Network optimization method and device, computer device, readable storage medium and program product

Abstract

The application relates to a network optimization method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: performing intention recognition on task creation instructions input by a user for a network optimization task, and determining network optimization requirement information of the task creation instructions; retrieving network optimization knowledge data matched with the network optimization requirement information, and generating a network optimization plan according to the network optimization knowledge data; the network optimization plan comprises at least one network optimization measure for completing the network optimization task, and operation steps in the network optimization measure; operation tasks of a plurality of RPA intelligent agents are arranged according to the network optimization plan, and an operation task arrangement result is obtained; and the respective operation tasks of the RPA intelligent agents are controlled to be executed according to the task arrangement result, so that the network optimization task is completed together. The method can improve the network optimization efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, and in particular to a network optimization method and device, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] Wireless network refers to a network that can realize interconnection of various communication devices without wiring. Wireless network technology covers a wide range, and more and more people use various Internet services through the use of wireless networks.

[0003] In the related art, network optimization of a wireless network often requires a large amount of time, and when a problem occurs in the wireless network, an optimization scheme for a target area cannot be quickly determined, so that the related art has the problem of low network optimization efficiency. SUMMARY

[0004] Therefore, it is necessary to provide a network optimization method, device, computer equipment, computer readable storage medium and computer program product capable of improving network optimization efficiency to solve the above technical problems.

[0005] In a first aspect, the present application provides a network optimization method, which comprises:

[0006] Intention recognition is performed on task creation instructions input by a user for a network optimization task to determine network optimization requirement information of the task creation instructions;

[0007] Network optimization knowledge data matching the network optimization requirement information is retrieved, and a network optimization plan is generated according to the network optimization knowledge data; the network optimization plan comprises at least one network optimization measure for completing the network optimization task and operation steps in the network optimization measure;

[0008] Operation tasks of a plurality of RPA intelligent agents are arranged according to the network optimization plan to obtain an operation task arrangement result; the operation tasks are used for the RPA intelligent agents to execute to make the RPA intelligent agents complete at least part of the operation steps in the network optimization measure;

[0009] According to the task arrangement result, the RPA intelligent agents are controlled to execute corresponding operation tasks to jointly complete the network optimization task.

[0010] In one of the embodiments, the network optimization plan is generated according to the network optimization knowledge data, which comprises:

[0011] Network working condition prediction information of the network optimization task is predicted according to the network optimization requirement information; the network working condition prediction information comprises network load and user demand;

[0012] determining at least one network optimization strategy meeting the network load and the user demand according to the network optimization knowledge data;

[0013] generating the network optimization plan according to the at least one network optimization strategy.

[0014] In one of the embodiments, the network optimization demand information includes a network optimization location and a network optimization time, and the predicting the network working condition information of the network optimization task according to the network optimization demand information includes:

[0015] querying historical network performance data matching the network optimization location from a network monitoring system;

[0016] training a network optimization model for the network optimization task according to the historical network performance data;

[0017] outputting, by the network optimization model, network load and user demand corresponding to the network optimization location at the network optimization time to obtain the network working condition prediction information.

[0018] In one of the embodiments, the method further includes:

[0019] in response to a command input operation implemented on a user interaction interface of the RPA intelligent agent, obtaining natural language text input by the command input operation;

[0020] in a case where the natural language text passes validity verification, determining the natural language text as the task creation instruction, and performing the intent recognition on the task creation instruction input by the user for the network optimization task to determine network optimization demand information carried by the task creation instruction;

[0021] wherein the validity verification is configured to verify whether the natural language text meets a preset intent recognition requirement.

[0022] In one of the embodiments, the intent recognition on the task creation instruction input by the user for the network optimization task to determine network optimization demand information of the task creation instruction includes:

[0023] inputting the task creation instruction into a pre-trained natural language processing model to obtain an intent recognition result output by the natural language processing model; the intent recognition result includes task intent information, key entity information and semantic understanding information in the task creation instruction;

[0024] determining network optimization demand information of the task creation instruction according to the task intent information, the key entity information and the semantic understanding information.

[0025] In one embodiment, the operation tasks of the plurality of RPA agents are arranged according to the network optimization plan, and an operation task arrangement result is obtained, including:

[0026] The network optimization plan is converted into task instructions for the plurality of RPA agents; different task instructions correspond to different operation tasks;

[0027] For any operation task, task decomposition is performed on the operation task to obtain at least two to-be-executed subtasks; each to-be-executed subtask is independently executed by at least one RPA agent;

[0028] According to the characteristic information of each to-be-executed subtask and the capability information of each RPA agent, each to-be-executed subtask is assigned to a corresponding RPA agent, and the operation task arrangement result is obtained.

[0029] In a second aspect, the present application further provides a network optimization device, the device comprising:

[0030] An identification module is configured to perform intent recognition on task creation instructions input by a user for a network optimization task, and determine network optimization requirement information of the task creation instructions;

[0031] A retrieval module is configured to retrieve network optimization knowledge data matched with the network optimization requirement information, and generate a network optimization plan according to the network optimization knowledge data; the network optimization plan comprises at least one network optimization measure for completing the network optimization task, and operation steps in the network optimization measure;

[0032] An arrangement module is configured to arrange operation tasks of a plurality of RPA agents according to the network optimization plan, and obtain an operation task arrangement result; the operation tasks are used for the RPA agents to execute, so that the RPA agents complete at least part of the operation steps in the network optimization measure;

[0033] A scheduling module is configured to control each RPA agent to execute a corresponding operation task according to the task arrangement result, so as to jointly complete the network optimization task.

[0034] In a third aspect, the present application further provides a computer device comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0035] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the above method.

[0036] In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described above.

[0037] The network optimization method, device, computer device, computer readable storage medium and computer program product described above determine network optimization requirement information of a task creation instruction input by a user for a network optimization task through intent recognition of the task creation instruction, retrieve network optimization knowledge data matching the network optimization requirement information, generate a network optimization plan according to the network optimization knowledge data, and the network optimization plan includes at least one network optimization measure for completing the network optimization task and operation steps in the network optimization measure. Then, operation tasks of multiple RPA agents are arranged according to the network optimization plan to obtain an operation task arrangement result. The operation tasks are used for the RPA agents to execute to make the RPA agents complete at least part of the operation steps in the network optimization measure. According to the task arrangement result, the RPA agents are controlled to execute corresponding operation tasks to jointly complete the network optimization task. Through cooperation between different RPA agents, more efficient and accurate wireless network optimization management is achieved. The user does not need to input a series of complex and tedious network optimization operations, but only needs to input the task creation instruction for the network optimization task to instruct the computer device to intelligently and comprehensively optimize the network environment to be optimized in combination with network optimization knowledge, thereby significantly improving network performance and user experience, and effectively improving network optimization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0039] Figure 1 An application environment diagram of a network optimization method in an embodiment;

[0040] Figure 2 A flowchart of a network optimization method in an embodiment;

[0041] Figure 3 A user interface diagram of a network optimization system in an embodiment;

[0042] Figure 4 A flowchart of a network optimization method in another embodiment;

[0043] Figure 5A system framework diagram of a network optimization system in an embodiment;

[0044] Figure 6 A structural block diagram of a network optimization apparatus in an embodiment;

[0045] Figure 7 An internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0046] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0047] In the present application, the data acquisition technical solution involved in the present application should comply with the requirements of national laws and regulations, meet the principles of legality, legitimacy and necessity, and not involve obtaining data types prohibited or restricted by laws and regulations, and will not hinder the normal operation of the target website when the above embodiments of the present application are applied to specific products or technologies.

[0048] The network optimization method provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 In the application environment, the terminal 102 communicates with the server 104 through a network. The data storage system can store data required to be processed by the server 104. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0049] The terminal 102 receives a task creation instruction input by a user for a network optimization task; the terminal 102 performs intent recognition on the task creation instruction input by the user for the network optimization task, and determines network optimization requirement information of the task creation instruction; the terminal 102 retrieves network optimization knowledge data matched with the network optimization requirement information from a knowledge base in the server 104, and generates a network optimization plan according to the network optimization knowledge data; the network optimization plan includes at least one network optimization measure for completing the network optimization task, and operation steps in the network optimization measure; the terminal 102 arranges operation tasks of multiple RPA intelligent agents according to the network optimization plan, and obtains an operation task arrangement result; the operation tasks are used for the RPA intelligent agents to execute, so that the RPA intelligent agents complete at least part of the operation steps in the network optimization measure; and the terminal 102 controls the RPA intelligent agents to execute corresponding operation tasks according to the task arrangement result, so as to jointly complete the network optimization task.

[0050] The terminal 102 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle-mounted device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0051] In an exemplary embodiment, as shown in Figure 2 , a network optimization method is provided. The method is applied to the terminal 102 in Figure 1 , and includes the following steps S202 to S208. Wherein:

[0052] Step S202, performing intent recognition on a task creation instruction input by a user for a network optimization task, to determine network optimization requirement information of the task creation instruction.

[0053] The network optimization task can refer to a task of optimizing network performance to meet specified requirements. In actual application, the network optimization task can be a task of focusing on guaranteeing wireless network performance in a certain place and time period.

[0054] The task creation instruction can refer to an instruction for creating the network optimization task. In actual application, the task creation instruction can be in natural language form. For example, the task creation instruction can be "Please help me create a focus guarantee task for A park during B holiday period, and execute."

[0055] The network optimization requirement information can refer to information for indicating a task target of the network optimization task. In actual application, the network optimization requirement information can include, but is not limited to, network optimization location, network optimization time, network optimization type, network optimization target, etc.

[0056] In specific implementation, the terminal displays a user interface of a network optimization system as a front-end interface for user interaction with the RPA multi-agent system. The terminal can provide an interface for user interaction with the RPA agent through the user interface, including command input, result display, and feedback collection. Specifically, the user interface can have the following functions:

[0057] 1) Data input function: Collect user queries or instructions through forms, text boxes, or other input controls. In this embodiment, the user can input "Please help me create a key protection task for A Park during B holiday period, and execute." in the text box.

[0058] 2) Request verification function: Verify validity to ensure that user input meets system processing requirements.

[0059] 3) Request forwarding function: Forward user requests to backend processing modules, such as search enhancement generation modules.

[0060] 4) Result display function: Receive backend processing results and display execution results to users on the user interface.

[0061] 5) User interaction function: Allow users to perform further operations through the interface, such as viewing detailed reports, adjusting optimization strategies, etc. To facilitate the understanding of those skilled in the art, Figure 3 An exemplary user interface diagram of a network optimization system is shown.

[0062] Users can input task creation instructions for network optimization tasks to the terminal through the data input function, such as "Please help me create a key protection task for A Park during B holiday period, and execute." The terminal then obtains the task creation instruction, and the terminal can perform intent recognition on the task creation instruction to determine the network optimization requirement information of the task creation instruction, to determine the network optimization location, network optimization time, network optimization type, network optimization target, etc. of the network optimization task.

[0063] In practical applications, the terminal can implement intent recognition on the task creation instruction by using the user intent understanding module in the network optimization system, accurately parse the user's input, and convert it into a task that the network optimization system can understand and execute. In practical applications, the user intent understanding module can have the following functions:

[0064] 1) Input receiving function: Receive text information input by users through the user interface module;

[0065] 2) Text preprocessing function: Cleanse user input, including removing irrelevant characters and standardizing text format.

[0066] 3) Intent recognition function: Determine the user's main purpose using large model prompting engineering technology. In the above example, the intent of the task creation instruction "Please help me create a key protection task for A Park during B holiday period, and execute." is "create a key protection task".

[0067] 4) Entity extraction function: Utilize large model slot recognition capabilities to identify and extract key information such as "A Park" and "B Holiday". These information are specific parameters for task execution.

[0068] 5) Semantic understanding function: Analyze user's request and understand its deep meaning, for example, the user may need to ensure the stability of A Park's network communication during B holiday.

[0069] Step S204, retrieve network optimization knowledge data matching the network optimization demand information, and generate network optimization plan based on the network optimization knowledge data.

[0070] Among them, the network optimization plan includes at least one network optimization measure to complete the network optimization task, and the operation steps in the network optimization measure.

[0071] In specific implementation, after the terminal obtains the network optimization demand information of the network optimization task, the terminal can access the pre-constructed and updated network optimization knowledge base accumulated in the wireless network optimization process to retrieve network optimization knowledge data matching the network optimization demand information from the network optimization knowledge base. In the above example, the network optimization knowledge data can be knowledge data in the historical key protection task record of A Park, network layout document, and special event arrangement data of B holiday.

[0072] After the terminal obtains the above knowledge data, the terminal can convert non-text data in the above knowledge data into text format using document loader and OCR technology for subsequent processing.

[0073] Then, the terminal can generate a network optimization plan based on the network optimization knowledge data. In practical applications, the terminal can use pre-set prompt words to instruct the large language model to generate a network optimization plan based on the network optimization knowledge data, including at least one network optimization measure to complete the network optimization task, and the operation steps in the network optimization measure, such as the key protection task plan for A Park's wireless network service during B holiday.

[0074] The key protection task plan can include a series of network optimization measures for optimizing A Park's wireless network during B holiday, and the operation steps in the network optimization measure.

[0075] In practical applications, the network optimization knowledge base can be maintained by a knowledge management and learning module in the network optimization system, which is responsible for managing and updating the knowledge base accumulated in the wireless network optimization process, including historical data, experience and lessons, best practices, etc., and improving the performance of the system through continuous learning. In practical applications, the knowledge management and learning module can include the following functions:

[0076] 1) Knowledge Collection Function: The input of this function includes historical optimization data, real-time optimization results, and external knowledge sources. Historical optimization data includes records of past network optimization activities, including measures taken, effectiveness evaluations, and user feedback. Real-time optimization results include real-time data and results from current network optimization activities. External data sources include industry reports, research papers, technical standards, and other external knowledge resources. Before task execution, the knowledge collection function collects relevant knowledge from historical data, external research, and previous experience.

[0077] 2) Vector Knowledge Base Construction Function: Store vectorized knowledge in a vector database to optimize retrieval efficiency.

[0078] 3) Retrieval Execution Function: Retrieve the most relevant knowledge fragments or documents from the vector database based on user requests.

[0079] 4) Knowledge Application Function: Combine the retrieved knowledge to generate a detailed key protection task plan, including network optimization strategies, resource allocation, expected risks, and countermeasures.

[0080] 5) Knowledge Update Function: Update the content in the knowledge base based on the results of task execution and user feedback, including adding new knowledge points and correcting outdated or inaccurate information.

[0081] Then, the network optimization system can also use the search enhancement generation module to retrieve network optimization knowledge data that matches the network optimization demand information in the network optimization knowledge base, and generate a network optimization plan based on the network optimization knowledge data. In practical applications, the working process of the search enhancement generation module can include the following stages:

[0082] 1) Retrieval Stage: First, receive user requests and convert them into retrievable queries. Use a pre-trained embedding model to convert queries into high-dimensional vector representations. Retrieve the most similar documents or information fragments in the knowledge base.

[0083] 2) Information Fusion Stage: Combine the retrieved information with the user's original request to form a rich context.

[0084] 3) Generation Stage: Use a pre-trained large language model to generate responses based on the fused context. Generate a key protection task plan, including specific network optimization measures and execution steps, through the generation model.

[0085] Step S206, according to the network optimization plan, arrange the operation tasks of multiple RPA intelligent agents, and obtain the operation task arrangement result.

[0086] The RPA agent can be the smallest execution unit in a multi-agent system (MAS) based on an RPA system.

[0087] The multi-agent system can be a system composed of multiple interacting agents, which can be software programs, robots, sensors, or human participants. Each agent has a certain degree of autonomy and can independently perceive the environment, make decisions, and perform actions.

[0088] The RPA system, i.e., Robotic Process Automation system, is a system that uses software robots (also known as automation scripts or "bots") to automatically execute repetitive and rule-based business processes. These software robots can simulate human users interacting with digital systems, such as data entry, file processing, web interaction, etc.

[0089] Network optimization RPA system: refers to a system that uses Robotic Process Automation (RPA) technology to optimize network performance.

[0090] It should be noted that the above information is only used to strengthen the understanding of the background of the present application, and therefore can include information that does not constitute information known to those of ordinary skill in the art.

[0091] The operation task is used for the RPA agent to execute, so that the RPA agent completes at least part of the operation steps in the network optimization measure.

[0092] After the terminal obtains the network optimization plan for the network optimization task, the terminal can convert each network optimization measure in the network optimization plan and the operation steps in each network optimization measure into task instructions for each RPA agent to execute. Then, the terminal can decompose the task instructions into a series of sub-task instructions, and according to the characteristics of the sub-task instructions and the capabilities of the RPA agents, assign the sub-task instructions to the corresponding RPA agents for execution.

[0093] Step S208, according to the task scheduling result, control each RPA agent to execute the corresponding operation task, so as to jointly complete the network optimization task.

[0094] In specific implementation, the terminal can assign the task instructions for each RPA agent to execute the corresponding operation task to the corresponding RPA agent according to the task scheduling result through RPA technology, so as to schedule the corresponding RPA agent to execute the respective operation task, realize the mutual coordination and cooperation of each RPA agent, and jointly complete the network optimization task indicated by the user.

[0095] In practical applications, the terminal can also monitor the process of RPA agents executing sub-tasks, ensuring that the tasks proceed according to the predetermined plan and timely adjust to any deviations. If an exception occurs during execution, the process engine module triggers an exception handling mechanism, invoking the corresponding error handling strategy. In practical applications, the terminal can coordinate multiple RPA agents to jointly execute wireless network optimization tasks through the collaborative RPA agent module in the network optimization system, ensuring efficient and accurate completion of the tasks. The collaborative RPA agent module can include the following functions:

[0096] 1) Automated execution function: RPA agents execute assigned tasks such as adjusting base station parameters and optimizing network configuration, while recording key data and states during the execution process.

[0097] 2) Collaborative work function: In tasks requiring collaboration among multiple RPA agents, RPA agents exchange information through predefined communication protocols.

[0098] During the execution of corresponding operation tasks by RPA agents, RPA agents in the network optimization system often need to interact with external systems or services. In practical applications, RPA agents can exchange data and functions through APIs (Application Programming Interfaces). Alternatively, the network optimization system can maintain integration between the network optimization system and external systems or services through an integration and API management module. The integration and API management module can have the following functions:

[0099] 1) API definition and development: According to the characteristics of wireless network optimization tasks, define API endpoints for receiving requests to create key protection tasks. Develop API interfaces that can receive requests containing parameters such as location, time, and event type, and trigger corresponding RPA task flows.

[0100] 2) API document management: Create API documents to specify how to call APIs, including request parameters, response formats, and examples. In the API document, specify the API call restrictions, error codes, and possible exceptions.

[0101] 3) Workflow automation: Use API management tools to automate API calls and RPA workflows, automatically starting RPA tasks after API calls. Design workflows so that after a successful API call, the network optimization system can execute subsequent network optimization tasks.

[0102] In addition, the terminal can also collect feedback information of system operation through the feedback and iterative learning module of the network optimization system, and use these information for iterative learning to continuously improve the effect of wireless network optimization. In practical application, the feedback and iterative learning module can include the following functions:

[0103] 1) Feedback collection function: collect feedback information from multiple sources.

[0104] 2) Iterative learning function: use machine learning algorithms to adjust and optimize the model in the network optimization system according to the feedback information.

[0105] 3) Strategy adjustment function: adjust the network optimization strategy and execution plan according to the learning results.

[0106] 4) Implementation improvement function: apply the updated strategy and plan to the network optimization system, and execute the corresponding improvement measures through the collaborative RPA agent module.

[0107] 5) Knowledge base update function: record the feedback analysis results and improvement measures in the above-mentioned knowledge base.

[0108] In the above network optimization method, the network optimization demand information of the task creation instruction input by the user for the network optimization task is determined through intent recognition of the task creation instruction, and the network optimization knowledge data matching the network optimization demand information is retrieved, and the network optimization plan is generated according to the network optimization knowledge data; the network optimization plan includes at least one network optimization measure for completing the network optimization task, and the operation steps in the network optimization measure; then, the operation tasks of multiple RPA agents are arranged according to the network optimization plan to obtain the operation task arrangement result; the operation task is used for the RPA agent to execute, so that the RPA agent completes at least part of the operation steps in the network optimization measure, and according to the task arrangement result, the corresponding operation task of each RPA agent is controlled to complete the network optimization task together, through the cooperation between different RPA agents, more efficient and accurate wireless network optimization management is realized, so that the user only needs to input the task creation instruction of the network optimization task to instruct the computer device to combine the network optimization knowledge to intelligently and comprehensively optimize the network environment to be optimized, which significantly improves the network performance and user experience, thereby effectively improving the network optimization efficiency.

[0109] In an exemplary embodiment, generating the network optimization plan according to the network optimization knowledge data includes: predicting network working condition prediction information of the network optimization task according to the network optimization demand information; determining at least one network optimization strategy that meets the network load and user demand according to the network optimization knowledge data; and generating the network optimization plan according to the at least one network optimization strategy.

[0110] The network working condition prediction information can be network working condition data of a network environment optimized by the network optimization task. In actual application, the network working condition prediction information can include network load and user demand of the network optimization location in the network optimization time.

[0111] In a specific implementation, the terminal generates the network optimization plan according to the network optimization knowledge data. The terminal can predict the network working condition prediction information of the network optimization task according to the network optimization demand information. Specifically, the terminal can query network performance data of the network optimization location in history according to the network optimization demand information. Then, the terminal can predict network performance data of the network optimization location in the network optimization time by using the network performance data in history, and obtain the network working condition prediction information of the network optimization task.

[0112] Then, the terminal determines at least one network optimization strategy that meets the network load and the user demand according to the network optimization knowledge data. Specifically, the terminal can generate at least one network optimization strategy, such as adjusting base station power, adding a temporary base station, optimizing frequency allocation, and the like, according to the network working condition prediction information and the network optimization knowledge data in a preset template.

[0113] The terminal can generate the network optimization plan according to the at least one network optimization strategy. Specifically, the terminal can use a pre-trained large language model to combine the fused context to fuse each network optimization strategy for the network optimization task, and generate a response, that is, generate the network optimization plan by the generation model, such as a key guarantee task plan for wireless network service of the A park during the B holiday. The key guarantee task plan can include a series of network optimization measures for optimizing the wireless network of the A park during the B holiday, and operation steps in the network optimization measures.

[0114] The technical scheme of the embodiment, in generating the network optimization plan according to the network optimization knowledge data, predicts the network working condition prediction information of the network optimization task according to the network optimization demand information, determines at least one network optimization strategy that meets the network load and the user demand according to the network optimization knowledge data, and generates the network optimization plan according to the at least one network optimization strategy, so that the network optimization plan can more effectively and scientifically optimize the wireless network of the network optimization location indicated by the network optimization task, and facilitate subsequent effective coordination of multiple RPA intelligent bodies based on the network optimization plan to work together to achieve the task goal of the network optimization task.

[0115] In an exemplary embodiment, the network working condition information of the network optimization task is predicted according to the network optimization demand information, including: querying historical network performance data matching the network optimization location from a network monitoring system; training a network optimization model for the network optimization task according to the historical network performance data; and outputting network load and user demand of the network optimization location corresponding to the network optimization time through the network optimization model to obtain network working condition prediction information.

[0116] The network optimization demand information includes a network optimization location and a network optimization time.

[0117] In a specific implementation, in the process of predicting the network working condition information of the network optimization task according to the network optimization demand information, the terminal can query historical network performance data matching the network optimization location from the network monitoring system. The historical network performance data can include past network load, signal quality, user satisfaction data, etc. Then, the terminal can analyze the historical network performance data to identify network performance trends and abnormalities. Specifically, the terminal can train a network optimization model for the network optimization task using historical network performance data; and extract network performance trends and abnormalities through the network optimization model, that is, output network load and user demand of the network optimization location corresponding to the network optimization time through the network optimization model to obtain network working condition prediction information. The network optimization model can be a time series model.

[0118] In actual application, after the terminal queries the historical network performance data matching the network optimization location in the network monitoring system, the terminal can use the data preprocessing and enhancement module in the network optimization system to preprocess the collected historical network performance data to improve data quality and availability, and expand the data set through data enhancement technology to enhance the robustness and generalization ability of the model. In actual application, the terminal can also use the multi-modal data processing module in the network optimization system to process and analyze historical network performance data from different sources and formats, such as text, image, sound and video, to support the decision-making process of wireless network optimization. In actual application, the data preprocessing and enhancement module can include the following functions:

[0119] 1) Data collection function: network management system, user feedback, historical optimization records, etc. Collect network performance data of network optimization locations from multiple sources.

[0120] 2) Data cleaning function: identify and process missing values, outliers, duplicate records to ensure data consistency and accuracy.

[0121] 3) Data conversion function: convert data into a format suitable for analysis, such as converting timestamps to dates and times, or converting categorical data to numerical representations.

[0122] 4) Data normalization function: normalize or standardize data to the same scale for model processing.

[0123] 5) Data augmentation function: apply data augmentation techniques such as random rotation, flipping, scaling images, or synonym replacement in text data to expand the dataset.

[0124] 6) Feature engineering function: create new features according to optimization goals, such as extracting holidays, weather conditions, etc. from timestamps that may affect network performance.

[0125] 7) Data labeling function: assign appropriate labels to each sample in the dataset, such as network congestion, weak signal, etc. for supervised learning.

[0126] 8) Data splitting function: divide the dataset into training set, validation set and test set for model training and evaluation.

[0127] In addition, the multi-modal data processing module can include the following functions:

[0128] 1) Data collection function: text data includes user feedback, network logs, technical documents, etc. Image data includes base station geographic location map, network coverage heat map, etc. User data includes user voice, video data includes user uploaded base station monitoring evaluation or field test video, etc.

[0129] 2) Data preprocessing function: clean, format and standardize different modalities of data for subsequent analysis, including removing irrelevant characters, standardizing text format, image denoising, audio noise reduction, etc.

[0130] 3) Feature extraction function: use deep learning models such as convolutional neural networks (CNN) to extract image features, recurrent neural networks (RNN) to extract text and audio features.

[0131] 4) Data fusion function: integrate features from different modalities to form a unified feature representation for analysis and decision-making.

[0132] The terminal can also use the adaptive learning module in the network optimization system to train a network optimization model for network optimization tasks based on historical network performance data; and output the network load and user demand corresponding to the network optimization location and time through the network optimization model. According to the historical performance data and real-time monitoring data of the network, automatically adjust and optimize the network configuration and strategy. The adaptive learning module can include the following functions:

[0133] 1) Data collection function: The module first receives the user's request from the user interface module and collects network performance data from the network monitoring system. Historical network performance data includes past network load, signal quality, user satisfaction data, etc.

[0134] 2) Data analysis function: Analyze the input data to identify network performance trends and abnormalities. In this embodiment, the data analysis module first receives the user's request from the user interface module and collects network performance data from the network monitoring system;

[0135] 3) Model training function: Use machine learning algorithms to train network optimization models. In this embodiment, a deep learning model is trained to predict network load and user demand at a target time;

[0136] 4) Strategy generation function: According to the training results, generate optimization strategies according to the preset template, such as adjusting base station power, adding temporary base stations, optimizing frequency allocation, etc.

[0137] The technical solution of this embodiment queries historical network performance data matching the network optimization location from the network monitoring system, and trains a network optimization model for network optimization tasks based on the historical network performance data. The network optimization model outputs the network load and user demand of the network optimization location at the network optimization time, obtaining network working condition prediction information, so that the network optimization model can effectively analyze the network performance trends and abnormalities in the historical network performance data matching the network optimization location, and predict the network working condition or network performance demand of the network optimization location at the network optimization time based on the network performance trends and abnormalities.

[0138] In an exemplary embodiment, the method further comprises: in response to the command input operation implemented on the user interaction interface of the RPA intelligent agent, obtaining the natural language text input by the command input operation; in the case that the natural language text passes the validity verification, determining the natural language text as a task creation instruction, and performing the step of intent recognition on the task creation instruction input by the user for the network optimization task to determine the network optimization demand information carried by the task creation instruction; wherein the validity verification is used to verify whether the natural language text meets the preset intent recognition requirement.

[0139] In a specific implementation, in the process of obtaining the task creation instruction input by the user for the network optimization task, the terminal can receive the command input operation of the user through the user interaction interface of the RPA intelligent agent, for example, the user inputs the natural language text "please help me create a key support task for A park during B holiday period, and execute." in the dialogue box in the user interaction interface. Then, the terminal can verify the validity of the user input, that is, the validity of the user input is verified, such as whether the number of words and the amount of information meet the preset conditions, whether the natural language text input by the user meets the preset intent recognition requirements.

[0140] For example, when "please create a task" is input in the dialogue box in the user interaction interface, the terminal starts to verify the validity of "please create a task", and determines that the natural language "please create a task" input by the user at this time cannot explicitly indicate the task parameters (such as task target, task location or task requirement, etc.), and the terminal displays a prompt information to prompt the user to re-input a natural language text that can explicitly indicate the network optimization task in the dialogue box in the user interaction interface. In the case that the natural language text input by the user meets the preset intent recognition requirements, the terminal can take the natural language text input by the user as the task creation instruction input for the network optimization task, and starts to perform intent recognition on the task creation instruction input for the network optimization task.

[0141] The technical scheme of the embodiment can perform preliminary validity judgment on the natural language text input by the user, avoid excessive redundant content in the backend processing, and improve the operation performance of the network optimization system.

[0142] In an exemplary embodiment, the intent recognition on the task creation instruction input by the user for the network optimization task to determine the network optimization requirement information of the task creation instruction includes: inputting the task creation instruction into a pre-trained natural language processing model to obtain an intent recognition result output by the natural language processing model; the intent recognition result includes task intent information, key entity information and semantic understanding information in the task creation instruction; and determining the network optimization requirement information of the task creation instruction according to the task intent information, the key entity information and the semantic understanding information.

[0143] In a specific implementation, in the process of performing intent recognition on the task creation instruction input by the user for the network optimization task and determining the network optimization requirement information of the task creation instruction, the terminal can input the task creation instruction into the pre-trained natural language processing model to obtain an intent recognition result output by the natural language processing model. The intent recognition result includes task intent information, key entity information, and semantic understanding information in the task creation instruction.

[0144] Taking the task creation instruction "Please help me create a key support task for A park during B holiday period, and execute it." as an example, the task intent information can be "create a key support task", the key entity information can include "A park" and "B holiday", and the semantic understanding information can be "the user may need to ensure the network communication stability of A park during B holiday period". Then, the terminal can determine the network optimization requirement information of the task creation instruction according to the task intent information, the key entity information, and the semantic understanding information.

[0145] In the process of performing intent recognition on the task creation instruction input by the user for the network optimization task and determining the network optimization requirement information of the task creation instruction, the technical solution of the embodiment can input the task creation instruction into the pre-trained natural language processing model to obtain an intent recognition result output by the natural language processing model. The intent recognition result includes task intent information, key entity information, and semantic understanding information in the task creation instruction. The network optimization requirement information of the task creation instruction is determined according to the task intent information, the key entity information, and the semantic understanding information. Therefore, the intent recognition on the task creation instruction can be performed from multiple aspects of task intent, key entity, and semantic understanding, and the network optimization requirement information of the user can be effectively mined from the task creation instruction.

[0146] In one exemplary embodiment, according to the network optimization plan, operation tasks of a plurality of RPA agents are arranged, and an operation task arrangement result is obtained, including: converting the network optimization plan into task instructions for the plurality of RPA agents; different task instructions correspond to different operation tasks; for any operation task, the operation task is decomposed to obtain at least two to-be-executed subtasks; each to-be-executed subtask is independently executed by at least one RPA agent; according to the characteristic information of each to-be-executed subtask and the capability information of each RPA agent, each to-be-executed subtask is assigned to a corresponding RPA agent to obtain the operation task arrangement result.

[0147] In a specific implementation, in the process of the terminal scheduling operation tasks of multiple RPA agents according to the network optimization plan and obtaining the operation task scheduling result, the terminal can convert the network optimization plan into task instructions for the multiple RPA agents; different task instructions correspond to different operation tasks. Specifically, the terminal can use the planner module in the network optimization system to design task processes using RPA technology, task execution order, required resources, and expected targets, and convert the network optimization plan into task instructions for the multiple RPA agents; different task instructions correspond to different operation tasks. The planner module includes at least the following functions:

[0148] 1) Rule definition function: define the execution rules of key protection tasks according to historical data and business rules, such as increasing network monitoring frequency during the National Day holiday and increasing the threshold of base station operation parameters.

[0149] 2) Task planning function: design task processes using RPA technology, including task execution order, required resources, and expected targets.

[0150] 3) Strategy generation function: generate optimization strategies according to analysis results, such as adjusting base station configuration, adding temporary base stations, and optimizing frequency allocation, and form detailed network optimization plans.

[0151] 4) Task scheduling function: convert the generated plan and strategy into executable task instructions and schedule them to the corresponding RPA agents for execution using RPA technology.

[0152] Then, the terminal can send the task instructions of the RPA agents to the process engine module in the network optimization system through the planner module in the network optimization system; the process engine module decomposes the different operation tasks corresponding to the task instructions into at least two to-be-executed sub-tasks; each to-be-executed sub-task is independently executed by at least one RPA agent; then, the process engine module can assign each to-be-executed sub-task to the corresponding RPA agent according to the characteristic information of each to-be-executed sub-task and the capability information of each RPA agent, and obtain the operation task scheduling result. In practical applications, the process engine module is used to manage and execute the automation process of wireless network optimization tasks; the process engine module can include the following functions:

[0153] 1) Task receiving and parsing function: receive task instructions from the planner module, parse task requirements, and determine network optimization targets and execution details.

[0154] 2) Task decomposition function: decompose complex tasks into a series of executable sub-tasks, each of which can be independently executed by one or more RPA agents.

[0155] 3) RPA agent allocation function: according to the characteristics of subtasks and the capabilities of RPA agents, tasks are allocated to suitable agents for execution.

[0156] 4) Execution monitoring RPA: monitor the process of RPA agents executing subtasks to ensure that tasks proceed according to the predetermined plan and adjust in a timely manner to address any deviations.

[0157] 5) Abnormal handling RPA: if an exception occurs during execution, the process engine module triggers the exception handling mechanism and invokes the corresponding error handling strategy.

[0158] 6) Task coordination RPA: for tasks that require collaboration between multiple agents, the process engine module is responsible for coordinating the work between different RPA agents to jointly complete the network optimization task.

[0159] The technical scheme of the embodiment converts the network optimization plan into task instructions for multiple RPA agents; different task instructions correspond to different operation tasks; for any operation task, the operation task is decomposed into at least two to-be-executed subtasks; each to-be-executed subtask is executed independently by at least one RPA agent; according to the characteristic information of each to-be-executed subtask and the capability information of each RPA agent, each to-be-executed subtask is allocated to the corresponding RPA agent to obtain an operation task arrangement result, so that the operation task arrangement result can be used to orderly and efficiently schedule the collaboration between RPA agents in a multi-agent system, realize more efficient and accurate wireless network optimization management, intelligently complete the network optimization task issued by the user, and effectively improve the network optimization efficiency.

[0160] In another embodiment, as shown in Figure 4 , a network optimization method is provided, which is applied to a terminal in Figure 1 for example, and includes the following steps:

[0161] Step S402, obtaining task creation instructions input by a user for a network optimization task.

[0162] Step S404, inputting the task creation instructions into a pre-trained natural language processing model to obtain an intent recognition result output by the natural language processing model; the intent recognition result includes task intent information, key entity information, and semantic understanding information in the task creation instructions.

[0163] Step S406, determining network optimization requirement information of the task creation instructions according to the task intent information, the key entity information, and the semantic understanding information; the network optimization requirement information includes a network optimization location and a network optimization time.

[0164] Step S408, querying the historical network performance data matching the network optimization location from the network monitoring system.

[0165] Step S410, training the network optimization model for the network optimization task according to the historical network performance data.

[0166] Step S412, outputting the network load and user demand corresponding to the network optimization time of the network optimization location by the network optimization model, to obtain network working condition prediction information; the network working condition prediction information includes the network load and the user demand.

[0167] Step S414, retrieving the network optimization knowledge data matching the network optimization demand information, and determining at least one network optimization strategy meeting the network load and the user demand according to the network optimization knowledge data.

[0168] Step S416, generating a network optimization plan according to the at least one network optimization strategy; the network optimization plan includes at least one network optimization measure for completing the network optimization task, and operation steps in the network optimization measure.

[0169] Step S418, converting the network optimization plan into task instructions for a plurality of RPA intelligent agents; different task instructions correspond to different operation tasks; the operation tasks are used for the RPA intelligent agents to execute, so that the RPA intelligent agents complete at least part of the operation steps in the network optimization measure.

[0170] Step S420, task decomposition is performed on the operation task to obtain at least two to-be-executed subtasks for any operation task; each to-be-executed subtask is independently executed by at least one RPA intelligent agent.

[0171] Step S422, assigning each to-be-executed subtask to a corresponding RPA intelligent agent according to the characteristic information of each to-be-executed subtask and the capability information of each RPA intelligent agent, to obtain an operation task arrangement result.

[0172] Step S424, controlling each RPA intelligent agent to execute a corresponding operation task according to the task arrangement result, to jointly complete the network optimization task.

[0173] It should be noted that the specific definition of the above steps can refer to the specific definition of the network optimization method described above.

[0174] In order to facilitate the understanding of those skilled in the art, Figure 5An exemplary system framework of a network optimization system is provided; wherein the network optimization system can include a user interface module, a user intent understanding module, a data preprocessing and enhancement module, a multi-modal data processing module, a knowledge management and learning module, an adaptive learning module, an integration and API management module, a search enhancement generation module, a planner module, an assisted RPA agent module, and a process engine module; wherein the user interface module can obtain task creation instructions input by a user for a network optimization task; then the user interface module sends the task creation instructions to the user intent understanding module; the user intent understanding module performs intent recognition on the task creation instructions input by the user for the network optimization task, and determines network optimization requirement information of the task creation instructions.

[0175] Then, the terminal obtains historical network performance data matching the network optimization location from the network monitoring system according to the network optimization requirement information. The historical network performance data is processed by the data preprocessing and enhancement module and the multi-modal data processing module; and the network optimization model for the network optimization task is trained according to the historical network performance data by the adaptive learning module; the network load and user demand corresponding to the network optimization time of the network optimization location are output by the network optimization model.

[0176] The network optimization knowledge data matching the network optimization requirement information is retrieved by the knowledge management and learning module and the search enhancement generation module, and at least one network optimization strategy meeting the network load and user demand is determined according to the network optimization knowledge data; and the network optimization plan is generated according to the at least one network optimization strategy.

[0177] The planner module and the process engine module are responsible for converting the agent decision specified by the network optimization plan into specific operation steps. The collaborative RPA agent module, the process engine module, the knowledge management and learning module, the integration and API management module, the feedback and iterative learning module jointly ensure the stability, security, performance and continuous improvement of the system. The design of the entire network optimization system ensures the standardization of the interface between the modules, the clarity of the data flow, and the rationality of the control process, so as to realize efficient automation and intelligent operation. It should be noted that the specific definition of the user interface module, the user intent understanding module, the data preprocessing and enhancement module, the multi-modal data processing module, the knowledge management and learning module, the adaptive learning module, the integration and API management module, the search enhancement generation module, the planner module, the assisted RPA agent module, and the process engine module can be referred to the specific definition of the network optimization method in the foregoing, which will not be repeated here.

[0178] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0179] Based on the same inventive concept, the embodiments of the present application also provide a network optimization apparatus for implementing the network optimization method described above. The implementation scheme for solving the problem provided by the apparatus is similar to the implementation scheme described in the above method, so the specific limitations in one or more network optimization apparatus embodiments provided below can refer to the limitations of the network optimization method described above, which will not be repeated here.

[0180] In an exemplary embodiment, as shown in Figure 6 a network optimization apparatus is provided, comprising:

[0181] The identification module 610 is configured to perform intent recognition on the task creation instruction input by the user for the network optimization task, and determine network optimization requirement information of the task creation instruction.

[0182] The retrieval module 620 is configured to retrieve network optimization knowledge data matched with the network optimization requirement information, and generate a network optimization plan according to the network optimization knowledge data; the network optimization plan includes at least one network optimization measure for completing the network optimization task, and operation steps in the network optimization measure.

[0183] The arrangement module 630 is configured to arrange operation tasks of a plurality of RPA intelligent agents according to the network optimization plan, and obtain an operation task arrangement result; the operation tasks are used for the RPA intelligent agents to execute, so that the RPA intelligent agents complete at least part of the operation steps in the network optimization measure.

[0184] The scheduling module 640 is configured to control each of the RPA intelligent agents to execute corresponding operation tasks according to the task arrangement result, so as to jointly complete the network optimization task.

[0185] In one of the embodiments, the retrieval module 620 is specifically configured to predict network working condition prediction information of the network optimization task according to the network optimization demand information; the network working condition prediction information includes network load and user demand; determine at least one network optimization strategy that meets the network load and the user demand according to the network optimization knowledge data; and generate the network optimization plan according to the at least one network optimization strategy.

[0186] In one of the embodiments, the retrieval module 620 is specifically configured to, the network optimization demand information includes network optimization location and network optimization time, and the prediction of the network working condition information of the network optimization task according to the network optimization demand information includes: querying historical network performance data matched with the network optimization location from a network monitoring system; training a network optimization model for the network optimization task according to the historical network performance data; and outputting network load and user demand of the network optimization location at the network optimization time by the network optimization model to obtain the network working condition prediction information.

[0187] In one of the embodiments, the device further includes a verification module configured to, in response to a command input operation implemented on a user interaction interface of the RPA intelligent agent, acquire natural language text input by the command input operation; in a case where the natural language text passes validity verification, determine the natural language text as the task creation instruction, and perform the step of identifying the intention of the task creation instruction input by the user for the network optimization task to determine network optimization demand information carried by the task creation instruction; and the validity verification is configured to verify whether the natural language text meets a preset intention recognition requirement.

[0188] In one of the embodiments, the identification module 610 is specifically configured to input the task creation instruction into a pre-trained natural language processing model to obtain an intention recognition result output by the natural language processing model; the intention recognition result includes task intention information, key entity information and semantic understanding information in the task creation instruction; and determine network optimization demand information of the task creation instruction according to the task intention information, the key entity information and the semantic understanding information.

[0189] In one of the embodiments, the orchestration module 630 is specifically configured to convert the network optimization plan into task instructions for the plurality of RPA agents; different task instructions correspond to different operation tasks; for any operation task, task decomposition is performed on the operation task to obtain at least two to-be-executed subtasks; each to-be-executed subtask is independently executed by at least one RPA agent; according to the characteristic information of each to-be-executed subtask and the capability information of each RPA agent, each to-be-executed subtask is assigned to the corresponding RPA agent to obtain an operation task orchestration result.

[0190] Each module in the network optimization apparatus described above can be realized wholly or partially by software, hardware, and a combination thereof. Each module described above can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform operations corresponding to each module.

[0191] In an exemplary embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 7 The computer device includes a processor, a memory, an input / output interface, and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a network optimization method.

[0192] In an exemplary embodiment, a computer device, which can be a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 7As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the external terminal in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. The computer program is executed by the processor to realize a network optimization method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0193] Those skilled in the art can understand that, Figure 7 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0194] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor to make the processor execute the steps of the network optimization method described above. The steps of the network optimization method described above can be the steps of the network optimization method in each of the embodiments described above.

[0195] In one embodiment, a computer readable storage medium is provided, storing a computer program, the computer program being executed by the processor to make the processor execute the steps of the network optimization method described above. The steps of the network optimization method described above can be the steps of the network optimization method in each of the embodiments described above.

[0196] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, causes the processor to perform the steps of the network optimization method described above. The steps of the network optimization method described above can be the steps of the network optimization method in each of the embodiments described above.

[0197] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0198] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0199] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0200] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A network optimization method, characterized by, The method comprises: intention recognition on task creation instructions input by a user for a network optimization task, to determine network optimization requirement information of the task creation instructions; retrieving network optimization knowledge data matching the network optimization requirement information, and generating a network optimization plan according to the network optimization knowledge data; the network optimization plan comprises at least one network optimization measure for completing the network optimization task, and operation steps in the network optimization measure; arranging operation tasks of a plurality of RPA agents according to the network optimization plan, to obtain an operation task arrangement result, comprising: converting the network optimization plan into task instructions for the plurality of RPA agents; different task instructions correspond to different operation tasks; for any operation task, task decomposition is performed on the operation task to obtain at least two to-be-executed subtasks; each to-be-executed subtask is independently executed by at least one RPA agent; according to the characteristic information of each to-be-executed subtask and the capability information of each RPA agent, each to-be-executed subtask is assigned to a corresponding RPA agent to obtain the operation task arrangement result; the operation task is used for execution by the RPA agent to enable the RPA agent to complete at least part of the operation steps in the network optimization measure; controlling each RPA agent to execute a corresponding operation task according to the task arrangement result, to jointly complete the network optimization task.

2. The method of claim 1, wherein, The network optimization plan is generated according to the network optimization knowledge data, comprising: predicting network working condition prediction information of the network optimization task according to the network optimization requirement information; the network working condition prediction information comprises network load and user demand; determining at least one network optimization strategy satisfying the network load and the user demand according to the network optimization knowledge data; generating the network optimization plan according to the at least one network optimization strategy.

3. The method of claim 2, wherein, The network optimization requirement information comprises a network optimization location and a network optimization time, and the network working condition information of the network optimization task is predicted according to the network optimization requirement information, comprising: querying historical network performance data matching the network optimization location from a network monitoring system; training a network optimization model for the network optimization task according to the historical network performance data; outputting, by the network optimization model, network load and user demand corresponding to the network optimization location at the network optimization time, to obtain the network working condition prediction information.

4. The method of claim 1, wherein, The method further comprises: in response to a command input operation implemented on a user interaction interface of the RPA agent, obtaining natural language text input by the command input operation; in the case that the natural language text passes validity verification, determining the natural language text as the task creation instruction, and performing the step of intention recognition on the task creation instruction input by the user for the network optimization task, to determine network optimization requirement information carried by the task creation instruction; The effectiveness verification is configured to verify whether the natural language text meets preset intent recognition requirements.

5. The method of claim 1, wherein, The intent recognition is performed on the task creation instruction input by the user for the network optimization task, and network optimization requirement information of the task creation instruction is determined. The task creation instruction is input into a pre-trained natural language processing model, and an intent recognition result output by the natural language processing model is obtained; the intent recognition result includes task intent information, key entity information, and semantic understanding information in the task creation instruction; The network optimization requirement information of the task creation instruction is determined according to the task intent information, the key entity information, and the semantic understanding information.

6. A network optimization apparatus, characterized by, The apparatus includes: An identification module configured to perform intent recognition on a task creation instruction input by a user for a network optimization task, and determine network optimization requirement information of the task creation instruction; A retrieval module configured to retrieve network optimization knowledge data matched with the network optimization requirement information, and generate a network optimization plan according to the network optimization knowledge data; the network optimization plan includes at least one network optimization measure for completing the network optimization task, and operation steps in the network optimization measure; An arrangement module configured to arrange operation tasks of a plurality of RPA agents according to the network optimization plan, and obtain an operation task arrangement result, including: converting the network optimization plan into task instructions for the plurality of RPA agents; different task instructions correspond to different operation tasks; for any operation task, task decomposition is performed on the operation task to obtain at least two to-be-executed subtasks; each to-be-executed subtask is independently executed by at least one RPA agent; according to characteristic information of each to-be-executed subtask and capability information of each RPA agent, each to-be-executed subtask is assigned to a corresponding RPA agent to obtain the operation task arrangement result; the operation task is used for execution by the RPA agent to enable the RPA agent to complete at least part of the operation steps in the network optimization measure; A scheduling module configured to control each RPA agent to execute a corresponding operation task according to the task arrangement result, to jointly complete the network optimization task.

7. The apparatus of claim 6, wherein, The retrieval module is specifically configured to predict network working condition prediction information of the network optimization task according to the network optimization requirement information; the network working condition prediction information includes network load and user demand; at least one network optimization strategy meeting the network load and the user demand is determined according to the network optimization knowledge data; The network optimization plan is generated according to the at least one network optimization strategy.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 6.

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

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

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