CN114971384A - Artificial intelligence machine learning experiment skill scoring method and system - Google Patents
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Abstract
The invention discloses a method and a system for scoring an artificial intelligence machine learning experiment skill, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring and constructing a basic management framework according to the information of the experimenters and the information of the experimental environment; constructing a target experiment skill evaluation model; acquiring and importing experimental data of corresponding target personnel into a target experimental skill evaluation model based on a basic management framework according to basic evaluation indexes, and generating evaluation information of each target personnel; matching the experimental subject in the experimental data of the target person with a preset subject scoring standard database to obtain scoring standard data of the experimental subject corresponding to the target person; and generating the scoring result of the experimental skill of the corresponding target person according to the evaluation information of each target person and the scoring standard data of the experimental subject of the corresponding target person. The invention can carry out high-efficiency, objective and accurate evaluation on the experimental skills of students, and improves the evaluation precision and the evaluation efficiency.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a system for scoring an experimental skill of artificial intelligence machine learning.
Background
The experiment is an important teaching means for checking the mastery level of the students on the theoretical knowledge, and is helpful for the students and teachers to know the corresponding experimental abilities. The existing experimental skill scoring method generally performs on-site scoring on experimental operation of students through a single teacher, and due to the fact that the students and the teachers are not matched in quantity and the complex conditions of the experimental process result in the fact that the workload of the scoring teachers is large, objective and efficient evaluation can not be performed on the experimental skills of each student carefully and comprehensively, and then the students and the teachers can not master the experimental skill capability conditions of the corresponding students accurately.
Disclosure of Invention
In order to overcome the above problems or at least partially solve the above problems, embodiments of the present invention provide a method and a system for scoring an experimental skill in artificial intelligence machine learning, which can efficiently, objectively and accurately evaluate an experimental skill of a student, and improve evaluation accuracy and evaluation efficiency.
The embodiment of the invention is realized by the following steps:
in a first aspect, an embodiment of the present invention provides an artificial intelligence machine learning experiment skill scoring method, including the following steps:
acquiring and constructing a basic management framework according to the information of the experimenters and the information of the experimental environment;
acquiring and training a preset initial evaluation model according to historical experimental skill scoring data and preset basic evaluation indexes to construct a target experimental skill evaluation model;
acquiring and importing experimental data of corresponding target personnel into a target experimental skill evaluation model based on a basic management framework according to basic evaluation indexes, and generating evaluation information of each target personnel;
matching the experimental subject in the experimental data of the target person with a preset subject scoring standard database to obtain scoring standard data of the experimental subject corresponding to the target person;
and generating the scoring result of the experimental skill of the corresponding target person according to the evaluation information of each target person and the scoring standard data of the experimental subject of the corresponding target person.
In order to solve the technical problem that the experimental skill of each student cannot be evaluated objectively and efficiently in the prior art, and further, the students and teachers cannot accurately master the experimental skill capability condition of the corresponding student, a targeted management frame is firstly constructed from the aspects of personnel, experimental environment and the like, and accurate support is provided for the acquisition or management of subsequent data; then, model training is carried out by combining comprehensive evaluation indexes and historical grading data to construct a comprehensive and accurate experimental skill evaluation model so as to carry out rapid and accurate evaluation subsequently; and then comprehensively and accurately evaluating the experimental skills of the target personnel by combining different reference scoring standards of different experimental subjects. According to the invention, the experimental skills of the personnel are accurately evaluated based on the artificial intelligence machine learning, so that the labor cost is greatly reduced, and the evaluation precision and the evaluation efficiency are also ensured.
Based on the first aspect, in some embodiments of the present invention, the method for obtaining and constructing a basic management framework according to the experimenter information and the experimental environment information includes the following steps:
acquiring experimenter information and experimental environment information;
extracting and constructing an initial personnel management frame according to personnel identity, personnel characteristics and personnel quantity in the experimental personnel information;
and extracting and importing the experimental subjects and the experimental scenes in the experimental environment information into each node in the initial personnel management frame to construct a basic management frame.
Based on the first aspect, in some embodiments of the present invention, the basic evaluation index includes an experimental theoretical evaluation index, an experimental observation evaluation index, and an experimental operation evaluation index.
Based on the first aspect, in some embodiments of the present invention, the method for obtaining and importing the experiment data of the corresponding target person into the target experiment skill evaluation model based on the basic management framework according to the basic evaluation index includes the following steps:
acquiring experimental theory learning data, experimental observation data and experimental operation data of corresponding target personnel based on each personnel management node in the basic management frame according to the experimental theory evaluation index, the experimental observation evaluation index and the experimental operation evaluation index;
and respectively importing the experimental theoretical learning data, the experimental observation data and the experimental operation data of each target person into a target experimental skill evaluation model to generate theoretical evaluation information, observation evaluation information and operation evaluation information of each target person.
Based on the first aspect, in some embodiments of the present invention, the method for scoring an experimental skill of artificial intelligence machine learning further includes the following steps:
acquiring and carrying out updating training on the target experiment skill evaluation model according to historical experiment class-hour data to obtain a target experiment skill evaluation optimization model;
acquiring and importing experimental class time data of corresponding target personnel into a target experimental skill evaluation optimization model based on each personnel management node in a basic management frame, and generating class time learning evaluation information of the corresponding target personnel;
and optimizing and adjusting the experimental skill scoring result according to the class hour learning evaluation information of the corresponding target person to generate a target experimental skill scoring result.
Based on the first aspect, in some embodiments of the present invention, the method for scoring an experimental skill of artificial intelligence machine learning further includes the following steps:
and optimizing and adjusting the experimental skill scoring result based on the student experimental skill scoring data of the scoring teachers corresponding to the target persons, and generating the optimal experimental skill scoring result.
Based on the first aspect, in some embodiments of the present invention, the method for scoring an experimental skill of artificial intelligence machine learning further includes the following steps:
and counting and generating a scoring statistical report form according to the experimental skill scoring result of each target person according to a preset arrangement template based on the basic management framework.
In a second aspect, an embodiment of the present invention provides an artificial intelligence machine learning experiment skill scoring system, including a framework building module, a model building module, an evaluation module, a standard matching module, and a skill scoring module, where:
the framework construction module is used for acquiring and constructing a basic management framework according to the information of the experimenters and the information of the experimental environment;
the model establishing module is used for acquiring and training a preset initial evaluation model according to historical experimental skill scoring data and preset basic evaluation indexes so as to establish a target experimental skill evaluation model;
the evaluation module is used for acquiring and importing the experimental data of the corresponding target personnel into a target experimental skill evaluation model based on a basic management frame according to the basic evaluation index, and generating evaluation information of each target personnel;
the standard matching module is used for matching the experimental subjects in the experimental data of the target personnel with a preset subject scoring standard database to obtain scoring standard data corresponding to the experimental subjects of the target personnel;
and the skill scoring module is used for generating an experimental skill scoring result of the corresponding target personnel according to the evaluation information of each target personnel and the scoring standard data of the experimental subject of the corresponding target personnel.
In order to solve the technical problem that in the prior art, the experimental skills of each student cannot be evaluated objectively and comprehensively, and therefore, both the student and the teacher cannot master the experimental skill capability condition of the corresponding student accurately, the system firstly constructs a targeted management frame from the aspects of personnel, experimental environment and the like through the cooperation of a plurality of modules such as a frame construction module, a model establishment module, an evaluation module, a standard matching module, a skill scoring module and the like, and provides accurate support for the acquisition or management of subsequent data; then, model training is carried out by combining comprehensive evaluation indexes and historical grading data to construct a comprehensive and accurate experimental skill evaluation model so as to carry out rapid and accurate evaluation subsequently; and then, the experimental skills of the target personnel are comprehensively and accurately evaluated by combining different reference scoring standards of different experimental subjects. According to the invention, the experimental skills of the personnel are accurately evaluated based on the artificial intelligence machine learning, so that the labor cost is greatly reduced, and the evaluation precision and the evaluation efficiency are also ensured.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory for storing one or more programs; a processor. The program or programs, when executed by a processor, implement the method of any of the first aspects as described above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method according to any one of the first aspect described above.
The embodiment of the invention at least has the following advantages or beneficial effects:
the embodiment of the invention provides a method and a system for scoring experimental skills in artificial intelligence machine learning, which solve the technical problem that in the prior art, the experimental skills of each student cannot be objectively and comprehensively evaluated in a meticulous manner, so that the students and teachers cannot accurately master the experimental skill capability condition of the corresponding student; then model training is carried out by combining comprehensive evaluation indexes and historical scoring data to construct a comprehensive and accurate experimental skill evaluation model for subsequent quick and accurate evaluation; and then, the experimental skills of the target personnel are comprehensively and accurately evaluated by combining different reference scoring standards of different experimental subjects. According to the invention, the experimental skills of the personnel are accurately evaluated based on the artificial intelligence machine learning, so that the labor cost is greatly reduced, and the evaluation precision and the evaluation efficiency are also ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flowchart of a skill scoring method for an artificial intelligence machine learning experiment according to an embodiment of the present invention;
FIG. 2 is a flowchart of the construction of a basic management architecture in the skill scoring method for an artificial intelligence machine learning experiment according to the embodiment of the present invention;
FIG. 3 is a flowchart of optimizing a scoring result in the skill scoring method for the artificial intelligence machine learning experiment in the embodiment of the present invention;
FIG. 4 is a schematic block diagram of an artificial intelligence machine learning experiment skill scoring system according to an embodiment of the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Description of reference numerals: 100. a framework building module; 200. a model building module; 300. an evaluation module; 400. a standard matching module; 500. a skill scoring module; 101. a memory; 102. a processor; 103. a communication interface.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the 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.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
Example (b):
as shown in fig. 1-2, in a first aspect, an embodiment of the present invention provides an artificial intelligence machine learning experiment skill scoring method, including the following steps:
s1, acquiring and constructing a basic management framework according to the information of the experimenters and the information of the experimental environment;
further, the step of building the basic management framework includes:
s11, acquiring experimenter information and experimental environment information;
s12, extracting and constructing an initial personnel management framework according to personnel identity, personnel characteristics and personnel quantity in the experimenter information;
and S13, extracting and importing the experiment subjects and the experiment scenes in the experiment environment information into each node in the initial personnel management framework to construct a basic management framework.
In some embodiments of the present invention, in order to improve the management effect on subsequent experimenters, targeted management is performed, and a basic management framework is constructed by combining data in various aspects, such as personnel identities, personnel characteristics, and personnel numbers of corresponding experimenters information, and experimental subjects and experimental scenes in experimental environment information, where the basic management framework includes a plurality of personnel management nodes and characteristic data of corresponding nodes, and the characteristic data includes personnel identifiers, experimental subject identifiers, and the like.
S2, acquiring and training a preset initial evaluation model according to the historical experimental skill scoring data and preset basic evaluation indexes to construct a target experimental skill evaluation model; the basic evaluation indexes comprise experiment theory evaluation indexes, experiment observation evaluation indexes and experiment operation evaluation indexes. The target experimental skill evaluation model is a mathematical model which is used for learning and training based on historical data and can evaluate the experimental ability of personnel based on experimental data.
S3, acquiring and importing the experiment data of the corresponding target personnel into a target experiment skill evaluation model based on a basic management framework according to the basic evaluation index, and generating evaluation information of each target personnel;
further, acquiring experimental theory learning data, experimental observation data and experimental operation data of corresponding target personnel based on each personnel management node in the basic management framework according to the experimental theory evaluation index, the experimental observation evaluation index and the experimental operation evaluation index; and respectively importing the experimental theoretical learning data, the experimental observation data and the experimental operation data of each target person into a target experimental skill evaluation model to generate theoretical evaluation information, observation evaluation information and operation evaluation information of each target person.
In some embodiments of the invention, the experimental ability of the target personnel is comprehensively evaluated from multiple levels of evaluation indexes such as theory, observation, operation and the like through the target experimental skill evaluation model, so that the comprehensiveness of evaluation is greatly improved, meanwhile, the subjectivity of artificial evaluation is avoided through evaluation through the target experimental skill evaluation model, the objectivity of evaluation is greatly improved, and the accuracy of an evaluation result is further ensured.
S4, matching the experimental subject in the experimental data of the target person with a preset subject scoring standard database to obtain scoring standard data of the experimental subject corresponding to the target person; the scoring standard data comprises scoring contents, scoring subject categories, assessment scores corresponding to the scoring contents and the like.
And S5, generating the scoring result of the experimental skill of the corresponding target person according to the evaluation information of each target person and the scoring standard data of the experimental subject of the corresponding target person. And grading the final experimental skills of the target personnel according to the evaluation information of multiple layers corresponding to each experimental subject in the evaluation information and the corresponding grading standard, so that the comprehensiveness of grading is greatly improved. The above-mentioned experimental skill scoring results include experimenters, experimental subjects, scoring contents, scores, etc.
In order to solve the technical problem that the experimental skill of each student cannot be evaluated objectively and efficiently in the prior art, and further, the students and teachers cannot accurately master the experimental skill capability condition of the corresponding student, a targeted management frame is firstly constructed from the aspects of personnel, experimental environment and the like, and accurate support is provided for the acquisition or management of subsequent data; then, model training is carried out by combining comprehensive evaluation indexes and historical grading data to construct a comprehensive and accurate experimental skill evaluation model so as to carry out rapid and accurate evaluation subsequently; and then, the experimental skills of the target personnel are comprehensively and accurately evaluated by combining different reference scoring standards of different experimental subjects. According to the invention, the experimental skills of the personnel are accurately evaluated based on the artificial intelligence machine learning, so that the labor cost is greatly reduced, and the evaluation precision and the evaluation efficiency are also ensured.
As shown in fig. 3, according to the first aspect, in some embodiments of the present invention, the method for scoring an experimental skill in an artificial intelligence machine learning further includes the following steps:
a1, obtaining and carrying out updating training on the target experiment skill evaluation model according to historical experiment class-hour data to obtain a target experiment skill evaluation optimization model; the historical experimental class time data comprises experimental subjects and corresponding class time arrangement data.
A2, acquiring and importing experimental class time data of corresponding target persons into a target experimental skill evaluation optimization model based on each person management node in a basic management frame, and generating class time learning evaluation information of the corresponding target persons;
and A3, optimizing and adjusting the experimental skill scoring result according to the class time learning evaluation information of the corresponding target person to generate a target experimental skill scoring result.
In order to ensure that the experimental skills of the experimenters are evaluated more reasonably and comprehensively, class-time evaluation indexes are added, the target experimental skill evaluation model is trained by combining historical data to obtain a more comprehensive target experimental skill evaluation optimization model, and then the target experimental skill evaluation optimization model is combined with class-time data of corresponding personnel to be analyzed based on the target experimental skill evaluation optimization model to obtain a more accurate and more comprehensive scoring result.
Based on the first aspect, in some embodiments of the present invention, the method for scoring an experimental skill of artificial intelligence machine learning further includes the following steps:
and acquiring and optimizing the experimental skill scoring result according to the student experimental skill scoring data of the scoring teachers corresponding to the target personnel based on the basic management framework, and generating the experimental skill scoring optimization result.
In order to further improve the reasonability and comprehensiveness of the scoring of the experimental skills of the personnel, the scoring result of the experimental skills is optimized and adjusted by combining the corresponding artificial scoring data of the scoring teacher, and a more comprehensive scoring optimization result is obtained.
Based on the first aspect, in some embodiments of the present invention, the method for scoring an experimental skill of artificial intelligence machine learning further includes the following steps:
and counting and generating a scoring statistical report form according to the experimental skill scoring result of each target person according to a preset arrangement template based on the basic management framework.
In order to further improve the management efficiency of the experimenters, the evaluation results of the experimental skills of the target personnel are counted based on the personnel management nodes in the basic management framework to obtain the statistical results, and in order to further facilitate checking and improve the management efficiency, the statistical results are typeset according to the preset arrangement template to further obtain a clear and comprehensive evaluation statistical report.
As shown in fig. 4, in a second aspect, an embodiment of the present invention provides an artificial intelligence machine learning experiment skill scoring system, which includes a framework building module 100, a model building module 200, an evaluation module 300, a standard matching module 400, and a skill scoring module 500, where:
the framework construction module 100 is used for acquiring and constructing a basic management framework according to the information of the experimenters and the information of the experimental environment;
the model establishing module 200 is used for acquiring and training a preset initial evaluation model according to historical experimental skill scoring data and preset basic evaluation indexes so as to establish a target experimental skill evaluation model;
the evaluation module 300 is used for acquiring and importing the experimental data of the corresponding target personnel into a target experimental skill evaluation model based on a basic management framework according to the basic evaluation index, and generating evaluation information of each target personnel;
the standard matching module 400 is configured to match an experimental subject in the experimental data of the target person with a preset subject scoring standard database to obtain scoring standard data of the experimental subject corresponding to the target person;
and the skill scoring module 500 is configured to generate an experiment skill scoring result of the corresponding target person according to the evaluation information of each target person and scoring standard data of the experiment subject of the corresponding target person.
In order to solve the technical problem that the experimental skill of each student cannot be evaluated objectively and efficiently in detail and comprehensively in the prior art, and further, the student and the teacher cannot accurately master the experimental skill capability condition of the corresponding student, the system firstly constructs a targeted management frame from the aspects of personnel, experimental environment and the like through the cooperation of a plurality of modules such as a frame construction module 100, a model establishment module 200, an evaluation module 300, a standard matching module 400, a skill scoring module 500 and the like, and provides accurate support for the acquisition or management of subsequent data; then, model training is carried out by combining comprehensive evaluation indexes and historical grading data to construct a comprehensive and accurate experimental skill evaluation model so as to carry out rapid and accurate evaluation subsequently; and then, the experimental skills of the target personnel are comprehensively and accurately evaluated by combining different reference scoring standards of different experimental subjects. According to the invention, the experimental skills of the personnel are accurately evaluated based on the artificial intelligence machine learning, so that the labor cost is greatly reduced, and the evaluation precision and the evaluation efficiency are also ensured.
As shown in fig. 5, in a third aspect, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. The one or more programs, when executed by the processor 102, implement the method of any of the first aspects as described above.
Also included is a communication interface 103, and the memory 101, processor 102 and communication interface 103 are electrically connected to each other, directly or indirectly, to enable transfer or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules, and the processor 102 executes the software programs and modules stored in the memory 101 to thereby execute various functional applications and data processing. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also 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.
In the embodiments provided in the present application, it should be understood that the disclosed method and system and method can be implemented in other ways. The method and system embodiments described above are merely illustrative, for example, the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by the processor 102, implements the method according to any one of the first aspect described above. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. An artificial intelligence machine learning experiment skill scoring method is characterized by comprising the following steps:
acquiring and constructing a basic management framework according to the information of the experimenters and the information of the experimental environment;
acquiring and training a preset initial evaluation model according to historical experimental skill scoring data and preset basic evaluation indexes to construct a target experimental skill evaluation model;
acquiring and importing experimental data of corresponding target personnel into a target experimental skill evaluation model based on a basic management framework according to basic evaluation indexes, and generating evaluation information of each target personnel;
matching the experimental subject in the experimental data of the target person with a preset subject scoring standard database to obtain scoring standard data of the experimental subject corresponding to the target person;
and generating the scoring result of the experimental skill of the corresponding target person according to the evaluation information of each target person and the scoring standard data of the experimental subject of the corresponding target person.
2. The method for scoring the skill of the artificial intelligence machine learning experiment, according to the claim 1, wherein the method for acquiring and constructing the basic management framework according to the information of the experimenter and the information of the experiment environment comprises the following steps:
acquiring information of experimenters and information of experimental environments;
extracting and constructing an initial personnel management frame according to personnel identity, personnel characteristics and personnel quantity in the experimental personnel information;
and extracting and importing the experimental subjects and the experimental scenes in the experimental environment information into each node in the initial personnel management frame to construct a basic management frame.
3. The method of claim 1, wherein the basic evaluation index comprises an experimental theoretical evaluation index, an experimental observation evaluation index and an experimental operation evaluation index.
4. The method for scoring the experimental skills of the artificial intelligence machine learning according to claim 3, wherein the method for obtaining and importing the experimental data of the corresponding target personnel into the target experimental skill evaluation model based on the basic management framework according to the basic evaluation indexes comprises the following steps:
acquiring experimental theory learning data, experimental observation data and experimental operation data of corresponding target personnel based on each personnel management node in the basic management frame according to the experimental theory evaluation index, the experimental observation evaluation index and the experimental operation evaluation index;
and respectively importing the experimental theoretical learning data, the experimental observation data and the experimental operation data of each target person into a target experimental skill evaluation model to generate theoretical evaluation information, observation evaluation information and operation evaluation information of each target person.
5. The method of claim 1, further comprising the steps of:
acquiring and carrying out updating training on the target experiment skill evaluation model according to historical experiment class-hour data to obtain a target experiment skill evaluation optimization model;
acquiring and importing experimental class time data of corresponding target personnel into a target experimental skill evaluation optimization model based on each personnel management node in a basic management frame, and generating class time learning evaluation information of the corresponding target personnel;
and optimizing and adjusting the experimental skill scoring result according to the class time learning evaluation information of the corresponding target person to generate a target experimental skill scoring result.
6. The method of claim 1, further comprising the steps of:
and acquiring and optimizing the experimental skill scoring result according to the student experimental skill scoring data of the scoring teachers corresponding to the target personnel based on the basic management framework, and generating the experimental skill scoring optimization result.
7. The method of claim 1, further comprising the steps of:
and counting and generating a scoring statistical report form according to the experimental skill scoring result of each target person according to a preset arrangement template based on the basic management framework.
8. The utility model provides an artificial intelligence machine learning experiment skill score system which characterized in that, includes frame construction module, model building module, evaluation module, standard matching module and skill score module, wherein:
the framework construction module is used for acquiring and constructing a basic management framework according to the information of the experimenters and the information of the experimental environment;
the model building module is used for acquiring and training a preset initial evaluation model according to historical experimental skill scoring data and preset basic evaluation indexes so as to build a target experimental skill evaluation model;
the evaluation module is used for acquiring and importing the experimental data of the corresponding target personnel into a target experimental skill evaluation model based on a basic management frame according to the basic evaluation indexes to generate evaluation information of each target personnel;
the standard matching module is used for matching the experimental subjects in the experimental data of the target personnel with a preset subject scoring standard database to obtain scoring standard data corresponding to the experimental subjects of the target personnel;
and the skill scoring module is used for generating an experimental skill scoring result of the corresponding target personnel according to the evaluation information of each target personnel and the scoring standard data of the experimental subject of the corresponding target personnel.
9. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the one or more programs, when executed by the processor, implement the 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 method according to any one of claims 1-7.
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| CN116664001A (en) * | 2023-06-13 | 2023-08-29 | 国信蓝桥教育科技股份有限公司 | Student skill assessment method and system based on artificial intelligence |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116664001A (en) * | 2023-06-13 | 2023-08-29 | 国信蓝桥教育科技股份有限公司 | Student skill assessment method and system based on artificial intelligence |
| CN116664001B (en) * | 2023-06-13 | 2024-02-09 | 国信蓝桥教育科技股份有限公司 | Student skill assessment method and system based on artificial intelligence |
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