Network signal operation and maintenance personnel compilation analysis method based on multiple linear regression

Abstract

The invention discloses a network signal operation and maintenance personnel compilation analysis method based on multiple linear regression. The method comprises the steps of obtaining operation and maintenance team member classification and post responsibilities according to a task type of network signal operation and maintenance work; wherein the operation and maintenance team members comprise a manager, an operation and maintenance architect, a development engineer and a hardware engineer; obtaining the number of hardware engineers based on the per capita controllable area and the personnel shift change coefficient of the operation and maintenance machine room; obtaining the number of operation and maintenance architects and the number of development engineers based on multiple linear regression analysis; obtaining the number of managers based on the number of hardware engineers, the number of operation and maintenance architects and the number of development engineers; and obtaining a quantized operation and maintenance team compiling model according to the number of each type of the operation and maintenance team members, the classification of the operation and maintenance team members and the post responsibilities, and completing network signal operation and maintenance personnel compiling analysis. According to the method, the MADE model compiled by the network signal operation and maintenance personnel is constructed based on the multiple linear regression theory, and the model has high interpretability and accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of network information operation and maintenance, and particularly relates to a method for analyzing the staffing of network information operation and maintenance personnel based on multiple linear regression. Background Art

[0002] The characteristic of network information work is "three parts construction and seven parts operation and maintenance". The life cycle of the network information system includes multiple stages such as planning, demonstration, design, implementation, operation and maintenance, etc. After the implementation stage of the information system ends, it enters the operation and maintenance stage. The operation and maintenance work in network information construction is the process of modifying and improving the system after it is delivered for use to correct errors or meet new needs. The service life of a general system is as short as four or five years and as long as more than 10 years. Therefore, the operation and maintenance stage is the longest stage in the whole life cycle of the system, accounting for about 70%. Whether the network information system can play its due role and achieve the designed purpose depends on the quality of system development on the one hand and the operation management of the system on the other hand. If the operation and maintenance work is not done well, it will lead to the network information system being "unusable and not easy to use", and instead weaken the effect of network information work.

[0003] Network information operation and maintenance is not a simple guarantee work, but a composite research field of management and technology. It mainly faces various components in network information construction and carries out work including hardware operation and maintenance, desktop operation and maintenance, application operation and maintenance, system operation and maintenance, security and confidentiality operation and maintenance, etc., as Figure 1 shown. The five task types put forward different ability requirements for network information operation and maintenance personnel, which means that the network information operation and maintenance team should fully consider influencing factors such as the educational background, ability preference, work experience of team members, and arrange appropriate job positions. The traditional operation and maintenance team staffing often relies on empirical judgment and lacks scientific basis. Constructing a scientific and accurate operation and maintenance team staffing model is crucial for improving the fluency, security and stability of the operation of the network information system. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a method for analyzing the staffing of network information operation and maintenance personnel based on multiple linear regression to solve the problems existing in the above prior art.

[0005] To achieve the above object, the present invention provides a method for analyzing the staffing of network information operation and maintenance personnel based on multiple linear regression, including:

[0006] Obtaining the classification of operation and maintenance team members and their job responsibilities according to the task types of network information operation and maintenance work; among them, the operation and maintenance team members include management personnel, operation and maintenance architects, development engineers and hardware engineers;

[0007] Obtaining the number of hardware engineers based on the per capita controllable area of the operation and maintenance computer room and the personnel shift coefficient;

[0008] Obtain the number of operation and maintenance architects and the number of development engineers based on multiple linear regression analysis;

[0009] Obtain the number of management personnel based on the number of hardware engineers, the number of operation and maintenance architects, and the number of development engineers;

[0010] According to the quantity of each type of operation and maintenance team members, the classification of operation and maintenance team members, and the job responsibilities, obtain a quantified operation and maintenance team establishment model to complete the analysis of the establishment of network information operation and maintenance personnel.

[0011] Optionally, the formula for obtaining the number of hardware engineers is as follows:

[0012]

[0013] In the formula, S is the total floor area of the unit facility computer room, S e is the maximum area that a single network operation and maintenance personnel can monitor the facility, and γ is the shift coefficient for computer room duty.

[0014] Optionally, the process of obtaining the number of operation and maintenance architects and the number of development engineers based on multiple linear regression analysis includes:

[0015] Based on the total task volume of network information operation and maintenance work and the average workload of operation and maintenance personnel, calculate the total number of operation and maintenance architects and development engineers; based on multiple linear regression analysis, obtain the ratio of operation and maintenance architects and development engineers, and combine the total number to obtain the number of operation and maintenance architects and the number of development engineers.

[0016] Optionally, the formula for obtaining the total task volume of network information operation and maintenance work is as follows:

[0017] R = ∑(nαα i );

[0018] In the formula, n is the total number of people, α is the proportion of the total task volume in the total number of people, α i is the proportion of each type of task.

[0019] Optionally, the formula for obtaining the average workload of operation and maintenance personnel is as follows:

[0020]

[0021] In the formula, is the average working hours per month, J + is the emergency handling duration, p i is the average processing time of each type of task per month, α i is the proportion of each type of task.

[0022] Optionally, the process of obtaining the ratio of operation and maintenance architects and development engineers based on multiple linear regression analysis includes:

[0023] Construct multiple linear regression models that affect the work efficiency of operation and maintenance architects and multiple linear regression models that affect the work efficiency of development engineers respectively, and perform fitting. Analyze the goodness of fit through the coefficient of determination and the adjusted coefficient, and use the F function and t function of linear hypothesis for significance verification. After the evaluation and verification pass, predict the work efficiency of operation and maintenance architects and development engineers respectively, and obtain the corresponding proportional values according to the predicted values.

[0024] Optionally, taking employee experience, task complexity, scientific research ability level, and security and confidentiality level as independent variables, construct a multiple linear regression model that affects the work efficiency of operation and maintenance architects; taking employee experience, task complexity, development ability, and error density as independent variables, construct a multiple linear regression model that affects the work efficiency of development engineers.

[0025] The present invention also provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the above-mentioned analysis method for the establishment of network information operation and maintenance personnel based on multiple linear regression.

[0026] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the above-mentioned analysis method for the establishment of network information operation and maintenance personnel based on multiple linear regression.

[0027] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the above-mentioned analysis method for the establishment of network information operation and maintenance personnel based on multiple linear regression.

[0028] Compared with the prior art, the present invention has the following advantages and technical effects:

[0029] Based on the theory of multiple linear regression, the present invention constructs a MADE model for the establishment of network information operation and maintenance personnel. By modeling work experience, task complexity, scientific research ability level, security and confidentiality level, development ability, code error density, etc., it simulates the work efficiency of team members in different positions, and takes workload and work efficiency as the core, combined with simulation data, to calculate the establishment of network information operation and maintenance teams. The results show that the model has high interpretability and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0031] Figure 1 It is a schematic diagram of the classification of network information system operation and maintenance work tasks and their work contents in the background technology of the present invention;

[0032] Figure 2 Schematic diagram of the MADE model structure for network information system operation and maintenance personnel in an embodiment of the present invention;

[0033] Figure 3 Diagram of the paired relationship of work efficiency in an embodiment of the present invention. (a) is the diagram of the paired relationship of work efficiency of operation and maintenance architects; (b) is the diagram of the paired relationship of work efficiency of development engineers;

[0034] Figure 4 Diagram of the true value and predicted value of work efficiency in an embodiment of the present invention. (a) is the true value and predicted value of work efficiency of operation and maintenance architects; (b) is the true value and predicted value of work efficiency of developers;

[0035] Figure 5 Flowchart of the method in an embodiment of the present invention. Specific implementation manners

[0036] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments may be combined with each other. The following will describe this application in detail with reference to the drawings and in combination with the embodiments.

[0037] It should be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than here.

[0038] Embodiment 1

[0039] As Figures 2-5 shown, this embodiment provides a method for analyzing the staffing of network information operation and maintenance personnel based on multiple linear regression, including:

[0040] Obtain the classification of operation and maintenance team members and their job responsibilities according to the task types of network information operation and maintenance work; among them, the operation and maintenance team members include management personnel, operation and maintenance architects, development engineers, and hardware engineers;

[0041] Specifically, due to the fact that network information operation and maintenance work faces different types of scenarios such as systems, applications, and basic environments, its work content often has the characteristics of professionalism and complexity. To ensure the efficient, safe, and stable operation of the system, it is usually necessary to divide job responsibilities according to the work content, such as Figure 2As shown in the figure, the members of the operation and maintenance team can be divided into management personnel (Management, M), operation and maintenance architects (Senior Architect, A), development engineers (Developer, D), and hardware engineers (Hardware Engineer, E). According to the specific work scenarios faced, the operation and maintenance team scientifically and reasonably arranges the positions and numbers of team members. This model for constructing the operation and maintenance team establishment is called the MADE model.

[0042] Figure 2 Among them, management personnel (M) are usually responsible for the planning, overall coordination, and management of network information operation and maintenance projects, and manage the daily work of the operation and maintenance team. For technical personnel such as operation and maintenance architects (A), development engineers (D), and hardware engineers (E), their job responsibilities need to be differentiated according to the member's ability background. As shown in Table 1, operation and maintenance architects are mainly responsible for system operation and maintenance and security and confidentiality work. This includes responsibilities such as system operation and maintenance, data governance, and security and confidentiality. Development engineers mainly focus on desktop operation and maintenance and application operation and maintenance. Their job responsibilities include communicating with user requirements, maintaining and updating desktop application programs, and ensuring application operation and maintenance. Hardware operation and maintenance engineers are mainly responsible for hardware operation and maintenance work, specifically including maintaining and upgrading network infrastructure, handling hardware failures, and optimizing network configurations.

[0043] Table 1

[0044]

[0045]

[0046] The number of hardware engineers is obtained based on the per capita controllable area of the operation and maintenance computer room and the personnel shift coefficient;

[0047] The number of operation and maintenance architects and development engineers is obtained based on multiple linear regression analysis;

[0048] The number of management personnel is obtained based on the number of hardware engineers, operation and maintenance architects, and development engineers;

[0049] According to the number of each type of member of the operation and maintenance team, the classification of operation and maintenance team members, and the job responsibilities, a quantified operation and maintenance team establishment model is obtained, and the analysis of the establishment of network information operation and maintenance personnel is completed.

[0050] In order to further clarify the number of members in each position of the operation and maintenance team, it is necessary to further quantify the MADE model. The total number of the operation and maintenance team should be the sum of the numbers of each position. As shown in formula (1), N is the total number of operation and maintenance team personnel, M is the number of management personnel, A is the number of operation and maintenance architects, D is the number of development engineers, and E is the number of hardware engineers.

[0051] N = M + A + D + E (1)

[0052] As the key coordination hub and command and management node of the team, the number of management personnel often needs to be calculated according to a reasonable ratio based on the number of technical personnel in the team. Here, it is assumed that there is a linear proportional relationship between management personnel and technical personnel:

[0053] ψ(A + D + E) (2)

[0054] Considering that the job responsibilities A and D of hardware engineer E have obvious boundaries and distinctions, it is necessary to consider its staffing separately. According to the actual operation and maintenance work environment of hardware engineers, the number of hardware engineers depends on the average controllable area of the operation and maintenance computer room and the shift coefficient of personnel. As shown in formula (3), S is the total floor area of the unit facility computer room, S e is the maximum area that a single network operation and maintenance personnel can monitor facilities, and γ is the shift coefficient of the computer room duty. Therefore, the calculation method of hardware engineer E is expressed as the total floor area of the computer room divided by the maximum area that a single operation and maintenance personnel can monitor, and then multiplied by the shift coefficient of the computer room.

[0055]

[0056] For the calculation of operation and maintenance architects and development engineers, since the types of tasks they handle cover desktop operation and maintenance, application operation and maintenance, system operation and maintenance, and security and confidentiality operation and maintenance work, the types of tasks they handle are complex and the task volume is huge. As shown in formula (5), using a unified calculation method, the total number L of operation and maintenance architects A and development engineers D is calculated from the total task volume and the average workload of operation and maintenance personnel.

[0057] L = A + D (4)

[0058]

[0059] Among them, R is the total workload of each type of task, is the average monthly workload of operation and maintenance personnel. The number of operation and maintenance architects and the number of development engineers are equal to the total monthly workload of the unit divided by the average monthly workload of operation and maintenance personnel. According to the experience of operation and maintenance task management, the task volume is often proportional to the number of people served by the operation and maintenance team. The calculation method of the total monthly workload R of the unit is shown in formula (6), where n is the total number of people, α is the proportion of the total task volume in the total number of people, α i is the proportion of each type of task in the unit.

[0060] R = ∑(nαα i ) (6)

[0061] Secondly, the average workload of operation and maintenance personnel should be calculated as the ratio of the average monthly working hours of operation and maintenance personnel to the expected value of the processing time of various types of tasks. As shown in formula (7), where, is the average monthly working hours, plus J + is the emergency handling duration. p i is the average processing time of various tasks per month. Depending on the task type, the average processing time of tasks varies. According to the actual situation of operation and maintenance management work, the specific average processing time of each task can be obtained.

[0062]

[0063] The number of operation and maintenance architects A and development engineers D can predict the ratio of the monthly work efficiency of the two types of personnel, that is, the ratio of the average monthly workload, by constructing a multiple linear regression model.

[0064] The multiple linear regression analysis method is used to analyze and predict the work efficiency of operation and maintenance personnel. The establishment of the regression analysis model is based on the causal relationship between the dependent variable and the independent variable. Substituting specific variable data can solve each parameter in the model. The regression model is evaluated through statistical indicators and tests to determine whether it has a good fitting degree. If the fitting degree is good, it means that the independent variable meets the basic prediction conditions. The work efficiency of members can be predicted by constructing a multiple linear regression model combined with historical data.

[0065] In the multiple regression model, according to the determination coefficient R 2 the proportion of the variation of the independent variable affected by the variation of the dependent variable can be determined. The determination coefficient R in formula (8) 2 is the ratio of the regression sum of squares to the total sum of squares. The closer R is to 1, the better the model fitting degree; the closer it is to 0, the worse the model fitting degree.

[0066]

[0067] The increase in the sample size will cause R to increase. As shown in formula (9), in order to reduce the influence of the sample size on the fitting result, the corrected coefficient R 2 is usually introduced. The residual sum of squares and the total sum of squared deviations are divided by their respective degrees of freedom to eliminate the influence of the number of variables on the goodness of fit, and can more accurately reflect the goodness of fit.

[0068]

[0069] As shown in formula (9), the F function is usually used to test the significance of the regression relationship of the entire equation. However, since there is a significant linear relationship between the independent variables after interaction on the target variable, it does not mean that each independent variable has a significant linear relationship with the target variable separately. Therefore, the t function is also needed to verify the significance of each parameter variable in the regression equation for the dependent variable, as shown in formula (10).

[0070] Therefore, a method combining the verification of the F function and the t function with linear assumptions is adopted to detect the linear relationship between variables. If F ≥ F α (p, n - p - 1), it means that the overall linear regression is significant, indicating that the regression equation generally conforms to the work efficiency of the operation and maintenance team members. If t j ≥ t α / 2 (n - p), it means that the independent variable has a significant impact on y, that is, it can be considered that the various factors selected in the experiment have a significant impact on the work efficiency of technical personnel.

[0071]

[0072]

[0073] The operation and maintenance work handled by operation and maintenance architects includes system operation and maintenance and security and confidentiality operation and maintenance. When staff handle daily work, there are many factors affecting their work efficiency. From the management experience of operation and maintenance work, the work experience of operation and maintenance architects, the complexity of tasks, their own scientific research ability level, security and confidentiality level, etc. all have a certain impact on the work efficiency of employees. For development engineers, the factors affecting their work efficiency include employee experience, task complexity, development ability, and error density, etc. For operation and maintenance architects, employee experience, task complexity, scientific research ability level, security and confidentiality level, etc. are selected as independent variables to construct a multiple linear regression model affecting the work efficiency of operation and maintenance architects. For development engineers, employee experience, task complexity, development ability, and error density are selected as independent variables to construct a multiple linear regression model affecting the work efficiency of development engineers. The model parameters and model parameter definitions are shown in Table 2.

[0074] Table 2

[0075]

[0076] β 0 is the regression constant, β 1 , β 2 , β 3 , β 4 are the regression coefficients, representing the influence value of the change of a certain independent variable on the change of the dependent variable when other independent variables remain unchanged. x 1 , x 2 , x 3 , x 4 are the regression factors, representing each quantity. ε is a random error obeying the normal distribution.

[0077]

[0078] is the regression constant, is the regression coefficient, representing the influence value of the change in an independent variable on the change in the dependent variable when other variables remain unchanged, y 1 and y 2 and y 3 and y 4 are regression factors, representing the respective independent variables, which are the empirical value of development, the complexity of the task, and the development ability value of the personnel. ρ is a random error following a normal distribution.

[0079]

[0080] The ratio of the work efficiency of the operation and maintenance architect to that of the development engineer is θ;

[0081]

[0082] Based on the ratio θ and the number of operation and maintenance architects and the number of development engineers L, the number of operation and maintenance architects can be calculated as:

[0083]

[0084] The number of development engineers is:

[0085]

[0086] Simulation calculation

[0087] The total number of employees in the unit is 10,000 people, the coefficient of the total number of tasks occupying the total number of people is 0.8, and the total number of tasks is 8,000. The ratio of each type of task to the total tasks and the average processing time are shown in Table 3.

[0088] Table 3

[0089]

[0090]

[0091] S is the total floor area of the unit's facility computer room, S e is the maximum area that a single operation and maintenance personnel can monitor the facilities, and γ is the shift coefficient for the computer room duty. The number of hardware engineers is γ.

[0092] Conduct modeling analysis on the existing data. Draw a paired relationship diagram of the work efficiency of the operation and maintenance architect and the work efficiency of the development engineer. The relationships between multiple independent variables and their relationships with the dependent variable can be observed, as Figure 3 shown. Figure 3 (a) The graph on the main diagonal shows the single-variable distributions such as work experience value, task complexity, scientific research ability, security and confidentiality level, work efficiency, etc. Figure 3(b) The figures on the main diagonal show the univariate distributions of work experience, task complexity, employee development ability, error density, work efficiency, etc. The diagonal uses kernel density estimation plots, which smoothly estimate the probability density function of the data. The scatter plots outside the diagonal show the bivariate relationships between any two different variables in the dataset. The points on each scatter plot represent the values of an observation on the two variables. Through these scatter plots, it is possible to observe whether there is a certain trend or correlation between the variables. A regression line is added to the scatter plot, which is the best fit line and shows the linear relationship between the two variables. It quantifies the strength of the correlation between the variables.

[0093] The influencing factors of the work efficiency of operation and maintenance architects, including work experience, task difficulty, scientific research ability, security and confidentiality level, and the influencing factors of the work efficiency of development engineers, including working years, task difficulty, employee development ability, error density. Analyze the above independent variables and dependent variables, and calculate their respective correlation coefficient matrices, as shown in Tables 7 and 8 specifically.

[0094] Conduct OLS analysis. Establish a multiple linear regression between the independent variables and the dependent variable, and perform least squares regression analysis. Output the regression results. Return the results of multiple regression analysis and relevant statistical parameters. As can be seen from Table 6, for the regression result of the work efficiency model of operation and maintenance architects, the coefficient of determination R = 0.989, R 2 ² = 0.977, adjusted R 2 ² = 0.977, indicating that the model has a good fit. For the regression result of the work efficiency model of development engineers, the coefficient of determination R = 0.963, R 2 ² = 0.928, and the adjusted R 2 ² = 0.927, indicating that the model has a good fit.

[0095] Table 9 is the analysis of variance table, showing the analysis of variance results after regression fitting. Significance represents the probability that the F value in the F test is greater than the critical value specified by F. The results show that when the regression equation contains the four different independent variables in the text, their significance probability values are all less than 0.001, rejecting the hypothesis that all regression coefficients are 0, and the equation has a good fitting effect.

[0096] Table 6

[0097]

[0098] Tables 10 and 11 show the regression process statistics and the coefficient analysis of the t test. From the statistics in the tables, the multiple linear regression analysis models can be obtained as follows:

[0099]

[0100] Table 7

[0101]

[0102] Table 8

[0103]

[0104]

[0105] Table 9

[0106]

[0107] Table 10

[0108]

[0109] According to the obtained regression models as shown in formulas (16) and (17), the work efficiency of operation and maintenance architects and the work efficiency of development engineers are predicted, and the predicted values of the multiple linear regression analysis as shown in Figure 4 (a), Figure 4 (b) can be obtained. It can be seen from the figure that the predicted values are closely around the fitting line, and the model prediction results meet the expectations.

[0110] The two constructed multiple linear regression models are predicted, and the average work efficiency value of operation and maintenance architects and the average work efficiency of developers are obtained respectively. The work efficiency ratio of the two types of personnel is obtained, and the number of personnel can be calculated.

[0111] As the key coordination hub and command and management node of the team, the number of management personnel often needs to be calculated according to the number of technical personnel in the team according to the given reasonable ratio That is,

[0112] The present invention also provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the steps of the above-mentioned network information operation and maintenance personnel establishment analysis method based on multiple linear regression.

[0113] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned network information operation and maintenance personnel establishment analysis method based on multiple linear regression are implemented.

[0114] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the above-mentioned network information operation and maintenance personnel establishment analysis method based on multiple linear regression are implemented.

[0115] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for analyzing the establishment of network security operation and maintenance personnel based on multiple linear regression, characterized in that: The following steps are involved: The classification and job responsibilities of the operation and maintenance team members are obtained according to the task types of network information operation and maintenance work; the operation and maintenance team members include managers, operation and maintenance architects, development engineers and hardware engineers; The number of hardware engineers is obtained based on the per capita controllable area of ​​the operation and maintenance room and the staff shift coefficient; The number of operation and maintenance architects and development engineers were obtained based on multivariate linear regression analysis; Get the number of managers based on the number of hardware engineers, operations architects, and development engineers; The staffing analysis of network security operation and maintenance personnel is completed by obtaining a quantified operation and maintenance team staffing model based on the number of each type of operation and maintenance team members, classification of operation and maintenance team members and job responsibilities.

2. The network information operation and maintenance personnel establishment analysis method based on multiple linear regression according to claim 1 is characterized in that: The formula to obtain the number of hardware engineers is as follows: In the formula, S is the total area of ​​the unit facility room, S e is the maximum area of ​​the facility that can be monitored by a single network operation and maintenance personnel, and γ is the shift coefficient of the computer room duty.

3. The network information operation and maintenance personnel establishment analysis method based on multiple linear regression according to claim 1 is characterized in that: The process of obtaining the number of operation and maintenance architects and development engineers based on multivariate linear regression analysis includes: The total number of operation and maintenance architects and development engineers is calculated based on the total task volume of network information operation and maintenance work and the average workload of operation and maintenance personnel; the ratio of operation and maintenance architects to development engineers is obtained based on multivariate linear regression analysis, and the number of operation and maintenance architects and development engineers is obtained by combining the total number of people.

4. The network information operation and maintenance personnel establishment analysis method based on multiple linear regression according to claim 3 is characterized in that: The formula for obtaining the total task volume of network security operation and maintenance work is as follows: R=∑(nαα i ); In the formula, n is the total number of people, α is the ratio of the total task volume to the total number of people, and α i The proportion of various tasks in the unit.

5. The network information operation and maintenance personnel establishment analysis method based on multiple linear regression according to claim 4 is characterized in that: The formula for obtaining the average workload of operation and maintenance personnel is as follows: In the formula, is the average monthly working hours, J + The time required to handle an emergency situation, p i is the average processing time of each type of task per month, α i The proportion of various tasks in the unit.

6. The network information operation and maintenance personnel establishment analysis method based on multiple linear regression according to claim 1 is characterized in that: The process of obtaining the ratio of operation and maintenance architects to development engineers based on multivariate linear regression analysis includes: A multiple linear regression model that affects the work efficiency of operation and maintenance architects and a multiple linear regression model that affects the work efficiency of development engineers were constructed and fitted respectively. The goodness of fit was analyzed by the determination coefficient and correction coefficient, and the F function and t function of the linear hypothesis were used to verify the significance. After the evaluation and verification, the work efficiency of operation and maintenance architects and the work efficiency of development engineers were predicted respectively, and the corresponding proportion values ​​were obtained according to the predicted values.

7. The network information operation and maintenance personnel establishment analysis method based on multiple linear regression according to claim 6 is characterized in that: Taking employee experience, task complexity, scientific research ability level, and security and confidentiality level as independent variables, a multiple linear regression model that affects the work efficiency of operation and maintenance architects is constructed; taking employee experience, task complexity, development ability, and error density as independent variables, a multiple linear regression model that affects the work efficiency of development engineers is constructed.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the network information operation and maintenance personnel compilation analysis method based on multivariate linear regression described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the network information operation and maintenance personnel compilation analysis method based on multivariate linear regression described in any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the network information operation and maintenance personnel compilation analysis method based on multivariate linear regression described in any one of claims 1 to 7 are implemented.

Images