# Enterprise Financial Management Control System considering Virtual Realization Technology Combined with Comprehensive Budget Management

Authors: Dan Zhang
Journal: Mobile Information Systems
 (2022)

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## Abstract

Nowadays, the corporate financial control management system has become more perfect with the development of the economy. With the continuous enrichment of financial management systems, enterprises will have to face more and more problems. The modern effective way to solve these financial problems is the combination of financial budget management and financial control. It is very important to combine financial control and financial management from the perspective of collaboration. Taking overall consideration into account, relevant personnel should consider both in the financial management system. They should make effective arrangements from the system and implementation links, so as to effectively prevent all enterprise development crises caused by the control risks. Relevant personnel must strictly control finances from the source and standardize various financial budget management systems. Virtual reality is an emerging digital technology in recent years, which is based on computer networks and transforms traditional paper-based information into a new way that is interactive, understandable, and easy to use. It is different from other things: in the traditional environment, people need to get the content they need through visual. Virtual reality can provide human-computer interconnection and various forms of interactive experiences; it also enables users to update the interface in real time and evaluate the feedback of related products or services, etc. Therefore, virtual reality is widely used in enterprises.


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## Body

## 1. Introduction

With the continuous reform of society, the domestic market competition in all walks of life is becoming increasingly fierce. During the operation of various enterprises, the external market environment and internal management are constantly changing [1]. Different from the past, the problems of financial management and control of modern enterprises are more complicated. These problems are not only crises for enterprises, but also opportunities for their own development [2]. With the ever-changing market competition, strengthening control from a financial perspective is undoubtedly of great significance for companies to win market competition, and it is also more conducive to the long-term development of companies [3].Total budget management refers to the overall planning and control of internal departments, various business activities, and the whole process of production and operation in accordance with the requirements of business objectives. It can effectively coordinate the company’s resource allocation to achieve the best status. In the network environment, virtual realization technology combined with strategy-oriented budgeting is an all-new form of networked organizational structure developed on the basis of the Internet; it is based on the network environment and integrates corporate resources into a virtual organization [4, 5]. On this platform, collaboration and communication among departments can be realized; at the same time, information sharing, resource allocation, and personnel deployment within the enterprise can be carried out through networked management. Networked virtual reality technology combined with strategy-oriented budgeting is based on the Internet, and the overall goal can be achieved through scientific management of each department’s budget management within the enterprise [6].At present, enterprises can use simulation systems based on virtual reality technology to create virtual worlds with real experiences. The virtual system must use a combination of multiple data sources to create an interactive three-dimensional dynamic view of the user entering the environment. This technology is an important part of simulation technology. It is a collection of simulation technology and other network technologies [7]. It is also the most cutting-edge field in which different disciplines are interconnected. This research is very important for the development path of enterprise informatization. Virtual reality technology has autonomy, interactivity, presence, and multiple sensitivities. Among them, autonomy is an important feature of this technology and also an important feature of realizing simulated reality [8]. Although virtual reality technology was born and invented at the beginning of this century, from a development perspective, it can play a role in many fields. The technology needs to be continuously researched and improved to promote its application in various fields, and then promote the development of other fields [9, 10].


## 2. Methods

### 2.1. Financial Control Method Based on Virtual Realization Technology

Virtual realization technology combined with the construction of a comprehensive budget management control system includes three main aspects: first, enterprise internal organization structure and functional departments between the information transfer and sharing mechanism. Second, each unit sets the corresponding authority in the financial index system. Third, the need for real-time monitoring and maintenance of the network system and the design and development of functional modules such as data analysis; fourth, the application software can be used to complete the forecast and management control of financial targets and cost expenditure situation, it is necessary to develop a perfect and reasonable effective and feasible plan to achieve the expected results in conjunction with the actual development status of the enterprise [11]. The composition of the virtual implementation system is shown in Figure 1.Figure 1
Virtual implementation system composition diagram.For the development, application, and management of virtual reality, enterprises need to establish a set of perfect financial control system in the process of implementation, and then it is monitored as a system. The main contents of the financial control are shown in Figure2:(1)
Construct a reasonable and effective budget target that is appropriate to the actual situation and development requirements. Through the analysis and evaluation of different stages, different periods and different types of projects to determine the proportion of expenditure of each project in each period and the relevant cost expenditure level and other indicators;(2)
Build the enterprise virtual realization management process framework, including preplanning and design, project schedule preparation, and construction preparation control; combined with budget management and construction project schedule preparation, analysis of virtual reality use in the virtual realization environment, which can enhance the overall strength of the enterprise [12]. Through its comprehensive systematization, dynamic monitoring and control to ensure the network planning and implementation effect and safe and stable operation; with the help of information technology to improve the efficiency of resource integration as well as reduce the cost of inputs and other aspects of the comprehensive ability.Figure 2
Contents of financial control.


### 2.2. Comprehensive Budget Management Based on Genetic Algorithm

Comprehensive budget management is a strategy-oriented management control system. It is the goal of various departments and employees within the enterprise to make a comprehensive and systematic decomposition of the company’s overall strategic planning, implementation. The application of genetic algorithm in comprehensive budget management is a relatively advanced method, which has strong flexibility and adaptability, and can be used for real-time prediction and control in virtual reality system.Genetic algorithm (GA) is a new global optimization algorithm developed on the basis of biological genetics. Genetic algorithm is based on the concept of Darwin’s theory of evolution. It is a data processing process based on the natural evolutionary law of “natural selection by nature, survival of the fittest”. Through the process of continuous evolution, genetic algorithms continue to improve the adaptability of all individuals in the population to achieve the global adaptation goals [13].The realization process of genetic algorithm is the performance of genetic operations such as hybridization and mutation of chromosomes or individuals in the population. These chromosomes are usually character strings represented by binary codes. Genetic algorithm is a kind of random search algorithm, it is an iterative optimization process, and genetic algorithm has self-compatibility. Starting from the initial population, and then through the role of selection, mutation, hybridization, and other genetic operators, evolve from generation to generation, and find the optimal solution through an iterative process [14]. The flow chart of genetic algorithm is shown in Figure 3.Figure 3
Flow chart of genetic algorithm.


#### 2.2.1. Coding Method

Encoding is the first problem that genetic algorithms must solve. The most commonly used encoding method is binary encoding. When using binary coding, a string of chromosome bits is mainly composed of binary symbols 0 and 1, in which the length of anL chromosome is a fixed number, as shown in formula:(1)X=10011100100011010100.The above formula represents an individual whose chromosome lengthL = 20.The length of the binary code symbol string is related to the accuracy of solving the problem. Assuming that a specific parameter has a value range [a, b], we use a binary code with a string length of L to represent the parameter, and a total of 2 different codes can be generated, if the parameter corresponds to the code, as shown in formula:(2)0000000000⋯⋯00000000⟶a00000000000⋯⋯00000001⟶a+δ⋮111111111111⋯⋯11111111=2L−1⟶b.Then, the coding accuracy of the binary code is as shown in formula:(3)δ=b−a2L−1.Assuming that the code of an individual isxk=ak1ak2⋯akl,, the corresponding decoding formula is shown in:(4)xk=a+b−a2L−1∑j=1Lakj2L−j.For example, forx ∈ [0, 1023], if a binary code with a length of 10 is used to represent the parameter, the symbol string is as shown in formula:(5)x=0010101111.The above formula represents an individual, and its corresponding parameter value isx = 175. At this time, the coding accuracy is 1.


#### 2.2.2. Individual Fitness

The core concept of individual fitness is to describe the standards of individual pros and cons, and it eliminates individuals based on this indicator. The core issue generally discussed in individual fitness is the issue of maximum and minimum [15]. For the minimum problem, its fitness is converted according to formula:(6)fx=Cmax−gx,ifgx<Cmax,0,other.In order to ensure that the fitness does not appear negative, for the maximum value that may have a negative value, the formula (7) can be used to transform:(7)fx=Ux+Cmin,ifUx+Cmin>0,0,other.


## 2.1. Financial Control Method Based on Virtual Realization Technology

Virtual realization technology combined with the construction of a comprehensive budget management control system includes three main aspects: first, enterprise internal organization structure and functional departments between the information transfer and sharing mechanism. Second, each unit sets the corresponding authority in the financial index system. Third, the need for real-time monitoring and maintenance of the network system and the design and development of functional modules such as data analysis; fourth, the application software can be used to complete the forecast and management control of financial targets and cost expenditure situation, it is necessary to develop a perfect and reasonable effective and feasible plan to achieve the expected results in conjunction with the actual development status of the enterprise [11]. The composition of the virtual implementation system is shown in Figure 1.Figure 1
Virtual implementation system composition diagram.For the development, application, and management of virtual reality, enterprises need to establish a set of perfect financial control system in the process of implementation, and then it is monitored as a system. The main contents of the financial control are shown in Figure2:(1)
Construct a reasonable and effective budget target that is appropriate to the actual situation and development requirements. Through the analysis and evaluation of different stages, different periods and different types of projects to determine the proportion of expenditure of each project in each period and the relevant cost expenditure level and other indicators;(2)
Build the enterprise virtual realization management process framework, including preplanning and design, project schedule preparation, and construction preparation control; combined with budget management and construction project schedule preparation, analysis of virtual reality use in the virtual realization environment, which can enhance the overall strength of the enterprise [12]. Through its comprehensive systematization, dynamic monitoring and control to ensure the network planning and implementation effect and safe and stable operation; with the help of information technology to improve the efficiency of resource integration as well as reduce the cost of inputs and other aspects of the comprehensive ability.Figure 2
Contents of financial control.


## 2.2. Comprehensive Budget Management Based on Genetic Algorithm

Comprehensive budget management is a strategy-oriented management control system. It is the goal of various departments and employees within the enterprise to make a comprehensive and systematic decomposition of the company’s overall strategic planning, implementation. The application of genetic algorithm in comprehensive budget management is a relatively advanced method, which has strong flexibility and adaptability, and can be used for real-time prediction and control in virtual reality system.Genetic algorithm (GA) is a new global optimization algorithm developed on the basis of biological genetics. Genetic algorithm is based on the concept of Darwin’s theory of evolution. It is a data processing process based on the natural evolutionary law of “natural selection by nature, survival of the fittest”. Through the process of continuous evolution, genetic algorithms continue to improve the adaptability of all individuals in the population to achieve the global adaptation goals [13].The realization process of genetic algorithm is the performance of genetic operations such as hybridization and mutation of chromosomes or individuals in the population. These chromosomes are usually character strings represented by binary codes. Genetic algorithm is a kind of random search algorithm, it is an iterative optimization process, and genetic algorithm has self-compatibility. Starting from the initial population, and then through the role of selection, mutation, hybridization, and other genetic operators, evolve from generation to generation, and find the optimal solution through an iterative process [14]. The flow chart of genetic algorithm is shown in Figure 3.Figure 3
Flow chart of genetic algorithm.


### 2.2.1. Coding Method

Encoding is the first problem that genetic algorithms must solve. The most commonly used encoding method is binary encoding. When using binary coding, a string of chromosome bits is mainly composed of binary symbols 0 and 1, in which the length of anL chromosome is a fixed number, as shown in formula:(1)X=10011100100011010100.The above formula represents an individual whose chromosome lengthL = 20.The length of the binary code symbol string is related to the accuracy of solving the problem. Assuming that a specific parameter has a value range [a, b], we use a binary code with a string length of L to represent the parameter, and a total of 2 different codes can be generated, if the parameter corresponds to the code, as shown in formula:(2)0000000000⋯⋯00000000⟶a00000000000⋯⋯00000001⟶a+δ⋮111111111111⋯⋯11111111=2L−1⟶b.Then, the coding accuracy of the binary code is as shown in formula:(3)δ=b−a2L−1.Assuming that the code of an individual isxk=ak1ak2⋯akl,, the corresponding decoding formula is shown in:(4)xk=a+b−a2L−1∑j=1Lakj2L−j.For example, forx ∈ [0, 1023], if a binary code with a length of 10 is used to represent the parameter, the symbol string is as shown in formula:(5)x=0010101111.The above formula represents an individual, and its corresponding parameter value isx = 175. At this time, the coding accuracy is 1.


### 2.2.2. Individual Fitness

The core concept of individual fitness is to describe the standards of individual pros and cons, and it eliminates individuals based on this indicator. The core issue generally discussed in individual fitness is the issue of maximum and minimum [15]. For the minimum problem, its fitness is converted according to formula:(6)fx=Cmax−gx,ifgx<Cmax,0,other.In order to ensure that the fitness does not appear negative, for the maximum value that may have a negative value, the formula (7) can be used to transform:(7)fx=Ux+Cmin,ifUx+Cmin>0,0,other.


## 2.2.1. Coding Method

Encoding is the first problem that genetic algorithms must solve. The most commonly used encoding method is binary encoding. When using binary coding, a string of chromosome bits is mainly composed of binary symbols 0 and 1, in which the length of anL chromosome is a fixed number, as shown in formula:(1)X=10011100100011010100.The above formula represents an individual whose chromosome lengthL = 20.The length of the binary code symbol string is related to the accuracy of solving the problem. Assuming that a specific parameter has a value range [a, b], we use a binary code with a string length of L to represent the parameter, and a total of 2 different codes can be generated, if the parameter corresponds to the code, as shown in formula:(2)0000000000⋯⋯00000000⟶a00000000000⋯⋯00000001⟶a+δ⋮111111111111⋯⋯11111111=2L−1⟶b.Then, the coding accuracy of the binary code is as shown in formula:(3)δ=b−a2L−1.Assuming that the code of an individual isxk=ak1ak2⋯akl,, the corresponding decoding formula is shown in:(4)xk=a+b−a2L−1∑j=1Lakj2L−j.For example, forx ∈ [0, 1023], if a binary code with a length of 10 is used to represent the parameter, the symbol string is as shown in formula:(5)x=0010101111.The above formula represents an individual, and its corresponding parameter value isx = 175. At this time, the coding accuracy is 1.


## 2.2.2. Individual Fitness

The core concept of individual fitness is to describe the standards of individual pros and cons, and it eliminates individuals based on this indicator. The core issue generally discussed in individual fitness is the issue of maximum and minimum [15]. For the minimum problem, its fitness is converted according to formula:(6)fx=Cmax−gx,ifgx<Cmax,0,other.In order to ensure that the fitness does not appear negative, for the maximum value that may have a negative value, the formula (7) can be used to transform:(7)fx=Ux+Cmin,ifUx+Cmin>0,0,other.


## 3. Genetic Operator

### 3.1. Select Operation

The selection operator selects individuals based on the fitness of the person. Commonly used selection methods include roulette selection, sort selection, and best individual preservation [16]. Roulette selection method is a selection method based on probability. This method determines an individual every time a random number is generated, and the error is relatively large. The ranking selection algorithm ranks according to individual fitness, and the probability of being selected is determined by the ranking; the best individual preservation is that individuals with high fitness directly enter the offspring group.It can be seen from the above that the selection operation uses the roulette method to select individuals, which is a method based on probability. Therefore, getting a suitable individual requires multiple implementations of the roulette method to be able to converge to the optimal solution. This makes it take too long to select a suitable individual, which affects the overall computational efficiency. In response to this shortcoming, we replaced the worst-adapted individuals in the group with better-adapted individuals. This method can effectively improve the overall fitness of the population. This method can protect the good individuals in each generation, eliminate bad individuals, and generate better individuals through crossover operations, so that the optimization speed of the algorithm can be improved.In addition, the traditional roulette operator determines an individual every time a random number is generated. This method greatly wastes random numbers, reduces the generation of random numbers, and makes the error of the algorithm larger. In response to this problem, the researchers proposed an improved multi-round roulette operator method. In this method, an individual can be determined every time.M random numbers are generated, which continuously increases the number of random numbers generated, and this can also reflect the role of random numbers more accurately, so as to reduce the error.Genetic algorithm relies on selection operations to guide the search direction of the algorithm, and selection operations use individual fitness as a deterministic index. It selects individuals with high fitness from the current population to generate a mating pool, and then performs crossover and mutation operations to find the optimal solution to the problem. If the fitness function is selected inappropriately, it may cause the loss of genetic information in the group, and the average similarity of individuals in the group will increase, which will eventually cause the genetic algorithm to mature prematurely [17].It can be seen from the above that the selection operation takes the fitness of the individual as a deterministic index. If the individual fitness function is inappropriately selected, it will cause the genetic algorithm to mature prematurely, so the selection of the fitness function is very important, so how to choose the appropriate fitness function? The researchers proposed a nonlinear dynamic fitness function, which can effectively select the appropriate fitness function.The basic sort selection algorithm is a comparison exchange sort algorithm, which is a simple method of sorting through exchange. The principle is: in a population composed ofn individuals, first compare the first individual with the second, if it is in the reverse order, then modify it, otherwise continue to compare and exchange. Only by n (n +  1) times of comparison and adjustment can the final sorting effect be obtained [18].From the above, it can be seen that the comparison and exchange sorting algorithm needs to go through a round of comparison and exchange, so it takes a long time. In response to this problem, we proposed a selection sorting algorithm. The principle of the algorithm is to compare the first individual with the entire row of individuals at once, and after all the individuals are compared, the smallest or largest one is selected for sorting. This method effectively shortens the algorithm sorting time.However, comparison and exchange sorting algorithms need to be compared in pairs when sorting. This method may cause larger errors in comparison and exchange. Therefore, the sorting efficiency of this algorithm is low. In response to this problem, the researchers proposed a two-way selection sorting algorithm. The principle of the algorithm is: the algorithm searches for the maximum value from the front to the back, and at the same time searches for the minimum value from the back to the front, and then finds the corresponding value and puts it on the first and last digits. By analogy, this method continuously effectively shortens the time and reduces the comparison calculation. The entire sorting process becomes simple, thereby effectively improving the sorting efficiency of the algorithm.


### 3.2. Crossover Operation

This operation is to select two chromosomes randomly as parents’ genetic chromosomes by the algorithm, and then select a suitable method from many crossover methods to perform crossover transformation, thereby generating new chromosomes as the chromosomes of the offspring. The characteristic of the crossover operation is: the closer the similarity (distance) of two crossover operators (genetic chromosomes) is, the fewer offspring will be produced by their crossover. The calculation of crossover probability is shown in formula:(8)Pc=Pc0−Pc0−Pcmin⋅tT.When the crossover operation selects two parents’ genetic chromosomes for crossover transformation, if the two parents’ genetic chromosomes are more similar, they will produce fewer offspring. This makes the crossover process little, and the difference between the offspring and the parent is very small, which also affects the subsequent crossover operations. In response to this problem, the main solution is to increase the distance between the two crossover individuals. When the two crossover individuals are larger, the similarity of the parent individual is low and enters the crossover operation. At the beginning of the iteration, thePc is larger, which can increase the diversity of the group and produce more new individuals.The calculation formula of the crossover probability in the crossover operation is shown in formula (8), where Pc is constant. Although a constant value can make the crossover probability more stable, it is also easy to destroy the good gene model. This makes the algorithm unable to converge or the convergence speed is significantly slower. In response to this problem, the best solution is to continuously increase the number of iterations t, so that in the later stage of evolution, the population will gradually approach the optimal solution. At this time, the smaller Pc can protect the individuals of the good mode to a certain extent, thus speeding up the convergence speed of the algorithm.The crossover operation of genetic algorithm refers to dividing a new problem into several children (usually parent nodes) in the process of solving. But this will not lead to the optimal solution for the calculation. On the basis of comprehensive budget management, the researchers first proposed a method with the GA paradigm as the core, combined with the actual situation of the enterprise, to analyze the economic benefits and costs and other indicators generated by each of its children, after the traditional method in the improved genetic algorithm calculates the optimal solution.In addition, in the process of crossover operation, some problems may occur in the running of genetic algorithm, which makes the budgeting result deviate from the actual situation and affects the realization of the overall budget management goal. The researchers proposed a solution based on the binary coding method and random generation function. This combined method can first use the fitness value as an evaluation index, and secondly, the global parameters can be obtained after calculating the optimal solution, so that the expected results can be obtained and the results are More consistent.In the crossover operation, when the number of populations is large and the differences between individuals are large, local extreme values will appear, and these extreme values will have a great impact on enterprise management, resulting in unsatisfactory budget execution results. In order to solve this problem, this paper introduces the fitness function based on the genetic algorithm, and establishes an improved comprehensive budget management system based on the actual situation, which can effectively improve the accuracy of budget information, and can also solve the traditional comprehensive budget management system, the local extreme value phenomenon that exists in the system.


### 3.3. Mutation Operation

This operation aims to simulate the connection between gene mutation and biological evolution. Genotype expression is affected by changes in nature or the external environment. Very simple mutation operations in chromosome binary codes are often used in genetic operations. The mutation operation can effectively select the optimal solution, but the mutation operation is easy to be in the local optimal solution in the later stage. If the position of the specific gene is 1, the mutation is 0; otherwise it changes from 0 to 1. Mutations in biological evolution can enrich the diversity of chromosomes, so that two species have different phenotypes and constantly change [12]. The formula for mutation probability of mutation operation is shown in:(9)pm=pmutate1−F.The mutation operation mainly simulates the connection between gene mutation and biological evolution, but when the mutation operation proceeds to the later stage, the individuals in the population are close to the optimal solution. As a result, some individuals may also be in the local optimum and cannot jump out of the local optimum. In response to this problem, the researchers improved the mutation probability algorithm, the improved probability ispm=pmmax−k⋅m⋅t/T, which enhances the competitiveness of individuals through the enhanced mutation probability, thereby better helping some individuals to jump out of the local optimum.The mutation operation assigns the same mutation probability to each individual in the binary code. This method is random and uncontrollable. For example, if the 1101001 individual is mutated, if the 5th locus has a higher similarity in the current population, so the 5th individual in the population has more genes with “0”. At this time, the probability of mutation of the 5th gene is shown in formula (9).In the mutation operation, the probability of using binary codes to mutate individuals is the same, but this method belongs to “full output”, so it lacks directionality. In response to this problem, the researchers proposed a genetic algorithm mutation operator that maintains the population diversity. The mutation probability and mutation location are adaptively determined by the diversity of the population gene locus and the individual fitness value. Excellent individuals can be preserved after mutation, and the ratio of two genes per gene in the population is controlled within the expected range.The main principle guiding genetic algorithm is model theory. This theory demonstrates the basic mechanism of genetic algorithm. The pattern refers to the part of the string that has the same attributes. The pattern sequence refers to the number of characters with clear meaning in the pattern, represented byO (h), where h represents the pattern. The lower the sequence, the stronger the pattern generalization, and the greater the number of different strings, and vice versa. If the mode order is zero, the universality of the clear character is the strongest. The length of the specified pattern represents the distance between the first and the last two characters in the pattern [13]. It has a clear meaning and is recorded as σH. Generally, the formula (10) is obtained:(10)σH=b−a.In the actual budget management process, we will have some uncertain and unpredictable situations, which require manual adjustment. So, this is also a waste of manpower.(1)
When the cross operation is performed, there may be deviations between the calculation results and the real data, resulting in low accuracy of the calculation results. In this paper, the linear programming method and genetic algorithm are used for improvement, which makes the calculation results more accurate, and provides a theoretical basis for enterprises to implement the comprehensive budget management.(2)
Due to the randomness and complexity of the algorithm itself, there are many problems in the practical application of the algorithm, such as: how to process the data and how to improve the accuracy of the operation. In order to solve these problems, the researchers used the improved particle swarm optimization technology to improve the algorithm, and introduced a fitness function into the genetic algorithm, which made the calculation method of particle swarm optimization more flexible, which can effectively solve the data processing and calculation and the problem of accuracy.(3)
In the mutation operation, when the random variable exceeds a certain value, the budget parameters cannot be redistributed. If the function changes too fast and the optimal solution cannot be obtained, the actual value needs to be adjusted or eliminated again. This will greatly increase the error of the calculation. On the basis of introducing the traditional natural selection and local optimal optimization theory, this paper proposes a comprehensive budget management method based on the principle of chaotic coding. This method can overcome the influence of randomness and improve the accuracy of data processing. At the same time, by introducing the niche search technology to solve the problem of a large number of uncertain factors in the global optimization, it also plays an important role in the local search. This method can effectively improve the level of budget management.


### 3.4. Construction of Enterprise Financial Management Control System

Budget management is an internal management tool commonly used in modern enterprises. In order to achieve long-term stable growth, companies often need to formulate long-term strategic goals and short-term business goals separately. In order to effectively achieve the established long-term and short-term goals, it is necessary to formulate an appropriate implementation plan in the implementation of actual work. In this way, the full implementation of the plan can be achieved, and appropriate management methods can be adopted [14]. Domestic and foreign companies often choose comprehensive budget management as an effective way to control corporate management activities and achieve long-term and short-term goals.The comprehensive budget refers to the overall financial budget of an enterprise in various aspects such as research and development, production, and sales at a specific time (usually no more than a year). These reflect all activities in the enterprise economy in the form of currency. Based on a comprehensive budget system, companies conduct business systematically, comply with legal goals, and they have stable operations and management. Comprehensive budget management is a comprehensive tool, control standard, and evaluation basis. This is the basis for the establishment of modern enterprises and the key to scientific management and corporate norms. This is an important part of building an effective corporate management structure. This is also an effective incentive for managers at all levels. This is an effective way for employees and all employees to innovate and improve their self-development skills. The flowchart of comprehensive budget management is shown in Figure4.Figure 4
Overall budget management process.Five links must be submitted to establish a modern enterprise scientific budget management system. Figure5 shows the framework of integrated budget management. Before making a budget, it is very important to conduct a good research and analysis of the budget to understand the true situation of the company’s budget and the problems that may arise; in the process of budgeting, each link must consider the technical and human aspects of the corporate budget. A scientific and reasonable budget system must also consider the impact and management of personnel and equipment. Personnel management can fully mobilize individual creativity and initiative, and equipment management can improve work efficiency. Both are indispensable.Figure 5
Overall budget management framework diagram.


#### 3.4.1. Synergy Between Comprehensive Budget Management and Financial Control

Target synergy: The emergence of modern enterprise management systems gave birth to the principal-agent theory, which aims to improve the scientific nature and work efficiency of professional organization management. However, the principal-agent system itself has inherent flaws: the asymmetry of information between the principal and the agent leads to the inconsistency of the goals of both parties, which affects the realization of the company’s overall goals. Comprehensive budget management and financial control can be used to coordinate and solve the problems that arise in this multi-level principal-agent relationship. Financial control aims to reduce the space for individual behavior choices. Relevant personnel should improve the control of the customer’s business management team and increase the transparency of the management activities of the corporate management team; it is also necessary to reduce conflicts between management teams, so as to lay the foundation for comprehensive budget and personnel management, review, and evaluation. Relevant personnel must have clear responsibilities and rights of all parties. Therefore, whether it is comprehensive budget management or financial control, it is in line with its purpose. They not only have to solve various problems faced in business management activities, but also realize the company’s overall development strategy and long-term goals.Process interaction: in modern enterprise management, comprehensive budget management is a systematic management tool. It not only evaluates and balances the rights, responsibilities adjustment, and profit distribution mechanism between the management team and employees, but also reflects the barriers of chief authority to agents and enterprise production capacity control. Financial control is the readjustment of the responsibilities, rights, and interests of corporate investors, managers, and employees in all aspects of comprehensive budget management. In the process of implementing comprehensive budget management and financial control, the two can complement each other. Scientific comprehensive budget management helps to enrich the control methods of internal control implementation.In Figure6, collective decision-making with the goal of scientific progress and value creation can be regarded as the goal of the control unit of the control system, with the integrated budget system and some of its subsystems as the execution unit. All system connections can be regarded as control links, and the exchange of budget information plays an important role in system-wide control links [15]. Figure 6 essentially presents a picture of the entire control system, which not only conforms to the basis of cybernetics, but also fully reflects the specific financial characteristics. Figure 7 shows the key link diagram under the financial control system based on comprehensive budget management.Figure 6
Financial control framework diagram based on comprehensive budget management.Figure 7
Diagram of key links under the financial control system based on comprehensive budget management.


## 3.1. Select Operation

The selection operator selects individuals based on the fitness of the person. Commonly used selection methods include roulette selection, sort selection, and best individual preservation [16]. Roulette selection method is a selection method based on probability. This method determines an individual every time a random number is generated, and the error is relatively large. The ranking selection algorithm ranks according to individual fitness, and the probability of being selected is determined by the ranking; the best individual preservation is that individuals with high fitness directly enter the offspring group.It can be seen from the above that the selection operation uses the roulette method to select individuals, which is a method based on probability. Therefore, getting a suitable individual requires multiple implementations of the roulette method to be able to converge to the optimal solution. This makes it take too long to select a suitable individual, which affects the overall computational efficiency. In response to this shortcoming, we replaced the worst-adapted individuals in the group with better-adapted individuals. This method can effectively improve the overall fitness of the population. This method can protect the good individuals in each generation, eliminate bad individuals, and generate better individuals through crossover operations, so that the optimization speed of the algorithm can be improved.In addition, the traditional roulette operator determines an individual every time a random number is generated. This method greatly wastes random numbers, reduces the generation of random numbers, and makes the error of the algorithm larger. In response to this problem, the researchers proposed an improved multi-round roulette operator method. In this method, an individual can be determined every time.M random numbers are generated, which continuously increases the number of random numbers generated, and this can also reflect the role of random numbers more accurately, so as to reduce the error.Genetic algorithm relies on selection operations to guide the search direction of the algorithm, and selection operations use individual fitness as a deterministic index. It selects individuals with high fitness from the current population to generate a mating pool, and then performs crossover and mutation operations to find the optimal solution to the problem. If the fitness function is selected inappropriately, it may cause the loss of genetic information in the group, and the average similarity of individuals in the group will increase, which will eventually cause the genetic algorithm to mature prematurely [17].It can be seen from the above that the selection operation takes the fitness of the individual as a deterministic index. If the individual fitness function is inappropriately selected, it will cause the genetic algorithm to mature prematurely, so the selection of the fitness function is very important, so how to choose the appropriate fitness function? The researchers proposed a nonlinear dynamic fitness function, which can effectively select the appropriate fitness function.The basic sort selection algorithm is a comparison exchange sort algorithm, which is a simple method of sorting through exchange. The principle is: in a population composed ofn individuals, first compare the first individual with the second, if it is in the reverse order, then modify it, otherwise continue to compare and exchange. Only by n (n +  1) times of comparison and adjustment can the final sorting effect be obtained [18].From the above, it can be seen that the comparison and exchange sorting algorithm needs to go through a round of comparison and exchange, so it takes a long time. In response to this problem, we proposed a selection sorting algorithm. The principle of the algorithm is to compare the first individual with the entire row of individuals at once, and after all the individuals are compared, the smallest or largest one is selected for sorting. This method effectively shortens the algorithm sorting time.However, comparison and exchange sorting algorithms need to be compared in pairs when sorting. This method may cause larger errors in comparison and exchange. Therefore, the sorting efficiency of this algorithm is low. In response to this problem, the researchers proposed a two-way selection sorting algorithm. The principle of the algorithm is: the algorithm searches for the maximum value from the front to the back, and at the same time searches for the minimum value from the back to the front, and then finds the corresponding value and puts it on the first and last digits. By analogy, this method continuously effectively shortens the time and reduces the comparison calculation. The entire sorting process becomes simple, thereby effectively improving the sorting efficiency of the algorithm.


## 3.2. Crossover Operation

This operation is to select two chromosomes randomly as parents’ genetic chromosomes by the algorithm, and then select a suitable method from many crossover methods to perform crossover transformation, thereby generating new chromosomes as the chromosomes of the offspring. The characteristic of the crossover operation is: the closer the similarity (distance) of two crossover operators (genetic chromosomes) is, the fewer offspring will be produced by their crossover. The calculation of crossover probability is shown in formula:(8)Pc=Pc0−Pc0−Pcmin⋅tT.When the crossover operation selects two parents’ genetic chromosomes for crossover transformation, if the two parents’ genetic chromosomes are more similar, they will produce fewer offspring. This makes the crossover process little, and the difference between the offspring and the parent is very small, which also affects the subsequent crossover operations. In response to this problem, the main solution is to increase the distance between the two crossover individuals. When the two crossover individuals are larger, the similarity of the parent individual is low and enters the crossover operation. At the beginning of the iteration, thePc is larger, which can increase the diversity of the group and produce more new individuals.The calculation formula of the crossover probability in the crossover operation is shown in formula (8), where Pc is constant. Although a constant value can make the crossover probability more stable, it is also easy to destroy the good gene model. This makes the algorithm unable to converge or the convergence speed is significantly slower. In response to this problem, the best solution is to continuously increase the number of iterations t, so that in the later stage of evolution, the population will gradually approach the optimal solution. At this time, the smaller Pc can protect the individuals of the good mode to a certain extent, thus speeding up the convergence speed of the algorithm.The crossover operation of genetic algorithm refers to dividing a new problem into several children (usually parent nodes) in the process of solving. But this will not lead to the optimal solution for the calculation. On the basis of comprehensive budget management, the researchers first proposed a method with the GA paradigm as the core, combined with the actual situation of the enterprise, to analyze the economic benefits and costs and other indicators generated by each of its children, after the traditional method in the improved genetic algorithm calculates the optimal solution.In addition, in the process of crossover operation, some problems may occur in the running of genetic algorithm, which makes the budgeting result deviate from the actual situation and affects the realization of the overall budget management goal. The researchers proposed a solution based on the binary coding method and random generation function. This combined method can first use the fitness value as an evaluation index, and secondly, the global parameters can be obtained after calculating the optimal solution, so that the expected results can be obtained and the results are More consistent.In the crossover operation, when the number of populations is large and the differences between individuals are large, local extreme values will appear, and these extreme values will have a great impact on enterprise management, resulting in unsatisfactory budget execution results. In order to solve this problem, this paper introduces the fitness function based on the genetic algorithm, and establishes an improved comprehensive budget management system based on the actual situation, which can effectively improve the accuracy of budget information, and can also solve the traditional comprehensive budget management system, the local extreme value phenomenon that exists in the system.


## 3.3. Mutation Operation

This operation aims to simulate the connection between gene mutation and biological evolution. Genotype expression is affected by changes in nature or the external environment. Very simple mutation operations in chromosome binary codes are often used in genetic operations. The mutation operation can effectively select the optimal solution, but the mutation operation is easy to be in the local optimal solution in the later stage. If the position of the specific gene is 1, the mutation is 0; otherwise it changes from 0 to 1. Mutations in biological evolution can enrich the diversity of chromosomes, so that two species have different phenotypes and constantly change [12]. The formula for mutation probability of mutation operation is shown in:(9)pm=pmutate1−F.The mutation operation mainly simulates the connection between gene mutation and biological evolution, but when the mutation operation proceeds to the later stage, the individuals in the population are close to the optimal solution. As a result, some individuals may also be in the local optimum and cannot jump out of the local optimum. In response to this problem, the researchers improved the mutation probability algorithm, the improved probability ispm=pmmax−k⋅m⋅t/T, which enhances the competitiveness of individuals through the enhanced mutation probability, thereby better helping some individuals to jump out of the local optimum.The mutation operation assigns the same mutation probability to each individual in the binary code. This method is random and uncontrollable. For example, if the 1101001 individual is mutated, if the 5th locus has a higher similarity in the current population, so the 5th individual in the population has more genes with “0”. At this time, the probability of mutation of the 5th gene is shown in formula (9).In the mutation operation, the probability of using binary codes to mutate individuals is the same, but this method belongs to “full output”, so it lacks directionality. In response to this problem, the researchers proposed a genetic algorithm mutation operator that maintains the population diversity. The mutation probability and mutation location are adaptively determined by the diversity of the population gene locus and the individual fitness value. Excellent individuals can be preserved after mutation, and the ratio of two genes per gene in the population is controlled within the expected range.The main principle guiding genetic algorithm is model theory. This theory demonstrates the basic mechanism of genetic algorithm. The pattern refers to the part of the string that has the same attributes. The pattern sequence refers to the number of characters with clear meaning in the pattern, represented byO (h), where h represents the pattern. The lower the sequence, the stronger the pattern generalization, and the greater the number of different strings, and vice versa. If the mode order is zero, the universality of the clear character is the strongest. The length of the specified pattern represents the distance between the first and the last two characters in the pattern [13]. It has a clear meaning and is recorded as σH. Generally, the formula (10) is obtained:(10)σH=b−a.In the actual budget management process, we will have some uncertain and unpredictable situations, which require manual adjustment. So, this is also a waste of manpower.(1)
When the cross operation is performed, there may be deviations between the calculation results and the real data, resulting in low accuracy of the calculation results. In this paper, the linear programming method and genetic algorithm are used for improvement, which makes the calculation results more accurate, and provides a theoretical basis for enterprises to implement the comprehensive budget management.(2)
Due to the randomness and complexity of the algorithm itself, there are many problems in the practical application of the algorithm, such as: how to process the data and how to improve the accuracy of the operation. In order to solve these problems, the researchers used the improved particle swarm optimization technology to improve the algorithm, and introduced a fitness function into the genetic algorithm, which made the calculation method of particle swarm optimization more flexible, which can effectively solve the data processing and calculation and the problem of accuracy.(3)
In the mutation operation, when the random variable exceeds a certain value, the budget parameters cannot be redistributed. If the function changes too fast and the optimal solution cannot be obtained, the actual value needs to be adjusted or eliminated again. This will greatly increase the error of the calculation. On the basis of introducing the traditional natural selection and local optimal optimization theory, this paper proposes a comprehensive budget management method based on the principle of chaotic coding. This method can overcome the influence of randomness and improve the accuracy of data processing. At the same time, by introducing the niche search technology to solve the problem of a large number of uncertain factors in the global optimization, it also plays an important role in the local search. This method can effectively improve the level of budget management.


## 3.4. Construction of Enterprise Financial Management Control System

Budget management is an internal management tool commonly used in modern enterprises. In order to achieve long-term stable growth, companies often need to formulate long-term strategic goals and short-term business goals separately. In order to effectively achieve the established long-term and short-term goals, it is necessary to formulate an appropriate implementation plan in the implementation of actual work. In this way, the full implementation of the plan can be achieved, and appropriate management methods can be adopted [14]. Domestic and foreign companies often choose comprehensive budget management as an effective way to control corporate management activities and achieve long-term and short-term goals.The comprehensive budget refers to the overall financial budget of an enterprise in various aspects such as research and development, production, and sales at a specific time (usually no more than a year). These reflect all activities in the enterprise economy in the form of currency. Based on a comprehensive budget system, companies conduct business systematically, comply with legal goals, and they have stable operations and management. Comprehensive budget management is a comprehensive tool, control standard, and evaluation basis. This is the basis for the establishment of modern enterprises and the key to scientific management and corporate norms. This is an important part of building an effective corporate management structure. This is also an effective incentive for managers at all levels. This is an effective way for employees and all employees to innovate and improve their self-development skills. The flowchart of comprehensive budget management is shown in Figure4.Figure 4
Overall budget management process.Five links must be submitted to establish a modern enterprise scientific budget management system. Figure5 shows the framework of integrated budget management. Before making a budget, it is very important to conduct a good research and analysis of the budget to understand the true situation of the company’s budget and the problems that may arise; in the process of budgeting, each link must consider the technical and human aspects of the corporate budget. A scientific and reasonable budget system must also consider the impact and management of personnel and equipment. Personnel management can fully mobilize individual creativity and initiative, and equipment management can improve work efficiency. Both are indispensable.Figure 5
Overall budget management framework diagram.


### 3.4.1. Synergy Between Comprehensive Budget Management and Financial Control

Target synergy: The emergence of modern enterprise management systems gave birth to the principal-agent theory, which aims to improve the scientific nature and work efficiency of professional organization management. However, the principal-agent system itself has inherent flaws: the asymmetry of information between the principal and the agent leads to the inconsistency of the goals of both parties, which affects the realization of the company’s overall goals. Comprehensive budget management and financial control can be used to coordinate and solve the problems that arise in this multi-level principal-agent relationship. Financial control aims to reduce the space for individual behavior choices. Relevant personnel should improve the control of the customer’s business management team and increase the transparency of the management activities of the corporate management team; it is also necessary to reduce conflicts between management teams, so as to lay the foundation for comprehensive budget and personnel management, review, and evaluation. Relevant personnel must have clear responsibilities and rights of all parties. Therefore, whether it is comprehensive budget management or financial control, it is in line with its purpose. They not only have to solve various problems faced in business management activities, but also realize the company’s overall development strategy and long-term goals.Process interaction: in modern enterprise management, comprehensive budget management is a systematic management tool. It not only evaluates and balances the rights, responsibilities adjustment, and profit distribution mechanism between the management team and employees, but also reflects the barriers of chief authority to agents and enterprise production capacity control. Financial control is the readjustment of the responsibilities, rights, and interests of corporate investors, managers, and employees in all aspects of comprehensive budget management. In the process of implementing comprehensive budget management and financial control, the two can complement each other. Scientific comprehensive budget management helps to enrich the control methods of internal control implementation.In Figure6, collective decision-making with the goal of scientific progress and value creation can be regarded as the goal of the control unit of the control system, with the integrated budget system and some of its subsystems as the execution unit. All system connections can be regarded as control links, and the exchange of budget information plays an important role in system-wide control links [15]. Figure 6 essentially presents a picture of the entire control system, which not only conforms to the basis of cybernetics, but also fully reflects the specific financial characteristics. Figure 7 shows the key link diagram under the financial control system based on comprehensive budget management.Figure 6
Financial control framework diagram based on comprehensive budget management.Figure 7
Diagram of key links under the financial control system based on comprehensive budget management.


## 3.4.1. Synergy Between Comprehensive Budget Management and Financial Control

Target synergy: The emergence of modern enterprise management systems gave birth to the principal-agent theory, which aims to improve the scientific nature and work efficiency of professional organization management. However, the principal-agent system itself has inherent flaws: the asymmetry of information between the principal and the agent leads to the inconsistency of the goals of both parties, which affects the realization of the company’s overall goals. Comprehensive budget management and financial control can be used to coordinate and solve the problems that arise in this multi-level principal-agent relationship. Financial control aims to reduce the space for individual behavior choices. Relevant personnel should improve the control of the customer’s business management team and increase the transparency of the management activities of the corporate management team; it is also necessary to reduce conflicts between management teams, so as to lay the foundation for comprehensive budget and personnel management, review, and evaluation. Relevant personnel must have clear responsibilities and rights of all parties. Therefore, whether it is comprehensive budget management or financial control, it is in line with its purpose. They not only have to solve various problems faced in business management activities, but also realize the company’s overall development strategy and long-term goals.Process interaction: in modern enterprise management, comprehensive budget management is a systematic management tool. It not only evaluates and balances the rights, responsibilities adjustment, and profit distribution mechanism between the management team and employees, but also reflects the barriers of chief authority to agents and enterprise production capacity control. Financial control is the readjustment of the responsibilities, rights, and interests of corporate investors, managers, and employees in all aspects of comprehensive budget management. In the process of implementing comprehensive budget management and financial control, the two can complement each other. Scientific comprehensive budget management helps to enrich the control methods of internal control implementation.In Figure6, collective decision-making with the goal of scientific progress and value creation can be regarded as the goal of the control unit of the control system, with the integrated budget system and some of its subsystems as the execution unit. All system connections can be regarded as control links, and the exchange of budget information plays an important role in system-wide control links [15]. Figure 6 essentially presents a picture of the entire control system, which not only conforms to the basis of cybernetics, but also fully reflects the specific financial characteristics. Figure 7 shows the key link diagram under the financial control system based on comprehensive budget management.Figure 6
Financial control framework diagram based on comprehensive budget management.Figure 7
Diagram of key links under the financial control system based on comprehensive budget management.


## 4. Simulation Experiment of Comprehensive Budget Management

The main contents involved in the construction phase of this project are:(1)
The main structure part, including foundation pit, foundation, and foundation works, where the foundation and footing are excavated, respectively.(2)
Each subproject has its own independent unit for construction operation. For example, the pile foundation treatment and reinforcement; concrete pouring, etc. are coordinated and controlled by the head office.Due to the large scale of the project and the complex and complicated construction process, it is necessary to make reasonable arrangements for the cost budget of each division during the construction phase. In the virtual reality technology environment, a large amount of resource information is involved, so the company needs to make reasonable planning and implementation of each division cost budget.Establishment of Comprehensive Budget Management: Firstly, the organizational structure of total budget management is established, which consists of three levels: strategic decision making, departmental implementation, and responsibility control. Secondly, the corporate objectives are refined to each functional module. Finally, it is necessary to decompose each layer into different levels of subplans as assessment indicators and use them to formulate corresponding rewards and punishments; in addition, it is necessary to conduct evaluation and analysis according to the completion of budget programming, so as to achieve effective restraint and supervision of employee behavior; it is also necessary to strengthen management control and efficiency in daily work.In traditional budget management, companies’ forecasts and plans for future planning are often derived from empirical summaries. This results in a lack of long-term goals and long-term strategies for the company. In contrast, comprehensive budgeting can scientifically and rationally control the company’s overall business situation in a global and holistic manner. It can link all departments together as a system to consider their development direction and budgeting; it can also identify and solve potential irregularities in budget implementation; it can also integrate the different degrees of correlation and conflict among various business units within the enterprise, and make reasonable coordination in the integration process, so as to achieve optimal resource allocation. This paper is based on the idea of total budget management. This paper is based on the idea of total budget management, which combines the actual study of how to effectively control the virtual realization system when formulating the development strategy of enterprises.Countermeasures for Comprehensive Budget Management: Build a perfect information system for comprehensive budget management. Firstly, a complete and unified budget plan should be established so as to realize the resource sharing. Secondly, it is necessary to strengthen the communication among all units to ensure that the accuracy of the information is improved; again, it is necessary to strengthen the whole process monitoring and improve and optimize it before implementing effective pre-control measures, which can guarantee the smooth operation of the total budget management system.(1)
Construct a comprehensive and integrated financial index system and realize resource sharing. Combined with the consideration of the overall strategic objectives of the enterprise, the enterprise financial index system is constructed. This enables comprehensive analysis of financial budgets, cost control, etc. At the same time, it is also necessary to take into account the synergistic effect of the construction and operation management of each subproject. Through comprehensive risk control, cost assessment, and evaluation, and performance appraisal mechanisms, the combination of resource sharing and value creation can be realized; while ensuring the safety and efficient use of funds, the reduction of investment loss rate and the enhancement of economic efficiency can be achieved; and targeted measures can be formulated in conjunction with actual business needs so that the overall operational efficiency of the enterprise can be improved, thus promoting the sustainable and stable development of the enterprise.(2)
Reasonable configuration of financial budget index system and control standards. Combined with the strategic development planning of enterprises, the application of virtual reality in economic business is analyzed and a reasonable configuration of financial budget index system and control standards is constructed. At the same time, through the combination of theory and practice to establish a set of perfect and scientific management system in line with the actual situation, it can effectively solve the real problems.(3)
Combined with the total budget management evaluation system. Combining the total budget management evaluation system is oriented to the strategic planning of the enterprise, and combining it with the financial objectives and business plans can realize the rational allocation of funds, thus realizing the effective use of resources, and at the same time, the balanced scorecard method can be used for strategic decision-making.


## 5. Conclusions

With the continuous deepening of China's reform and opening up, market-oriented competition has become more intense, and the level of risks faced by enterprises and changes in the operating environment have become more complex. They are facing a more difficult situation than ever before. This kind of poverty is a crisis of enterprise development, and it is also an opportunity for enterprise development. The implementation of the budget control system controls the company’s production behavior and business activities, and it uses virtual realization technology to maintain scientific management of various economic activities of the enterprise and strengthen the financial matters under unexpected circumstances. Control plays an important role in the success or failure of a company in market competition, and it is essential to the sustainable and stable development of a company. Virtual reality technology is a new industry; its application in enterprise financial management is still in its infancy. However, it has become a factor affecting and restricting the development of Internet economy, and it is an indispensable foundation for the rapid development of informationization and digitalization in the future society. This paper takes the application of virtual reality in the Internet environment as the research object, and conducts an in-depth analysis of its development status as well as the relevant theoretical basis. This paper combines actual cases to propose that enterprises need to consider various aspects such as cost factors and risk control when enhancing the advantages and competitiveness of their own network platforms, so that they can build a system based on a combination of a strategy-oriented management model and a comprehensive budget.



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*Source: 8578817-2022-08-08.xml*