CN113361935B - Electric power energy scheduling method, device and energy scheduling system - Google Patents
Electric power energy scheduling method, device and energy scheduling system Download PDFInfo
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Abstract
The application discloses a method and a device for scheduling electric power energy and an energy scheduling system, wherein the method comprises the following steps: determining electricity demand information, wherein the electricity demand information comprises a target user identifier; determining a power scheduling model adapted to the power demand information through a designated query interface; inputting the electricity consumption demand information into the power dispatching model, and obtaining electricity consumption allocation information corresponding to the electricity consumption demand information output by the power dispatching model, wherein the electricity consumption allocation information comprises electricity consumption of different electricity consumption periods; and carrying out electric energy allocation of the corresponding electric quantity on the target user identification in different electric utilization periods based on the electric utilization allocation information. Therefore, energy allocation is carried out according to different electricity utilization characteristics of users, the electricity utilization efficiency is improved, and the energy waste is reduced.
Description
Technical Field
The embodiment of the application relates to the technical field of power data processing, in particular to a power energy scheduling method, a power energy scheduling device and an energy scheduling system.
Background
Grid dispatching management refers to management of production operation of a grid, a grid dispatching system and personnel and job activities by a grid dispatching mechanism according to related regulations in order to ensure safe, high-quality and economical operation of the grid. It generally includes dispatch operation management, dispatch plan management, relay protection and safety automation management, grid dispatch automation management, power communication management, hydropower plant reservoir dispatch management, power system personnel training management, and the like.
In the related art, when the power grid dispatching management platform realizes power grid dispatching management, energy allocation cannot be performed according to different power utilization characteristics of users, so that the power utilization efficiency is low, and the energy waste is caused.
Disclosure of Invention
The application provides a method, a device and an energy scheduling system for scheduling electric power energy, which are used for solving the problems that in the prior art, energy allocation cannot be performed according to different electricity utilization characteristics of users, so that the electricity utilization efficiency is low and energy waste is caused.
In a first aspect, an embodiment of the present application provides a method for power energy scheduling, which is applied to an energy scheduling system, where the method includes:
determining electricity demand information, wherein the electricity demand information comprises a target user identifier;
determining a power scheduling model adapted to the power demand information through a designated query interface;
inputting the electricity consumption demand information into the power dispatching model, and obtaining electricity consumption allocation information corresponding to the electricity consumption demand information output by the power dispatching model, wherein the electricity consumption allocation information comprises electricity consumption of different electricity consumption periods;
and carrying out electric energy allocation of the corresponding electric quantity on the target user identification in different electric utilization periods based on the electric utilization allocation information.
Optionally, the energy scheduling system comprises a user layer, and the user equipment accesses the energy scheduling system via the user layer;
the determining electricity demand information includes:
and receiving the electricity demand information input by the user equipment through the user layer.
Optionally, the energy scheduling system includes a data service layer, where the data service layer is configured to display a trained data model, provide a model query function, and provide the specified query interface to the outside;
the determining, by the designated query interface, a power scheduling model adapted to the power demand information includes:
displaying a data model list through the data service layer;
when the appointed query interface is detected to be triggered, acquiring search keywords input by a user, wherein the search keywords comprise keywords related to a power dispatching model;
and searching a corresponding power scheduling model according to the search keyword.
Optionally, the energy scheduling system further comprises a data source layer and a data model layer;
the method further comprises the steps of:
collecting data source information through the data source layer, wherein the data source information comprises power consumption information of different users in different power consumption periods;
and acquiring the data source information from the data source layer through the data model layer, and training different data models based on different training targets.
Optionally, the data model layer comprises a source data model layer;
the step of obtaining the data source information from the data source layer through the data model layer and training different data models based on different training targets comprises the following steps:
and constructing source data models of corresponding types according to different types of data source information based on different first training targets through the source data model layer.
Optionally, the data model layer comprises a fusion data model layer;
the step of obtaining the data source information from the data source layer through the data model layer and training different data models based on different training targets comprises the following steps:
and fusing the data source information of different types based on different second training targets through the fusion data model layer to obtain one or more fusion data sets, and constructing corresponding fusion data models for different fusion data sets.
In a second aspect, an embodiment of the present application further provides an apparatus for power energy scheduling, where the apparatus exists in an energy scheduling system, and the apparatus includes:
the power consumption requirement information determining module is used for determining power consumption requirement information, wherein the power consumption requirement information comprises a target user identifier;
the power scheduling model determining module is used for determining a power scheduling model matched with the power demand information through a designated query interface;
the user allocation information determining module is used for inputting the electricity consumption requirement information into the power scheduling model and obtaining electricity consumption allocation information corresponding to the electricity consumption requirement information output by the power scheduling model, wherein the electricity consumption allocation information comprises electricity consumption of different electricity consumption periods;
and the electricity utilization allocation module is used for allocating the electric energy corresponding to the electric quantity to the target user identification in different electricity utilization periods based on the electricity utilization allocation information.
In a third aspect, embodiments of the present application further provide an energy scheduling system, where the energy scheduling system includes: the system comprises a user layer, a data source layer, a data model layer, a data service layer and a deployment layer;
the user layer is used for providing an interface for user equipment to access the energy scheduling system;
the data source layer is used for collecting data source information, and the data source information comprises power consumption information of different users in different power consumption periods;
the data model layer is used for acquiring the data source information from the data source layer and training different data models based on different training targets;
the data service layer is used for displaying the trained data model, providing a model query function and providing the appointed query interface to the outside;
the allocation layer is used for allocating electric energy corresponding to the electric quantity to the users in different electricity utilization periods based on electricity utilization allocation information of different users.
In a fourth aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program to implement the method described above.
In a fifth aspect, embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the above-described method.
The application has the following beneficial effects:
in this embodiment, after determining the electricity demand information, an electricity scheduling model adapted to the electricity demand information may be determined through a designated query interface, and then the electricity demand information is input into the electricity scheduling model, and electricity allocation information corresponding to the electricity demand information output by the electricity scheduling model is obtained, where the electricity allocation information includes electricity consumption of different electricity consumption periods. Based on the electricity allocation information, electric energy allocation of corresponding electric quantity to the target user identification is realized in different electricity utilization periods, so that energy allocation is performed according to different electricity utilization characteristics of users, the electricity utilization efficiency is improved, and the energy waste is reduced.
Drawings
FIG. 1 is a block diagram of an embodiment of an energy scheduling system according to an embodiment of the present application;
fig. 2 is a flowchart of an embodiment of a method for scheduling electric power energy according to the second embodiment of the present application;
fig. 3 is a flowchart of an embodiment of a method for scheduling electric power energy according to the third embodiment of the present application;
fig. 4 is a block diagram of an embodiment of an apparatus for scheduling electric power energy according to the fourth embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
Example 1
Fig. 1 is a block diagram of an embodiment of an energy scheduling system according to an embodiment of the present application, where, as shown in fig. 1, the energy scheduling system may at least include: user layer 10, data source layer 20, data model layer 30, data service layer 40, and deployment layer 50. Wherein,,
the user layer 10 is configured to provide an interface for a user equipment to access the energy scheduling system. As an example, the interface may include a human-machine interface for a user to connect to the energy scheduling system through a web page and/or a machine interface; the machine interface is used for the electronic equipment to be connected with the energy scheduling system.
The data source layer 20 is configured to collect data source information, where the data source information includes power consumption information of different users in different power consumption periods. In implementation, the data source layer 20 may define a data access model, and different data source acquisition devices may send acquired data source information to the data source layer 20 by invoking the data access model. The data source layer 20 then stores the received data source information in hadoop background. At the same time, the data source layer 20 also supports monitoring of the data acquisition situation. Through continuously rich data source information, a complete data flow is formed in the hadoop background, and the complete and accurate transmission of data is ensured from an interface to storage.
Illustratively, the data source information may include monitoring data, archive data, topology data, and the like, wherein the monitoring data may include electrical energy monitoring data, gas energy monitoring data, water energy monitoring data, cold energy data, heat, new energy data, stored energy data, and the like; the profile data may include profile data internal and external to the enterprise; the topology data may include various topology data.
In other examples, the data source information may also include scheduling automation data, enterprise information system data, external system data, and the like. The dispatching automation data are data generated after the monitoring data are automatically dispatched; the enterprise information system data are obtained by counting the archive data and establishing a database; the external system data is data obtained by counting the data of the external system.
The data model layer 30 is configured to obtain the data source information from the data source layer, and train different data models based on different training targets.
In one embodiment, as shown in fig. 1, the data model layer 30 may include a source data model layer 301, where the source data model layer 301 is configured to construct a corresponding type of source data model for different types of data source information based on different first training objectives. For example, for monitoring data, archive data, and topology data, the corresponding source data models are a monitoring data model, an archive data model, and a topology data model, respectively. The monitoring data model is used for supporting modeling and understanding of various monitoring data, providing batch automatic processing capacity of internal and external system data, configuring the data synchronous flow of Hadoop into a system automatic scheduling mode for processing, realizing front-end dependence and operation running state monitoring functions and providing data cleaning operation before warehouse entry; the archive data model is used for supporting modeling and understanding of various archive data, providing service for accessing the archive data outside the power grid enterprise, and providing a foundation for model fusion understanding; the topology data model is used for supporting modeling and understanding of various topology data, and the multi-energy collaboration relates to analysis of different energy topological structures, so that the relation between energy production and supply can be conveniently known.
In another embodiment, as shown in fig. 1, the data model layer 30 may further include a fusion data model layer 302, where the fusion data model layer 302 is configured to fuse different types of data source information based on different second training targets, to obtain one or more fusion data sets, and construct a corresponding fusion data model for the different fusion data sets. For example, for monitoring data, profile data, and topology data, the corresponding fused data models are a fused monitoring data model, a fused profile data model, and a fused topology data model, respectively. The method comprises the steps of integrating a monitoring data model to provide modes and methods of data query, processing and calculation, and facilitating the processing of data relevance analysis and collaborative services by upper-layer application; the fusion archive data model is a fusion model of multi-energy archive data and is used for establishing archive association relations among alternative energy users and supporting calculation and analysis of multiple modes such as multi-energy coordination, corresponding multi-energy demand sides and the like; the fusion topology data model is a fusion model of a multi-energy data topology, supports unified modeling of the topology model of various data, supports collaborative understanding and analysis, and provides a fusion model for upper-layer application.
The data service layer 40 is configured to display the trained data model, provide a model query function, and provide a specified query interface to the outside. The data models in the data model layer 30 are queried and presented by the data services layer 40, which includes model services, data services, and visualization services. The model service is used for supporting the inquiry and display of the data model in the data model layer 30, so that the upper layer application can conveniently realize the operation of the model inquiry, and provides an entrance for the upper layer application to related resources; the data service is used for supporting the inquiry and the display of the data model in the data model layer 30, and displaying big data and data mining capability to an upper layer application as a service form; the visualization service is used for providing a visualization support, supporting the visual display of the data access, storage and use conditions of the data model layer 30, and providing the visual display of the completeness, the data storage scale, the use frequency, the update frequency and the like of the data model layer 30 through the visualization service.
The allocation layer 50 is configured to allocate electric energy corresponding to the electric power consumption to the users in different electricity consumption periods based on electricity consumption allocation information of different users, so as to allocate and manage electricity consumption requirements of different users.
In this embodiment, the allocation layer dynamically allocates the power consumption according to different power consumption periods of different users, so as to allocate the power consumption of the users in different power consumption sections. The data service layer inquires and displays the data model in the data model layer, the data of the energy station and the user can be modeled, the electricity consumption of the user is further accurately analyzed through analysis of the model, the data access model is defined by the data source layer, the data required by the intelligent energy is stored on the platform, and the management capability is improved through datamation of the intelligent energy.
Example two
Fig. 2 is a flowchart of an embodiment of a method for scheduling electric power energy according to a second embodiment of the present application, where the method may be applied to the energy scheduling system in the first embodiment, and may include the following steps:
at step 210, electricity demand information is determined, the electricity demand information including a target user identification.
In implementation, the electricity demand information may be electricity allocation demands for a specific user, or may be electricity allocation demands for a specific power station or a specific area, which is not limited in this embodiment. Illustratively, the electricity demand information may include a target user identifier, which may be an identifier of a specific user, an identifier of a region, or other entity identifiers, which is not limited in this embodiment.
In one embodiment, the energy scheduling system comprises a user layer via which user equipment accesses the energy scheduling system; step 210 may include the steps of:
and receiving the electricity demand information input by the user equipment through the user layer.
Illustratively, the user layer may include a web page, an app client page, an applet page, etc., which the present embodiment does not display. After the user accesses the energy scheduling system through the user layer, operations such as user registration and the like can be performed, including registering user identification, the region where the user is located, the type of electricity (such as household electricity, factory electricity, store electricity, irrigation electricity and the like) and the like. After user registration, it means that the authorized energy scheduling system can collect historical electricity data of the user, and the historical electricity data can be used as a data source to construct a data model.
When electricity allocation is needed for a certain user, the electricity demand information can be input through the user layer so as to instruct the energy scheduling system to allocate the electricity for the target user identification.
And 220, determining a power scheduling model adapted to the power demand information through a designated query interface.
In this embodiment, when it is determined that the current energy allocation requirement is electricity allocation, the adapted power scheduling model may be found out through the designated query interface.
In one embodiment, the energy scheduling system may include a data service layer, where the data service layer is configured to display a trained data model and provide a model query function, and provide the specified query interface to the outside; step 220 may further comprise the steps of:
displaying a data model list through the data service layer; when the appointed query interface is detected to be triggered, acquiring search keywords input by a user, wherein the search keywords comprise keywords related to a power dispatching model; and searching a corresponding power scheduling model according to the search keyword.
In this embodiment, the data model list may include a plurality of data models that have been trained. When the data service layer displays the data model list, the data model display can be performed according to different energy types and/or different electricity utilization types. For example, when the power source type is electric energy, one or more data models under each power type are displayed according to different power types such as household power, factory power, store power, irrigation power and the like. In one embodiment, one or more data models for each electricity usage type may be further subdivided by administrative area or electricity usage size.
The user may trigger a specific query interface to query the desired data model. When the energy scheduling system detects that the designated query interface is triggered, search keywords input by a user can be obtained, wherein the search keywords can comprise keywords related to the power scheduling model. For example, the search keywords may include one or more of administrative area names (or identifications), electricity usage types, energy types, electricity usage ranges, and the like.
After the search keyword is obtained, a corresponding power scheduling model can be found out from all the stored data models according to the search keyword.
In other embodiments, the power scheduling model may also be determined by detecting a user's selection of a data model in the list of data models.
In order to better improve the adaptation degree of the determined power dispatching model, the estimated power consumption range of the user can be determined according to the historical power consumption data of the target user identification in the power consumption demand information, and then the power dispatching model in the range is selected.
In this embodiment, after determining the power scheduling model, the power demand information may be input into the power scheduling model, and the power scheduling model performs power allocation analysis according to information such as the target user identifier and the region where the target user identifier is located in the power demand information, and then outputs power allocation information corresponding to the power demand information. The electricity allocation information comprises electricity consumption of different electricity consumption periods, namely the electricity allocation information comprises a plurality of electricity consumption periods and estimated electricity consumption corresponding to each electricity consumption period.
In other embodiments, when the electricity demand information includes the target electricity consumption time period, the power scheduling model outputs the estimated electricity consumption corresponding to the target electricity consumption time period.
And 240, performing electric energy allocation of the corresponding electric quantity on the target user identification in different electric utilization periods based on the electric utilization allocation information.
After the power consumption allocation information corresponding to the current power consumption demand information is determined, the energy scheduling system can take the power consumption allocation information as power consumption allocation guidance, and allocate the electric energy of the corresponding electric quantity to the target user identification when different power consumption periods arrive.
In this embodiment, after determining the electricity demand information, an electricity scheduling model adapted to the electricity demand information may be determined through a designated query interface, and then the electricity demand information is input into the electricity scheduling model, and electricity allocation information corresponding to the electricity demand information output by the electricity scheduling model is obtained, where the electricity allocation information includes electricity consumption of different electricity consumption periods. Based on the electricity allocation information, electric energy allocation of corresponding electric quantity to the target user identification is realized in different electricity utilization periods, so that energy allocation is performed according to different electricity utilization characteristics of users, the electricity utilization efficiency is improved, and the energy waste is reduced.
Example III
Fig. 3 is a flowchart of an embodiment of a method for scheduling electric power energy according to the third embodiment of the present application, where, based on the second embodiment, before step 210, the method may include the following steps:
in step 310, data source information is collected through the data source layer, where the data source information includes power consumption information of different users in different power consumption periods.
In this embodiment, the data source layer is responsible for collecting data source information, so as to implement data and standardized storage of various source data. For example, the data source information may include power usage information of different users in different power usage periods, for example, the data source information may include a user identification, a region where the user is located, a power usage period where the user is located, a corresponding power usage amount, a power usage type, and the like.
In this embodiment, different training targets may be preset, and for a specific training target, corresponding data source information may be first selected from the data source layer, and then a data model corresponding to the training target may be trained according to a preset model training algorithm. The present embodiment is not limited to the model training algorithm.
In one embodiment, the data model layer may comprise a source data model layer, and step 320 may further comprise the steps of:
and constructing source data models of corresponding types according to different types of data source information based on different first training targets through the source data model layer.
In this embodiment, a corresponding source data model may be built for a certain type of data source information, and the first training object is a training object of the data model building the certain type of data source information.
In another embodiment, the data model layer may comprise a fusion data model layer, and step 320 may further comprise the steps of:
and fusing the data source information of different types based on different second training targets through the fusion data model layer to obtain one or more fusion data sets, and constructing corresponding fusion data models for different fusion data sets.
In this embodiment, after the data source information of multiple types is fused, a corresponding fused data model is constructed, and then the second training target is a training target for constructing the fused data model.
In the embodiment, by constructing different types of data models, allocation management analysis is conveniently carried out on different energy sources through the data models, allocation individuation of the system is improved, and management capacity is improved.
Example IV
Fig. 4 is a block diagram of an embodiment of an apparatus for power energy scheduling according to a fourth embodiment of the present application, where the apparatus exists in an energy scheduling system, and may include the following modules:
a power demand information determining module 410 configured to determine power demand information, where the power demand information includes a target user identifier;
the power scheduling model determining module 420 is configured to determine, through a specified query interface, a power scheduling model adapted to the power demand information;
the user allocation information determining module 430 is configured to input the electricity consumption requirement information into the electricity scheduling model, and obtain electricity consumption allocation information corresponding to the electricity consumption requirement information output by the electricity scheduling model, where the electricity consumption allocation information includes electricity consumption of different electricity consumption periods;
and the electricity allocation module 440 is configured to allocate the electric energy of the corresponding electric quantity to the target user identifier in different electricity utilization periods based on the electricity allocation information.
In one embodiment, the energy scheduling system comprises a user layer via which user equipment accesses the energy scheduling system;
the electricity demand information determining module 410 is specifically configured to:
and receiving the electricity demand information input by the user equipment through the user layer.
In one embodiment, the energy scheduling system includes a data service layer, where the data service layer is configured to display a trained data model, provide a model query function, and provide the specified query interface to the outside;
the power scheduling model determining module 420 is specifically configured to:
displaying a data model list through the data service layer;
when the appointed query interface is detected to be triggered, acquiring search keywords input by a user, wherein the search keywords comprise keywords related to a power dispatching model;
and searching a corresponding power scheduling model according to the search keyword.
In one embodiment, the energy scheduling system further comprises a data source layer and a data model layer;
the apparatus may further comprise the following modules:
the data source acquisition module is used for acquiring data source information through the data source layer, wherein the data source information comprises power consumption information of different users in different power consumption periods;
and the model training module is used for acquiring the data source information from the data source layer through the data model layer and training different data models based on different training targets.
In one embodiment, the data model layer comprises a source data model layer;
the model training module may include the following sub-modules:
and the source data model training submodule is used for respectively constructing source data models of corresponding types for different types of data source information based on different first training targets through the source data model layer.
In one embodiment, the data model layer comprises a fusion data model layer;
the model training module may include the following sub-modules:
and the fusion data model training sub-module is used for fusing different types of data source information based on different second training targets through the fusion data model layer to obtain one or more fusion data sets, and constructing corresponding fusion data models for different fusion data sets.
It should be noted that the above-mentioned device for power energy scheduling provided in the embodiment of the present application may perform a method for power energy scheduling provided in the second embodiment and the third embodiment of the present application, and has a functional module and beneficial effects corresponding to the performing method.
Example five
Fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present application, as shown in fig. 5, the electronic device includes a processor 510, a memory 520, an input device 550 and an output device 540; the number of processors 510 in the electronic device may be one or more, one processor 510 being taken as an example in fig. 5; the processor 510, memory 520, input device 550, and output device 540 in the electronic device may be connected by a bus or other means, for example in fig. 5.
The memory 520 is a computer-readable storage medium, and may be used to store software programs, computer-executable programs, and modules, such as program instructions/modules, corresponding to the methods in the embodiments of the present application. The processor 510 performs various functional applications of the electronic device and data processing, i.e., implements the methods described above, by running software programs, instructions, and modules stored in the memory 520.
The input device 550 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the electronic device. The output 540 may include a display device such as a display screen.
Example six
The sixth embodiment of the present application also provides a storage medium containing computer-executable instructions for performing the method of the first or second embodiments when executed by a processor of a server.
From the above description of embodiments, it will be clear to a person skilled in the art that the present application may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk, or an optical disk of a computer, where the instructions include a number of instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the embodiments of the present application.
It should be noted that, in the embodiment of the apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding function can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. Those skilled in the art will appreciate that the present application is not limited to the particular embodiments described herein, but is capable of numerous obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the present application. Therefore, while the present application has been described in connection with the above embodiments, the present application is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the present application, the scope of which is defined by the scope of the appended claims.
Claims (7)
1. A method for power energy scheduling, characterized in that the method is applied to an energy scheduling system, the method comprising:
determining electricity demand information, wherein the electricity demand information comprises a target user identifier;
determining a power scheduling model adapted to the power demand information through a designated query interface;
inputting the electricity consumption demand information into the power dispatching model, and obtaining electricity consumption allocation information corresponding to the electricity consumption demand information output by the power dispatching model, wherein the electricity consumption allocation information comprises electricity consumption of different electricity consumption periods;
based on the electricity allocation information, carrying out electric energy allocation of the corresponding electric quantity on the target user identification in different electricity utilization periods;
the energy scheduling system comprises a data source layer and a data model layer;
the method further comprises the steps of:
collecting data source information through the data source layer, wherein the data source information comprises power consumption information of different users in different power consumption periods;
acquiring the data source information from the data source layer through the data model layer, and training different data models based on different training targets;
the data model layer comprises a source data model layer;
the step of obtaining the data source information from the data source layer through the data model layer and training different data models based on different training targets comprises the following steps:
constructing a source data model of a corresponding type according to different types of data source information based on different first training targets through the source data model layer;
the data model layer comprises a fusion data model layer;
the step of obtaining the data source information from the data source layer through the data model layer and training different data models based on different training targets comprises the following steps:
and fusing the data source information of different types based on different second training targets through the fusion data model layer to obtain one or more fusion data sets, and constructing corresponding fusion data models for different fusion data sets.
2. The method of claim 1, wherein the energy scheduling system comprises a user plane via which user equipment accesses the energy scheduling system;
the determining electricity demand information includes:
and receiving the electricity demand information input by the user equipment through the user layer.
3. The method according to claim 1 or 2, wherein the energy scheduling system comprises a data service layer for exposing a trained data model and providing a model query function, and providing the specified query interface externally;
the determining, by the designated query interface, a power scheduling model adapted to the power demand information includes:
displaying a data model list through the data service layer;
when the appointed query interface is detected to be triggered, acquiring search keywords input by a user, wherein the search keywords comprise keywords related to a power dispatching model;
and searching a corresponding power scheduling model according to the search keyword.
4. An apparatus for power energy scheduling, the apparatus residing in an energy scheduling system, the apparatus comprising:
the power consumption requirement information determining module is used for determining power consumption requirement information, wherein the power consumption requirement information comprises a target user identifier;
the power scheduling model determining module is used for determining a power scheduling model matched with the power demand information through a designated query interface;
the user allocation information determining module is used for inputting the electricity consumption requirement information into the power scheduling model and obtaining electricity consumption allocation information corresponding to the electricity consumption requirement information output by the power scheduling model, wherein the electricity consumption allocation information comprises electricity consumption of different electricity consumption periods;
the electricity utilization allocation module is used for allocating electric energy corresponding to the electric quantity of the target user identifier in different electricity utilization periods based on the electricity utilization allocation information;
the energy scheduling system also comprises a data source layer and a data model layer;
the apparatus further comprises: the data source acquisition module is used for acquiring data source information through the data source layer, wherein the data source information comprises power consumption information of different users in different power consumption periods;
the model training module is used for acquiring the data source information from the data source layer through the data model layer and training different data models based on different training targets;
the data model layer comprises a source data model layer;
the data model layer comprises a fusion data model layer;
the model training module comprises the following sub-modules:
the source data model training submodule is used for constructing source data models of corresponding types according to different types of data source information based on different first training targets through the source data model layer;
and the fusion data model training sub-module is used for fusing different types of data source information based on different second training targets through the fusion data model layer to obtain one or more fusion data sets, and constructing corresponding fusion data models for different fusion data sets.
5. An energy scheduling system, the energy scheduling system comprising: the system comprises a user layer, a data source layer, a data model layer, a data service layer and a deployment layer;
the user layer is used for providing an interface for user equipment to access the energy scheduling system;
the data source layer is used for collecting data source information, and the data source information comprises power consumption information of different users in different power consumption periods;
the data model layer is used for acquiring the data source information from the data source layer and training different data models based on different training targets;
the data service layer is used for displaying the trained data model, providing a model query function and providing a specified query interface to the outside;
the allocation layer is used for allocating electric energy corresponding to the electric quantity to the users in different electricity utilization periods based on electricity utilization allocation information of different users;
the data model layer comprises a source data model layer, wherein the source data model layer is used for constructing source data models of corresponding types according to different types of data source information based on different first training targets;
the data model layer further comprises a fusion data model layer, wherein the fusion data model layer is used for fusing different types of data source information based on different second training targets to obtain one or more fusion data sets, and a corresponding fusion data model is built for the different fusion data sets.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-3 when the program is executed by the processor.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-3.
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