Network planning method and device, computer equipment, storage medium and program product

Abstract

The invention relates to a network planning method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: receiving business demand information input by a user, and performing intention analysis on the business demand information to obtain a structured intention object; converting the intention object into a logic element, and adding the logic element into a preset meta-model to trigger a logic element updating event; the meta-model is used for providing semantic standards of logic elements and data elements; responding to the logic element updating event through a co-processing framework, and calling an intelligent mapping engine to convert the logic element into a corresponding data parameter; and filling the data parameters in a data filling interface, and generating a network planning scheme containing the logic elements and the data parameters. By adopting the method, collaboration of a logic arrangement layer and a data filling layer can be realized, and the problem that logic design and data filling are disjointed is solved.

Description

Technical Field

[0001] This application relates to the field of computer network planning technology, and in particular to a network planning method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With the rapid development of information technology, modern network structures are becoming increasingly complex, posing unprecedented challenges to network planning. Traditional network planning methods typically employ a phased, manual approach, where engineers first design and orchestrate the logic based on business requirements to form a preliminary plan, followed by the data entry and configuration distribution of specific equipment parameters. However, this linear workflow suffers from a disconnect between logical design and data entry, making it prone to deviations when translating design intent into underlying configurations, resulting in discrepancies between the final deployment and the initial plan. Summary of the Invention

[0003] Therefore, it is necessary to provide a network planning method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can achieve collaboration between the logic orchestration layer and the data entry layer, and solve the problem of the disconnect between logic design and data entry, in order to address the above-mentioned technical problems.

[0004] Firstly, this application provides a network planning method, including:

[0005] Receive business requirement information input by the user, perform intent parsing on the business requirement information, and obtain a structured intent object;

[0006] The intent object is converted into a logical element and added to a preset metamodel to trigger a logical element update event; the metamodel is used to provide semantic standards for logical elements and data elements.

[0007] In response to the logical element update event, the collaborative processing framework calls the intelligent mapping engine to convert the logical element into corresponding data parameters.

[0008] Fill in the data parameters in the data entry interface to generate a network planning scheme that includes the logical elements and the data parameters.

[0009] In some embodiments, converting the intent object into a logical element includes:

[0010] Create the logical rules corresponding to the intent object;

[0011] Add corresponding constraints to the logical rules to obtain the logical elements corresponding to the intent object.

[0012] In some embodiments, the intelligent mapping engine has a built-in mapping rule library for logical elements and data elements;

[0013] The intelligent mapping engine searches the mapping rule base based on the logical element to obtain the data element associated with the logical element; it then calculates the data parameters of the data element using a transformation function to determine the data parameters corresponding to the logical element.

[0014] In some embodiments, after filling in the data parameters on the data entry interface, the method further includes:

[0015] Suggestions for pre-filling relevant parameters for generating the data parameters;

[0016] The pre-fill suggestions are displayed on the data entry interface to instruct the user to fill in the relevant parameters.

[0017] In some embodiments, the method further includes:

[0018] Throughout the entire process of monitoring data entry, the consistency between the configuration parameters required by the logical elements in the logical layer and the data parameters entered by the data layer is maintained.

[0019] If a data deviation is detected, an alarm is issued, and / or the data deviation is corrected according to a predefined repair strategy.

[0020] In some embodiments, the collaborative processing framework is an event-driven management framework that uses an asynchronous message mechanism to process the various events being monitored.

[0021] The collaborative processing framework is also used to respond to user changes to the data parameters on the data entry interface, call the intelligent mapping engine to convert the changed data parameters into corresponding logical elements, and update the logic layer based on the converted logical elements.

[0022] Secondly, this application also provides a network planning apparatus, comprising:

[0023] The intent parsing unit is used to receive business requirement information input by the user, perform intent parsing on the business requirement information, and obtain an intent object.

[0024] An intent conversion unit is used to convert the intent object into a logical element and add it to a preset metamodel to trigger a logical element update event; the metamodel is used to provide semantic standards for logical elements and data elements.

[0025] The element conversion unit is used to respond to the logical element update event through the collaborative processing framework and call the intelligent mapping engine to convert the logical element into the corresponding data parameters.

[0026] The scheme generation unit is used to fill in the data parameters in the data entry interface and generate a network planning scheme containing the logical elements and the data parameters.

[0027] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0028] Receive business requirement information input by the user, perform intent parsing on the business requirement information, and obtain a structured intent object;

[0029] The intent object is converted into a logical element and added to a preset metamodel to trigger a logical element update event; the metamodel is used to provide semantic standards for logical elements and data elements.

[0030] In response to the logical element update event, the collaborative processing framework calls the intelligent mapping engine to convert the logical element into corresponding data parameters.

[0031] Fill in the data parameters in the data entry interface to generate a network planning scheme that includes the logical elements and the data parameters.

[0032] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0033] Receive business requirement information input by the user, perform intent parsing on the business requirement information, and obtain a structured intent object;

[0034] The intent object is converted into a logical element and added to a preset metamodel to trigger a logical element update event; the metamodel is used to provide semantic standards for logical elements and data elements.

[0035] In response to the logical element update event, the collaborative processing framework calls the intelligent mapping engine to convert the logical element into corresponding data parameters.

[0036] Fill in the data parameters in the data entry interface to generate a network planning scheme that includes the logical elements and the data parameters.

[0037] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0038] Receive business requirement information input by the user, perform intent parsing on the business requirement information, and obtain a structured intent object;

[0039] The intent object is converted into a logical element and added to a preset metamodel to trigger a logical element update event; the metamodel is used to provide semantic standards for logical elements and data elements.

[0040] In response to the logical element update event, the collaborative processing framework calls the intelligent mapping engine to convert the logical element into corresponding data parameters.

[0041] Fill in the data parameters in the data entry interface to generate a network planning scheme that includes the logical elements and the data parameters.

[0042] The aforementioned network planning method, apparatus, computer equipment, computer-readable storage medium, and computer program product receive user-input business requirement information, perform intent parsing on the business requirement information to obtain structured intent objects; the intent objects are converted into logical elements and added to a preset metamodel to trigger logical element update events; the metamodel provides semantic standards for logical elements and data elements; a collaborative processing framework responds to logical element update events by calling an intelligent mapping engine to convert logical elements into corresponding data parameters; data parameters are filled in the data entry interface to generate a network planning scheme containing logical elements and data parameters. This method provides a standardized semantic description framework for network planning by constructing a metamodel, and achieves unambiguous automatic conversion from logical intent to data parameters through a collaborative processing framework and an intelligent mapping engine, thereby realizing collaboration between the logic orchestration layer and the data entry layer and solving the problem of disconnect between logic design and data entry; furthermore, through the collaborative action of various components, it achieves fully automatic conversion from high-level user business intent to low-level configuration parameters, enabling business personnel to directly describe requirements using natural language, reducing the technical threshold for network planning and improving business response speed. It can achieve fully automated generation and management, from business intent input to final configuration output, reducing manual intervention and ensuring the accuracy and reliability of network planning solutions. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a diagram showing the components of a network planning scheme in one embodiment;

[0045] Figure 2 This is a flowchart illustrating a network planning method in one embodiment;

[0046] Figure 3 This is a flowchart illustrating the network planning method in another embodiment;

[0047] Figure 4 This is a structural block diagram of a network planning device in one embodiment;

[0048] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. It should be noted that the terms "comprising" and "having," and any variations thereof, as used in this application, are intended to cover non-exclusive inclusion. The term "multiple" as used in this application refers to two or more. The term "and / or" as used in this application refers to one of the solutions, or any combination of multiple solutions.

[0050] To facilitate understanding of this application, the relevant concepts involved in this application are briefly explained below.

[0051] Network planning refers to the systematic design process for building or transforming a network (such as a telecommunications network, data center network, or enterprise network). Traditionally, this process involves a series of complex steps, often requiring manual intervention, including requirements analysis, topology design, equipment selection, site planning, and strategy formulation. This application aims to fundamentally revolutionize this process.

[0052] Logical orchestration refers to the process of abstracting, defining, and combining network service intentions and high-level policies. It focuses on "what" rather than "how." For example, defining a policy of "guaranteeing minimum bandwidth for VIP users" is a form of logical orchestration; it doesn't concern itself with which port on which device it is implemented on.

[0053] Data entry refers to the process of translating the high-level intents generated by logical orchestration into specific configuration parameters that network devices can recognize and execute. It focuses on "how," such as translating the "guarantee minimum bandwidth" policy into QoS (Quality of Service) queue configuration commands, bandwidth values, priority parameters, etc., on specific routers.

[0054] It is understandable that in related technologies, network planning methods usually adopt a phased manual operation mode. This linear workflow has significant defects: (1) The logical design and data entry are seriously disconnected. When the design intention is converted into the underlying configuration, it is easy to produce deviations, resulting in the final deployment being inconsistent with the initial plan; (2) Change management is extremely difficult. Any modification of the logical layer is difficult to be efficiently synchronized to the data layer, and vice versa, making network optimization and adjustment costly; (3) There is a lack of effective real-time consistency verification mechanism. Errors often need to be discovered in the deployment and implementation stage or even the operation stage, causing serious operation and maintenance risks and economic losses; (4) The automation level of the whole process is low, and it is highly dependent on the manual operation of professional engineers. Not only is it inefficient, but it is also very easy to introduce errors due to human factors.

[0055] However, existing research on related technologies mainly focuses on document information extraction and automated configuration at the device level. For example, some technical solutions are dedicated to automatically extracting network topology keyword information from natural language documents, but they do not address the essential issue of collaboration between logical design and data configuration; other intelligent topology commissioning methods, while achieving automated configuration at the device level, lack the ability to perform bidirectional mapping and collaborative management from high-level business intents to low-level configuration parameters.

[0056] Therefore, there is an urgent need in the current technology field for an innovative solution that can break down the barriers between logical orchestration and data entry, and achieve efficient collaboration and automatic generation between the two.

[0057] Based on this, this application provides a network planning technology that enables collaborative generation of logical orchestration and data entry. It provides a standardized semantic description framework for network planning by constructing a unified meta-model definition; achieves unambiguous automatic conversion and bidirectional synchronization from logical intent to data parameters through an intelligent mapping engine; ensures real-time verification and conflict resolution of any modifications during the planning process through a built-in consistency verification module; and finally, manages change propagation and version control throughout the entire planning lifecycle through an event-driven collaborative processing framework. This significantly improves the accuracy, efficiency, and automation level of network planning, reduces reliance on manual operation and the associated error risks, and enables network planning to quickly respond to changes in business needs, providing solid technical support for the construction and operation of complex network environments. Figure 1 As shown, this technical solution has the following five main components:

[0058] Intent parser: Used to receive natural language input from users. It employs natural language processing (NLP) technology to identify and decompose the user's business requirements described in everyday language (such as "increase core switch bandwidth"), and convert them into structured logical rules that the system can understand (such as IntentCode: BANDWIDTH_ENHANCEMENT, parameters: {value: 500}). NLP technology enables computers to understand, interpret, and manipulate human language. In this application, identifying the key intents, parameters (such as device type "switch" and value "500") and operations (such as "increase" and "update") of user input through NLP technology is the technological foundation for realizing the conversion from "human language" to "machine rules."

[0059] Metamodel: A unified data model and semantic standard established for the entire system. It defines the basic attributes, relationships, and constraint rules of "logical elements" (such as policies and constraints) and "data elements" (such as parameters and configurations). It is the cornerstone for achieving intelligent mapping and consistency verification, ensuring that both parties communicate within the same semantic framework.

[0060] The intelligent mapping engine is the core execution component that enables the collaborative generation of logical orchestration and data entry. It has a built-in mapping rule library that automatically converts logical elements defined in the metamodel into corresponding data elements (logic to data), and conversely, it can also feed back changes in the data layer and affect the logical layer (data to logic). It can be seen as a two-way translator and converter.

[0061] The collaborative processing framework is an event-driven software framework responsible for managing and coordinating the workflow and communication between various components (mapping engine, verification module, etc.) in the system. It listens for change events in the logic or data layer and automatically triggers corresponding mapping, verification, and other tasks, ensuring the entire collaborative process is orderly, efficient, and reliable. The event-driven working mode means that the collaborative processing framework's operation is triggered and driven by a series of events (such as "logical element updated" or "data element modified").

[0062] Consistency verification module: Used to continuously and in real time check whether the design intent of the logical layer is consistent with the specific configuration of the data layer during and after data entry. Once a deviation is found (e.g., the logical layer requires 500Mbps, but the data layer is only configured with 100Mbps), an alarm will be issued immediately and / or an automatic repair mechanism will be triggered.

[0063] This solution achieves the following functions through the synergistic effect of the aforementioned components:

[0064] (1) Two-way collaborative generation of logical orchestration and data entry. Through a systematic two-way collaborative mechanism (such as intelligent mapping and consistency verification), the two sides automatically and in real time drive and synchronize each other, thereby generating the final network planning scheme efficiently and without errors. This two-way collaborative mechanism enables the logical layer and the data layer to interact equally and in real time. Not only can changes in the logical layer be automatically synchronized to the data layer (forward), but configuration changes in the data layer can also be transmitted in reverse and affect the logical layer (reverse). This ensures that any modification on one side can be reflected on the other side in real time, solving the problem of asynchronous changes in the traditional one-way process.

[0065] (2) Change management based on a collaborative mechanism. This refers to the ability to modify, update, and version control the network planning scheme. Through the two-way collaboration and event-driven framework of this scheme, any change can be automatically and accurately propagated and synchronized between the logic layer and the data layer, which greatly simplifies the change process and reduces management complexity and risk.

[0066] (3) Good scalability. The collaborative processing framework adopts an event-driven working mode, which can realize asynchronous processing. The asynchronous processing mechanism enables the system to have high decoupling, scalability and high performance, and can flexibly cope with various change requests in high-concurrency scenarios.

[0067] (4) Real-time consistency between logical orchestration and data entry. This is achieved through monitoring and verification by the consistency verification module. Moreover, the monitoring and verification behavior does not occur only after the network planning is completed, but happens immediately and continuously during the data entry process. This can detect and correct errors almost instantly, eliminating risks in their infancy and avoiding the high cost problem of errors not being discovered until the deployment stage in the traditional approach.

[0068] (5) Automated configuration. This refers to the system's ability to automatically generate detailed configuration parameters required by devices based on the intent of logical orchestration through an intelligent mapping engine, and to automatically distribute these parameters to network devices through downstream systems. This function is the ultimate goal of network planning and deployment, namely, to minimize manual intervention and improve efficiency and accuracy.

[0069] refer to Figure 2 This is a flowchart illustrating a network planning method according to an embodiment of this application. This embodiment uses the application of this method to a terminal as an example for illustration. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0070] Step S210: Receive business requirement information input by the user, perform intent parsing on the business requirement information, and obtain a structured intent object.

[0071] Among them, business requirement information is used to describe the user's business requirements, which can be described in natural language.

[0072] Specifically, the user inputs business requirement information through natural language. After receiving the business requirement information, the intent parser is invoked to parse the business requirement information, identify the key information, and parse it into a structured intent object.

[0073] For example, if a user inputs the business requirement information "increase the bandwidth of the core switch to 500Mbps", after the intent parser parses it, it obtains the intent code "bandwidth enhancement" and the parameters {value: 500, device type: switch}, which serve as the intent object.

[0074] In this step, natural language processing and machine learning technologies are integrated into an intent parser, which can automatically transform the business requirements described by users in natural language into structured logical rules. Through pattern matching and parameter extraction technologies, vague business intents are transformed into precise executable rules, achieving intelligent intent parsing, significantly reducing the technical threshold and improving planning efficiency.

[0075] Step S220: Convert the intent object into a logical element and add it to the preset metamodel to trigger a logical element update event; the metamodel is used to provide semantic standards for logical elements and data elements.

[0076] Specifically, a meta-model defining logical and data elements can be pre-established to provide a unified meta-language description framework for network planning. Logical elements include high-level abstractions such as strategies, rules, and constraints, while data elements contain concrete implementation details such as parameters, configurations, and values. This model establishes the semantic foundation for the relationship between the logical and data layers.

[0077] The metamodel defines logical elements and data elements. Therefore, after obtaining the intent object corresponding to the business requirement information, the intent object needs to be converted into a logical element. Then, the logical element is added to the metamodel, thereby triggering a logical element update event so that the subsequent collaborative processing framework can capture and process the event.

[0078] In this step, a unified meta-model definition system is established to construct the relationship between logical elements and data elements, and a standardized network planning meta-language description framework is provided to ensure that the logical layer and the data layer have a consistent semantic foundation.

[0079] In step S230, in response to the logical element update event, the collaborative processing framework calls the intelligent mapping engine to convert the logical element into the corresponding data parameters.

[0080] In practical implementation, an intelligent mapping engine can be pre-designed, with a built-in mapping rule library between logical elements and data elements. Thus, when the collaborative processing framework listens for logical element update events, and responds by calling the intelligent mapping engine, the engine can convert the logical element into corresponding data parameters according to the mapping rule library.

[0081] In this step, the design of a collaborative processing framework and an intelligent mapping engine enables the automatic conversion of the user's logical intent into data parameters.

[0082] Step S240: Fill in the data parameters in the data entry interface to generate a network planning scheme containing logical elements and data parameters.

[0083] Specifically, after filling in the data parameters on the data entry interface, the consistency between the data parameters and the configuration parameters in the logical elements will be verified. After the verification is passed, a network planning scheme including a bandwidth enhancement strategy description of the logical layer and detailed device configuration parameters of the data layer will be generated.

[0084] In the aforementioned network planning method, user-inputted business requirements are received, and intent parsing is performed to obtain structured intent objects. These intent objects are converted into logical elements and added to a pre-defined metamodel, triggering logical element update events. The metamodel provides semantic standards for logical and data elements. A collaborative processing framework responds to logical element update events by calling an intelligent mapping engine to convert logical elements into corresponding data parameters. Data parameters are then filled in the data entry interface, generating a network planning scheme containing logical elements and data parameters. This method provides a standardized semantic description framework for network planning by constructing a metamodel. The collaborative processing framework and intelligent mapping engine achieve unambiguous automatic conversion from logical intent to data parameters, thereby enabling collaboration between the logic orchestration layer and the data entry layer, solving the problem of disconnect between logic design and data entry. Furthermore, through the collaborative action of various components, fully automatic conversion from high-level user business intent to low-level configuration parameters is achieved, allowing business personnel to directly describe requirements using natural language, lowering the technical threshold for network planning and improving business response speed. It enables fully automated generation and management of the entire process, from business intent input to final configuration output, reducing manual intervention and ensuring the accuracy and reliability of the network planning scheme.

[0085] In an exemplary embodiment, step S220 above converts the intent object into a logical element, including: creating a logical rule corresponding to the intent object; adding corresponding constraints to the logical rule to obtain the logical element corresponding to the intent object.

[0086] Specifically, logical elements include high-level abstractions such as strategies, rules, and constraints. Therefore, the specific process of converting an intent object into a logical element involves creating a corresponding logical strategy or rule and adding appropriate constraints to it to obtain the corresponding logical element. In some embodiments, the constraints can be determined based on the user's intent; if the user's intent is to adjust bandwidth, then the constraints are bandwidth-related constraints.

[0087] For example, the business requirement "increase the bandwidth of the core switch to 500Mbps" is parsed by the intent parser, resulting in the intent code "bandwidth enhancement" and the parameters {value: 500, device type: switch}, which serve as the intent object. Converting this into a logical element involves creating a logical rule named "bandwidth enhancement policy," setting the target bandwidth attribute to 500Mbps, and adding corresponding constraints (such as the bandwidth value not exceeding 10Gbps), thus forming the logical element.

[0088] In this embodiment, by converting intent objects into logical elements, the element determination of the logical layer in network planning technology is realized, laying the foundation for the subsequent collaboration between the logical layer and the data layer.

[0089] In an exemplary embodiment, the intelligent mapping engine in step S230 above has a built-in mapping rule library for logical elements and data elements; the intelligent mapping engine searches the mapping rule library according to the logical element to obtain the data element associated with the logical element; and calculates the data parameters of the data element through a conversion function to determine the data parameters corresponding to the logical element.

[0090] Transformation functions are predefined mapping rules, typically mathematical functions, logical conditions, or business rules. They are the core component of the intelligent mapping engine, used to convert the abstract attributes of logical elements into concrete data parameters. Transformation functions can include functions for semantic transformation; for example, the numerical value in a logical element might be a high-level description (e.g., "500Mbps"), while the data parameter needs to be in a device-recognizable format (e.g., the integer 500). Transformation functions handle unit conversions, format normalization, or computational dependencies (e.g., calculating actual available bandwidth based on network topology) to obtain data parameters. Transformation functions can also be functions that adapt the intelligent mapping engine's mapping rules to different scenarios; for example, bandwidth values ​​might be adjusted based on logical constraints (e.g., maximum bandwidth limits) to ensure parameter appropriateness.

[0091] Specifically, the intelligent mapping engine can automatically convert logical elements into data parameters and supports bidirectional mapping relationships. It maintains a complete mapping rule library, which can automatically convert logical element attributes into corresponding data parameter values ​​according to predefined conversion functions. It also supports updating relevant attributes of the logical layer from changes in the data layer.

[0092] For example, if the logical element is about increasing the bandwidth to 500Mbps, the intelligent mapping engine will find the data element (such as the interface bandwidth parameter) associated with the bandwidth policy according to the predefined mapping rule library, and calculate the specific value 500 through the conversion function as the data parameter.

[0093] Understandably, the consistency verification module continuously monitors the consistency status of the logical layer and the data layer. If it detects that the data parameters calculated by the transformation function are inconsistent with the logical element (e.g., the logical element requires 500Mbps, but the transformation function outputs 400Mbps), it will issue an alarm or perform automatic repair. For example, the verification module compares the "minBandwidth" attribute of the logical layer with the "interfaceBandwidth" value of the data layer. If an inconsistency is found, it will trigger a repair strategy (such as forcibly overwriting the data value or prompting the user to adjust it).

[0094] In this embodiment, real-time synchronization and collaboration between logical elements and data elements are achieved through configurable mapping rules and transformation functions in the mapping rule base.

[0095] In an exemplary embodiment, after filling in the data parameters on the data entry interface in step S240, the method further includes: generating pre-filling suggestions for related parameters of the data parameters; and displaying the pre-filling suggestions on the data entry interface to instruct the user to fill in the related parameters.

[0096] In practice, after filling in the data parameters on the data entry interface, the user can see the results. For example, the user can see that the interface bandwidth field has been automatically filled with 500. In some embodiments, the data parameters being filled may have some related or affected parameters. Therefore, after completing the data parameter filling, pre-filling suggestions for related parameters can be generated and displayed in the corresponding field of the data entry interface to instruct the user to fill in the relevant parameters. The user can then make fine adjustments based on the pre-filling suggestions.

[0097] In this embodiment, after the data parameters are filled in on the data entry interface, pre-filling suggestions for related parameters are generated and displayed on the data entry interface to provide users with guidance on filling in related parameters, thereby improving the filling efficiency.

[0098] In an exemplary embodiment, the method further includes: monitoring the consistency between the configuration parameters required by the logical elements of the logical layer and the data parameters filled in by the data layer throughout the data filling process; issuing an alarm if a data deviation is detected, and / or correcting the data deviation according to a predefined repair strategy.

[0099] In the specific implementation, a consistency verification module is pre-built to continuously monitor the consistency status of the logic layer and the data layer, detecting conflicts and mismatches between them in real time. If the detection results are consistent, the network planning scheme can be generated. If data deviation is detected, i.e., an inconsistency problem exists, an alarm can be issued, and the data deviation can be corrected according to a predefined repair strategy to ensure that the logic layer and the data layer always maintain a consistent state.

[0100] In this embodiment, a real-time consistency verification mechanism is used to continuously monitor the consistency status of the logic layer and the data layer. It can automatically detect configuration conflicts and provide repair suggestions, ensuring that the design intent and the implementation configuration are always highly consistent. This avoids the problem that errors in related technologies are often only discovered during the deployment and implementation stage or even the operation stage, which can cause serious operational risks and economic losses.

[0101] In an exemplary embodiment, the collaborative processing framework is an event-driven management framework that uses an asynchronous message mechanism to process various monitored events. The collaborative processing framework is also used to respond to user changes to data parameters on the data entry interface by calling the intelligent mapping engine to convert the changed data parameters into corresponding logical elements and updating the logic layer based on the converted logical elements.

[0102] Specifically, the collaborative processing framework constructed in this application is an event-driven management framework responsible for coordinating the entire workflow of logical orchestration and data entry. This framework employs an asynchronous message mechanism to handle various change events, which can be either logic-layer or data-layer changes. Therefore, change propagation based on this collaborative processing framework includes not only forward collaboration from the logic layer to the data layer but also reverse collaboration from the data layer to the logic layer, forming a bidirectional collaboration mechanism. Wherein:

[0103] Forward change propagation: When a logical layer element (such as a logical rule parsed from a user's intent) changes, the collaborative processing framework automatically converts the logical element into data parameters through the intelligent mapping engine and updates the data layer. For example, after a user modifies their business intent, the logical element updates and triggers an event, and the intelligent mapping engine recalculates the data parameters and populates them into the data entry interface.

[0104] Reverse change propagation: When a data layer element changes (such as parameters manually modified by a user in the data entry interface), the collaborative processing framework automatically converts the data parameters into logical elements through the intelligent mapping engine, updating the logical layer. For example, if a user changes the interface bandwidth from 500Mbps to 1000Mbps in the data entry interface, or adjusts the data due to other business needs, the system will update the bandwidth attribute in the logical elements in reverse.

[0105] The following example illustrates the complete collaborative process.

[0106] When a user inputs the intent "increase bandwidth to 500Mbps", the intent parser generates a logical element (targetBandwidth: 500Mbps).

[0107] Positive Collaboration: The intelligent mapping engine converts logical elements into data parameters 500 and populates the data entry interface.

[0108] When a user manually modifies a data parameter to 1000, a data element update event is triggered.

[0109] Reverse collaboration: The intelligent mapping engine maps the data value 1000 back to the logical element and updates the targetBandwidth to 1000Mbps (possibly by setting a new attribute such as mappedValue).

[0110] Therefore, reverse collaboration can also update the representation of logical elements within the system to reflect the latest state, allowing users to adjust details through the data layer while keeping the logical layer synchronized.

[0111] In some embodiments, version control can also be implemented for changes to the data layer and logic layer to manage different states and change histories of network planning schemes, ensuring traceability and consistency. Specific implementation methods include:

[0112] Version history: Assign version numbers to logical and data elements, and record the content, time, and user of each change. For example, when a logical element is updated, the system saves the old version and creates a new one, supporting version comparison and rollback.

[0113] Change propagation and version synchronization: When handling change events, the collaborative processing framework checks version compatibility. For example, if data layer parameters are modified based on older version logical elements, the system will detect version conflicts and prompt the user to resolve them.

[0114] Version Tagging and Status Management: Supports tagging of solution status (such as "Draft", "Under Review", "Released") to ensure changes propagate to the correct version. In Java code, a version field can be added by extending the "MetaModel" class, and version information can be included in the "CoordinationEvent".

[0115] In this embodiment, the collaborative processing framework uses an asynchronous message mechanism to handle various change events and supports version control and bidirectional change propagation, which can ensure that any modification can be correctly synchronized between the logic layer and the data layer.

[0116] refer to Figure 3The diagram below illustrates the specific process of a network planning method in another embodiment. It mainly includes four stages: Stage 1 and Stage 2 are forward collaborations; Stage 4 (shown by the dotted line in the diagram) is a reverse collaboration, caused by the user directly modifying data parameters. Both stages trigger real-time verification in Stage 3. The descriptions of each stage are as follows:

[0117] Phase 1: Intent Analysis.

[0118] After the user inputs business requirements described in natural language, the intent parser parses them to obtain a structured intent object. Logical elements are then created based on the intent object, added to the metamodel, and a logical element change event is triggered.

[0119] Phase Two: Intelligent Mapping.

[0120] When the event-driven engine of the collaborative processing framework detects a change event in a logical element, it calls the intelligent mapping engine to perform intelligent mapping from logic to data, obtains the data parameters corresponding to the logical element, causes the data element to be updated, and triggers real-time verification.

[0121] Phase 3: Real-time verification.

[0122] The consistency verification module monitors the consistency between the logic layer and the data layer in real time. It checks the consistency between the data parameters entered by the data layer and those of the logic layer. If they match, the verification passes, and a network planning scheme containing logical documentation and detailed configuration data is output. If they do not match, the verification fails (warning or error), triggering a verification result event. The collaborative processing framework uses this event to issue an alert, notify the user, pause scheme generation, or initiate an automatic repair process. After repair, consistency verification is performed again. It should be noted that this verification result event does not cause the collaborative processing framework to call the intelligent mapping engine; the collaborative processing framework primarily handles change propagation events.

[0123] Phase Four: Reverse Collaboration.

[0124] When a data element is updated due to direct modification by the user in the data layer (non-forward mapping), a data change event is triggered. After the event-driven engine of the collaborative processing framework listens for the data element change event, it calls the intelligent mapping engine to perform intelligent mapping from data to logic and updates the logical elements in the logic layer.

[0125] This solution ensures a high degree of consistency between design intent and actual configuration through a collaborative mechanism, significantly reducing errors and deviations caused by manual operation and solving the problem of disconnect between logical design and data entry in traditional network planning. Furthermore, by automating the generation of most configuration content, it reduces work that traditionally took hours or even days to minutes, significantly lowering operating and time costs and greatly improving network planning efficiency. Further, it constructs an event-driven collaborative processing framework, employing an asynchronous messaging mechanism to manage the entire workflow of logical orchestration and data entry, supporting bidirectional change propagation and version control, making network planning modifications and optimizations more flexible and reliable, adapting to rapidly changing business needs, and enhancing the system's change management capabilities. Furthermore, through a unified meta-model and modular design, it achieves a scalable technical framework capable of adapting to various complex network planning scenarios, providing a solid technical foundation for future network technology development. Finally, by improving the automation level and accuracy of network planning, it significantly enhances the reliability and stability of network services, providing strong technical support for digital transformation and intelligent operation.

[0126] To more clearly illustrate the network planning method provided in the embodiments of this application, the following uses a network bandwidth planning scenario as an example to further explain this solution. The specific process is as follows:

[0127] (1) The user first inputs the business requirement in natural language: "Increase the bandwidth of the core switch to 500Mbps". The intent parser identifies the key information in it and parses it into a structured intent object, which contains the intent code "bandwidth enhancement" and the parameters {value: 500, device type: switch}.

[0128] (2) Convert the parsed intent object into a logical element, create a logical rule named "Bandwidth Enhancement Strategy", set the target bandwidth attribute to 500Mbps, and add corresponding constraints (such as the bandwidth value must not exceed 10Gbps). This logical element is added to the metamodel, triggering a logical element update event.

[0129] (3) After the collaborative processing framework captures the update event of the logical element, it calls the intelligent mapping engine to perform the mapping from logic to data. The intelligent mapping engine finds the data element associated with the bandwidth policy (such as the interface bandwidth parameter) according to the predefined mapping rule library, calculates the specific value 500 through the conversion function, and automatically updates the value of the data element.

[0130] (4) On the data entry interface, users can see that the interface bandwidth field has been automatically filled with 500, and the system provides suggestions for pre-filling other related parameters. Users can make fine adjustments or add other configuration details based on this.

[0131] (5) The consistency verification module continuously monitors the entire process to ensure that the 500Mbps bandwidth required by the logic layer is consistent with the value configured in the data layer. If the data is found to have been mistakenly changed to 100Mbps, the system will immediately issue a warning and may attempt to automatically restore it to the correct value.

[0132] (6) Once the user confirms all configurations, the system generates a complete network planning scheme, including a description of the bandwidth enhancement strategy for the logical layer and detailed device configuration parameters for the data layer. Throughout the process, any modifications will be bidirectionally synchronized through the collaborative processing framework to ensure real-time consistency between the logical layer and the data layer.

[0133] This embodiment demonstrates a fully automated processing flow from business intent input to final configuration generation, effectively solving the problem of disconnect between logical design and data entry in traditional network planning, and significantly improving planning efficiency and accuracy.

[0134] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0135] Based on the same inventive concept, this application also provides a network planning apparatus for implementing the network planning method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more network planning apparatus embodiments provided below can be found in the limitations of the network planning method described above, and will not be repeated here.

[0136] In one exemplary embodiment, such as Figure 4 As shown, a network planning device is provided, comprising:

[0137] The intent parsing unit 410 is used to receive business requirement information input by the user, perform intent parsing on the business requirement information, and obtain an intent object.

[0138] The intent conversion unit 420 is used to convert intent objects into logical elements and add them to a preset metamodel to trigger logical element update events; the metamodel is used to provide semantic standards for logical elements and data elements.

[0139] The element conversion unit 430 is used to respond to a logical element update event through the collaborative processing framework and call the intelligent mapping engine to convert the logical element into the corresponding data parameter.

[0140] The scheme generation unit 440 is used to fill in data parameters on the data entry interface and generate a network planning scheme containing logical elements and data parameters.

[0141] In some embodiments, the intent conversion unit 420 is further configured to create logical rules corresponding to the intent object; add corresponding constraints to the logical rules to obtain logical elements corresponding to the intent object.

[0142] In some embodiments, the intelligent mapping engine has a built-in mapping rule library between logical elements and data elements; the intelligent mapping engine searches the mapping rule library based on the logical element to obtain the data element associated with the logical element; and calculates the data parameters of the data element through a transformation function to determine the data parameters corresponding to the logical element.

[0143] In some embodiments, the apparatus further includes a suggestion generation unit for generating pre-fill suggestions for relevant parameters of the data parameters; and displaying the pre-fill suggestions on the data entry interface to instruct the user to fill in the relevant parameters.

[0144] In some embodiments, the system further includes a consistency verification unit, which monitors the consistency between the configuration parameters required by the logical elements of the logical layer and the data parameters filled in by the data layer throughout the data filling process; if a data deviation is detected, an alarm is issued, and / or the data deviation is corrected according to a predefined repair strategy.

[0145] In some embodiments, the collaborative processing framework is an event-driven management framework that uses an asynchronous message mechanism to process various monitored events. The collaborative processing framework is also used to respond to user changes to data parameters on the data entry interface, call the intelligent mapping engine to convert the changed data parameters into corresponding logical elements, and update the logic layer according to the converted logical elements.

[0146] Each module in the aforementioned network planning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0147] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a network planning method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0148] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0149] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0150] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0151] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0152] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0153] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0154] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0155] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A network planning method, characterized in that, The method includes: Receive business requirement information input by the user, perform intent parsing on the business requirement information, and obtain a structured intent object; The intent object is converted into a logical element and added to a preset metamodel to trigger a logical element update event; the metamodel is used to provide semantic standards for logical elements and data elements. In response to the logical element update event, the collaborative processing framework calls the intelligent mapping engine to convert the logical element into corresponding data parameters. Fill in the data parameters in the data entry interface to generate a network planning scheme that includes the logical elements and the data parameters.

2. The method according to claim 1, characterized in that, The step of converting the intent object into logical elements includes: Create the logical rules corresponding to the intent object; Add corresponding constraints to the logical rules to obtain the logical elements corresponding to the intent object.

3. The method according to claim 1, characterized in that, The intelligent mapping engine has a built-in mapping rule library for logical elements and data elements; The intelligent mapping engine searches the mapping rule base based on the logical element to obtain the data element associated with the logical element; The data parameters of the data element are calculated using a conversion function and then determined to be the data parameters corresponding to the logical element.

4. The method according to claim 1, characterized in that, After filling in the data parameters on the data entry interface, the process further includes: Suggestions for pre-filling relevant parameters for generating the data parameters; The pre-fill suggestions are displayed on the data entry interface to instruct the user to fill in the relevant parameters.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Throughout the entire process of monitoring data entry, the consistency between the configuration parameters required by the logical elements in the logical layer and the data parameters entered by the data layer is maintained. If a data deviation is detected, an alarm is issued, and / or the data deviation is corrected according to a predefined repair strategy.

6. The method according to any one of claims 1 to 4, characterized in that, The collaborative processing framework is an event-driven management framework that uses an asynchronous message mechanism to process the various events it listens for. The collaborative processing framework is also used to respond to user changes to the data parameters on the data entry interface, call the intelligent mapping engine to convert the changed data parameters into corresponding logical elements, and update the logic layer based on the converted logical elements.

7. A network planning device, characterized in that, The device includes: The intent parsing unit is used to receive business requirement information input by the user, perform intent parsing on the business requirement information, and obtain an intent object. An intent conversion unit is used to convert the intent object into a logical element and add it to a preset metamodel to trigger a logical element update event; the metamodel is used to provide semantic standards for logical elements and data elements. The element conversion unit is used to respond to the logical element update event through the collaborative processing framework and call the intelligent mapping engine to convert the logical element into the corresponding data parameters. The scheme generation unit is used to fill in the data parameters in the data entry interface and generate a network planning scheme containing the logical elements and the data parameters.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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