Enterprises are the pillars of the national economy, and their operating conditions are directly related to the development of China’s national economy and the consolidation of the socialist system [1, 2]. The benefit is the key to the survival and development of any enterprise. At present, for state-owned enterprises, only by carrying out effective reforms can improve the economic benefits, strengthen the management, coordinate the development and maximise the economic benefits in the face of fierce market competition. The economic benefit is the soul of an enterprise and improving economic benefits remains the core issue of all economic management activities [3]. With the deepening of the standardisation of financial information disclosure in numerous listed companies under the post-financial crisis stage and the strengthening of financial management, more listed companies pay attention to the early warning of the impending financial crisis [4, 5]. Therefore, the prediction, tracking, analysis and application with regard to an impending financial crisis with early warning information in listed companies have become the focus of recent academic research.
The economic benefit analysis of enterprises can adopt specific methods such as statistics and mathematics, and enterprises should decide according to the purpose of analysis, the characteristics of enterprises, and the nature and content of data to be mastered. Traditional methods of enterprise economic benefit analysis include comparative analysis, ratio analysis, trend analysis, proportion analysis and factor analysis [6–8]. However, the disadvantages are that they are inaccurate and can neither reflect the internal relationship between financial indicators nor can they analyse the prominent items reflected in the economic benefit indicators of enterprises. When compared with the traditional principal component analysis, kernel function analysis usually involves more evaluation indexes, where the first fitting process is essentially the development of orthogonal transformation in feature space, and the correlation calculation of principal components is completed [9, 10]. In addition, it can be used to calculate nonlinear data so that some hidden and difficult-to-find internal relations among indicators can be fully displayed, and the contribution rate of principal components is higher [11, 12]. But among them, the selection and parameters of kernel functions have not been well solved [13]. Therefore, this paper analyses the development of enterprises through the combination of different kernel functions, aiming at accurately and completely reflecting the economic benefit indicators of enterprises, and providing a basis for the follow-up financial analysis.
From the angle of combining macro-economic benefits with micro-economic benefits, the economic benefit of enterprise analysis is the management work of checking, examining and evaluating the formation and realisation degree of economic benefits in the process of production and operation [14, 15]. To realise the development of diversified enterprises, a modern information system that can reflect the internal relations of economic development must be established. Economic benefit analysis of enterprises can effectively, quickly, timely and correctly correct wrong macroeconomic decisions. In a certain sense, it is a modern management means to provide information to decision-making departments at all levels, which is also an indispensable way of thinking for enterprise development.
Among them, the most complicated and key problem is to use an objective and correct standard to make a correct judgement on the evaluation of the economic benefits of enterprises. As shown in Figure 1, the establishment of criteria for measuring the economic benefits of enterprises should proceed from the internal structure of economic benefits, and make a scientific basis as the benchmark for evaluation.
In the analysis of economic benefits of enterprises, the objective reality and social benefits are deviated and mutually restricted, which can be divided into the following four principles: the unity of use-value and value; unity of enterprise economic benefits and social-economic benefits; unity of long-term economic benefits and current economic benefits; unity of direct economic benefits and indirect economic benefits.
Through investigation and summary, as shown in Figure 2, there are four traditional analysis methods for the economic benefits of enterprises [16, 17]:
Factor analysis method
A variety of different factors will eventually have a comprehensive impact on the enterprise, and they will become comprehensive influencing factors. These factors are not fixed and will be influenced by external factors. By decomposing the influencing factors of a comprehensive economy, we can find out the specific factors that affect the completion of comprehensive indicators, and the responsibilities will be divided.
Ratio analysis method
Ratio analysis is essentially a special form of comparative analysis, which calculates the relative number of indexes and analyses the economic phenomena. The commonly used ratio analysis methods include correlation ratio analysis, composition ratio analysis and dynamic ratio analysis.
Balance analysis method
The balance analysis method is an analysis method used to find out the interdependence among various economic factors with a balanced relationship, and compare the actual balance relationship with the planned balance relationship to determine the impact of changes in various economic factors on economic indicators. By adopting this method, we can find the imbalance in the production and business activities of enterprises, to take timely measures to organise a new balance, which ensures the sustained and stable development of economic activities. The balance analysis method includes the balance average method, full balance method and growth rate balance method.
Linear programming method
How to make rational use of the existing resources of enterprises to obtain the greatest economic benefits is the core issue of enterprise management decisions. Using linear programming for economic benefit analysis is mainly a process of establishing a mathematical model according to relevant data and adopting certain methods to solve the optimal scheme. The mathematical model of linear programming is divided into two parts, namely, constraints and objective functions. The constraint condition refers to the condition that a set of inequalities or equations reflect the relationship between limited resources and unknown numbers and limit the range of unknown numbers. While the objective function refers to the best result that can be achieved under certain conditions in the form of a certain function. However, there are some weaknesses of traditional enterprise economic benefit analysis methods, as shown in Figure 3.
The traditional method determines the selection range of evaluation indexes, which makes the classification and division of evaluation indexes more difficult and cannot be directly selected and adopted. Different evaluation criteria should be adopted for different evaluation indexes, and the process is complicated; The analysis method is not unified, and the relationship among various economic indicators cannot be clearly expressed; The evaluation accuracy is low and its error is large, which can’t clearly reflect the contribution rate of each economic index of the enterprise.
When using a support vector machine for classification, regression and data analysis, the basis and premise of obtaining better classification, approximation effect and principal component expression are being applied to understand the ways and means to select and construct appropriate kernel functions and choose appropriate parameters [18]. Therefore, it is necessary to research the theory and application of kernel function for better and deeper research.
Application of kernel function is a method with an obvious dimension reduction effect. It does not operate in the whole high-dimensional feature space, but in a relatively small linear subspace, and its dimension is much smaller than that of the high-dimensional space, which has the following advantages in data analysis of enterprise economic benefits [19]:
It abides by the principle of structural risk minimisation, and has a good operation effect;
For different space requirements, selecting different kernel functions and parameters can change the nature of feature space and the functions of various kernel functions;
Kernel function method can simply reduce the dimension of vector space, thus solving the problem of difficult operation and avoiding the phenomenon that the dimension is too high to be calculated;
It does not need to know the specific function forms and parameters in nonlinearity, but only needs to calculate the inner product.
It is not easily interfered with by other factors, and even if the data is nonlinear, the implicit correlation can be found.
In the support vector machine classifier, we need to select function
When we evaluate similarity, a mapping must be studied from the input space
Assuming a mapping is:
At this time, the space mapped to Hilbert
Therefore, the kernel function (kernel or positive definite kernel) lets
Then,
In economic benefit analysis, it is not necessary to know the specific form of
Polynomial kernel: the common form is:
Gaussian kernel:
Exponential radial basis function:
Neural network core:
In addition, there are some kernel functions, such as Fourier series kernel function, spline kernel function and tensor product kernel function.
The analysis method mainly uses the idea of kernel function to increase the dimension of data, extracts the eigenvalues and eigenvectors after inner product operation in the feature space, and then reduces the dimension [20]. The specific steps are shown in Figure 4.
After standardising the original index, input sample
Because obtaining
The eigenvalues
Find the largest eigenvalue
Calculate the contribution rate of the principal component, find out the first
Assuming that
For principal component analysis, by solving the characteristic equation
The nonlinear mapping function
Then the covariance matrix in the feature space
Therefore, Principal Components Analysis (PCA) in the eigenspace
Among them,
Therefore
Define kernel function as follows:
Then
By solving the type, can get the characteristic value of
For the choice of principal components, it is only necessary to calculate the projection of the test sample data vector
And the comprehensive evaluation function of economic benefits can be defined as:
If
Profit index is an important evaluation index to evaluate the economic benefits and decision-making schemes of enterprises. According to the internal relations of accounting elements, it can be found that total profit, product sales revenue, total output value and overall sales rate are all important indicators to evaluate the economic benefits and decision-making level of enterprises. As shown in Table 1, we select the relevant economic benefit evaluation indexes of five enterprises in a certain area in 2020, analyse the main components of these indexes, evaluate the overall economic benefits of the enterprises and provide correct suggestions for the management to make more reasonable and scientific decisions.
Evaluation index of economic benefits of enterprises.
Number | Total output value (ten thousand yuan) | Product sales rate (%) | Sales revenue (ten thousand yuan) | Total profit (ten thousand yuan) | Total profits and taxes (ten thousand yuan) |
---|---|---|---|---|---|
A | 1500 | 100.6 | 1172 | 25 | 67 |
B | 1002 | 105.3 | 954 | 80 | 76 |
C | 4890 | 83.9 | 2754 | 198 | 407 |
D | 20,205 | 94.2 | 22082 | 1021 | 983 |
E | 589 | 98.0 | 674 | 493 | 26 |
As mentioned above, the kernel principal component analysis method is used to comprehensively evaluate the economic benefits of enterprises:
The indicators of economic benefits are converged, the possible negative values are directly converted into positive indicators, and the original data are standardised by Matlab6.5 [21]:
The results of data processing are shown in Table 2.
Select the appropriate kernel function for many times of trials and comparison according to this data. In this paper, a variety of kernel functions are selected for comparison. Taking the polynomial kernel function as an example, the matrix
Find the eigenvalue of matrix.
Find the maximum eigenvalue, that is the maximum eigenvector under the condition of
Then the evaluation function is
Standardisation results of evaluation index data.
Number | |||||
---|---|---|---|---|---|
A | −0.267 | 0.642 | −0.239 | −0.593 | −0.502 |
B | 0.557 | 0.901 | 0.583 | −0.237 | −0.347 |
C | −0.429 | −1.290 | −0.493 | −0.320 | −0.352 |
D | 3.278 | 1.829 | 3.512 | 3.290 | 3.200 |
E | −0.015 | −0.483 | −0.089 | −0.072 | −0.126 |
The contribution rate of each characteristic value under this analysis is shown in Table 3.
The contribution rate of each characteristic value under polynomial kernel function.
Eigenvalue | 1.127 | 0.063 | 0.003 |
Contribution rate | 93.98% | 4.75% | 1.27% |
Cumulative contribution rate | 93.98% | 98.73% | 100% |
According to the nuclear principal component analysis method, after evaluating the actual operation of the enterprise, it is found that the contribution rate of
Ranking of Economic Benefits of Enterprises.
Enterprise number | Evaluation coefficient | Ranking |
---|---|---|
A | 0.837 | 2 |
B | −0.509 | 4 |
C | −0.378 | 3 |
D | 3.233 | 1 |
E | −0.835 | 5 |
It can be seen from Table 4 that enterprise D has the best economic benefit, and enterprise E has the lowest economic benefit, which is positively related to its total output value and sales revenue. From the perspective of the main components that affect the economic benefits, the total industrial output value is the most important factor in the enterprise’s economic benefits. Therefore, to achieve high economic benefits, the total industrial output value must be increased. While only the self-value of the products be improved, can we occupy a place in the complicated market and really enhance the economic benefits of enterprises.
When selecting kernel functions to solve data problems, one of the usual methods is to select different kernel functions to find the kernel function with the smallest result error, which is called the effective kernel function; The second is the kernel function given by many experiments the second way are selected in this paper.
When the Gaussian function is selected as the kernel function, the values in matrix
The contribution rate of enterprise economic benefit under Gaussian kernel function when σ = 50.
Eigenvalue | Contribution rate | Cumulative contribution rate | |
---|---|---|---|
0.286 | 45.65% | 45.65% | |
0.203 | 22.58% | 68.23% | |
0.128 | 19.59% | 87.82% | |
0.059 | 7.51% | 95.33% | |
0.012 | 4.67% | 100% |
Although this analysis method requires that the cumulative contribution rate should be higher than 85%, compared with the polynomial kernel function, its first principal component contribution rate is as high as 93.98%. Therefore, the Gaussian function is not suitable for the evaluation data of the five enterprises selected in this paper.
When using the neural network kernel function, it is necessary to determine the values of
Contribution rate of enterprise economic benefit under neural network kernel function.
Eigenvalue | Contribution rate | Cumulative contribution rate | |
---|---|---|---|
0.016 | 52.38% | 52.38% | |
0.008 | 41.82% | 94.20% | |
0.001 | 5.80% | 100% |
Similarly, when the kernel function is selected as the neural network kernel function, the first two principal components need to be selected to ensure that the cumulative contribution rate is higher than 85%. Compared with the polynomial kernel function, the neural network kernel function whose first principal component contribution rate is as high as 93.98%, is also not suitable for the enterprise data selected in this paper.
The basic idea of the hybrid kernel method is that the combination of different kernels will have better properties. Because polynomial kernel functions have good global and poor locality, while Gaussian kernel functions have good locality and poor extrapolation ability, these two kernel functions can be mixed together to form a new mixed kernel function as follows:
Among them,
When polynomial kernel function and Gaussian kernel function are selected as the kernel function, the Optimal solution of
The contribution rate of enterprise economic benefits obtained according to the mixed kernel function is shown in Table 7.
Contribution rate of enterprise economic benefit under mixed kernel function.
Eigenvalue | Contribution rate | Cumulative contribution rate | |
---|---|---|---|
0.016 | 99.80 | 99.80% | |
0.008 | 0.2% | 100% |
When the mixed kernel function is selected, the contribution rate of the first principal component is 98.80, which is higher than the 93.98% obtained from the polynomial kernel function, indicating that it is more appropriate to apply a mixed kernel function to this data than polynomial kernel function. Therefore, the application of the kernel function mixed with polynomial kernel function and Gaussian kernel function to the economic benefit analysis of enterprises is more effective than the application of a single kernel function.
This paper introduces and compares the traditional analysis methods of enterprise economic benefits, and studies the application of mixed kernel function in enterprise economic benefits, where the economic benefits of five enterprises are evaluated by nuclear principal component analysis. The analysis results show that the total industrial output value is the most important factor in the economic benefits of enterprises. By comparing the contribution rate of the first principal component, when the mixed kernel function is used, the contribution rate of the first principal component is 98.80%, which is higher than 93.98% of the polynomial kernel function and 87.82% of the Gaussian kernel function. It shows that the mixed kernel function proposed in this study has higher accuracy and better effect in analysing and evaluating the economic benefits of enterprises, which can provide a basis for the follow-up financial analysis.
Evaluation index of economic benefits of enterprises.
Number | Total output value (ten thousand yuan) | Product sales rate (%) | Sales revenue (ten thousand yuan) | Total profit (ten thousand yuan) | Total profits and taxes (ten thousand yuan) |
---|---|---|---|---|---|
A | 1500 | 100.6 | 1172 | 25 | 67 |
B | 1002 | 105.3 | 954 | 80 | 76 |
C | 4890 | 83.9 | 2754 | 198 | 407 |
D | 20,205 | 94.2 | 22082 | 1021 | 983 |
E | 589 | 98.0 | 674 | 493 | 26 |
The contribution rate of each characteristic value under polynomial kernel function.
Eigenvalue | 1.127 | 0.063 | 0.003 |
Contribution rate | 93.98% | 4.75% | 1.27% |
Cumulative contribution rate | 93.98% | 98.73% | 100% |
The contribution rate of enterprise economic benefit under Gaussian kernel function when σ = 50.
Eigenvalue | Contribution rate | Cumulative contribution rate | |
---|---|---|---|
0.286 | 45.65% | 45.65% | |
0.203 | 22.58% | 68.23% | |
0.128 | 19.59% | 87.82% | |
0.059 | 7.51% | 95.33% | |
0.012 | 4.67% | 100% |
Contribution rate of enterprise economic benefit under neural network kernel function.
Eigenvalue | Contribution rate | Cumulative contribution rate | |
---|---|---|---|
0.016 | 52.38% | 52.38% | |
0.008 | 41.82% | 94.20% | |
0.001 | 5.80% | 100% |
Contribution rate of enterprise economic benefit under mixed kernel function.
Eigenvalue | Contribution rate | Cumulative contribution rate | |
---|---|---|---|
0.016 | 99.80 | 99.80% | |
0.008 | 0.2% | 100% |
Standardisation results of evaluation index data.
Number | |||||
---|---|---|---|---|---|
A | −0.267 | 0.642 | −0.239 | −0.593 | −0.502 |
B | 0.557 | 0.901 | 0.583 | −0.237 | −0.347 |
C | −0.429 | −1.290 | −0.493 | −0.320 | −0.352 |
D | 3.278 | 1.829 | 3.512 | 3.290 | 3.200 |
E | −0.015 | −0.483 | −0.089 | −0.072 | −0.126 |
Ranking of Economic Benefits of Enterprises.
Enterprise number | Evaluation coefficient | Ranking |
---|---|---|
A | 0.837 | 2 |
B | −0.509 | 4 |
C | −0.378 | 3 |
D | 3.233 | 1 |
E | −0.835 | 5 |
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Index Evaluation Based on Logistic Distribution Fitting Transition Probability Function Children's Educational Curriculum Evaluation Management System in Mathematical Equation Model Query Translation Optimization and Mathematical Modeling for English-Chinese Cross-Language Information Retrieval The Effect of Children’s Innovative Education Courses Based on Fractional Differential Equations Fractional Differential Equations in the Standard Construction Model of the Educational Application of the Internet of Things Optimization research on prefabricated concrete frame buildings based on the dynamic equation of eccentric structure and horizontal-torsional coupling Optimization in Mathematics Modeling and Processing of New Type Silicate Glass Ceramics Green building considering image processing technology combined with CFD numerical simulation Research on identifying psychological health problems of college students by logistic regression model based on data mining Abnormal Behavior of 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 Function Model Design of Tennis Mobile Teaching Assistant System Based on Ordinary Differential Equations Financial Crisis Early Warning Model of Listed Companies Based on Fisher Linear Discriminant Analysis High Simulation Reconstruction of Crowd Animation Based on Optical Flow Constraint Equation Construction of Intelligent Search Engine for Big Data Multimedia Resource Subjects Based on Partial Least Squares Structural Equation 3D Animation Simulation of Computer Fractal and Fractal Technology Combined with Diamond-Square Algorithm Analysis of the Teaching Quality of Physical Education Class by Using the Method of Gradient Difference The Summation of Series Based on the Laplace Transformation Method in Mathematics Teaching Optimal Solution of the Fractional Differential Equation to Solve the Bending Performance Test of Corroded Reinforced Concrete Beams under Prestressed Fatigue Load Animation VR scene mosaic modeling based on generalized Laplacian equation Radial Basis Function 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