As an effective clustering method, K-means clustering needs to be combined with the minimum distance criterion to accurately classify research samples. It is usually used in the clustering analysis of data streams, and has the characteristics of convenience, speed and simplicity. The actual core idea is to randomly select K objects, and all objects represent a certain cluster center. For other objects, it is necessary to allocate them to the most similar cluster based on the distance formed between the objects and the cluster center, so as to ensure the maximum similarity between the objects in the cluster and the minimum similarity between the objects in the cluster.^{[1]}

_{i} research samples in C_{i} of class I, and their mean value is MI, the following formula can be obtained^{[2]}:

_{1}, m_{2}…, m_{k} represents K sample subset C_{1}, C_{2}…, the total error squared constituted by C_{k}. If Je is different, the clustering result will be biased. Therefore, the result guaranteeing Je to obtain the minimum distance can be regarded as the optimal result under the sum of error squares criterion.

^{[3]}

Third, calculate the distance between the remaining N-2 objects and XP and XQ respectively. Assuming that this condition is met, xi should be integrated into the corresponding category of XP, and vice versa. Their corresponding categories can be represented by Z1 and Z2.

Fourth, calculate the farthest distance between the target arrival contained in S1, the specific formula is as follows:

And you can get _{max} = max {_{1}, _{2}}

Fifthly, if the condition _{max} > _{pq}

Sixth, repeat steps three to five.

The modeling flow chart of the overall algorithm is as FIG. 1 follows^{[4]}:

Clustering analysis based on self-organizing feature mapping network is a new unsupervised clustering method in neural network algorithm research. The specific flow chart is as Figure 2 follows:

In the formula above, X and Y represent vectors of two modes.

On the other hand, full-vector programming is required, and the specific formula is as follows, which meets the condition

During the learning of the self-organizing feature mapping network, the weight coefficients between the output neurons and the input phasor should be adjusted in the following ways:

^{[5]}.

Assume that the input vector is _{1}, _{2}, Λ, _{p}^{T}, which contains K output neurons, then the weight vector of output neuron J is _{j}_{1j}, _{2j}, Λ, _{pj}^{T}, (^{*}) ≥ _{j}

In the above formula, p represents the dimension of input vector X, and the maximum point of the value corresponding to _{j}

In the above equation, C represents the winning unit, which can also be regarded as the best matching unit. R represents the second-best unit, which meets the following conditions:

And we can get

Finally, the weight value should be adjusted according to the following formula:

In this case, d (xi) represents the density of XI, and the specific definition formula is:

In the above formula, α represents the rate of learning and β represents the rate of forgetting, which meets the condition

In order to construct the portrait of the car user, the driving posture of the car is described primarily, which involves the torso azimuth, the upper arm azimuth, the elbow Angle, the thigh Angle, the knee Angle and so on. At the same time, during the design of automobile driving layout, priority should be given to defining the main feature points of automobile users, including S point representing the center of the connecting line of the left and right joints of human body; Point P represents the center of the connecting line of left and right hand marking points during the symmetry of human upper limb posture; Point E represents the center of the connection line between the marked points of left and right ankle joints in the symmetry of human lower limb posture; Point H represents the intersection of the trunk line and the center line of the thigh in the two-dimensional human body sample. In the THREE-DIMENSIONAL model, it represents the center of the connection line of the marking point of the left and right hip joints of the human body. It can also be regarded as the Point H in car seat design^{[6]}.

Cluster analysis based on the k-means clustering algorithm proposed above shall be performed according to the following steps:

First, suppose that the domain U={x1, x2…, xm} represents the target waiting for classification, all targets contain N indicators belonging to the corresponding attribute features, then it can be concluded that Xi ={xi1, xi2… Xin}, where xil represents the data value of the l th indicator of the I th target. In practical studies, data with different attributes and characteristics usually have diverse dimensions. To make comparative analysis of data combinations with different dimensions, scientific transformation of corresponding data is required, which is also known as the process of standardization. The most common operation method is translation and range transformation, and the specific formula is as follows:^{[7]}

In the above formula,

Secondly, combined with the set number of clustering k, k targets are selected as the initial clustering centers after standardized processing, specifically {xj (l)}, and j conforms to 1,2… K. The selection principle followed at this time is that the distance between each initial cluster center should reach the maximum.

Thirdly, for all standardization targets, the distance between them and the cluster center

Fourthly, the minimum distance between all m standardized targets and all K clustering centers is

Fifth, calculate the centroid of k categories accurately. Assume that there are Q targets divided into j category, then the calculation formula of the centroid of j category is as follows:

In the above formula, u= 1,2…, the condition q,

Sixth, regard

After the RTH iteration clustering, the formula of the sum of the squares of the distances between the standardized target and the distance center contained in class J is shown as follows:

The formula of the sum of the square of the distance between the standardized target in the included k class and the corresponding distance center is as follows:

Assume that the actual classification is not reasonable, so distance sum of squares (Dr) the calculation of the trip will continue to rise, and following the increase of the actual number of iterations, the corresponding numerical will continue to decline and tend to be stable, if the given sufficiently small amount is in line with

In addition, the determination of clustering quantity K directly affects the final result. If the value k is too small, the sum of the squares of the distance between the target and the corresponding distance center will become larger and larger, and the final distance result is not ideal. If the value is too small, the practical significance of cluster analysis cannot be demonstrated. Under normal circumstances, the minimum cluster number should reach 2, and with the continuous increase of cluster number, the corresponding Dr Will continue to decline. Therefore, the k value during the decreasing trend of Dr From fast to slow can be regarded as the optimal cluster number.

Combined with the above proposed k-means clustering analysis algorithm for driving attitude of automobile users, 50 volunteers were selected for experimental verification, including 30 males and 20 females, with actual driving age of more than two years. Before formal experimental analysis, height and body mass should be tested according to relevant standards, so as to obtain the corresponding positions of human marker points. The final results are shown in the following table 1:

Characteristic information of measurement

Statistic | Mean (standard deviation) | Minimum - Maximum | ||
---|---|---|---|---|

Male (30) | Female (30) | Male (30) | Female (30) | |

Age | 28.5(5.0) | 28(4.1) | 23–41 | 23–40 |

Height/CM | 173.6(5.0) | 164.5(3.8) | 166.0–183.0 | 158.0–172.0 |

Quality/KG | 71.6(7.8) | 54.3(5.4) | 55.0–91.0 | 45.0–65.0 |

The trunk _{s} |
58.6(2.5) | 52.4(3.1) | 54.0–63.0 | 46.0–57.0 |

Arm length _{u} |
62.2(1.7) | 57.1(2.8) | 59.0–65.0 | 53.0–62.0 |

The driving sitting position test experiment will conduct simulated operation according to the layout design of ordinary cars, in which the seat distance, inclination Angle and steering wheel height contained in the platform can be adjusted independently, and the seats that participants sit on are adjustable seats of ordinary cars. During the test, all volunteers were required to scientifically adjust the height, inclination and distance in the platform seat according to their own preferences, so as to ensure that they could meet the grip posture used in daily driving. According to the analysis of the calculation results shown in the table above, this operation can directly omit the data standardization process and conduct clustering analysis directly. In this paper, S P S S software is used for cluster analysis, and a variety of clustering data is proposed to compare and analyze the actual results, so as to obtain the optimal cluster number. At the same time, 3d images are used to visually present the clustering results and finally obtain the effective distance center. According to the analysis of the set number of different clusters, it can be seen that the optimal cluster number is 5, and the variance results of each cluster result are shown in the following table:

Analysis of variance of clustering results

Indicators | Mean square value of clustering | Mean square of error | F | Sig |
---|---|---|---|---|

_{1} / _{s} |
1.17×10^{−}^{2} |
1.05×10^{−}^{3} |
11.093 | 2.40×10^{−}^{6} |

_{2} / _{u} |
2.18×10^{−}^{2} |
1.47×10^{−}^{2} |
14.799 | 8.61×10^{−}^{8} |

_{3} / _{1} |
5.64×10^{−}^{2} |
1.01×10^{−}^{2} |
55.761 | 7.21×10^{−}^{17} |

Based on the 3d scatter graph, the final cluster distribution characteristics can be studied and more feature information can be obtained on the basis of visual presentation. Each of the five types of mounting can be represented by hollow markers in various forms, and the final distance center of each type of mounting has its corresponding solid marker. Finally, the categories and sample numbers of the three index parameters corresponding to the five cluster centers proposed in this paper are shown in Table 3 below^{[5,6]}:

Cluster centers and sample numbers

Indicators | Clustering | ||||
---|---|---|---|---|---|

1 | 2 | 3 | 4 | 4 | |

_{1} / _{s} |
0.784 | 0.801 | 0.744 | 0.790 | 0.836 |

_{2} / _{u} |
0.814 | 0.732 | 0.842 | 0.804 | 0.863 |

_{3} / _{1} |
0.787 | 0.894 | 0.914 | 0.994 | 0.874 |

Number of samples | 11 | 8 | 12 | 9 | 10 |

In the local distribution results, d1 represents the vertical distance between points S and H, d2 represents the horizontal distance between points P and S, d3 represents the vertical distance between points P and H, D4 represents the horizontal distance between points E and H, and D5 represents the vertical distance between points E and H. Hs stands for height when the human body sits up straight, and LU stands for leg length. D1 / HS represents relative sitting height, D2 / LU represents the horizontal distance of the steering wheel, and D3 / D1 represents the vertical distance of the steering wheel^{[9,10]}.

Combined with the above distribution characteristics and value range analysis, it can be seen that the driving sitting characteristics of the five types of car users are as follows: First, when the driver holds the steering wheel, the height of his hand is lower than that of his shoulder, and the other two characteristics are relatively moderate; Second, the distance between the driver and the steering wheel is relatively close, the degree of arm bending is too large, and the other two characteristics are relatively moderate; Third, the driver's upper part of the body tilt is too large, the relative distance between the steering wheel is too far, the degree of arm bending is too low, and other characteristics are relatively moderate; Fourth, the driver's hand lifting height when holding the steering wheel is higher than his own shoulder, and the other two characteristics are moderate; Fifth, the driver's upper part of the body tilt is too low, and the distance between the steering wheel is too far, the arm bending degree is too low, other features moderate. Analysis of driver anthropometric data based on the value of cluster center can not only provide effective basis for the layout design of car seat and steering wheel, but also construct a perfect portrait of car users. The method studied in this paper not only analyzes the driving posture preference of the target group, but also studies the characteristics of differences in individual physique. This analysis can provide effective basis for the current staff involved in vehicle design and satisfy the attitude preference of different drivers^{[11]}.

To sum up, by simply presenting the driving portraits and corresponding poses of automobile users with dimension parameters, and selecting 50 groups of sample data and k-means clustering model algorithm for cluster analysis, on the basis of defining the optimal number of clusters, the corresponding upper body pose characteristics of different classes can be obtained in the visualization operation. Finally, the empirical analysis shows that the clustering algorithm model can effectively obtain the upper body posture characteristics of car users during driving, which can provide effective basis for differentiated driving posture and comfort research. It should be noted that this paper did not analyze the sitting preference of drivers during the static period, nor did it consider the dynamic characteristics of limbs and the scientific range of activities. Therefore, in the continuous technological update in the future, researchers of various countries should strengthen the research on static sitting preference of automobile users according to existing technical algorithms on the basis of sorting out previous research experience, and conduct in-depth research according to the actual operating conditions and processes, so as to finally obtain more specific design information.

#### Cluster centers and sample numbers

Indicators | Clustering | ||||
---|---|---|---|---|---|

1 | 2 | 3 | 4 | 4 | |

_{1} / _{s} |
0.784 | 0.801 | 0.744 | 0.790 | 0.836 |

_{2} / _{u} |
0.814 | 0.732 | 0.842 | 0.804 | 0.863 |

_{3} / _{1} |
0.787 | 0.894 | 0.914 | 0.994 | 0.874 |

Number of samples | 11 | 8 | 12 | 9 | 10 |

#### Characteristic information of measurement

Statistic | Mean (standard deviation) | Minimum - Maximum | ||
---|---|---|---|---|

Male (30) | Female (30) | Male (30) | Female (30) | |

Age | 28.5(5.0) | 28(4.1) | 23–41 | 23–40 |

Height/CM | 173.6(5.0) | 164.5(3.8) | 166.0–183.0 | 158.0–172.0 |

Quality/KG | 71.6(7.8) | 54.3(5.4) | 55.0–91.0 | 45.0–65.0 |

The trunk _{s} |
58.6(2.5) | 52.4(3.1) | 54.0–63.0 | 46.0–57.0 |

Arm length _{u} |
62.2(1.7) | 57.1(2.8) | 59.0–65.0 | 53.0–62.0 |

#### Analysis of variance of clustering results

Indicators | Mean square value of clustering | Mean square of error | F | Sig |
---|---|---|---|---|

_{1} / _{s} |
1.17×10^{−}^{2} |
1.05×10^{−}^{3} |
11.093 | 2.40×10^{−}^{6} |

_{2} / _{u} |
2.18×10^{−}^{2} |
1.47×10^{−}^{2} |
14.799 | 8.61×10^{−}^{8} |

_{3} / _{1} |
5.64×10^{−}^{2} |
1.01×10^{−}^{2} |
55.761 | 7.21×10^{−}^{17} |

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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 Optimisation of Modelling of Finite Element Differential Equations with Modern Art Design Theory Mathematical function data model analysis and synthesis system based on short-term human movement Human gait modelling and tracking based on motion functionalisation The Control Relationship Between the Enterprise's Electrical Equipment and Mechanical Equipment Based on Graph Theory Financial Accounting Measurement Model Based on Numerical Analysis of Rigid Normal Differential Equation and Rigid Functional Equation Mathematical Modeling and Forecasting of Economic Variables Based on Linear Regression Statistics Nonlinear Differential Equations in Cross-border E-commerce Controlling Return Rate Differential equation model of financial market stability based on Internet big data 3D Mathematical Modeling Technology in Visualized Aerobics Dance Rehearsal System Children’s cognitive function and mental health based on finite element nonlinear mathematical model 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 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 -groupsThe internal mechanism of corporate social responsibility fulfillment affecting debt risk in China: analysis of intermediary transmission effect based on degree of debt concentration and product market competitive advantage Study on transmission characteristics in three kinds of deformed finlines based on edge-based finite element method Asymptotic stability problem of predator–prey system with linear diffusion 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 Research on threat assessment problems of island air defence system based on the leader-follower model 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 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 Evaluation of ecosystem health in Futian mangrove wetland based on the PSR-AHP model A study of local smoothness-informed convolutional neural network models for image inpainting Towards more efficient control of the ironmaking blast furnace: modelling gaseous reduction of iron ores in H _{2}-N_{2}atmosphereAlgorithm of overfitting avoidance in CNN based on maximum pooled and weight decay 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 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 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 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 Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model