CN112131408A - Cognitive ability analysis method and system based on knowledge graph - Google Patents
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Abstract
The invention provides a cognitive competence analysis method and a system based on a knowledge graph, which can construct and form a corresponding knowledge graph according to historical knowledge learning records of different target objects, determine cognitive analysis error conditions of the target objects according to the knowledge graph, and correct the knowledge graph according to the cognitive analysis error conditions, so that the occurrence of knowledge graph construction deviation conditions caused by data defects of the historical knowledge learning records can be effectively avoided, and finally, a cognitive competence evaluation value of the target object is re-determined according to the corrected knowledge graph, so that the target object is accurately and reliably evaluated, and the learning progress of knowledge data of the target object is adjusted according to the cognitive competence evaluation value, thereby improving the learning efficiency and the learning quality of the target object to the maximum extent.
Description
Technical Field
The invention relates to the technical field of intelligent education, in particular to a cognitive ability analysis method and system based on a knowledge graph.
Background
At present, knowledge maps are generally used for describing the mastery degree of a target object on different knowledge point data in a historical learning process and the relevance between the different knowledge point data, the knowledge maps of different target objects are different, and the cognitive ability of the corresponding target object can be accurately determined by performing corresponding research and analysis on the knowledge maps. However, in actual operation, since the knowledge graph is constructed based on the historical learning knowledge points of the target object, and the acquisition of the historical learning knowledge points is not completely matched with the actual learning process of the target object, it is easy to cause that the constructed knowledge graph cannot truly reflect the actual cognitive ability of the target object, and thus a certain deviation exists in the cognitive ability of the target object determined subsequently. Therefore, there is a need in the art for a method mode that can effectively correct the cognitive ability analysis result obtained based on the knowledge graph of the user according to the actual knowledge learning records of different target objects.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a cognitive competence analysis method and a system based on a knowledge graph, which comprises the steps of obtaining a historical knowledge learning record of a target object, preprocessing the historical knowledge learning record and extracting knowledge points, constructing and forming the knowledge graph corresponding to the target object according to data of the extracted knowledge points, determining a cognitive analysis error value of the target object according to the knowledge graph, correcting the knowledge graph according to the cognitive analysis error value to eliminate a knowledge point association error of the knowledge graph, re-determining a cognitive competence evaluation value of the target object according to a result of correcting the knowledge graph, and adjusting the learning progress of the knowledge data of the target object according to the cognitive competence evaluation value; therefore, the cognitive ability analysis method and the system based on the knowledge graph can construct and form the corresponding knowledge graph according to the historical knowledge learning records of different target objects, determine the cognitive analysis error condition of the target object according to the knowledge graph, and modify the knowledge graph according to the cognitive analysis error condition, so that the situation that the knowledge graph construction deviation is caused by the data defect of the historical knowledge learning records can be effectively avoided, and finally, the cognitive ability evaluation value of the target object is re-determined according to the modified knowledge graph, so that the target object is accurately and reliably evaluated, and the learning progress of the knowledge data of the target object is adjusted according to the cognitive ability evaluation value, so that the learning efficiency and the learning quality of the target object are improved to the maximum extent.
The invention provides a cognitive ability analysis method based on a knowledge graph, which is characterized by comprising the following steps:
step S1, acquiring historical knowledge learning records of a target object, preprocessing the historical knowledge learning records and extracting knowledge points, and constructing and forming a knowledge graph corresponding to the target object according to the extracted knowledge point data;
step S2, determining a cognitive analysis error value of the target object according to the knowledge graph, and correcting the knowledge graph according to the cognitive analysis error value so as to eliminate a knowledge point association error of the knowledge graph;
step S3, re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge map, and adjusting the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value;
further, in step S1, acquiring a historical knowledge learning record of the target object, preprocessing the historical knowledge learning record and extracting knowledge points, and then constructing and forming a knowledge graph corresponding to the target object according to the extracted knowledge point data specifically includes:
step S101, obtaining curriculum knowledge data correspondingly browsed in the online learning process of the target object, comparing the actual browsing duration of each curriculum knowledge data with a preset browsing time threshold, and taking the corresponding curriculum knowledge data as the historical knowledge learning record if the actual browsing duration is greater than or equal to the preset browsing time threshold;
step S102, carrying out data deduplication preprocessing on all course knowledge data contained in the historical knowledge learning record, and extracting corresponding knowledge point keywords from each course knowledge data subjected to data deduplication preprocessing;
step S103, constructing and forming a knowledge graph corresponding to the target object according to the semantic association degree between the extracted different knowledge point keywords;
further, in step S2, determining a cognitive analysis error value of the target object according to the knowledge graph, and correcting the knowledge graph according to the cognitive analysis error value, so as to eliminate a knowledge point association error of the knowledge graph specifically includes:
step S201, determining the cognitive analysis result of the target object on the problems related to different knowledge points according to the knowledge graph;
step S202, determining a cognitive analysis error value W of the target object according to the cognitive analysis result of the target object on the problems related to different knowledge points and the following formula (1):
in the above formula (1), Pk(t) a quantitative result value, P, corresponding to the cognitive analysis result of the kth correlation problem of the t-th knowledge point by the target objectk0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point, and n represents the total number of related problems of the knowledge point;
step S203, according to the following formula (2), correcting the quantization result value corresponding to the cognitive analysis result of the knowledge point related problem, so as to obtain a corrected quantization result value:
in the above formula (2), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of a target object for a t-th problem related to a knowledge point, W represents a cognitive analysis error value of the target object, and P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of the target object for a t-th problem related to a knowledge pointk0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point;
further, in step S3, the step of re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph, and the adjusting the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value specifically includes:
step S301, RE-determining the cognitive ability evaluation value RE of the target object according to the following formula (3):
in the above formula (3), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a recognition analysis result of the target object for the t-th problem related to the knowledge point, P (t)k0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point;
step S302, comparing the cognitive ability evaluation value RE with a preset cognitive ability evaluation threshold value, if the cognitive ability evaluation value RE is smaller than or equal to the preset cognitive ability evaluation threshold value, reducing the total learning duration of the knowledge course of the target object, otherwise, increasing the total learning duration of the knowledge course of the target object.
The invention also provides a cognitive ability analysis system based on the knowledge graph, which is characterized by comprising a historical knowledge learning record acquisition and processing module, a knowledge graph construction module, a cognitive analysis error determination and correction module, a cognitive ability evaluation module and a learning progress adjustment module; wherein,
the historical knowledge learning record acquisition and processing module is used for acquiring a historical knowledge learning record of a target object, and preprocessing and knowledge point extraction processing are carried out on the historical knowledge learning record;
the knowledge graph construction module is used for constructing and forming a knowledge graph corresponding to the target object according to the extracted knowledge point data;
the cognitive analysis error determining and correcting module is used for determining a cognitive analysis error value of the target object according to the knowledge graph and correcting the knowledge graph according to the cognitive analysis error value so as to eliminate a knowledge point association error of the knowledge graph;
the cognitive ability evaluation module is used for re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph;
the learning progress adjusting module is used for adjusting the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value;
further, the historical knowledge learning record obtaining and processing module obtains the historical knowledge learning record of the target object, and the preprocessing and knowledge point extraction processing on the historical knowledge learning record specifically comprises:
acquiring curriculum knowledge data correspondingly browsed in the online learning process of the target object, comparing the actual browsing duration of each curriculum knowledge data with a preset browsing time threshold, and taking the corresponding curriculum knowledge data as the historical knowledge learning record if the actual browsing duration is greater than or equal to the preset browsing time threshold;
carrying out data deduplication preprocessing on all course knowledge data contained in the historical knowledge learning record, and extracting corresponding knowledge point keywords from each item of course knowledge data subjected to data deduplication preprocessing;
and the number of the first and second groups,
the method for constructing the knowledge graph corresponding to the target object by the knowledge graph construction module according to the extracted knowledge point data specifically comprises the following steps:
constructing and forming a knowledge graph corresponding to the target object according to the semantic association degree between the extracted different knowledge point keywords;
further, the cognitive analysis error determination and correction module determines a cognitive analysis error value of the target object according to the knowledge graph, and corrects the knowledge graph according to the cognitive analysis error value, so as to eliminate the knowledge point association error of the knowledge graph specifically includes:
determining the cognitive analysis result of the target object on the problems related to different knowledge points according to the knowledge graph;
and determining a cognitive analysis error value W of the target object according to the cognitive analysis result of the target object on the problems related to different knowledge points and the following formula (1):
in the above formula (1), Pk(t) a quantitative result value, P, corresponding to the cognitive analysis result of the kth correlation problem of the t-th knowledge point by the target objectk0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point, and n represents the total number of related problems of the knowledge point;
and then correcting the quantitative result value corresponding to the cognitive analysis result of the knowledge point related problem according to the following formula (2) to obtain a corrected quantitative result value:
in the above formula (2), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of a target object for a t-th problem related to a knowledge point, W represents a cognitive analysis error value of the target object, and P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of the target object for a t-th problem related to a knowledge pointk0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point;
further, the step of, by the cognitive ability evaluation module, re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph specifically includes:
RE-determining the cognitive ability evaluation value RE of the target object according to the following formula (3):
in the above formula (3), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a recognition analysis result of the target object for the t-th problem related to the knowledge point, P (t)k0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point;
and the number of the first and second groups,
the learning progress adjusting module adjusts the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value, and specifically includes:
and comparing the cognitive ability evaluation value RE with a preset cognitive ability evaluation threshold value, if the cognitive ability evaluation value RE is smaller than or equal to the preset cognitive ability evaluation threshold value, reducing the total learning time of the knowledge course of the target object, otherwise, increasing the total learning time of the knowledge course of the target object.
Compared with the prior art, the cognitive ability analysis method and system based on the knowledge graph acquire the historical knowledge learning record of the target object, preprocess and extract the knowledge points from the historical knowledge learning record, construct and form the knowledge graph corresponding to the target object according to the extracted knowledge point data, determine the cognitive analysis error value of the target object according to the knowledge graph, correct the knowledge graph according to the cognitive analysis error value, eliminate the knowledge point association error of the knowledge graph, re-determine the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph, and adjust the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value; therefore, the cognitive ability analysis method and the system based on the knowledge graph can construct and form the corresponding knowledge graph according to the historical knowledge learning records of different target objects, determine the cognitive analysis error condition of the target object according to the knowledge graph, and modify the knowledge graph according to the cognitive analysis error condition, so that the situation that the knowledge graph construction deviation is caused by the data defect of the historical knowledge learning records can be effectively avoided, and finally, the cognitive ability evaluation value of the target object is re-determined according to the modified knowledge graph, so that the target object is accurately and reliably evaluated, and the learning progress of the knowledge data of the target object is adjusted according to the cognitive ability evaluation value, so that the learning efficiency and the learning quality of the target object are improved to the maximum extent.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a cognitive ability analysis method based on a knowledge graph provided by the invention.
FIG. 2 is a schematic structural diagram of a cognitive ability analysis system based on a knowledge-graph according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, a flow chart of a cognitive ability analysis method based on a knowledge graph according to an embodiment of the present invention is schematically shown. The cognitive ability analysis method based on the knowledge graph comprises the following steps:
step S1, acquiring historical knowledge learning records of the target object, preprocessing the historical knowledge learning records and extracting knowledge points, and constructing and forming a corresponding knowledge map of the target object according to the extracted knowledge point data;
step S2, determining the cognitive analysis error value of the target object according to the knowledge graph, and correcting the knowledge graph according to the cognitive analysis error value so as to eliminate the knowledge point association error of the knowledge graph;
step S3 is to re-determine the cognitive ability evaluation value of the target object based on the result of correcting the knowledge map, and to adjust the learning progress of the knowledge data of the target object based on the cognitive ability evaluation value.
The beneficial effects of the above technical scheme are: the cognitive ability analysis method based on the knowledge graph can construct and form a corresponding knowledge graph according to historical knowledge learning records of different target objects, then determine cognitive analysis error conditions of the target objects according to the knowledge graph, and modify the knowledge graph according to the cognitive analysis error conditions, so that the situation that the knowledge graph is constructed and deviated due to data defects of the historical knowledge learning records can be effectively avoided, finally, the cognitive ability evaluation value of the target object is re-determined according to the modified knowledge graph, the target object is accurately and reliably evaluated, the learning progress of knowledge data of the target object is adjusted according to the cognitive ability evaluation value, and therefore the learning efficiency and the learning quality of the target object are improved to the maximum extent.
Preferably, in step S1, the acquiring a historical knowledge learning record of the target object, preprocessing the historical knowledge learning record and extracting knowledge points, and then constructing a knowledge graph corresponding to the target object according to the extracted knowledge point data specifically includes:
step S101, acquiring curriculum knowledge data correspondingly browsed in the on-line learning process of the target object, comparing the actual browsing duration of each curriculum knowledge data with a preset browsing time threshold, and taking the corresponding curriculum knowledge data as the historical knowledge learning record if the actual browsing duration is greater than or equal to the preset browsing time threshold;
step S102, carrying out data deduplication preprocessing on all course knowledge data contained in the historical knowledge learning record, and extracting corresponding knowledge point keywords from each course knowledge data subjected to data deduplication preprocessing;
and step S103, constructing and forming a knowledge graph corresponding to the target object according to the semantic association degree between the extracted different knowledge point keywords.
The beneficial effects of the above technical scheme are: the course knowledge data browsed in the online learning process of the target object is compared and judged according to the actual browsing duration, so that the course knowledge data obtained by screening can be guaranteed to be the knowledge data really interested by the target object to the maximum extent, and the reliability of obtaining the historical knowledge learning record is guaranteed; in addition, the data deduplication preprocessing is carried out on the historical knowledge learning record, so that the data redundancy of the historical knowledge learning record can be reduced, and the data processing workload of the historical knowledge learning record is greatly reduced.
Preferably, in the step S2, determining a cognitive analysis error value of the target object according to the knowledge graph, and correcting the knowledge graph according to the cognitive analysis error value, so as to eliminate the knowledge point association error of the knowledge graph specifically includes:
step S201, determining a cognitive analysis result of the target object on different knowledge point related questions according to the knowledge map, where the different knowledge point related questions refer to objective or subjective question answers related to different knowledge point contents, and the cognitive analysis result refers to actual answer answers of the target object to the objective or subjective question answers.
Step S202, determining a cognitive analysis error value W of the target object according to the cognitive analysis result of the target object on the problems related to different knowledge points and the following formula (1):
in the above formula (1), Pk(t) a quantitative result value, P, corresponding to the cognitive analysis result of the kth correlation problem of the t-th knowledge point by the target objectk0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point, and n represents the total number of related problems of the knowledge point;
step S203, according to the following formula (2), correcting the quantization result value corresponding to the cognitive analysis result of the knowledge point related problem, so as to obtain a corrected quantization result value:
in the above formula (2), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of a target object for a t-th problem related to a knowledge point, W represents a cognitive analysis error value of the target object, and P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of the target object for a t-th problem related to a knowledge pointk0(t) represents a quantitative result value corresponding to a standard cognitive analysis result of the kth related problem of the t-th knowledge point according to the knowledge graph, and m represents the total number of the related problems of the knowledge points.
The beneficial effects of the above technical scheme are: the cognitive analysis error value of the target object and the result obtained by correcting the quantitative result value corresponding to the cognitive analysis result of the target object on the problem related to the knowledge point are respectively determined through the formulas (1) and (2), so that the cognitive analysis state of the target object can be quantitatively determined, and the targeted and controllable cognitive analysis result correction can be conveniently carried out on the target object.
Preferably, in step S3, the step of re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph, and the adjusting the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value specifically includes:
step S301, RE-determining the cognitive ability evaluation value RE of the target object according to the following formula (3):
in the above formula (3), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a recognition analysis result of the target object for the t-th problem related to the knowledge point, P (t)k0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point;
step S302, comparing the cognitive ability evaluation value RE with a preset cognitive ability evaluation threshold value, if the cognitive ability evaluation value RE is less than or equal to the preset cognitive ability evaluation threshold value, reducing the total learning duration of the knowledge course of the target object, otherwise, increasing the total learning duration of the knowledge course of the target object.
The beneficial effects of the above technical scheme are: the cognitive ability evaluation value of the target object is quantitatively determined through the formula (3), and a reliable and effective basis can be provided for the learning progress of knowledge data of the target object to be adjusted subsequently, so that the total learning duration of the adjusted knowledge course can be matched with the cognitive ability of the target object to the maximum extent.
Referring to fig. 2, a schematic structural diagram of a cognitive ability analysis system based on a knowledge graph according to an embodiment of the present invention is shown. The cognitive ability analysis system based on the knowledge graph comprises a historical knowledge learning record acquisition and processing module, a knowledge graph construction module, a cognitive analysis error determination and correction module, a cognitive ability evaluation module and a learning progress adjustment module; wherein,
the historical knowledge learning record acquisition and processing module is used for acquiring a historical knowledge learning record of a target object and carrying out preprocessing and knowledge point extraction processing on the historical knowledge learning record;
the knowledge graph construction module is used for constructing and forming a knowledge graph corresponding to the target object according to the extracted knowledge point data;
the cognitive analysis error determining and correcting module is used for determining a cognitive analysis error value of the target object according to the knowledge graph and correcting the knowledge graph according to the cognitive analysis error value so as to eliminate a knowledge point association error of the knowledge graph;
the cognitive ability evaluation module is used for re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph;
the learning progress adjusting module is used for adjusting the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value.
The beneficial effects of the above technical scheme are: the cognitive ability analysis system based on the knowledge graph can construct and form a corresponding knowledge graph according to historical knowledge learning records of different target objects, then determine cognitive analysis error conditions of the target objects according to the knowledge graph, and modify the knowledge graph according to the cognitive analysis error conditions, so that the situation that the knowledge graph is constructed and deviated due to data defects of the historical knowledge learning records can be effectively avoided, finally, the cognitive ability evaluation value of the target object is re-determined according to the modified knowledge graph, the target object is accurately and reliably evaluated, the learning progress of knowledge data of the target object is adjusted according to the cognitive ability evaluation value, and therefore the learning efficiency and the learning quality of the target object are improved to the maximum extent.
Preferably, the historical knowledge learning record obtaining and processing module obtains the historical knowledge learning record of the target object, and the preprocessing and knowledge point extracting processing on the historical knowledge learning record specifically includes:
acquiring curriculum knowledge data correspondingly browsed in the online learning process of the target object, comparing the actual browsing duration of each curriculum knowledge data with a preset browsing time threshold, and taking the corresponding curriculum knowledge data as the historical knowledge learning record if the actual browsing duration is greater than or equal to the preset browsing time threshold;
carrying out data deduplication preprocessing on all course knowledge data contained in the historical knowledge learning record, and extracting corresponding knowledge point keywords from each course knowledge data subjected to data deduplication preprocessing;
and the number of the first and second groups,
the method for constructing the knowledge graph corresponding to the target object by the knowledge graph construction module according to the extracted knowledge point data specifically comprises the following steps:
and constructing and forming a knowledge graph corresponding to the target object according to the semantic association degree between the extracted different knowledge point keywords.
The beneficial effects of the above technical scheme are: the course knowledge data browsed in the online learning process of the target object is compared and judged according to the actual browsing duration, so that the course knowledge data obtained by screening can be guaranteed to be the knowledge data really interested by the target object to the maximum extent, and the reliability of obtaining the historical knowledge learning record is guaranteed; in addition, the data deduplication preprocessing is carried out on the historical knowledge learning record, so that the data redundancy of the historical knowledge learning record can be reduced, and the data processing workload of the historical knowledge learning record is greatly reduced.
Preferably, the cognitive analysis error determination and correction module determines a cognitive analysis error value of the target object according to the knowledge graph, and corrects the knowledge graph according to the cognitive analysis error value, so as to eliminate the knowledge point association error of the knowledge graph specifically includes:
determining cognitive analysis results of the target object on different knowledge point related questions according to the knowledge map, wherein the different knowledge point related questions refer to objective questions or subjective questions related to different knowledge point contents, and the cognitive analysis results refer to actual answer answers of the target object to the objective questions or the subjective questions;
and determining the cognitive analysis error value W of the target object according to the cognitive analysis result of the target object on the problems related to different knowledge points and the following formula (1):
in the above formula (1), Pk(t) a quantitative result value, P, corresponding to the cognitive analysis result of the kth correlation problem of the t-th knowledge point by the target objectk0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point, and n represents the total number of related problems of the knowledge point;
and then correcting the quantitative result value corresponding to the cognitive analysis result of the knowledge point related problem according to the following formula (2) to obtain a corrected quantitative result value:
in the above formula (2), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of a target object for a t-th problem related to a knowledge point, W represents a cognitive analysis error value of the target object, and P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of the target object for a t-th problem related to a knowledge pointk0(t) represents a quantitative result value corresponding to a standard cognitive analysis result of the kth related problem of the t-th knowledge point according to the knowledge graph, and m represents the total number of the related problems of the knowledge points.
The beneficial effects of the above technical scheme are: the cognitive analysis error value of the target object and the result obtained by correcting the quantitative result value corresponding to the cognitive analysis result of the target object on the problem related to the knowledge point are respectively determined through the formulas (1) and (2), so that the cognitive analysis state of the target object can be quantitatively determined, and the targeted and controllable cognitive analysis result correction can be conveniently carried out on the target object.
Preferably, the determining, again by the cognitive ability evaluation module, the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph specifically includes:
the cognitive ability evaluation value RE of the target object is newly determined according to the following formula (3):
in the above formula (3), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a recognition analysis result of the target object for the t-th problem related to the knowledge point, P (t)k0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point;
and the number of the first and second groups,
the learning progress adjusting module adjusts the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value, and specifically includes:
and comparing the cognitive ability evaluation value RE with a preset cognitive ability evaluation threshold, if the cognitive ability evaluation value RE is less than or equal to the preset cognitive ability evaluation threshold, reducing the total learning time of the knowledge course of the target object, otherwise, increasing the total learning time of the knowledge course of the target object.
The beneficial effects of the above technical scheme are: the cognitive ability evaluation value of the target object is quantitatively determined through the formula (3), and a reliable and effective basis can be provided for the learning progress of knowledge data of the target object to be adjusted subsequently, so that the total learning duration of the adjusted knowledge course can be matched with the cognitive ability of the target object to the maximum extent.
As can be seen from the content of the above embodiment, the cognitive competence analysis method and system based on a knowledge graph construct and form a knowledge graph corresponding to a target object by obtaining a historical knowledge learning record of the target object, preprocessing and extracting knowledge points from the historical knowledge learning record, according to the extracted knowledge point data, determine a cognitive analysis error value of the target object according to the knowledge graph, correct the knowledge graph according to the cognitive analysis error value, so as to eliminate a knowledge point association error of the knowledge graph, according to the result of correcting the knowledge graph, re-determine a cognitive competence evaluation value of the target object, and adjust a learning progress of knowledge data of the target object according to the cognitive competence evaluation value; therefore, the cognitive ability analysis method and the system based on the knowledge graph can construct and form the corresponding knowledge graph according to the historical knowledge learning records of different target objects, determine the cognitive analysis error condition of the target object according to the knowledge graph, and modify the knowledge graph according to the cognitive analysis error condition, so that the situation that the knowledge graph construction deviation is caused by the data defect of the historical knowledge learning records can be effectively avoided, and finally, the cognitive ability evaluation value of the target object is re-determined according to the modified knowledge graph, so that the target object is accurately and reliably evaluated, and the learning progress of the knowledge data of the target object is adjusted according to the cognitive ability evaluation value, so that the learning efficiency and the learning quality of the target object are improved to the maximum extent.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. The cognitive ability analysis method based on the knowledge graph is characterized by comprising the following steps:
step S1, acquiring historical knowledge learning records of a target object, preprocessing the historical knowledge learning records and extracting knowledge points, and constructing and forming a knowledge graph corresponding to the target object according to the extracted knowledge point data;
step S2, determining a cognitive analysis error value of the target object according to the knowledge graph, and correcting the knowledge graph according to the cognitive analysis error value so as to eliminate a knowledge point association error of the knowledge graph;
step S3, re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph, and adjusting the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value.
2. A method of knowledge-graph-based cognitive ability analysis according to claim 1, wherein: in step S1, acquiring a historical knowledge learning record of a target object, preprocessing the historical knowledge learning record and extracting knowledge points, and constructing a knowledge graph corresponding to the target object according to the extracted knowledge point data specifically includes:
step S101, obtaining curriculum knowledge data correspondingly browsed in the online learning process of the target object, comparing the actual browsing duration of each curriculum knowledge data with a preset browsing time threshold, and taking the corresponding curriculum knowledge data as the historical knowledge learning record if the actual browsing duration is greater than or equal to the preset browsing time threshold;
step S102, carrying out data deduplication preprocessing on all course knowledge data contained in the historical knowledge learning record, and extracting corresponding knowledge point keywords from each course knowledge data subjected to data deduplication preprocessing;
and step S103, constructing and forming a knowledge graph corresponding to the target object according to the semantic association degree between the extracted different knowledge point keywords.
3. A method of knowledge-graph-based cognitive ability analysis according to claim 2, wherein: in step S2, determining a cognitive analysis error value of the target object according to the knowledge graph, and correcting the knowledge graph according to the cognitive analysis error value, so as to eliminate the knowledge point association error of the knowledge graph specifically includes:
step S201, determining the cognitive analysis result of the target object on the problems related to different knowledge points according to the knowledge graph;
step S202, determining a cognitive analysis error value W of the target object according to the cognitive analysis result of the target object on the problems related to different knowledge points and the following formula (1):
in the above formula (1), Pk(t) a quantitative result value, P, corresponding to the cognitive analysis result of the kth correlation problem of the t-th knowledge point by the target objectk0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point, and n represents the total number of related problems of the knowledge point;
step S203, according to the following formula (2), correcting the quantization result value corresponding to the cognitive analysis result of the knowledge point related problem, so as to obtain a corrected quantization result value:
in the above formula (2), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of a target object for a t-th problem related to a knowledge point, W represents a cognitive analysis error value of the target object, and P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of the target object for a t-th problem related to a knowledge pointk0(t) represents a quantitative result value corresponding to a standard cognitive analysis result of the kth related problem of the t-th knowledge point according to the knowledge graph, and m represents the total number of the related problems of the knowledge points.
4. A method of knowledge-graph-based cognitive ability analysis according to claim 3, wherein: in step S3, the step of re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph, and the adjusting the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value specifically includes:
step S301, RE-determining the cognitive ability evaluation value RE of the target object according to the following formula (3):
in the above formula (3), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a recognition analysis result of the target object for the t-th problem related to the knowledge point, P (t)k0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point;
step S302, comparing the cognitive ability evaluation value RE with a preset cognitive ability evaluation threshold value, if the cognitive ability evaluation value RE is smaller than or equal to the preset cognitive ability evaluation threshold value, reducing the total learning duration of the knowledge course of the target object, otherwise, increasing the total learning duration of the knowledge course of the target object.
5. The cognitive competence analysis system based on the knowledge graph is characterized by comprising a historical knowledge learning record acquisition and processing module, a knowledge graph construction module, a cognitive analysis error determination and correction module, a cognitive competence evaluation module and a learning progress adjustment module; wherein,
the historical knowledge learning record acquisition and processing module is used for acquiring a historical knowledge learning record of a target object, and preprocessing and knowledge point extraction processing are carried out on the historical knowledge learning record; the knowledge graph construction module is used for constructing and forming a knowledge graph corresponding to the target object according to the extracted knowledge point data;
the cognitive analysis error determining and correcting module is used for determining a cognitive analysis error value of the target object according to the knowledge graph and correcting the knowledge graph according to the cognitive analysis error value so as to eliminate a knowledge point association error of the knowledge graph;
the cognitive ability evaluation module is used for re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph;
the learning progress adjusting module is used for adjusting the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value.
6. The knowledge-graph-based cognitive ability analysis system of claim 5, wherein: the historical knowledge learning record obtaining and processing module obtains the historical knowledge learning record of the target object, and the preprocessing and knowledge point extraction processing on the historical knowledge learning record specifically comprises the following steps:
acquiring curriculum knowledge data correspondingly browsed in the online learning process of the target object, comparing the actual browsing duration of each curriculum knowledge data with a preset browsing time threshold, and taking the corresponding curriculum knowledge data as the historical knowledge learning record if the actual browsing duration is greater than or equal to the preset browsing time threshold;
carrying out data deduplication preprocessing on all course knowledge data contained in the historical knowledge learning record, and extracting corresponding knowledge point keywords from each item of course knowledge data subjected to data deduplication preprocessing;
and the number of the first and second groups,
the method for constructing the knowledge graph corresponding to the target object by the knowledge graph construction module according to the extracted knowledge point data specifically comprises the following steps:
and constructing and forming a knowledge graph corresponding to the target object according to the semantic association degree between the extracted different knowledge point keywords.
7. The knowledge-graph-based cognitive ability analysis system of claim 6, wherein: the cognitive analysis error determination and correction module determines a cognitive analysis error value of the target object according to the knowledge graph, and corrects the knowledge graph according to the cognitive analysis error value, so that eliminating the knowledge point association error of the knowledge graph specifically comprises:
determining the cognitive analysis result of the target object on the problems related to different knowledge points according to the knowledge graph;
and determining a cognitive analysis error value W of the target object according to the cognitive analysis result of the target object on the problems related to different knowledge points and the following formula (1):
in the above formula (1), Pk(t) a quantitative result value, P, corresponding to the cognitive analysis result of the kth correlation problem of the t-th knowledge point by the target objectk0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point, and n represents the total number of related problems of the knowledge point;
and then correcting the quantitative result value corresponding to the cognitive analysis result of the knowledge point related problem according to the following formula (2) to obtain a corrected quantitative result value:
in the above formula (2), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of a target object for a t-th problem related to a knowledge point, W represents a cognitive analysis error value of the target object, and P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a cognitive analysis result of the target object for a t-th problem related to a knowledge pointk0(t) represents a quantitative result value corresponding to a standard cognitive analysis result of the kth related problem of the t-th knowledge point according to the knowledge graph, and m represents the total number of the related problems of the knowledge points.
8. A knowledge-graph-based cognitive ability analysis system according to claim 7, wherein: the step of, by the cognitive ability evaluation module, re-determining the cognitive ability evaluation value of the target object according to the result of correcting the knowledge graph specifically includes:
RE-determining the cognitive ability evaluation value RE of the target object according to the following formula (3):
in the above formula (3), P (t) represents a quantization result value obtained by correcting a quantization result value corresponding to a recognition analysis result of the target object for the t-th problem related to the knowledge point, P (t)k0(t) representing a quantitative result value corresponding to a standard cognitive analysis result of a kth related problem of the t-th knowledge point according to the knowledge graph, wherein m represents the total number of related problems of the knowledge point;
and the number of the first and second groups,
the learning progress adjusting module adjusts the learning progress of the knowledge data of the target object according to the cognitive ability evaluation value, and specifically includes:
and comparing the cognitive ability evaluation value RE with a preset cognitive ability evaluation threshold value, if the cognitive ability evaluation value RE is smaller than or equal to the preset cognitive ability evaluation threshold value, reducing the total learning time of the knowledge course of the target object, otherwise, increasing the total learning time of the knowledge course of the target object.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112786202A (en) * | 2021-01-27 | 2021-05-11 | 安徽华米健康科技有限公司 | Health awareness parameter detection method and device, electronic equipment and storage medium |
| CN113344204A (en) * | 2021-06-10 | 2021-09-03 | 合肥工业大学 | Cognitive data processing method and device for multiple logic problems |
| CN113468077A (en) * | 2021-09-06 | 2021-10-01 | 北京无疆脑智科技有限公司 | Cognitive ability testing method and device, electronic equipment and storage medium |
| CN113515641A (en) * | 2021-04-30 | 2021-10-19 | 浙江工业大学 | Method and system for constructing evaluator cognitive structure map by utilizing comments |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112786202A (en) * | 2021-01-27 | 2021-05-11 | 安徽华米健康科技有限公司 | Health awareness parameter detection method and device, electronic equipment and storage medium |
| CN113515641A (en) * | 2021-04-30 | 2021-10-19 | 浙江工业大学 | Method and system for constructing evaluator cognitive structure map by utilizing comments |
| CN113344204A (en) * | 2021-06-10 | 2021-09-03 | 合肥工业大学 | Cognitive data processing method and device for multiple logic problems |
| CN113344204B (en) * | 2021-06-10 | 2022-11-18 | 合肥工业大学 | Cognitive data processing method and device for multiple logic problems |
| CN113468077A (en) * | 2021-09-06 | 2021-10-01 | 北京无疆脑智科技有限公司 | Cognitive ability testing method and device, electronic equipment and storage medium |
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