With the rapid development of science and technology, more and more novel and functional new materials are gradually entering our real life. Graphene, which is the most popular material in recent years, exists in nature; but it is difficult to peel out the monolayer structure of graphene, layer by layer, which is graphite.A quantity of 1 mm thick graphite contains about 3 million layers of graphene. The main functions of graphene are mainly reflected in the following aspects, as shown in Table 1: Firstly, graphene has good mechanical properties [1–3], which is one of the materials with the highest known strength. At the same time, though graphene is tough, it is also flexible and can be bent. In addition, graphene has a good electronic effect [4–6], and the carrier mobility at room temperature is about 10 times that of commonly used silicon materials,which is not affected by ambient temperature. Graphene material can also be used for various purposes of heat dissipation [7–9], which can increase the heat dissipation efficiency of objects. The optical properties of graphene [10–12] show that it looks almost transparent in a wide wavelength range. Finally, graphene shows good solubility in non-polar solvents, superhydrophobicity and superlipophilicity, and can adsorb and desorb various molecules and atoms [13–15]. It claims the distinction of being the first material to be applied in commercially, such as in mobile devices, aerospace, new energy batteries [16] and other relevant fields. For industrialisation of graphene project, however, the influences of external factors are still very strong. As the most critical part of project research, finance is the basis to guarantee the stability of industrialisation. Therefore, for the emergence of graphene industry projects, it is imperative to comply with its system of risk evaluation which is crucial to help graphene industry projects to solve some of the common risks, and to provide financial support for the industrialisation of graphene to become bigger and stronger.

Main functional characteristics of graphene

Mechanical properties | High strength, but also has good toughness |

Electronic effect | The carrier mobility is high at room temperature and is little affected by temperature |

Thermal performance | It has very good heat conduction performance |

Optical properties | Graphene films have excellent optical properties, and their optical properties change with the thickness of graphene |

Solubility | It is super hydrophobic and super lipophilic, and can adsorb and desorbed various molecules and atoms |

Biomass in industrialisation of grapheme; graphene material and differences and similarities with the biomass [17–19]: structurally, there is essentially no difference between graphene which is the basic element of carbon and biomass and the biomass materials which contain carbon, and if they are added with other elements and the impurities removed, graphene can be prepared. In terms of properties, graphene prepared from biomass materials and non-biomass materials has the same physical and chemical properties. However, the sources of the two materials are different. Biomass graphene is prepared from leaves and other biomass materials, while non-biomass materials are graphite ore and other biomass materials. In the preparation of grapheme, it mainly uses biomass materials such as carbon tubesfollowing the mechanical stripping method, epitaxial growth method, chemical vapour deposition method, oxidation-reduction method, and cutting method. With the use of biomass and cellulose catalysts, complex ion formation occurs, and with the deoxidisation process under high temperature, graphene precursor can be obtained. It goes through heat treatment, acid treatment, drying and so on in the process of graphene formation. In contrast, biomass graphene is cost-effective, and the materials come from a wide range of sources with high yield, suitable for industrial large-scale production. In the financial risk system of mass production, Cincinelli

Bellavite Pellegrini [21] studied how the characteristics of different financial institutions are related to systemic risk. They compared traditional banks with shadow entities such as money market funds and financial services. It was found that the size of large financial institutions, especially money market funds, significantly increased systemic risk. Gao [22] studied the final boundary region of the financial risk system through numerical analysis. Based on the optimisation method and Lagrange multiplier method, the analytical expression of the limit boundary region is derived, the economic variables can be detected and observed and the financial risk is reduced. Financial risk handling is crucial for emerging enterprises. Gu [23] studied the different influences of major leaders of companies on financial risk handling, and their research shows that the overseas experience of CEOS reduces the risk of financial misconduct in Chinese enterprises. Cheng

According to the above analysis, financial risk is crucial for the development of the biomass graphene industry. In the emerging field of biomass, the evaluation of the financial risk system is more important. In this paper, we compare several models commonly used in financial risk research [25], choose the TOPSIS method for industrial project analysis, and use a mathematical model to analyse the financial risk of biomass graphene fibre industrialisation project. By comparing the advantages and disadvantages of each method, the method suitable for financial risk evaluation of biomass graphene fibre industrialisation project is selected. In the process of model and method evaluation, this paper will comprehensively consider the external environment of the market and the factors of the industrial project to analyse the applicability of each model to the industrial project.

The key to the success of the project is to evaluate the financial risk correctly and prevent it beforehand. The financial condition of the project can reflect the good condition of the project, so the risk evaluation not only evaluates the financial problems but also analyses the comprehensive condition of the project. Through the establishment of a financial risk evaluation model, financial personnel can evaluate the financial risk of the project based on historical data and the market environment. By analysing the historical financial data of industrial projects, we can get the development trend of industrial projects and the risks they face. Therefore, through the construction of a financial evaluation model, we can predict the operation and management risks of an industrial project, find the shortcomings in the operation process and implement effective measures.

The author selected several models commonly used in financial risk research for comparison and selected the method suitable for financial risk evaluation of biomass graphene fibre industrialisation project by comparing the advantages and disadvantages of each method. In the process of model and method evaluation, this paper will comprehensively consider the external environment of the market and the factors of the industrial project to analyse the applicability of each model to the industrial project.

Financial indicators are screened by mathematical statistics. The specific equation is as follows:
_{1} is working capital/total assets; _{2} is retained earnings/total assets; _{3} is EBIT/total assets; _{4} represents the market value of ownership/total liabilities, and finally, _{5} refers to total sales/total assets. When,

This method evaluates the financial risks of enterprises by establishing a linear regression model and determines whether the evaluation objects have risks through the probability of event occurrence. The equation is as follows:
_{i}_{i}

This method simulates the human brain and builds an artificial network model that can ‘learn’, and uses knowledge accumulation and utilisation, thus reducing the difference between the obtained solution and the actual value. This method is introduced into the study of financial risk early warning. A neural network structure has an input layer, an intermediate layer and an output layer. This three-layer neural network model has a super powerful learning ability. However, this method requires a large amount of input data.

This method analyses the nature of the problem and the target to be completed, decomposes the problem layer by layer, stratifies it according to the correlation and subordination of the influencing factors of the problem, and constructs a multi-level risk warning model. However, when determining the index weight, this method makes a pairwise comparison between the influencing factors, which requires a high level of professional judgement and strong subjectivity.

By setting two values for each evaluation index, one is a satisfactory value, and one is not an allowed value. The satisfactory value is the upper limit of the evaluation index, and the unpermitted value is the lower limit. The two values are used to calculate the score of each index, and then the weighted average is used to calculate the financial risk of the evaluation object. But this method is difficult to determine the satisfactory value and not allowed value, so the operation is difficult. In addition, the influence degree of the index is different, so it is necessary to combine the objective weighting method to ensure the validity of the calculation results.

This method transforms multiple variables into a few unrelated comprehensive variables to reflect the whole data set comprehensively. At present, the model needs to ensure that the principal components contain a high level of information and can give meaningful and realistic explanations. In addition, the principal component may be fuzzy, not clear or precise.

In this method, the evaluation objects are ranked by their proximity to the set of optimal and worst solutions. By constructing the evaluation matrix, the financial risk of the evaluation object is ranked by a mathematical model, which is more objective. It can be compared with both historical data and industry data.

Through the comparison of various models, this paper chooses the TOPSIS method to analyse industrial projects. The financial risk of the biomass graphene fibre industrialisation project was analysed by a mathematical model.

The application of the model can be divided into six steps: data standardisation processing, using an evaluation matrix to determine the weight, constructing weighted normalisation matrix, determining positive and negative ideal solutions, calculating Euclidean distance, and calculating relative closeness degree. See Figure 1 below:

Where _{ij}

The standardisation of the decision matrix is mainly to eliminate the dimension inconsistency of the evaluation index. For indicators of different properties, the treatment method will be different.

Positive indicators:

Negative indicators:
_{s}

_{i}

This step determines the weight of each evaluation index to prepare for the subsequent construction of the weighted evaluation matrix.

Because the evaluation criteria of indicators are not consistent, the sixth step is used to cochemize negative indicators, which is convenient for the calculation of positive and negative ideal solutions in the following paper.

Where

In order to improve the comparability of evaluation indexes, the author normalised the matrix on the basis of cochemotaxis.

This step is mainly for the objective weight assignment of each evaluation index in the subsequent ranking of evaluation objects by USING the TOPSIS method:

The positive ideal solution of the positive index and the negative index is the set composed of the maximum value of the positive index and the maximum value of the negative index, respectively.

By calculating Euclidean distance, the relative closeness degree of the evaluation object can be further obtained

Among them, the greater the closeness degree _{j}

The graphene preparation process is short, highly efficient, cost-effective and pollution-free. At present, the single-layer rate of finished products in China can reach more than 99%, achieving a higher level in the global industry. High-quality and low-cost graphene raw materials have laid a solid foundation for the wide application of downstream composite fibrosis materials. Finally, this paper puts forward corresponding countermeasures according to the identified risks and analysed reasons. The research conclusions are as follows. The financial risks of the biomass graphene fibre industrialisation project from 2017 to 2021 were calculated and ranked according to the relevant risk assessment parameters of the project and related material utilisation projects in each year. The details are shown in Table 2. The risk index for each year is as follows: 2017>2021>2019>2020>2018. In order to specifically present the trend of the average financial risk of the project in 2018-2021, Figure 2 shows the rule of the average security index changing with years, which is also the financial security trend chart. As can be seen from the figure, in early 2017, the average financial situation of all companies in the development of the whole project industry was at the bottom point, but there was a huge improvement in 2015, and the financial situation tended to be stable after the decline of the average safety index in 2019. In terms of financial ratios for 2018, cash flow capacity, operational capacity and growth capacity are optimal for the 2018-2021 period. However, in 2019 and 2020, with the continuous financing expansion of companies in the industry project, the overall risk remains at a high level, which to some extent indicates that most enterprises sacrifice certain cash flow in order to make their business revenue follow the pace of expansion. However, based on the development trend in recent years, the financial status of each company in the project is still relatively stable.

Risk ranking table of listed companies in the biomass graphene fibre industrialisation project

Year | 2017 | 2018 | 2019 | 2020 | 2021 |
---|---|---|---|---|---|

Risk index (5 is the highest risk index) | 5 | 1 | 3 | 2 | 4 |

Graphene nanoribbon is a new type of carbon nanomaterial, because it has an ideal planar two-dimensional structure, unique thermal properties, mechanical properties, electrical properties, optical properties, etc., and has a good application prospect in the fields of electronics, machinery and medicine. We must seek benefits from both production and management. Management is also a productive force. Financial management is closely related to economic efficiency. The central goal of the enterprise is around how to obtain as much economic benefit as possible with less consumption, to strengthen financial management which can promote enterprises to save potential, and to control costs and reduce consumption. Through raising and dispatching of funds and its joint use, it helps improve the effective use of funds and prevents wasteful expenditure. Therefore, giving full play to the leading role of financial management can more effectively improve economic benefits. Therefore, this paper makes a detailed analysis of the above financial risk performance and index. The specific quantitative data are shown in Table 3. During 2017-2021, the average total assets of companies in the biomass graphene fibre industrialisation project show an increasing trend, with the most significant increase in 2019, which is 514,135,000 yuan. This is an increase of 119.14% compared to 2018. Secondly, the average value of debt also reached the highest in 2019, with the value of debt reaching 383.248 million yuan, an increase of 427.54% compared to 2018. The average financial position of each company is shown in Figure 3. It can be observed that 2019 is indeed a year of great transformation in the engineering application of graphene fibre industrialisation, but the value appreciation of assets and liabilities have also greatly increased this year, along with the degree of risk which has also risen sky high.

Average financial data of companies in the biomass graphene fibre industrialisation Project from 2017 to 2021 (unit: 10,000 Yuan)

Average gross assets | 14,561.2 | 23,461.5 | 51,413.5 | 61,748.8 | 68,523.2 |

Average total liabilities | 4187.7 | 7264.8 | 38,324.8 | 11,574.97 | 14,123.85 |

Average net profit | 1345.1 | 2343.2 | 2124.4 | 3251.7 | 6123.5 |

Using the mathematical model of entropy weight TOPSIS method can realise the optimisation of an inventory structure, reduce inventory overstocking, and achieve economic inventory; through the price pull, can increase the enterprise income; Through the management of assets, enterprises can make reasonable and effective use of assets, and achieve the value preservation and increase of assets. Finally, the effective reduction of the economic risk in the biomass graphene fibre industrialisation project of each company in the industry will be realised. Figure 4 shows the correlation between calculation accuracy and calculation times of entropy weight TOPSIS method. It is observed that the calculation accuracy fluctuates slightly with the increase in the number of words, but the maximum oscillation is less than 3%, which indicates that the model is reliable for the economic risk assessment of projects.

The graphene preparation process is short, highly efficiencient, cost-effective and pollution-free. At present, the single-layer rate of finished products in China can reach more than 99%, achieving a higher level in the global industry. High-quality and low-cost graphene raw materials have laid a solid foundation for the wide application of downstream composite fibrosis materials. In this paper, the biomass graphene fibre industrialisation project is taken as the research object, the development characteristics of the project in the field of industrial application and the financial status of the current company are taken into comprehensive consideration, and the financial data from 2014 to 2018 are selected for quantitative analysis. Finally, this paper puts forward corresponding countermeasures according to the identified risks and analysed reasons. The research conclusions are as follows:

The financial risk status of the biomass graphene fibre industrialisation project was analysed, and the preliminary identification of the risk was realised. The risk identification and analysis of this project focuses on financing, investment, operation and other risks. In terms of investment risks, the main risks faced by the biomass graphene fibre industrialisation project are the poor sales performance of some subsidiaries and the difficulty in fund recovery. At the same time, the lack of investment in research and development of the project will easily lead to difficulties in the follow-up business development of enterprises. The results show that the risk index for each year is: 2017>2021>2019>2020>2018. In early 2017, the average financial status of all companies in the development of the whole project industry was at the bottom point, but in 2015, there was a huge improvement, and in 2019, the financial status stabilised after the decline of the average safety index.

Use the entropy weight TOPSIS method to build a financial risk evaluation model and make a horizontal and a vertical comparison. In this paper, a risk evaluation model is constructed, and the entropy value and entropy weight of evaluation indicators are realised by MATLAB software. In addition, the financial risks of enterprises in the industry are ranked, and the industry status of the graphene fibre industrialisation project is analysed. According to the study, companies in the biomass graphene fibre industrialisation project should focus on cash flow risk and growth risk. The calculation results show that the average total assets of each company in the biomass graphene fibre industrialisation project showed an increasing trend from 2017 to 2021, with the most significant increase in 2019 (514.135 million yuan), an increase of 119.14% compared to 2018. Secondly, the average value of debt also reached the highest in 2019, with the value of debt reaching 383.248 million yuan, an increase of 427.54% compared to 2018.

#### Average financial data of companies in the biomass graphene fibre industrialisation Project from 2017 to 2021 (unit: 10,000 Yuan)

Average gross assets | 14,561.2 | 23,461.5 | 51,413.5 | 61,748.8 | 68,523.2 |

Average total liabilities | 4187.7 | 7264.8 | 38,324.8 | 11,574.97 | 14,123.85 |

Average net profit | 1345.1 | 2343.2 | 2124.4 | 3251.7 | 6123.5 |

#### Main functional characteristics of graphene

Mechanical properties | High strength, but also has good toughness |

Electronic effect | The carrier mobility is high at room temperature and is little affected by temperature |

Thermal performance | It has very good heat conduction performance |

Optical properties | Graphene films have excellent optical properties, and their optical properties change with the thickness of graphene |

Solubility | It is super hydrophobic and super lipophilic, and can adsorb and desorbed various molecules and atoms |

#### Risk ranking table of listed companies in the biomass graphene fibre industrialisation project

Year | 2017 | 2018 | 2019 | 2020 | 2021 |
---|---|---|---|---|---|

Risk index (5 is the highest risk index) | 5 | 1 | 3 | 2 | 4 |

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Fractional Differential Equations in Processing Computer Big Data Mathematical Modeling Thoughts and Methods Based on Fractional Differential Equations in Teaching Research on evaluation system of cross-border E-commerce platform based on the combined model A mathematical model of PCNN for image fusion with non-sampled contourlet transform Nonlinear Differential Equations in Computer-Aided Modeling of Big Data Technology The Uniqueness of Solutions of Fractional Differential Equations in University Mathematics Teaching Based on the Principle of Compression Mapping Financial customer classification by combined model Influence of displacement ventilation on the distribution of pollutant concentrations in livestock housing Recognition of Electrical Control System of Flexible Manipulator Based on Transfer Function Estimation Method Automatic Knowledge Integration Method of English Translation Corpus Based on Kmeans Algorithm Real Estate Economic Development Based on Logarithmic Growth 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Neural Network in Vibration Control of Civil Engineering Structure Optimal Model Combination of Cross-border E-commerce Platform Operation Based on Fractional Differential Equations The influence of accounting computer information processing technology on enterprise internal control under panel data simultaneous equation Research on Stability of Time-delay Force Feedback Teleoperation System Based on Scattering Matrix BIM Building HVAC Energy Saving Technology Based on Fractional Differential Equation Construction of comprehensive evaluation index system of water-saving irrigation project integrating penman Montei the quation Human Resource Management Model of Large Companies Based on Mathematical Statistics Equations Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression Analysis of technical statistical indexes of college tennis players under the win-lose regression function equation Automatic extraction and discrimination of vocal main melody based on quadratic wave equation Analysis of wireless English multimedia communication based on spatial state model equation Optimization of Linear Algebra Core Function Framework on Multicore Processors Application of hybrid kernel function in economic benefit analysis and evaluation of enterprises Research on classification of e-commerce customers based on BP neural network The Control Relationship Between the Enterprise's Electrical Equipment and Mechanical Equipment Based on Graph Theory Mathematical Modeling and Forecasting of Economic Variables Based on Linear Regression Statistics Nonlinear Differential Equations in Cross-border E-commerce Controlling Return Rate 3D Mathematical Modeling Technology in Visualized Aerobics Dance Rehearsal System Fractional Differential Equations in Electronic Information Models BIM Engineering Management Oriented to Curve Equation Model Leakage control of urban water supply network and mathematical analysis and location of leakage points based on machine learning Analysis of higher education management strategy based on entropy and dissipative structure theory Prediction of corporate financial distress based on digital signal processing and multiple regression analysis Mathematical Method to Construct the Linear Programming of Football Training Multimedia sensor image detection based on constrained underdetermined equation The Size of Children's Strollers of Different Ages Based on Ergonomic Mathematics Design Application of Numerical Computation of Partial Differential Equations in Interactive Design of Virtual Reality Media Stiffness Calculation of Gear Hydraulic System Based on the Modeling of Nonlinear Dynamics Differential Equations in the Progressive Method Knowledge Analysis of Charged Particle Motion in Uniform Electromagnetic Field Based on Maxwell Equation Relationship Between Enterprise Talent Management and Performance Based on the Structural Equation Model Method Term structure of economic management rate based on parameter analysis of estimation model of ordinary differential equation Influence analysis of piano music immersion virtual reality cooperation based on mapping equation Chinese painting and calligraphy image recognition technology based on pseudo linear directional diffusion equation Label big data compression in Internet of things based on piecewise linear regression Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation Design of language assisted learning model and online learning system under the background of artificial intelligence Study on the influence of adolescent smoking on physical training vital capacity in eastern coastal areas Application of machine learning in stock selection Comparative analysis of CR of ideological and political education in different regions based on improved fuzzy clustering Action of Aut( G ) on the set of maximal subgroups ofp -groupsStudy on transmission characteristics in three kinds of deformed finlines based on edge-based finite element method Research on loyalty prediction of e-commerce customer based on data mining Algebraic Equations in Educational Model of College Physical Education Course Education Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Semiparametric Spatial Econometric Analysis of Household Consumption Based on Ordinary Linear Regression Model Video adaptive watermark embedding and detection algorithm based on phase function equation English Learning Motivation of College Students Based on probability Distribution Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity Research on mobile Awareness service and data privacy Protection based on Linear Equations computing protocol Vocal Music Teaching Model Based on Finite Element Differential Mathematical Equations Studying a matching method combining distance proximity and buffer constraints The trend and influence of media information Propagation based on nonlinear Differential equation Research on the construction of early warning model of customer churn on e-commerce platform Evaluation and prediction of regional human capital based on optimised BP neural network Study on inefficient land use determination method for cities and towns from a city examination perspective A sentiment analysis method based on bidirectional long short-term memory networks A study of local smoothness-informed convolutional neural network models for image inpainting Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Computational Algorithm to Solve Two–Body Problem Using Power Series in Geocentric System Decisions of competing supply chain with altruistic retailer under risk aversion Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Cross-border E-commerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory Application of Linear Partial Differential Equation Theory in Guiding Football Scientific Training Nonlinear Channel Estimation for Internet of Vehicles Some Necessary Conditions for Feedback Functions of de Bruijn Sequences The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model An empirical research on economic growth from industrial structure optimisation in the Three Gorges Reservoir area The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation Study of agricultural finance policy information extraction based on ELECTRA-BiLSTM-CRF Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Innovative research of vertical video creation under the background of mobile communication Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model Genetic algorithm-based congestion control optimisation for mobile data network