Dynamic Evaluation Model Research Articles

Population-level impact of switching to 1-dose human papillomavirus vaccination in high-income countries: examining uncertainties using mathematical modeling.

A concern in high-income countries is that switching to 1-dose human papillomavirus (HPV) vaccination could cause a rebound in HPV infection and cervical cancer if 1-dose efficacy or duration were inferior to 2 doses. Using mathematical modeling and up-to-date trial-based data, we projected the population-level effectiveness of switching from 2-dose to 1-dose vaccination under different vaccine efficacy and duration assumptions in high-income countries. We used HPV-ADVISE (Agent-based Dynamic model for VaccInation and Screening Evaluation), a transmission-dynamic model of HPV infection and cervical cancer, varying key model assumptions to identify those with the greatest impact on projections of HPV-16 and cervical cancer incidence over time: 1) 1-dose vaccine efficacy and vaccine duration, 2) mechanisms of vaccine efficacy and duration over time, 3) midadult (>30 years of age) sexual behavior, 4) progression to cervical cancer among midadults, and 5) vaccination coverage and programs. In high-income countries, 1-dose vaccination would cause no appreciable rebound in HPV-16 infection, except for a limited rebound under the most pessimistic assumptions of vaccine duration (average, 25 years), because 1) the switch would occur when HPV prevalence is low because of high 2-dose vaccination coverage and 2) individuals would be protected during their peak ages of sexual activity (<35 to 40 years of age). Our model projects a more limited rebound in cervical cancer because of a shift to older age at infection, resulting in fewer life-years left to potentially develop cancer. Projections were robust when varying key model assumptions. High protection during peak ages of sexual activity in high-income countries would likely mitigate any potential rebounds in HPV infection and cervical cancer under the most pessimistic assumptions of 1-dose efficacy and duration.

Optimization and Application of Comprehensive Environmental-Economic Evaluation Indicator System in Sustainable Development Policies of Chinese Manufacturing Industry

This paper focuses on optimizing the comprehensive environmental-economic evaluation index system to enhance the sustainable development policy of China’s manufacturing industry. The current indicator system, while useful, has notable shortcomings that limit its effectiveness. To address these issues, the paper proposes the inclusion of crucial indicators such as green productivity, carbon footprint, and water footprint, which are essential for providing a more holistic evaluation. These additions aim to capture better the environmental impacts and resource efficiency within the manufacturing sector. The paper also discusses the challenges in data acquisition, emphasizing the need for a standardized national environmental data platform. Such a platform would facilitate the collection of accurate, reliable, and accessible data, thereby improving the precision of evaluations. To further refine the assessment process, dynamic evaluation models, like the system dynamics model, are introduced. These models are designed to provide a more nuanced understanding of the environmental impacts over time, allowing for more responsive and adaptive policy-making. The optimized indicator system is expected to have significant applications in policy formulation, enterprise management, and social oversight. By providing a scientific basis for decision-making, it will play a critical role in advancing the sustainable development of China’s manufacturing industry, ensuring that environmental considerations are fully integrated into economic planning and industrial practices.

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

A Dynamic Trust evaluation and update model using advance decision tree for underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) are an emerging research area that is rapidly gaining popularity. However, it has several challenges, including security, node mobility, limited bandwidth, and high error rates. Traditional trust models fail to adapt to the dynamic underwater environment. Thus, to address these issues, we propose a dynamic trust evaluation and update model using a modified decision tree algorithm. Unlike baseline methods, which often rely on static and generalized trust evaluation approaches, our model introduces several innovations tailored specifically for UWSNs. These include energy-aware decision-making, real-time adaptation to environmental changes, and the integration of multiple underwater-specific factors such as water currents and acoustic signal properties. Our model enhances trust accuracy, reduces energy consumption, and lowers data overhead, achieving a 96% accuracy rate with a 2% false positive rate. Additionally, it outperforms baseline models by improving energy efficiency by 50 mW and reducing response time to 20 ms per packet. These innovations demonstrate the proposed model’s effectiveness in addressing the unique challenges of UWSNs, ensuring both security and operational efficiency goals. The proposed model effectively enhances the trust evaluation process in UWSNs, providing both security and operational benefits. These key findings validate the potential of integrating modified decision tree algorithms to improve the performance and sustainability of UWSNs.

Advanced Risk Assessment for Deep Excavation in Karst Regions Using Improved Dempster–Shafer and Dynamic Bayesian Networks

This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios.

Resilience evaluation and spatio-temporal evolution analysis of China's science and technology financial ecosystem

ABSTRACT This paper discusses the resilience of science and technology (sci-tech) financial ecosystem. It constructs an evaluation system from four dimensions: resistance, adaptability, recovery and innovation. The data are from 29 provinces in China from 2010 to 2020. It is evaluated using entropy weights–TOPSIS and dynamic evaluation model. The spatial evolution law is revealed by spatial autocorrelation analysis. The results show that: (1) The overall resilience level of sci-tech financial ecosystem is not high. Its evolution and development are generally in a relatively stable state. It has significant regional heterogeneity, showing a distribution pattern of ‘high east and low west’. (2) It has significant spatial positive correlation. The structure of the sci-tech financial ecosystem in the eastern region is highly complex, most provinces are in high–high agglomeration area. Provinces in other regions are mainly in low–low agglomeration area. Only a few provinces have experienced changes in the spatial distribution. (3) The dynamic value in the eastern region shows polarisation characteristics. Although the static value of resilience in the central and western regions is low, the dynamic evaluation value in some provinces in the central and western regions is high. The subsequent growth momentum of resilience in the north-eastern region is insufficient.

Evaluating the Public Health and Health Economic Impacts of Baloxavir Marboxil and Oseltamivir for Influenza Pandemic Control in China: A Cost-Effectiveness Analysis Using a Linked Dynamic Transmission-Economic Evaluation Model.

Pandemic influenza poses a recurring threat to public health. Antiviral drugs are vital in combating influenza pandemics. Baloxavir marboxil (BXM) is a novel agent that provides clinical and public health benefits in influenza treatment. We constructed a linked dynamic transmission-economic evaluation model combining a modified susceptible-exposed-infected-recovered (SEIR) model and a decision tree model to evaluate the cost-effectiveness of adding BXM to oseltamivir in China's influenza pandemic scenario. The cost-effectiveness was evaluated for the general population from the Chinese healthcare system perspective, although the users of BXM and oseltamivir were influenza-infected persons. The SEIR model simulated the transmission dynamics, dividing the population into four compartments: susceptible, exposed, infected, and recovered, while the decision tree model assessed disease severity and costs. We utilized data from clinical trials and observational studies in the literature to parameterize the models. Costs were based on 2021 CN¥ and not discounted due to a short time-frame of one year in the model. One-way, two-way, and probabilistic sensitivity analyses were also conducted. The integrated model demonstrated that adding BXM to treatment choices reduced the cumulative incidence of infection from 49.49% to 43.26% and increased quality-adjusted life years (QALYs) by 0.00021 per person compared with oseltamivir alone in the base-case scenario. The intervention also amounted to a positive net monetary benefit of CN¥77.85 per person at the willingness to pay of CN¥80,976 per QALY. Sensitivity analysis confirmed the robustness of these findings, with consistent results across varied key parameters and assumptions. Adding BXM to treatment choices instead of only treating with oseltamivir for influenza pandemic control in China appears to be cost-effective compared with oseltamivir alone. The dual-agent strategy not only enhances population health outcomes and conserves resources, but also mitigates influenza transmission and alleviates healthcare burden.

Dynamic evaluation of road and bridge engineering construction safety risk based on Fuzzy Dynamic Bayesian Network research

To comprehensively and objectively evaluate the actual safety condition in road and bridge engineering construction, the road and bridge engineering construction safety risk evaluation index system is constructed combined with the factors induced by emergencies in the road and bridge engineering construction process. Aiming at the dynamic uncertainty of road and bridge construction safety risk, using Fuzzy Set Theory and an improved similar aggregation method to determine the prior probabilities and conditional probabilities of network nodes, and then selecting the transition probabilities of nodes through expert opinions and incident reports, leading to the development of a dynamic evaluation model for safety risks in road and bridge engineering construction based on Fuzzy Dynamic Bayesian Network, this model can make the construction safety risk prediction result accurately. Taking the Hebi City Provincial Highway 304 reconstruction project as an example for analysis, the results indicate that the model can accurately predict the probability of changes in safety risks in road and bridge engineering construction. Additionally, it can identify critical risk factors and provide crucial supporting information for decision-makers to optimize risk management strategies.

Dynamic Comprehensive Evaluation of a 660 MW Ultra-Supercritical Coal-Fired Unit Based on Improved Criteria Importance through Inter-Criteria Correlation and Entropy Weight Method

To address the issue of traditional static evaluation models being unable to comprehensively analyze the performance of ultra-supercritical coal-fired units under varying loads, we propose a dynamic comprehensive evaluation model based on the improved Criteria Importance Through Inter-criteria Correlation (CRITIC) method and entropy weight method (EWM). The comprehensive performance evaluation index system of ultra-supercritical coal fired units is constructed by examining the boiler performance, turbine performance, plant power performance, environmental performance, and flexible performance of coal-powered units. The CRITIC and EWM methods are used to calculate the weights of the indicators, which are then combined with the static evaluation results. Using a dynamic comprehensive evaluation model, we analyze ultra-supercritical coal-fired units, taking into account time weight. This allows us to obtain the comprehensive dynamic real-time evaluation value of the units under different loads. The research indicates that the weight of the evaluation index is changed when using the dynamic comprehensive evaluation model of the improved CRITIC and EWM. The index with lower weight is increased by 6.2%, while the index with higher weight is decreased by 0.22%. This alteration in weight range can provide a more objective reflection of the relationship between evaluation indicators. This model offers significant advantages in improving evaluation accuracy, weight balance distribution, and generality.

Research on establishment of digital-twin system for intelligent control of cutting tools sintering process driven by data-model combination

In the manufacturing process of tungsten cemented carbide cutting tools, the changes in temperature, pressure and vacuum during the sintering process of the cutting tools can have a significant impact on the quality of the cutting tools. This effect is particularly significant in the vacuum sintering stage, so this paper focuses on cutting tools sintering in the vacuum stage. The cutting tools sintering process is carried out in a closed space in a sintering furnace with a complex internal structure. Therefore, it is extremely difficult to accurately predict the heating condition of the cutting tools in the furnace and monitor the sintering quality of the cutting tools. In this study, a digital twin system for the sintering process of cemented carbide cutting tools is proposed, which combines monitoring rolling data analysis and sintering quality prediction model. Firstly, the radiative thermodynamic model of cutting tools sintering temperature was established in this system. The model predicts the time-varying state of the heat distribution at any location in the furnace during the vacuum phase. Subsequently, based on the convolutional neural network, the mapping relationship between the sintering parameters and the grain size in the vacuum stage is constructed, and a dynamic evaluation model of cutting tools sintering quality is proposed. The model enables real-time prediction of sintering quality in the vacuum stage. The system realizes the prediction of the time-varying state of the sintering quality of the cutting tools. The digital twin system realizes the adaptive temperature control of the vacuum stage of cutting tools sintering based on the fuzzy control algorithm. The application of this system has increased the yield rate of cemented carbide cutting tools manufacturing to 98.9%. This breaks through the problem of timing modulation of cutting tools sintering quality in the vacuum stage of cutting tools sintering.

Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction

Due to factors such as the availability of assembly equipment, technology, and site management level, prefabricated building construction safety accidents often occur. To ensure the safety of prefabricated buildings and effectively reduce the accident rate, the concept of resilience is introduced into the safety management of prefabricated buildings. Based on the resilience absorption capacity, adaptation capacity, recovery capacity, and optimization capacity, a comprehensive evaluation index system for the safety resilience of prefabricated buildings is established. By combining prior knowledge with structural learning and parameter learning, a dynamic Bayesian network (DBN) model is constructed to dynamically evaluate the safety resilience of prefabricated buildings. Through forward causal reasoning and backward diagnostic reasoning, the dynamic safety resilience value of prefabricated buildings and the chain of maximum failure causes are obtained. Finally, by conducting a sensitivity analysis on the target nodes, the key influencing factors of the safety resilience of prefabricated construction are identified, and improvement suggestions for enhancing resilience are proposed. The results indicate that establishing a resilience safety culture, preventing unsafe behaviors of personnel, safety management, and supervision on the construction site, emergency management actions, and building a risk management information system are crucial factors influencing the safety resilience of prefabricated buildings. The enhancement of absorption capacity has the greatest impact on the safety resilience of prefabricated buildings.

Control parameter optimization of underwater gliders with uncertainty using hierarchical optimization method and interval analysis theory

This article attempts to solve the engineering optimization problem for control parameter configuration of underwater gliders with parameter uncertainty. The optimization objectives are maximizing the glider energy utilization rate and average voyage velocity; design variables include the net buoyancy adjustment amount and movable mass block translation amount; and uncertain parameters include the glider control parameter errors, manufacturing errors and sensor measurement errors. First, a dynamic simulation-based performance evaluation model of the glider is established, and interval number order theory is used to quantify the uncertainty. Then, surrogate models of the performance evaluation model are established, and the Sobol’ method-based sensitivity analysis is executed to identify the key uncertain parameters. Thus, a hierarchical optimization framework is introduced, and its system level and sub-level are used for determining optimal control parameter values and performance evaluation parameter intervals, respectively. Finally, optimization calculation is executed under different parameter configurations, and some rules are summarized.

Analysis of sustainable water resource management and driving mechanism in arid region: a case study of Xinjiang, China, from 2005 to 2020.

The long-term dynamic comprehensive evaluation of the water resource carrying capacity (WRCC) and the analysis of its potential driving mechanism in arid areas are contemporary research issues and technical means of mitigating and coordinating the conflict between severe resource shortages and human needs. The purpose of this study was to explore the distribution of the WRCC and the spatiotemporal heterogeneity of drivers in arid areas based on an improved two-dimensional spatiotemporal dynamic evaluation model. The results show that (1) the spatial distribution of the WRCC in Xinjiang, China, is high in the north, low in the south, high in the west, and low in the east. (2) From 2005 to 2020, the centers of gravity of the WRCC in northern and southern Xinjiang moved to the southeast and west, respectively, and the spatial distribution exhibited slight diffusion. (3) The factors influencing the WRCC exhibit more obvious spatial and temporal heterogeneity. The domestic waste disposal rate and ecological water use rate were the main factors influencing the WRCC in the early stage, while the GDP per capita gradually played a dominant role in the later stage. (4) In the next 30 years, the WRCC in Xinjiang will increase. The results provide a theoretical reference for the sustainable development of water resources in arid areas.

The Construction and Application of the Effectiveness and Dynamic Teaching Evaluation Model of Big Civic and Political Science Courses

Abstract How to make the teaching quality of the Civics and Political Science class continuously improve, the teaching effectiveness is highlighted in the growth of students, and the comprehensive development of students is effectively promoted, which has become an important part of the educational evaluation reform of the Civics and Political Science class in colleges and universities. Based on the construction concept of the objectivity evaluation index system, this paper determines the evaluation index system of the effect and dynamic teaching of the big Civic and Political Science class. It adopts expert knowledge to pre-process the research data. According to the judgment affiliation degree and weight set, determine the single-factor evaluation matrix of evaluation indexes, calculate the weight value of evaluation indexes, and then complete the construction of a fuzzy comprehensive evaluation model oriented to the effect of big Civic and Political Science teaching and dynamic teaching and based on the data analysis software, carry out the comprehensive evaluation and analysis. The results show that the size of the weight value of the first-level indexes in order of magnitude, dynamic teaching process A2 (0.5478) &gt; teaching preparation A1 (0.2353) &gt; big Civics effect A3 (0.2169), indicating that the weight accounted for by the dynamic teaching and teaching preparation is relatively large and there is a not-so-small gap between the weight value of the big Civics effect A3, so the teaching of big Civics courses should focus on teaching preparation and dynamic teaching. This study can not only enrich the teacher’s Civics teaching method, but also improve the students’ Civics level, which has a promoting effect on promoting the development of the teaching of Big Civics courses in colleges and universities.

Research on Dynamic Monitoring and Evaluation Model of Cadre Building in Colleges and Universities

Abstract In today’s context of rapid development of higher education, college cadres as the core force to promote the development of the school, its construction and management is particularly important. This paper adopts B/S architecture and MVC design, introduces the dynamic monitoring mechanism of the construction of college cadres, and builds a dynamic monitoring system. The assessment model adopting the multiple regression method is used to evaluate the cadre force’s response to changes in work efficiency through real-time and comprehensive data collection and analysis. The dynamic monitoring system’s online situation on Saturday and Sunday is not as good as it is on Monday to Friday, as found in this paper’s research. And dynamic monitoring can increase the willingness of cadres to learn different subjects and improve their knowledge. Dynamic monitoring can also enhance the average grade of the cadres, and the average grade after the implementation of dynamic monitoring is approximately 9.88 points higher than before monitoring. Overall, the cadre’s satisfaction with dynamic monitoring is high, with an evaluated satisfaction score of 4.92 on the nine indicators of the survey. A one-unit increase in “Monitoring management” is associated with a 0.694 increase in the work efficiency of the cadres, which is the most influential factor in dynamic monitoring. In conclusion, dynamic monitoring helps to provide strong data support for the construction of college cadres and to build high-quality, professional college cadres.

A hierarchical byzantine fault tolerance consensus protocol for the Internet of Things

The inefficiency of Consensus protocols is a significant impediment to blockchain and IoT convergence development. To solve the problems like inefficiency and poor dynamics of the Practical Byzantine Fault Tolerance (PBFT) in IoT scenarios, a hierarchical consensus protocol called DCBFT is proposed. Above all, we propose an improved k-sums algorithm to build a two-level consensus cluster, achieving an hierarchical management for IoT devices. Next, A scalable two-level consensus protocol is proposed, which uses a multi-primary node mechanism to solve the single-point-of-failure problem. In addition, a data synchronization process is introduced to ensure the consistency of block data after view changes. Finally, A dynamic reputation evaluation model is introduced to update the nodes’ reputation values and complete the rotation of consensus nodes at the end of each consensus round. The experimental results show that DCBFT has a more robust dynamic and higher consensus efficiency. Moreover, After running for some time, the performance of DCBFT shows some improvement.

Research on Grading and Evolution of International Trade Development Quality of City Clusters Based on Logit-ISM Modeling

Abstract In order to further accelerate the realization of high-quality development of international trade and enhance the discourse power in international trade activities, this paper explores the grading and evolution of the quality of international trade development of city clusters. After analyzing the DEA static model and DEA-Malmquist dynamic evaluation model, combined with the entropy value method, the entropy value-DEA model is constructed to measure the comprehensive evaluation of the quality of international trade development of the city cluster, and the results are comprehensively analyzed. In addition, combined with the Logit-ISM model to analyze the factors affecting the development quality of international trade of city cluster and explore the grading and evolution of the development quality of international trade of city cluster from two perspectives of global statistics and global spatial trend. The results show that the fluctuation of the trade structure score is smaller than the trade scale, with an average growth rate of 0.165, and the trade competitiveness score basically stays in a state of growth except for 2008 and 2009, with an average annual growth rate of 0.972. The urban agglomerations in the eastern region account for a total of 0.38 of the city agglomerations in the city agglomerations synthesis, and the number of prefectural-level cities included is 106, which accounts for a total of 0.52 of the number of prefectural-level cities included in the city agglomerations. The quality of international trade development in city clusters in the eastern and northeastern regions stands out as the most significant. Based on the research in this paper, we advocate for the balanced development of the quality rating and evolution of each city cluster in international trade development.

A Network-gene-mutation-based model for large network dynamic application reliability evaluation

Network evolution model (NEM) is a framework proposed recently for dynamic application reliability evaluation which integrates various factors that affect application failure. The existing component-based NEM relies on an exhaustive enumeration of all potential states of network components, which can lead to a computational bottleneck, especially when dealing with large networks. Therefore, to tackle this challenge, this paper presents a Network-gene-mutation-based NEM. The model introduces novel concepts of Network-cells and Network-genes inspired by the gene-cell-function model framework used in system biology to address complex disease problems. Network-cells are constructed according to the statistical classification of components. Network-genes are constructed by the average failure rate of components within a Network-cell. Dynamic network states are simulated by the Network-gene-mutation function of Network-cells instead of the failure and repair function of network components. Validation of our proposed model is performed through the comparison with benchmark models. The results demonstrate the validity of our proposed model in calculating dynamic application reliability. Besides, the comparison results with the component-based NEM also suggest that our proposed model reduces the time complexity of the algorithm from square order [Formula: see text] to constant order [Formula: see text] and improves the simulation efficiency by roughly 104. Applied to networks of varying scales, our proposed model proves to be an efficient method for calculating dynamic application reliability in large networks. In summary, this paper offers a promising solution to address the computational bottlenecks encountered in the component-based NEM when evaluating the reliability of dynamic applications.

Refined Urban Grid Simulation Planning Based on Low Carbon Goal Achievement

Abstract In this paper, a new grid planning combined with low-carbon operation simulation is proposed, a new grid planning and low-carbon operation simulation model is proposed, and the new grid planning model is investigated and illustrated from the four dimensions of constraints. For the new grid simulation planning model, the solution efficiency of the new grid planning model combined with low-carbon operation simulation is improved by the time series clustering method and the extreme scenario identification method based on the amount of lost load. Starting from the low-carbon benefit dynamics of the generation side and the consumption side, a refined urban new grid low-carbon benefit dynamic evaluation model is constructed, while the model parameters and table functions are set, and the low-carbon urban grid simulation planning based on low-carbon simulation is empirically analyzed by means of simulation analysis. The results show that in grid planning, when the carbon emission intensity is 1000gCO2/kWh, the S4 scenario only needs to add an extreme scenario in the iterative process to achieve a relative error accuracy of 0.1%, and the method of using the new grid planning and the low-carbon operation simulation model is sufficient to achieve the target of the loss of load rate and carbon emission intensity. In the evaluation of the low-carbon effect, with the increase in investment amount, the installed capacity of clean energy and the total CO2 emission reduction of the refined urban grid will show two relatively obvious upward trends.

Problems and Countermeasures of Enterprise Financial Sharing in the Context of Big Data

Abstract In this paper, a dynamic evaluation model of the functional requirements of enterprise finance centers is first established. Then a technology and business evolution monitoring model is constructed based on the Markov chain. According to the model of the technology-business evolution chain, the functional demand dynamic evaluation indexes are summarized, and the indexes are reduced by a rough set algorithm to improve the functional demand dynamic evaluation effect. Establish the functional demand dynamic evaluation method. A new decision tree algorithm is used to confirm the branches of the evaluation so as to decompose the evaluation system, realize the grounding of indicators, and provide auxiliary information for the enterprise’s financial sharing function optimization decision. Since the establishment of the financial shared service center of Enterprise S in 2014, its sustainable growth rate has decreased by 18.89% from 2014 to 2016. However, from the beginning of 2017 to 2019, the rise is as high as 20%. Thus, the establishment of the financial shared service center has improved the sustainable growth of the enterprise.