a continue analysis signal is generated. At this time, there is no need to immediately trigger the adjustment of user buckets. The continue analysis signal believes that the user's service demand has not changed much (but it does not mean that there is no change, and it does not mean that the change in the user's dependence on network services has no impact on the user), and further analysis is needed.

[0070] The setting of dependency change thresholds should be based on historical data analysis, business needs, and understanding of user behavior patterns. Generally, threshold settings need to balance sensitivity and practical feasibility to ensure that important changes in user needs can be responded to in a timely manner without frequent adjustments. In the actual setting process, you can optimize the selection by simulating the impact of different threshold settings on the user bucketing effect to ensure that the threshold can capture key changes while not overreacting to every small change.

[0071] Analyze the entropy changes of user network traffic and assess the potential risks of abnormal user traffic distribution, specifically: By analyzing the entropy value of the user's network traffic, we can identify areas with abnormal traffic distribution and predict possible network failure hotspots in advance. The change in entropy can reveal potential traffic hotspots and bottleneck areas, thereby helping to adjust the priority of user buckets.

[0072] The traffic data of each service is extracted from the network monitoring system. Services include but are not limited to video traffic, online games, social media, file downloads, and general browsing.

[0073] Determine the time window for data collection (e.g., hourly, daily). To capture the dynamic changes in traffic, a shorter time window can be chosen.

[0074] Calculate the traffic ratio of each service, the expression is: ;in, It is The traffic ratio of the service It is The total traffic volume of the service, is the number of services, is the service ID.

[0075] The entropy value is used to measure the distribution of network traffic and identify areas with abnormal traffic distribution.

[0076] Calculate network traffic entropy, the expression is: ;in, is the network traffic entropy.

[0077] Set the entropy monitoring interval. The entropy monitoring interval is a real-time monitoring interval (that is, the end point is always the real-time time point). The length of the entropy monitoring interval is set based on the actual monitoring needs of network traffic entropy and other actual conditions, and will not be repeated here.

[0078] The change of network traffic entropy within the entropy value monitoring interval is analyzed, and the traffic entropy variation coefficient is calculated. The expression of the traffic entropy variation coefficient is: ;in, is the coefficient of variation of flow entropy, are the number of network traffic entropies monitored within the entropy monitoring interval and the serial number of network traffic entropies monitored within the entropy monitoring interval. is the first Network traffic entropy, is the first Network traffic entropy.

[0079] Among them, the monitoring intervals between the network traffic entropies monitored within the entropy value monitoring interval should be set to be equal.

[0080] The larger the coefficient of variation of traffic entropy, the greater the hidden danger of abnormal user traffic distribution, which reflects that the fluctuation of network traffic entropy within the monitoring interval is greater, which means that the distribution of network traffic has changed significantly in a short period of time, indicating that there may be potential traffic hotspots or bottleneck areas, which in turn suggests that the area may have a higher risk of network failure.

[0081] By monitoring the traffic entropy variation coefficient, we can dynamically track changes in network traffic and promptly discover abnormal areas of traffic distribution. This method can predict potential network failure hotspots, provide data support, and provide a scientific basis for failure prevention and resource optimization configuration, ensuring the stability of network services and improving user experience. A high traffic entropy variation coefficient can serve as an early warning signal for network failures, helping the operation and maintenance team take preventive measures before problems occur and reduce the probability of network interruptions.

[0082] Using user traffic data and network resource consumption, the fluid dynamics model is used to simulate network pressure and assess the user demand for network resources and the potential risk of service failure during peak hours. Specifically: Obtain user traffic data, which includes the user's total traffic, the user's active time period, and the user's traffic within the time period.

[0083] Use network monitoring systems (such as Deep Packet Inspection, DPI) to obtain the user's total traffic, the user's active time period, and the user's traffic within the time period.

[0084] Use network resource monitoring tools to collect resource usage information in different time periods. Resource usage includes the total amount of network resources in a specific time period and the amount of resources consumed by users in the time period.

[0085] The network traffic is regarded as a fluid, and the propagation and pressure distribution of data flow in the network are simulated by the fluid dynamics model. The specific simulation can be performed by the following formula: ;in, is the fluid density, which corresponds to the network traffic density, i.e., the traffic of users in a time period.

[0086] is the flow rate, corresponding to the transmission speed of the data stream; is the pressure, which corresponds to the consumption pressure of network resources, that is, the amount of resources consumed by users in a time period.

[0087] The above equations are used to simulate network traffic and resource consumption in different time periods.

[0088] Actual network traffic data is mapped into the fluid dynamics model to simulate network stress during peak hours.

[0089] Calculate the network service pressure index, the expression is: ;in, is the network service pressure index, is the number of time periods in the statistical period, is the number of the time period within the statistical period. Is the user in the time period The flow rate within It is the time period Length, For users in time period The amount of resources used within It is a specific time The total amount of network resources within.

[0090] The larger the network service pressure index is, the more significantly the user demand for network resources during peak hours is higher than the available resources, and the greater the risk of service failure. The greater the user demand for network resources during peak hours and the risk of service failure.

[0091] By using user traffic data and network resource consumption, simulating network pressure through a fluid dynamics model, and evaluating the user's demand for network resources and the potential degree of service failure during peak hours, the method has the following beneficial effects: First, the method can provide accurate predictions of network resource usage and potential failures, which helps network service providers to identify and solve potential problems during peak hours in advance, reducing the risk of service interruptions and performance degradation. Second, by calculating the network service pressure index, service providers can allocate resources more effectively and ensure that key users receive high-quality services during high-demand periods, thereby improving overall user satisfaction and network service quality. In addition, the method is based on a complex fluid dynamics model and can fully consider the dynamic changes in network traffic and resource consumption.

[0092] When there is no need to immediately trigger the adjustment of user buckets, a comprehensive analysis is conducted on the potential risk of abnormal user traffic distribution and the potential risk of network resource demand and service failure during peak hours. Based on the comprehensive analysis results and the user buckets of preset rules, the priority of the user's corresponding user bucket group is adjusted, specifically: When a continue analysis signal is generated, the traffic entropy variation coefficient and the network service pressure index are normalized, and after the normalized traffic entropy variation coefficient and the network service pressure index are assigned preset proportional coefficients respectively, the bucket priority evaluation coefficient is calculated.

[0093] The specific implementation method of calculating the bucket priority evaluation coefficient is not specifically limited here. Any method that can calculate the bucket priority evaluation coefficient by assigning the normalized traffic entropy variation coefficient and the network service pressure index to preset proportional coefficients respectively is acceptable. In order to implement the technical solution of the present invention, the present invention provides a specific calculation method for the bucket priority evaluation coefficient, and its expression is: ;in, is the bucket priority evaluation coefficient, are the normalized traffic entropy variation coefficient and network service pressure index, are the preset proportional coefficients of the traffic entropy variation coefficient and the network service pressure index, respectively, and Both are greater than 0.

[0094] The setting of preset proportionality coefficients is usually based on historical data analysis, expert experience, and actual test results. First, through statistical analysis of past network service failure cases and user behavior data, we can preliminarily evaluate the impact of different indicators (such as traffic entropy variation coefficient and network service stress index) on network stability. Then, combined with the expertise of network engineers and data scientists, determine the relative importance of these indicators in the fault prediction model. Finally, through a series of AB tests or simulation experiments, adjust these proportionality coefficients to ensure that they can effectively reflect the user's risk level and optimize the allocation of network resources and fault prevention measures.

[0095] The method of calculating the bucket priority evaluation coefficient by normalizing the traffic entropy variation coefficient and the network service pressure index and assigning a preset proportional coefficient can effectively quantify the user's network risk level. This calculation method provides a standardized measurement method to ensure that the evaluation results of different users are comparable, which facilitates the systematic sorting and prioritization. Through the size of the bucket priority evaluation coefficient, network service providers can clearly identify which users or user groups have a higher risk of service failures and therefore require more attention or resource investment. Such a strategy not only helps prevent possible failures in advance, but also more effectively allocates limited network resources and ensures the service quality of key users, thereby improving the overall network stability and user satisfaction.

[0096] It can be seen from the expression that the larger the bucket priority evaluation coefficient is, the greater the risk of potential failures in the user's network service is, and the higher the priority of the corresponding user bucket group is required for resource allocation and fault prevention measures.

[0097] Based on the preset rules, the user bucket groups and bucket priority evaluation coefficients assigned to the user are re-determined to prioritize the user bucket groups: All users are sorted according to the calculated bucket priority evaluation coefficient. A higher bucket priority evaluation coefficient means that the user is more likely to encounter service problems during peak hours and should be assigned to a higher priority user bucket group.

[0098] Consider users with high bucket priority evaluation coefficients as potential high-risk or high-demand users. These users may require more network resources or faster responses.

[0099] According to the value of the bucket priority evaluation coefficient, the user's position in the current bucket group is adjusted or the user is moved to a more appropriate bucket group.

[0100] Determine the threshold: For example, promote the 10% of users with the highest bucket priority evaluation coefficient to a higher priority group, or demote the 10% of users with the lowest bucket priority evaluation coefficient to a lower priority group.

[0101] Consider business impact: Ensure that the adjusted buckets better align with business needs and network performance goals.

[0102] For users identified as requiring priority resources, ensure they receive the necessary support during network peak hours.

[0103] For users with lower risks or less demand, their resource allocation should be appropriately adjusted to optimize the overall utilization efficiency of network resources.

[0104] For example, you may need to readjust the user buckets based on the sorting results and actual business needs. For example, you may set a threshold to place the top 20% of users with the highest bucket priority evaluation coefficients into the highest priority bucket.

[0105] For example, the user's buckets can also be dynamically adjusted based on the result of the bucket priority evaluation coefficient. For example, if the user's bucket priority evaluation coefficient is significantly higher than the average value of the bucket priority evaluation coefficients of its current bucket group, it can be considered to be moved to a higher priority bucket group.

[0106] The method of calculating the bucket priority evaluation coefficient by normalizing the traffic entropy variation coefficient and the network service pressure index and assigning a preset proportional coefficient provides a standardized and quantified means for network service providers to accurately identify and sort users who may encounter service problems during peak hours based on scientific data analysis. This not only helps to identify and prevent potential network failures in advance, but also allocates limited network resources more effectively, ensuring that key users receive the necessary service support during peak demand, thereby avoiding or reducing the impact of service interruptions. In addition, by dynamically adjusting the user's bucket group, it is possible to continuously optimize the network's operational efficiency, improve user satisfaction, enhance service reliability and stability, and ultimately promote business growth and expansion of the user base.

[0107] Define response and processing strategies for each user bucket group, and implement AB testing to optimize the fault handling process, specifically: First, define specific response and handling strategies for each bucket group based on the results of user bucketing (such as risk level, resource requirements, etc.). These strategies should reflect the principles of the bucketing standards, such as ensuring that high-risk or high-demand users receive priority resources and rapid fault response.

[0108] Specific strategies include but are not limited to: Priority management: Allocate more bandwidth and faster response time to high-risk bucket groups.

[0109] Resource allocation: Dynamically adjust bandwidth and storage resources based on the user's business importance and traffic demand.

[0110] Preventive maintenance: Perform regular network maintenance and inspections on high-risk bucket groups.

[0111] Test design: Design AB tests to compare the impact of different fault handling strategies or response times on user satisfaction and network performance. This can include testing different resource allocation models, fault response strategies, or technical support methods.

[0112] Grouping: Randomly divide users in the same bucket group into Group A and Group B to ensure that the two groups are statistically comparable.

[0113] Apply strategy: Implement one strategy in group A and another strategy in group B.

[0114] Data collection: Monitor and record the performance indicators of both groups, such as fault recovery time, system stability, user feedback, etc.

[0115] Data analysis: Analyze the collected data to determine which strategy is more effective and whether there is room for improvement.

[0116] Adjust based on results: Based on the results of AB testing, make necessary adjustments to the fault handling process. This may include changing the way resources are allocated, adjusting the fault response strategy, or introducing new technical solutions.

[0117] Continuous improvement: Treat AB testing as an ongoing process and regularly evaluate and optimize troubleshooting strategies.

[0118] Defining response and processing strategies for each user bucket group and implementing AB testing to optimize the fault handling process not only improves the response speed and efficiency of fault handling, but also helps operators optimize resource allocation and user services through a data-driven decision support system. It is an advanced application that meets the needs of modern network services.

[0119] Embodiment 2: Embodiment 2 of the present invention differs from Embodiment 1 in that this embodiment introduces a configuration system for dynamically adjusting business processes.

[0120] Figure 2 A structural schematic diagram of a configuration system for dynamically adjusting business processes of the present invention is given, a configuration system for dynamically adjusting business processes, including a preset grouping module, an adjustment trigger judgment module, a traffic entropy value analysis module, a peak risk analysis module, a bucket priority adjustment module and a strategy generation optimization module.

[0121] The preset grouping module buckets users according to preset rules and assigns users to corresponding user bucket groups.

[0122] The adjustment trigger judgment module evaluates the user's dependence on different network services; by analyzing the change in the user's dependence on different network services, it determines whether it is necessary to immediately trigger the adjustment of user bucketing.

[0123] The traffic entropy analysis module analyzes the entropy changes of user network traffic and assesses the potential risk of abnormal user traffic distribution.

[0124] The peak risk analysis module uses user traffic data and network resource consumption to simulate network pressure through a fluid dynamics model to assess user demand for network resources and the degree of service failure risk during peak hours.

[0125] When there is no need to immediately trigger the adjustment of user buckets, the bucket priority adjustment module will conduct a comprehensive analysis of the potential risks of abnormal user traffic distribution and the potential risks of user demand for network resources and service failures during peak hours, and adjust the priority of the user's corresponding user bucket group based on the comprehensive analysis results.

[0126] The strategy generation and optimization module defines response and processing strategies for each user bucket group and implements AB testing to optimize the fault handling process.

[0127] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0128] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0129] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0132] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0134] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0135] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0136] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for dynamically adjusting a business process configuration, characterized in that: The steps include: Bucketize users according to preset rules and assign users to corresponding user bucket groups; Evaluate the degree of user dependence on different network services; by analyzing the degree of change in the degree of user dependence on different network services, determine whether it is necessary to immediately trigger the adjustment of user buckets; Analyze the entropy changes of user network traffic and evaluate the potential risks of abnormal user traffic distribution. Use user traffic data and network resource consumption to simulate network pressure through a fluid dynamics model to evaluate user demand for network resources and service failure risks during peak hours. When there is no need to immediately trigger the adjustment of user buckets, a comprehensive analysis is conducted on the potential risk of abnormal user traffic distribution and the user's demand for network resources and service failures during peak hours, and the priority of the user's corresponding user bucket group is adjusted based on the comprehensive analysis results; Define response and processing strategies for each user bucket group, and implement AB testing to optimize the fault handling process.

2. A method for dynamically adjusting a business process configuration according to claim 1, characterized in that: Users are bucketed according to preset rules and assigned to corresponding user bucket groups, specifically: Collect multi-dimensional data related to users, including but not limited to geographic location, service level, historical fault records and network usage behavior; Define preset rules for user bucketing, including service type, usage, and impact; Users are divided into buckets based on their service type, usage, and impact.

3. A method for dynamically adjusting business process configuration according to claim 2, characterized in that: Assess the degree of user dependence on different network services, specifically: Obtain the frequency and duration of users’ visits to the Services; Define each service type as a node in the graph; define each user as a node in the graph; Create a directed graph; calculate the user center dependency index based on Katz centrality.

4. A method for dynamically adjusting business process configuration according to claim 3, characterized in that: By analyzing the changes in the degree of user dependence on different network services, it is determined whether it is necessary to immediately trigger the adjustment of user buckets. Specifically: Obtain the user-centered dependency index within the statistical period, and calculate the average value of the user-centered dependency index within the statistical period; The ratio of the absolute value of the difference between the average value of the user-centered dependency index in the statistical period and the average value of the user-centered dependency index in the previous statistical period to the average value of the user-centered dependency index in the previous statistical period is marked as the dependency change value; Set the dependency change threshold and compare the dependency change value with the dependency change threshold: When the dependency change value is greater than the dependency change threshold, it is determined that a signal for adjusting the user bucket group is generated, and the adjustment of the user bucket group is triggered; When the dependency change value is less than or equal to the dependency change threshold, it is determined that a continue analysis signal is generated, and there is no need to immediately trigger the adjustment of the user bucket.

5. A method for dynamically adjusting business process configuration according to claim 4, characterized in that: Analyze the entropy changes of user network traffic and assess the potential risks of abnormal user traffic distribution, specifically: Extract traffic data for each service; services include video streaming, online gaming, social media, file downloads, and general browsing; Calculate the traffic ratio of each service; Calculate network traffic entropy, the expression is: ;in, is the network traffic entropy, It is The traffic ratio of the service is the number of services, is the service number; Set the entropy monitoring interval; analyze the change of network traffic entropy within the entropy monitoring interval, and calculate the traffic entropy variation coefficient, which is expressed as: ;in, is the coefficient of variation of flow entropy, are the number of network traffic entropies monitored within the entropy monitoring interval and the serial number of network traffic entropies monitored within the entropy monitoring interval. is the first Network traffic entropy, is the first Network traffic entropy.

6. A method for dynamically adjusting business process configuration according to claim 5, characterized in that: Using user traffic data and network resource consumption, the fluid dynamics model is used to simulate network pressure and assess the user demand for network resources and the potential risk of service failure during peak hours. Specifically: Obtain user traffic data; collect resource usage in different time periods; The propagation and pressure distribution of data flow in the network are simulated through fluid dynamics model; Simulate network traffic and resource consumption in different time periods; Mapping real network traffic data into the fluid dynamics model; Calculate the network service pressure index, the expression is: ;in, is the network service pressure index, is the number of time periods in the statistical period, is the number of the time period within the statistical period. Is the user in the time period The flow rate within It is the time period Length, For users in time period The amount of resources used within It is a specific time The total amount of network resources within.

7. A method for dynamically adjusting business process configuration according to claim 6, characterized in that: When there is no need to immediately trigger the adjustment of user buckets, a comprehensive analysis is conducted on the potential risk of abnormal user traffic distribution and the potential risk of network resource demand and service failure during peak hours. Based on the comprehensive analysis results and the user buckets of preset rules, the priority of the user's corresponding user bucket group is adjusted, specifically: When a continue analysis signal is generated, the traffic entropy variation coefficient and the network service pressure index are normalized, and after the normalized traffic entropy variation coefficient and the network service pressure index are assigned preset proportional coefficients respectively, the bucket priority evaluation coefficient is calculated; Based on the preset rules, the user bucket groups and bucket priority evaluation coefficients assigned to the user are re-determined to prioritize the user bucket groups: All users are sorted according to the calculated bucket priority evaluation coefficient; the higher the bucket priority evaluation coefficient, the more likely they should be assigned to a higher priority user bucket group.

8. A system for dynamically adjusting a business process, used to implement a method for dynamically adjusting a business process according to any one of claims 1 to 7, characterized in that: It includes preset grouping module, adjustment trigger judgment module, traffic entropy value analysis module, peak risk analysis module, bucket priority adjustment module and strategy generation optimization module; The preset grouping module buckets users according to preset rules and assigns users to corresponding user bucket groups; The adjustment trigger judgment module evaluates the user's dependence on different network services; by analyzing the change in the user's dependence on different network services, it determines whether it is necessary to immediately trigger the adjustment of user bucketing; The traffic entropy analysis module analyzes the entropy changes of user network traffic and assesses the potential risks of abnormal user traffic distribution. The peak risk analysis module uses user traffic data and network resource consumption to simulate network pressure through a fluid dynamics model to assess user demand for network resources and the degree of service failure risk during peak hours. When there is no need to immediately trigger the adjustment of user buckets, the bucket priority adjustment module will conduct a comprehensive analysis of the potential risk of abnormal user traffic distribution and the user's demand for network resources and service failures during peak hours, and adjust the priority of the user's corresponding user bucket group based on the comprehensive analysis results; The strategy generation and optimization module defines response and processing strategies for each user bucket group and implements AB testing to optimize the fault handling process.

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