CN112633566A - Autonomic capacity assessment method and device and computer equipment - Google Patents
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Abstract
The invention discloses an autonomic capability assessment method, a device and computer equipment, wherein the method comprises the following steps: acquiring the autonomic capacity evaluation feature information of each participating subject in a preset period and in a target scene, wherein the autonomic capacity evaluation feature information is determined according to a preset autonomic capacity evaluation index; determining the weight of the autonomic capacity evaluation characteristic information according to the autonomic capacity evaluation characteristic information and an AHP algorithm based on a triangular fuzzy matrix; analyzing the self-discipline capability of each participating subject according to the weight of the self-discipline capability evaluation feature information and the self-discipline capability evaluation feature information to obtain an analysis result; determining the time weight of each day in a preset period according to the analysis result of each participating subject and the preset period; and determining the comprehensive self-discipline capability score of each participating subject in a preset period according to the analysis result of each participating subject and the time weight of each day.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an autonomic capacity assessment method, an autonomic capacity assessment device and computer equipment.
Background
The self-discipline ability is self-restraint and self-control ability, is a behavior habit and is gradually formed in the process of receiving education by students. It is not only an educational result, but also an important condition for students to learn, develop and receive education.
In the related art, evaluation of the autonomic capacity usually depends on subjective judgment of manpower, and evaluation is performed only by means of short-time performance, but the evaluation of the autonomic capacity by manpower has a certain subjective color, which is not beneficial to really understanding the autonomic capacity of students, and the evaluation accuracy is low, so that an autonomic capacity evaluation method is urgently needed to be provided for evaluating the autonomic capacity of the students, so that the autonomic capacity evaluation accuracy is improved, and the cultivation of the autonomic capacity of the students is enhanced.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defect of low evaluation accuracy rate caused by subjective colors in the prior art for evaluating the autonomic capability by manpower, so as to provide a method, a device and a computer device for evaluating the autonomic capability.
According to a first aspect, an embodiment of the present invention discloses an autonomic capability assessment method, including the following steps: acquiring the autonomic capacity evaluation feature information of each participating subject in a preset period and in a target scene, wherein the autonomic capacity evaluation feature information is determined according to a preset autonomic capacity evaluation index; determining the weight of the autonomic capacity evaluation characteristic information according to the autonomic capacity evaluation characteristic information and an AHP algorithm based on a triangular fuzzy matrix; analyzing the self-discipline capability of each participating subject according to the weight of the self-discipline capability evaluation feature information and the self-discipline capability evaluation feature information to obtain an analysis result; determining the time weight of each day in a preset period according to the analysis result of each participating subject and the preset period; and determining the comprehensive self-discipline capability score of each participating subject in a preset period according to the analysis result of each participating subject and the time weight of each day.
Optionally, the method further comprises: acquiring video information and audio information under a target scene; carrying out structuring processing on the video information and the audio information, obtaining behavior characteristic information and expression characteristic information according to the video information after the structuring processing, and obtaining the audio characteristic information according to the audio information after the structuring processing; and performing information fusion on the behavior characteristic information, the expression characteristic information and the audio characteristic information to obtain the autonomic capacity evaluation characteristic information.
Optionally, after the structuring the video information and the audio information, the method further includes: and cleaning and filing the video information and the audio information which are subjected to the structured processing.
Optionally, the method further comprises: comparing the facial feature information corresponding to the expression feature information and the voiceprint information corresponding to the audio feature information with personal information of each participating subject stored in a preset database in a correlated manner, and determining the behavior feature information, the expression feature information and the identity information to which the audio feature information belongs, wherein the personal information comprises the identity information, the voiceprint information and the facial feature information of each participating subject.
Optionally, after obtaining the autonomic capability assessment feature information of each participating subject in the preset period and in the target scene, before determining the weight of the autonomic capability assessment feature information according to the autonomic capability assessment feature information and an AHP algorithm based on a triangular fuzzy matrix, the method further includes: carrying out consistency processing on the autonomic capacity evaluation feature information according to a preset feature value; and carrying out non-dimensionalization processing on the self-discipline capability evaluation characteristic information after the consistency processing according to the average value and the standard deviation of the self-discipline capability evaluation characteristic information after the consistency processing.
Optionally, the determining the weight of the autonomic capability assessment feature information according to the autonomic capability assessment feature information and an AHP algorithm based on a triangular fuzzy matrix includes: determining a plurality of unit triangular fuzzy judgment matrixes according to the autonomic capacity evaluation characteristic information; fusing the unit fuzzy judgment matrixes according to a plurality of preset expert weights to obtain a fusion matrix; performing triangular fuzzy number conversion on the fusion matrix to obtain a non-fuzzy matrix; and determining the weight of the autonomic capacity evaluation feature information according to an AHP algorithm and the non-fuzzy matrix.
Optionally, the method further comprises: calculating the average value of the analysis results of the target participating main bodies in the preset period; and determining the stability score of the autonomous ability of the target participating subject according to the analysis result of the target participating subject in the preset period and the average value of the analysis results of the target participating subject in the preset period.
According to a second aspect, an embodiment of the present invention further discloses an autonomic capability assessment apparatus, including: the acquisition module is used for acquiring the autonomic capacity evaluation feature information of each participating subject in a preset period and a target scene, and the autonomic capacity evaluation feature information is determined according to a preset autonomic capacity evaluation index; the first weight determination module is used for determining the weight of the autonomic capacity evaluation characteristic information according to the autonomic capacity evaluation characteristic information and an AHP algorithm based on a triangular fuzzy matrix; the analysis module is used for analyzing the autonomic capability of each participating subject according to the weight of the autonomic capability assessment feature information and the autonomic capability assessment feature information to obtain an analysis result; the second weight determining module is used for determining the time weight of each day in a preset period according to the analysis result of each participating subject and the preset period; and the comprehensive scoring determination module is used for determining the comprehensive scoring of the autonomic capacity of each participating subject in a preset period according to the analysis result of each participating subject and the time weight of each day.
According to a third aspect, an embodiment of the present invention further discloses a computer device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the autonomic capability assessment method of the first aspect or any of the alternative embodiments of the first aspect.
According to a fourth aspect, an embodiment of the present invention further discloses a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the autonomic capability assessment method according to the first aspect or any of the optional embodiments of the first aspect.
The technical scheme of the invention has the following advantages:
the autonomic capacity assessment method and the device provided by the invention can be used for determining the autonomic capacity assessment characteristic information according to the preset autonomic capacity assessment indexes by acquiring the autonomic capacity assessment characteristic information of each participating subject in the preset period and the target scene, determining the weight of the self-discipline ability evaluation characteristic information according to the self-discipline ability evaluation characteristic information and an AHP algorithm based on a triangular fuzzy matrix, and according to the weight of the self-discipline ability evaluation characteristic information and the self-discipline ability evaluation characteristic information, analyzing the self-discipline ability of each participating subject to obtain an analysis result, determining the time weight of each day in a preset period according to the analysis result of each participating subject and the preset period, and determining the comprehensive self-discipline capability score of each participating subject in a preset period according to the analysis result of each participating subject and the time weight of each day. According to the method, the self-discipline capability assessment characteristic information is obtained, the weight of the self-discipline capability assessment characteristic information is determined through an AHP algorithm based on a triangular fuzzy matrix, the self-discipline capability analysis result of each participating subject is determined intelligently, meanwhile, the comprehensive evaluation of the self-discipline capability of each participating subject is determined according to the self-discipline capability analysis result of students in a preset period, and therefore teachers are assisted to know the class attendance of the students, timely intervention is facilitated, the culture of the self-discipline capability of the students is enhanced, and compared with the manual judgment that the self-discipline capability of the students is easily affected by subjective factors of evaluators, the method is more scientific and.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of an autonomic capability evaluation method in an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a specific example of an autonomic capability assessment apparatus in an embodiment of the present invention;
FIG. 3 is a diagram of an embodiment of a computer device.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment of the invention discloses an autonomic capability assessment method, which comprises the following steps as shown in figure 1:
s11: acquiring the autonomic capacity evaluation feature information of each participating subject in a preset period and in a target scene, wherein the autonomic capacity evaluation feature information is determined according to a preset autonomic capacity evaluation index.
For example, the preset period may be 1 month, and the preset period is not particularly limited in the embodiment of the present invention, and may be set by a person skilled in the art according to actual situations. The target scene may include a class of lectures and a lesson. The participating subjects may be classes of lectures and students in lessons. As shown in table 1 below, the preset autonomic capability assessment indicators may include timeliness, continence, autonomy, and the like. The timekeeping property can comprise on-time class, preparation before class and the like; the self-control force can comprise eastern westness, head and ear jointing, sitting posture, dozing, sleeping, quarrying, fighting and the like; autonomy may include listening carefully, talking actively, thinking actively, etc. The autonomic capability assessment feature information refers to specific information corresponding to each preset autonomic capability assessment index. The target scene and the preset evaluation index of the autonomic capacity are not particularly limited, and can be set by a person skilled in the art according to actual conditions. The autonomic capability assessment feature information can be directly called from a preset database.
For different target scenes, the preset assessment indexes of the self-discipline ability can be the same, for example, for classes and lessons of lectures, the preset assessment indexes of the self-discipline ability can comprise timeliness and self-discipline; for different target scenes, the preset assessment indexes of the self-discipline ability may also be different, for example, the preset assessment indexes of the self-discipline ability for the class of the lecture may include timekeeping, self-discipline ability and autonomy, and for the lesson, the preset assessment indexes of the self-discipline ability may include timekeeping and self-discipline ability. The preset autonomic capacity assessment indexes under different target scenes are not particularly limited, and can be set by a person skilled in the art according to actual conditions.
TABLE 1 evaluation index of autonomic ability
S12: and determining the weight of the self-discipline capability evaluation characteristic information according to the self-discipline capability evaluation characteristic information and an AHP algorithm based on the triangular fuzzy matrix.
For example, the weight of the autonomic capability assessment feature information determined according to the autonomic capability assessment feature information and the AHP algorithm based on the triangular fuzzy matrix may specifically be:
firstly, a plurality of unit triangular fuzzy judgment matrixes are determined according to the autonomic capacity evaluation characteristic information.
Illustratively, for a triangular blur number S1＝(l1，r1，μ1)，S2＝(l2，r2，μ2) Defining the operation rule as follows:
and (3) addition operation: s1+S2＝(l1+l2，r1+r2，μ1+μ2)；
Multiplication operation: s1×S2＝(l1×l2，r1×r2，μ1×μ2)；
And (3) reciprocal operation:
wherein l represents the lower bound of the fuzzy evaluation interval; mu represents the upper bound of the fuzzy evaluation interval; r represents the importance of the two indexes and is determined by a 1-9 scaling method, for example, the 1 scaling means that the two indexes are equally important, the 3 scaling means that one index is slightly more important than the other index, 5-obviously important, 7-obviously important and 9-absolutely important, and can be given by experts.
Using triangular fuzzy numbers Smn＝(lmn，rmn，μmn) Representing autonomic capability assessment feature information mumEvaluating characteristic information mu for autonomic abilitynResult of importance judgment of (1), μmTo munThe importance of is:
determining a plurality of unit triangular fuzzy judgment matrixes according to the autonomic capacity evaluation feature information specifically can be as follows:
supposing that W-bit experts are provided, wherein W-th (W is 1, 2,., W) experts compare every two pieces of autonomic capacity evaluation characteristic information to obtain a unit fuzzy judgment matrix: sw＝Smn (w)In which S ismn (w)＝(lmn (w)，rmn (w)，μmn (w))。
Secondly, fusing the unit fuzzy judgment matrixes according to a plurality of preset expert weights to obtain a fusion matrix;
according to the specific conditions of W experts, preset expert weights z are respectively givenwThe predetermined expert weights may be set according to authority degrees of experts, for example, the predetermined expert weights with a higher authority degree may be set, or all the predetermined expert weights may be set to be the same, and then their respective unit fuzzy judgment matrices may be aggregated into a fuzzy judgment matrix S ═ S according to the operation rule of the triangular fuzzy numbermnThe method comprises the following steps:
thirdly, performing triangular fuzzy number conversion on the fusion matrix to obtain a non-fuzzy matrix;
exemplarily, the triangular fuzzy number conversion is performed on the fusion matrix, and the obtained non-fuzzy matrix may specifically be:
for triangular blur number S in fusion matrixmn＝(lmn，rmn，μmn) Convertible to non-fuzzy numbers (real numbers)
From a to amnThat is, the non-fuzzy matrix A ═ a (a) can be formedmn). If amnanmNot equal to 1, then A is not a reciprocal matrix and can be adjusted as follows
The adjusted matrix A is a reciprocal matrix and can be used for consistency verification.
And thirdly, determining the weight of the autonomic capacity evaluation feature information according to the AHP algorithm and the non-fuzzy matrix.
Illustratively, the eigenvalue and eigenvector of the non-fuzzy matrix a are calculated according to the AHP algorithm, and the weight C is determined as (C)1，c2，…，cn) Is prior art and is not described herein.
According to the embodiment of the invention, the judgment matrix is constructed through the triangular fuzzy number, the weight is determined by using the AHP method on the basis of the judgment matrix, and the problem that the subjective redundancy and the objective analysis processing are insufficient when the judgment matrix is formed by only using expert scoring is solved. Compared with the weight of the autonomic capacity assessment feature information obtained according to the experience preset weight, the method is more accurate.
S13: and analyzing the self-discipline capability of each participating subject according to the weight of the self-discipline capability evaluation feature information and the self-discipline capability evaluation feature information to obtain an analysis result.
Illustratively, the analysis results may include classroom analysis results and lesson analysis results. And analyzing the self-discipline ability of each participating subject according to the weight of the self-discipline ability evaluation characteristic information and the self-discipline ability evaluation characteristic information to obtain an analysis result, wherein the analysis result can be determined by weighted average sum of the weight of each self-discipline ability evaluation characteristic and the self-discipline ability evaluation characteristic information, and can also be determined by dividing the sum of the products of the weight of each self-discipline ability evaluation characteristic information evaluation characteristic and the self-discipline ability evaluation characteristic information by the sum of the self-discipline ability evaluation characteristic information. The determination method of the analysis result in the embodiment of the present invention is not particularly limited, and may be set by a person skilled in the art according to actual conditions.
S14: and determining the time weight of each day in the preset period according to the analysis result of each participating subject and the preset period.
For example, determining the time weight of each day in the preset period according to the analysis result of each participant and the preset period may specifically be:
wherein,
a time weight representing a classroom;
time weight representing a lesson; t represents a preset period; v represents all participating subjects; g_ktv(t) represents the result of classroom autonomy analysis of day t of the participating subject v; g_zxv(t) represents the result of the autonomic ability analysis of the lessons participating in the subject v on the tth day.
S15: and determining the comprehensive self-discipline capability score of each participating subject in a preset period according to the analysis result of each participating subject and the time weight of each day.
For example, the determining, according to the analysis result of each participating subject in the preset period and the time weight of each day, the comprehensive self-discipline capability score of each participating subject in the preset period may specifically be:
wherein G iskExpressing the comprehensive self-discipline capability score of each participating subject k in a preset period; g_ktRepresenting the analysis result of the autonomic capacity of the class of each participating subject on the tth day; g_zxAnd (4) showing the analysis result of the autonomic ability of each subject on the day t of the lesson.
The autonomic capacity assessment method provided by the invention comprises the steps of obtaining autonomic capacity assessment feature information of each participating body in a target scene, determining the autonomic capacity assessment feature information according to a preset autonomic capacity assessment index, determining the weight of the autonomic capacity assessment feature information according to the autonomic capacity assessment feature information and an AHP algorithm based on a triangular fuzzy matrix, analyzing the autonomic capacity of each participating body according to the weight of the autonomic capacity assessment feature information and the autonomic capacity assessment feature information to obtain an analysis result, determining the time weight of each day in a preset period according to the analysis result of each participating body in the preset period and the preset period, and determining the comprehensive assessment of the autonomic capacity of each participating body in the preset period according to the analysis result of each participating body and the time weight of each day. According to the method, the self-discipline capability assessment characteristic information is obtained, the weight of the self-discipline capability assessment characteristic information is determined through an AHP algorithm based on a triangular fuzzy matrix, the self-discipline capability analysis result of each participating subject is determined intelligently, meanwhile, the comprehensive evaluation of the self-discipline capability of each participating subject is determined according to the self-discipline capability analysis result of students in a preset period, and therefore teachers are assisted to know the class attendance of the students, timely intervention is facilitated, the culture of the self-discipline capability of the students is enhanced, and compared with the manual judgment that the self-discipline capability of the students is easily affected by subjective factors of evaluators, the method is more scientific and.
As an optional implementation manner of the embodiment of the present invention, the autonomic capability evaluation method further includes:
first, video information and audio information in a target scene are acquired.
Illustratively, the video information comprises video images of each participant in the target scene, and in one embodiment, a video capture device, such as a surveillance camera, is disposed in the classroom, and the video information can be captured by the camera. To ensure that video information for all participating subjects can be captured, in one particular embodiment, multiple video capture devices are provided in the classroom, for example, at the 4 corners of the classroom ceiling.
The audio information includes the speaking information of each participant in the target participation scene, and the audio information can be obtained by an audio acquisition device such as a sound pickup, a sound recorder or an array microphone. The audio acquisition device in the embodiment of the present invention is not particularly limited, and those skilled in the art may set the audio acquisition device according to actual situations. To ensure that audio information of all participating subjects can be captured, multiple audio capture devices may be provided in a classroom. In one embodiment, to ensure that the audio information of each participating subject is accurately collected, each participating subject may be worn with a wearable microphone.
It should be noted that the collection of the video information and the audio information is authorized by each participating subject.
Secondly, carrying out structuring processing on the video information and the audio information, obtaining behavior characteristic information and expression characteristic information according to the video information after the structuring processing, and obtaining audio characteristic information according to the audio information after the structuring processing.
The video information collected by the video collecting device and the audio information collected by the audio collecting device are unstructured data, and the structured processing of the video information may be inputting the video information into a preset recognition model, performing sequence modeling on the video information through a video structuring algorithm, and performing expression feature recognition and action recognition on a participant in the video, for example, a video sequence is recognized as a word (e.g., a curriculity) representing an expression feature or a behavior feature.
The audio information is structured, that is, the audio information is input into a preset recognition model, so that a series of English words are obtained. In the embodiment of the invention, the voice print tracking technology is utilized to compare the audio information with the voice print information of the participating main bodies stored in the preset database, so as to identify the audio characteristic information of each participating main body.
As an optional implementation manner of the embodiment of the present invention, the autonomic capability evaluation method further includes:
first, personal information of each participating subject is acquired and stored in association in advance, and the personal information includes identity information, voiceprint information, and facial feature information of the participating subject.
Illustratively, the personal information includes identity information, voiceprint information and facial feature information of the participating subject, wherein the identity information may include: the name, age, etc. of the participating subject. The personal information of the participating main bodies can be independently sent to the preset database by each participating main body for storage, or can be collected in a centralized manner and then stored in the preset database.
In the embodiment of the invention, the course information and the class information of each participating subject can be stored, so that the result can be conveniently fed back to any lesson teacher of the corresponding course. The course information and the class information may be stored in a format preset in advance, for example, a class date: the format is XX year-XX month-XX day, class: the format is XX grade-XX class, subject: the format is as follows: chinese/math/english/study … …, class number: the format is as follows: 1-12.
Secondly, storing the video information, the audio information and the analysis result;
illustratively, as for the video information and the audio information, they may be stored in a unified storage format and named form, such as collectively storing the audio information in Mp3 format, collectively storing the video information in Mp4 format; and names the video information and the audio information uniformly, such as: 20201203_01 class _ section 8 _ language. mp 4. For the analysis results, storage can be performed directly.
And thirdly, performing information fusion on the behavior characteristic information, the expression characteristic information and the audio characteristic information to obtain the autonomic capacity evaluation characteristic information.
Illustratively, information fusion is carried out on the recognized action characteristic information, expression characteristic information and audio characteristic information, and the action characteristic information, the expression characteristic information and the audio characteristic information are arranged into self-discipline capability evaluation information.
As an optional implementation manner of the embodiment of the present invention, after performing the structuring process on the video information and the audio information, the autonomic capability evaluation method further includes:
and cleaning and filing the video information and the audio information which are subjected to the structured processing.
Illustratively, archiving the video information and audio information after the structuring process means that the video information and audio information after the structuring process and the existing structured data (name, gender, etc.) are stored in a row for each participating subject and the observed variables are stored in columns. The cleaning processing of the video information and the audio information subjected to the structured processing refers to that the acquired information behavior of a certain participating subject is deleted; the collected data units are unified, and the data units are not unified and are irregular, and are corrected into standard formats and units.
The invention can be used for cleaning and filing the video information and the audio information which are subjected to the structured processing, thereby being convenient for direct analysis after subsequent calling.
As an optional implementation manner of the embodiment of the present invention, the autonomic capability evaluation method further includes:
comparing the facial feature information corresponding to the expression feature information and the voiceprint information corresponding to the audio feature information with personal information of each participating subject stored in a preset database in a correlated manner, and determining the identity information of the behavior feature information, the expression feature information and the audio feature information, wherein the personal information comprises the identity information, the voiceprint information and the facial feature information of each participating subject.
For example, the video information and the audio information collected by the video collecting device include video information and audio information of all participating subjects, and therefore, the video information and the audio information are structured to obtain action feature information, expression feature information and audio feature information of each participating subject, the face feature information and the voiceprint information corresponding to the expression features are compared with the voiceprint information and the face feature information stored in preset data one by one, and identity information of each action feature information, expression feature information and audio feature information attached to the audio feature information is determined, that is, the action feature information, the expression feature information and the audio feature information of each participating subject can be determined.
As an optional implementation manner of the embodiment of the present invention, after the step S11 and before the step S12, the autonomic capability assessment method further includes:
firstly, according to a preset characteristic value, the self-discipline capability evaluation characteristic information is processed in a consistent mode.
For example, the preset feature value may be set in advance as long as the preset feature value is greater than the autonomic capability assessment feature information. In the embodiment of the present invention, the autonomic capability assessment feature information corresponding to the timeliness and the autonomic capability is referred to as the extremely small feature information, and the autonomic capability assessment feature information corresponding to the autonomic capability is referred to as the extremely large feature information.
The consistency processing of the self-discipline capability evaluation feature information according to the preset feature value can be realized by uniformly converting each type of self-discipline capability evaluation feature information into extremely large feature information, and specifically, for extremely small self-discipline capability evaluation feature information xi：
x′i＝M-xi
Wherein, x'iRepresentation reconciliationThe processed maximum characteristic information; m is a preset characteristic value, and characteristic information x is evaluated for the extremely small autonomic capacityiOne allows an upper bound.
And secondly, carrying out dimensionless processing on the self-discipline capability evaluation characteristic information after the consistency processing according to the average value and the standard deviation of the self-discipline capability evaluation characteristic information after the consistency processing.
Illustratively, the average value and the standard deviation of the autonomic capability evaluation feature information after the unification process are calculated as follows:
wherein,
and SiRespectively is x after the uniformization treatmentiAverage value and standard deviation of (I ═ 1, 2.. I), I denotes the number of autonomic ability evaluation feature information.
Carrying out non-dimensionalization processing on the consistent self-discipline capability evaluation characteristic information according to the average value and the standard deviation of the consistent self-discipline capability evaluation characteristic information, wherein the specific formula is as follows:
wherein,
and (4) representing the autonomy capability evaluation characteristic information after the non-dimensionalization processing. And carrying out autonomous capability evaluation according to the nondimensionalized autonomous capability evaluation characteristic information.
According to the embodiment of the invention, the dimension and magnitude of all the autonomic capacity evaluation characteristic information are consistent by carrying out non-dimensionalization processing on the autonomic capacity evaluation characteristic information, so that the subsequent calculation is facilitated.
As an optional implementation manner of the embodiment of the present invention, the autonomic capability evaluation method further includes:
firstly, an average value calculation formula is used for calculating the average value of the analysis results of the target participating subjects in the preset period.
And secondly, determining the stability score of the autonomic capacity of the target participating subject according to the daily analysis result of the target participating subject in the preset period and the average value of the analysis results of the target participating subject in the preset period.
For example, within the preset period T, the stability score of the autonomic ability of the target participant j can be calculated by the following formula:
wherein,
representing the average value of the analysis results of the target participant j in a preset period; fjThe smaller the value, the higher the stability. G (t) shows the result of analysis of the autonomic capacity of the subject j on day t, and G (t) ═ G_ktj(t)+G_zxj(t)，G_ktj(t) representing the result of the analysis of the t-th day class self-discipline ability of the target participant j; g_zxj(t) represents the result of the analysis of the self-discipline ability of the subject j on the tth day.
According to the embodiment of the invention, the self-discipline capability condition of the student can be known by calculating the self-discipline capability stability of each participating subject, and the teacher and the parents can be helped to conduct effective guidance.
The embodiment of the present invention further discloses an autonomic capability assessment apparatus, as shown in fig. 2, including:
the acquiring module 21 is configured to acquire the autonomic capability evaluation feature information of each participating subject in a preset period and in a target scene, where the autonomic capability evaluation feature information is determined according to a preset autonomic capability evaluation index; the specific implementation manner is described in the above embodiment in relation to step S11, and is not described herein again.
The first weight determining module 22 is configured to determine the weight of the autonomic capability assessment feature information according to the autonomic capability assessment feature information and an AHP algorithm based on a triangular fuzzy matrix; the specific implementation manner is described in the above embodiment in relation to step S12, and is not described herein again.
And the analysis module 23 is configured to analyze the autonomic capability of each participating subject according to the weight of the autonomic capability assessment feature information and the autonomic capability assessment feature information to obtain an analysis result. The specific implementation manner is described in the above embodiment in relation to step S13, and is not described herein again.
The second weight determining module 24 is configured to determine a time weight of each day in a preset period according to an analysis result of each participating subject and the preset period; the specific implementation manner is described in the above embodiment in relation to step S14, and is not described herein again.
And the comprehensive score determining module 25 is configured to determine an autonomic capability comprehensive score of each participating subject in a preset period according to the analysis result of each participating subject and the time weight of each day. The specific implementation manner is described in the above embodiment in relation to step S15, and is not described herein again.
The autonomic capacity evaluation device provided by the invention obtains the autonomic capacity evaluation characteristic information of each participating body in a target scene, the autonomic capacity evaluation characteristic information is determined according to a preset autonomic capacity evaluation index, the weight of the autonomic capacity evaluation characteristic information is determined according to the autonomic capacity evaluation characteristic information and an AHP algorithm based on a triangular fuzzy matrix, the autonomic capacity of each participating body is analyzed according to the weight of the autonomic capacity evaluation characteristic information and the autonomic capacity evaluation characteristic information to obtain an analysis result, the time weight of each day in a preset period is determined according to the analysis result of each participating body in the preset period and the preset period, and the comprehensive autonomic capacity score of each participating body in the preset period is determined according to the analysis result of each participating body and the time weight of each day. According to the method, the self-discipline capability assessment characteristic information is obtained, the weight of the self-discipline capability assessment characteristic information is determined through an AHP algorithm based on a triangular fuzzy matrix, the self-discipline capability analysis result of each participating subject is determined intelligently, meanwhile, the comprehensive evaluation of the self-discipline capability of each participating subject is determined according to the self-discipline capability analysis result of students in a preset period, and therefore teachers are assisted to know the class attendance of the students, timely intervention is facilitated, the culture of the self-discipline capability of the students is enhanced, and compared with the manual judgment that the self-discipline capability of the students is easily affected by subjective factors of evaluators, the method is more scientific and.
As an optional implementation manner of the embodiment of the present invention, the autonomic capability assessment apparatus further includes:
the information acquisition module is used for acquiring video information and audio information in a target scene; the specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
The structuralization processing module is used for structuralizing the video information and the audio information; the specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
The characteristic information obtaining module is used for obtaining behavior characteristic information and expression characteristic information according to the video information after the structural processing, and obtaining audio characteristic information according to the audio information after the structural processing; the specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
And the information fusion module is used for carrying out information fusion on the behavior characteristic information, the expression characteristic information and the audio characteristic information to obtain the autonomic capacity evaluation characteristic information. The specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
As an optional implementation manner of the embodiment of the present invention, the autonomic capability assessment apparatus further includes:
and the storage module is used for cleaning and filing the video information and the audio information which are structurally processed. The specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
As an optional implementation manner of the embodiment of the present invention, the autonomic capability assessment apparatus further includes:
and the comparison module is used for comparing the facial feature information corresponding to the expression feature information and the voiceprint information corresponding to the audio feature information with the personal information of each participating subject stored in a preset database in a correlated manner, and determining the identity information to which the behavior feature information, the expression feature information and the audio feature information belong, wherein the personal information comprises the identity information, the voiceprint information and the facial feature information of each participating subject. The specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
As an optional implementation manner of the embodiment of the present invention, the autonomic capability assessment apparatus further includes:
the consistency processing module is used for carrying out consistency processing on the self-discipline capability evaluation characteristic information according to the preset characteristic value; the specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
And the dimensionless processing module is used for carrying out dimensionless processing on the agreed self-discipline capability evaluation characteristic information according to the average value and the standard deviation of the agreed self-discipline capability evaluation characteristic information. The specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
As an optional implementation manner of the embodiment of the present invention, the determining module 22 includes:
the first determining submodule is used for determining a plurality of unit triangular fuzzy judging matrixes according to the self-discipline capability evaluation characteristic information; the specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
The fusion module is used for fusing the unit fuzzy judgment matrixes according to the preset expert weights to obtain a fusion matrix; the specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
The conversion module is used for carrying out triangular fuzzy number conversion on the fusion matrix to obtain a non-fuzzy matrix; the specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
And the second determining submodule is used for determining the weight of the self-discipline capability evaluation characteristic information according to the AHP algorithm and the non-fuzzy matrix. The specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
As an optional implementation manner of the embodiment of the present invention, the autonomic capability assessment apparatus further includes:
the calculation module is used for calculating the average value of the analysis results of the target participating main bodies in the preset period; the specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
And the stability score determining module is used for determining the stability score of the autonomous ability of the target participating subject according to the analysis result of the target participating subject in the preset period and the average value of the analysis result of the target participating subject in the preset period. The specific implementation manner is described in the relevant description of the corresponding steps in the above embodiments, and is not described herein again.
An embodiment of the present invention further provides a computer device, as shown in fig. 3, the computer device may include a processor 31 and a memory 32, where the processor 31 and the memory 32 may be connected by a bus or in another manner, and fig. 3 takes the example of being connected by a bus as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 32, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the autonomic capability assessment method in the embodiment of the present invention (for example, the obtaining module 21, the first weight determination module 22, the analysis module 23, the second weight determination module 24, and the composite score determination module 25 shown in fig. 2). The processor 31 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 32, that is, the autonomic capability assessment method in the above method embodiment is implemented.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 31, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 32 and, when executed by the processor 31, perform the autonomic capability assessment method in the embodiment shown in FIG. 1.
The details of the computer device can be understood with reference to the corresponding related descriptions and effects in the embodiment shown in fig. 1, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-0nly Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. An autonomic capability assessment method, comprising the steps of:
acquiring the autonomic capacity evaluation feature information of each participating subject in a preset period and in a target scene, wherein the autonomic capacity evaluation feature information is determined according to a preset autonomic capacity evaluation index;
determining the weight of the autonomic capacity evaluation characteristic information according to the autonomic capacity evaluation characteristic information and an AHP algorithm based on a triangular fuzzy matrix;
analyzing the self-discipline capability of each participating subject according to the weight of the self-discipline capability evaluation feature information and the self-discipline capability evaluation feature information to obtain an analysis result;
determining the time weight of each day in a preset period according to the analysis result of each participating subject and the preset period;
and determining the self-discipline capability comprehensive score of each participating subject in a preset period according to the analysis result of each participating subject and the time weight of each day.
2. The method of claim 1, further comprising:
acquiring video information and audio information under a target scene;
carrying out structuring processing on the video information and the audio information, obtaining behavior characteristic information and expression characteristic information according to the video information after the structuring processing, and obtaining the audio characteristic information according to the audio information after the structuring processing;
and performing information fusion on the behavior characteristic information, the expression characteristic information and the audio characteristic information to obtain the autonomic capacity evaluation characteristic information.
3. The method of claim 2, wherein after said structuring said video information and said audio information, said method further comprises:
and cleaning and filing the video information and the audio information which are subjected to the structured processing.
4. The method of claim 2, further comprising:
comparing the facial feature information corresponding to the expression feature information and the voiceprint information corresponding to the audio feature information with personal information of each participating subject stored in a preset database in a correlated manner, and determining the behavior feature information, the expression feature information and the identity information to which the audio feature information belongs, wherein the personal information comprises the identity information, the voiceprint information and the facial feature information of each participating subject.
5. The method according to claim 1, wherein after obtaining the autonomic capability assessment feature information of each participant in a target scene within a preset period, before determining the weight of the autonomic capability assessment feature information according to the autonomic capability assessment feature information and an AHP algorithm based on a triangular fuzzy matrix, the method further comprises:
carrying out consistency processing on the autonomic capacity evaluation feature information according to a preset feature value;
and carrying out non-dimensionalization processing on the self-discipline capability evaluation characteristic information after the consistency processing according to the average value and the standard deviation of the self-discipline capability evaluation characteristic information after the consistency processing.
6. The method according to claim 1, wherein the determining the weight of the autonomic capability assessment feature information according to the autonomic capability assessment feature information and an AHP algorithm based on a triangular fuzzy matrix comprises:
determining a plurality of unit triangular fuzzy judgment matrixes according to the autonomic capacity evaluation characteristic information;
fusing the unit fuzzy judgment matrixes according to a plurality of preset expert weights to obtain a fusion matrix;
performing triangular fuzzy number conversion on the fusion matrix to obtain a non-fuzzy matrix;
and determining the weight of the autonomic capacity evaluation feature information according to an AHP algorithm and the non-fuzzy matrix.
7. The method of claim 1, further comprising:
calculating the average value of the analysis results of the target participating main bodies in the preset period;
and determining the stability score of the autonomous ability of the target participating subject according to the analysis result of the target participating subject in the preset period and the average value of the analysis results of the target participating subject in the preset period.
8. An autonomic capability assessment apparatus, comprising:
the acquisition module is used for acquiring the autonomic capacity evaluation feature information of each participating subject in a preset period and a target scene, and the autonomic capacity evaluation feature information is determined according to a preset autonomic capacity evaluation index;
the first weight determination module is used for determining the weight of the autonomic capacity evaluation characteristic information according to the autonomic capacity evaluation characteristic information and an AHP algorithm based on a triangular fuzzy matrix;
the analysis module is used for analyzing the autonomic capability of each participating subject according to the weight of the autonomic capability assessment feature information and the autonomic capability assessment feature information to obtain an analysis result;
the second weight determining module is used for determining the time weight of each day in a preset period according to the analysis result of each participating subject and the preset period;
and the comprehensive scoring determination module is used for determining the comprehensive scoring of the autonomic capacity of each participating subject in a preset period according to the analysis result of each participating subject and the time weight of each day.
9. A computer device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the autonomic capability assessment method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the autonomic capability assessment method according to any of claims 1-7.
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