Research on the construction and application of an invention patent model based on TFAHP-FAHP algorithm

Abstract

The TFAHP-FAHP algorithm model constructed in this article can preferably avoid subjective assumptions caused by human factor. The new energy vehicle power battery is a core component for improving the performance of electric vehicles, and constitutes one of the most valuable parts of the invention patent for new energy vehicles. Patent pledge financing is a brand new form with great potential. However, as a result of the imperfect system of the patent pledge financing, banks and other financial institutions are exposed to an increased risk in the process of the patent pledge financing, which seriously limits the development of the patent pledge financing for power battery. In view of the above issues, this paper uses the SPSS reliability and validity tests in order to develop a relatively complete and accurate index system. TFAHP algorithm model and fuzzy comprehensive are combined to determine the final correction coefficient. In this study, the income method is used to determine the pledge valuation value of a new energy vehicle power battery patent portfolio, and the AHP algorithm method is used to determine the weight of the value of the invention patent portfolio within the patent portfolio. As a result, in this paper, the pledge value of the patent portfolio is multiplied by a proportion of the invention patent portfolio’s value, and then multiplied by a correction coefficient to obtain the final new energy vehicle power battery invention patent portfolio pledge value. Using this reference value, technology-based energy companies can pledge financing for new energy vehicle power battery invention patent portfolios.

Keywords

TFAHP algorithm FAHP algorithm invention patent portfolio for new energy power battery SPSS reliability and validity test algorithm

1. Introduction

The application of loans to banks as collateral for the invention patent portfolio of new energy vehicle power battery can expand the financing channels of scientific and technology-based energy enterprises, enhance the core competitiveness of scientific and technology-based energy enterprises, and finally assist scientific and technology-based energy enterprises in securing a position in market competition.

Eom et al. when using machine learning, quantitative evaluations based on large amounts of data are possible, and evaluations can be conducted quickly and cheaply, helping to activate patent transactions [1]. Liu et al. proposed a patent valuation model based on probability graphs. In this model, the text part is combined with some structured parts of the patent to represent the appraisal object. A heterogeneous association network was constructed as an evaluation scenario. Then, a patent valuation model is formed through the generation process, which is represented by a probability graph model. The distribution of patent value is learned through inference using valuation models. The results indicate that the constructed model outperforms other models in terms of evaluation and measurement [2]. Huang et al. considered the fuzziness of decision-makers’ thinking and the uncertainty of patent indicators, extended the traditional TOPSIS method to the field of triangular fuzzy numbers, and proposed a TFN-TOPSIS multi-criteria decision-making model based on the possibility degree relationship model. In addition, this study established a core patent value system from three aspects: technology, law, and economy, and applied the TFN-TOPSIS model to the top 20 balanced automotive patents with the highest comprehensive evaluation, ranking and analyzing the measurement results. And on this basis, provide reference opinions for relevant industry professionals from aspects such as future product technology updates and patent layout [3]. Wang and Li computed its weight for the pledge value evaluation of battery patents using the index of the trapezoidal FAHP model, which can mitigate the impact of its subjective variables [4]. J. Contreras used Tesla’s patent commitment as the launching point for his research. It is thought that enterprises in industries other than ICT can expand enterprise financing channels [5]. J. Yang provided feasible specific measures for the patent pledge in four areas: enhancing relevant laws and regulations, introducing the evaluation of professional third-party institutions, developing a unified patent value evaluation standard, and expanding the patent trading platform [6].

2. Construction of an evaluation model based on TFAHP-FAHP

2.1 TF-AHP (triangular fuzzy-analysis hierarchy process) model

AHP is a quantitative and qualitative method being proposed in the 1970s by Saaty, a renowned American professor of operational research. The traditional AHP model uses subjective factors to determine the index weight, so it cannot guarantee the reliability of the final conclusion. With the continuous development of scientific research, the concept of triangular fuzzy mathematics has been introduced into AHP, which can avoid people’s subjective judgement to a certain extent and ensure the conclusion’s reliability [7]. In this paper, MATLAB2017a is used for the corresponding data analysis.

Triangular fuzzy numbers imitate the process of development and change of things, and accurately describe uncertain things using a numerical order composed of possible maximum, minimum, and most likely values. By combining triangular fuzzy numbers with Analytic Hierarchy Process, it is more in line with the decision-making process of things in practice than traditional Fuzzy AHP. The basic idea of the triangular fuzzy analytic hierarchy process is to compare two indices at the same index level in pairs, and represent the comparison results quantitatively by a triangular fuzzy number. Finally, it is possible to obtain the complementary judgement matrix of triangular fuzzy numbers.

(1) Experts score in the form of a triangular fuzzy number, from which the triangular fuzzy complementary judgement matrix $W_{N}$ is constructed, and on the basis of this information, a subsequent analysis is conducted.

(2) Due to the fact that the triangular fuzzy complementary judgement matrix is constructed in accordance with the TFAHP model’s definition, i.e., fuzzy consistency is fully accounted for in the construction of the judgement matrix [8]. It’s unnecessary to perform consistency transformation and consistency inspection using mathematical methods.

(3) According to the formula $D_{i}^{k}=\sum\limits_{j=1}^{n}{a_{ij}^{k}}\otimes\left({\sum\limits_{i=1}^{n}% {\sum\limits_{j=1}^{n}{a_{ij}^{k}}}}\right)^{-1}(i=1,2,\ldots n;j=1,2,\ldots n)$ .

Calculate the fuzzy synthesis value $D_{i}^{k}$ of element $i$ in layer $K$ , and compare the comprehensive importance degree pairwise by using formula

$\displaystyle V(D_{B1}\leqslant D_{B2})=\left[{\frac{s_{2}-m_{1}}{{(h}_{1}-m_{% 1})-(h_{2}-s_{2})}}\right]\wedge 1.$

(4) The calculated index $B_{i}$ is superior to the purity measurement of other indexes.

According to the formula $d(B_{i})=V(D_{Bi}\geqslant D_{B1},\ldots,D_{Bi-1},\ldots D_{Bn})=\min\{{V(D_{% Bi}\geqslant D_{Bk})}\}$ ( $k=1,2,\ldots n$ , and $k\neq i$ ), the fuzzy weight vector ${W}^{\prime}=(d(B_{1}),d(B_{2}),\ldots d(B_{n}))^{T}$ of the index is obtained, and finally the ${W}^{\prime}$ is normalized to obtain $W_{B}$ .

(5) Obtain the weight vector at this level based on Equation $W_{\textit{Cij}}=W_{Bi}{W}^{\prime}_{\textit{Cij}}$ .

2.2 FAHP evaluation model

Fuzzy Comprehensive Evaluation is a comprehensive evaluation method based on the theory of fuzzy mathematics. Its basic principle is to determine the membership function relationship between the evaluation value and the evaluation factor value based on the characteristics of the evaluated object in a certain aspect and the membership degree of the object [9]. The basic steps of the fuzzy comprehensive evaluation model are as follows:

Step 1:
Determine the set of safety evaluation factors U.

Based on the established evaluation index system in the above chapter, there are four main factors that affect the safety of urban rail transit: U $=$ {U1, U2, U3, U4} $=$ {human factors, equipment and facilities factors, environmental factors, and management factors}.
Step 2:
Determine the evaluation set V for safety evaluation.

A rating table is a set of evaluation results written by the evaluator for the evaluated item. In this article, five levels of evaluation were divided, namely V $=$ {V1, V2, V3, V4, V5} $=$ {Very safe, relatively safe, safe, relatively dangerous, dangerous} $=$ {90, 80, 70, 60, 30}.
Step 3:
Establish a fuzzy relationship evaluation matrix R between factor set U and comment set V.

Using a survey questionnaire method, score and determine $r_{ij}$ , where $r_{ij}=d_{ij}\left/\sum\limits_{j=1}^{n}d_{ij}\right.$ , D represents the number of endorsements of a certain evaluation factor towards the evaluation level. Obtained fuzzy judgment matrix:

$\displaystyle R=\left[\begin{array}[]{c}R_{1}\\ R_{2}\\ R_{3}\\ R_{4}\\ \end{array}\right]=\left[\begin{array}[]{c}r_{11}r_{12}\ldots r_{1n}\\ r_{21}r_{22}\ldots r_{2n}\\ r_{31}r_{32}\ldots r_{3n}\\ r_{41}r_{42}\ldots r_{4n}\\ \end{array}\right]$
Step 4:
Perform fuzzy comprehensive evaluation on the factor set.

Based on the evaluation index weight w and fuzzy judgment matrix R calculated earlier, a first level fuzzy evaluation can be obtained through calculation.

$\displaystyle B_{1}=w_{1}R_{1}$ $\displaystyle B_{2}=w_{2}R_{2}$ $\displaystyle B_{3}=w_{3}R_{3}$ $\displaystyle B_{4}=w_{4}R_{4}$

Perform a two-level fuzzy comprehensive evaluation on the factor set, with the total evaluation matrix R $=$ (B1, B2, B3, B4) T. The final result is:

$\displaystyle B=w^{}R=w^{}\left[\begin{array}[]{c}B_{1}\\ B_{2}\\ B_{3}\\ B_{4}\\ \end{array}\right]$

3. Model construction: Taking the patent evaluation of new energy vehicle power batteries as an example

3.1 Value evaluation of invention patents

Innovation refers to the new intellectual achievements created by humans in the process of using nature to transform nature, which have positive significance and take the shape of technical devices. Briefly, an invention is a novel and inventive technical scheme. Nowadays, invention patents, utility model patents, and design patents are the three types of patents protected by law in China. The sorts of patents protected by patent law in other nations vary, and this study focuses solely on the situation in China. Among these three sorts of patents, innovation patents are undoubtedly the most valuable.

Market method, income method, and cost method are three conventional asset valuation approaches. The income method is one of the three traditional methods to determine the value of the entrusted patent by capitalizing or discounting the expected excess income of the entrusted patent. In this case study, due to the lack of market transactions, the market approach cannot be used. In this paper, we can obtain the sales revenue, market demand, and other relevant financial information of the company’s invention patent portfolio of new energy vehicle power battery. At the same time, the income method is also the most widely used and the most reasonable evaluation method. Therefore, the income method can be used to evaluate it [10].

3.2 Construction of the evaluation index system for the pledge value of invention patent of new energy vehicle power battery

Several factors will influence the evaluation of the pledge value of the invention patent for new energy vehicle power battery. This paper investigates the influencing factors of the pledge value of the invention patent for new energy vehicle power battery, and then constructs a relatively complete evaluation index system for the pledge value using the SPSS 22 software for corresponding analysis in accordance with the systematic principle, the operability principle, and the representativeness principle.

3.2.1 Preliminary selection of pledge value evaluation indicators for invention patent of new energy vehicle power battery

Scope of patent protection, in general, refers to the scope of patent right that is claimed to be covered by law at the time of patent application, i.e. patent right item; the Operating Manual of Patent Value Analysis Index System serves as the reference for this indication.

Remaining validity period refers to the time remaining between the issuance of a patent and its expiration. In general, as the remaining patent validity time increases, so does the financial value of the patent pledge.

Because of the ownership problem of the patent with clear property right and unclear property right, the legitimate rights and interests of the patent cannot be effectively guaranteed. The clearer the patent property right is, the higher its pledge financing value.

Patent independence involves whether the implementation of a patent requires the licensing of an existing patent, and whether this patent serves as the cornerstone for subsequent patent applications. The stronger the patent’s independence, the greater the value of its pledge financing.

Legal status is the current legal status of the innovation patent, including whether or whether it is valid, pending, open, or another legal status? Legal status will have a significant impact on the assessment of the value of the patent pledge.

Patent licensing status involves whether the patentee has licensed the patent to others for usage, and whether there is a patent infringement lawsuit.

Technology maturity involves the development of patented technology. Enterprise development all have an experience process, and the patent pledge value in maturity ranks highest at all stages of patent technology development.

Technical application scope is the field that the patent can serve. In general, the wider the patent’s technical application scope, the higher the patent’s pledge value.

The less substitutable a patented technology is on the market, or, alternatively, the less threatened it is by potential substitutes, the greater its competitive advantage. Patents with low technology substitutability have a comparatively high pledge value.

Technical innovation primarily refers to how much a patented technology contributes to the technical field to which it belongs. If the degree of technological innovation of the patent is greater, the value of the patent pledge will be higher. The reference for this indicator is Wan and Zhu.

Technical content refers to the degree of complexity of a patented technology, the greater the degree of complexity, the higher the patent pledge value.

Technological advancement refers to unique technical advantages and characteristics relative to other patents at a particular moment.

Technology dependency is the extent to which the application of patented technology is dependent on other corresponding technologies. In general, the lower the technology dependency of patents, the higher the value of the patent pledge. This indicator is based on Jin and Qiu.

Life cycle refers to the entire time span from the beginning to the end of existing patents, as well as the current development stage and future development trend of the patent.

Market share represents the proportion of patents in the market.

Market adaptability reveals patents’ market popularity and future development potential. Patent pledge values are greater for patents with superior market applicability.

Industry development trend is the future industry development trend of the current industry in the technical field of the patent.

Enterprise profitability refers to the ability of an enterprise to obtain profitability through its own production and operation, capital and human resources, so as to realize the interests of shareholders and the long-term development of the enterprise.

Enterprise Credit Rating. Enterprises are often inevitably required to interact with banks in the course of production and operation. Enterprises’ credit performance with banks is the best indicator of their credit rating. The better an enterprise’s credit rating, the easier it is for banks to lend to that enterprise.

Enterprise solvency refers to the ability of an enterprise to repay loans. The reference of this indicator is Li and Xia.

To continuously create patented technologies, enterprises must engage a significant amount of manpower and material resources in R&D investment to assure the advancement of patented technologies and enhance their patent market competitiveness.

Inflation. The rate of inflation also influences the value of the patent pledge to some extent. The reference for this indicator is Sun and Chen.

Interest rate. The level of the interest rate is likewise intricately related to the value of the patent pledge.

The stronger the patent liquidity, the stronger the liquidity of the patent, which proves that the patent has strong liquidity in the market. The stronger the patent liquidity, the higher its pledge value. The reference for this indicator is Qian et al.

The risk degree of patent financing is relatively high within a certain range, and the value of the patent pledge is relatively low.

3.2.2 Questionnaire design of the evaluation index system of the pledge value of invention patent

In this questionnaire, the Likert five-point scale is utilized, with each number representing a gradual transition from 1 to 5, with 1 indicating strong disagreement, 3 indicating neutrality, and 5 indicating strong agreement. Experts working in this element of research in colleges and universities are also included in the dissemination of the questionnaire, as there are currently relatively few cases of pledge for innovation patents for new energy vehicle power battery. Therefore, the respondents to this questionnaire are categorized into five groups: banks, experts from scientific research institutions at colleges and universities, government intellectual property departments, scientific and technology-based energy enterprises, and asset evaluation institutions.

A total of 150 questionnaires are distributed, and 130 are collected, with a questionnaire recovery rate of 86.7%, of which 119 are valid, with a valid questionnaire ratio of 79.3%, which indicates that the questionnaire’s validity satisfies the requirements. The questionnaire is available from January until May of 2022. 20 banks, 40 experts from scientific research institutions in colleges and universities, 15 government intellectual property departments, 32 scientific and technological energy enterprises, and 12 asset evaluation institutions submitted 119 valid questionnaires. It is evident from the preceding that this sample is representative to a certain extent and can provide strong support for subsequent data analysis.

Figure 1.

Source composition of questionnaire survey objects.

3.2.3 Factor analysis of questionnaire results

The method of factor analysis is used to examine the relationship between variables, and the majority of variables are divided into multiple categories in order to accomplish dimension reduction. Table 1 is obtained after eliminating the enterprise R&D investment index. Then, an analysis of sample validity is performed to obtain Table 2; factor adjustment is performed to exclude the two indicators of enterprise development status and enterprise solvency; and the test results of 22 indicators are finally obtained and displayed in Table 3.

Table 1
KMO and Bartleet Spherical Test Results

25 Indicators
Measured Value of KMO Sampling Appropriateness		0.843
Bartleet’s Sphericity Test	Last Read ChiSquare	1908.389
	Degree of Freedom	300
	Significance	0.000

Table 2

KMO and Bartleet Spherical Test Results

24 Indicators
Measured Value of KMO Sampling Appropriateness		0.845
Bartleet’s Sphericity Test	Last Read ChiSquare	1872.206
	Degree of Freedom	276
	Significance	0.000

Table 3

KMO and Bartleet Spherical Test Results

22 Indicators
Measured Value of KMO Sampling Appropriateness		0.858
Bartleet’s Sphericity Test	Last Read ChiSquare	1732.299
	Degree of Freedom	231
	Significance	0.000

The KMO value of the remaining 22 indicators is 0.858 $>$ 0.845, as illustrated in the table, indicating that after removing these two indicators, the reliability of the questionnaire sample is enhanced, allowing for a more accurate description of the relationship between these influencing factors.

Next, after extracting the common factor and reclassifying the factor, the factor is rotated, and the rotated factor factor load matrix table is obtained after seven iterations of the maximum variance method; the results of factor reclassification can be obtained from the factor loading matrix table, as shown in Table 4.

Table 4

Composition matrix after rotation

	Factors
	1	2	3	4	5	6
Scope of patent protection	0.757
Remaining validity period	0.730
Clear property right	0.783
Patent independence	0.752
Legal status	0.713
Patent licensing status	0.802
Technical content		0.708
Technical application range		0.799
Technology substitutability		0.782
Technology dependency		0.658
Technological advancement		0.630
Technology maturity		0.748
Degree of technological innovation		0.680
Life cycle		0.722
Market share			0.858
Market adaptability			0.834
Enterprise profitability				0.909
Enterprise credit rating				0.876
Interest rate					0.900
Inflation					0.900
Patent liquidity						0.889
Risk degree of patent financing						0.892

Extraction method: Principal component analysis. Rotation method: Kaiser standardized maximum variance method. The rotation converges after 7 iterations.

Based on the above analysis, Table 5 illustrates the new evaluation index of the pledge value of an invention patent for new energy vehicle power battery.

Table 5

New evaluation index of the pledge value of an invention patent of new energy vehicle power battery

Target layer	Primary index	Secondary index
Evaluation index F of the	B1 Legal factors	C11 Legal status
pledge value of invention		C12 Remaining validity period
patent of new energy		C13 Clear property right
vehicle power battery		C14 Patent independence
		C15 Patent licensing status
		C16 Scope of patent protection
	B2 External macro factors	C21 Interest rate
		C22 Inflation
	B3 Market factors	C31 Market share
		C32 Market adaptability
	B4 Technical factors	C41 Technology maturity
		C42 Technical scope
		C43 Technology substitutability
		C44 Degree of technological innovation
		C45 Technical content
		C46 Technological advancement
		C47 Technology dependency
		C48 Life cycle
	B5 Factors of the patent pledge financing status	C51 Patent liquidity
		C52 Risk degree of patent financing
	B6 Enterprise factors	C61 Enterprise profitability
		C62 Enterprise credit rating

3.3 Determination of the weight of indicators for assessing the pledge value of invention patent of new energy vehicle power battery

3.3.1 Data description

As the result of pairwise comparison in the construction of a triangular fuzzy complementary judgement matrix, triangular fuzzy numbers are utilized. The median value of triangular fuzzy numbers is the most important degree that represents the true will of experts, i.e., the importance degree of comparison between the two, whereas the upper and lower values represent the fluctuation range of importance degree. In this context, that expert score is performed in step of 0.05 for ease of calculation, e.g. (0.35, 0.4, 0.45).

Table 6
Fuzzy scale and its meaning

Scale	Meaning	Scale	Meaning
0.1	The latter is extremely important than the former.	0.6	The former is slightly more important than the latter.
0.2	The latter is strongly more important than the former.	0.7	The former is obviously more important than the latter.
0.3	The latter is obviously more important than the former.	0.8	The former is strongly more important than the latter.
0.4	The latter is slightly more important than the former.	0.9	The former is extremely important than the latter.
0.5	Both are equally important.

The data in this paper are gathered using an expert scoring method. In order to ensure that the data samples are representative, three experienced experts are selected from university institutions, banks, and government intellectual property departments.

3.3.2 Weight calculation

(1) Finally, using the expert scoring method, the element data of B-level are sorted to obtain the matrix, as shown in Table 7.

Table 7
B-level element data matrix obtained by expert scoring method

	B1			B2			B3			B4			B5			B6
B1	0.5	0.5	0.5	0.55	0.6	0.65	0.55	0.6	0.65	0.55	0.6	0.65	0.35	0.4	0.45	0.35	0.4	0.45
	0.5	0.5	0.5	0.6	0.65	0.7	0.6	0.65	0.7	0.6	0.65	0.7	0.35	0.4	0.45	0.3	0.35	0.4
	0.5	0.5	0.5	0.65	0.7	0.75	0.55	0.6	0.65	0.65	0.7	0.75	0.3	0.35	0.4	0.35	0.4	0.45
B2	0.35	0.4	0.45	0.5	0.5	0.5	0.3	0.35	0.4	0.35	0.4	0.45	0.55	0.6	0.65	0.35	0.4	0.45
	0.3	0.35	0.4	0.5	0.5	0.5	0.35	0.4	0.45	0.4	0.45	0.5	0.6	0.65	0.7	0.3	0.35	0.4
	0.25	0.3	0.35	0.5	0.5	0.5	0.3	0.35	0.4	0.3	0.35	0.4	0.55	0.6	0.65	0.4	0.45	0.5
B3	0.35	0.4	0.45	0.55	0.6	0.65	0.5	0.5	0.5	0.4	0.45	0.5	0.55	0.6	0.65	0.35	0.4	0.45
	0.3	0.35	0.4	0.55	0.6	0.65	0.5	0.5	0.5	0.35	0.4	0.45	0.6	0.65	0.7	0.4	0.45	0.5
	0.35	0.4	0.45	0.6	0.65	0.7	0.5	0.5	0.5	0.4	0.45	0.5	0.65	0.7	0.75	0.35	0.4	0.45
B4	0.35	0.4	0.45	0.55	0.6	0.65	0.5	0.55	0.6	0.5	0.5	0.5	0.35	0.4	0.45	0.55	0.6	0.65
	0.3	0.35	0.4	0.5	0.55	0.6	0.55	0.6	0.65	0.5	0.5	0.5	0.4	0.45	0.5	0.55	0.6	0.65
	0.25	0.3	0.35	0.55	0.6	0.65	0.5	0.55	0.6	0.5	0.5	0.5	0.35	0.4	0.45	0.6	0.65	0.7
B5	0.55	0.6	0.65	0.35	0.4	0.45	0.35	0.4	0.45	0.55	0.6	0.65	0.5	0.5	0.5	0.35	0.4	0.45
	0.55	0.6	0.65	0.3	0.35	0.4	0.3	0.35	0.4	0.5	0.55	0.6	0.5	0.5	0.5	0.35	0.4	0.45
	0.6	0.65	0.7	0.35	0.4	0.45	0.25	0.3	0.35	0.55	0.6	0.65	0.5	0.5	0.5	0.4	0.45	0.5
B6	0.55	0.6	0.65	0.55	0.6	0.65	0.55	0.6	0.65	0.35	0.4	0.45	0.55	0.6	0.65	0.5	0.5	0.5
	0.6	0.65	0.7	0.6	0.65	0.7	0.5	0.55	0.6	0.35	0.4	0.45	0.55	0.6	0.65	0.5	0.5	0.5
	0.55	0.6	0.65	0.5	0.55	0.6	0.55	0.6	0.65	0.3	0.35	0.4	0.5	0.55	0.6	0.5	0.5	0.5

(2) Due to the fact that the triangular fuzzy complementary judgement matrix is constructed according to the definition in the TFAHP model, it is unnecessary to perform consistency transformations and consistency tests using the mathematical method.

(3) Finally, by substituting the data into MATLAB2017a, the fuzzy weight of the final B-level index is obtained.

$W_{B}=$ (0.2065, 0.0921, 0.1681, 0.1753, 0.1340, 0.2240)^T, Similarly, the fuzzy weight of C-level indicators is as follows:

$\displaystyle W_{C1}^{\prime}=(0.6667,0.4333,0.4000,0.6333,0.6333,0.5000)$ $\displaystyle W_{C2}^{\prime}=0.6833,0.5$ $\displaystyle W_{C3}^{\prime}=(0.7667,0.5000)$ $\displaystyle W_{C4}^{\prime}=(0.6500,0.6500,0.4667,0.633,0.6167,0.6167,0.4833% ,0.5000)$ $\displaystyle W_{C5}^{\prime}=(0.3500,0.5000)$ $\displaystyle W_{C6}^{\prime}=(0.7167,0.5000)$

Finally, the total vector of fuzzy weight ranking of C-level index is obtained as follows:

$\displaystyle W_{C1}=(0.0419,0.0196,0.0353,0.0393,0.0353,0.0352)$ $\displaystyle W_{C2}=(0.0629,0.0461)$ $\displaystyle W_{C3}=(0.1289,0.0841)$ $\displaystyle W_{C4}=(0.0075,0.0193,0.0366,0.0202,0.0245,0.012,0.0262,0.0288)$ $\displaystyle W_{C5}=(0.0469,0.067)$ $\displaystyle W_{C6}=(0.1605,0.112)$

The weights of indicators at all levels can be obtained from the above analysis, as shown in Table 8.

Table 8

Weight of indicators at different levels

Target layer	Weight of primary index	Weight of secondary index
Evaluation on the pledge value of	B1 Legal factors 0.2065	C11 Legal status 0.0419
invention patent of new energy vehicle		C12 Remaining validity period 0.0393
power battery		C13 Clear property right 0.0353
		C14 Patent independence 0.0353
		C16 Patent licensing status 0.0193
		C15 Scope of patent protection 0.0352
	B2 External macro factors 0.0921	C21 Interest rate 0.0660
		C22 Inflation 0.0461
	B3 Market factors 0.1681	C31 Market share 0.1289
		C32 Market adaptability 0.0841
	B4 Technical factors 0.1753	C41 Technology maturity 0.0245
		C42 Technical scope 0.0288
		C43 Technology substitutability 0.0262
		C44 Degree of technological innovation
		C45 Technical content 0.0202
		C46 Technological advancement 0.0193
		C47 Technology dependency 0.012
		C48 Life cycle 0.0075
	B5 Factors of the patent pledge	C51 Patent liquidity 0.067
	financing status 0.1340	C52 Risk degree of patent financing 0.0469
	B6 Enterprise factors 0.2240	C61 Enterprise profitability 0.1605
		C62 Enterprise credit rating 0.112

It can be seen from Table 8 that in the weight of B-level indicators, enterprise factor accounts for the highest proportion, followed by legal factor, indicating that banks consider enterprise factors more in the process of pledging invention patent of new energy vehicle power battery, while legal factor accounts for the highest proportion regardless of enterprise factor. The highest proportion of the C-level indicators is comprised of enterprise profitability, market share, and enterprise credit rating weight, which indicates that banks have always adhered to the principle of low risk and high income in the operation process.

One of the largest battery suppliers in China, Penghui Energy Co., Ltd., has a number of industrial parks. Additionally, Penghui Energy Co., Ltd., is a high-tech enterprise listed on the A-share market, a provincial enterprise technology center. It specializes in R&D, manufacturing, and sales of lithium iron phosphate batteries, including power battery, energy storage batteries, etc. Through a questionnaire of 10 experts, this paper evaluates the value evaluation index system of the invention patent pledge of new energy vehicle power battery applied to the pledge value evaluation of invention patent of the company, and finally the membership matrix is constructed to calculate the final correction coefficient.

(4) Weight of primary and secondary indicators

It is possible to obtain fuzzy weights (normalized) for the first-level index and fuzzy weights (unnormalized) for the second-level index using TFAHP, i.e.

$\displaystyle W_{B}=(0.2065,0.0921,0.1681,0.1753,0.1340,0.2240)^{\text{T}}$ $\displaystyle W_{C1}^{\prime}=(0.6667,0.4333,0.4000,0.6333,0.6333,0.5000)$ $\displaystyle W_{C2}^{\prime}=0.6833,0.5$ $\displaystyle W_{C3}^{\prime}=(0.7667,0.5000)$ $\displaystyle W_{C4}^{\prime}=(0.6500,0.6500,0.4667,0.6333\,0.6167,0.6167,0.48% 33,0.5000)$ $\displaystyle W_{C5}^{\prime}=(0.3500,0.5000)$ $\displaystyle W_{C6}^{\prime}=(0.7167,0.5000)$

Following normalization, the secondary index fuzzy weights of the invention patent are as follows:

$\displaystyle W_{C1}^{\prime\prime}(0.2031,0.0947,0.1709,0.1901,0.1709,0.1703)$ $\displaystyle W_{C2}^{\prime\prime}=(0.5775,0.4225)$ $\displaystyle W_{C3}^{\prime\prime}=(0.6053,0.3947)$ $\displaystyle W_{C4}^{\prime\prime}=(0.0430,0.1103,0.2086,0.1152,0.1399,0.0693% ,0.1496,0.1641)$ $\displaystyle W_{C5}^{\prime\prime}(0.4118,0.5882)$ $\displaystyle W_{C6}^{\prime\prime}=(0.5891,0.4109)$

(5) A questionnaire survey of 10 experts is conducted in this paper in order to establish a membership matrix for the evaluation index of the invention patent pledge value of new energy vehicle power battery of Penghui Energy Co., Ltd., which can be obtained as follows:

$\displaystyle R_{1}=\left|{{\begin{array}[]{*{20}c}{0.6}&{0.2}&{0.2}&0&0\\ {0.4}&{0.4}&{0.2}&0&0\\ {0.6}&{0.4}&0&0&0\\ {0.2}&{0.6}&{0.2}&0&0\\ {0.4}&{0.2}&{0.4}&0&0\\ {0.2}&{0.8}&0&0&0\\ \end{array}}}\right|,R_{2}=\left|{{\begin{array}[]{*{20}c}0&{0.8}&{0.2}&0&0\\ 0&{0.6}&{0.4}&0&0\\ \end{array}}}\right|,R_{3}=\left|{{\begin{array}[]{*{20}c}{0.4}&{0.2}&{0.4}&0&% 0\\ 0&{0.6}&{0.4}&0&0\\ \end{array}}}\right|$ $\displaystyle R_{4}=\left|{{\begin{array}[]{*{20}c}{0.4}&{0.6}&0&0&0\\ 0&1&0&0&0\\ {0.4}&{0.6}&0&0&0\\ {0.4}&{0.4}&{0.2}&0&0\\ {0.4}&{0.2}&{0.4}&0&0\\ {0.4}&{0.2}&{0.4}&0&0\\ {0.2}&{0.4}&{0.4}&0&0\\ {0.4}&{0.4}&{0.2}&0&0\\ \end{array}}}\right|,R_{5}=\left|{{\begin{array}[]{*{20}c}{0.4}&{0.2}&{0.4}&0&% 0\\ 0&{0.6}&{0.4}&0&0\\ \end{array}}}\right|,R_{6}=\left|{{\begin{array}[]{*{20}c}{0.4}&{0.6}&0&0&0\\ {0.2}&{0.8}&0&0&0\\ \end{array}}}\right|$

Using the fuzzy hierarchy comprehensive evaluation model $B_{1}=W_{C1}\ast R_{1}$ , this paper takes the first-level index legal factor as an example.

$\displaystyle B_{1}=W_{C1}\ast R_{1}=(0.2031,0.0947,0.1709,0.1901,0.1709,0.1703)$ $\displaystyle{}*\left|{{\begin{array}[]{*{20}c}{0.6}&{0.2}&{0.2}&0&0\\ {0.4}&{0.4}&{0.2}&0&0\\ {0.6}&{0.4}&0&0&0\\ {0.2}&{0.6}&{0.2}&0&0\\ {0.4}&{0.2}&{0.4}&0&0\\ {0.2}&{0.8}&0&0&0\\ \end{array}}}\right|=(0.4027,0.4313,0.1660,0,0)$

Similarly, we can obtain: $B_{2}=$ (0, 0.7155, 0.2845, 0, 0), $B_{3}=$ (0.2421, 0.3579, 0.4, 0, 0), $B_{4}=$ (0.3260, 0.4747, 0.1993, 0, 0), $B_{5}=$ (0.2421, 0.6, 0.1579, 0, 0), $B_{6}=$ (0.3178, 0.6822, 0, 0, 0)

$\displaystyle B=W_{Bi}\ast R=(0.2065,0.0921,0.1681,0.1753,0.1340,0.2240)$ $\displaystyle{}*\left|{{\begin{array}[]{*{20}c}{0.4027}&{0.4313}&{0.1660}&0&0% \\ 0&{0.7155}&{0.2845}&0&0\\ {0.2421}&{0.3579}&{0.4}&0&0\\ {0.3260}&{0.4747}&{0.1993}&0&0\\ {0.2421}&{0.6}&{0.1579}&0&0\\ {0.3178}&{0.6822}&0&0&0\\ \end{array}}}\right|=(0.2846,0.5316,0.1838,0,0)$

Therefore, the correction coefficient of the evaluation of the pledge value of the invention patent of new energy vehicle power battery of Penghui Energy Co., Ltd., is as follows:

M $=$ BX^T $=$ (0.2846, 0.5316, 0.1838, 0, 0)*(1, 0.8, 0.6, 0.4, 0.2)^T $=$ 0.8202. Through the above calculation and analysis, it is reasonable to conclude that the correction coefficient for the pledge value evaluation of invention patent for New Energy Vehicle power battery of Penghui Energy Co., Ltd., is 0.2 $<$ 0.8202 $<$ 1.

4. Model application: Evaluate the value of the invention patent portfolio for battery vehicle power battery of Penghui Energy Co., Ltd.

4.1 Evaluation method

In this paper, the revenue sharing method is used, and there are two main entry points in the revenue sharing model, one is to predict the expected future income of the invention patent through net profit, and the other is to predict the expected future income of the invention patent portfolio through sales revenue. Because sales revenue has a higher degree of reliability compared with net profit, the expected future revenue of the invention patent portfolio is predicted through sales revenue in this paper. Finally, considering the completeness of the data of Penghui Energy Co., Ltd., the evaluation benchmark date selected in this paper is June 30, 2018.

4.2 Relevant parameter setting of Penghui Energy Co., Ltd.

4.2.1 Expected return

The sales revenue of Penghui Energy Co., Ltd.’s new energy vehicle power battery can be obtained from the information obtained from Tonghuashun Financial Network combined with the company’s annual report. Therefore, Table 9 is obtained.

Table 9
Historical sales revenue table of power battery patent portfolio

Item	Year 2015	Year 2016	Year 2017	January to June Year 2018
Sales revenue	74770000	253000000	593000000	238110731
Growth rate		238%	134%

The prediction period of the invention patent is determined to be 8.5 years after considering the life cycle, technological advancement, protection period and other factors of the invention patent, and the prediction period of the invention patent comprehensively.

Table 10

Forecast of annual sales revenue of the power battery patent portfolio

Item	Jul–Dec Year 2018	Year 2019	Year 2020	Year 2021 and beyond
Sales revenue	397889269	1144800000	1545480000	2086398000
Growth rate		80%	35%	35%

According to the company ’s financial report, as shown in Table 10, it can be seen that the sales revenue growth rate of new energy vehicle power battery of the company is about 7% in 2018 and 76% in 2019. However, due to the impact of the new Coronavirus epidemic in 2020, the sales revenue growth rate of new energy vehicle power battery of the company is only 10%. Because this is a force majeure factor, the prediction is reasonable in terms of the current development prospects of the new energy vehicle industry.

4.2.2 Discount rate

In this case, the inverse algorithm is used to predict the discount rate, and the specific calculation process is as follows:

(1) Estimated equity capital costs

Estimate $R_{f}$

$R_{f}$ is defined as the risk-free return rate. According to this case analysis, $R_{f}$ is the arithmetic mean of the yield-to-maturity of treasury bonds that have a remaining maturity period that is close to the predicted return period on the date of the evaluation. $R_{f}=$ 3.13% is obtained by calculation, and this data comes from the China Bond Information Network.

Calculate ERP

ERP is a market risk premium, which is a very important parameter when CAPM model is used to estimate equity capital value.

By calculating the closing price of CSI 300 constituent stocks at the end of the year as the basic data, the closing price data of CSI 300 constituent stocks at the end of the year are derived from wind database. Through calculation, it is known that the average yield to maturity of CSI 300 constituent stocks from 2008 to 2017 is about 11.11%, ERP $=$ 11.11% $-$ 3.13% $=$ 7.98%.

Calculate $\beta$ coefficient

Since the selected Penghui Energy Co., Ltd., is a listed company, the $\beta$ coefficient of the listed company in 2018 can be directly obtained through the wind database. Finally, it is found through the wind database that the original $\beta$ coefficient of Penghui Energy Co., Ltd., is 1.0783 and the adjusted $\beta$ coefficient is 1.0525.

Calculate $R_{S}$ . $R_{S}$ is the individual risk premium for the company under evaluation.

Scale risk. Because Penghui Energy Co., Ltd., belongs to listed companies, its new energy vehicle power battery products have strong competitiveness in the market. The installed capacity of square, cylindrical and soft pack power battery ranks 9,11 and 19 respectively in the market. Therefore, the company has strong risk resistance, so the scale risk takes as 2%.

Market competition risk. At present, the market competition of new energy vehicle power battery in China is still relatively fierce, so the market competition risk is taken as 0.5%.

Therefore, $R_{S}=$ 2.5%.

Calculate $K_{e}$ . $K_{e}$ is the equity capital cost.

$\displaystyle K_{e}=R_{f}+\textit{ERP}\times\beta_{1}+R_{s}$

(1) $\displaystyle K_{e}=3.13\%+7.98\%\ast 1.0525+2.5\%=14.03\%$

(2) Calculate the WACC (Weighted Cost of Capital)

$\displaystyle\textit{WACC}=K_{e}\times W_{e}+K_{d}\times(1-t)\times W_{d}$ (2) $\displaystyle W_{d}=(\text{Interest Bearing Liabilities})(\text{Interest Bearing Liabilities}+\text{Owner's Equity}),$ $\displaystyle W_{e}=1-W_{d}$ (3)

$W_{d}=$ 0.1687, $W_{e}=$ 0.8313. According to the official website of Penghui Energy Co., Ltd., the income tax rate of the enterprise is 15%.

WACC $=$ 14.03% 0.8313 $+$ 4.90% 85% 0.1687 $=$ 0.1166 $+$ 0.0070 $=$ 12.36%.

(3) Calculate the discount rate of invention patent

In this case, the inverse algorithm is used to calculate the discount rate of the invention patent, and its specific calculation Eq. (4) is shown in Eq. (4):

$\displaystyle R_{i}=\frac{\text{All Assets}}{\text{Intangible Assets}}\left(% \textit{WACC}-R_{a}\frac{\text{Working Capital}}{\text{All Assets}}-R_{b}\frac% {\text{Tangible non Current Assets}}{\text{All Assets}}\right)$ (4)

In Eq. (4), total assets $=$ equity value $+$ interest payment debt value, working capital $=$ current assets $-$ non-interest payment liabilities, tangible non-current assets $=$ book value of tangible non-current assets, intangible assets $=$ total assets $-$ working capital $-$ tangible non-current assets; $R_{a}$ is the rate of return on working capital, $R_{b}$ is the rate of return on tangible and non-current assets, and $R_{i}$ is the discount rate of invention patents.

$R_{a}$ takes the one-year loan interest rate of 4.35%, and $R_{b}$ takes the five-year loan interest rate of 4.75%.

Through Guotai’an database, Penghui Energy Co., Ltd.,’s working capital/total assets $=$ 23.66%, tangible non-current assets/total assets $=$ 29.26%, intangible assets/total assets $=$ 47.08%.

Final $R_{i}=$ (12.36% $-$ 4.35% 23.66% $-$ 4.75% 29.26%)/47.08% $=$ (12.36% $-$ 0.0107 $-$ 0.0139)/ 47.08% $=$ 21.03%. The patent discount rate level assessed for intangible assets is generally high, and 19–24% belongs to the normal range.
4.2.3 Share ratio

In this case, the comparative company method is used to determine the share rate.

The three companies selected are Yiwei Lithium Energy, Gotion High-tech Co., Ltd. and Shengyang. All three companies are A-share listed companies and only issue one A-share stock, with a public trading history of at least 2 years. These three companies belong to the same industry company as Penghui Energy Co., Ltd., and are the same as Penghui Energy Co., Ltd., in their main business. Therefore, through Guotai’an database, the data of the three enterprises from 2015 to 2017 are selected and finally Table 11 is obtained.

Table 11
Capital structure of the comparative companies

Category	Comparative company name	Year 2015	Year 2016	Year 2017	Three-year average	MEAN value
Proportion of working capital	Eve Energy Co., Ltd.	33.47%	27.86%	14.63%	25.32%	37.96%
to total assets	Gotion High-tech Co., Ltd.	37.85%	26.74%	37.88%	34.15%
	Shandong Sacred Sun Power	54.40%	58.36%	50.47%	54.41%
Proportion of tangible non-current	Eve Energy Co., Ltd.	24.62%	31.24%	41.17%	32.34%	26.17%
assets to total assets	Gotion High-tech Co., Ltd.	20.23%	26.65%	21.28%	22.72%
	Shandong Sacred Sun Power	24.42%	21.19%	24.76%	23.46%
Proportion of intangible assets	Eve Energy Co., Ltd.	41.91%	40.90%	44.20%	42.34%	35.86%
to total assets	Gotion High-tech Co., Ltd.	41.92%	46.61%	40.85%	43.12%
	Shandong Sacred Sun Power	21.17%	20.45%	24.77%	22.12%

According to the enterprise inspection website, as of the evaluation benchmark date, there are a total of 250 intangible assets in Penghui Energy Co., Ltd., of which 104 are self-owned patents. The analysis determined that the proportion of self-owned power battery patents in all intangible assets in this evaluation is determined to be (104/250)*100% $=$ 42%. The arithmetic mean value of the proportion of

Table 12

Comparison of share ratio of self-owned patents of the comparative companies

Comparative companies	Year	Intangible assets/ total assets	Proportion of proprietary patents in intangible
assets	Proportion of owned patents in assets	Total ebitda		Contribution of proprietary patents to cash flow	Operating income	Share rate of proprietary patent
Eve Energy Co., Ltd.	2015	41.91%	42%	17.60%	247810011	.8	43620013.90	1348932918	3.23%
	2016	40.90%	42%	17.19%	445606354	.4	76546259.56	2339712008	3.27%
	2017	44.20%	42%	18.56%	614498818	.9	114075560.74	2982304750	3.83%
Gotion High-tech Co., Ltd.	2015	41.92%	42%	17.61%	747638482	.2	131632221.73	2745496179	4.79%
	2016	46.61%	42%	19.58%	1325589638		259500078.71	4757931944	5.45%
	2017	40.85%	42%	17.16%	1252647953		214916809.30	4838098642	4.44%
Shandong Sacred Sun Power	2015	21.17%	42%	8.89%	81384768	.06	7236245.27	1379761643	0.52%
	2016	20.45%	42%	8.59%	103313537	.5	8873599.74	1551654658	0.57%
	2017	24.77%	42%	10.40%	80015275	.41	8324309.16	1706468317	0.49%

self-owned power battery patents in all intangible assets in the three comparison companies is about 42%, as shown in Table 12.

As can be seen from the above table, the average value calculated according to the patented technology share rate is 2.95%. The gross profit margin of comparative company sales is shown in Table 13 below.

Table 13

Technical share rate

Item	Comparative company sales gross profit margin	Gross profit margin of sales of the target
company	Difference rate	The proportion of comparative company patents to the
total capital	Average patent share rate of comparison company	Technology share ratio of target company
	A	B	C $=$ B $-$ A	D	E	F $=$ E $+$ DC
Patent	30.96%	24%	$-$ 6.96%	15.78%	2.95%	1.85%

Therefore, the sales revenue sharing rate (before tax) $=$ 1.85%. According to the official website of the enterprise, it can be seen that the income tax rate of the enterprise is 15%, so the sales revenue sharing rate (after tax) $=$ 1.57%.

4.3 Finally obtaining the evaluation value of the invention patent portfolio of the new energy vehicle power battery

Table 14
Evaluation results unit: (yuan)

Item	Jul–Dec Year 2018	Year 2019	Year 2020	Year 2021 and beyond
Sales revenue	397889269	1144800000	1545480000	2086398000
Net share ratio	1.57%
Net income after sharing	6246861.52	17973360	24264036	32756448.6
Discount rate	21.03%
Year	0.5	1.5	2.5	8.5
Discount coefficient	0.9090	0.7510	0.6205	2.0118
Present value of net income after sharing	5678397.12	13497993.36	15055834.34	65899423.29
Evaluation value	100131648.1 (rounded to
	approximately 100 million
	yuan)

According to the income method, the evaluation value of the patent portfolio of new energy vehicle power battery is 100131648.1 yuan. According to the official website of the enterprise, there are 12 invention patents authorized in the self-owned patents of new energy vehicle power battery from the application date to the evaluation base date, while the total number of self-authorized patents of power battery is 76. Seven indexes are selected to measure the value proportion of the three types of patents in the patent portfolio, which are the clarity of property rights, legal status and scope of patent protection in legal factors, the technical content and technological innovation in technical factors, and the market share and market adaptability in market factors. Five experts in the field are invited to score, and the weight of invention patent value is calculated by yaahp software as 0.5571. Therefore, the evaluation value of pledge of invention patent portfolio of new energy vehicle power battery is obtained ( $=$ 100131648.1*0.5571 $=$ 55783341.16 yuan). At the same time, because the appraisal value of the invention patent pledge is affected by many factors, it needs to be multiplied by a correction coefficient. Therefore, the evaluation value of pledge of invention patent portfolio of new energy vehicle power battery of Penghui Energy Company is finally obtained ( $=$ 55783341.16*0.8202 $=$ 45753496.42 yuan). In this case evaluation, only the invention patent for NEV power battery with authorized legal status is considered. For banks, only the authorized invention patent has the possibility of pledge financing. Thus, the method used in the paper has a certain reliability.

5. Conclusion

There are many factors in the process of evaluating the pledge value of invention patent of new energy vehicle power battery. The progress of this evaluation lies in combining the income method and TFAHP-fuzzy comprehensive evaluation correction coefficient to obtain the final pledge evaluation value of the invention patent portfolio. First, a relatively complete set of the evaluation index system for the pledge value evaluation of invention patent of new energy vehicle power battery is constructed by SPSS 22. Second, the correction coefficient of the pledge value evaluation of the invention patent of the company is obtained by TFAHP and fuzzy comprehensive evaluation. Finally, a patent portfolio evaluation value is obtained by the revenue sharing model. Then, the pledge evaluation value of the invention patent portfolio of Penghui Energy Co., Ltd., is obtained by multiplying the value weight proportion of the invention patent portfolio and the correction coefficient, respectively. The rationality of the case evaluation results in this article can only be further demonstrated from a theoretical perspective, and the rationality of the methods used needs to be tested through more practical cases in future research.

Footnotes

Acknowledgments

The authors acknowledge the Key Topics Foundation of the 13th Five Year Plan for Education Science in Hunan Province (Grant: XJK20AGD003).

References

Eom

Choi

. Marketable value estimation of patents using ensemble learning methodology: Focusing on US patents for the electricity sector. Plos One. 2021; 16(9).

Liu

Qiao

. Probabilistic graph-based valuation model for measuring the relative patent value in a valuation scenario. Pattern Recognition Letters. 2020; 138: 204-210.

Huang

Yue

. Method of TFN-TOPSIS based on possibility degree relation model and its application in the patent value estimation of self-balancing vehicles. Sustainability. 2023; 15(4).

Wang

. Research on Evaluation of financing value of the patent pledge based on trapezoidal FAHP battery. Journal of Systems Science. 2017; 25(03): 64-68.

Contreras

. Tesla Motors and the Rise of Non-ICT Patent Pledges. ResearchGate. 2014; 3(2): 8.

Yang

. Solution to the value evaluation in enterprise patent pledge financing. Theoretical Exploration. 2015; (05): 116-121.

Pan

Wang

. Advanced FMECA Method Based on Intuitionistic 2-Tuple Linguistic Variables and the Triangular Fuzzy Analytic Hierarchy Process. WIRELESS COMMUNICATIONS & MOBILE COMPUTING. 2022.

Yang

Wang

Zhang

Wang

. Intelligent Optimization of Power Grid Corridor based on TFAHP-CA Algorithm. ACM International Conference Proceeding Series. 2022; 2022(23): 759-765.

Dong

Zhang

. Approaches to group decision making with incomplete information based on power geometric operators and triangular fuzzy AHP. Expert Systems with Applications. 2015; 42(21): 7846-7857.

10.

Park

Choi

. A study on the appropriateness of intellectual property valuation: The patent valuation of korea technology finance corporation. Knowledge Management Review. 2020; 21(4): 195-210.

Research on the construction and application of an invention patent model based on TFAHP-FAHP algorithm

Abstract

Keywords

1. Introduction

2. Construction of an evaluation model based on TFAHP-FAHP

2.1 TF-AHP (triangular fuzzy-analysis hierarchy process) model

2.2 FAHP evaluation model

3.1 Value evaluation of invention patents

3.2 Construction of the evaluation index system for the pledge value of invention patent of new energy vehicle power battery

3.2.1 Preliminary selection of pledge value evaluation indicators for invention patent of new energy vehicle power battery

3.2.2 Questionnaire design of the evaluation index system of the pledge value of invention patent

Table 1 KMO and Bartleet Spherical Test Results

3.3.1 Data description

Table 6 Fuzzy scale and its meaning

Table 7 B-level element data matrix obtained by expert scoring method

4.1 Evaluation method

4.2 Relevant parameter setting of Penghui Energy Co., Ltd.

4.2.1 Expected return

Table 9 Historical sales revenue table of power battery patent portfolio

Estimate R f

Calculate ERP

Calculate β coefficient

Table 11 Capital structure of the comparative companies

Table 14 Evaluation results unit: (yuan)

Footnotes

Acknowledgments

References

Table 1
KMO and Bartleet Spherical Test Results

Table 6
Fuzzy scale and its meaning

Table 7
B-level element data matrix obtained by expert scoring method

Table 9
Historical sales revenue table of power battery patent portfolio

Estimate $R_{f}$

Calculate $\beta$ coefficient

Table 11
Capital structure of the comparative companies

Table 14
Evaluation results unit: (yuan)