High vocational college online enrollment safety risk assessment method

Abstract

The invention relates to a higher vocational college online enrollment safety risk assessment method, and belongs to the technical field of risk assessment methods. According to the technical scheme, a higher vocational college online enrollment safety risk assessment index system based on a mutation series method is established; collecting original data, and performing dimensionless processing on the data by using a range transformation method; determining a mutation model and a normalization formula according to the number of the evaluation indexes of each level; performing step-by-step evaluation on the indexes of each level from bottom to top by using a normalization formula according to complementary and non-complementary principles; and analyzing and evaluating the online enrollment safety risk of the higher vocational colleges according to an evaluation result. The method has the advantages that calculation is easy and accurate, weights do not need to be given to indexes, the evaluation result is more objective, the higher vocational college online enrollment safety risk can be effectively evaluated, and the higher vocational college online enrollment safety risk situation is scientifically reflected.

Description

Technical Field

[0001] This invention relates to a method for assessing the security risks of online enrollment in higher vocational colleges, belonging to the technical field of risk assessment methods. Background Technology

[0002] In the digital and information age, online enrollment for vocational colleges has become an important means of admissions work. It breaks the time and space limitations of traditional enrollment models, providing students with a more convenient and efficient way to choose schools. However, with the popularization of online enrollment, security risks have also become prominent. From information leaks to online fraud, from data tampering to human error, each risk point can become a "time bomb" in the enrollment process, not only harming the interests of schools and students but also causing widespread concern and anxiety in society. These risk events not only affect the fairness and transparency of enrollment work but also seriously damage the credibility and reputation of the education sector. Assessing the security risks of online enrollment in vocational colleges is of great significance for improving the enrollment security governance capabilities of enrollment units. Summary of the Invention

[0003] The purpose of this invention is to provide a method for assessing the security risks of online enrollment in higher vocational colleges. By constructing an indicator system using the catastrophe series method, and then evaluating each level of indicators step by step according to the catastrophe model and normalization formula, the calculation is simple and accurate, eliminating the need to assign weights to the indicators, thus making the evaluation results more objective. This method can effectively assess the security risks of online enrollment in higher vocational colleges, overcoming the shortcomings of previous studies that often required assigning weights to indicators, allowing subjective factors to significantly influence the results. It scientifically reflects the security risks of online enrollment in higher vocational colleges and effectively solves the aforementioned problems in the background technology.

[0004] The technical solution of this invention is: a method for assessing the security risks of online enrollment in higher vocational colleges, comprising the following steps:

[0005] (1) Establish a risk assessment index system for online enrollment security of higher vocational colleges based on the mutation series method;

[0006] (2) Collect the raw data and use the range transformation method to perform dimensionless processing on the data;

[0007] (3) Determine the mutation model and normalization formula based on the number of evaluation indicators at each level;

[0008] (4) Using the normalization formula, the indicators at each level are evaluated step by step from bottom to top according to the principles of complementarity and non-complementarity.

[0009] (5) Analyze and evaluate the online enrollment security risks of higher vocational colleges based on the assessment results. You can compare them horizontally with other higher vocational colleges or vertically with the enrollment security risks of the higher vocational college over the years.

[0010] In step (1), the mutation series method uses no more than four evaluation indicators at each level.

[0011] In step (2), the formula for dimensionless processing is:

[0012] Formula for processing positive indicators:

[0013]

[0014] Formula for processing contrarian indicators:

[0015]

[0016] Formula for processing the appropriateness index:

[0017]

[0018] In the formula, x max(i) x represents the maximum value of the index in the i-th row. min(i) Let x represent the minimum value of the index in the i-th row. 0(i) This indicates the appropriate value for the indicator; the data after dimensionless processing ranges from 0 to 1.

[0019] In step (3), there are seven forms of mutation models, the three most common being cusp mutation, swallowtail mutation, and butterfly mutation. When two indicators are included at the same level, the corresponding mutation model is the cusp mutation model. When three indicators are included at the same level, the corresponding mutation model is the swallowtail mutation model. When four indicators are included at the same level, the corresponding mutation model is the butterfly mutation model.

[0020] In step (3), when the mutation model is a cusp mutation model, the normalization formula is: x a =a 1 / 2 x b =b 1 / 3 When the mutation model is a swallowtail mutation model, the normalization formula is: x a =a 1 / 2 x b =b 1 / 3 x c =c 1 / 4 When the mutation model is a butterfly mutation model, the normalization formula is: x a =a 1 / 2 x b =b 1 / 3 x c =c 1 / 4 x d =d 1 / 5 .

[0021] In step (4), when using the normalization formula for step-by-step evaluation, if there is an interaction between indicators at the same level, the principle of averaging based on complementarity is followed; if there is no obvious interaction between indicators at the same level, the principle of selecting the smaller of the larger non-complementary indicators is followed.

[0022] The beneficial effects of this invention are: by constructing an index system using the mutation series method, and then evaluating each level of index according to the mutation model and normalization formula, the calculation is simple and accurate, and there is no need to assign weights to the indicators, making the evaluation results more objective. It can effectively assess the security risks of online enrollment in higher vocational colleges, making up for the shortcomings of previous studies, which often required assigning weights to the indicators, making subjective factors have a greater impact on the results. It reflects the security risks of online enrollment in higher vocational colleges more scientifically. Detailed Implementation

[0023] To make the purpose, technical solutions, and advantages of the embodiments of the invention clearer, the technical solutions in the embodiments of the invention are described clearly and completely below. Obviously, the embodiments described are only a small part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without creative effort are within the protection scope of the invention.

[0024] A method for assessing the security risks of online enrollment in higher vocational colleges includes the following steps:

[0025] (1) Establish a risk assessment index system for online enrollment security of higher vocational colleges based on the mutation series method;

[0026] (2) Collect the raw data and use the range transformation method to perform dimensionless processing on the data;

[0027] (3) Determine the mutation model and normalization formula based on the number of evaluation indicators at each level;

[0028] (4) Using the normalization formula, the indicators at each level are evaluated step by step from bottom to top according to the principles of complementarity and non-complementarity.

[0029] (5) Analyze and evaluate the online enrollment security risks of higher vocational colleges based on the assessment results. You can compare them horizontally with other higher vocational colleges or vertically with the enrollment security risks of the higher vocational college over the years.

[0030] In step (1), the mutation series method uses no more than four evaluation indicators at each level.

[0031] In step (2), the formula for dimensionless processing is:

[0032] Formula for processing positive indicators:

[0033]

[0034] Formula for processing contrarian indicators:

[0035]

[0036] Formula for processing the appropriateness index:

[0037]

[0038] In the formula, x max(i) x represents the maximum value of the index in the i-th row. min(i) Let x represent the minimum value of the index in the i-th row. 0(i) This indicates the appropriate value for the indicator; the data after dimensionless processing ranges from 0 to 1.

[0039] In step (3), there are seven forms of mutation models, the three most common being cusp mutation, swallowtail mutation, and butterfly mutation. When two indicators are included at the same level, the corresponding mutation model is the cusp mutation model. When three indicators are included at the same level, the corresponding mutation model is the swallowtail mutation model. When four indicators are included at the same level, the corresponding mutation model is the butterfly mutation model.

[0040] In step (3), when the mutation model is a cusp mutation model, the normalization formula is: x a =a 1 / 2 x b =b 1 / 3 When the mutation model is a swallowtail mutation model, the normalization formula is: x a =a 1 / 2 x b =b 1 / 3 x c =c 1 / 4 When the mutation model is a butterfly mutation model, the normalization formula is: x a =a 1 / 2 x b =b 1 / 3 x c =c 1 / 4 x d =d 1 / 5 .

[0041] In step (4), when using the normalization formula for step-by-step evaluation, if there is an interaction between indicators at the same level, the principle of averaging based on complementarity is followed; if there is no obvious interaction between indicators at the same level, the principle of selecting the smaller of the larger non-complementary indicators is followed.

[0042] Example:

[0043] An assessment of the online enrollment security risks of three higher vocational colleges, A, B, and C, was conducted.

[0044] (1) Based on the problems that have occurred in the past enrollment process and the potential risks that may arise in online enrollment, this paper establishes a risk indicator system for online enrollment of higher vocational colleges with seven levels and 20 indicators, including market risk, environmental risk, technological risk, human risk, legal and financial risk, user verification risk and disaster recovery preparation risk, as shown in the table below.

[0045] Risk indicator system for online enrollment in higher vocational colleges

[0046]

[0047]

[0048] (2) After the indicators were established, staff from the Admissions Office, the Information Network Center, teaching management personnel, professors, and associate professors were invited to score the risk of each indicator for the three institutions (A, B, and C). The maximum score was 100 points, with scores from highest to lowest indicating risk, i.e., 100 points for high risk and 0 points for zero risk. The final scoring results are shown in the table below:

[0049] Risk Indicator Score Statistics Table

[0050]

[0051]

[0052] (3) The data was dimensionless, and the processed data is shown in the table below:

[0053] Standardized data

[0054]

[0055]

[0056] (4) Using the normalization formula, the index values ​​of B1 to B20 are obtained, as shown in the table below.

[0057] Risk indicator normalized data

[0058] Serial Number School <![CDATA[X B1 ]]> <![CDATA[X B2 ]]> <![CDATA[X B3 ]]> <![CDATA[X B4 ]]> <![CDATA[X B5 ]]> <![CDATA[X B6 ]]> <![CDATA[X B7 ]]> <![CDATA[X B8 ]]> <![CDATA[X B9 ]]> <![CDATA[X B10 ]]> 1 A 0.0000 0.0000 1.0000 0.0000 0.3014 1.0000 1.0000 1.0000 0.0000 1.0000 2 B 1.0000 0.2609 0.0000 0.9354 1.0000 0.6764 0.0000 0.0000 1.0000 0.9370 3 C 0.6509 1.0000 0.8011 1.0000 0.0000 0.0000 0.8676 0.7211 0.031 0.0000 Serial Number School <![CDATA[X B11 ]]> <![CDATA[X B12 ]]> <![CDATA[X B13 ]]> <![CDATA[X B14 ]]> <![CDATA[X B15 ]]> <![CDATA[X B16 ]]> <![CDATA[X B17 ]]> <![CDATA[X B18 ]]> <![CDATA[X B19 ]]> <![CDATA[X B20 ]]> 1 A 0.1900 0.2222 0.0000 0.0000 1.0000 1.0000 0.0000 1.0000 0.3019 1.0000 2 B 0.0000 0.0000 1.0000 1.0000 0.3299 0.0000 0.1515 0.5890 1.0000 0.0000 3 C 1.0000 1.0000 0.6580 0.7824 0.0000 0.5773 1.0000 0.0000 0.0000 0.7298

[0059] (5) Then, the normalization formula is used to evaluate step by step. According to the principle of complementary averaging, the upper-level evaluation index value and the final online enrollment safety risk value X of higher vocational colleges are obtained as shown in the table below.

[0060] Risk assessment results of online enrollment security for higher vocational colleges

[0061] Serial Number School A1 A2 A3 A4 A5 A6 A7 X 1 A 0.3333 0.5754 0.6667 0.1374 0.5000 0.5000 0.7673 0.4953 2 B 0.4203 0.6530 0.6457 0.3333 0.6650 0.0758 0.5297 0.5157 3 C 0.8173 0.4669 0.2507 0.8860 0.3912 0.7887 0.2433 0.5370

[0062] After conducting an online enrollment security risk assessment on various indicators of three vocational colleges (A, B, and C), the results showed that college A had the lowest online enrollment security risk, followed by college B, while college C had the highest online enrollment security risk. This method can be used to compare with other vocational colleges horizontally, or to compare the online enrollment security risk of the college over the years vertically.

[0063] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing the security risks of online enrollment in higher vocational colleges, characterized in that... Includes the following steps: (1) Establish a risk assessment index system for online enrollment security of higher vocational colleges based on the mutation series method; (2) Collect the raw data and use the range transformation method to perform dimensionless processing on the data; (3) Determine the mutation model and normalization formula based on the number of evaluation indicators at each level; (4) Using the normalization formula, the indicators at each level are evaluated step by step from bottom to top according to the principles of complementarity and non-complementarity. (5) Analyze and evaluate the online enrollment security risks of higher vocational colleges based on the assessment results. You can compare them horizontally with other higher vocational colleges or vertically with the enrollment security risks of the higher vocational college over the years.

2. The method for assessing the security risks of online enrollment in higher vocational colleges according to claim 1, characterized in that: In step (1), the mutation series method uses no more than four evaluation indicators at each level.

3. The method for assessing the security risks of online enrollment in higher vocational colleges according to claim 1, characterized in that: In step (2), the formula for dimensionless processing is: Formula for processing positive indicators: Formula for processing contrarian indicators: Formula for processing the appropriateness index: In the formula, x max(i) x represents the maximum value of the index in the i-th row. min(i) Let x represent the minimum value of the index in the i-th row. 0(i) This indicates the appropriate value for the indicator; The data after dimensionless processing has a value range of 0 to 1.

4. The method for assessing the security risks of online enrollment in higher vocational colleges according to claim 1, characterized in that: In step (3), there are seven forms of mutation models, the three most common being cusp mutation, swallowtail mutation, and butterfly mutation. When two indicators are included at the same level, the corresponding mutation model is the cusp mutation model. When three indicators are included at the same level, the corresponding mutation model is the swallowtail mutation model. When four indicators are included at the same level, the corresponding mutation model is the butterfly mutation model.

5. The method for assessing the security risks of online enrollment in higher vocational colleges according to claim 1, characterized in that: In step (3), when the mutation model is a cusp mutation model, the normalization formula is: x a =a 1 / 2 x b =b 1 / 3 When the mutation model is a swallowtail mutation model, the normalization formula is: x a =a 1 / 2 x b =b 1 / 3 x c =c 1 / 4 When the mutation model is a butterfly mutation model, the normalization formula is: x a =a 1 / 2 x b =b 1 / 3 x c =c 1 / 4 x d =d 1 / 5 .

6. The method for assessing the security risks of online enrollment in higher vocational colleges according to claim 1, characterized in that: In step (4), when using the normalization formula for step-by-step evaluation, if there is an interaction between indicators at the same level, the principle of averaging based on complementarity is followed; if there is no obvious interaction between indicators at the same level, the principle of selecting the smaller of the larger non-complementary indicators is followed.

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