Power distribution network layering and business data flow design method and device based on cloud edge collaboration

Abstract

The invention relates to the technical field of quadratic programming of a power system, in particular to a power distribution network layered and business data flow design method and device based on cloud edge collaboration, and the method comprises the steps: carrying out the data demand analysis of a target power grid according to a preset dimension, and obtaining a plurality of data flows; based on the plurality of data streams, designing an initial power distribution network layered and business data stream architecture according to a preset power grid business and a preset voltage grade; performing digital deduction on the initial power distribution network layered and business-divided data flow architecture according to a target power grid construction demand to obtain a final power distribution network layered and business-divided data flow architecture; and constructing a cloud side end collaborative architecture, and carrying out differentiated uploading and downloading on various services in the final layered and sub-service data flow architecture of the power distribution network by utilizing the cloud side end collaborative architecture. Therefore, the problems that in the prior art, large-scale and high-frequency data processing is delayed, and specific data processing and analysis requirements of different service requirements are difficult to meet are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of secondary planning of power systems, and in particular to a method and device for designing layered and business-based data flows of distribution networks based on cloud-edge collaboration. Background Art

[0002] In the current field of new power systems, efficient and accurate data management is the key to achieving smart grid optimization. With the widespread deployment of smart meters and sensor technology, the amount of data generated by distribution networks has increased dramatically, bringing huge challenges. Current distribution network data management systems mostly use centralized data processing and control methods, which often lead to processing delays when processing large-scale, high-frequency data, thereby affecting decision-making and response speed. In addition, these systems usually adopt a unified data processing mode, which makes it difficult to provide specific data processing and analysis solutions for different business needs. In the event of equipment failure or network problems, the centralized processing architecture may lead to reduced system operation efficiency or service interruption, further exacerbating these problems.

[0003] Therefore, in response to the current problems faced by the layered and business-based data flow architecture of distribution networks, it is necessary to propose a layered and business-based data flow design method and device for distribution networks based on cloud-edge collaboration. Summary of the invention

[0004] The present invention provides a method and device for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration, so as to solve the problem that the current centralized data processing and control methods are difficult to provide specific data processing and analysis solutions for different business needs.

[0005] The first aspect of the present invention provides a method for designing layered and business-based data flows for a distribution network based on cloud-edge collaboration, comprising the following steps: performing data demand analysis on a target power grid according to preset dimensions to obtain multiple data streams; designing an initial layered and business-based data flow architecture for a distribution network based on the multiple data streams and according to preset power grid services and preset voltage levels; digitally deducing the initial layered and business-based data flow architecture for a distribution network according to the construction requirements of the target power grid to obtain a final layered and business-based data flow architecture for a distribution network; constructing a cloud-edge-end collaborative architecture, and utilizing the cloud-edge-end collaborative architecture to perform differentiated uploading and downloading of various types of services in the final layered and business-based data flow architecture for a distribution network.

[0006] Optionally, the preset dimension includes at least one of data timeliness, data attributes, data sources, data formats and data interaction methods.

[0007] Optionally, based on the multiple data streams, the initial distribution network hierarchical and business-specific data flow architecture is designed according to preset power grid services and preset voltage levels, including: dividing the target power grid according to the preset power grid services to obtain production services, information services and new services; dividing the target power grid according to the preset voltage level to obtain provincial dispatching and control systems, regional dispatching and control systems, medium-voltage distribution networks, low-voltage distribution networks and users; designing a hierarchical and business-specific framework based on the production services, the information services, the new services, the provincial dispatching and control systems, the regional dispatching and control systems, the medium-voltage distribution networks, the low-voltage distribution networks and the users; based on the hierarchical and business-specific framework, the multiple data streams are designed for acquisition, transmission, storage and use to generate the initial distribution network hierarchical and business-specific data flow architecture.

[0008] Optionally, the initial distribution network hierarchical and business-based data flow architecture is digitally deduced according to the construction requirements of the target power grid to obtain the final distribution network hierarchical and business-based data flow architecture, including: performing measurement preprocessing on data with poor observability in the initial distribution network hierarchical and business-based data flow architecture to obtain a preprocessed data flow architecture; constructing a typical load power curve template library according to the construction requirements of the target power grid; constructing a soft measurement generation model based on the typical load power curve template library using a non-contact load identification method; and digitally deducing the preprocessed data flow architecture according to the soft measurement generation model to obtain the final distribution network hierarchical and business-based data flow architecture.

[0009] Optionally, the measurement preprocessing includes at least one of missing value filling, outlier replacement and data normalization.

[0010] Optionally, the building of the cloud-edge-end collaborative architecture includes: building the cloud-edge-end collaborative architecture based on a preset distribution network cloud platform, a preset edge IoT agent, and a preset intelligent distribution device.

[0011] The second aspect of the present invention provides a distribution network layered and business-based data flow design device based on cloud-edge collaboration, including: a demand analysis module, used to perform data demand analysis on a target power grid according to preset dimensions to obtain multiple data streams; a design architecture module, used to design an initial distribution network layered and business-based data flow architecture based on the multiple data streams, according to preset power grid services and preset voltage levels; an optimization architecture module, used to digitally deduce the initial distribution network layered and business-based data flow architecture according to the construction requirements of the target power grid to obtain a final distribution network layered and business-based data flow architecture; a cloud-edge collaboration module, used to construct a cloud-edge-end collaboration architecture, and use the cloud-edge-end collaboration architecture to perform differentiated uploading and downloading of various types of services in the final distribution network layered and business-based data flow architecture.

[0012] An embodiment of the third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for designing layered and business-specific data flows for distribution networks based on cloud-edge collaboration as described in the above embodiments.

[0013] The fourth aspect of the present invention provides a computer program product, which, when executed by a processor, implements the above-mentioned method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration.

[0014] A fifth aspect of the present invention provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for designing layered and business data flows of distribution networks based on cloud-edge collaboration.

[0015] The embodiments of the present invention propose a method and device for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration. By designing a layered and business-based data flow architecture for distribution networks, data processing and management are divided into multiple levels. Each level is optimized for specific business needs, thereby improving the flexibility and scalability of data management, so that the system can more accurately meet the needs of various business scenarios. By designing a data flow processing solution for cloud-edge collaboration, cloud computing resources and edge computing devices work together, and the distribution network data is processed together in a differentiated manner through layered and business-based architecture design and cloud-edge collaboration, thereby effectively improving the efficiency and reliability of data processing, reducing dependence on centralized systems, and enhancing the stability and robustness of the system when facing equipment failures or network problems.

[0016] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0018] Figure 1 A flow chart of a method for designing layered and business-based data flows for a distribution network based on cloud-edge collaboration provided by an embodiment of the present invention;

[0019] Figure 2 An execution block diagram of a method for designing layered and business-based data flows for a distribution network based on cloud-edge collaboration provided by an embodiment of the present invention;

[0020] Figure 3 A digital deduction structure diagram provided by an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of the final distribution network layered and service-based data flow architecture provided by an embodiment of the present invention;

[0022] Figure 5 A schematic diagram of cloud-edge-device collaboration provided by an embodiment of the present invention;

[0023] Figure 6 A block diagram of a device for designing layered and business-based data flows for a distribution network based on cloud-edge collaboration provided by an embodiment of the present invention;

[0024] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0026] The following describes the method and device for designing layered and business-based data flows of distribution networks based on cloud-edge collaboration in the embodiment of the present application with reference to the accompanying drawings. In view of the fact that in the field of new power systems mentioned in the above background technology center, due to the widespread deployment of smart meters and sensor technologies, the amount of data generated by distribution networks has increased dramatically, bringing huge challenges to data processing and management. Traditional centralized data processing and control methods often cause processing delays when processing large-scale, high-frequency data, affecting decision-making and response speed. Therefore, the present invention provides a method and device for designing layered and business-based data flows of distribution networks based on cloud-edge collaboration. In this method, data processing is divided into multiple levels, and each level is customized and optimized for specific business needs. Through the collaborative work of cloud computing resources and edge computing devices, distribution network data is processed together. As a result, not only the flexibility and scalability of data management are improved, ensuring that the system can accurately meet the needs of different business scenarios, significantly improving the efficiency and reliability of data processing, reducing dependence on centralized systems, and enhancing the stability and robustness of the system in the face of equipment failures or network problems, but also achieving significant optimization of smart grid data management, providing strong technical support for the efficient operation and intelligent decision-making of power systems.

[0027] The following describes the method and device for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration in an embodiment of the present invention with reference to the accompanying drawings.

[0028] Figure 1 A flow chart of a method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration provided in an embodiment of the present invention.

[0029] like Figure 1 As shown, the distribution network layered and business-based data flow design method based on cloud-edge collaboration includes the following steps:

[0030] In step S101, data demand analysis is performed on the target power grid according to preset dimensions to obtain multiple data streams.

[0031] In some embodiments, the preset dimensions include at least one of data timeliness, data attributes, data sources, data formats, and data interaction methods.

[0032] In the actual implementation process, the data characteristics of the target power grid are analyzed from five dimensions: data timeliness, data attributes, data sources, data formats, and data interaction methods. According to data timeliness, the data of the target power grid are divided into real-time data, quasi-real-time data, and historical data; according to data attributes, the data of the target power grid are divided into electrical data and non-electrical data, among which electrical data are divided into operation data and metering data, and non-electrical data are divided into equipment data, images and videos, and external data; according to data sources, the data of the target power grid are divided into measurement data, ledger data, auxiliary data, and external data; according to data formats, the data of the target power grid are divided into digital data, image data, and text data; according to the interaction method, the data of the target power grid are divided into remote interaction data and local interaction data.

[0033] In step S102, based on multiple data streams, an initial distribution network hierarchical and business-based data stream architecture is designed according to preset power grid services and preset voltage levels.

[0034] In some embodiments, the target power grid is divided according to the power grid level and multiple business aspects, wherein the target power grid is divided according to the preset power grid business, divided into production business, information business, and new business, and the target power grid is divided according to the preset voltage level, divided into five levels: provincial dispatching and control system, regional dispatching and control system, medium-voltage distribution network, low-voltage distribution network, and users; a hierarchical and business-divided framework is designed according to production business, information business, new business, provincial dispatching and control system, regional dispatching and control system, medium-voltage distribution network, low-voltage distribution network, and users; based on the hierarchical and business-divided framework, multiple data streams are designed for collection, transmission, storage, and use to generate an initial distribution network hierarchical and business-divided data flow architecture.

[0035] In step S103, the initial distribution network hierarchical and business-based data flow architecture is digitally deduced according to the construction requirements of the target power grid to obtain the final distribution network hierarchical and business-based data flow architecture.

[0036] In some embodiments, the initial distribution network hierarchical and business-based data flow architecture is digitally deduced according to the target power grid construction requirements to obtain the final distribution network hierarchical and business-based data flow architecture, including:

[0037] The data with poor observability in the initial distribution network layered and business-based data flow architecture is measured and preprocessed to obtain the preprocessed data flow architecture, where the measurement preprocessing includes missing value filling, outlier replacement and data normalization;

[0038] Build a typical load power curve template library according to the target power grid construction requirements;

[0039] Based on the typical load power curve template library, a soft measurement generation model is constructed using a non-contact load identification method;

[0040] The preprocessed data flow architecture is digitally deduced according to the soft measurement generation model to obtain the final distribution network layered and business-based data flow architecture.

[0041] In the actual implementation process, the missing values ​​appearing at the beginning, end and middle of the data in the initial distribution network hierarchical and business data flow architecture are supplemented by trend ratio calculation and non-nearest neighbor mean generation method respectively; a corresponding reasonable range is set for each load, and the values ​​exceeding this range in the initial distribution network hierarchical and business data flow architecture are regarded as abnormal values, and then replaced as missing values; the time window size of the initial distribution network hierarchical and business data flow architecture is set so that the window data segment includes the complete operation cycle of each load as much as possible, for example, the window size is set to 72 and 24 respectively, and the data is sliced ​​according to the time window size, as shown in the following formula (1), and the preprocessed data flow architecture is obtained.

[0042]

[0043] Among them, x′ is the normalized pseudo-measurement value, x is the actual value, and x min is the actual minimum value, x max is the actual maximum value.

[0044] Furthermore, historical data of the electricity collection system and local distribution station area are obtained, and dynamic clustering analysis of power curves is performed in combination with labeled information such as load type, season, climate, holidays, etc. Based on the iterative self-organizing data analysis algorithm, power curve templates with too few samples are dynamically removed during the clustering process, and power curve templates with too dispersed samples are dynamically split to establish a power curve template library for typical loads such as air conditioners, electric vehicles, and electric heating. For example, the typical load power curve template library is set to K=20.

[0045] like Figure 3 As shown in the figure, based on the typical load power curve template library, a power decomposition identification method based on deep learning is adopted. The load power curve is identified through non-contact identification and other computing power deduction technologies to obtain a soft measurement generation model. The specific formula is shown in formula (2).

[0046]

[0047] Among them, y t represents the total power at time t, x i,t is the power of the ith electrical device at time t, u t is the total power of the unknown electrical equipment at time t, ∈ t is random noise, satisfying ∈ t ~N(0,σ 2 ), I represents the number of electrical equipment.

[0048] like Figure 4 As shown in the figure, the preprocessed data flow architecture is input into the soft measurement generation model for training, so that the network can fully capture the input data characteristics and timing dependencies; the data to be deduced is input into the trained model, and its output value is denormalized, so that the load state or specific power value can be deduced, thereby obtaining the final distribution network layered and business-based data flow architecture.

[0049] In step S104, a cloud-edge-end collaborative architecture is constructed, and the cloud-edge-end collaborative architecture is used to perform differentiated uploading and downloading of various types of services in the final distribution network's layered and business-based data flow architecture.

[0050] In some embodiments, such as Figure 5 As shown in the figure, a cloud-edge-end collaborative architecture is constructed based on the preset distribution network cloud platform, the preset edge IoT agent, and the preset intelligent distribution equipment. Among them, the distribution network cloud platform is responsible for processing global data services with low real-time requirements and high complexity; the edge side uses edge computing technology to be responsible for small real-time local data services, and will not incur high equipment costs in computing and storage; the end-side equipment is deployed on the user side and is responsible for data collection and user interaction.

[0051] Specifically, the end side collects relevant data in real time and uploads it to the intelligent terminal in the substation to which it belongs, and accepts and executes adjustment instructions to achieve orderly power consumption. The relevant data include: primary equipment monitoring data, secondary equipment operation data, environmental monitoring data, power quality monitoring, distributed source load storage data, etc. The intelligent terminal in the substation aggregates the real-time data of all power-consuming equipment in the area, and combines it with historical data for edge-side computing and analysis applications, and sends the required data and downward adjustment instructions according to the specific situation of the area; the cloud platform performs calculations and analysis based on the data uploaded by each area, formulates real-time power distribution plans for each area, and sends them to each edge device (intelligent terminal in the substation) in the form of air conditioning instructions.

[0052] In summary, the layered and business-based data flow design method for distribution network based on cloud-edge collaboration proposed in an embodiment of the present invention can effectively reduce the delay in processing large-scale and high-frequency data, and optimize the response speed and accuracy to various business needs; the layered and business-based architecture provides a more flexible and efficient data management mechanism by targeted optimization of data processing at each level, enhances the adaptability of data management, and enables the system to more accurately meet the needs of different business scenarios; the cloud-edge collaborative processing solution not only improves the efficiency of data processing, but also enhances the stability and robustness of the system when facing equipment failures or network problems; it optimizes resource utilization, reduces operation and maintenance costs, and provides an innovative solution for the efficient management of smart grids.

[0053] Secondly, the distribution network layered and business-based data flow design device based on cloud-edge collaboration proposed according to an embodiment of the present invention is described with reference to the accompanying drawings.

[0054] Figure 6 It is a block diagram of a device for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration according to an embodiment of the present invention.

[0055] like Figure 6 As shown, the distribution network layered and business-based data flow design device 60 based on cloud-edge collaboration includes: a demand analysis module 601, a design architecture module 602, an optimization architecture module 603 and a cloud-edge collaboration module 604.

[0056] Among them, the demand analysis module 601 is used to analyze the data demand of the target power grid according to preset dimensions to obtain multiple data streams. The design architecture module 602 is used to design the initial distribution network hierarchical and business-based data flow architecture based on multiple data streams, according to preset power grid services and preset voltage levels. The optimization architecture module 603 is used to digitally deduce the initial distribution network hierarchical and business-based data flow architecture according to the construction requirements of the target power grid to obtain the final distribution network hierarchical and business-based data flow architecture. The cloud-edge collaboration module 604 is used to build a cloud-edge-end collaboration architecture, and use the cloud-edge-end collaboration architecture to differentiate the various types of services in the final distribution network hierarchical and business-based data flow architecture.

[0057] In some embodiments, the preset dimensions include at least one of data timeliness, data attributes, data sources, data formats, and data interaction methods.

[0058] In some embodiments, the design architecture module 602 includes:

[0059] Divide the target power grid according to the preset power grid services to obtain production services, information services and new services;

[0060] Divide the target power grid according to the preset voltage level to obtain the provincial dispatching control system, regional dispatching control system, medium voltage distribution network, low voltage distribution network and users;

[0061] Design a hierarchical and business-based framework based on production services, information services, new services, provincial dispatching and control systems, regional dispatching and control systems, medium-voltage distribution networks, low-voltage distribution networks, and users;

[0062] Based on the hierarchical and business-divided framework, multiple data streams are designed for collection, transmission, storage and use to generate the initial hierarchical and business-divided data stream architecture of the distribution network.

[0063] In some embodiments, the optimization architecture module 603 includes:

[0064] Performing measurement preprocessing on data with poor observability in the initial distribution network layered and business-based data flow architecture to obtain a preprocessed data flow architecture, wherein the measurement preprocessing includes at least one of missing value completion, outlier replacement, and data normalization;

[0065] Build a typical load power curve template library based on the construction requirements of the target power grid;

[0066] Based on the typical load power curve template library, a soft measurement generation model is constructed using a non-contact load identification method;

[0067] The preprocessed data flow architecture is digitally deduced according to the soft measurement generation model to obtain the final distribution network layered and business-based data flow architecture.

[0068] In some embodiments, the cloud-edge collaboration module 604 builds a cloud-edge collaboration architecture based on a preset distribution network cloud platform, a preset edge IoT agent, and a preset smart distribution device.

[0069] It should be noted that the above explanation of the embodiment of the method for designing layered and business-based data flows for distribution network based on cloud-edge collaboration is also applicable to the device for designing layered and business-based data flows for distribution network based on cloud-edge collaboration of this embodiment, and will not be repeated here.

[0070] The distribution network layered and business-specific data flow design device based on cloud-edge collaboration proposed in an embodiment of the present invention can effectively reduce the delay in processing large-scale and high-frequency data, and optimize the response speed and accuracy to various business needs; the layered and business-specific architecture provides a more flexible and efficient data management mechanism by targeted optimization of data processing at each level, enhances the adaptability of data management, and enables the system to more accurately meet the needs of different business scenarios; the cloud-edge collaborative processing solution not only improves the efficiency of data processing, but also enhances the stability and robustness of the system when facing equipment failures or network problems; it also optimizes resource utilization, reduces operation and maintenance costs, and provides an innovative solution for the efficient management of smart grids.

[0071] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0072] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0073] When the processor 702 executes the program, it implements the distribution network layered and business-based data flow design method based on cloud-edge collaboration provided in the above embodiment.

[0074] Furthermore, the electronic device further comprises:

[0075] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0076] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0077] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0078] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0079] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0080] The processor 702 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0081] An embodiment of the present invention also provides a computer program product, which, when executed by a processor, implements the above-mentioned method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration.

[0082] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration.

[0083] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0084] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0085] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0086] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0087] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0088] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0089] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0090] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration, characterized in that: The following steps are involved: Perform data demand analysis on the target power grid according to preset dimensions to obtain multiple data streams; Based on the multiple data streams, designing an initial distribution network hierarchical and business-based data stream architecture according to preset power grid services and preset voltage levels; Digitally derive the initial distribution network hierarchical and business-specific data flow architecture according to the construction requirements of the target power grid to obtain a final distribution network hierarchical and business-specific data flow architecture; Construct a cloud-edge-end collaborative architecture, and use the cloud-edge-end collaborative architecture to perform differentiated uploading and downloading of various types of services in the final distribution network's layered and business-based data flow architecture.

2. The method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration according to claim 1 is characterized in that: The preset dimensions include at least one of data timeliness, data attributes, data sources, data formats, and data interaction methods.

3. The method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration according to claim 1 is characterized in that: The design of the initial distribution network hierarchical and business-based data flow architecture based on the multiple data flows and according to the preset power grid services and preset voltage levels includes: Dividing the target power grid according to the preset power grid services to obtain production services, information services and new services; Dividing the target power grid according to the preset voltage level to obtain a provincial dispatching control system, a regional dispatching control system, a medium voltage distribution network, a low voltage distribution network and users; Design a hierarchical and sub-service framework according to the production service, the information service, the new service, the provincial dispatching control system, the regional dispatching control system, the medium-voltage distribution network, the low-voltage distribution network and the user; Based on the hierarchical and business-divided framework, the multiple data streams are designed for collection, transmission, storage and use to generate the initial distribution network hierarchical and business-divided data stream architecture.

4. The method for designing layered and business-based data flows for distribution network based on cloud-edge collaboration according to claim 1 is characterized in that: The digital deduction of the initial distribution network hierarchical and business-based data flow architecture according to the construction requirements of the target power grid to obtain the final distribution network hierarchical and business-based data flow architecture includes: Performing measurement preprocessing on the data with poor observability in the initial distribution network hierarchical and business-based data flow architecture to obtain a preprocessed data flow architecture; Constructing a typical load power curve template library according to the construction requirements of the target power grid; Based on the typical load power curve template library, a soft measurement generation model is constructed using a non-contact load identification method; The pre-processed data flow architecture is digitally deduced according to the soft measurement generation model to obtain the final distribution network layered and business-based data flow architecture.

5. The method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration according to claim 4 is characterized in that: The measurement preprocessing includes at least one of missing value filling, outlier replacement and data normalization.

6. The method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration according to claim 1 is characterized in that: The construction of the cloud-edge-device collaborative architecture includes: The cloud-edge-end collaborative architecture is constructed based on the preset distribution network cloud platform, the preset edge IoT agent, and the preset intelligent distribution equipment.

7. A device for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration, characterized in that: include: A demand analysis module is used to analyze the data demand of the target power grid according to preset dimensions to obtain multiple data streams; A design architecture module is used to design an initial distribution network hierarchical and business-based data flow architecture based on the multiple data flows and according to preset power grid services and preset voltage levels; An optimization architecture module is used to digitally deduce the initial distribution network hierarchical and business-based data flow architecture according to the construction requirements of the target power grid to obtain a final distribution network hierarchical and business-based data flow architecture; The cloud-edge collaboration module is used to build a cloud-edge-end collaboration architecture, and use the cloud-edge-end collaboration architecture to perform differentiated uploading and downloading of various types of services in the final distribution network's layered and business-based data flow architecture.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for designing layered and business-specific data flows for distribution networks based on cloud-edge collaboration as described in any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program / instruction is executed by the processor, it implements the method for designing layered and business-based data flows of distribution networks based on cloud-edge collaboration as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for designing layered and business-based data flows for distribution networks based on cloud-edge collaboration as described in any one of claims 1-6.

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