CN118036993A

CN118036993A - Team budget management system based on micro-service architecture

Info

Publication number: CN118036993A
Application number: CN202410284728.5A
Authority: CN
Inventors: 胡俊博; 王波; 孙剑; 王正刚; 付杰; 吕宁; 徐剑; 龚贵富; 韩合彦; 刘金艳; 杜唯准; 徐欣; 李辉; 余红军; 危琪; 陶韦文; 陈美菱; 周敏; 郭治平; 黎红梅
Original assignee: State Grid Hubei Electric Power Co Ltd Laohekou Power Supply Co; Xiangyang Power Supply Co of State Grid Hubei Electric Power Co Ltd
Current assignee: State Grid Hubei Electric Power Co Ltd Laohekou Power Supply Co; Xiangyang Power Supply Co of State Grid Hubei Electric Power Co Ltd
Priority date: 2024-03-13
Filing date: 2024-03-13
Publication date: 2024-05-14

Abstract

The invention discloses a group budget management system based on a micro-service architecture, which aims to realize automation and intellectualization of a group budget management flow. The dynamic micro-service arrangement mechanism is responsible for organizing and managing deployment and expansion and contraction configuration of micro-services according to real-time changes of service demands and system loads. The intelligent resource allocation mechanism analyzes the upcoming resource demands through a predictive algorithm and intelligently allocates computing and storage resources. The personalized intelligent assistant provides customized budgeting and optimization suggestions by learning the user's operating habits and preferences. The budget optimizing engine analyzes historical budget data by utilizing data analysis and machine learning technology, identifies saving opportunities and improves fund use efficiency. The self-adaptive interface is dynamically adjusted according to the roles and habits of the users, so that the user experience is greatly optimized. The system provides a set of comprehensive and intelligent solution for team budget management, effectively improves the efficiency and accuracy of budget planning, and supports the digital transformation of enterprises.

Description

Team budget management system based on micro-service architecture

Technical Field

The invention relates to the technical field of computers, in particular to a team budget management system based on a micro-service architecture.

Background

In modern enterprise management, budget management is a key component in ensuring financial health and supporting strategic decisions. With the expansion of enterprise scale and the complexity of business process, the traditional budget management method has the problems of low efficiency, slow response speed, difficult adaptation to rapidly changing business demands and the like. Especially at the base level, such as on the class, budget management tasks tend to become more cumbersome and inefficient due to resource misallocation, islanding, complex operations, and the like. These problems not only consume a great deal of manpower and material resources, but also affect the agility and competitiveness of enterprises.

The prior art often adopts a centralized system architecture, the architecture is not easy to expand, service changes are difficult to respond quickly, and great challenges are brought to maintenance and upgrading of the system. In addition, existing solutions often ignore the personalized needs of the user experience, resulting in that the actual problems and challenges encountered by the user during budget management are not effectively resolved.

Therefore, there is a need to develop a group budget management system based on a micro-service architecture to solve the problems of the conventional budget management method.

Disclosure of Invention

The application provides a group budget management system based on a micro-service architecture, which is used for improving the efficiency and accuracy of group budget establishment.

The application provides a group budget management system based on a micro-service architecture, which comprises:

The dynamic micro-service arrangement mechanism is used for dynamically organizing, managing, deploying and adjusting the expansion and contraction configuration of the micro-service according to the real-time change of the service demand and the system load; the dynamic micro-service arrangement mechanism supports various links of the group budget management, including demand reporting, budget planning and budget decomposition issuing of the group budget, so as to optimize the utilization rate of system resources and improve response speed;

the intelligent resource allocation mechanism is connected with the dynamic micro-service arrangement mechanism and is used for intelligently allocating computing and storing resources according to the current resource condition of the system and the priority of the service; analyzing the resource demand through a prediction algorithm, ensuring that the key business process obtains priority resources, and improving the system operation efficiency and stability;

The personalized intelligent assistant is connected with the dynamic micro-service arrangement mechanism and the intelligent resource allocation mechanism and is used for assisting in completing budget arrangement, adjustment and approval work by analyzing inquiry and operation intention of a user; learning user preference, actively proposing budget optimization suggestions, and simplifying user operation flow;

The budget optimization engine is connected with the personalized intelligent assistant and used for analyzing historical budget data through data analysis and machine learning technology and identifying saving opportunities and waste points; providing budget optimization suggestions, helping to adjust budget allocation and improving fund efficiency;

The self-adaptive interface is connected with the personalized intelligent assistant, can dynamically adjust the interface layout according to the roles and the operation habits of the user, provides visual navigation and operation guidance, and optimizes the user experience.

Still further, the personalized intelligent assistant comprises a user behavior analysis module; the user behavior analysis module is responsible for collecting operation histories and preference settings of the user and providing accurate and personalized budgeting and optimization suggestions for the user.

Furthermore, the personalized intelligent assistant can dynamically adjust the provided functions and data access levels according to the roles and the authorities of the user, ensure the data security and improve the user experience.

Still further, the budget optimization engine is capable of automatically updating budget optimization suggestions based on real-time market data and internal business metrics to accommodate rapidly changing economic environments and business requirements.

Still further, the adaptive interface employs responsive design and dynamic content loading to ensure that an excellent user experience is provided across a variety of device and screen sizes.

Furthermore, the self-adaptive interface can automatically adjust the layout and the shortcut of the main interface according to the operation habit and the frequently used function of the user, thereby improving the working efficiency and the satisfaction degree of the user.

Still further, the dynamic micro-service orchestration mechanism comprises:

The real-time business demand and system load analysis module is used for collecting and analyzing the types and the quantity of business operations and the demands of the business operations on resources, and the current computing capacity and the use condition of the storage capacity of the system in real time;

the micro-service configuration adjustment algorithm execution module performs dynamic expansion and contraction configuration of the micro-service using the following formula 1 based on the data collected from the real-time service demand and system load analysis module:

Wherein C _new represents a new microservice configuration state; c _current represents the current microservice configuration state; d _demand represents the currently detected traffic demand; d _capacity represents the current processing power of the system; p _priority represents the priority of the service; t _sensitivity represents the time sensitivity of the service; f _historical represents trend factors obtained based on historical data analysis; alpha, beta and gamma are preset adjustment coefficients respectively used for controlling the influence of the sensitivity of resource expansion and time sensitivity and the weight of historical data trend;

The micro service deployment management module is used for automatically executing deployment, starting, stopping and resource allocation adjustment of the micro service according to the new configuration state calculated by the micro service configuration adjustment algorithm so as to ensure that each service link obtains enough resource support according to actual requirements, and meanwhile, the high-efficiency operation and resource utilization optimization of the whole system are maintained.

Still further, the micro service configuration adjustment algorithm execution module is further configured to calculate the trend factor F _historical according to the following equation 2:

Wherein EMA _latest is the last day's moving average of the indices; average _year is the Average traffic demand a over the past year;

The calculation formula of the exponential moving average is shown in the following formula 3:

Wherein EMA _t is the exponential moving average on day t; v _t is the traffic demand on day t; s is a smoothing constant; d is the number of days of the selected time period; EMA _t-1 is the running average of the index on day t-1.

Still further, the intelligent resource allocation mechanism applies a predictive model to predict the resource demand in the short term, the predictive model being expressed by equation 4 as follows:

R_future＝λ×(w₁×R_historical+w₂×R_current+w₃×P_future) (4)

Wherein R _future represents the predicted future resource demand; r _historical represents the analysis result based on the historical resource usage trend; r _current represents the current real-time monitored resource usage; p _future is the predicted business demand increase based on current business activity and known future events; w ₁、w₂, and w ₃ are historical data weight, current data weight, and predicted business requirement growth weight, respectively; lambda is an adjustment factor used for adjusting a prediction result according to the overall resource management strategy of the system so as to adapt to different running environments and business strategies;

The intelligent resource allocation mechanism comprises a resource priority scheduling module, wherein the resource priority scheduling module adopts the following formula 5 to intelligently allocate computing and storage resources according to the priority of service, the predicted resource demand R _future and the current resource condition of the system:

Wherein R _alloc represents the amount of resources allocated for the service; p _biz represents the priority of the service; r _future is the future resource demand according to the prediction model; r _total represents the total amount of resources of the system, including computing and storage resources; u _current represents the current resource utilization of the system, calculated as the current amount of resources being used divided by the total amount of resources; sigma, delta, and epsilon are adjustment coefficients; a _adaptive is an adaptive adjustment factor for self-adjusting based on past resource allocation effects and traffic satisfaction to optimize future resource allocation policies.

The application has the following beneficial technical effects:

(1) The budget management efficiency and the response speed are improved: through a dynamic micro-service arrangement mechanism, the system can rapidly organize, manage, deploy and adjust the configuration of micro-services according to the real-time change of service demands and system loads. This means that the whole team budget management flow from demand reporting to budget planning and budget decomposition can be supported more smoothly and efficiently, and the response speed and processing efficiency of budget management are improved significantly.

(2) Optimizing resource utilization and system stability: the intelligent resource allocation mechanism ensures that computing and storage resources of the system can be intelligently allocated according to the current resource conditions and traffic priorities. The resource demand is analyzed through the prediction algorithm, and the key business process can obtain the priority resource, so that the overall utilization rate of the resource is optimized, and the running stability of the system is improved.

(3) Simplifying the operation flow of the user: the personalized intelligent assistant can provide immediate assistance and advice by resolving the user's query and operational intent, including proactively proposing budget optimization advice. The mechanism for learning the user preference and providing the help according to the user preference greatly simplifies the operation flow of the user and improves the working efficiency of the user.

(4) Accuracy and fund efficiency of budget planning are improved: the budget optimization engine can identify savings opportunities and waste points in historical budget data using data analysis and machine learning techniques. The provided budget optimization suggestions help the user adjust the budget allocation, thereby improving the fund use efficiency and the budgeting accuracy.

(5) Optimizing user experience: the self-adaptive interface is dynamically adjusted according to the roles and operation habits of the user, and visual navigation and operation guidance are provided. The personalized interface design not only improves the convenience of user operation, but also greatly improves the overall experience of the user.

Drawings

FIG. 1 is a schematic diagram of a group budget management system based on a micro-service architecture according to a first embodiment of the present application.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application may be embodied in many other forms than those herein described, and those skilled in the art will readily appreciate that the present application may be similarly embodied without departing from the spirit or essential characteristics thereof, and therefore the present application is not limited to the specific embodiments disclosed below.

A first embodiment of the present application provides a group budget management system based on a micro-service architecture. Referring to fig. 1, a schematic diagram of a first embodiment of the present application is shown. A first embodiment of the present application is described in detail below with reference to fig. 1, which provides a group budget management system based on a micro-service architecture.

The team budget management system includes a dynamic micro-service orchestration mechanism 101, an intelligent resource allocation mechanism 102, a personalized intelligent assistant 103, a budget optimization engine 104, and an adaptive interface 105.

In order to more quickly understand the group budget management system provided in this embodiment, the workflow of this group budget management system based on the micro-service architecture is described below by way of a detailed example:

Assume that a team leader needs to prepare a budget for the upcoming quarter. The team leader needs to submit the budget requirement, build a detailed budget, and conduct budget approval.

The workflow comprises the following steps:

1. a demand reporting stage:

the team responsible person logs in to the team budget management system, and the system displays an intuitive and easy-to-use operation interface according to the role of the team responsible person and the previous operation habit through the self-adaptive interface.

Team leader submitted a new budget requirement to the system via interface.

2. The dynamic micro-service orchestration mechanism starts:

Once the demand is submitted, the dynamic micro-service orchestration mechanism of the system responds immediately. Related micro-services, such as start-up budgeting services, data analysis services, etc., are dynamically organized, managed, and their configuration automatically adjusted as needed, based on current business needs (i.e., budgeting) and system load.

3. Intelligent resource allocation:

The intelligent resource allocation mechanism connected with the micro-service scheduling mechanism analyzes the resource condition of the current system and the priority required by the team leader, intelligently allocates computing and storing resources, and ensures that the budget scheduling process is smoothly carried out.

4. Personalized intelligent assistant intervention:

When the team leader starts to budget, the personalized intelligent assistant provides an initial template for budget planning and some suggestions, such as common budget items and budget ranges, based on the past operating habits and preferences of the team leader.

The assistant also parses the team leader's specific queries for budgeting, such as detailed descriptions of a particular expense, providing immediate feedback and guidance.

5. Budget optimization:

When the team leader fills in the preliminary budget draft, the system's budget optimization engine analyzes the historical budget data associated therewith, identifying possible savings opportunities and points of waste in the budget.

Then, through the personalized intelligent assistant, the system proposes a budget optimization suggestion to Li Gong, such as adjusting the allocation of certain budget items, to increase the efficiency of the use of funds.

6. Budget approval:

The team leader adjusts and perfects the budget draft according to the system's advice and then submits the approval.

In the approval process, the approver also obtains an approval interface customized according to the roles of the approver through the self-adaptive interface, so that the approval process is simple and efficient.

By this system, the budgeting process of team leader is greatly simplified and accelerated. The dynamic micro-service orchestration and intelligent resource allocation of the system ensures efficient processing performance and response speed. The personalized advice provided by the personalized intelligent assistant and the budget optimization engine helps the team responsible person optimize the budget, and improves the fund use efficiency. Finally, the adaptive interface ensures that all users obtain the best operation experience according to their own roles and habits.

The team budget management system presented in this example embodies the core principles and features of a micro-service architecture. The micro-service architecture is a method of developing a single application into a set of small services, each running in its own separate process, and communicating through a lightweight communication mechanism (typically an HTTP resource API). These services are built around business capabilities and can be deployed independently through fully automated deployment mechanisms. In addition, these services may be written in different programming languages and use different data storage techniques.

In this example, the interconnections and mechanisms of action between the components embody the following key features of the microservice architecture:

dynamic microservice orchestration mechanism: dynamic deployment and management capability of micro services are presented, and service configuration and expansion capacity are allowed to be automatically adjusted according to real-time service requirements and system loads, which is a manifestation of flexibility and responsiveness of micro service architecture.

Intelligent resource allocation mechanism: the method is tightly combined with micro-service arrangement, and the resources are intelligently allocated according to the current system resource status and service priority, so that the efficiency of the micro-service architecture in terms of resource utilization and optimization is demonstrated.

Personalized intelligent assistant: through interaction with the micro-service arrangement and resource allocation mechanism, customized services are provided, and the micro-service architecture is embodied to support highly personalized and flexible service function expansion capability.

Budget optimization engine: with data analysis and machine learning techniques, the characteristics of micro-service architecture that allow services to be implemented using multiple technologies and languages are further emphasized, facilitating innovations and optimal application of technologies.

Adaptive interface: interaction with the personalized intelligent assistant ensures that the best experience is provided according to different requirements of users, and the micro-service architecture is embodied to support front-end and back-end separation, so that a User Interface (UI) can be quickly iterated and optimized independent of the back-end service.

Overall, this example highlights the distributed nature, independence, flexibility of the micro-service architecture, and the ability to support complex business processes by combining different services, through interactions and linkages between the components.

A dynamic micro-service orchestration mechanism 101 for dynamically organizing, managing, deploying, and adjusting the scaling configuration of micro-services according to real-time changes in business needs and system load; the dynamic micro-service arrangement mechanism supports each link of the group budget management, wherein the links comprise demand reporting, budget planning and budget decomposition issuing of the group budget so as to optimize the utilization rate of system resources and improve response speed.

Dynamic microservice orchestration mechanism 101 is a core component of the present teams budget management system, and aims to provide a flexible and efficient way to respond to real-time changes in business needs and system load. The design and implementation of the present mechanism takes into account the features of the micro-service architecture, i.e., breaking down the application into a set of small, loosely coupled services, each of which implements a particular function or business logic of the application and can be deployed, extended, or updated independently.

The dynamic micro-service orchestration mechanism 101 works on the basis of real-time monitoring of all micro-services within the system, including but not limited to changes in the operational status, resource consumption, load conditions, and business requirements of each micro-service. The monitoring data will be used for decision support to dynamically organize, manage, deploy and adjust the configuration of the micro-services. This includes operations of micro-services start, stop, expand (add instance), contract (reduce instance), etc. to ensure optimal utilization of system resources and quick response to traffic demand changes.

Dynamic micro-service orchestration mechanism 101, when implemented, typically relies on containerization techniques and orchestration tools (e.g., kubernetes) that provide flexible deployment, automatic scaling, service discovery, and load balancing functions. By defining resource limitations and automatic scaling rules, the mechanism can automatically manage the lifecycle of each micro service instance, thereby maintaining the performance and reliability of the system without human intervention.

Still further, the dynamic micro-service orchestration mechanism comprises:

In this embodiment, a dynamic micro-service orchestration mechanism is provided, which, by analyzing service demands and system loads in real time and performing complex resource allocation adjustments, ensures that a team budget management system can run with optimized resource usage while responding to actual demands of each service link.

The core responsibility of the real-time business requirement and system load analysis module is to continuously monitor and analyze business operations occurring within the system. It details the type (e.g., demand reporting, budgeting, or budgeting resolution) and number of each business operation, as well as the immediate needs of those operations for computing power and storage capacity. The module provides an adjustment basis for micro-service configuration by collecting the current computing power (such as CPU usage, memory occupancy) and the usage of storage capacity (e.g., hard disk space usage) of the system.

The micro service configuration adjustment algorithm execution module executes a complex adjustment algorithm (equation 1) to dynamically expand or contract the configuration of the micro service based on the real-time traffic demand and the data collected by the system load analysis module.

C _new is the new micro-service configuration state obtained after calculation, which determines the future resource configuration of the micro-service, such as the allocated CPU and memory quantity.

C _current is the configuration state of the current micro-service, reflecting the current resource configuration of the micro-service.

D _demand is the current detected traffic demand, and measures the number and complexity of the traffic operation requests faced by the current system.

D _capacity is the current processing power of the system, which represents the overall power of the system to handle service requests, and is typically determined by the overall configuration of resources such as CPU, memory, and storage.

P _prioity represents the priority of the service, and different service links may be given different priorities, so as to ensure that the key service can obtain the resource preferentially.

T _sensitivity denotes the time sensitivity of the traffic, which is higher for traffic requiring immediate response.

And F _historical is a trend factor obtained based on historical data analysis, and reflects the change trend of past business requirements.

Alpha, beta and gamma are preset adjustment coefficients for adjusting the sensitivity of the resource expansion, the influence of time sensitivity and the weight of the historical data trend respectively. These adjustment coefficients can be obtained by calculation from experimental data or set directly by expert knowledge.

The micro service configuration state refers to the resource configuration and its related settings required for the micro service to run, and these configurations determine the performance, availability and response speed of the micro service. In a micro-service architecture based system, each micro-service may be deployed and extended independently, with configuration states including, but not limited to, CPU quota, memory capacity, number of instances (or containers), network bandwidth limitations, storage space, and service-specific configuration parameters (e.g., database connection numbers, timeout settings, etc.). The configuration state of the micro service is adjusted, so that the operation efficiency of the service can be optimized, and the service demand change is responded.

Taking equation 1 as an example:

Assuming that the micro-services in a team budget management system are responsible for handling budgeting functions, the current configuration state C _current includes 2 CPU cores and 4GB of memory. The traffic demand D _demand suddenly increases, the system detects that the processing capacity D _capacity is approaching saturation, the traffic priority P _priority is higher, and the time sensitivity to budgeting T _sensitivity is also higher, the trend factor F _historical shows that this demand increase is continuous.

In this case, by applying the above formula, a new micro service configuration state C _new can be calculated. Let the adjustment coefficient α=1, β=0.5, γ=0.2, and based on real-time monitoring and historical data analysis, we getP_priority＝1.5,T_sensitivity,F_historical＝0.8。

Substituting formula 1 for calculation:

C_new＝7.16

this means that based on the current traffic demand and real-time changes in system load, the configuration of micro-services should be scaled up from the original 2 CPU cores and 4GB memory to approximately 7 CPU cores and 14GB memory (assuming that the CPU and memory demands are scaled up).

Through the dynamic adjustment mechanism, the system can ensure that the budgeting micro-service can maintain high-efficiency operation performance even under the condition of sudden increase of demand, avoid response delay and promote user experience.

For the calculation of the trend factor F _historical, a specific method is provided below to calculate this factor. The trend factor F _historical is intended to reflect the trend of past traffic demands, for which time series analysis methods, such as Simple Moving Average (SMA) or Exponential Moving Average (EMA), can be used to determine the direction and intensity of the trend.

Assuming that an Exponential Moving Average (EMA) is selected as the method for calculating F _historical, the EMA better reflects the latest trend of change because it gives higher weight to the latest data.

The calculation formula of EMA is:

wherein:

EMA _t is the exponential moving average on day t,

V _t is the traffic demand on day t,

S is a smoothing constant, typically taking a value of 2,

D is the number of days of the selected time period, e.g., if looking at trends over the past 30 days, d=29,

EMA _t-1 is the running average of the index on day t-1.

To convert EMA to trend factor F _historical, a baseline (e.g., average traffic demand over the past year) and a threshold are determined to determine the significance of the trend. The specific calculation of F _historical is as follows:

1. Average traffic demand Average _year over the past year is calculated.

2. An observation period is selected, for example, 30 days in the past, and the EMA value is calculated for each day of the period.

3. The latest EMA value EMA _iatest, the last day of EMA, is calculated.

4. The trend factor F _historical is determined as the ratio of the latest EMA value EMA _latest to the Average service demand Average _year over the past year.

If F _historical is more than 1, the latest business demand is in an ascending trend; if F _historical < 1, the traffic demand is in a decreasing trend. The specific value of F _historical reflects the intensity of the trend, with larger or smaller values indicating more pronounced trend.

Through the method, the trend factor F _historical can be calculated clearly so as to be used in a dynamic micro-service arrangement mechanism, and the system resource configuration is ensured to adapt to the historical change trend of the service requirement.

The micro-service deployment management module is responsible for automatically executing deployment, starting, stopping and resource allocation adjustment of the micro-service according to the new configuration state calculated by the micro-service configuration adjustment algorithm. This ensures that each business link can obtain sufficient resource support according to actual demands while maintaining efficient operation and optimal resource utilization of the system as a whole.

By the mechanism, the team budget management system can realize dynamic management and optimization of resources, and can ensure that the system can maintain an efficient and stable running state even when facing the continuously changing business demands.

By implementing the dynamic micro-service orchestration mechanism 101, the team budget management system can realize efficient management and scheduling of micro-services, ensure reasonable allocation and utilization of system resources, and improve response speed and flexibility of the system to service demand changes. The implementation of the mechanism provides strong technical support for team budget management, so that the budget management process is more efficient and flexible, thereby helping enterprises to better adapt to market changes and improving competitiveness.

The intelligent resource allocation mechanism 102 is connected with the dynamic micro-service arrangement mechanism and is used for intelligently allocating computing and storing resources according to the current resource condition of the system and the priority of the service; and the resource demand is analyzed through a prediction algorithm, so that the key business process is ensured to obtain priority resources, and the system operation efficiency and stability are improved.

In this embodiment, the intelligent resource allocation mechanism 102 is a key technology, which aims to improve the operation efficiency and stability of the team budget management system. The mechanism is closely connected with a dynamic micro-service orchestration mechanism 101, which is specifically designed to intelligently allocate computing and storage resources based on the current resource status of the system and the priorities of the various services. The process covers the real-time monitoring of the system resource condition and the dynamic identification of the service priority, and ensures that the key service flow can obtain necessary resource support under the changeable service demand and system load conditions.

The core of realizing intelligent resource allocation is to adopt a prediction algorithm which can accurately predict future resource demands based on historical data and current operation modes. In this way, the system not only reacts quickly on the resource allocation, but also predictably adjusts the resource allocation, thereby avoiding resource bottlenecks and overload situations. In addition, the predictive resource management strategy can also reduce the idle and waste of resources and improve the overall resource utilization rate of the system.

In terms of technical implementation, the intelligent resource allocation mechanism 102 relies on a complex set of software logic that integrates a variety of factors including, but not limited to, the current computing power of the system, storage capacity, network bandwidth, and the urgency and importance of business operations. These factors are weighted and comprehensively analyzed by an algorithm to determine the allocation strategy of the resources. When in implementation, the mechanism is tightly integrated with a monitoring tool of the system, and the system performance data and the service request data are collected in real time, so that basis is provided for intelligent decision.

R_future＝λ×(w₁×R_historical+w₂×R_current+w₃×P_future) (4)

In a team budget management system, the intelligent resource allocation mechanism is a core component that utilizes predictive models to predict short-term resource demands, thereby ensuring that the system can intelligently allocate computing and storage resources according to the actual needs of the business. Implementation of this mechanism relies on two key mathematical formulas for predicting future resource requirements and allocating resources based on these predictions, respectively.

Equation 4 is used to predict future resource demands R _future. It combines three main input factors: analysis results R _historical based on historical resource usage trends, current real-time monitored resource usage R _current, and predicted business demand growth P _future based on current business activities and known future events. Each factor is assigned a weight w ₁、w₂、w₃ that reflects their relative importance in the predictive model. The adjustment factor lambda is used for adjusting the prediction result according to the overall resource management strategy of the system. These weights and adjustment factors can be obtained from experimental data or set directly by expert knowledge.

The analysis result R _historical based on the historical resource usage trend may be calculated by the following steps:

1. collecting historical resource usage data: first, it is necessary to collect resource usage data for the system over a period of time (e.g., the last 12 months), which may include CPU usage, memory usage, storage space usage, network bandwidth usage, and the like.

2. Trend analysis: historical data is analyzed using time series analysis methods, such as moving average or exponential smoothing methods, to identify trends in resource usage. For example, average resource usage per month in the past may be calculated and then an exponential smoothing algorithm applied to derive a smoothed trend line.

3. Determination of R _historical: based on the trend analysis results, R _historical can be set to the expected resource usage over the next time period, which is typically predicted by a trend line.

The current real-time monitored resource usage R _current can be calculated by the following steps:

1. And (3) real-time monitoring: current resource usage data, including CPU, memory, storage and network bandwidth usage, is collected in real-time using a system monitoring tool, such as promethaus or Zabbix.

2. Summarizing data: a simple mathematical summary of the collected real-time data is performed, e.g., calculating the average resource usage over the last few minutes, to obtain a snapshot of the current resource usage.

3. Determination of R _current: and taking the summarized result of the last step as R _current to represent the current resource usage.

The service demand increase P _future, predicted based on current service activity and known future events, may be calculated by:

1. analyzing business activity: the currently ongoing business activity and its resource requirements, including the ongoing tasks, the planned activities, and any known future events, are evaluated, which may affect the resource requirements.

2. Predicting the demand growth: based on the business activity analysis results, in combination with historical resource usage data and business growth patterns, statistical models or machine learning algorithms are used to predict business demand growth over a period of time in the future. For example, if a large sales activity is known to occur, it can be predicted how this will increase the resource requirements based on data from past similar activities.

3. Determination of P _future: the rate or amount of increase in the output of the predictive model is referred to as P _future, indicating an expected increase in business demand.

In summary, computing R _historical、R_current and P _future requires comprehensive historical data analysis, real-time monitoring, and prediction of future business activities. These computational steps provide the necessary inputs to the intelligent resource allocation mechanism so that it can intelligently adjust the resource allocation according to the predicted resource demand, ensuring that the system can operate efficiently and stably.

Equation 5 describes how the resources R _alloc are intelligently allocated based on the priority of traffic P _biz, the predicted resource demand R _future, and the current resource status of the system. This includes the total resource amount of the system R _total and the current resource utilization U _current. The adjustment coefficients sigma, delta, and epsilon and the adaptive adjustment factor a _adaptive allow the system to optimize the resource allocation policy based on past resource allocation effects and business satisfaction. The adjustment coefficients sigma, delta and epsilon can be obtained through experimental data or can be set directly according to expert knowledge.

The calculating step of the service priority P _biz includes:

Business priorities are typically assessed based on the importance and urgency of the business processes. This can be achieved by a simple scoring system in which different business processes are assigned different scores according to their contribution to the organizational targets and the time sensitivity of completing the processes. For example, a scoring criteria of 1 to 10 may be set, where 10 represents the highest priority. This score may be set by the business analyst based on business needs and strategic goals, and determined by team discussion and management layer approval.

The calculating step of the total resource amount R _total of the system comprises the following steps:

The total amount of resources of the system includes all available computing resources (e.g., CPU cores), memory size (GB), storage space (GB), and network bandwidth (Mbps). This may be calculated by summarizing resource configuration information provided by the data center or cloud service management platform. For example, if a system is made up of 10 servers, each with 8 CPU cores and 32GB of memory, the total amount of system resources is 80 CPU cores and the total amount of memory is 320GB. Similarly, the total amount of storage space and network bandwidth may be obtained by similar summaries.

The calculating step of the current resource utilization U _current comprises the following steps:

The current resource utilization refers to the ratio of the amount of resources currently being used to the total amount of resources. This can be calculated by the monitoring tool collecting the usage of each resource in real time. For example, if the current system uses 40 CPU cores, the CPU utilization is 40/80=0.5 or 50%. The utilization of memory, storage, and network bandwidth may also be calculated in the same manner. The total resource utilization may be a weighted average of these individual utilizations, where the weights reflect the extent to which different resource types affect system performance.

The step of calculating the adaptive adjustment factor a _adaptive includes:

The adaptive adjustment factor a _adaptive is used to make self-adjustment according to the past resource allocation effect and service satisfaction, so as to optimize the future resource allocation policy. This may be accomplished by analyzing historical monitoring data and user feedback. A particular method may include utilizing a machine learning algorithm, such as regression analysis or neural networks, to identify which resource allocation parameters most effectively support business needs and improve user satisfaction. The coefficients or model parameters output by the algorithm may be used as a value of a _adaptive for adjusting future resource allocations. As more data accumulates, a _adaptive can be updated periodically to reflect the latest business needs and system performance trends.

An example of the calculation of the adaptive adjustment factor a _adaptive is given below. This example employs a simplified version of the regression analysis method, a common machine learning algorithm, for predicting or determining the interdependence relationship between variables. In this example, it is assumed that the resource allocation efficiency (i.e., the relationship between resource utilization and business satisfaction) can be predicted by a regression model through historical data analysis.

Step 1, defining variables:

Business satisfaction Satisfaction: this is the goal of desiring optimization, which can be measured by user surveys or business performance metrics (e.g., response time).

Resource Utilization ratio optimization: indicating the use of system resources during a particular time period.

Other factors (X ₁,X₂,.): there may be other factors that affect business satisfaction, such as the number of concurrent users, the type of request, etc.

Step 2, collecting data:

Business satisfaction data and corresponding resource utilization data over a period of time are collected, as well as other factor data that may affect satisfaction.

Step 3, constructing a regression model:

A model is built by using linear regression analysis, business satisfaction is taken as an independent variable Y, and resource utilization rate and other factors are taken as the independent variable Y:

Y＝β_o+β₁×Utilization+β₂×X₁+β₃×X₂+...+∈1

Where Y is business satisfaction, β ₀ is intercept term, (β ₁,β₂,β₃,) is the coefficient of the respective variable, ε 1 is the error term.

Step 4, training a model:

This regression model is trained using historical data to determine the individual coefficients (beta values).

Step 5, calculate a _adaptive:

Once the model is trained, the coefficient of resource utilization β ₁ can be taken as the adaptive adjustment factor a _adaptive. This coefficient reflects the strength of the impact of resource utilization on business satisfaction and can be used to adjust future resource allocation policies.

After the regression model training is completed, the obtained model is:

Satisfaction＝0.5+0.8×Utilization-0.2×X₁+0.05×X₂

in this model, the coefficient of resource utilization is 0.8, indicating that the increase in resource utilization has a positive impact on business satisfaction. Therefore, a _adaptive =0.8, meaning that in the resource allocation policy, an increase in resource allocation should be considered to improve service satisfaction.

Step 6, application a _adaptive:

This a _adaptive value may be used in future resource allocation decisions to optimize resource allocation, such as by increasing resource allocation for high priority traffic in hopes of improving traffic satisfaction.

This simplified example demonstrates how the adaptive adjustment factor a _adaptive is determined by regression analysis, enabling the resource allocation mechanism to make more intelligent decisions based on data-driven insight. Over time and with the accumulation of more data, the model may be periodically retrained and updated to ensure that a _adaptive always reflects the latest business needs and system performance trends.

By the calculation method, an intelligent resource allocation mechanism can be accurately implemented, so that the resource allocation decision reflects the actual requirements of the service and is adapted to the current state and future trend of the system.

By implementing the intelligent resource allocation mechanism 102, the team budget management system can maximize the efficient use of resources while ensuring smooth operation of key business processes, and significantly improve the performance and user satisfaction of the system.

A personalized intelligent assistant 103 connected to the dynamic micro-service orchestration mechanism and the intelligent resource allocation mechanism for assisting in the completion of budgeting, adjustment and approval by resolving the user's query and operational intent; and learning user preference, actively proposing budget optimization suggestions, and simplifying user operation flow.

In the group budget management system based on the micro-service architecture provided in this embodiment, the personalized intelligent assistant 103 is a component, and aims to greatly simplify the operation flow of the user in the processes of budget planning, adjustment, approval and the like through a highly personalized interaction mode. The personalized intelligent assistant 103 is designed to take into account the specific needs and preferences of the user, and to provide accurate assistance and advice by resolving the user's query and operational intent, thereby improving the efficiency and user satisfaction of the overall budget management process.

The personalized intelligent assistant 103 adopts natural language processing technology and machine learning algorithm, can understand natural language input of a user, recognize requirements and intentions of the user, and provide corresponding services and suggestions according to the information. For example, when a user asks how to allocate a particular budget item, the personalized intelligent assistant can recommend one or more best practices based on the user's historical operating habits and existing budget data.

The key to implementing a personalized intelligent assistant is its learning mechanism. The assistant needs to be able to learn from the user's interactions continuously to better understand the user's preferences and needs. This includes analyzing the user's query history, modes of operation and feedback, as well as budgeting and approval results. Through this continuous learning and adaptation process, personalized intelligent assistants are able to provide increasingly accurate and personalized services over time.

In addition, the implementation of the personalized intelligent assistant needs to be integrated into the micro-service architecture of the team budget management system, and closely cooperate with other system components such as the dynamic micro-service orchestration mechanism 101 and the intelligent resource allocation mechanism 102. This means that the personalized intelligent assistant needs to be able to access and process data from various parts of the system, including budget data, resource usage and user behavior data, to support its decision making and suggestion generation.

In particular, data privacy and security need to be considered, so that safe storage and processing of user data are ensured. At the same time, the design of the personalized intelligent assistant should be flexible and extensible so as to be updated and upgraded according to new requirements and technical development in the future.

The implementation of the personalized intelligent assistant 103 may be implemented through artificial intelligence techniques such as an API interface that integrates a core language, which provides an efficient and flexible way to introduce natural language processing and machine learning capabilities into a teams budget management system. In this way, the personalized intelligent assistant can understand natural language query and operation instructions of the user, and provide customized budgeting, adjustment and approval support.

Specifically, when a user interacts with the system, making budget-related queries or requests, the personalized intelligent assistant translates the user's language input into a machine-understandable format by communicating with the API interface of the heart-to-speech. The discounts API processes these inputs and returns corresponding parsing results or performs corresponding operations according to its powerful natural language processing capabilities. For example, if the user asks about how to optimize budget allocation for a particular department, the personalized intelligent assistant sends this query to a centralized mono-lingual API, which analyzes the query content and returns specific optimization suggestions or related operational steps.

In addition, the personalized intelligent assistant can learn the preference and behavior mode of the user, and the response is continuously optimized through the machine learning component of the text-to-speech API so as to better meet the user demands. For example, if the system notices that a certain user is often reviewing budget execution at the end of the month, the personalized intelligent assistant may actively alert the user to this activity and provide relevant data support.

In a teams budget management system, the introduction of personalized intelligent assistants aims to provide customized services by in-depth analyzing user behavior and preferences, thereby greatly simplifying and optimizing the budgeting and management process. The following is a detailed description of the user behavior analysis module in a personalized intelligent assistant.

The main responsibility of the user behavior analysis module is to collect and analyze the user's operational history and preference settings in the teams budget management system. This includes details of how the user interacts with the system, such as frequently accessed pages, frequently used functions, frequently performed operations, and user settings preferences in the system. By collecting this data, the module can build a detailed user behavior model.

The technician needs to implement a data collection mechanism that may include user interface event tracking at the front end (e.g., click, scroll, page access time, etc.), and back end logging user operations and settings changes. All the data should be stored separately per user and the security and privacy of the data is guaranteed.

The collected data will be used to analyze the behavior patterns and preferences of the user. This may be accomplished by applying machine learning algorithms such as cluster analysis (to identify common patterns of behavior) and association rule learning (to discover relationships between user preferences). For example, if the user is found to frequently budget a report at the end of a month, the system may infer that the user may need to automatically hint or pre-load relevant data at the end of a month.

Based on the analysis results, the user behavior analysis module may provide personalized budgeting and optimization suggestions to the user. For example, if the system finds that a user often adjusts the budget allocation for a particular item, it may proactively provide optimization suggestions for the item, or alert the user to budget changes for the item.

In the team budget management system, the design of the personalized intelligent assistant takes the roles and the authorities of different users into consideration, so that the personalized intelligent assistant becomes a flexible and safe tool, and the user experience is improved.

Dynamic role and rights management functions in the personalized intelligent assistant are implemented by maintaining a detailed role and rights database at the back end of the system. Each user account is assigned a specific role, each role having a predefined set of permissions defining a range of data and functions that can be accessed and operated by the user.

Roles are defined in terms of the user's responsibilities and requirements during team budget management. For example, the system may define the following roles: budget administrators, department managers, financial analysts, etc. Each role is associated with a different level of authority that specifies the types of data that the role can access, the operations that can be performed, and the scope of these operations.

Rights are specific grants to a single operation or data access request. They are mapped onto roles, forming a relationship of roles to rights. For example, a budget manager may have the right to create and modify all budget items, while a department manager can only access and modify budget data for its department.

The personalized intelligent assistant processes each user request using a middleware component that is responsible for checking the user's role and rights associated therewith. When a user attempts to perform an operation or access certain data, the system dynamically determines whether to allow the request based on the user's role and permissions. If the user attempts to perform an operation outside of their rights, the system will reject the request and possibly display a message to the user indicating that they do not have sufficient rights.

By the method, the personalized intelligent assistant can provide customized services for users with different roles, so that the data safety is ensured, and the user experience is optimized. The dynamic role and authority management mechanism makes the team budget management system a flexible and safe tool, and is suitable for various business requirements and operating environments.

The personalized intelligent assistant provided by the embodiment remarkably improves the user experience of the team budget management system, and enables the budget management process to be more efficient, flexible and user-friendly.

A budget optimization engine 104, coupled to the personalized intelligent assistant, for analyzing historical budget data through data analysis and machine learning techniques, identifying savings opportunities and waste points; and providing a budget optimization suggestion, helping to adjust budget allocation and improving fund efficiency.

The budget optimization engine 104 plays a core role in this embodiment, which is a key component of a team budget management system based on a micro-service architecture, specifically designed to analyze historical budget data, identify savings opportunities and points of wastage, and provide budget optimization suggestions. By combining data analysis and machine learning techniques, the engine not only makes the budget management process more efficient, but also more accurate and intelligent.

The budget optimization engine 104 operating principle is based on in-depth analysis of large amounts of historical budget data. By collecting and processing data from various aspects of past budget execution, including but not limited to, budget planning, execution, adjustment, and actual spending, the engine is able to build detailed representations of budget execution. Next, using machine learning models, the engine analyzes the data to identify patterns and trends therein, such as which budget items are often hyperbranched and which are often underutilized.

To achieve this, the budget optimization engine 104 incorporates a variety of data analysis algorithms, including regression analysis, cluster analysis, and deep learning, to accommodate data sets of different types and sizes. These algorithms can identify key savings opportunities and waste points from complex data, providing specific and viable budget optimization suggestions for users. For example, if a department's office product budget remains for several consecutive periods, the engine may recommend reducing the budget allocation for that item, and use the saved funds on other items that may be under tension.

In addition, the budget optimization engine 104 can also dynamically adjust its analytical model and recommendation strategies based on current business environments and market conditions. This means that over time and data accumulation, the engine's recommendations will become more and more accurate, fitting more into the actual business needs and budget management objectives.

By implementing the budget optimization engine 104, the team budget management system can help users identify and utilize saving opportunities, reduce unnecessary waste, improve the fund efficiency and decision quality of the whole budget management process, and finally help enterprises to realize more refined and intelligent financial management.

In a teams budget management system, one of the core functions of the budget optimization engine is to be able to automatically update budget optimization suggestions with real-time market data and internal business metrics. This feature ensures that the budget management process can flexibly adapt to rapid changes in economic environment and business requirements, thereby improving the timeliness and accuracy of decisions. The following is a detailed description of the steps required to achieve this function.

First, the budget optimization engine needs to be able to access external market data sources to obtain real-time economic and market information. This may include, but is not limited to, raw material prices, exchange rates, interest rates, and other key economic indicators. This may be accomplished by pulling the relevant data in real time using an API interface in cooperation with the financial market data provider. The technician needs to ensure that the accuracy of the data and the update frequency can meet the requirements of budget optimization.

At the same time, the budget optimization engine needs to be able to analyze business metrics from within the organization, including key performance metrics (KPIs) such as sales, costs, inventory levels, production efficiency, etc. This requires that the system be able to integrate with internal ERP, CRM, etc. business management systems, collect and process these data in real time.

In combination with real-time market data and internal business metrics, the budget optimization engine applies a data analysis model to evaluate the impact of current economic environment and business performance on the budget. This may include using statistical analysis, trend prediction, machine learning, etc. techniques to identify key factors that affect budget execution and predict their future trend of change.

Based on the analysis, the budget optimization engine can automatically generate budget optimization suggestions for different business links. These suggestions will automatically push to relevant decision makers, helping them make more data driven decisions. For this reason, the system needs to have a set of mechanisms to ensure timeliness, relevance and understandability of the advice.

In order to improve the user experience, the budget optimization engine should also include an intuitive user interface for presenting budget optimization suggestions in the form of charts, reports, notifications, and the like. Furthermore, the user should be able to feedback the recommended adoption and make manual adjustments if necessary via the interface.

The self-adaptive interface 105 is connected with the personalized intelligent assistant, can dynamically adjust the interface layout according to the roles and the operation habits of the user, provides visual navigation and operation guidance, and optimizes the user experience.

In this embodiment, the adaptive interface 105 is designed and implemented to ensure that each user is able to obtain an optimal user experience according to their roles and operating habits. The adaptive interface 105 is a key component that interfaces directly with the personalized intelligent assistant 103, thereby enabling dynamic adjustment of interface layout and provided navigation and operational guidance based on the user's behavior and preferences, as well as data obtained from the personalized intelligent assistant 103, in real time.

The adaptive interface 105 is developed to take into account the diversity requirements of the User Interface (UI), including but not limited to different devices (e.g., personal computers, tablet computers, smartphones, etc.), different operating systems, and different browsers. The method adopts a responsive design principle, ensures that the user interface can be automatically adapted to different screen sizes and resolutions, and provides clear and consistent visual experience for users.

To achieve this goal, the adaptive interface 105 uses some front-end technology, such as HTML5, CSS3, and JavaScript framework. Among other things, the media query function of CSS3 is key to implementing a responsive design, which allows the interface to dynamically adjust layout and style according to different device characteristics (e.g., screen size, resolution, etc.). The JavaScript framework is used for processing the interaction between the user and the interface, capturing the operation intention and preference of the user in real time, and interacting with the personalized intelligent assistant 103 to realize the dynamic adjustment of the content and layout of the interface.

In addition, the adaptive interface 105 is designed to fully take into account the user's operating habits and role differences. The system identifies common functions and preference settings of the user by analyzing operation history and role information of the user, optimizes interface layout by an algorithm, places the most frequently used functions of the user at a conspicuous position, simplifies operation paths of the user, reduces operation steps and improves working efficiency.

Implementing the adaptive interface 105 also includes a complete set of testing and optimization procedures that ensure that the interface provides a consistent and reliable user experience under different devices and environments. This includes cross-browser testing, cross-device testing, and performance testing, ensuring fast response and efficient data loading.

In a team budget management system, the design of the adaptive interface is critical, which ensures that the user gets a consistent and quality experience regardless of the device used or screen size. The self-adaptive interface is realized by adopting a responsive design and a dynamic content loading technology, and the combination of the two technologies enables the interface to be automatically adjusted to adapt to different browsing environments, so that the interactive experience of a user and the usability of a system are improved.

The response type design is a webpage design methodology, and the core principle is to use CSS media inquiry to detect the characteristics of screen size, resolution and the like of an operation environment, and dynamically adjust the webpage layout, element size and typesetting according to the characteristics. This means that designers and developers need to create multiple sets of interface layouts to fit a variety of devices, from cell phones and tablets to large screen desktop displays. For example, a three-column layout may be well-displayed on a large screen, but on a cell phone it is necessary to adjust to a single-column layout to maintain the readability and accessibility of content.

Dynamic content loading, also known as asynchronous loading, refers to loading content on demand during user interaction with an interface, rather than loading the entire page content at once. This is accomplished through JavaScript and Ajax techniques, allowing the web page to exchange data with the server and update portions of the web page content without reloading the entire page. The method not only can reduce data transmission and reduce the burden of the server, but also can obviously improve the user experience, especially when the mobile equipment is connected by a network.

The implementation steps comprise:

1. Layout design: multiple sets of layouts are designed to accommodate different screen sizes and device types. Techniques such as fluid grid (layouts), scalable picture and media queries (media queries) are used to ensure that the content is properly displayed on any device.

2. Application of media query: with CSS media queries, different style rules are applied according to different screen sizes and resolutions. For example, different CSS files or style segments are defined for the small screen device, tablet, and desktop.

3. Dynamic content management: front-end scripts are developed for dynamically requesting and loading content upon user interaction. This may involve modifying an existing Content Management System (CMS) to support dynamic loading and rendering of content.

4. Performance optimization: the implementation of the adaptive interface is ensured not to negatively affect the performance of the website. This includes optimizing the picture size, using the Content Delivery Network (CDN), minimizing CSS and JavaScript files, etc.

5. Testing and verifying: and testing the self-adaptive interfaces on different devices and browsers to ensure that all users can obtain consistent experience. Cross-browser testing is performed using tools such as BrowserStack, as well as testing on real devices to verify the effects of responsive design and dynamic content loading.

By following the steps, a flexible and efficient adaptive interface can be realized, ensuring that a smooth and consistent user experience is enjoyed no matter what device the user accesses. This not only improves user satisfaction, but also enhances accessibility and usability of the system.

In the team budget management system, the design of the adaptive interface is further extended to automatically adjust the main interface layout and shortcuts according to the user's operating habits and frequently used functions. The core of the design concept is to personalize the user experience so that each user can have a customized interface layout and function access path according to the specific requirements and use modes of the user. To ensure that one skilled in the art can implement this feature, a detailed description of the implementation of this function follows.

First, the system needs to be able to capture and analyze the behavior data of the user. This includes which functions are accessed frequently, the distribution of time the user spends in the system, how the user navigates, and which operations are repeatedly performed, etc. Such data may be collected through front-end user interface event tracking techniques, such as click events, page access durations, scrolling behavior, etc., and aggregated and analyzed through back-end services.

The collected user behavior data then needs to be analyzed to identify patterns and preferences. This may be achieved through data mining and machine learning techniques, such as cluster analysis, which may help identify different modes of user operation, while predictive models may be used to predict new functions or required resources that may be of interest to a user. Based on these analysis results, the system can identify the interface layout of the most frequently used functions and preferences of the user.

The system then needs to translate these analysis results into specific interface adjustments. This means that a dynamic interface rendering module is developed that can automatically adjust the interface layout and shortcuts based on the results of the user's behavioral analysis. For example, if the analysis shows that the user frequently accesses a particular report generating tool, the system may automatically place a shortcut to that tool in a conspicuous location on the main interface.

Furthermore, in order to ensure consistency of user experience and persistence of personalized settings, the user's personalized interface configuration needs to be stored in the user's personal profile. This requires the system back-end to provide enough memory to hold the configurations and load the personalization settings every time the user logs in.

In summary, the design and implementation of the adaptive interface 105 is intended to provide an intuitive, easy-to-use, highly personalized user interface that allows each user to obtain the best operating experience and efficiency when using the teams budget management system, according to their own specific needs and preferences. By tightly integrating with the personalized intelligent assistant 103, it can ensure real-time adaptive adjustment of the user interface, thereby greatly improving user satisfaction and overall performance of the system.

While the application has been described in terms of preferred embodiments, it is not intended to be limiting, but rather, it will be apparent to those skilled in the art that various changes and modifications can be made herein without departing from the spirit and scope of the application as defined by the appended claims.

Claims

1.A group budget management system based on a micro-service architecture, comprising:

2. The team budget management system of claim 1, wherein said personalized intelligent assistant includes a user behavior analysis module; the user behavior analysis module is responsible for collecting operation histories and preference settings of the user and providing accurate and personalized budgeting and optimization suggestions for the user.

3. The team budget management system of claim 1, wherein said personalized intelligent assistant is capable of dynamically adjusting offered functions and data access levels according to roles and permissions of a user, ensuring data security while enhancing user experience.

4. The team budget management system of claim 1, wherein said budget optimization engine is capable of automatically updating budget optimization suggestions based on real-time market data and internal business metrics to accommodate rapidly changing economic environments and business requirements.

5. The team budget management system of claim 1, wherein said adaptive interface employs responsive design and dynamic content loading to ensure an excellent user experience across a variety of devices and screen sizes.

6. The team budget management system according to claim 1, wherein said adaptive interface is capable of automatically adjusting a main interface layout and a shortcut according to a user's operation habit and a frequently used function, thereby improving a user's work efficiency and satisfaction.

7. The group budget management system recited in claim 1, wherein said dynamic microservice orchestration mechanism comprises:

wherein C _new represents a new microservice configuration state; c _current represents the current microservice configuration state; d _demand represents the currently detected traffic demand; d _capacity represents the current processing power of the system; p _priiority represents the priority of the service; t _sensitivity represents the time sensitivity of the service; f _historical represents trend factors obtained based on historical data analysis; alpha, beta and gamma are preset adjustment coefficients respectively used for controlling the influence of the sensitivity of resource expansion and time sensitivity and the weight of historical data trend;

8. The teams budget management system in accordance with claim 7, wherein said micro-service configuration adjustment algorithm execution module is further operative to calculate a trend factor F _historical in accordance with equation 2 as follows:

9. The teams budget management system in accordance with claim 1, wherein said intelligent resource allocation mechanism applies a predictive model for predicting resource demand in the short term, said predictive model being expressed by equation 4 as follows:

R_future＝λ×(w₁×R_historical+w₂×R_current+w₃×P_future) (4)

The intelligent resource allocation mechanism comprises a resource priority scheduling module, wherein the resource priority scheduling module adopts the following formula 5 to intelligently allocate computing and storage resources according to the priority of service, the predicted resource demand H _future and the current resource condition of the system: