Electrical transient model data processing method and device, equipment and storage medium

Abstract

The invention provides an electrical transient model data processing method and device, equipment and a storage medium. Storage of target model data is achieved through a transient model classification library, when data corresponding to a target model needs to be applied, model attributes and model parameters of the target model can be directly called, or after the model attributes and the model parameters of the target model are called, a first model is obtained through further processing, and the first model is used for applying the data corresponding to the target model. Therefore, the target model and / or the first model are / is used for a specific application scene, for example, corresponding model data can be obtained through calling or processing in a power system operation mode configuration process. Therefore, under the condition of realizing standardized storage and flexible storage of the transient model, the data storage and data processing cost is reduced, and the effectiveness and practicability of data processing of the electrical transient model are also improved.

Description

Technical Field

[0001] The present application relates to the field of power system data storage, and in particular to an electrical transient model data processing method, device, equipment and storage medium. Background Art

[0002] In the electrical field, both electromechanical models and electromagnetic models are transient models. The number of models is large and the types are complex. In addition, the number of attributes that need to be labeled in the transient models changes flexibly according to the actual working conditions. In the existing grid operation mode compilation process, the transient models involved in each grid operation mode need to be copied from the previous grid operation mode plan, and then the attributes and content need to be added or deleted according to the needs of the operation mode. This method cannot store transient models in a standardized manner, resulting in a complex operation mode configuration process and high data storage and data processing costs.

[0003] Therefore, a method for processing electrical transient model data is needed to achieve standardized storage of transient models and reduce data storage and data processing costs. Summary of the invention

[0004] The purpose of the present application is to solve at least one of the above-mentioned technical defects, especially the technical defects in the prior art that the standardization of transient model storage is not high and the data storage and data processing costs are relatively high.

[0005] In a first aspect, the present application provides a method for processing electrical transient model data, the method comprising:

[0006] Obtain the target model in the transient model classification library;

[0007] A first model is generated according to the model attributes and model parameters of the target model.

[0008] As an optional implementation, before obtaining the target model in the transient model classification library, the method further includes:

[0009] Establish a transient model classification library;

[0010] The data structure of the transient model classification library includes a tree structure, and the root node and the internal nodes are used to indicate the classification status of each of the target models, and the leaf nodes are used to indicate the subdivision type of each of the target models.

[0011] As an optional implementation, the model attributes of the target model include fixed attributes and dynamic attributes;

[0012] The method further comprises:

[0013] Storing the fixed attributes and corresponding fixed parameters of each target model through a relational database;

[0014] The dynamic attributes and corresponding dynamic parameters of each target model are stored through a non-relational database.

[0015] As an optional implementation manner, generating the first model according to the model attributes and model parameters of the target model includes:

[0016] Taking the target model as a parent model;

[0017] Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a first sub-attribute and a first sub-parameter;

[0018] Using the model attribute of the parent model and the first sub-attribute as the first target attribute of the first model, and using the model parameter of the parent model and the first sub-parameter as the first target parameter of the first model;

[0019] The first model is generated according to the first target attribute and the first target parameter.

[0020] As an optional implementation manner, generating the first model according to the model attributes and model parameters of the target model includes:

[0021] Taking the target model as a parent model;

[0022] Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a second sub-attribute and a second sub-parameter;

[0023] According to the second sub-attribute, the model attribute of the parent model is updated to obtain the updated model attribute; and, according to the second sub-parameter, the model parameter of the parent model is updated to obtain the updated model parameter;

[0024] Using the updated model attribute of the parent model and the second sub-attribute as the second target attribute of the first model, and using the updated model parameter of the parent model and the second sub-parameter as the second target parameter of the first model;

[0025] The first model is generated according to the second target attribute and the second target parameter.

[0026] As an optional implementation manner, there are multiple target models, and generating the first model according to the model attributes and model parameters of the target models includes:

[0027] Taking each of the target models as a parent model;

[0028] Performing inheritance operation on the model attributes and model parameters of each of the parent models, and creating a third sub-attribute and a third sub-parameter;

[0029] Using the model attribute of each parent model and the third sub-attribute as the third target attribute of the first model, or directly using the model attribute of each parent model as the third target attribute of the first model and using the model parameter of the parent model and the third sub-parameter as the third target parameter of the first model, or directly using the model parameter of the parent model as the third target parameter of the first model;

[0030] The first model is generated according to the third target attribute and the third target parameter.

[0031] As an optional implementation, the method further includes:

[0032] In the target model and / or the first model, determining at least one operating model;

[0033] According to each of the operation models, a target operation mode is determined.

[0034] In a second aspect, the present application provides an electrical transient model data processing device, the device comprising:

[0035] A determination module is used to obtain a target model in a transient model classification library;

[0036] The processing module is used to generate a first model according to the model attributes and model parameters of the target model.

[0037] As an optional implementation, the processing module is further configured to, before the determining module obtains the target model in the transient model classification library,

[0038] Establish a transient model classification library;

[0039] The data structure of the transient model classification library includes a tree structure, and the root node and the internal nodes are used to indicate the classification status of each of the target models, and the leaf nodes are used to indicate the subdivision type of each of the target models.

[0040] As an optional implementation, the model attributes of the target model include fixed attributes and dynamic attributes;

[0041] The processing module is also used for:

[0042] Storing the fixed attributes and corresponding fixed parameters of each target model through a relational database;

[0043] The dynamic attributes and corresponding dynamic parameters of each target model are stored through a non-relational database.

[0044] As an optional implementation manner, the specific manner in which the processing module generates the first model according to the model attributes and model parameters of the target model includes:

[0045] Taking the target model as a parent model;

[0046] Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a first sub-attribute and a first sub-parameter;

[0047] Using the model attribute of the parent model and the first sub-attribute as the first target attribute of the first model, and using the model parameter of the parent model and the first sub-parameter as the first target parameter of the first model;

[0048] The first model is generated according to the first target attribute and the first target parameter.

[0049] As an optional implementation manner, the specific manner in which the processing module generates the first model according to the model attributes and model parameters of the target model includes:

[0050] Taking the target model as a parent model;

[0051] Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a second sub-attribute and a second sub-parameter;

[0052] According to the second sub-attribute, the model attribute of the parent model is updated to obtain the updated model attribute; and, according to the second sub-parameter, the model parameter of the parent model is updated to obtain the updated model parameter;

[0053] Using the updated model attribute of the parent model and the second sub-attribute as the second target attribute of the first model, and using the updated model parameter of the parent model and the second sub-parameter as the second target parameter of the first model;

[0054] The first model is generated according to the second target attribute and the second target parameter.

[0055] As an optional implementation, there are multiple target models, and the specific manner in which the processing module generates the first model according to the model attributes and model parameters of the target model includes:

[0056] Taking each of the target models as a parent model;

[0057] Performing inheritance operation on the model attributes and model parameters of each of the parent models, and creating a third sub-attribute and a third sub-parameter;

[0058] Using the model attribute of each parent model and the third sub-attribute as the third target attribute of the first model, or directly using the model attribute of each parent model as the third target attribute of the first model and using the model parameter of the parent model and the third sub-parameter as the third target parameter of the first model, or directly using the model parameter of the parent model as the third target parameter of the first model;

[0059] The first model is generated according to the third target attribute and the third target parameter.

[0060] As an optional implementation, the processing module is further configured to:

[0061] In the target model and / or the first model, determining at least one operating model;

[0062] According to each of the operation models, a target operation mode is determined.

[0063] In a third aspect, the present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method described in the first aspect are performed.

[0064] In a fourth aspect, the present application provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, causes the one or more processors to perform the steps of the method described in the first aspect.

[0065] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0066] Based on any of the above embodiments, the present application implements the storage of target model data through a transient model classification library. When the data corresponding to the target model needs to be applied, the model attributes and model parameters of the target model can be directly called, or the model attributes and model parameters of the target model can be called and further processed to obtain the first model, so that the target model and / or the first model can be used in specific application scenarios, such as the corresponding model data can be called or processed during the configuration of the power system operation mode. Therefore, under the condition of realizing standardized storage and flexible storage of transient models, the data storage and data processing costs are reduced, and the effectiveness and practicality of electrical transient model data processing are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0068] Figure 1 A flow chart of an electrical transient model data processing method provided in one embodiment of the present application;

[0069] Figure 2 A schematic diagram showing the effect of an electrical transient model data processing method provided by an embodiment of the present application;

[0070] Figure 3 A schematic diagram showing the effect of an electrical transient model data processing method provided by an embodiment of the present application;

[0071] Figure 4 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0073] In recent years, with the development of economy and the acceleration of industrialization, the demand for electricity has continued to grow, which has led to the continuous expansion of the scale and complexity of the power grid. As the foundation of the power system, the operation mode of the new energy power grid plays a very important role in the stable operation and optimized dispatch of the power system. Therefore, scientifically and rationally compiling and implementing the operation mode of the power grid is not only an optimization of the current operation status of the power grid, but also a forward-looking layout for the future development of the power grid.

[0074] In the process of compiling the grid operation mode, it is necessary to solve problems such as how to improve the efficiency of compilation, how to use existing models, conduct standardized management, and reduce repeated modifications during the compilation process.

[0075] Both electromechanical models and electromagnetic models are transient models. The number of models is large and the types are complex. Even the number of equipment attributes to be marked in the transient model changes flexibly according to the situation. In the existing grid operation mode compilation process, the transient model involved in each grid operation mode needs to be copied from the previous grid operation mode plan (essentially copying the text file), and then add or delete attributes and edit the content according to the needs of the operation mode. This editing method can realize the editing of the transient model of the grid operation mode, but it is impossible to standardize the management and storage of the transient model. It can only be used once and modified once, which cannot well accumulate knowledge and standardize management, and the user learning management cost is very high.

[0076] In each implementation mode of the present application, a relational database is specifically used to implement the classification of transient model types, storage and management of various transient model fixed parameters (parameters here are attributes rather than attribute values), and the naming and data type standardization of type management and fixed parameter management are realized; NoSQL database technology is used to implement the management of various parameters and parameter values ​​in the transient model. Because the parameter types concerned by the transient model are different each time, they cannot be managed in a standard table format (or relational database). NoSQL database technology is a good storage solution. When the user needs to add a new attribute type, it can be added directly without affecting the operation of the system; the idea of ​​inheritance is used to solve the problem of transient parameter reuse, and a new power grid operation mode is compiled. The existing transient model parameters can be directly called, or the current transient model parameters can be called by inheritance, and then the unique parameters are added to form a new transient model; the polymorphic technology is used to inherit the existing transient model and form a new transient model. On the basis of inheritance, the parameter values ​​in the parent model are reassigned or set to invalid, the parameters in the parent model are adjusted, and then the parameters of the parent model are added. The new transient model can be fully utilized and formed by adding its own model parameters.

[0077] The above method realizes the standardized storage management of transient models using database technology, satisfies the flexible editing and application of transient models, realizes the real-time sharing of models, and avoids the editing and management of transient models in the form of text editing.

[0078] The technical concept of the present application is that the present application realizes the storage of target model data through a transient model classification library. When the data corresponding to the target model needs to be applied, the model attributes and model parameters of the target model can be directly called, or the model attributes and model parameters of the target model can be called and then further processed to obtain the first model, so that the target model and / or the first model can be used in specific application scenarios, such as the corresponding model data can be called or processed during the configuration of the power system operation mode. Therefore, under the condition of realizing standardized storage and flexible storage of transient models, the data storage and data processing costs are reduced, and the effectiveness and practicality of electrical transient model data processing are improved.

[0079] See also Figure 1 , Figure 1 A flow chart of an electrical transient model data processing method provided in one embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0080] S101, obtaining a target model in a transient model classification library;

[0081] The target model can be a templated model framework or a typical transient model with specific parameters. For example, a typical dynamic model can be created in advance in the transient model classification library, and the parameters and parameter values ​​of some typical dynamic models can be pre-edited and saved in the model tree. These models can be used directly or as parent classes of other transient models. For related applications of the parent model, refer to the relevant descriptions of other embodiments.

[0082] S102: Generate a first model according to model attributes and model parameters of the target model.

[0083] Among them, the model attributes of the target model include fixed attributes and dynamic attributes. The first model is generated by processing the model attributes and model parameters of the target model through inheritance, polymorphism or nesting. For details, please refer to the description of the relevant implementation method.

[0084] This application implements the storage of target model data through a transient model classification library. When the data corresponding to the target model needs to be applied, the model attributes and model parameters of the target model can be directly called, or the model attributes and model parameters of the target model can be called and then further processed to obtain the first model, so that the target model and / or the first model can be used in specific application scenarios, such as the corresponding model data can be called or processed during the configuration of the power system operation mode. Therefore, under the condition of realizing standardized storage and flexible storage of transient models, the data storage and data processing costs are reduced, and the effectiveness and practicality of electrical transient model data processing are improved.

[0085] In some specific implementations, non-relational databases are used to store and process dynamic data, such as the NoSQL data storage method. In addition, the inheritance development concept is introduced to solve the problem of uncertain parameter types of transient models, maximize the use of existing models and standardize storage management, and facilitate the implementation of specific application scenarios such as power grid operation mode editing.

[0086] See also Figure 2 , Figure 2 A schematic diagram of the effect of an electrical transient model data processing method provided by an embodiment of the present application is shown in FIG. Figure 2 As shown, as an optional implementation, before obtaining the target model in the transient model classification library, the method further includes:

[0087] Establish a transient model classification library;

[0088] The data structure of the transient model classification library includes a tree structure, and the root node and the internal nodes are used to indicate the classification status of each of the target models, and the leaf nodes are used to indicate the subdivision type of each of the target models.

[0089] This application uses a recursive table to store the classification of transient models in the form of a tree structure as shown in the figure. This tree structure has an infinite level of drill-down. The nodes are divided into two categories: classification nodes (root nodes and internal nodes) and model nodes (leaf nodes). The storage content can include: model number, model (type) name, category, model model, etc.

[0090] This implementation method can realize tree-like storage of electrical transient models by establishing a transient model classification library. For various target models, they can be classified into specific classifications at each level through a tree structure, thereby realizing standardized storage of target model data, facilitating later calls, and reducing data storage and data processing costs.

[0091] See also Figure 3 , Figure 3 A schematic diagram of the effect of an electrical transient model data processing method provided by an embodiment of the present application is shown in FIG. Figure 3 As shown, as an optional implementation, the model attributes of the target model include fixed attributes and dynamic attributes;

[0092] The method further comprises:

[0093] Storing the fixed attributes and corresponding fixed parameters of each target model through a relational database;

[0094] The dynamic attributes and corresponding dynamic parameters of each target model are stored through a non-relational database.

[0095] As shown in the figure, each type of transient model has fixed parameters. Such parameters are the important difference between the major categories of transient models. This type of parameters is needed to classify and filter the models. This type of parameters is stored in a relational database. The number and category of parameters cannot be modified at will, and there are standard specifications for the input parameter values.

[0096] For dynamic attributes and dynamic parameters, NoSQL database can be used to dynamically store various dynamic parameters of the model. The dynamic parameters of the model are often not fixed. For example, 10 excitation parameters are needed for annual operation, and 30 excitation parameters are needed for summer operation to make the simulation more accurate. Use NoSQL database to store these dynamic parameters. When using them, the adjustment of parameters and attributes is not restricted, which facilitates the flexible configuration of model parameters.

[0097] This implementation separates the two types of attributes in the transient model and stores them separately in databases with different characteristics. Specifically, a relational database is used to store the fixed attributes of each target model and the fixed parameters corresponding to the fixed attributes, and a non-relational database is used to store the dynamic attributes and dynamic parameters of each target model. This achieves standardized storage of target model data, facilitates later calls, and reduces data storage and data processing costs.

[0098] The following implementation is used to illustrate an implementation method of generating a first model by inheriting a target model.

[0099] As an optional implementation manner, generating the first model according to the model attributes and model parameters of the target model includes:

[0100] Taking the target model as a parent model;

[0101] Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a first sub-attribute and a first sub-parameter;

[0102] Using the model attribute of the parent model and the first sub-attribute as the first target attribute of the first model, and using the model parameter of the parent model and the first sub-parameter as the first target parameter of the first model;

[0103] The first model is generated according to the first target attribute and the first target parameter.

[0104] Specifically, in practical applications, a new transient model is created. First, the model is allowed to inherit all parameter information of a parent model, and then its own parameters and parameter values ​​are created to form a new child transient model, namely, the first model.

[0105] This embodiment provides a model data processing process, in which one of the target models is taken as the parent model by inheritance, thereby completely inheriting the model attributes and model parameters of the parent model, and generating personalized first sub-attributes and first sub-parameters at the same time, and integrating the inheritance results and personalized data to generate the first model, thereby simplifying the generation process of the first model and improving the practicality of standardized storage of the target model.

[0106] The following implementation is used to illustrate an implementation method of generating a first model by performing polymorphic processing on a target model.

[0107] As an optional implementation manner, generating the first model according to the model attributes and model parameters of the target model includes:

[0108] Taking the target model as a parent model;

[0109] Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a second sub-attribute and a second sub-parameter;

[0110] According to the second sub-attribute, the model attribute of the parent model is updated to obtain the updated model attribute; and, according to the second sub-parameter, the model parameter of the parent model is updated to obtain the updated model parameter;

[0111] Using the updated model attribute of the parent model and the second sub-attribute as the second target attribute of the first model, and using the updated model parameter of the parent model and the second sub-parameter as the second target parameter of the first model;

[0112] The first model is generated according to the second target attribute and the second target parameter.

[0113] Specifically, in practical applications, a new transient model is created. First, the model is allowed to inherit all parameter information of a parent model, and then its own parameters and parameter values ​​are created to form a new child transient model. Some parameters in the child transient model require modification of the parameter values ​​in the parent model. In this case, the parameters are directly added to the child model to overwrite the parameters of the parent model. When the model is called, the parameters in the child model are used and the same parameter values ​​in the parent model are ignored.

[0114] This embodiment provides a model data processing process, in which one of the target models is used as a parent model through inheritance to generate personalized second sub-attributes and second sub-parameters. After inheriting the model attributes and model parameters of the parent model, the inheritance result is also modified to update part of the data in the model attributes and model parameters of the parent model. At the same time, the inheritance result, update result and personalized data are integrated to generate a first model, thereby simplifying the generation process of the first model and improving the practicality of standardized storage of the target model.

[0115] The following implementation is used to illustrate an implementation method of generating a first model by performing nesting processing on a target model.

[0116] As an optional implementation manner, there are multiple target models, and generating the first model according to the model attributes and model parameters of the target models includes:

[0117] Taking each of the target models as a parent model;

[0118] Performing inheritance operation on the model attributes and model parameters of each of the parent models, and creating a third sub-attribute and a third sub-parameter;

[0119] Using the model attribute of each parent model and the third sub-attribute as the third target attribute of the first model, or directly using the model attribute of each parent model as the third target attribute of the first model and using the model parameter of the parent model and the third sub-parameter as the third target parameter of the first model, or directly using the model parameter of the parent model as the third target parameter of the first model;

[0120] The first model is generated according to the third target attribute and the third target parameter.

[0121] Specifically, in actual application scenarios, when creating a new transient model, multiple existing model parameters can be included and nested into the new model, and then unique parameters can be added or not added to form a new transient model.

[0122] This embodiment provides a model data processing process, which uses inheritance to take multiple target models as parent models, thereby completely inheriting the model attributes and model parameters of each parent model, and simultaneously generating personalized third sub-attributes and third sub-parameters, and integrating the inheritance results and personalized data to generate a first model, or the inheritance results of multiple parent models can be directly used to generate the first model, thereby simplifying the generation process of the first model and improving the practicality of standardized storage of target models.

[0123] As an optional implementation, the method further includes:

[0124] In the target model and / or the first model, determining at least one operating model;

[0125] According to each of the operation models, a target operation mode is determined.

[0126] In this embodiment, at least one operating model can be determined through the existing target models in the transient model classification library and / or the first model generated according to the target model, so that the determined operating model can be directly used to compile the target operating mode of the current working cycle, thereby realizing the simple calling of model data in an actual application scenario, thereby improving the practicability of transient model storage and transient model data processing.

[0127] The present application also provides an electrical transient model data processing device, the device comprising:

[0128] A determination module is used to obtain a target model in a transient model classification library;

[0129] The processing module is used to generate a first model according to the model attributes and model parameters of the target model.

[0130] This application implements the storage of target model data through a transient model classification library. When the data corresponding to the target model needs to be applied, the model attributes and model parameters of the target model can be directly called, or the model attributes and model parameters of the target model can be called and then further processed to obtain the first model, so that the target model and / or the first model can be used in specific application scenarios, such as the corresponding model data can be called or processed during the configuration of the power system operation mode. Therefore, under the condition of realizing standardized storage and flexible storage of transient models, the data storage and data processing costs are reduced, and the effectiveness and practicality of electrical transient model data processing are improved.

[0131] As an optional implementation, the processing module is further configured to, before the determining module obtains the target model in the transient model classification library,

[0132] Establish a transient model classification library;

[0133] The data structure of the transient model classification library includes a tree structure, and the root node and the internal nodes are used to indicate the classification status of each of the target models, and the leaf nodes are used to indicate the subdivision type of each of the target models.

[0134] This implementation method can realize tree-like storage of electrical transient models by establishing a transient model classification library. For various target models, they can be classified into specific classifications at each level through a tree structure, thereby realizing standardized storage of target model data, facilitating later calls, and reducing data storage and data processing costs.

[0135] As an optional implementation, the model attributes of the target model include fixed attributes and dynamic attributes;

[0136] The processing module is also used for:

[0137] Storing the fixed attributes and corresponding fixed parameters of each target model through a relational database;

[0138] The dynamic attributes and corresponding dynamic parameters of each target model are stored through a non-relational database.

[0139] This implementation separates the two types of attributes in the transient model and stores them separately in databases with different characteristics. Specifically, a relational database is used to store the fixed attributes of each target model and the fixed parameters corresponding to the fixed attributes, and a non-relational database is used to store the dynamic attributes and dynamic parameters of each target model. This achieves standardized storage of target model data, facilitates later calls, and reduces data storage and data processing costs.

[0140] As an optional implementation manner, the specific manner in which the processing module generates the first model according to the model attributes and model parameters of the target model includes:

[0141] Taking the target model as a parent model;

[0142] Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a first sub-attribute and a first sub-parameter;

[0143] Using the model attribute of the parent model and the first sub-attribute as the first target attribute of the first model, and using the model parameter of the parent model and the first sub-parameter as the first target parameter of the first model;

[0144] The first model is generated according to the first target attribute and the first target parameter.

[0145] This embodiment provides a model data processing process, in which one of the target models is taken as the parent model by inheritance, thereby completely inheriting the model attributes and model parameters of the parent model, and generating personalized first sub-attributes and first sub-parameters at the same time, and integrating the inheritance results and personalized data to generate the first model, thereby simplifying the generation process of the first model and improving the practicality of standardized storage of the target model.

[0146] As an optional implementation manner, the specific manner in which the processing module generates the first model according to the model attributes and model parameters of the target model includes:

[0147] Taking the target model as a parent model;

[0148] Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a second sub-attribute and a second sub-parameter;

[0149] According to the second sub-attribute, the model attribute of the parent model is updated to obtain the updated model attribute; and, according to the second sub-parameter, the model parameter of the parent model is updated to obtain the updated model parameter;

[0150] Using the updated model attribute of the parent model and the second sub-attribute as the second target attribute of the first model, and using the updated model parameter of the parent model and the second sub-parameter as the second target parameter of the first model;

[0151] The first model is generated according to the second target attribute and the second target parameter.

[0152] This embodiment provides a model data processing process, in which one of the target models is used as a parent model through inheritance to generate personalized second sub-attributes and second sub-parameters. After inheriting the model attributes and model parameters of the parent model, the inheritance result is also modified to update part of the data in the model attributes and model parameters of the parent model. At the same time, the inheritance result, update result and personalized data are integrated to generate a first model, thereby simplifying the generation process of the first model and improving the practicality of standardized storage of the target model.

[0153] As an optional implementation, there are multiple target models, and the specific manner in which the processing module generates the first model according to the model attributes and model parameters of the target model includes:

[0154] Taking each of the target models as a parent model;

[0155] Performing inheritance operation on the model attributes and model parameters of each of the parent models, and creating a third sub-attribute and a third sub-parameter;

[0156] Using the model attribute of each parent model and the third sub-attribute as the third target attribute of the first model, or directly using the model attribute of each parent model as the third target attribute of the first model and using the model parameter of the parent model and the third sub-parameter as the third target parameter of the first model, or directly using the model parameter of the parent model as the third target parameter of the first model;

[0157] The first model is generated according to the third target attribute and the third target parameter.

[0158] This embodiment provides a model data processing process, which uses inheritance to take multiple target models as parent models, thereby completely inheriting the model attributes and model parameters of each parent model, and simultaneously generating personalized third sub-attributes and third sub-parameters, and integrating the inheritance results and personalized data to generate a first model, or the inheritance results of multiple parent models can be directly used to generate the first model, thereby simplifying the generation process of the first model and improving the practicality of standardized storage of target models.

[0159] As an optional implementation, the processing module is further configured to:

[0160] In the target model and / or the first model, determining at least one operating model;

[0161] According to each of the operation models, a target operation mode is determined.

[0162] In this embodiment, at least one operating model can be determined through the existing target models in the transient model classification library and / or the first model generated according to the target model, so that the determined operating model can be directly used to compile the target operating mode of the current working cycle, thereby realizing the simple calling of model data in an actual application scenario, thereby improving the practicability of transient model storage and transient model data processing.

[0163] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the processing module can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The function of the above-determined module. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0164] Indicatively, Figure 4 As shown, Figure 4 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 may be provided as a server. Figure 4The computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by a memory 301 for storing instructions executable by the processing component 302, such as an application. The application stored in the memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 302 is configured to execute the instructions to perform the text recognition method of any of the above embodiments.

[0165] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0166] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0167] An embodiment of the present application provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute a method provided in any embodiment.

[0168] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0169] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0170] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing electrical transient model data, characterized in that: The method comprises: Obtain the target model in the transient model classification library; Generate a first model according to the model attributes and model parameters of the target model; The model attributes of the target model include fixed attributes and dynamic attributes, and the first model is generated by processing the model attributes and model parameters of the target model in an inheritance, polymorphism or nesting manner.

2. The method according to claim 1, characterized in that Before obtaining the target model in the transient model classification library, the method further includes: Establish a transient model classification library; The data structure of the transient model classification library includes a tree structure, and the root node and the internal nodes are used to indicate the classification status of each of the target models, and the leaf nodes are used to indicate the subdivision type of each of the target models.

3. The method according to claim 1, characterized in that The method further comprises: Storing the fixed attributes and corresponding fixed parameters of each target model through a relational database; The dynamic attributes and corresponding dynamic parameters of each target model are stored through a non-relational database.

4. The method according to claim 1, characterized in that The step of generating a first model according to the model attributes and model parameters of the target model comprises: Taking the target model as a parent model; Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a first sub-attribute and a first sub-parameter; Using the model attribute of the parent model and the first sub-attribute as the first target attribute of the first model, and using the model parameter of the parent model and the first sub-parameter as the first target parameter of the first model; The first model is generated according to the first target attribute and the first target parameter.

5. The method according to claim 1, characterized in that The step of generating a first model according to the model attributes and model parameters of the target model comprises: Taking the target model as a parent model; Performing inheritance operation on the model attributes and model parameters of the parent model, and creating a second sub-attribute and a second sub-parameter; According to the second sub-attribute, the model attribute of the parent model is updated to obtain the updated model attribute; and, according to the second sub-parameter, the model parameter of the parent model is updated to obtain the updated model parameter; Using the updated model attribute of the parent model and the second sub-attribute as the second target attribute of the first model, and using the updated model parameter of the parent model and the second sub-parameter as the second target parameter of the first model; The first model is generated according to the second target attribute and the second target parameter.

6. The method according to claim 1, characterized in that There are multiple target models, and generating a first model according to model attributes and model parameters of the target models includes: Taking each of the target models as a parent model; Performing inheritance operation on the model attributes and model parameters of each of the parent models, and creating a third sub-attribute and a third sub-parameter; Using the model attribute of each parent model and the third sub-attribute as the third target attribute of the first model, or directly using the model attribute of each parent model as the third target attribute of the first model and using the model parameter of the parent model and the third sub-parameter as the third target parameter of the first model, or directly using the model parameter of the parent model as the third target parameter of the first model; The first model is generated according to the third target attribute and the third target parameter.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: In the target model and / or the first model, determining at least one operating model; According to each of the operation models, a target operation mode is determined.

8. An electrical transient model data processing device, characterized in that: The device comprises: A determination module is used to obtain a target model in a transient model classification library; The processing module is used to generate a first model according to the model attributes and model parameters of the target model.

9. A computer device, characterized in that: The method comprises one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method according to any one of claims 1 to 7 are executed.

10. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method according to any one of claims 1 to 7.

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