CN117540034A - Test question model generation method and device and computer equipment - Google Patents
Test question model generation method and device and computer equipment Download PDFInfo
- Publication number
- CN117540034A CN117540034A CN202311642154.6A CN202311642154A CN117540034A CN 117540034 A CN117540034 A CN 117540034A CN 202311642154 A CN202311642154 A CN 202311642154A CN 117540034 A CN117540034 A CN 117540034A
- Authority
- CN
- China
- Prior art keywords
- test question
- test
- target
- feature information
- questions
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 442
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 40
- 238000005457 optimization Methods 0.000 claims abstract description 36
- 238000004590 computer program Methods 0.000 claims description 36
- 230000004044 response Effects 0.000 claims description 4
- 230000003068 static effect Effects 0.000 description 9
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 238000013500 data storage Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
- G06Q50/2057—Career enhancement or continuing education service
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Educational Technology (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Educational Administration (AREA)
- Human Resources & Organizations (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- General Business, Economics & Management (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Computational Linguistics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application relates to a test question model generation method, a test question model generation device, a test question model generation computer device, a test question model generation program and a test question model generation program. The method comprises the following steps: acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set; performing iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm to obtain target test question feature information; constructing a target knowledge graph according to the characteristic information of the target test questions; responding to a test question generation request, and selecting test questions in the test question set according to the target knowledge graph so as to generate a test question model. By adopting the method, the examination questions can be flexibly generated so as to meet different examination scenes.
Description
Technical Field
The present application relates to the field of artificial intelligence, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for generating a test question model.
Background
With the continuous development of the power system, at the present stage, related researches on a distributed photovoltaic cluster refined modeling method and simulation model suitable for large power grid stability analysis and a power grid multi-scale coordinated control system architecture and operation control key strategy under a high-permeability distributed photovoltaic access scene are developed at home and abroad. However, no research on training simulation systems in the environment of a novel power system is currently being conducted.
At present, a dispatcher culture mode is also a traditional old zone new mode, the dispatcher culture system is not enough, the training and the assessment lack the support of a professional information system, the training does not have a high-quality interaction environment, the operation level of a related system can only depend on subjective assessment, and objective and quantitative assessment means are lacked.
In the related art, in the examination of related personnel, a preset test question is generally adopted, and the preset test question cannot meet different examination scenes.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a test question model generating method, apparatus, computer device, computer readable storage medium, and computer program product capable of satisfying different examination scenes.
In a first aspect, the present application provides a method for generating a test question model, including:
acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set;
performing iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm to obtain target test question feature information;
constructing a target knowledge graph according to the characteristic information of the target test questions;
responding to a test question generation request, and selecting test questions in the test question set according to the target knowledge graph so as to generate a test question model.
In one embodiment, the current test question feature information includes a test question keyword and current test question relation data; the step of performing iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm to obtain target test question feature information comprises the following steps:
adopting an alternate recommendation algorithm, and carrying out iterative optimization on the test question feature information according to the current test question relation data so as to obtain target test question relation data;
and generating target test question feature information according to the test question keywords and the target test question relation data.
In one embodiment, the constructing the target knowledge graph according to the target test question feature information includes:
generating a basic knowledge graph according to the test question keywords;
and updating the basic knowledge graph according to the characteristic information of the target test questions to obtain a target knowledge graph.
In one embodiment, the responding to the test question generation request, selecting the test questions in the test question set according to the target knowledge graph, and generating the test question model includes:
responding to a test question generation request, and selecting test questions in the test question set according to the target knowledge graph to obtain simulated test question data;
checking the simulated test question data according to the test question keywords;
and generating a test question model according to the simulated test question data under the condition that the test passes.
In one embodiment, the test question sets are obtained step by step according to the operation steps of the distributed energy system.
In one embodiment, the method is used for operational assessment of a nonlinear system.
In a second aspect, the present application further provides a test question model generating device, including:
the information acquisition module is used for acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set;
the iterative optimization module is used for carrying out iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm so as to obtain target test question feature information;
the map generation module is used for constructing a target knowledge map according to the target test question characteristic information;
and the test question generation module is used for responding to a test question generation request, selecting test questions in the test question set according to the target knowledge graph so as to generate a test question model.
In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set;
performing iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm to obtain target test question feature information;
constructing a target knowledge graph according to the characteristic information of the target test questions;
responding to a test question generation request, and selecting test questions in the test question set according to the target knowledge graph so as to generate a test question model.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set;
performing iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm to obtain target test question feature information;
constructing a target knowledge graph according to the characteristic information of the target test questions;
responding to a test question generation request, and selecting test questions in the test question set according to the target knowledge graph so as to generate a test question model.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set;
performing iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm to obtain target test question feature information;
constructing a target knowledge graph according to the characteristic information of the target test questions;
responding to a test question generation request, and selecting test questions in the test question set according to the target knowledge graph so as to generate a test question model.
According to the test question model generation method, the device, the computer equipment, the storage medium and the computer program product, the connection between the current existing test question main body and different test questions in the test question set is obtained by acquiring the test question set and the current test question characteristic information, the current test question characteristic information is subjected to iterative optimization by adopting an alternative recommendation algorithm to obtain target test question characteristic information, and then a target knowledge graph is constructed according to the target test question characteristic information. And responding to the test question generation request, and selecting the test questions in the test question set according to the target knowledge graph so as to generate a test question model. The alternate recommendation algorithm is a universal end-to-end depth recommendation framework, and aims to assist in recommending tasks by utilizing knowledge graph embedding, so that examination questions can be flexibly generated to meet different examination scenes.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is an application environment diagram of a test question model generation method in one embodiment;
FIG. 2 is a flow chart of a method for generating a test question model according to an embodiment;
FIG. 3 is a flowchart illustrating steps S204 to S206 in the test question model generating method according to one embodiment;
FIG. 4 is a block diagram of a test question model generating device according to an embodiment;
FIG. 5 is an internal block diagram of a computer device in one embodiment;
fig. 6 is an internal structural view of a computer device in another embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The test question model generation method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The data storage system is used for storing the test question set, the current test question characteristic information, cache data in the iterative optimization process and the like. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In an exemplary embodiment, as shown in fig. 2, a test question model generating method is provided, and an example of application of the method to the server 104 in fig. 1 is described, which includes the following steps S202 to S208. Wherein:
step S202, acquiring a test question set and current test question feature information.
Wherein the current test question characteristic information is used for representing the relation between different test questions in the test question set,
illustratively, the method may be used for operational assessment of a nonlinear system. The server 104 can firstly establish a power system model based on a Hammerstein model, and the established power system model is formed by connecting static nonlinear links and dynamic linear links in series, so that the dynamic characteristics of the nonlinear system can be approximated to a high degree, the dynamic links and the static links can be modeled separately, and the method has the advantages of small simulation calculation amount and simplicity in nonlinear control design. And the server 104 executes the method for establishing a test question model formed based on the operation points of the dispatcher, wherein the operation points of the dispatcher are the operation points of the dispatcher in the power system scene.
Wherein the Hammerstein model is a mathematical model describing a nonlinear system. It consists of two parts connected in series: a static nonlinear section and a dynamic linear section. The two parts together form an integral model of the nonlinear system. The static nonlinear section describes the nonlinear relationship between the input and output, typically represented by a static nonlinear function. The output of this function is input dependent but is not affected by the dynamics of the system. The dynamic linear part describes the dynamic behavior of the system, i.e. the linear relationship between input and output. It is usually expressed by differential equations or differential equations, which contain the dynamics of the system. This section is typically used to describe the delay, inertia, and dynamic response of the system.
Illustratively, the step of acquiring the test question set by the server 104 may be acquired step by step according to the operation steps of the distributed energy system, and the operation bit corresponding to each test question may be recorded when acquiring the test question set.
And S204, performing iterative optimization on the characteristic information of the current test question by adopting an alternative recommendation algorithm to obtain the characteristic information of the target test question.
Among them, the alternate recommendation algorithm is a technique for recommendation systems, mainly for optimizing the representation of users and items to improve the accuracy of recommendations. Such algorithms optimize the representation of the user and the item by alternating iterations to better capture the potential relationship between them. These algorithms typically fall into the category of collaborative filtering, where representations of users and items are learned by mining user behavior data. The alternate recommendation algorithm may be, for example, the MKR algorithm (Multi-task Learning for KG enhanced Recommendation). The MKR algorithm is a recommendation algorithm based on a knowledge graph, which uses information in the knowledge graph to enhance the performance of the recommendation system. MKR focuses mainly on two tasks: an Automatic generation Task (Automatic Task) and an Automatic scoring Task (Rating Prediction Task). The two tasks are independent of each other but are related to each other by entity relationships in the knowledge graph.
For example, the server 104 may establish a test question library including a test question keyword index without establishing the test question library including the test question keyword index, so as to count current test question feature information; under the condition that a test question library containing test question keyword indexes is established, recording the existing test question information into the test question library containing the test question keyword indexes so as to count the current test question characteristic information. And then, carrying out iterative optimization on the characteristic information of the current test question by adopting an alternative recommendation algorithm so as to obtain the characteristic information of the target test question.
And S206, constructing a target knowledge graph according to the characteristic information of the target test questions.
In the MKR algorithm, a Knowledge Graph (knowledgegraph) is a Graph structure, which is used to represent relationships and attributes between entities. In a recommendation system, a knowledge graph generally includes information about entities (such as users and articles) related to a recommendation task, relationships (such as preference relationships of users to articles), attributes (such as characteristics of articles), and the like. Specifically, the knowledge graph in MKR algorithm includes elements such as entity, relationship, attribute, triplet, etc.: entities (Entities) are Entities that include information related to recommended tasks, such as users and items. Each entity has a unique identifier. Relationships (relationships) are relationships describing associations between entities, which may be user-to-item favorites, item attributes, and the like. Relationships are typically represented by directed edges, connecting two entities. Attributes (Attributes) are characteristics or attribute information describing an entity or relationship. In a recommendation system, attributes of items may include categories, tags, scores, etc., while attributes of users may include historical behavior, preferences, etc. In addition, the information in the knowledge-graph is stored in the form of triples, i.e. (entity 1, relationship, entity 2). This representation helps organize and retrieve information in the knowledge-graph. Wherein, the triples can include the test question content, the test question attribute category and the relation between different test questions.
In MKR, the purpose of the knowledge graph is to provide enhanced information on recommended tasks by learning relationships between entities. Entity relationships in the knowledge-graph can be used to generate task-specific representations, thereby improving performance of the recommendation system. The MKR algorithm enables the information of the task-specific representation and the knowledge graph to mutually promote by alternately optimizing the recommending task and the automatically generating task in the knowledge graph so as to promote the recommending effect.
Step S208, responding to the test question generation request, selecting the test questions in the test question set according to the target knowledge graph so as to generate a test question model.
The test question generation request is request instruction information sent by the terminal 102 and used for instructing the server 104 to generate the simulation test questions. For example, the question generation request may be issued by the terminal 102 according to an input instruction from the receiving assessment person.
For example, the server 104 may analyze the test question information carried in the test question generation request according to the test question generation request sent by the terminal 102, analyze the corresponding portion in the target knowledge graph according to the test question information, thereby selecting the test questions in the test question set, and combine and package the selected test questions according to the test question sequence to form the test question model.
According to the test question model generation method, the relation between the current existing test question main body and different test questions in the test question set is obtained by acquiring the test question set and the current test question characteristic information, the current test question characteristic information is subjected to iterative optimization by adopting an alternative recommendation algorithm to obtain target test question characteristic information, and then a target knowledge graph is constructed according to the target test question characteristic information. And responding to the test question generation request, and selecting the test questions in the test question set according to the target knowledge graph so as to generate a test question model. The alternate recommendation algorithm is a generic, end-to-end, deep recommendation framework that aims to assist in recommending tasks using knowledge graph embedding. The two tasks are respectively an automatic generation task and an automatic scoring task, and the two tasks are independent of each other, but are highly relevant because item in the embedding layer RS and entity in the embedding layer KG are mutually related. The whole framework can be trained by alternately optimizing two tasks, and high flexibility and adaptability of MKR in a real recommended scene are given, so that assessment questions can be flexibly generated.
In an exemplary embodiment, as shown in fig. 3, step S204 includes steps S302 to S306, and correspondingly, step S206 includes steps S306 to S308. Wherein:
step S302, adopting an alternate recommendation algorithm to perform iterative optimization on the test question feature information according to the current test question relation data so as to obtain target test question relation data.
The current test question feature information comprises test question keywords and current test question relation data. The test question keywords are used for representing the current test question characteristics, and the current test question relation data are used for representing the association information among different test questions, such as association degree, test question difficulty proportion and the like.
For example, the server 104 may perform iterative optimization on the test question feature information according to the current test question relationship data by using an alternative recommendation algorithm, so as to fill a partial gap of the current test question relationship data, and make the test question relationship data more close to the actual requirement, thereby obtaining the target test question relationship data.
And step S304, generating target test question feature information according to the test question keywords and the target test question relation data.
For example, the server 104 may perform one-to-one correspondence between the newly generated target test question relation data and the test question keywords, and correspond the target test question relation data to the set corresponding to the test question keywords in a searching manner, so as to form new target test question feature information.
And step S306, generating a basic knowledge graph according to the test question keywords.
The basis can be a static priori knowledge constructed according to the test question keywords.
Step S308, updating the basic knowledge graph according to the characteristic information of the target test questions to obtain a target knowledge graph.
For example, the server 104 may use the underlying knowledge-graph and the target test question feature information to derive a target knowledge-graph, and learn representations of the user and the item (or other entity), including predicted targets and knowledge regularization terms, as an optimization objective function using the MKR algorithm. Further, in optimizing the objective function, knowledge regularization terms are introduced. This term is used to ensure that the learned representation fits as well as possible with the relationships defined in the knowledge-graph by transforming the relationships in the initial knowledge-graph into constraints on the model representation.
In an exemplary embodiment, step S208 includes selecting a test question in a test question set according to a target knowledge graph in response to a test question generation request, to obtain simulated test question data; checking the simulated test question data according to the test question keywords; and generating a test question model according to the simulated test question data under the condition that the test passes.
For example, the server 104 may gradually select the questions in the question set according to the target knowledge graph according to the operation sequence of the power system, and then gradually verify the questions in the question model, so as to generate a final question model.
In another embodiment, the server 104 first establishes a power system model formed by connecting static nonlinear links and dynamic linear links in series based on a Hammerstein model, and the server 104 executes the method to establish a test question model formed based on operation points of a dispatcher, wherein the operation points of the dispatcher are operation points of the dispatcher in a power system scene. The server 104 acquires a test question set under the power system model, acquires the test questions step by step according to the operation steps of the distributed energy system to form the test question set, and records the operation bit and the test question characteristic information corresponding to each test question when acquiring the test question set.
Then, the server 104 establishes a test question library containing the test question keyword index under the condition that the test question library containing the test question keyword index is not established, so as to count the characteristic information of the current test question; under the condition that a test question library containing test question keyword indexes is established, recording the existing test question information into the test question library containing the test question keyword indexes so as to count the current test question characteristic information. And then, carrying out iterative optimization on the characteristic information of the current test question by adopting an alternative recommendation algorithm, and establishing a target knowledge graph so as to obtain the characteristic information of the target test question.
In the use process, the server 104 analyzes the test question information carried in the test question generation request according to the test question generation request sent by the terminal 102, analyzes the corresponding part in the target knowledge graph according to the test question information, so as to select the test questions in the test question set, and combines and encapsulates the selected test questions according to the test question sequence to form the test question model.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a test question model generating device for realizing the test question model generating method. The implementation scheme of the solution provided by the device is similar to the implementation scheme recorded in the method, so the specific limitation in the embodiment of the device for generating the test question model provided below can be referred to the limitation of the method for generating the test question model, which is not repeated herein.
In an exemplary embodiment, as shown in fig. 4, there is provided a test question model generating apparatus, including: an information acquisition module 402, an iterative optimization module 404, a map generation module 406, and a test question generation module 408, wherein:
the information acquisition module 402 is configured to acquire a test question set and current test question feature information, where the current test question feature information is used to characterize a connection between different test questions in the test question set;
the iterative optimization module 404 is configured to perform iterative optimization on the current test question feature information by using an alternative recommendation algorithm, so as to obtain target test question feature information;
the map generation module 406 is configured to construct a target knowledge map according to the target test question feature information;
the test question generation module 408 is configured to select, in response to the test question generation request, a test question in the test question set according to the target knowledge graph, so as to generate a test question model.
In one embodiment, the current test question feature information includes a test question keyword and current test question relation data; the iterative optimization module 404 includes:
the data optimization unit is used for carrying out iterative optimization on the test question feature information according to the current test question relation data by adopting an alternative recommendation algorithm so as to obtain target test question relation data;
and the information generating unit is used for generating target test question feature information according to the test question keywords and the target test question relation data.
In one embodiment, the atlas generation module 406 comprises:
the map initialization unit is used for generating a basic knowledge map according to the test question keywords;
and the map updating unit is used for updating the basic knowledge map according to the characteristic information of the target test questions so as to obtain the target knowledge map.
In one embodiment, the question generation module 408 includes:
the test question generation unit is used for responding to the test question generation request, selecting test questions in the test question set according to the target knowledge graph, and obtaining simulated test question data;
the test question checking unit is used for checking the simulated test question data according to the test question keywords;
and the model generating unit is used for generating a test question model according to the simulated test question data under the condition that the test passes.
In one embodiment, the test question sets are obtained step by step according to the operating steps of the distributed energy system.
In one embodiment, the method is used for operational assessment of a nonlinear system.
The modules in the test question model generating device can be realized by all or part of software, hardware and combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing the test question set, the current test question characteristic information and cache data in the iterative optimization process. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method for generating a test question model.
In an exemplary embodiment, a computer device, which may be a terminal, is provided, and an internal structure diagram thereof may be as shown in fig. 6. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements a method for generating a test question model. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one exemplary embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of: acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set; performing iterative optimization on the characteristic information of the current test question by adopting an alternative recommendation algorithm to obtain the characteristic information of the target test question; constructing a target knowledge graph according to the characteristic information of the target test questions; and responding to the test question generation request, and selecting the test questions in the test question set according to the target knowledge graph so as to generate a test question model.
In one embodiment, the processor when executing the computer program further performs the steps of: adopting an alternate recommendation algorithm to perform iterative optimization on test question feature information according to the current test question relation data so as to obtain target test question relation data; and generating target test question feature information according to the test question keywords and the target test question relation data.
In one embodiment, the processor when executing the computer program further performs the steps of: generating a basic knowledge graph according to the test question keywords; and updating the basic knowledge graph according to the characteristic information of the target test questions to obtain a target knowledge graph.
In one embodiment, the processor when executing the computer program further performs the steps of: responding to a test question generation request, and selecting test questions in a test question set according to a target knowledge graph to obtain simulated test question data; checking the simulated test question data according to the test question keywords; and generating a test question model according to the simulated test question data under the condition that the test passes.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set; performing iterative optimization on the characteristic information of the current test question by adopting an alternative recommendation algorithm to obtain the characteristic information of the target test question; constructing a target knowledge graph according to the characteristic information of the target test questions; and responding to the test question generation request, and selecting the test questions in the test question set according to the target knowledge graph so as to generate a test question model.
In one embodiment, the computer program when executed by the processor further performs the steps of: adopting an alternate recommendation algorithm to perform iterative optimization on test question feature information according to the current test question relation data so as to obtain target test question relation data; and generating target test question feature information according to the test question keywords and the target test question relation data.
In one embodiment, the computer program when executed by the processor further performs the steps of: generating a basic knowledge graph according to the test question keywords; and updating the basic knowledge graph according to the characteristic information of the target test questions to obtain a target knowledge graph.
In one embodiment, the computer program when executed by the processor further performs the steps of: responding to a test question generation request, and selecting test questions in a test question set according to a target knowledge graph to obtain simulated test question data; checking the simulated test question data according to the test question keywords; and generating a test question model according to the simulated test question data under the condition that the test passes.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of: acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set; performing iterative optimization on the characteristic information of the current test question by adopting an alternative recommendation algorithm to obtain the characteristic information of the target test question; constructing a target knowledge graph according to the characteristic information of the target test questions; and responding to the test question generation request, and selecting the test questions in the test question set according to the target knowledge graph so as to generate a test question model.
In one embodiment, the computer program when executed by the processor further performs the steps of: adopting an alternate recommendation algorithm to perform iterative optimization on test question feature information according to the current test question relation data so as to obtain target test question relation data; and generating target test question feature information according to the test question keywords and the target test question relation data.
In one embodiment, the computer program when executed by the processor further performs the steps of: generating a basic knowledge graph according to the test question keywords; and updating the basic knowledge graph according to the characteristic information of the target test questions to obtain a target knowledge graph.
In one embodiment, the computer program when executed by the processor further performs the steps of: responding to a test question generation request, and selecting test questions in a test question set according to a target knowledge graph to obtain simulated test question data; checking the simulated test question data according to the test question keywords; and generating a test question model according to the simulated test question data under the condition that the test passes.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. The test question model generation method is characterized by comprising the following steps:
acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set;
performing iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm to obtain target test question feature information;
constructing a target knowledge graph according to the characteristic information of the target test questions;
responding to a test question generation request, and selecting test questions in the test question set according to the target knowledge graph so as to generate a test question model.
2. The method according to claim 1, wherein the current test question feature information comprises test question keywords and current test question relation data; the step of performing iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm to obtain target test question feature information comprises the following steps:
adopting an alternate recommendation algorithm, and carrying out iterative optimization on the test question feature information according to the current test question relation data so as to obtain target test question relation data;
and generating target test question feature information according to the test question keywords and the target test question relation data.
3. The method of claim 2, wherein constructing a target knowledge-graph from the target test question feature information comprises:
generating a basic knowledge graph according to the test question keywords;
and updating the basic knowledge graph according to the characteristic information of the target test questions to obtain a target knowledge graph.
4. The method of claim 2, wherein the selecting the questions in the set of questions according to the target knowledge-graph in response to the question generation request to generate the question model comprises:
responding to a test question generation request, and selecting test questions in the test question set according to the target knowledge graph to obtain simulated test question data;
checking the simulated test question data according to the test question keywords;
and generating a test question model according to the simulated test question data under the condition that the test passes.
5. The method of claim 1, wherein the test question sets are obtained step by step according to the operating steps of the distributed energy system.
6. The method according to any one of claims 1 to 5, wherein the method is used for operational assessment of a nonlinear system.
7. A test question model generation device, characterized in that the device comprises:
the information acquisition module is used for acquiring a test question set and current test question feature information, wherein the current test question feature information is used for representing the relation between different test questions in the test question set;
the iterative optimization module is used for carrying out iterative optimization on the current test question feature information by adopting an alternative recommendation algorithm so as to obtain target test question feature information;
the map generation module is used for constructing a target knowledge map according to the target test question characteristic information;
and the test question generation module is used for responding to a test question generation request, selecting test questions in the test question set according to the target knowledge graph so as to generate a test question model.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311642154.6A CN117540034A (en) | 2023-12-04 | 2023-12-04 | Test question model generation method and device and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311642154.6A CN117540034A (en) | 2023-12-04 | 2023-12-04 | Test question model generation method and device and computer equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117540034A true CN117540034A (en) | 2024-02-09 |
Family
ID=89784091
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311642154.6A Pending CN117540034A (en) | 2023-12-04 | 2023-12-04 | Test question model generation method and device and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117540034A (en) |
-
2023
- 2023-12-04 CN CN202311642154.6A patent/CN117540034A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xu et al. | Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services | |
| KR102342604B1 (en) | Method and apparatus for generating neural network | |
| CN111680219B (en) | Content recommendation method, device, equipment and readable storage medium | |
| Torrens et al. | Cellular automata and urban simulation: where do we go from here? | |
| Silva et al. | Using vistrails and provenance for teaching scientific visualization | |
| CN110008397A (en) | A kind of recommended models training method and device | |
| CN114579584B (en) | Data table processing method and device, computer equipment and storage medium | |
| Bergero et al. | A vectorial DEVS extension for large scale system modeling and parallel simulation | |
| CN111782951A (en) | Method and device for determining display page, computer system and medium | |
| US20240078473A1 (en) | Systems and methods for end-to-end machine learning with automated machine learning explainable artificial intelligence | |
| Qin et al. | [Retracted] Evaluation of College Students’ Ideological and Political Education Management Based on Wireless Network and Artificial Intelligence with Big Data Technology | |
| Shao et al. | Beyond imitation: Generating human mobility from context-aware reasoning with large language models | |
| CN117540915A (en) | Big data technology-based selection scheme generation method, device, equipment and medium | |
| CN116681470A (en) | Store location method, store location device, computer equipment, storage medium and product | |
| CN117540034A (en) | Test question model generation method and device and computer equipment | |
| Fan et al. | Exploring open-ended design space of mechatronic systems | |
| CN106919946B (en) | A kind of method and device of audience selection | |
| Jávor et al. | Simulation on the Web with distributed models and intelligent agents | |
| DeMars | Big data | |
| Li | [Retracted] Design of Online Ideological and Political Teaching of Building Architecture from the Perspective of Machine Learning | |
| Jeong et al. | Multi-wsn simulator with log data for efficient sensing on internet of things | |
| Ma et al. | Learning resource recommendation via knowledge graphs and learning style clustering | |
| CN116880886A (en) | Updating method and device of product recommendation model | |
| Liang | Construction of a Personalized Recommendation Model for MOOC Courses Based on LGAT | |
| CN118467813A (en) | Media recommendation method, device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |