CN114996930A - Modeling method and device, electronic equipment and storage medium - Google Patents

Abstract

The application discloses a modeling method, a modeling device, electronic equipment and a storage medium, and relates to the technical field of computers. Wherein, the method comprises the following steps: classifying a plurality of power grid entities in a power distribution network database based on a service scene to obtain a data set corresponding to the service scene, wherein the data set comprises at least one necessary power grid entity corresponding to the service scene; acquiring information records of necessary power grid entities from a power distribution grid database; determining a dynamically modelable power grid object from a data set corresponding to a business scene based on the information record; and adopting a JavaScript object representation method to build a model for the dynamically modelled power grid object to obtain a memory resident dynamic model corresponding to the service scene. The technical scheme provided by the application can save the memory and improve the processing efficiency of the calculation program.

Description

A modeling method, device, electronic device and storage medium

technical field

The present application relates to the field of computer technology, and in particular, to a modeling method, an apparatus, an electronic device, and a storage medium.

Background technique

In recent years, with the rapid improvement of my country's national economy, the distribution network has developed rapidly in response to electricity demand, and its scale and power supply quality requirements have become higher and higher. Under this condition, it is required to carry out various kinds of analysis and calculation on the distribution network to solve daily operations such as maintenance and power supply adjustment of the distribution network without affecting the power supply. However, due to the huge scale of the distribution network, various analysis and calculations of the distribution network are difficult. On the one hand, the processing efficiency of computing programs is slow due to the large scale and scattered data.

SUMMARY OF THE INVENTION

The present application provides a modeling method, apparatus, electronic device and storage medium, which can save memory and improve the processing efficiency of computing programs.

In a first aspect, the present application provides a modeling method, the method comprising:

Classifying multiple power grid entities in the distribution network database based on the business scenario, to obtain a data set corresponding to the business scenario, where the data set includes at least one necessary power grid entity corresponding to the business scenario;

obtaining information records of the necessary grid entities from the distribution grid database;

Determine a dynamically modelable power grid object from the data set corresponding to the business scenario based on the information record;

The dynamic modelable grid object is modeled by using JavaScript object notation, and a memory-resident dynamic model corresponding to the business scenario is obtained.

In a second aspect, the present application provides a modeling device, the device comprising:

A data set determination module, configured to classify multiple power grid entities in the distribution network database based on business scenarios, and obtain a data set corresponding to the business scenario, the data set including at least one necessary power grid entity corresponding to the business scenario ;

an information acquisition module, configured to acquire the information records of the necessary grid entities from the distribution grid database;

an object determination module, configured to determine a dynamically modelable power grid object from the data set corresponding to the business scenario based on the information record;

A model building module is used to build a model for the dynamically modelable power grid object by using JavaScript object notation to obtain a memory-resident dynamic model corresponding to the business scenario.

In a third aspect, the application provides an electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the construction described in any of the embodiments of the present application. model method.

In a fourth aspect, the present application provides a computer-readable storage medium, where computer instructions are stored in the computer-readable storage medium, and the computer instructions are used to enable a processor to implement the modeling described in any embodiment of the present application when executed. method.

Embodiments of the present application provide a modeling method, device, electronic device, and storage medium. The method includes: classifying multiple power grid entities in a distribution network database based on a business scenario, and obtaining a data set corresponding to the business scenario. The set includes at least one necessary grid entity corresponding to the business scenario; obtains the information records of the necessary grid entity from the distribution network database; determines the dynamic modelable grid object from the data set corresponding to the business scenario based on the information record; uses JavaScript object representation The method builds a model for the power grid objects that can be dynamically modeled, and obtains the memory-resident dynamic model corresponding to the business scenario. This application classifies and sorts the distribution network database based on business scenarios, obtains necessary power grid entities and necessary fields corresponding to the business scenarios, and then obtains a tree-shaped data structure dataset corresponding to the business scenarios; Select grid entities that occupy too much memory, grid entities or fields that are used infrequently, or grid entities or fields with a high degree of repetition, and use them as grid objects that can be dynamically modeled. The grid object of the modulo constructs the model. In the present application, the dynamic modeling of the power grid objects that can be dynamically modeled can save memory, and can also improve the processing efficiency of computing programs.

It should be understood that the content described in this section is not intended to identify key or critical features of the embodiments of the application, nor is it intended to limit the scope of the application. Other features of the present application will become readily understood from the following description.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort.

1 is a first schematic flowchart of a modeling method provided by an embodiment of the present application;

FIG. 2 is a schematic diagram of a data set corresponding to a business scenario provided by an embodiment of the present application;

3 is a second schematic flowchart of a modeling method provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a modeling apparatus provided by an embodiment of the present application;

FIG. 5 is a block diagram of an electronic device used to implement a modeling method according to an embodiment of the present application.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments It is only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the scope of protection of the present application.

It should be noted that the terms "first", "second", "target" and "original" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used for Describe a particular order or sequence. It is to be understood that data so used may be interchanged under appropriate circumstances such that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein. Furthermore, the terms "comprising", "having" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed Rather, those steps or units may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

FIG. 1 is a first schematic flowchart of a modeling method provided by an embodiment of the present application, and this embodiment can be applied to the dynamic modeling of power distribution network data. A modeling method provided in this embodiment may be executed by the modeling apparatus provided in this embodiment of the present application, and the apparatus may be implemented in software and/or hardware, and integrated in an electronic device that executes the method.

Referring to FIG. 1, the method of this embodiment includes but is not limited to the following steps:

S110. Classify multiple power grid entities in the distribution network database based on the business scenario, and obtain a data set corresponding to the business scenario, where the data set includes at least one necessary power grid entity corresponding to the business scenario.

The business scenario refers to a specific application scenario of the distribution network in the operation process, for example, the business scenario may be a distribution network maintenance scenario, a power consumption collection scenario, and the like. Power grid entity refers to a collection of certain types of data in the distribution network database. Power grid entities include abstract entities such as conductive equipment, including specific entities such as feeders, switches, and transformers, including topological entities such as endpoints and connection nodes, including acquisition devices, control terminals, etc. Secondary equipment entities also include marketing entities such as users and electric energy metering. Necessary grid entities refer to important grid entities that are essential for a business scenario.

Optionally, the distribution network database may be a large-scale 10kV distribution network database, and the grid entities in the distribution network database may perform information management in accordance with IEC61970 and IEC61968 standards.

In the embodiment of the present application, the electronic device may mark the business scene to obtain the corresponding scene label. Taking the scene label as the root node, the grid entities in the distribution network database are classified according to the different scene labels, and the data set of the tree data structure corresponding to the different scene labels is determined. Optionally, in addition to the tree data structure, the structure type of the data set may also be other data structures, which are not limited here. The data set includes at least one necessary grid entity corresponding to the business scenario, and the necessary grid entity is regarded as a child node of the scene label, that is, the necessary grid entity is a child node under the root node (ie, the scene label).

Optionally, the required grid entity includes at least one required field, and the required field is used as a leaf node of the required grid entity, that is, the required field is a leaf node under a child node (ie, the required grid entity). Required fields refer to important fields that are essential for a grid entity.

Further, classify multiple power grid entities in the distribution network database based on the business scenario, and obtain a data set corresponding to the business scenario, including: from the perspective of the business scenario's demand for power grid entities, based on the business scenario and according to the first necessary principle from the Determine at least one necessary power grid entity corresponding to the business scenario from multiple power grid entities; then perform usage analysis on the fields of the power grid entity, conduct demand assessment for each field in the necessary power grid entity according to the second necessary principle, and select the necessary power grid entity corresponding at least one necessary field; obtain a data set corresponding to the business scenario based on the at least one necessary grid entity and the at least one necessary field. Among them, the first necessary principle refers to the set of the minimum number of entities necessary to complete the business corresponding to the business scenario. The second essential principle refers to the set of the minimum number of fields necessary to complete the business corresponding to the business scenario.

In the embodiment of the present application, the root node of the tree-shaped data structure of the scene label is established based on the business scene. Traverse multiple power grid entities in the distribution network database, and evaluate whether the power grid entity is necessary for the scene according to the business scenario. If it is necessary, add a child node to the root node of the scene label. Traverse the specific fields in the necessary power grid entities, and evaluate whether the field is necessary for the scene according to the business scenario. If it is necessary, add leaf nodes to the child nodes of the model. In particular, record identification information (such as record ID, record name, etc.) must be included in the required fields.

Figure 2 is a schematic diagram of a data set corresponding to a business scenario. The data set corresponding to a business scenario includes a scenario label (ie, root node), at least one necessary grid entity (ie, child node) and at least one necessary field (ie, leaf node). The purpose of this setting is to appropriately tailor the IEC61970 and IEC61968 standards in the distribution network database, so as to generate a data set (such as a tree data structure) corresponding to the business scenario, which is convenient for the management of the distribution network database.

Optionally, data sets in different business scenarios can be expressed in E _case , and the necessary grid entities in all business scenarios can be represented by the following formula (1):

In the formula, E is the necessary power grid entity in all business scenarios, n is the index number of the business scenario, Ecase, n is the necessary power grid entity in the nth business scenario, and U is the union set.

S120: Obtain information records of necessary grid entities from the distribution grid database.

In this embodiment of the present application, the information record of a grid entity may be any information related to the grid entity, such as historical information, website information, and the like. The electronic device sends the information acquisition request of the necessary grid entity to the server, and the server receives and parses the information acquisition request, and sends the information record of the necessary grid entity to the electronic device.

S130. Determine a dynamically modelable power grid object from the data set corresponding to the business scenario based on the information record.

The grid object can be a grid entity or a field. The power grid objects that can be dynamically modeled can be power grid entities that take up too much memory, power grid entities or fields that are used infrequently, or power grid entities or fields that are highly repetitive. For example, the power grid object that can be dynamically modeled may be all fields of a certain power grid entity (ie, the entire power grid entity), or may be part of the fields of a certain power grid entity (ie, the aforementioned fields).

In the embodiment of the present application, after the information record is obtained from the distribution network database through the above step S120, all the contents in the information record are traversed, so as to determine whether to perform dynamic modeling processing, and obtain a dynamic modeling grid object. Specifically, the memory occupied by the grid entity can be determined according to the number of information records, and whether the grid entity needs to perform dynamic modeling processing can be determined according to the size of the occupied memory. For example, if a grid entity occupies too much memory, the grid entity will be Dynamic modeling processing; statistical analysis can also be performed on the repetition degree of field values according to the field content of the information record to determine whether the field needs to undergo dynamic modeling processing. For example, if a certain field is used with a low frequency or a high degree of repetition , then perform dynamic modeling processing on it.

Optionally, the power grid objects that can be dynamically modeled in different business scenarios can be expressed by D _case , and the power grid objects that can be dynamically modeled in all business scenarios can be represented by the following formula (2):

In the formula, D is the power grid object that can be dynamically modeled in all business scenarios, n is the index number of the business scenario, D _{case, n} is the power grid object that can be dynamically modeled in the nth business scenario, and U is the union set.

Further, after determining the dynamically modelable power grid objects from the data set corresponding to the business scenario based on the information records, the method further includes: taking the data set corresponding to the business scenario as a complete set; taking the dynamically modelable objects corresponding to the business scenario as a subset ; Calculate the complement of the subset based on the complete set, and use the complement as the grid object that can be statically modeled corresponding to the business scenario; use a preset programming language to build a model for the grid object that can be statically modeled, and obtain the memory resident corresponding to the business scenario. Keep the original model. Among them, the grid objects that can be statically modeled refer to grid entities or fields that are frequently used and do not have a high degree of repetition. The grid objects that can be statically modeled must satisfy the following formula (3):

为所有业务场景下的可静态建模的电网对象，E为所有业务场景下的必要电网实体，D为所有业务场景下的可动态建模的电网对象，

表示空集。In the formula,

is the grid object that can be modeled statically in all business scenarios, E is the necessary grid entity in all business scenarios, D is the grid object that can be dynamically modeled in all business scenarios,

represents the empty set.

S140. Use JavaScript object notation to construct a model for a dynamically modelable power grid object, and obtain a memory-resident dynamic model corresponding to the business scenario.

Among them, JavaScript Object Notation (JavaScript Object Notation, Json) is a lightweight data exchange format, which is widely used in asynchronous communication of applications, such as in the communication between the Web client and the server. The data structure of the memory-resident dynamic model is expressed in the Json data format, which is independent of the programming language.

Further, the JavaScript object notation is used to construct a model for the dynamically modelable object, and a memory-resident dynamic model corresponding to the business scenario is obtained, including: determining the data characteristics of the dynamically modelable object; Assuming that a number of power grid objects have the same data feature, a model is constructed for a preset number of power grid objects according to the first modeling method; other power grid objects in the dynamically modelable power grid objects are constructed according to the second modeling method. A memory-resident dynamic model corresponding to the business scenario is obtained, and other power grid objects are power grid objects other than a preset number of power grid objects among the power grid objects that can be dynamically modeled. That is, according to the different data characteristics of the dynamically modelable objects, the constructed memory-resident dynamic model data structures are also different.

In the embodiment of the present application, the first modeling method is a modeling method for grid objects with a large amount of repeated data, and the model building method may be based on data characteristics as fields, and field values are grid objects with the data characteristics. The second modeling method is a modeling method for a power grid object without a large amount of repeated data. The model building method may be based on the power grid object as a field, and the field value is a data feature of the power grid object. Optionally, an empty memory-resident dynamic model can be constructed as a reservation to facilitate the subsequent dynamic management of the model during the running process.

Further, after obtaining the memory-resident dynamic model corresponding to the business scenario, the method further includes: extracting key fields (such as keyField) shared by the memory-resident inherent model and the memory-resident dynamic model; establishing a memory-resident based on the key fields The mapping relationship between the intrinsic model and the memory-resident dynamic model; based on the mapping relationship, the memory-resident intrinsic model and the memory-resident dynamic model are spliced to obtain the complete model corresponding to the business scenario.

The technical solution provided by this embodiment classifies multiple power grid entities in the distribution network database based on business scenarios, and obtains a data set corresponding to the business scenario, where the data set includes at least one necessary power grid entity corresponding to the business scenario; Obtain the information records of the necessary power grid entities from the database; determine the power grid objects that can be dynamically modeled from the data sets corresponding to the business scenarios based on the information records; use the JavaScript object notation to build models for the power grid objects that can be dynamically modeled to obtain the corresponding business scenarios. memory-resident dynamic model. This application classifies and sorts the distribution network database based on business scenarios, obtains necessary power grid entities and necessary fields corresponding to the business scenarios, and then obtains a tree-shaped data structure dataset corresponding to the business scenarios; Select grid entities that occupy too much memory, grid entities or fields that are used infrequently, or grid entities or fields with a high degree of repetition, and use them as grid objects that can be dynamically modeled. The grid object of the modulo constructs the model. In the present application, the dynamic modeling of the power grid objects that can be dynamically modeled can save memory, and can also improve the processing efficiency of computing programs.

The modeling method provided by the embodiment of the present invention is further described below, and FIG. 3 is a second schematic flowchart of a modeling method provided by the embodiment of the present application. The embodiment of the present application is optimized on the basis of the above-mentioned embodiment, and the specific optimization is as follows: this embodiment provides a detailed explanation of the determination process of the power grid object that can be dynamically modeled and the adjustment process of the memory resident dynamic model.

Referring to FIG. 3, the method of this embodiment includes but is not limited to the following steps:

S210. Classify multiple power grid entities in the distribution network database based on the business scenario to obtain a data set corresponding to the business scenario.

For the relevant content of this step, refer to step S110 in the embodiment of FIG. 1 , which will not be repeated here.

S220. Obtain information records of necessary grid entities from the distribution grid database.

For the relevant content of this step, refer to step S120 in the embodiment of FIG. 1 , and details are not repeated here.

S230. Determine whether the necessary power grid entity meets the first condition for dynamic modeling based on the number of information records; if so, obtain a mapping table of at least one necessary field in the necessary power grid entity.

Wherein, the first condition for dynamic modeling is that the memory occupation of the power grid entity is within the preset standard.

In the embodiment of the present application, after the information records are acquired from the distribution network database, the number of pieces of information records is counted. If the number of information records is less than the preset value (such as 100), it is considered that the necessary power grid entity will not cause excessive memory usage, does not meet the first condition of dynamic modeling, and does not need to perform dynamic modeling processing, and then determine the next Necessary grid entity. If the number of information records is greater than or equal to the preset value (such as 100), it is considered that the necessary power grid entity will cause excessive memory usage, and the first condition for dynamic modeling is satisfied, and dynamic modeling processing is required. At this time, it is necessary to judge Which fields in the necessary power grid entity need dynamic modeling processing, so a mapping table of at least one necessary field in the necessary power grid entity needs to be obtained. Among them, the mapping table is used to record the field values of the necessary fields, the number of key values of the field values, and the number of repetitions of the field values.

The establishment process of the mapping table can be: traverse the fields of the power grid entity, and establish a mapping table of field value-repetition times. exists in, and each time it occurs, the number of repetitions in the mapping table is incremented by 1.

S240. Determine whether the number of key values exceeds the first preset percentage of the number of information records, and if not, select the maximum number of repetitions in the mapping table.

The second condition for dynamic modeling is that the number of key values and the number of repetitions of the fields in the power grid entity are within preset standards. The number of key values is the number of types of values of the field value.

In the embodiment of the present application, after obtaining the mapping table of at least one necessary field in the necessary grid entity, the number of key values in the mapping table is counted. If the number of key values is greater than the first preset percentage (such as 33%) of the number of records, Then it is considered that the field does not meet the second condition of dynamic modeling, and then the next necessary field is judged. If the number of key values is less than or equal to the first preset percentage (eg, 33%) of the number of records, the number of repetitions of the field value in the mapping table is selected to determine the maximum number of repetitions.

S250. Determine whether the maximum number of repetitions exceeds the second preset percentage of the information record, and if so, use the necessary field corresponding to the maximum number of repetitions as a dynamic modelable grid object.

In this embodiment of the present application, after determining the maximum number of repetitions, it is determined whether the maximum number of repetitions exceeds a second preset percentage (such as 60%) of the information record. If not, it is considered that the field does not have the dynamic modeling second condition, and then determine the next required field. If it exceeds, it is considered that the field has the second condition for dynamic modeling, and the grid entity and the field are included in the list that can be dynamically modeled.

S260. Use JavaScript object notation to construct a model for a dynamically modelable power grid object, and obtain a memory-resident dynamic model corresponding to the business scenario.

For the relevant content of this step, refer to step S140 in the embodiment of FIG. 1 , which will not be repeated here.

S270. When running the memory-resident dynamic model, re-acquire new information records of necessary power grid entities from the distribution network database; evaluate the memory occupation of the memory-resident dynamic model based on the new information records to obtain an evaluation result, and based on the new information records The evaluation results make adjustments to the memory-resident dynamic model.

In the embodiment of the present application, after the memory resident dynamic model is obtained through the above steps, the memory resident dynamic model needs to be adjusted. Specifically: when running the memory resident dynamic model, since the information records of the grid entities may be updated, it is necessary to re-obtain new information records of the necessary grid entities from the distribution network database, according to the rules in steps S230-S250. The evaluation results are obtained by evaluating the memory footprint of the memory-resident dynamic model. Grid entities or fields that no longer satisfy the dynamic modeling conditions are recorded in the evaluation results. If the memory occupancy saving effect of the dynamic model is not obvious, according to the evaluation results, in the next initialization work, a memory-resident inherent model is constructed for the grid entities or fields that no longer meet the dynamic modeling conditions, so as to adjust the memory-resident dynamic model.

In the technical solution provided by this embodiment, a plurality of grid entities in the distribution network database are classified based on business scenarios, and a data set corresponding to the business scenario is obtained; information records of necessary grid entities are obtained from the distribution network database; based on the information records Determine whether the necessary grid entity has the first condition for dynamic modeling; if yes, obtain a mapping table of at least one necessary field in the necessary grid entity; determine whether the number of key values exceeds the first preset percentage of the number of information records, If it does not exceed, select the maximum number of repetitions in the mapping table; determine whether the maximum number of repetitions exceeds the second preset percentage of the information record, if so, take the necessary field corresponding to the maximum number of repetitions as a dynamic modelable grid Object; use JavaScript object notation to build a model for dynamically modelable power grid objects, and obtain the memory-resident dynamic model corresponding to the business scenario; when running the memory-resident dynamic model, re-acquire the necessary power grid entities from the distribution network database. A new information record; the memory occupancy of the memory-resident dynamic model is evaluated based on the new information record to obtain an evaluation result, and the memory-resident dynamic model is adjusted based on the evaluation result. This application classifies and sorts the distribution network database based on business scenarios, obtains necessary power grid entities and necessary fields corresponding to the business scenarios, and then obtains a tree-shaped data structure dataset corresponding to the business scenarios; Select grid entities that occupy too much memory, grid entities or fields that are used infrequently, or grid entities or fields with a high degree of repetition, and use them as grid objects that can be dynamically modeled. The resident dynamic model can also be adjusted for the memory resident dynamic model. In the present application, the dynamic modeling of the power grid objects that can be dynamically modeled can save memory, and can also improve the processing efficiency of computing programs.

FIG. 4 is a schematic structural diagram of a modeling apparatus provided by an embodiment of the present application. As shown in FIG. 4 , the apparatus 400 may include:

A data set determination module 410, configured to classify multiple power grid entities in the distribution network database based on business scenarios, and obtain a data set corresponding to the business scenario, where the data set includes at least one necessary power grid corresponding to the business scenario entity;

an information acquisition module 420, configured to acquire the information records of the necessary grid entities from the distribution grid database;

an object determination module 430, configured to determine a dynamically modelable power grid object from the data set corresponding to the business scenario based on the information record;

The model building module 440 is configured to use JavaScript object notation to build a model for the dynamically modelable power grid object to obtain a memory-resident dynamic model corresponding to the business scenario.

Optionally, the necessary grid entity includes at least one necessary field.

Further, the above data set determination module 410 may be specifically configured to: determine at least one necessary power grid entity corresponding to the business scenario from the plurality of power grid entities according to the first necessary principle based on the business scenario; According to the necessity principle, a demand assessment is performed for each field in the necessary grid entity, and at least one necessary field corresponding to the necessary grid entity is selected; the service is obtained based on the at least one necessary grid entity and the at least one necessary field. The dataset corresponding to the scene.

Further, the above-mentioned object determination module 430 may be specifically configured to: determine whether the necessary power grid entity has the first condition for dynamic modeling based on the number of pieces of information records; A mapping table of at least one necessary field; based on the mapping table of the at least one necessary field, a necessary field with the second condition for dynamic modeling is determined from the at least one necessary field, and the second condition with the second condition for dynamic modeling is determined. Required fields as a dynamically modelable grid object.

Optionally, the mapping table is used to record the field value of the necessary field, the number of key values of the field value, and the number of repetitions of the field value.

Further, the above-mentioned object determination module 430 can also be specifically used for: judging whether the number of key values exceeds the first preset percentage of the number of information records, and if not, selecting the largest repetition in the mapping table. number of times; determine whether the maximum number of repetitions exceeds the second preset percentage of the information record, and if so, use the necessary field corresponding to the maximum number of repetitions as a dynamically modelable grid object.

Further, the above-mentioned model building module 440 can be specifically used to: determine the data feature of the dynamically modelable object; if a preset number of power grid objects in the dynamically modelable power grid objects have the same data feature, then Build models for the preset number of power grid objects according to the first modeling method; build models for other power grid objects in the dynamically modelable power grid objects according to the second modeling method, so as to obtain the memory corresponding to the business scenario A dynamic model resides, and the other grid objects are grid objects other than the preset number of grid objects in the dynamically modelable grid objects.

Further, the above-mentioned modeling device may further include: a model adjustment module;

The model adjustment module is used for re-acquiring new information records of the necessary grid entities from the distribution grid database when running the memory resident dynamic model; based on the new information records, the The memory occupation of the memory-resident dynamic model is evaluated to obtain an evaluation result, and the memory-resident dynamic model is adjusted based on the evaluation result.

Further, the above-mentioned object determination module 430 may also be specifically configured to: after determining the power grid object that can be dynamically modeled from the data set corresponding to the business scenario based on the information record, the data set corresponding to the business scenario is determined. As a complete set; take the dynamically modelable objects corresponding to the business scenario as a subset; find the complement of the subset based on the complete set, and use the complement as the statically modelable object corresponding to the business scenario A power grid object; a preset programming language is used to construct a model for the statically modelable power grid object, and a memory-resident inherent model corresponding to the business scenario is obtained.

Further, the above-mentioned model building module 440 can also be specifically configured to: after obtaining the memory-resident dynamic model corresponding to the business scenario, extract the key shared by the memory-resident inherent model and the memory-resident dynamic model. field; establish a mapping relationship between the memory-resident intrinsic model and the memory-resident dynamic model based on the key field; based on the mapping relationship, the memory-resident intrinsic model and the memory-resident The dynamic models are spliced to obtain a complete model corresponding to the business scenario.

The modeling apparatus provided in this embodiment can be applied to the modeling method provided in any of the foregoing embodiments, and has corresponding functions and beneficial effects.

FIG. 5 is a block diagram of an electronic device used to implement a display method according to an embodiment of the present application. Electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (eg, helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are by way of example only, and are not intended to limit implementations of the application described and/or claimed herein.

As shown in FIG. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a read only memory (ROM) 12, a random access memory (RAM) 13, etc., connected in communication with the at least one processor 11, wherein the memory stores There is a computer program executable by at least one processor, and the processor 11 can be executed according to a computer program stored in a read only memory (ROM) 12 or loaded from a storage unit 18 into a random access memory (RAM) 13. Various appropriate actions and processes are performed. In the RAM 13, various programs and data necessary for the operation of the electronic device 10 can also be stored. The processor 11 , the ROM 12 and the RAM 13 are connected to each other through a bus 14 . An input/output (I/O) interface 15 is also connected to the bus 14 .

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc. etc.; and a communication unit 19, such as a network card, modem, wireless communication transceiver, and the like. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

The processor 11 may be various general and/or special purpose processing components having processing and computing capabilities. Some examples of processors 11 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as modeling methods.

In some embodiments, the modeling method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18 . In some embodiments, part or all of the computer program may be loaded and/or installed on the electronic device 10 via the ROM 12 and/or the communication unit 19 . When a computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the modeling method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the modeling method by any other suitable means (eg, by means of firmware).

Various implementations of the systems and techniques described herein above may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips system (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that The processor, which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an output device.

Computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/operations specified in the flowcharts and/or block diagrams to be carried out. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in connection with the instruction execution system, apparatus or device. Computer-readable storage media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), fiber optics, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having a display device (eg, a CRT (cathode ray tube) or an LCD (liquid crystal display)) for displaying information to the user monitor); and a keyboard and pointing device (eg, a mouse or trackball) through which a user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback); and can be in any form (including acoustic input, voice input, or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the Internet.

A computing system can include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or a cloud host. It is a host product in the cloud computing service system to solve the traditional physical host and VPS services, which are difficult to manage and weak in business scalability. defect.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, the steps described in the present application may be executed in parallel, sequentially or in different orders. As long as the desired results of the technical solutions of the present application can be achieved, no limitation is imposed herein.

The above-mentioned specific embodiments do not constitute a limitation on the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application shall be included within the protection scope of this application.

Claims (11)

1. a modeling method, it is characterised in that the method comprises: Classifying multiple power grid entities in the distribution network database based on the business scenario, to obtain a data set corresponding to the business scenario, where the data set includes at least one necessary power grid entity corresponding to the business scenario; obtaining information records of the necessary grid entities from the distribution grid database; Determine a dynamically modelable power grid object from the data set corresponding to the business scenario based on the information record; The dynamic modelable grid object is modeled by using JavaScript object notation, and a memory-resident dynamic model corresponding to the business scenario is obtained. 2 . The modeling method according to claim 1 , wherein the necessary power grid entity includes at least one necessary field, and the classification of multiple power grid entities in a distribution network database based on business scenarios is performed to obtain the Data sets corresponding to business scenarios, including: determining at least one necessary power grid entity corresponding to the business scenario from the plurality of power grid entities based on the business scenario according to the first necessary principle; Perform demand assessment on each field in the necessary grid entity according to the second necessary principle, and select at least one necessary field corresponding to the necessary grid entity; A data set corresponding to the business scenario is obtained based on the at least one necessary grid entity and the at least one necessary field. 3 . The modeling method according to claim 2 , wherein, determining a dynamically modelable power grid object from a data set corresponding to the business scenario based on the information record, comprising: 3 . Determine whether the necessary grid entity meets the first condition for dynamic modeling based on the number of information records; if yes, obtain a mapping table of the at least one necessary field in the necessary grid entity; Based on the mapping table of the at least one necessary field, a necessary field meeting the second condition for dynamic modeling is determined from the at least one necessary field, and the necessary field meeting the second condition for dynamic modeling is regarded as a dynamic modelable field grid object. 4. The modeling method according to claim 3, wherein the mapping table is used to record the field value of the necessary field, the number of key values of the field value and the number of repetitions of the field value. The necessary fields that meet the second condition for dynamic modeling are determined from the at least one necessary field based on the mapping table of the at least one necessary field, and the necessary fields that meet the second condition for dynamic modeling are regarded as dynamically buildable. Modular grid objects, including: Judging whether the number of key values exceeds the first preset percentage of the number of information records, if not, selecting the maximum number of repetitions in the mapping table; It is judged whether the maximum number of repetitions exceeds a second preset percentage of the information record, and if it exceeds, the necessary field corresponding to the maximum number of repetitions is used as a dynamically modelable power grid object. 5. The modeling method according to claim 1, characterized in that, by using JavaScript object notation to construct a model for the dynamically modelable power grid object, a memory-resident dynamic model corresponding to the business scenario is obtained, include: determining data characteristics of the dynamically modelable object; If a preset number of power grid objects in the dynamically modelable power grid objects have the same data feature, building a model for the preset number of power grid objects according to the first modeling method; Build models for other power grid objects in the dynamically modelable power grid objects according to the second modeling method, so as to obtain a memory-resident dynamic model corresponding to the business scenario, and the other power grid objects are the dynamically modelable power grid objects. grid objects other than the preset number of grid objects among the grid objects. 6. The modeling method according to claim 1, wherein the method further comprises: retrieving new information records of the necessary grid entities from the distribution grid database while running the memory resident dynamic model; An evaluation result is obtained by evaluating the memory occupation of the memory-resident dynamic model based on the new information record, and the memory-resident dynamic model is adjusted based on the evaluation result. 7 . The modeling method according to claim 1 , wherein, after determining the power grid object that can be dynamically modeled from the data set corresponding to the business scenario based on the information record, the method further comprises: 8 . Taking the data set corresponding to the business scenario as a complete set; Taking the dynamically modelable objects corresponding to the business scenario as a subset; Find the complement of the subset based on the complete set, and use the complement as a statically modelable power grid object corresponding to the business scenario; A preset programming language is used to construct a model for the statically modelable power grid object, and a memory-resident inherent model corresponding to the business scenario is obtained. 8. The modeling method according to claim 7, wherein after obtaining the memory-resident dynamic model corresponding to the business scenario, the method further comprises: extracting key fields common to the memory-resident intrinsic model and the memory-resident dynamic model; establishing a mapping relationship between the memory-resident intrinsic model and the memory-resident dynamic model based on the key field; Based on the mapping relationship, the memory-resident inherent model and the memory-resident dynamic model are spliced to obtain a complete model corresponding to the business scenario. 9. A modeling device, characterized in that the device comprises: A data set determination module, configured to classify multiple power grid entities in the distribution network database based on business scenarios, and obtain a data set corresponding to the business scenario, the data set including at least one necessary power grid entity corresponding to the business scenario ; an information acquisition module, configured to acquire the information records of the necessary grid entities from the distribution grid database; an object determination module, configured to determine a dynamically modelable power grid object from the data set corresponding to the business scenario based on the information record; A model building module is used to build a model for the dynamically modelable power grid object by using JavaScript object notation to obtain a memory-resident dynamic model corresponding to the business scenario. 10. An electronic device, characterized in that the electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform any one of claims 1 to 8. the modelling method described. 11. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the construction described in any one of claims 1 to 8 when executed. model method.

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