Student comprehensive ability assessment method and device, electronic equipment and storage medium

Abstract

The invention discloses a student comprehensive ability assessment method and device, electronic equipment and a storage medium, and belongs to the technical field of student comprehensive ability assessment, and the method comprises the steps: calculating a first membership degree of a theoretical knowledge score of a to-be-assessed student relative to each preset assessment level; calculating a second membership degree of the practical operation score of the to-be-evaluated student relative to each preset evaluation grade; calculating a third membership degree of the classroom performance score of the to-be-evaluated student relative to each preset evaluation grade; calculating a fourth membership degree of the teacher score of the to-be-evaluated student relative to each preset evaluation grade; calculating a fifth membership degree of the to-be-evaluated student relative to each preset evaluation grade; and taking the preset evaluation grade corresponding to the maximum membership degree in the fifth membership degrees as the evaluation grade of the to-be-evaluated student, so that the problems of single evaluation index and one-sided evaluation result in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of student comprehensive ability assessment, and in particular to a student comprehensive ability assessment method, device, electronic equipment and storage medium. Background Art

[0002] Traditional student comprehensive ability assessment systems usually use fixed weights and scoring standards for assessment, which cannot accurately reflect students' comprehensive abilities and have the following defects: difficulty in fusing multi-source data: it is difficult to integrate performance data, behavioral data (such as online learning time), unstructured data (such as classroom performance) and other data for evaluation; single indicator limitation: excessive reliance on test scores, relying on only a single indicator for assessment, ignoring invisible indicators such as classroom participation and practical ability, resulting in one-sided evaluation results. Summary of the Invention

[0003] The present invention provides a student comprehensive ability assessment method, device, electronic device and storage medium, which can solve the problems of single assessment indicators and one-sided evaluation results in the prior art.

[0004] In order to solve the above technical problems, an embodiment of the present invention provides a method for evaluating students' comprehensive abilities, comprising:

[0005] Obtain training parameters of the trainees during the training process; wherein the training parameters include: scores of the power system basic knowledge test, the power system safety management and regulations test, the power equipment operation and maintenance practical test, the power system fault handling and emergency response practical test, classroom behavior monitoring data, classroom discussion texts, online learning platform logs, student peer evaluation data, and teacher evaluation data;

[0006] Based on the scores of the power system basic knowledge examination and the power system safety management and regulations examination, the first degree of membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level is calculated; based on the scores of the power equipment operation and maintenance practical examination and the power system fault handling and emergency response practical examination, the second degree of membership of the practical score of the student to be evaluated relative to each preset evaluation level is calculated; based on the classroom behavior monitoring data, classroom discussion texts, online learning platform logs and student mutual evaluation data, the third degree of membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level is calculated; based on the teacher rating data, the fourth degree of membership of the teacher rating of the student to be evaluated relative to each preset evaluation level is calculated;

[0007] Calculating a fifth degree of membership of the student to be assessed relative to each preset assessment level based on the first degree of membership, the second degree of membership, the third degree of membership, and the fourth degree of membership;

[0008] The preset evaluation grade corresponding to the maximum degree of membership among the fifth degrees of membership is used as the evaluation grade of the student to be evaluated.

[0009] As a preferred solution, the calculation of the first degree of membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level based on the scores of the power system basic knowledge test and the power system safety management and regulations test includes:

[0010] Calculating, based on the power system basic knowledge test score, a sixth degree of membership of the power system basic knowledge test score relative to each preset assessment level;

[0011] Calculating, based on the power system safety management and regulations examination score, a seventh degree of membership of the power system safety management and regulations examination score relative to each preset assessment level;

[0012] Based on the sixth degree of membership, the seventh degree of membership, the weight coefficient corresponding to the score of the basic knowledge examination of power systems, and the weight coefficient corresponding to the score of the power system safety management and regulations examination, the first degree of membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level is calculated.

[0013] As a preferred solution, the calculation of the second degree of membership of the practical score of the student to be evaluated relative to each preset evaluation level based on the scores of the practical test on power equipment operation and maintenance and the practical test on power system fault handling and emergency response includes:

[0014] Calculating, based on the scores of the practical examination on operation and maintenance of electric power equipment, the eighth degree of membership of the scores of the practical examination on operation and maintenance of electric power equipment relative to each preset evaluation level;

[0015] Calculating, based on the scores of the practical examination on power system fault handling and emergency response, the ninth degree of membership of the scores of the practical examination on power system fault handling and emergency response relative to each preset assessment level;

[0016] Based on the eighth degree of membership, the ninth degree of membership, the weight coefficient corresponding to the score of the practical examination on power equipment operation and maintenance, and the weight coefficient corresponding to the score of the practical examination on power system fault handling and emergency response, the second degree of membership of the practical score of the student to be evaluated relative to each preset evaluation level is calculated.

[0017] As a preferred solution, the third degree of membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level is calculated based on the classroom behavior monitoring data, classroom discussion texts, online learning platform logs, and student peer evaluation data, including:

[0018] calculating, based on the classroom behavior monitoring data, a tenth degree of membership of the classroom behavior monitoring data relative to each preset evaluation level;

[0019] Calculating, based on the classroom discussion text, an eleventh membership degree of the classroom discussion text relative to each preset evaluation level;

[0020] Calculating, based on the online learning platform log, a twelfth membership degree of the online learning platform log relative to each preset evaluation level;

[0021] Calculating the thirteenth degree of membership of the student mutual evaluation data relative to each preset evaluation level based on the student mutual evaluation data;

[0022] Based on the tenth degree of membership, the eleventh degree of membership, the twelfth degree of membership, the thirteenth degree of membership, the weight coefficient corresponding to the classroom behavior monitoring data, the weight coefficient corresponding to the classroom discussion text, the weight coefficient corresponding to the online learning platform log, and the weight coefficient corresponding to the student mutual evaluation data, the third degree of membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level is calculated.

[0023] As a preferred solution, the calculating of the fifth degree of membership of the student to be assessed relative to each preset assessment level based on the first degree of membership, the second degree of membership, the third degree of membership, and the fourth degree of membership includes:

[0024] Based on the first degree of membership, the second degree of membership, the third degree of membership, the fourth degree of membership, the weight coefficient corresponding to the theoretical knowledge score, the weight coefficient corresponding to the practical score, the weight coefficient corresponding to the classroom performance score, and the weight coefficient corresponding to the teacher score, the fifth degree of membership of the student to be evaluated relative to each preset evaluation level is calculated.

[0025] As a preferred solution, the weight coefficients corresponding to the theoretical knowledge score, the practical operation score, the classroom performance score, and the teacher score are calculated according to the following method:

[0026] Construct the first judgment matrix between theoretical knowledge scores, practical scores, classroom performance scores, and teacher scores;

[0027] According to the first judgment matrix, the weight coefficients corresponding to the theoretical knowledge score, the weight coefficients corresponding to the practical score, the weight coefficients corresponding to the classroom performance score, and the weight coefficients corresponding to the teacher score are calculated respectively.

[0028] As a preferred solution, the weight coefficient corresponding to each training parameter is calculated according to the following method:

[0029] Constructing a second judgment matrix between the scores of the power system basic knowledge examination and the scores of the power system safety management and regulations examination, and calculating the weight coefficients corresponding to the scores of the power system basic knowledge examination and the power system safety management and regulations examination based on the second judgment matrix;

[0030] Constructing a third judgment matrix between the scores of the power equipment operation and maintenance practical examination and the scores of the power system fault handling and emergency response practical examination, and calculating the weight coefficients corresponding to the scores of the power equipment operation and maintenance practical examination and the scores of the power system fault handling and emergency response practical examination based on the third judgment matrix;

[0031] A fourth judgment matrix is ​​constructed between classroom behavior monitoring data, classroom discussion texts, online learning platform logs, tunnel burial depth, and student mutual evaluation data. According to the fourth judgment matrix, the weight coefficients corresponding to the classroom behavior monitoring data, the weight coefficients corresponding to the classroom discussion texts, the weight coefficients corresponding to the online learning platform logs, and the weight coefficients corresponding to the student mutual evaluation data are calculated respectively.

[0032] Based on the above embodiment, another embodiment of the present invention provides a student comprehensive ability assessment device, comprising: a training parameter acquisition module, an assessment factor membership calculation module, a to-be-assessed student membership calculation module, and an assessment grade determination module;

[0033] The training parameter acquisition module is used to obtain the training parameters of the trainees to be evaluated during the training process; wherein the training parameters include: the scores of the power system basic knowledge examination, the power system safety management and regulations examination, the power equipment operation and maintenance practical examination scores, the power system fault handling and emergency response practical examination scores, classroom behavior monitoring data, classroom discussion texts, online learning platform logs, student peer evaluation data, and teacher evaluation data;

[0034] The evaluation factor membership calculation module is used to calculate the first membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level based on the score of the power system basic knowledge test and the score of the power system safety management and regulations test; calculate the second membership of the practical score of the student to be evaluated relative to each preset evaluation level based on the score of the power equipment operation and maintenance practical test and the score of the power system fault handling and emergency response practical test; calculate the third membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level based on the classroom behavior monitoring data, classroom discussion texts, online learning platform logs and student mutual evaluation data; calculate the fourth membership of the teacher score of the student to be evaluated relative to each preset evaluation level based on the teacher rating data;

[0035] The membership degree calculation module of the student to be evaluated is used to calculate the fifth membership degree of the student to be evaluated relative to each preset evaluation level based on the first membership degree, the second membership degree, the third membership degree and the fourth membership degree;

[0036] The evaluation grade determination module is configured to use the preset evaluation grade corresponding to the maximum degree of membership among the fifth degrees of membership as the evaluation grade of the student to be evaluated.

[0037] Based on the above embodiments, another embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the student comprehensive ability assessment method described in the above invention embodiment is implemented.

[0038] Based on the above embodiment, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the student comprehensive ability assessment method described in the above invention embodiment.

[0039] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0040] The present invention provides a method for evaluating the comprehensive ability of students, which obtains the training parameters of the students to be evaluated during the training process; wherein the training parameters include: the scores of the basic knowledge test of power system, the scores of the safety management and regulations test of power system, the scores of the practical test of power equipment operation and maintenance, the scores of the practical test of power system fault handling and emergency response, classroom behavior monitoring data, classroom discussion texts, online learning platform logs, student mutual evaluation data, and teacher evaluation data; according to the scores of the basic knowledge test of power system and the scores of the safety management and regulations test of power system, the first membership degree of the theoretical knowledge score of the students to be evaluated relative to each preset evaluation level is calculated; according to the scores of the practical test of power equipment operation and maintenance and the scores of the practical test of power system fault handling and emergency response ... The method comprises the following steps: first, calculating the second membership of the practical test score of the student to be evaluated relative to each preset evaluation level; second, calculating the third membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level according to the classroom behavior monitoring data, classroom discussion text, online learning platform log and student mutual evaluation data; third, calculating the fourth membership of the teacher score of the student to be evaluated relative to each preset evaluation level according to the teacher scoring data; fifth, calculating the fifth membership of the student to be evaluated relative to each preset evaluation level according to the first, second, third and fourth memberships; and taking the preset evaluation level corresponding to the maximum membership in each fifth membership as the evaluation level of the student to be evaluated. Through the present invention, multi-source data (theoretical knowledge scoring data, practical scoring data, classroom performance scoring data and teacher scoring data) can be fused and analyzed, and multi-level factors (theoretical knowledge scoring, practical scoring, classroom performance scoring and teacher scoring) can be fuzzy synthesized layer by layer to avoid the one-sidedness of a single indicator and improve the comprehensiveness and accuracy of the student comprehensive ability evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of a method for evaluating comprehensive student abilities provided by one embodiment of the present invention;

[0042] Figure 2 It is a schematic diagram of the master-slave structure of each evaluation factor;

[0043] Figure 3 It is a structural diagram of a student comprehensive ability evaluation device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0046] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0047] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0049] In the description of the embodiments of the present application, the terms "multiple" and "several" refer to more than two (including two). Similarly, "multiple groups" refer to more than two groups (including two groups), and "multiple pieces" refer to more than two pieces (including two pieces).

[0050] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0051] Example 1

[0052] Please refer to Figure 1 To solve the problem of single evaluation indicators and one-sided evaluation results in the prior art, an embodiment of the present invention provides a flow chart of a method for evaluating students' comprehensive abilities, which includes the following specific steps:

[0053] S1. Obtain training parameters of the trainee during the training process; wherein the training parameters include: scores of the power system basic knowledge examination, the power system safety management and regulations examination, the power equipment operation and maintenance practical examination, the power system fault handling and emergency response practical examination, classroom behavior monitoring data, classroom discussion texts, online learning platform logs, student peer evaluation data, and teacher evaluation data;

[0054] Specifically, the present invention constructs a comprehensive evaluation model factor set through expert scoring. A factor set is a collection of various factors that influence the evaluation object. To reflect the importance of each factor, each factor is assigned a corresponding weight. The weights are also distributed through expert scoring to create a reasonable weight set. This factor set and the corresponding weights are then used to conduct a comprehensive assessment of the candidate's abilities.

[0055] When conducting a comprehensive ability assessment of the trainees, we first obtain the training parameters of the trainees during their daily power training: power system basic knowledge test scores, power system safety management and regulations test scores, power equipment operation and maintenance practical test scores, power system fault handling and emergency response practical test scores, classroom behavior monitoring data, classroom discussion texts, online learning platform logs, student peer evaluation data, and teacher rating data.

[0056] S2. Calculate the first degree of membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level based on the scores of the power system basic knowledge test and the power system safety management and regulations test; calculate the second degree of membership of the practical score of the student to be evaluated relative to each preset evaluation level based on the scores of the power equipment operation and maintenance practical test and the power system fault handling and emergency response practical test; calculate the third degree of membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level based on the classroom behavior monitoring data, classroom discussion texts, online learning platform logs, and student mutual evaluation data; calculate the fourth degree of membership of the teacher score of the student to be evaluated relative to each preset evaluation level based on the teacher rating data;

[0057] Preferably, the weight coefficients corresponding to the theoretical knowledge score, the weight coefficients corresponding to the practical score, the weight coefficients corresponding to the classroom performance score, and the weight coefficients corresponding to the teacher score are calculated according to the following method: a first judgment matrix is ​​constructed between the theoretical knowledge score, the practical score, the classroom performance score, and the teacher score; and the weight coefficients corresponding to the theoretical knowledge score, the practical score, the classroom performance score, and the teacher score are respectively calculated according to the first judgment matrix.

[0058] Preferably, the weight coefficient corresponding to each of the training parameters is calculated according to the following method: a second judgment matrix is ​​constructed between the score of the basic knowledge test of the power system and the score of the safety management and regulations test of the power system, and the weight coefficient corresponding to the score of the basic knowledge test of the power system and the weight coefficient corresponding to the score of the safety management and regulations test of the power system are calculated according to the second judgment matrix; a third judgment matrix is ​​constructed between the score of the practical test of the power equipment operation and maintenance and the score of the practical test of the power system fault handling and emergency response, and the weight coefficient corresponding to the practical test score of the power system fault handling and emergency response are calculated according to the third judgment matrix; a fourth judgment matrix is ​​constructed between the classroom behavior monitoring data, the classroom discussion text, the online learning platform log, the tunnel burial depth and the student mutual evaluation data, and the weight coefficient corresponding to the classroom behavior monitoring data, the classroom discussion text, the online learning platform log and the student mutual evaluation data are calculated according to the fourth judgment matrix.

[0059] Preferably, the first degree of membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level is calculated based on the score of the basic knowledge of power system test and the score of the safety management and regulations of power system test, including: calculating the sixth degree of membership of the basic knowledge of power system test score relative to each preset evaluation level based on the score of the basic knowledge of power system test; calculating the seventh degree of membership of the safety management and regulations of power system test score relative to each preset evaluation level based on the score of the safety management and regulations of power system test; calculating the first degree of membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level based on the sixth degree of membership, the seventh degree of membership, the weight coefficient corresponding to the score of the basic knowledge of power system test, and the weight coefficient corresponding to the score of the safety management and regulations of power system test.

[0060] Preferably, the second membership of the practical score of the trainee to be evaluated relative to each preset evaluation level is calculated based on the practical examination score of the power equipment operation and maintenance and the practical examination score of the power system fault handling and emergency response, including: calculating the eighth membership of the practical examination score of the power equipment operation and maintenance relative to each preset evaluation level based on the practical examination score of the power equipment operation and maintenance; calculating the ninth membership of the practical examination score of the power system fault handling and emergency response relative to each preset evaluation level based on the practical examination score of the power system fault handling and emergency response; calculating the second membership of the practical score of the trainee to be evaluated relative to each preset evaluation level based on the eighth membership, the ninth membership, the weight coefficient corresponding to the practical examination score of the power equipment operation and maintenance, and the weight coefficient corresponding to the practical examination score of the power system fault handling and emergency response.

[0061] Preferably, the third membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level is calculated based on the classroom behavior monitoring data, classroom discussion text, online learning platform log and student mutual evaluation data, including: calculating the tenth membership of the classroom behavior monitoring data relative to each preset evaluation level based on the classroom behavior monitoring data; calculating the eleventh membership of the classroom discussion text relative to each preset evaluation level based on the classroom discussion text; calculating the twelfth membership of the online learning platform log relative to each preset evaluation level based on the online learning platform log; calculating the thirteenth membership of the student mutual evaluation data relative to each preset evaluation level based on the student mutual evaluation data; calculating the third membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level based on the tenth membership, the eleventh membership, the twelfth membership, the thirteenth membership, the weight coefficient corresponding to the classroom behavior monitoring data, the weight coefficient corresponding to the classroom discussion text, the weight coefficient corresponding to the online learning platform log, and the weight coefficient corresponding to the student mutual evaluation data.

[0062] Specifically, the following steps are included:

[0063] 1. Establish factor set:

[0064] The main factors and sub-factors are determined based on the training parameters. The main factors include: theoretical knowledge score, practical operation score, classroom performance score and teacher score; sub-factors include: power system basic knowledge test score, power system safety management and regulations test score, power equipment operation and maintenance practical test score, power system fault handling and emergency response practical test score, classroom behavior monitoring data, classroom discussion text, online learning platform log, student mutual evaluation data, and teacher score data. Please refer to Figure 2 , is a schematic diagram of the master-slave structure of each evaluation factor, and the factor set is shown in Table 1 below.

[0065]

[0066]

[0067] Table 1 Comprehensive ability evaluation factor set

[0068] The importance of each factor in the factor set is different. In order to reflect the importance of each factor, each factor is assigned a corresponding weight. The weight coefficient corresponding to each comprehensive ability evaluation factor is calculated by the following method:

[0069] (1) First, the four comprehensive ability evaluation factors (main factors) are scored according to the expert scoring method, as shown in Table 2 below. According to the expert scoring method, a judgment matrix is ​​constructed between the theoretical knowledge score, practical operation score, classroom performance score, and teacher score comprehensive ability evaluation factor set.

[0070]

[0071] Table 2 Judgment matrix between each evaluation factor set

[0072] In Table 2, there are currently 20 experts who score the four indicators. Using the 1-9 point scale, the scores of the 20 experts are averaged to obtain the final judgment matrix table.

[0073] According to the hierarchical analysis method, the weight coefficient of each evaluation factor relative to the comprehensive evaluation can be obtained by analyzing the contents of Table 2, as shown in the following Table 3.

[0074]

[0075] Table 3 Weight coefficients of each evaluation factor relative to the comprehensive evaluation

[0076] From Table 3, we can determine the weights of the four indicators of theoretical knowledge scoring factor, practical operation scoring factor, classroom performance scoring factor and teacher scoring factor using the AHP hierarchical analysis method (calculation method is: sum product method), and the eigenvector obtained by analysis is (1.332, 2.102, 0.374, 0.193); the weight values ​​are 33.29%, 52.54%, 9.35%, and 4.82% respectively, and the weight vector is W = (w1, w2, w3, w4) = (0.3329, 0.5254, 0.0935, 0.0482); combined with the eigenvector, the maximum eigenroot is calculated to be 4.139; then the CI value is calculated using the maximum eigenroot value to be 0.046, and the CI value is used for the following consistency test.

[0077] The consistency test is analyzed using the consistency ratio CR value. A CR value less than 0.1 indicates that the data has passed the consistency test. CR = CI / RI. The CI value has been obtained in the previous step and is 0.046. The present invention is of order 4 (4 indicators), and the corresponding RI value is 0.89. After the consistency test, the weight coefficient result of the judgment matrix is ​​reasonable. There is no logical problem in the judgment matrix, and the calculated weight is scientific. The weight coefficient result can be applied to subsequent evaluation calculations.

[0078] (2) Based on the calculation method of the weight coefficients of the above-mentioned evaluation factors (main factors), the judgment matrix can be constructed to calculate the weight coefficients of the evaluation factors of power system basic knowledge and power system safety management and regulations relative to the theoretical knowledge score; the weight coefficients of the evaluation factors of power equipment operation and maintenance and power system fault handling and emergency response relative to the practical operation score; the weight coefficients of classroom behavior monitoring, classroom discussion texts, online learning platform logs, and student mutual evaluation relative to the classroom performance score; and the weight coefficients of the teacher's evaluation of staged learning scores relative to the teacher's score. Table 4 below shows the weight coefficients corresponding to each evaluation factor and each sub-factor.

[0079]

[0080] Table 4 Weight coefficients corresponding to each evaluation factor and sub-factor

[0081] 2. Establish an evaluation set:

[0082] According to the existing evaluation standards and relevant specifications, the comprehensive evaluation level is divided into five evaluation levels, and each evaluation level corresponds to a qualitative indicator, as shown in Table 5 below.

[0083]

[0084]

[0085] Table 5 Evaluation criteria for each evaluation level

[0086] The evaluation levels have two types of division indicators: quantitative and qualitative. According to the fuzzy comprehensive evaluation method, the qualitative division indicators of each level in Table 5 can be quantified to obtain the quantitative division indicators of each level. The quantitative division indicators of each evaluation level in the present invention are shown in the following Table 6. The quantitative division indicators of each evaluation level include the value range of each basic parameter.

[0087]

[0088]

[0089] Table 6 Quantitative classification indicators for each evaluation level

[0090] 3. Calculate the membership of each main factor:

[0091] The first degree of membership of the theoretical knowledge score relative to each preset evaluation level is determined based on the scores of the power system basic knowledge test and the power system safety management and regulations theoretical knowledge test;

[0092] According to the quantitative division indicators of each safety level, the membership of each training parameter relative to the evaluation factor can be obtained, and then according to the membership of each training parameter relative to the evaluation factor and the weight coefficient corresponding to each training parameter, the membership of each evaluation factor relative to each preset evaluation level is calculated, specifically: the sixth membership of the basic knowledge of the power system relative to each preset evaluation level is calculated according to the score of the basic knowledge of the power system; the seventh membership of the power system safety management and regulations score relative to each preset evaluation level is calculated according to the score of the power system safety management and regulations test; the first membership of the theoretical knowledge scoring factor relative to each preset evaluation level is calculated according to the sixth membership, the seventh membership and the weight coefficient corresponding to the basic knowledge of the power system test score and the weight coefficient corresponding to the power system safety management and regulations test score.

[0093] The present invention uses a trapezoidal membership function to calculate the membership of each evaluation factor and training parameter relative to the evaluation level, wherein the membership function is classified into small, medium and large types. The specific calculation formula is as follows:

[0094]

[0095]

[0096] Similarly, the above method can be used to determine the second membership of the practical score relative to each preset evaluation level based on the practical test score of power equipment operation and maintenance and the practical test score of power system fault handling and emergency response. Specifically, the eighth membership of the practical test score of power equipment operation and maintenance relative to each preset evaluation level is calculated based on the practical test score of power equipment operation and maintenance; the ninth membership of the practical test score of power system fault handling and emergency response relative to each preset evaluation level is calculated based on the practical test score of power system fault handling and emergency response; the second membership of the practical score factor relative to each preset evaluation level is calculated based on the eighth membership, the ninth membership and the weight coefficient corresponding to the practical test score of power equipment operation and maintenance and the weight coefficient corresponding to the practical test score of power system fault handling and emergency response.

[0097] Similarly, the above method can be used to determine the third degree of membership of the classroom performance score relative to each preset evaluation level based on classroom behavior monitoring data, classroom discussion text, online learning platform log, and student mutual evaluation data. Specifically, the tenth degree of membership of the classroom behavior monitoring data relative to each preset evaluation level is calculated based on the classroom behavior monitoring data; the eleventh degree of membership of the classroom discussion text relative to each preset evaluation level is calculated based on the classroom discussion text; the twelfth degree of membership of the online learning platform log relative to each preset evaluation level is calculated based on the online learning platform log; the thirteenth degree of membership of the student mutual evaluation data relative to each preset evaluation level is calculated based on the student mutual evaluation data; the third degree of membership of the classroom performance scoring factor relative to each preset evaluation level is calculated based on the tenth degree of membership, the eleventh degree of membership, the twelfth degree of membership, the thirteenth degree of membership and the weight coefficients corresponding to the classroom behavior monitoring data, the weight coefficients corresponding to the classroom discussion text, the weight coefficients corresponding to the online learning platform log, and the weight coefficients corresponding to the student mutual evaluation data.

[0098] Similarly, the above method can be used to determine the fourth degree of membership of the teacher rating factor relative to each preset evaluation level based on the teacher rating data.

[0099] S3. Calculating a fifth degree of membership of the student to be assessed relative to each preset assessment level based on the first degree of membership, the second degree of membership, the third degree of membership, and the fourth degree of membership;

[0100] Preferably, the fifth membership of the student to be evaluated relative to each preset evaluation level is calculated based on the first membership, the second membership, the third membership and the fourth membership, including: calculating the fifth membership of the student to be evaluated relative to each preset evaluation level based on the first membership, the second membership, the third membership, the fourth membership, the weight coefficient corresponding to the theoretical knowledge score, the weight coefficient corresponding to the practical score, the weight coefficient corresponding to the classroom performance score, and the weight coefficient corresponding to the teacher score.

[0101] Specifically, a fuzzy matrix synthesis operation is performed based on the membership matrix R1 of each evaluation factor relative to each preset evaluation level, and the weight vector A1 of the evaluation factor relative to the comprehensive evaluation to obtain a first-level fuzzy comprehensive evaluation result, that is, based on the first membership, the second membership, the third membership, the fourth membership, the weight coefficient corresponding to the theoretical knowledge score, the weight coefficient corresponding to the practical score, the weight coefficient corresponding to the classroom performance score, and the weight coefficient corresponding to the teacher score, the fifth membership of the student to be evaluated relative to each preset evaluation level is calculated. The calculation formula is as follows:

[0102]

[0103] The "°" in the above formula is a generalized fuzzy synthesis operator, which can have different comprehensive evaluation models according to different situations. The present invention adopts the "weighted summation type" generalized fuzzy operator M(·,⊕) to calculate b1 j, and its specific calculation formula is:

[0104] S4. Using the preset evaluation grade corresponding to the maximum degree of membership among the fifth degrees of membership as the evaluation grade of the student to be evaluated.

[0105] Specifically, after the calculation is completed according to the above matrix data, the preset evaluation level corresponding to the maximum membership degree in the matrix is ​​used as the final evaluation level of the student to be evaluated.

[0106] In a specific embodiment, the dynamic comprehensive performance evaluation system model based on the fuzzy comprehensive evaluation algorithm provided by the present invention is further illustrated below in combination with a specific case of a project. The reference values ​​of various training parameters that affect the comprehensive ability evaluation of trainees are shown in the following Table 7.

[0107] Training parameters <![CDATA[U 11 ]]> <![CDATA[U 12 ]]> <![CDATA[U 21 ]]> <![CDATA[U 22 ]]> <![CDATA[U 31 ]]> Reference value 84 73 92 84 22m Training parameters <![CDATA[U 32 ]]> <![CDATA[U 33 ]]> <![CDATA[U 34 ]]> <![CDATA[U 41 ]]> / Reference value 71％ 8 88 91 /

[0108] Table 7 Reference values ​​of various training parameters

[0109] The theoretical knowledge scoring factor has a first degree of membership relative to each preset evaluation level. As shown in Table 7, the reference values ​​of the two training parameters corresponding to the theoretical knowledge scoring factor are: the power system basic knowledge test score U11 = 85, and the power system safety management and regulations test score U12 = 73. Combining the quantitative division indicators of each evaluation level and the membership function diagram in Table 6, the calculation formula for each evaluation level is inferred to be:

[0110]

[0111] According to the above formula, the fifth membership of the power system basic knowledge test score U11 relative to each evaluation level is (0, 0, 0, 0.6, 0.4), and the sixth membership of the power system safety management and regulations test score U12 relative to each evaluation level is (0, 0, 0.7, 0.3, 0). That is, the evaluation matrix R1 of the theoretical knowledge scoring factor is:

[0112]

[0113] According to the weight vector of the two training parameters corresponding to the theoretical knowledge scoring factor in Table 4, A1=(0.5, 0.5), the membership vector calculation formula of the theoretical knowledge scoring factor relative to each preset evaluation level can be obtained as B1=A1°R1=(0, 0, 0.35, 0.45, 0.2). Similarly, the membership vectors of the practical scoring factor, classroom performance scoring factor, and teacher scoring factor relative to each preset evaluation level can be obtained. Thus, the evaluation matrix K of the comprehensive evaluation of students can be obtained as follows:

[0114]

[0115] According to the weight vector W = (0.3329, 0.5254, 0.0935, 0.0482) of each evaluation factor relative to the comprehensive evaluation in Table 4, combined with the above-mentioned comprehensive evaluation matrix K, the student's comprehensive evaluation membership vector is: B = W°K = (0.0187, 0.0224, 0.1595, 0.3092, 0.4898). According to the principle of maximum membership, the preset evaluation level corresponding to the maximum membership of the membership vector in B is used as the evaluation level of the student's comprehensive evaluation. It can be seen that the student's evaluation level combined with the overall evaluation factors is excellent.

[0116] It can be seen that the present invention provides a method for evaluating students' comprehensive abilities. Through the present invention, multi-source data (theoretical knowledge scoring data, practical operation scoring data, classroom performance scoring data, and teacher scoring data) can be integrated and analyzed, and multi-level factors (theoretical knowledge scoring, practical operation scoring, classroom performance scoring, and teacher scoring) can be fuzzy synthesized layer by layer to avoid the one-sidedness of a single indicator and improve the comprehensiveness and accuracy of the results of students' comprehensive ability evaluation.

[0117] Example 2

[0118] Please refer to Figure 3 , is a schematic diagram of the structure of a student comprehensive ability assessment device provided by an embodiment of the present invention, the device comprising: a training parameter acquisition module, an assessment factor membership calculation module, a to-be-assessed student membership calculation module, and an assessment grade determination module;

[0119] The training parameter acquisition module is used to obtain the training parameters of the trainees to be evaluated during the training process; wherein the training parameters include: the scores of the power system basic knowledge examination, the power system safety management and regulations examination, the power equipment operation and maintenance practical examination scores, the power system fault handling and emergency response practical examination scores, classroom behavior monitoring data, classroom discussion texts, online learning platform logs, student peer evaluation data, and teacher evaluation data;

[0120] The evaluation factor membership calculation module is used to calculate the first membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level based on the score of the power system basic knowledge test and the score of the power system safety management and regulations test; calculate the second membership of the practical score of the student to be evaluated relative to each preset evaluation level based on the score of the power equipment operation and maintenance practical test and the score of the power system fault handling and emergency response practical test; calculate the third membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level based on the classroom behavior monitoring data, classroom discussion texts, online learning platform logs and student mutual evaluation data; calculate the fourth membership of the teacher score of the student to be evaluated relative to each preset evaluation level based on the teacher rating data;

[0121] The membership degree calculation module of the student to be evaluated is used to calculate the fifth membership degree of the student to be evaluated relative to each preset evaluation level based on the first membership degree, the second membership degree, the third membership degree and the fourth membership degree;

[0122] The evaluation grade determination module is configured to use the preset evaluation grade corresponding to the maximum degree of membership among the fifth degrees of membership as the evaluation grade of the student to be evaluated.

[0123] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0124] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0125] Example 3

[0126] Accordingly, an embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the student comprehensive ability assessment method described in the above-mentioned embodiment of the invention.

[0127] The electronic device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0128] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device and connects various parts of the entire device using various interfaces and lines.

[0129] Example 4

[0130] Accordingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the student comprehensive ability assessment method described in the above-mentioned embodiment of the invention.

[0131] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0132] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0133] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating students' comprehensive abilities, characterized in that: include: Obtain training parameters of the trainees during the training process; wherein the training parameters include: scores of the power system basic knowledge test, the power system safety management and regulations test, the power equipment operation and maintenance practical test, the power system fault handling and emergency response practical test, classroom behavior monitoring data, classroom discussion texts, online learning platform logs, student peer evaluation data, and teacher evaluation data; Based on the scores of the power system basic knowledge examination and the power system safety management and regulations examination, the first degree of membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level is calculated; based on the scores of the power equipment operation and maintenance practical examination and the power system fault handling and emergency response practical examination, the second degree of membership of the practical score of the student to be evaluated relative to each preset evaluation level is calculated; based on the classroom behavior monitoring data, classroom discussion texts, online learning platform logs and student mutual evaluation data, the third degree of membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level is calculated; based on the teacher rating data, the fourth degree of membership of the teacher rating of the student to be evaluated relative to each preset evaluation level is calculated; Calculating a fifth degree of membership of the student to be assessed relative to each preset assessment level based on the first degree of membership, the second degree of membership, the third degree of membership, and the fourth degree of membership; The preset evaluation grade corresponding to the maximum degree of membership among the fifth degrees of membership is used as the evaluation grade of the student to be evaluated.

2. The method for evaluating students' comprehensive abilities as claimed in claim 1, wherein: The step of calculating the first degree of membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level based on the scores of the power system basic knowledge test and the power system safety management and regulations test includes: Calculating, based on the power system basic knowledge test score, a sixth degree of membership of the power system basic knowledge test score relative to each preset assessment level; Calculating, based on the power system safety management and regulations examination score, a seventh degree of membership of the power system safety management and regulations examination score relative to each preset assessment level; Based on the sixth degree of membership, the seventh degree of membership, the weight coefficient corresponding to the score of the basic knowledge examination of power systems, and the weight coefficient corresponding to the score of the power system safety management and regulations examination, the first degree of membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level is calculated.

3. The method for evaluating students' comprehensive abilities as claimed in claim 1, wherein: The calculating, based on the scores of the electric power equipment operation and maintenance practical examination and the scores of the electric power system fault handling and emergency response practical examination, the second degree of membership of the practical score of the student to be evaluated relative to each preset evaluation level includes: Calculating, based on the scores of the practical examination on operation and maintenance of electric power equipment, the eighth degree of membership of the scores of the practical examination on operation and maintenance of electric power equipment relative to each preset evaluation level; Calculating, based on the scores of the practical examination on power system fault handling and emergency response, the ninth degree of membership of the scores of the practical examination on power system fault handling and emergency response relative to each preset assessment level; Based on the eighth degree of membership, the ninth degree of membership, the weight coefficient corresponding to the score of the practical examination on power equipment operation and maintenance, and the weight coefficient corresponding to the score of the practical examination on power system fault handling and emergency response, the second degree of membership of the practical score of the student to be evaluated relative to each preset evaluation level is calculated.

4. The method for evaluating students' comprehensive abilities as claimed in claim 1, wherein: The calculating, based on the classroom behavior monitoring data, classroom discussion texts, online learning platform logs, and student peer evaluation data, of the third degree of membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level includes: calculating, based on the classroom behavior monitoring data, a tenth degree of membership of the classroom behavior monitoring data relative to each preset evaluation level; Calculating, based on the classroom discussion text, an eleventh membership degree of the classroom discussion text relative to each preset evaluation level; Calculating, based on the online learning platform log, a twelfth membership degree of the online learning platform log relative to each preset evaluation level; Calculating the thirteenth degree of membership of the student mutual evaluation data relative to each preset evaluation level based on the student mutual evaluation data; Based on the tenth degree of membership, the eleventh degree of membership, the twelfth degree of membership, the thirteenth degree of membership, the weight coefficient corresponding to the classroom behavior monitoring data, the weight coefficient corresponding to the classroom discussion text, the weight coefficient corresponding to the online learning platform log, and the weight coefficient corresponding to the student mutual evaluation data, the third degree of membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level is calculated.

5. The method for evaluating students' comprehensive abilities as claimed in claim 1, wherein: Calculating the fifth degree of membership of the student to be assessed relative to each preset assessment level based on the first degree of membership, the second degree of membership, the third degree of membership, and the fourth degree of membership includes: Based on the first degree of membership, the second degree of membership, the third degree of membership, the fourth degree of membership, the weight coefficient corresponding to the theoretical knowledge score, the weight coefficient corresponding to the practical score, the weight coefficient corresponding to the classroom performance score, and the weight coefficient corresponding to the teacher score, the fifth degree of membership of the student to be evaluated relative to each preset evaluation level is calculated.

6. The method for evaluating students' comprehensive abilities as claimed in claim 1, wherein: The weight coefficients corresponding to the theoretical knowledge score, the practical operation score, the classroom performance score, and the teacher score are calculated according to the following method: Construct the first judgment matrix between theoretical knowledge scores, practical scores, classroom performance scores, and teacher scores; According to the first judgment matrix, the weight coefficients corresponding to the theoretical knowledge score, the weight coefficients corresponding to the practical score, the weight coefficients corresponding to the classroom performance score, and the weight coefficients corresponding to the teacher score are calculated respectively.

7. The method for evaluating students' comprehensive abilities according to claim 1, wherein: The weight coefficient corresponding to each training parameter is calculated according to the following method: Constructing a second judgment matrix between the scores of the power system basic knowledge examination and the scores of the power system safety management and regulations examination, and calculating the weight coefficients corresponding to the scores of the power system basic knowledge examination and the power system safety management and regulations examination based on the second judgment matrix; Constructing a third judgment matrix between the scores of the power equipment operation and maintenance practical examination and the scores of the power system fault handling and emergency response practical examination, and calculating the weight coefficients corresponding to the scores of the power equipment operation and maintenance practical examination and the scores of the power system fault handling and emergency response practical examination based on the third judgment matrix; A fourth judgment matrix is ​​constructed between classroom behavior monitoring data, classroom discussion texts, online learning platform logs, tunnel burial depth, and student mutual evaluation data. According to the fourth judgment matrix, the weight coefficients corresponding to the classroom behavior monitoring data, the weight coefficients corresponding to the classroom discussion texts, the weight coefficients corresponding to the online learning platform logs, and the weight coefficients corresponding to the student mutual evaluation data are calculated respectively.

8. A performance evaluation device based on a fuzzy comprehensive evaluation algorithm, characterized in that: include: Training parameter acquisition module, evaluation factor membership calculation module, to-be-evaluated trainee membership calculation module, and evaluation grade determination module; The training parameter acquisition module is used to obtain the training parameters of the trainees to be evaluated during the training process; wherein the training parameters include: the scores of the power system basic knowledge examination, the power system safety management and regulations examination, the power equipment operation and maintenance practical examination scores, the power system fault handling and emergency response practical examination scores, classroom behavior monitoring data, classroom discussion texts, online learning platform logs, student peer evaluation data, and teacher evaluation data; The evaluation factor membership calculation module is used to calculate the first membership of the theoretical knowledge score of the student to be evaluated relative to each preset evaluation level based on the score of the power system basic knowledge test and the score of the power system safety management and regulations test; calculate the second membership of the practical score of the student to be evaluated relative to each preset evaluation level based on the score of the power equipment operation and maintenance practical test and the score of the power system fault handling and emergency response practical test; calculate the third membership of the classroom performance score of the student to be evaluated relative to each preset evaluation level based on the classroom behavior monitoring data, classroom discussion texts, online learning platform logs and student mutual evaluation data; calculate the fourth membership of the teacher score of the student to be evaluated relative to each preset evaluation level based on the teacher rating data; The membership degree calculation module of the student to be evaluated is used to calculate the fifth membership degree of the student to be evaluated relative to each preset evaluation level based on the first membership degree, the second membership degree, the third membership degree and the fourth membership degree; The evaluation grade determination module is configured to use the preset evaluation grade corresponding to the maximum degree of membership among the fifth degrees of membership as the evaluation grade of the student to be evaluated.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for evaluating the comprehensive ability of students as claimed in any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the student comprehensive ability assessment method according to any one of claims 1 to 7.

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