CN117726484A - Method and device for generating simulation model of power system and computer equipment - Google Patents
Method and device for generating simulation model of power system and computer equipment Download PDFInfo
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Abstract
The application relates to a method, a device, a computer device, a storage medium and a computer program product for generating a simulation model of a power system. The method comprises the following steps: determining an operation scene identifier of the power system, wherein the operation scene identifier is determined by classifying the power data; based on the operation scene identification, acquiring an entity corresponding to the operation scene identification and a relation between the entities from the knowledge graph; the knowledge graph is constructed based on the classified power data; determining an execution rule of simulation operation in a target training task based on the entity and the relation between the entities, and storing the execution rule in a target task model corresponding to the operation scene identifier; based on the target task model and a preset control target, a dynamic simulation model corresponding to the operation scene identifier is constructed, so that the dynamic simulation model outputs an execution result of the target training task according to the input simulation operation. By adopting the method, the simulation reduction degree of the power system can be improved.
Description
Technical Field
The present invention relates to the field of power systems, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for generating a simulation model of a power system.
Background
Along with the progress of science and technology, the demand of users for electric energy is higher and higher, and in order to ensure the normal supply of electric energy, operators at all positions in the power grid are required to perform operations such as equipment operation, fault treatment and the like in a standardized manner.
At present, a simulation system is generally established for a power system to simulate and predict the behavior of the power system, so that each station personnel running on the power grid can be trained through the simulation system. However, with the continuous development of the power grid, the power grid structure is more and more complex, and the reduction degree of the simulation system to the power system in the related technology is lower, so that the training effect by the simulation system is poor.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer-readable storage medium, and a computer program product for generating a power system simulation model capable of improving a simulation restoration degree of a power system.
In a first aspect, the present application provides a method for generating a simulation model of an electric power system, including:
determining an operation scene identifier of the power system, wherein the operation scene identifier is determined by classifying the power data;
based on the operation scene identification, acquiring an entity corresponding to the operation scene identification and a relation between the entities from a knowledge graph; wherein the knowledge graph is constructed based on the classified power data;
determining an execution rule of simulation operation in a target training task based on the entity and the relation between the entities, and storing the execution rule in a target task model corresponding to the operation scene identifier;
based on the target task model and a preset control target, constructing a dynamic simulation model corresponding to the operation scene identifier, so that the dynamic simulation model outputs an execution result of the target training task according to the input simulation operation.
In one embodiment, the determining, based on the entity and the relationship between the entities, an execution rule of the simulation operation in the target training task, and storing the execution rule in a target task model corresponding to the operation scene identifier includes:
determining a training task corresponding to the knowledge graph and simulation operation in the training task based on a recommended algorithm model; the recommendation algorithm model is obtained by training an initial recommendation algorithm model based on a sample knowledge graph.
In one embodiment, the determining an execution rule of a simulation operation in a target training task based on the entity and the relationship between entities, and storing the execution rule in a target task model corresponding to the operation scene identifier includes:
acquiring target authority data, wherein the target authority data corresponds to an authority range of a target role;
based on the target authority data, the entity and the relation among the entities, determining an execution rule of simulation operation in a target training task, and storing the execution rule in a target task model corresponding to the operation scene identification, so that the target task model guides the target training task to be executed in the authority range of the target role.
In one embodiment, the determining, based on the entity and the relationship between the entities, an execution rule of the simulation operation in the target training task, and storing the execution rule in a target task model corresponding to the operation scene identifier, and then includes:
the generated target model is stored in a model library for providing a plurality of task models corresponding to an application system for performing training tasks.
In one embodiment, the determining, based on the target task model and a preset control target, a dynamic simulation model corresponding to the operation scene identifier includes:
determining an input matrix and an output matrix of a dynamic simulation model corresponding to the operation scene identification based on the target task model;
and constructing the dynamic simulation model based on the input matrix, the output matrix and the preset control target.
In one embodiment, the determining, based on the target task model and a preset control target, a dynamic simulation model corresponding to the operation scene identifier includes:
constructing an application system for executing training tasks based on the dynamic simulation model;
acquiring operation node data of a user in the application system, wherein the operation node data corresponds to simulation operation of the user;
generating training data of the user and corresponding label values based on the operation node data;
and inputting the training data of the user and the corresponding label value into a pre-trained classifier to obtain the training score of the user.
In a second aspect, the present application further provides a device for generating a simulation model of a power system, including:
the first determining module is used for determining an operation scene identifier of the power system, wherein the operation scene identifier is determined by classifying the power data;
the first acquisition module is used for acquiring the electric power data corresponding to the operation scene identification and the relation between the electric power data from the knowledge graph based on the operation scene identification;
the second determining module is used for determining the execution rule of the simulation operation in the target training task based on the electric power data and the relation between the electric power data, and storing the execution rule in a target task model corresponding to the operation scene identifier;
the first construction module is used for determining a dynamic simulation model corresponding to the operation scene identifier based on the target task model and a preset control target, so that the dynamic simulation model outputs an execution result of the target training task according to the input simulation operation.
In a third aspect, the present application also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any one of the methods described above when the computer program is executed by the processor.
In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method of any of the preceding claims.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of any of the preceding claims.
According to the method, the device, the computer equipment, the storage medium and the computer program product for generating the simulation model of the electric power system, the collected electric power data are classified according to the actual service scene, and the knowledge graph is constructed based on the classified electric power data, so that the entity corresponding to the actual service scene and the relation between the entities can be obtained from the knowledge graph through the identification corresponding to the actual service scene, further, the execution rule of the training task in the specific scene and the dynamic simulation model for executing the training task can be obtained, the simulation reduction degree of the electric power system is improved, and further, the training effect of training through the simulation model is guaranteed.
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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 method for generating a simulation model of a power system in one embodiment;
FIG. 2 is a flow chart of a method for generating a simulation model of a power system in one embodiment;
FIG. 3 is a flowchart of a method for generating a simulation model of a power system according to another embodiment;
FIG. 4 is a block diagram of a power system simulation model generation apparatus in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one 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 method for generating the simulation model of the power system, 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 user may input a simulation operation to the server 104 through the terminal 102. 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 method for generating a simulation model of a power system is provided, and the method is applied to the server 104 in fig. 1 for illustration, and includes the following steps 201 to 204. Wherein:
step 201, determining an operation scene identifier of the power system, where the operation scene identifier is determined by classifying the power data.
Wherein the operation scenario identification of the power system corresponds to an actual traffic scenario of the power system. For example, the actual business scenario may include: power scheduling scenarios, fault handling scenarios, etc. The power data may include: operational flow data, equipment data, fault data, etc. for the power system. In one possible implementation manner, after the power data of the power system is collected, according to the data source and the collection time of the power data, an actual service scene corresponding to the power data is determined, and then a corresponding operation scene identifier is added to the power data as a tag value.
Step 202, acquiring an entity corresponding to the operation scene identifier and a relation between the entities from the knowledge graph based on the operation scene identifier; the knowledge graph is constructed based on the classified power data.
The knowledge graph may store a plurality of operation scene identifiers corresponding to the entities and relationships between the entities. In one possible implementation, a corresponding entity may be defined for each operation scenario identification in the knowledge graph, and the attributes of the entities and the relationships between the entities may be determined according to the collected power data. Here, the entities in the knowledge graph may correspond to actual devices, faults, operations, etc., and the relationship between the entities may include, for example, a relationship between a state of the generator and an operation condition of the power grid, a relationship between a scheduling instruction and start and stop of the generator, etc.
Optionally, the power data may be collected and analyzed again at intervals of a preset update time, so as to perform maintenance operations such as error correction, data supplementation, and the like on the entities in the knowledge graph and the relationships between the entities.
Step 203, determining an execution rule of the simulation operation in the target training task based on the entity and the relation between the entities, and storing the execution rule in the target task model corresponding to the operation scene identifier.
Wherein the target training task corresponds to a work task in an actual business scenario of the power system. For example, the work task in the actual business scenario may be a task such as starting, stopping, maintaining, etc. for a specific device, or a task such as power scheduling, fault handling, load prediction, etc. for the entire power system. The target training task simulates and executes the work task in the actual business scene by executing the simulation operation. The execution rules of the simulation operation may include: information such as sequence, dependency relationship, operation specification and the like among simulation operations.
By way of example, the executing rule of the simulation operation can be obtained by performing semantic analysis on the knowledge graph, and then the executing rule is stored in the target task model corresponding to the operation scene identifier. In one possible implementation, the execution rules of the simulation operation may be stored according to a predefined data structure, resulting in a target task model. Here, the target task model may further include: the name, description, object of the simulation operation, time, etc. of the target training task.
And 204, constructing a dynamic simulation model corresponding to the operation scene identifier based on the target task model and a preset control target, so that the dynamic simulation model outputs an execution result of the target training task according to the input simulation operation.
The target task model is used for determining the input and output of the dynamic simulation model, and the preset control target is used for determining a control algorithm of the dynamic simulation model.
According to the method for generating the simulation model of the electric power system, the collected electric power data are classified according to the actual service scene, and the knowledge graph is constructed based on the classified electric power data, so that the entity corresponding to the actual service scene and the relation between the entities can be obtained from the knowledge graph through the identification corresponding to the actual service scene, further, the execution rule of the training task under the specific scene and the dynamic simulation model for executing the training task can be obtained, the simulation reduction degree of the electric power system is improved, and further, the training effect of training through the simulation model is guaranteed.
In an exemplary embodiment, as shown in fig. 3, another method for generating a simulation model of a power system is provided, including steps 301 to 310. Wherein:
step 301, determining a training task corresponding to a knowledge graph and simulation operation in the training task based on a recommended algorithm model; the recommendation algorithm model is obtained by training an initial recommendation algorithm model based on a sample knowledge graph.
The recommendation algorithm model may be a MKR (Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation) model which is trained alternately, and MKR is a Multi-task feature learning method for knowledge graph enhancement recommendation, and a knowledge graph is used to embed tasks to assist in recommending tasks. In one possible implementation, a training task list of knowledge maps may be generated based on the MKR model, the training task list comprising: names, descriptions of a plurality of training tasks; a plurality of simulation operations included for each training task, and an object, time, etc. for each simulation operation.
Therefore, by adopting the recommendation algorithm model, training tasks corresponding to all scenes can be rapidly and accurately determined according to the knowledge in the knowledge graph, and the simulation efficiency of the power system is improved.
Step 302, determining an operation scene identifier of the power system, wherein the operation scene identifier is determined by classifying the power data.
In one possible implementation manner, according to the operation scene identifier corresponding to the power data for determining the attribute of the entity, corresponding attribute information may be added to the entity, so that the corresponding relationship between the entity and the operation scene identifier can be determined through the attribute information of the entity.
Step 303, based on the operation scene identification, acquiring an entity corresponding to the operation scene identification and a relation between the entities from the knowledge graph; the knowledge graph is constructed based on the classified power data.
Step 304, determining an execution rule of the simulation operation in the target training task based on the entity and the relation between the entities, and storing the execution rule in the target task model corresponding to the operation scene identifier.
In an exemplary embodiment, the step 304 includes:
step 304A, obtaining target authority data, where the target authority data corresponds to an authority range of the target role.
Wherein the target role may correspond to a particular post in the power system, e.g., dispatcher, operator, maintenance personnel, system administrator, etc. The scope of authority of the target character may include: responsibility for the target role, scope of operation, etc.
Step 304B, determining an execution rule of the simulation operation in the target training task based on the target authority data, the entity and the relationship among the entities, and storing the execution rule in the target task model corresponding to the operation scene identifier, so that the target task model guides the target training task to be executed within the authority range of the target role.
For example, corresponding permission attribute information/prohibition attribute information can be added for the entity in the knowledge graph based on the target authority data, and then simulation operation in the target training task corresponding to the target role can be screened out according to the permission information of the entity; or deleting part of simulation operation in the target training task according to the forbidden attribute information of the entity, so that the target task model can guide the target training task to be executed within the authority range of the target role.
In the embodiment, the simulation operation included in the training task is limited by the authority information of the roles, so that the training task meeting the authority range of each role can be constructed, the targeted training of the training personnel can be realized, and the training quality of training through the simulation model is improved.
In step 305, the generated target model is stored in a model library, which is used to provide a plurality of task models, and the plurality of task models correspond to the application systems for executing training tasks.
For example, a model library may be stored in the server 104, and an application system may obtain a plurality of task models from the model library through a network, and construct a corresponding simulation model based on the plurality of task models, so that training tasks corresponding to a plurality of actual business scenarios can be performed by the application system.
And 306, constructing a dynamic simulation model corresponding to the operation scene identifier based on the target task model and a preset control target, so that the dynamic simulation model outputs an execution result of the target training task according to the input simulation operation.
In an exemplary embodiment, the step 306 includes:
step 306A, based on the target task model, determines an input matrix and an output matrix of the dynamic simulation model corresponding to the operation scene identification.
And 306B, constructing a dynamic simulation model based on the input matrix, the output matrix and a preset control target.
Wherein the dynamic simulation model may be a Hammerstein model. The input matrix and the output matrix of the target training task can be determined according to the target task model, the static nonlinear link and the dynamic linear link of the Hammerstein model are determined through a system identification method, the Hammerstein model corresponding to the operation scene identification is obtained through serial connection of the static nonlinear link and the dynamic linear link, and a proper controller is designed for the Hammerstein model according to a preset control target, so that the Hammerstein model can process the input simulation operation according to the preset control target.
Step 307, building an application system for executing training tasks based on the dynamic simulation model.
Step 308, obtaining operation node data of the user in the application system, wherein the operation node data corresponds to simulation operation of the user.
Step 309, generating training data of the user and corresponding tag values based on the operation node data.
Step 310, the training data of the user and the corresponding label value are input into the pre-trained classifier, and the training score of the user is output.
The user's operational node data set may be determined, for example, by recording the user's operational track in the application system. Furthermore, based on the standard operation track corresponding to the operation scene identifier, a corresponding label value is added to the operation node data of the user, so that training data of the user and the corresponding label value are obtained; wherein the standard operation track comprises operation node data arranged in a correct order. The pre-trained classifier can be constructed based on a random forest model, and accurate training scores are given by introducing random attributes to reduce error results of individual score generalization.
Therefore, the quantitative analysis of the operation grasping condition of the training personnel can be realized by analyzing the operation information of the user and determining the training score of the user through the classifier, the training quality of training through the simulation model is further improved, and the safety of the power system is further ensured.
In summary, in the method for generating the simulation model of the electric power system, the collected electric power data are classified according to the actual service scene, and the knowledge graph is constructed based on the classified electric power data, so that the entity corresponding to the actual service scene and the relation between the entities can be obtained from the knowledge graph through the identification corresponding to the actual service scene, further, the execution rule of the training task under the specific scene and the dynamic simulation model for executing the training task can be obtained, the simulation reduction degree of the electric power system is improved, and further, the training effect of training through the simulation model is ensured.
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 generating device of the electric power system simulation model for realizing the generating method of the electric power system simulation model. The implementation scheme of the device for solving the problem is similar to the implementation scheme described in the method, so the specific limitation in the embodiment of the device for generating one or more power system simulation models provided below may refer to the limitation of the method for generating the power system simulation model hereinabove, and will not be repeated herein.
In an exemplary embodiment, as shown in fig. 4, there is provided a generating apparatus 400 of a simulation model of a power system, including: a first determination module 401, a first acquisition module 402, a second determination module 403, and a first construction module 404, wherein:
the first determining module 401 is configured to determine an operation scenario identifier of the power system, where the operation scenario identifier is determined by classifying the power data.
The first obtaining module 402 is configured to obtain, based on the operation scene identifier, power data corresponding to the operation scene identifier and a relationship between the power data from the knowledge graph.
The second determining module 403 is configured to determine an execution rule of the simulation operation in the target training task based on the power data and the relationship between the power data, and store the execution rule in the target task model corresponding to the operation scenario identifier.
The first building module 404 is configured to determine a dynamic simulation model corresponding to the operation scene identifier based on the target task model and a preset control target, so that the dynamic simulation model outputs an execution result of the target training task according to the input simulation operation.
In an exemplary embodiment, the generating device 400 of the power system simulation model includes:
the third determining module is used for determining training tasks corresponding to the knowledge maps and simulation operations in the training tasks based on the recommended algorithm model; the recommendation algorithm model is obtained by training an initial recommendation algorithm model based on a sample knowledge graph.
In an exemplary embodiment, the second determining module 403 includes:
and the acquisition sub-module is used for acquiring target authority data, wherein the target authority data corresponds to the authority range of the target role.
The first determining submodule is used for determining an execution rule of the simulation operation in the target training task based on the target authority data, the entity and the relation among the entities, and storing the execution rule in a target task model corresponding to the operation scene identifier so that the target task model guides the target training task to be executed in the authority range of the target role.
In an exemplary embodiment, the generating device 400 of the power system simulation model includes:
the storage module is used for storing the generated target model in a model library, and the model library is used for providing a plurality of task models which correspond to the application system for executing the training task.
In an exemplary embodiment, the first building block 404 includes:
and the second determination submodule is used for determining an input matrix and an output matrix of the dynamic simulation model corresponding to the operation scene identification based on the target task model.
The construction submodule is used for constructing a dynamic simulation model based on the input matrix, the output matrix and a preset control target.
In an exemplary embodiment, the generating device 400 of the power system simulation model includes:
and the second construction module is used for constructing an application system for executing training tasks based on the dynamic simulation model.
The second acquisition module is used for acquiring operation node data of the user in the application system, wherein the operation node data corresponds to simulation operation of the user.
And the label value generation module is used for generating training data of the user and corresponding label values based on the operation node data.
And the score output module is used for inputting training data of the user and corresponding label values into the pre-trained classifier and outputting training scores of the user.
The above-described respective modules in the generation apparatus of the power system simulation model may be implemented in whole or in part by software, hardware, and combinations 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 device is used for storing the knowledge graph. 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 of generating a simulation model of an electrical power system.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 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 an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor performing the steps of the method embodiments described above when the computer program is executed.
In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method embodiments described above.
In an exemplary embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
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. A method for generating a simulation model of an electrical power system, the method comprising:
determining an operation scene identifier of the power system, wherein the operation scene identifier is determined by classifying the power data;
based on the operation scene identification, acquiring an entity corresponding to the operation scene identification and a relation between the entities from a knowledge graph; wherein the knowledge graph is constructed based on the classified power data;
determining an execution rule of simulation operation in a target training task based on the entity and the relation between the entities, and storing the execution rule in a target task model corresponding to the operation scene identifier;
based on the target task model and a preset control target, constructing a dynamic simulation model corresponding to the operation scene identifier, so that the dynamic simulation model outputs an execution result of the target training task according to the input simulation operation.
2. The method of claim 1, wherein determining execution rules for simulation operations in a target training task based on the entities and relationships between entities and storing the execution rules in a target task model corresponding to the operation scenario identification, previously comprises:
determining a training task corresponding to the knowledge graph and simulation operation in the training task based on a recommended algorithm model; the recommendation algorithm model is obtained by training an initial recommendation algorithm model based on a sample knowledge graph.
3. The method of claim 1, wherein determining execution rules for simulation operations in a target training task based on the entities and relationships between entities and storing the execution rules in a target task model corresponding to the operation scenario identification comprises:
acquiring target authority data, wherein the target authority data corresponds to an authority range of a target role;
based on the target authority data, the entity and the relation among the entities, determining an execution rule of simulation operation in a target training task, and storing the execution rule in a target task model corresponding to the operation scene identification, so that the target task model guides the target training task to be executed in the authority range of the target role.
4. The method of claim 1, wherein determining execution rules for simulation operations in a target training task based on the entities and relationships between entities and storing the execution rules in a target task model corresponding to the operation scenario identification, then comprises:
the generated target model is stored in a model library for providing a plurality of task models corresponding to an application system for performing training tasks.
5. The method according to claim 1, wherein the determining a dynamic simulation model corresponding to the operation scenario identification based on the target task model and a preset control target includes:
determining an input matrix and an output matrix of a dynamic simulation model corresponding to the operation scene identification based on the target task model;
and constructing the dynamic simulation model based on the input matrix, the output matrix and the preset control target.
6. The method according to claim 1, wherein the determining a dynamic simulation model corresponding to the operation scenario identification based on the target task model and a preset control target, then comprises:
constructing an application system for executing training tasks based on the dynamic simulation model;
acquiring operation node data of a user in the application system, wherein the operation node data corresponds to simulation operation of the user;
generating training data of the user and corresponding label values based on the operation node data;
and inputting the training data of the user and the corresponding label value into a pre-trained classifier to obtain the training score of the user.
7. A device for generating a simulation model of an electric power system, the device comprising:
the first determining module is used for determining an operation scene identifier of the power system, wherein the operation scene identifier is determined by classifying the power data;
the first acquisition module is used for acquiring power data corresponding to the operation scene identifier and a relation between the power data from a knowledge graph based on the operation scene identifier;
the second determining module is used for determining an execution rule of the simulation operation in the target training task based on the electric power data and the relation between the electric power data, and storing the execution rule in a target task model corresponding to the operation scene identifier;
the first construction module is used for determining a dynamic simulation model corresponding to the operation scene identifier based on the target task model and a preset control target, so that the dynamic simulation model outputs an execution result of the target training task according to the input simulation operation.
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.
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