As modern society continues to evolve, artificial intelligence technology is also advancing rapidly. At present, most of the domestic population has a high dependence on intelligent digital cameras and can adapt to changes in environmental needs and other characteristics; however, at present, China's existing hardware equipment there are a number of problems: such as poor stability and low anti-interference ability and can not meet the current scene of the application function, which leads to most of the scenes in China's camera quality is poor, poor stability, and this can not meet user needs; In addition, part of the crowd animation application function is single, it does not have stable motion pose as well as motion rate and other characteristics. Based on the above problems[1,2]. This paper proposes a new concept and method to model the robot 3D environment. Firstly, the existing optical flow constraint equation model is analyzed and summarized and combined with the actual situation to give two mainstream design ideas: the first is to use multi-objective optimization algorithm (M-HMM) to achieve the quality of the camera in the simulated scene on the basis of the original; the second is to use mobile video screen technology to achieve the control of the camera motion rate in the simulated scene[3].

The optical flow constraint equation describes a two-dimensional plane formed in a three-dimensional space in a mobile table column, illuminated by a certain number of light sources, so that light waves are reflected from a certain place to another point[4].

There is a certain connection between the illumination and the motion field. Therefore, the control function can be used to simulate factors such as the direction and position of light propagation and the angle of solar radiation, so as to model and analyze the object and study its trajectory change law; control variables can also be used. After the mathematical model is established by the method and the equation is converted into an algebraic expression, it can be directly applied to other fields, such as laser interference and kinematics simulation. Based on the animation model of the optical flow constraint equation, researchers can conduct a deeper study: establish the mathematical model by the control variable method and the graph under certain influence conditions on its parameters to simulate the real scene, and transform the image into a functional relationship; analyze the regularity according to the experimental results; finally use MATLAB software to generate experimental data and graphs[5].

At present, there are many ways to construct animation models based on crowd motion. One of them builds a 2D scene based on optical flow constraint equations. In this way, all elements in the entire video picture can be combined, and then the overall effect can be evaluated and analyzed, so as to achieve the expected function, and finally obtain a complete and excellent target animation work with visual characteristics. In addition, there is another way of thinking to determine the relationship between each area according to the motion law and motion speed corresponding to each element in the scene, and finally complete the design of the entire animation scene[6]. This method can synthesize the required research content and reduce the difficulty of model modeling to a certain extent. At the same time, it can also have a clear understanding of the motion area and spatial position[7].

The process of high fidelity drawing is as follows: firstly, the researcher needs to understand the user's history of the occurrence and evolution of light flow events and human visual psychological reaction to the scene; secondly, the simulation design is carried out by building a model to observe what kind of picture effect it produces in different environments. Finally, based on the changes in the scene and the results of human brain processing, a conclusion is drawn that the application has some feasibility. The specific flow chart is shown in Figure 1.

In traditional crowd animation, two-dimensional drawings and text are mostly used. Now, the 3D space modeling technology is applied in the intelligent scene based on the optical flow constraint equation model. The workflow of this method is shown in Figure 2. It uses the computer to simulate the interaction between people, objects or other objects to generate information transmission paths to analyze the entire scene. We need to deal with a large amount of data and graphics and other complex interface information and difficult to directly obtain a complete and realistic animation screen effect, so in the post-production process is usually used in combination with laser synthesis method and optical flow constraint equation model reconstruction algorithm to use, so as to obtain the complete scene animation effect[8].

In a complete crowd animation, information acquisition plays a crucial role. It is the most important part of the whole system operation and data processing process. It not only includes the decision making after analyzing the scene, character model and the surrounding environment, it also contains the information related to the content that the user is interested in; at the same time, there will be some other aspects such as scene characteristics and background, time change, etc., which need to be expressed through these contents or can be shown visually to the audience group of crowd animation. This will make it easier to get feedback and understanding from people[9]. This is an integral part of the whole system. The current flow chart of animation information collection is shown in Figure 3.

The information of the collected population data can be used in equations (1) and (2):

The equations for constructing the crowd information are shown in equations (3) and (4).

The distribution map of the population images can be shown in Equation (5).

According to the above analysis, the frame scan technique is used to classify the feature domain of the crowd image information, and the template matching function f(gi) for correlation detection of the crowd image information is constructed as shown in Equation (6).

In the above way, we can obtain the template matching function of the multidimensional dynamic feature domain of the crowd image information and carry out the crowd image information tracking and acquisition.

Through the above method, the template matching function of the multi-dimensional dynamic feature domain of the crowd image information can be obtained, and the crowd image information can be tracked and collected.

In practical applications, optical flow can be divided into different types. Such as: active, passive and random noise. The crowd has high requirements on the speed of movement, so in order to ensure the scene environment is more complete, safe and stable, it is necessary to establish a group that conforms to the characteristics of the group, so that the optical flow operation can be completed in a short time and the result data can be obtained to evaluate its feasibility., which can provide a basis for later design and production[10]. The schematic diagram of optical flow constraint is shown in Figure 4.

Assuming that there is a quality point m on the collected video image, formula (7) is used to express its properties:

The grayscale value of m at time t is represented by I(x, y, t), where x and y are represented as the space vector of m. The grayscale value of m at time t is represented by I(x, y, t). Then, at the moment t + dt, its grayscale value is I(x + dx, y + dy, t + dt), so that when d tends to zero value, it is as shown in Equation (8).

Equation (9) can be obtained by the above test.

The partial derivative function of the grayscale values in the acquired population images in the case of x, y, t, as shown in Equation (11):

Then the above equation is expressed as a constant equation of the optical flow field, expressed in vectors as shown in Equation (12):

Horn-Schunc is an optical flow-based algorithm that plays an important role in describing the interaction between light and objects in an animated scene. Simply put, it is a function to represent an animation, video or other scenario. This method can be a good solution for model construction and parameter setting. Heberip OS (system composition diagram shown in Figure 5) is a software system controlled by a computer written program, which is also known as a user interface (MISG) system. It is a computer-based software system.

The mathematical model of Horn-Schunck algorithm can be carried out by the following formula. Assuming that in the collected crowd image sequence, it is expressed as I(x, y, t) by letters, the optical flow field that can be obtained is expressed as

The minimum value of optical flow field can be obtained by solving the extreme value of the above formula. The minimum value formula can be expressed by equations (14) and (15):

In the above formula, there is a situation such as equation (16):

After derivation of the above, equations (17) and (18) can be obtained:

In the above formula, Δ is used to represent the Laplace operator, and the calculation equation of Δ is shown in equation (19):

The above identities can be transformed as shown in equations (20) and (21):

Under the set light intensity, when people are in different positions, the light will be different. For example, when the lights are bright and dim at night, the human body will feel dizzy; at night, people will feel tired. Therefore, this visual environment and physiological phenomenon can be improved by adjusting the light, so as to achieve the best visual effect. This simulation mainly analyzes the crowd animation scene, establishes the experimental object through the optical flow model, uses the optical flow constraint equation to calculate its illumination intensity in the actual environment, and simulates the scene in the laboratory for testing. By verifying the error between the experimental test results and the theoretical value, and analyzing the reasons, the accuracy of the optical flow constraint equation in the crowd animation scene is finally determined[11].

In our optical flow model reconstruction process, a series of processing is needed for the initial data. First, the original image is grayed out and the noise is removed by using filtering. Then it is converted to smooth and output to the subsequent frames; then the number of times the corresponding point of the pixel block is taken away and its position in different cycles is calculated using the step method, and the position coordinates of the pixel block are calculated. Finally, the reconstruction and synthesis of the target point and the surrounding environment brightness, shadows and other factors are carried out according to this trajectory, and a more restorative and effective reduction of the noise impact of a smaller degree of optical flow model is obtained.

According to the above methods, we can get the following results:

After the model is optimized, the animation production cycle is reduced to a certain extent; and the cost is reduced to a certain extent; finally, after the model is optimized, the quality of its animation is greatly improved. Since the optical flow constraint equation adds part of the inverse noise. Therefore, it can be concluded that for the scene the state of motion matches the actual situation. While for other parameters without verification directly through the experiment to determine whether to meet the performance index of the application requirements, it is not possible to get the best results; so we must take into account the influence of noise in the experiment, that is, the scene model is reconstructed, rather than just rely on the parameters to calculate the results to be reconstructed. The optimization of the model improves the dynamics and the stability and visualization of the model, which is a better way for users to learn.

The designed model in the initial state, optical flow data and acceleration parameters are 0, and its change pattern is also relatively stable compared with the original simulation. However, due to the formation time and propagation speed of the laser beam, the laser signal cannot be accurately predicted to the frequency of the current scene. Therefore, the actual situation needs to be adjusted and optimized to improve the simulation degree and accuracy, so as to adapt to different needs to get a model that meets the user's usage requirements; meanwhile, the initial parameter settings are verified to be reasonable through several tests at the later stage of this design, and the model parameter values are modified according to the experimental results.

According to the experimental data, the scene of the subject of this study is based on the model reconstruction under the optical flow constraint. The model is initialized before it is built. Then the laser scanner is used to complete the modeling. The distribution table between the template size and the number of correlation coefficients when the layout calculation is performed is shown in Table 1.

Template size and correlation coefficient distribution

Template size | 4*5 | 12* 10 | 25*18 | 20*14 | 25*50 |

Setting parameters | 0.345 | 0.675 | 0.636 | 0.487 | 0.543 |

According to the above parameter settings, the virtual object entity is constructed and the crowd information data is obtained, and the crowd animation effect is shown in Figure 6.

Firstly, a complete prototype interface and database table are built; secondly, the required parameter values are obtained after the camera is fixed into three-dimensional space and recorded by laser interferometer; finally, a series of graphical information such as mapping relationship diagram between data and animation scene and motion curve diagram are established by MATLAB software. Through the study of the laser interferometer, the motion characteristics of the optical flow are analyzed, and the MATLAB software is used to establish a model based on the mapping relationship between the optical flow constraint equation and the experimental data, etc. After the above operations are completed, the simulation reconstruction can be performed. Finally, the curve corresponding to all parameter values and required parameters and related graphical information are obtained using high simulation; then the prototype interface effect is verified to reach the expected goal according to the stepper motor drive module and algorithm implementation function, and the problems arising during its application are studied accordingly and the reasons are analyzed, and feasible solutions are proposed.

Scene simulation refers to the simulation of a complete scene to verify whether the model is correct or not, rather than by simply using software. Here we mainly use the optical flow constraint equation and related optical theory. First, the basic parameters such as the corresponding light environment, camera trajectory map and animation effects are established before starting to create; then the camera model and size are selected to match the desired image with the laser camera; finally, the lighting is set and the luminance value of each pixel position in the scene is set to correspond to the video area, thus ensuring that it meets the normal use requirements. Through a series of experiments and tests, it can be concluded that the crowd animation scene, the camera trajectory and the laser camera parameters match the desired effect.

This paper mainly simulates some functions through the establishment and simulation of optical flow constraint equations. First of all, this paper summarizes the change rules of crowd movements according to the analysis of experimental data, and then adds factors such as different positions, different materials and lighting brightness in the scene into the software, and combines the actual situation to design the motion trajectory diagram and human-computer interaction in the corresponding scene. interface. Finally, the animation effect curve is generated by MATLAB, and the required model is generated by the stepper motor drive module and the button control module, and the simulation experiment is carried out after the rationality of the algorithm is verified by simulation. In the future, similar to “Internet +”, it will become more common and frequent to be applied to various affairs in people's life such as study and work; At the same time, the animation industry will also face more severe challenges and opportunities; in the future, with the continuous development and improvement in the field of artificial intelligence and the advent of the era of networking and digitalization, people's living standards and quality will also be higher. Level up. For animation producers, they will pay more attention to research on new technologies and products in the future, so that they can better meet user needs and gain more market share; for users, they will pay more attention to animation. The improvement of the overall quality enables it to occupy an advantageous position in the fierce competition.

#### Template size and correlation coefficient distribution

Template size | 4*5 | 12* 10 | 25*18 | 20*14 | 25*50 |

Setting parameters | 0.345 | 0.675 | 0.636 | 0.487 | 0.543 |

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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 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