Power distribution network collaborative design system and method based on cloud native architecture

Abstract

The invention discloses a cloud native architecture-based power distribution network collaborative design system and method, the system comprises a user terminal and a cloud server cluster, the user terminal and the cloud server cluster establish data transmission through WebSocket, and real-time interaction between an incremental data packet and a calculation result is carried out; the user terminal is used for providing an interactive interface for a user, carrying out lightweight rendering on the power distribution network drawing and capturing the operation of the user on the power distribution network drawing; and the cloud server cluster is used for centralizing service logic, electrical calculation and cooperative control of the drawing of the power distribution network. The invention provides a power distribution network collaborative design system and method based on a cloud native architecture, which can greatly reduce network bandwidth occupation and guarantee operation fluency in a weak network environment. The electrical topology cascading conflict is solved, and the data security of multi-person collaboration is ensured. A design-calculation real-time closed loop is realized, and the engineering design efficiency is remarkably improved. And cross-platform lightweight access is realized.

Description

Technical Field

[0001] This invention relates to a cloud-native architecture-based collaborative design system and method for power distribution networks, belonging to the fields of power engineering design and cloud computing technology. Background Technology

[0002] Currently, distribution network design is a crucial aspect of power engineering construction, directly impacting the safe and stable operation of the power system and the reliability of power supply. Existing distribution network design technologies primarily rely on traditional local CAD software and file-transfer collaborative models, which have the following significant drawbacks: 1. Traditional power distribution network design relies on large-scale CAD software installed locally. Such software has stringent hardware requirements and obvious platform compatibility limitations. It cannot be seamlessly switched between different operating systems such as Windows, Mac, and Linux, resulting in insufficient flexibility in design scenarios.

[0003] 2. Design drawing and electrical calculations (such as power flow calculations and short-circuit calculations) usually rely on independent software systems. Data between the two types of software cannot be directly exchanged, and core data such as equipment parameters and topology relationships need to be manually entered repeatedly. This not only reduces design efficiency, but also easily introduces data errors due to human operation errors, affecting the accuracy of design results.

[0004] 3. Existing collaborative design models rely on file transfer such as dwg, leading to chaotic version management and an inability to support simultaneous online work by multiple professionals, including electrical and civil engineers. Design conflicts easily arise during multi-user parallel operations, and the delayed detection and high cost of resolving these conflicts severely hinder the design progress of power distribution network projects. Summary of the Invention

[0005] The technical problem this invention aims to solve is to overcome the shortcomings of existing technologies and provide a cloud-native architecture-based collaborative design system and method for power distribution networks. This system can significantly reduce network bandwidth consumption and ensure smooth operation in weak network environments. It resolves electrical topology cascading conflicts, ensuring data security for multi-user collaboration. It achieves a real-time closed-loop design-computation process, significantly improving engineering design efficiency. It decouples terminal hardware dependencies, enabling lightweight cross-platform access.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: The present invention provides a power distribution network collaborative design system based on cloud-native architecture, which includes a user terminal and a cloud server cluster. The user terminal and the cloud server cluster establish data transmission through WebSocket to perform real-time interaction of incremental data packets and calculation results. User terminal, the user terminal is used to provide an interactive interface for users, perform lightweight rendering of power distribution network drawings and capture the operations performed by users on power distribution network drawings; A cloud server cluster is used for centralized business logic, electrical calculations, and collaborative control of power distribution network drawings.

[0007] Furthermore, the user terminal includes a user interface, a rendering engine, an object storage pool, an incremental data extraction module, and a WebSocket client; The user interface is used to receive layout instructions and attribute modification instructions from users for power distribution network components, display power distribution network rendering drawings, and provide real-time feedback on cloud electrical calculation results and conflict alarm information. The rendering engine is used to achieve lightweight rendering of large-scale power distribution network drawings; The object storage pool stores electrical object data in the power distribution network drawings in JSON format. Each electrical object is assigned a globally unique UUID, which includes the object type, version number, attribute parameters, and association information. The incremental data extraction module is used to capture user operations and extract incremental data packets encapsulated in changed attribute fields; The WebSocket client is used to establish a fully bidirectional real-time communication link with the cloud server cluster, send the incremental data packets encapsulated by the incremental data extraction module to the cloud, and receive the electrical verification results and collaborative status broadcast information returned by the cloud server cluster in real time.

[0008] Furthermore, the cloud server cluster includes an API gateway, a microservice business layer, and a data storage layer; The API gateway is used to uniformly receive and distribute operation instructions from user terminals; The microservice business layer includes an electrical computing engine, user and project management services, drawing parsing and topology construction services, and collaborative conflict control services. The electrical computing engine receives the distribution network topology and constructs a real-time model provided by the service, performs professional verification calculations on the distribution network drawings such as short-circuit current and voltage deviation, and feeds the calculation results back to the user terminal in real time. The user and project management services are used for user authentication and access control, and for version tracking, cloud storage, and archiving management of the distribution network drawings. The drawing parsing and topology construction services parse incremental data packets uploaded by the user terminal, dynamically construct and update electrical connection relationships in cloud memory using graph theory algorithms. The collaborative conflict control service performs primitive locking management based on an object occupancy table, combined with a timestamp sequencing mechanism and topology domain verification logic. The data storage layer is used to provide multi-dimensional data persistence and high-speed caching support.

[0009] Furthermore, the data storage layer includes relational databases, NoSQL databases, and Redis caches.

[0010] Another aspect of the present invention provides a control method for a distribution network collaborative design system based on a cloud-native architecture, which includes the following steps: Step S1: When multiple people operate the same power distribution network drawing at the same time, perform collaborative conflict handling based on topology constraints. Step S2: Perform real-time electrical calculations on the power distribution network drawings in the cloud.

[0011] Furthermore, when multiple people are simultaneously operating the same power distribution network drawing, collaborative conflict handling based on topology constraints is performed, specifically including the following steps: Step S11: Abandon traditional file locks and establish a primitive object lock based on UUID. Use Redis cache to maintain a dynamic object occupancy table and broadcast the status via WebSocket. Step S12: Use a timestamp-based sequencing mechanism based on cloud server clusters to process concurrent modification requests based on the first-come, first-served principle; Step S13: When a user operates any component in the power distribution network drawing, automatically detect the status of the upstream and downstream related objects of that device; Step S14: After receiving the blocking information, the user terminal does not perform deletion on the canvas.

[0012] Furthermore, in step S13, when the user operates any component in the power distribution network drawing, the status of the upstream and downstream related objects of that device is automatically detected, specifically including the following steps: Step S131: When the user operates on component T on the interface, the server receives the request; first, it uses the UUID to lock component T, and then calls the topology breadth-first search algorithm to retrieve all adjacent nodes T that have a direct electrical connection with component T in the graph data structure in memory. s Construct the affected topology domain {T s1 , T s2 , …T sn}; Step S132: The system queries the global object status table in the Redis cache, performs status retrieval on all object IDs in the affected topology domain, determines whether component T is locked by other users, and then checks whether component T in the affected topology domain is locked by other users. Step S133: If all objects in the affected topology are in an unlocked state, mark component T and its associated connection points as write-protected, execute user operations, and trigger topology reconstruction. If all objects within the affected topology are locked, a cascading block is triggered, an operation failure instruction is sent to the user, and metadata containing the specific reason is pushed.

[0013] Furthermore, in step S2, the real-time electrical calculation of the power distribution network drawing is performed in the cloud, specifically including the following steps: Step S21: When a user performs an operation on a component, the incremental data extraction module captures the operation and adds the UUID of the operated component to a temporary patch model. Step S22: Traverse the patch model, compare the current attribute values ​​of the component to be operated with the previous version snapshot, and extract the attribute fields that have changed. Step S23: Encapsulate the changed attribute fields extracted in step S22 into an incremental data packet based on JSON Patch format; Step S24: Using the WebSocket channel, push the serialized incremental data packet into the sending queue. When the cloud server cluster returns a confirmation signal, the user terminal updates the version number of the local object and clears the patch model. If the serialized incremental data packet fails to be sent, a retry mechanism is triggered.

[0014] By adopting the above technical solution, the present invention has the following beneficial effects: 1. This invention employs an incremental extraction and JSON Patch transmission mechanism based on patch detection. Compared to traditional full file synchronization technology, this system only transmits changed attribute fields via WebSocket (reducing the transmission volume from MB to KB), effectively overcoming network congestion problems during online editing of large-scale power distribution network drawings. Simultaneously, combined with pre-version number verification, it effectively solves the risk of data out-of-order processing caused by network latency, ensuring data consistency between the cloud and front-end within millisecond-level latency.

[0015] 2. This invention addresses the electrical logic dependencies between equipment in power distribution networks by proposing a cascading locking mechanism based on electrical dependencies and a BFS topology search algorithm. Unlike simple file locks or single-object locks in existing technologies, this system automatically detects and intercepts conflicting operations involving upstream and downstream related equipment (such as the branch being edited and its power source) when a user performs a deletion or modification operation. This fundamentally eliminates topology silos or electrical logic breaks caused by concurrent operations, achieving fine-grained multi-user concurrent collaboration while ensuring data integrity.

[0016] 3. This invention transforms the traditional "drawing and then exporting calculations" model into an "online real-time feedback" model through real-time cloud topology analysis and dynamic microservice computing power scheduling. The system can instantly perform power flow or short-circuit calculations in the cloud when a user adjusts graphic element parameters (such as changing conductor types), and directly drive the front-end view update through visual mapping technology (such as turning red when voltage exceeds limits). This "what you see is what you get" interactive method eliminates the cumbersome steps of cross-software data conversion, significantly shortening the design iteration cycle of power distribution network projects.

[0017] 4. This invention employs a lightweight WebGL rendering and a separate image / model storage architecture. The front-end is only responsible for vector graphics rendering, while core electrical attributes and topology structures are stored as JSON objects in a cloud database. This architecture allows the heavy electrical verification and data processing tasks to be handled by a cloud server cluster, overcoming the dependence of traditional CAD software on local high-performance workstations. This enables users to smoothly handle large-scale power distribution network design tasks through a browser on a regular PC or mobile device. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the cloud-native architecture-based collaborative design system for power distribution networks according to the present invention. Figure 2 This is a flowchart of the control method for the cloud-native architecture-based collaborative design system for power distribution networks according to the present invention. Detailed Implementation

[0019] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0020] Example 1 like Figure 1 As shown, this embodiment provides a power distribution network collaborative design system based on a cloud-native architecture, which includes: a user terminal and a cloud server cluster. The user terminal and the cloud server cluster establish data transmission through a WebSocket long connection to perform real-time interaction of incremental data packets and calculation results.

[0021] The user terminal provides an interactive interface for users, performs lightweight rendering of power distribution network drawings, and captures user actions on the drawings. The user terminal runs on a browser and requires no plugins. It includes a user interface, a WebGL / Canvas rendering engine, an object storage pool, an incremental data extraction module, and a WebSocket client.

[0022] The user interface is used to receive layout instructions and attribute modification instructions from users for power distribution network components, display power distribution network rendering drawings, and provide real-time feedback on cloud-based electrical calculation results and conflict alarm information.

[0023] The WebGL / Canvas rendering engine is used to achieve lightweight rendering of large-scale power distribution network drawings.

[0024] The object storage pool stores electrical object data from the distribution network drawings in JSON format. Each electrical object is assigned a globally unique UUID (Unique Identifier), which includes the object type, version number, attribute parameters, and association information. The version number (typically an integer such as 1, 2, 3, etc.) identifies the current logical state of the electrical object, enabling optimistic locking concurrency control during collaborative processes. Optimistic locking concurrency control is a data conflict arbitration mechanism based on version number verification. Its core logic is as follows: when the user terminal encapsulates an incremental data packet, it pushes the previous version number of the component to be modified to the cloud server. The cloud server determines the legitimacy of the modification request by comparing the version number in the incremental data packet with the version number currently stored in the database. Its main function is to ensure that in a multi-user collaborative environment, any incremental modification is based on the latest state of the electrical object, logically eliminating the risk of data overwrite due to concurrent operations and ensuring the rigor and consistency of the distribution network topology data.

[0025] The incremental data extraction module is used to capture user operations and extract incremental data packets encapsulated in changed attribute fields.

[0026] The WebSocket client is used to establish a fully bidirectional real-time communication link with the cloud server cluster, send the incremental data packets encapsulated by the incremental data extraction module to the cloud, and receive the electrical verification results and collaborative status broadcast information returned by the cloud server cluster in real time.

[0027] A cloud server cluster is used for centralized business logic, electrical calculations, and collaborative control of power distribution network drawings. It receives operation commands from user terminals via an API gateway, utilizes a microservice business layer for topology parsing, conflict detection, and electrical verification, and relies on a data storage layer to achieve persistent storage of device attributes and topology data, as well as state management under high concurrency. The cloud server cluster comprises an API gateway, a microservice business layer, and a data storage layer.

[0028] API gateways are used to uniformly receive and distribute operation commands from user terminals.

[0029] The microservice business layer includes an electrical computing engine, user and project management services, drawing parsing and topology construction services, and collaborative conflict control services.

[0030] The electrical calculation engine receives the distribution network topology and builds a real-time model provided by the service. It performs professional verification calculations on the distribution network drawings, such as short-circuit current and voltage deviation, and feeds the calculation results back to the user terminal in real time.

[0031] The user and project management service is used for user authentication and access control, as well as for version tracking, cloud storage, and archiving management of power distribution network drawings.

[0032] The drawing parsing and topology construction service is used to parse incremental data packets uploaded by user terminals, dynamically construct and update electrical connection relationships in cloud memory using graph theory algorithms, and provide topology data support for electrical computing engine and conflict control.

[0033] The collaborative conflict control service is used to perform primitive locking management based on the object occupancy table. It combines a nanosecond-level timestamp sequencing mechanism with topology domain verification logic to achieve conflict arbitration and cascading blocking for concurrent operations by multiple users.

[0034] The data storage layer provides multi-dimensional data persistence and high-speed caching support. It includes relational databases, NoSQL databases, and a Redis cache. Relational databases store device parameters, NoSQL databases store topology structures, and the Redis cache manages object lock states under high concurrency. The high-concurrency lock refers to a mutual exclusion mechanism for primitive objects implemented using a high-performance cloud cache to ensure the consistency and security of distribution network topology data when multiple users are editing online simultaneously (in a high-concurrency environment). Its working principle involves maintaining a real-time "object occupancy table" in conjunction with the Redis cache. This table records the UUIDs of all locked components and their holder information, and uses WebSocket to broadcast status in milliseconds, enabling all collaborators to instantly perceive the editable status of components.

[0035] Example 2 like Figure 2 As shown in the figure, this embodiment provides a control method for a distribution network collaborative design system based on a cloud-native architecture, which includes the following steps: Step S1: When multiple people are simultaneously operating the same power distribution network drawing, collaborative conflict handling based on topology constraints is performed, thereby resolving the data consistency and electrical security issues when multiple people are simultaneously operating the same power distribution network drawing. Specifically, this includes the following steps: Step S11: Abandon traditional file locks and establish primitive object locks based on UUID (Unique Identifier). Use Redis cache to maintain a dynamic object occupancy table and broadcast millisecond-level status via WebSocket.

[0036] Step S12: Adopt a sequencing mechanism based on nanosecond-level timestamps of cloud server clusters to process concurrent modification requests based on the first-come, first-served principle.

[0037] Step S13: When a user manipulates any component in the power distribution network drawing (e.g., deleting a transformer), the status of the upstream and downstream related objects of that device is automatically detected. Specifically: Step S131, Operation Intent Parsing and Topology Domain Construction: When a user performs an operation on component T on the interface (e.g., add, move, delete), the server receives the request; first, it locks component T using its UUID, and then calls the topology breadth-first search (BFS) algorithm to retrieve all adjacent nodes T that have a direct electrical connection with component T in the graph data structure in memory. s Construct the affected topology domain {T s1 , T s2 , …T sn}

[0038] Step S132, Parallel verification of associated states: The system queries the global object status table in the Redis cache to retrieve the status of all object IDs within the affected topology domain, determining whether component T is locked by other users. Then, it checks whether component T within the affected topology domain is locked by other users. The global object status table is a dynamic set of key-value pairs stored in the Redis cache, indexed by the UUID of the electrical elements, and records the locking status and user information of each element in real time. The system concurrently retrieves this table to obtain the real-time occupancy status of all components within the affected topology domain, serving as the authoritative basis for determining cascading blocking.

[0039] Step S133, Priority-based conflict resolution: If all objects within the affected topology are in an unlocked state, mark component T and its associated connection points as write-protected, execute user operations, and trigger topology refactoring.

[0040] If all objects within the affected topology are locked, a cascading block is triggered, an operation failure instruction is sent to the user, and metadata containing the specific reason is pushed.

[0041] Step S14: After receiving the blocking information, the user terminal does not perform deletion on the canvas.

[0042] In this embodiment, elements, components, and objects all refer to the smallest independent design unit in a power distribution network, including but not limited to poles, conductors, and transformers. Their emphasis varies slightly in different subsystems. In engineering, a component refers to a specific object; A primitive emphasizes a visual unit of a "component" in a CAD drawing; The object focuses on data description, referring to the data form of "component".

[0043] Step S2: Perform real-time electrical calculations on the power distribution network diagram using cloud computing. This includes the following steps: When a user modifies the power distribution network drawing (e.g., changes the conductor type), the system only extracts the JSON data packet of the modified portion, rather than uploading the entire drawing. In this embodiment, each graphic element (e.g., pole, conductor, transformer) is an independent object, and each object has a globally unique ID (UUID). The user terminal stores this data in JSON format, for example: "obj1": { "type": pole, "ver": 5, "x": 100, "y": 200, "obj id ":111111}; "obj2": { "type": line, "ver": 3, "from": 111111, "to": 222222, "len": 50 ,"obj id ":222222}.

[0044] Where `type` refers to the object's type; `ver` refers to the version number; `x`, `y` refer to the x and y coordinates in a two-dimensional global coordinate system; and `obj`... id Refers to the UUID; from and to are used to describe the UUID of the object to which the wire is connected; len refers to the length of the wire.

[0045] Step S21: When a user performs a change operation on a component, the incremental data extraction module captures the operation. Instead of immediately sending the data to the cloud server cluster, the system adds the UUID of the component being changed to a temporary patch model.

[0046] Step S22: Traverse the patch model and compare the current attribute values ​​of the component to be operated on with the previous version snapshot. Only extract the attribute fields that have changed, not the entire object. The previous version snapshot refers to the electrical object data state that was most recently synchronized with the cloud server before the current user operation, stored in the user terminal object storage pool. It serves as the benchmark for attribute comparison and is used in this step to identify and extract the changed local attribute fields through difference calculation.

[0047] Step S23: Encapsulate the changed attribute fields extracted in Step S22 into an incremental packet based on JSON Patch format. This incremental packet specifically includes: the operation type indicating the nature of the action (e.g., modification, move), the UUID of the target component, the specific change data, and the previous version number of the component in local storage. The previous version number corresponds to the version information of the previous snapshot in Step S22, and is used to perform optimistic locking concurrency control on the cloud server, ensuring that the incremental modification is based on the latest state of the object by verifying version consistency.

[0048] Step S24: Using the WebSocket channel, push the serialized incremental data packet into the sending queue. When the cloud server cluster returns an acknowledgment signal, the user terminal updates the version number of the local object and clears the patch model. If the serialization of the incremental data packet fails to send, a retry mechanism is triggered. Here, the local object refers to the electrical component data entity expressed in JSON format stored in the user terminal's object storage pool. In this step, the user terminal synchronously updates the version number of this entity to the new version value assigned by the cloud based on the acknowledgment signal from the cloud server cluster, thus completing the final confirmation of the local data status.

[0049] The specific embodiments described above further illustrate the technical problems, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cloud-native architecture-based collaborative design system for power distribution networks, characterized in that: It includes a user terminal and a cloud server cluster. The user terminal and the cloud server cluster establish data transmission through WebSocket to perform real-time interaction between incremental data packets and calculation results. User terminal, the user terminal is used to provide an interactive interface for users, perform lightweight rendering of power distribution network drawings and capture the operations performed by users on power distribution network drawings; A cloud server cluster is used for centralized business logic, electrical calculations, and collaborative control of power distribution network drawings.

2. The cloud-native architecture-based collaborative design system for power distribution networks according to claim 1, characterized in that: The user terminal includes a user interface, a rendering engine, an object storage pool, an incremental data extraction module, and a WebSocket client; The user interface is used to receive layout instructions and attribute modification instructions from users for power distribution network components, display power distribution network rendering drawings, and provide real-time feedback on cloud electrical calculation results and conflict alarm information. The rendering engine is used to achieve lightweight rendering of large-scale power distribution network drawings; The object storage pool stores electrical object data in the power distribution network drawings in JSON format. Each electrical object is assigned a globally unique UUID, which includes the object type, version number, attribute parameters, and association information. The incremental data extraction module is used to capture user operations and extract incremental data packets encapsulated in changed attribute fields; The WebSocket client is used to establish a fully bidirectional real-time communication link with the cloud server cluster, send the incremental data packets encapsulated by the incremental data extraction module to the cloud, and receive the electrical verification results and collaborative status broadcast information returned by the cloud server cluster in real time.

3. The cloud-native architecture-based collaborative design system for power distribution networks according to claim 1, characterized in that: The cloud server cluster includes an API gateway, a microservice business layer, and a data storage layer; The API gateway is used to uniformly receive and distribute operation instructions from user terminals; The microservice business layer includes an electrical computing engine, user and project management services, drawing parsing and topology construction services, and collaborative conflict control services. The electrical computing engine receives the distribution network topology and constructs a real-time model provided by the service, performs professional verification calculations on the distribution network drawings such as short-circuit current and voltage deviation, and feeds the calculation results back to the user terminal in real time. The user and project management services are used for user authentication and access control, and for version tracking, cloud storage, and archiving management of the distribution network drawings. The drawing parsing and topology construction services parse incremental data packets uploaded by the user terminal, dynamically construct and update electrical connection relationships in cloud memory using graph theory algorithms. The collaborative conflict control service performs primitive locking management based on an object occupancy table, combined with a timestamp sequencing mechanism and topology domain verification logic. The data storage layer is used to provide multi-dimensional data persistence and high-speed caching support.

4. The cloud-native architecture-based collaborative design system for power distribution networks according to claim 3, characterized in that: The data storage layer includes relational databases, NoSQL databases, and Redis cache.

5. A control method for a cloud-native architecture-based collaborative design system for power distribution networks as described in any one of claims 1 to 4, characterized in that, It includes the following steps: Step S1: When multiple people operate the same power distribution network drawing at the same time, perform collaborative conflict handling based on topology constraints. Step S2: Perform real-time electrical calculations on the power distribution network drawings in the cloud.

6. The control method according to claim 5, characterized in that, In step S1, when multiple people are simultaneously operating the same power distribution network drawing, collaborative conflict handling based on topology constraints is performed, specifically including the following steps: Step S11: Abandon traditional file locks and establish a primitive object lock based on UUID. Use Redis cache to maintain a dynamic object occupancy table and broadcast the status via WebSocket. Step S12: Use a timestamp-based sequencing mechanism based on cloud server clusters to process concurrent modification requests based on the first-come, first-served principle; Step S13: When a user operates any component in the power distribution network drawing, automatically detect the status of the upstream and downstream related objects of that device; Step S14: After receiving the blocking information, the user terminal does not perform deletion on the canvas.

7. The control method according to claim 6, characterized in that, In step S13, when a user operates any component in the power distribution network drawing, the status of the upstream and downstream related objects of that device is automatically detected, specifically including the following steps: Step S131: When the user operates on component T on the interface, the server receives the request; first, it uses the UUID to lock component T, and then calls the topology breadth-first search algorithm to retrieve all adjacent nodes T that have a direct electrical connection with component T in the graph data structure in memory. s Construct the affected topology domain {T s1 , T s2 , …T sn }; Step S132: The system queries the global object status table in the Redis cache, performs status retrieval on all object IDs in the affected topology domain, determines whether component T is locked by other users, and then checks whether component T in the affected topology domain is locked by other users. Step S133: If all objects in the affected topology are in an unlocked state, mark component T and its associated connection points as write-protected, execute user operations, and trigger topology reconstruction. If all objects within the affected topology are locked, a cascading block is triggered, an operation failure instruction is sent to the user, and metadata containing the specific reason is pushed.

8. The control method according to claim 5, characterized in that, In step S2, the real-time electrical calculation of the power distribution network drawing is performed in the cloud, which specifically includes the following steps: Step S21: When a user performs an operation on a component, the incremental data extraction module captures the operation and adds the UUID of the operated component to a temporary patch model. Step S22: Traverse the patch model, compare the current attribute values ​​of the component to be operated with the previous version snapshot, and extract the attribute fields that have changed. Step S23: Encapsulate the changed attribute fields extracted in step S22 into an incremental data packet based on JSON Patch format; Step S24: Using the WebSocket channel, push the serialized incremental data packet into the sending queue. When the cloud server cluster returns a confirmation signal, the user terminal updates the version number of the local object and clears the patch model. If the serialized incremental data packet fails to be sent, a retry mechanism is triggered.

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