Optimization scheme determination method and device, storage medium and program product

Abstract

The embodiment of the invention provides an optimization scheme determination method and device, a storage medium and a program product, relates to the technical field of wireless communication, and can improve the detection precision of problem root causes, generate a refined network optimization scheme and improve the working efficiency of network optimization. The method comprises the steps that a complaint work order sent by a terminal is acquired, a target root cause causing the performance of the terminal to be in a to-be-optimized state is determined based on the complaint work order, then a target optimization scheme is generated based on the target root cause, the complaint work order is used for identifying that the performance of the terminal is in the to-be-optimized state, and the target optimization scheme is used for improving the performance of the terminal.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method, device, storage medium and program product for determining an optimization solution. Background Art

[0002] With the rapid development of mobile network technology, the number of devices connected to mobile networks continues to grow, and users' demand for data transmission has also shown an explosive growth trend, which has put forward higher requirements for the stability of mobile networks. In this context, efficient mobile network optimization operations have become a key issue that operators need to solve urgently.

[0003] However, the existing network optimization operation system has certain limitations, which are as follows: although the current network optimization technology can automatically detect problems in the network, it lacks further analysis and decision-making capabilities and cannot automatically provide specific optimization suggestions. This results in operators needing to distribute work orders to front-line personnel for processing after discovering problems. However, the front-line personnel have weak skills and lack user perception analysis and problem root cause location tools. This makes it difficult to perform refined operations when handling network optimization tasks, and often only some relatively extensive optimization measures can be taken, which makes network optimization work inefficient and difficult to meet users' needs for high-quality mobile network services. Summary of the invention

[0004] The present application provides a method, device, storage medium and program product for determining an optimization solution, which can improve the detection accuracy of the root cause of the problem, generate a refined and targeted network optimization solution, and thus improve the efficiency of network optimization work.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for determining an optimization solution, which is applied to a server, and the method includes:

[0007] Obtain the complaint ticket sent by the terminal, and based on the complaint ticket, determine the target root cause that causes the terminal's performance to be in a state to be optimized, and then generate a target optimization plan based on the target root cause.

[0008] Among them, the complaint ticket is used to indicate that the performance of the terminal is in a state to be optimized, and the target optimization plan is used to improve the performance of the terminal.

[0009] Based on the above solution, the server can accurately locate the root cause of the terminal being in the state to be optimized based on the information in the complaint ticket, and can generate an optimization solution that can improve the terminal performance based on the root cause. This can provide a basis for subsequent optimization work, improve the efficiency of network optimization work, and thus meet users' needs for high-quality mobile network services.

[0010] Optionally, the above-mentioned determination of the target root cause causing the terminal's performance to be in a state to be optimized based on the complaint work order may specifically include: determining the network device associated with the terminal's performance, and then performing fault detection on the network device to obtain the detection result, and when the detection result indicates that the network device has a fault, determining the root cause of the fault of the network device, and then using the root cause of the fault as the target root cause causing the terminal's performance to be in a state to be optimized.

[0011] Optionally, the root cause of the fault may indicate that a first component of the network device fails, and the target optimization solution is to repair the first component.

[0012] Optionally, the server may store optimization solutions corresponding to multiple root causes, including a target root cause. On this basis, the above-mentioned generation of a target optimization solution based on the target root cause may specifically include: determining at least one optimization solution corresponding to the target root cause, and then determining the target optimization solution based on the at least one optimization solution corresponding to the target root cause.

[0013] Optionally, the server may be deployed with an information extraction model, on which basis, optimization solutions corresponding to multiple root causes may be obtained in the following manner: obtaining text information, and then extracting the optimization solution corresponding to each root cause from the text information through the information extraction model.

[0014] The text information includes multiple root causes and an optimization solution corresponding to each of the multiple root causes.

[0015] Optionally, the information extraction model may be trained in the following manner: obtaining training data with optimization solutions corresponding to different root causes identified, and using the training data to train a large language model to obtain the information extraction model.

[0016] Optionally, the server may be deployed with a root cause determination model and a solution generation model. The root cause determination model is used to determine the target root cause that causes the performance of the terminal to be optimized based on the complaint ticket. The solution generation model is used to generate a target optimization solution based on the target root cause.

[0017] In a second aspect, the present application provides an optimization scheme determination device, comprising:

[0018] The transceiver unit is used to obtain the complaint ticket sent by the terminal.

[0019] The determination unit is used to determine the target root cause that causes the performance of the terminal to be in a state to be optimized based on the complaint ticket.

[0020] The processing unit is used to generate a target optimization solution based on the target root cause.

[0021] Optionally, the determination unit is further configured to determine a network device associated with the performance of the terminal.

[0022] Optionally, the processing unit is further used to perform fault detection on the network device and obtain a detection result.

[0023] Optionally, the determination unit is further configured to, when the detection result indicates that a network device has failed, determine a root cause of the failure of the network device, and use the root cause of the failure as a target root cause causing the performance of the terminal to be in a state to be optimized.

[0024] Optionally, the determination unit is further used to determine at least one optimization solution corresponding to the target root cause.

[0025] Optionally, the determination unit is further used to determine a target optimization solution based on at least one optimization solution corresponding to the target root cause.

[0026] Optionally, the processing unit is further used to extract an optimization solution corresponding to each root cause from the text class information through an information extraction model.

[0027] Optionally, the processing unit is further used to train the large language model using the training data to obtain an information extraction model.

[0028] In a third aspect, the present application provides an optimization solution determination device, the device comprising: a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run a computer program or instruction to implement the optimization solution determination method as described in any one of the first aspect and any possible implementation of the first aspect.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed on a terminal, the terminal executes an optimization solution determination method as described in any one of the first aspect and any possible implementation of the first aspect.

[0030] In a fifth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are executed on a computer, the computer executes an optimization solution determination method as described in any one of the first aspect and any possible implementation of the first aspect.

[0031] It can be understood that the beneficial effects that can be achieved in the second to fifth aspects provided above can refer to the beneficial effects in any possible design method of the optimization scheme determination method described in any one of the first aspect and any possible implementation method of the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1An architecture diagram of an optimization solution determination system provided in an embodiment of the present application;

[0033] Figure 2 A flowchart of a method for determining an optimization solution provided in an embodiment of the present application;

[0034] Figure 3 A diagram of a knowledge graph construction method provided in an embodiment of the present application;

[0035] Figure 4 A relationship diagram between entities and entities contained in the knowledge graph provided in the embodiment of the present application;

[0036] Figure 5 A schematic diagram of the types of large language models provided in the embodiments of the present application;

[0037] Figure 6 A schematic diagram of a large language model training process provided in an embodiment of the present application;

[0038] Figure 7 A schematic diagram of the types of root cause location models provided in the embodiments of the present application;

[0039] Figure 8 An interactive flow chart of a method for determining an optimization solution provided in an embodiment of the present application;

[0040] Fig. 9 A schematic diagram of the structure of an optimization solution determination device provided in an embodiment of the present application;

[0041] Fig.10 A schematic diagram of the structure of another optimization scheme determination device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] A method and device for determining an optimization solution provided in an embodiment of the present application are described in detail below in conjunction with the accompanying drawings.

[0043] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0044] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0045] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0046] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0047] In recent years, with the rapid development and widespread popularity of the fifth generation mobile communication technology (5G), mobile networks have become an indispensable infrastructure in modern society, profoundly affecting people's daily lives. Through mobile networks, users can instantly obtain social media information, conduct video communications, browse global news, learn new knowledge, and enjoy convenient services such as mobile payments and online shopping.

[0048] However, as the number of devices connected to mobile networks continues to rise, users' demand for data transmission has also shown an explosive growth trend, which has put forward higher requirements for the stability of mobile networks and user experience. In order to ensure the service quality, stability and efficiency of mobile networks, efficient mobile network optimization operations have become an urgent problem that operators need to solve. Effective network optimization can not only improve network performance, but also significantly enhance user satisfaction.

[0049] However, the existing network optimization operation system has certain limitations when facing these diverse challenges. Specifically, although the current network optimization technology can automatically detect problems in the network, it lacks further analysis and decision-making capabilities and cannot automatically provide specific optimization suggestions. This results in operators needing to distribute work orders to front-line personnel for processing after discovering problems. However, the front-line personnel have weak skills and lack user perception analysis and problem root cause location tools. This makes it difficult to perform refined operations when handling network optimization tasks, and often only some relatively extensive optimization measures can be taken, which makes network optimization work inefficient and difficult to meet users' needs for high-quality mobile network services.

[0050] In order to solve the above technical problems, the embodiment of the present application provides a method for determining an optimization solution. After receiving the complaint ticket sent by the terminal, the server can accurately locate the root cause of the terminal being in the state to be optimized based on the information in the complaint ticket, and then generate an optimization solution that can improve the performance of the terminal based on the root cause of the terminal being in the state to be optimized. In this way, it can provide a basis for subsequent optimization work, improve the efficiency of network optimization work, and thus meet the user's demand for high-quality mobile network services.

[0051] Figure 1 An optimization solution provided in the embodiment of the present application is provided to determine the architecture of the system, such as Figure 1 As shown, the system framework includes: a terminal 101 and a server 102.

[0052] The terminal 101 may be a device that provides voice and / or data connectivity to a user, a device with wireless connection function, or other device connected to a wireless modem. The terminal device may be at least one of a desktop computer, a laptop, a wireless terminal, and a laptop portable computer. In one embodiment, the electronic device has a communication function and can access a wired network or a wireless network.

[0053] The embodiment of the present application does not limit the number of terminals 101 in the optimization solution determination system, and may include Figure 1 More or fewer terminals 101.

[0054] The server 102 may be a high-performance server that provides various services online, an independent physical server, a server cluster composed of multiple physical servers, or at least one of cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, and big data or artificial intelligence platforms, etc. The embodiments of the present application do not limit this. Of course, the server can also include other functions to provide more comprehensive and diverse services.

[0055] In some embodiments, a user holding a terminal 101 can submit a complaint ticket to a telecom operator through the terminal 101 with the intention of improving the performance of the terminal 101 (e.g., improving the network connection stability of the terminal 101). Accordingly, the user can fill out a complaint ticket in the official application (application, APP) of the telecom operator installed on the terminal 101 and trigger a submission operation. The terminal 101 sends a complaint ticket to the server 102 (i.e., the server of the telecom operator) in response to the user's submission operation. After receiving the complaint ticket, the server 102 can determine the target root cause that causes the performance of the terminal 101 to be optimized based on the complaint ticket, and then generate a target optimization solution based on the target root cause.

[0056] Figure 2 A flowchart of a method for determining an optimization solution provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, this method is Figure 1 The server shown executes the method, comprising:

[0057] S201, obtaining the complaint ticket sent by the terminal.

[0058] Among them, the complaint ticket is used to indicate that the performance of the terminal is in a state to be optimized.

[0059] The embodiments of the present application do not limit the content contained in the complaint ticket. For example, the complaint ticket may include basic information of the terminal, such as the terminal identification (ID) and the terminal model. For another example, the complaint ticket may include the complaint content, which includes information related to terminal performance issues, such as unstable network connection, application freeze or long-term unresponsiveness. For another example, the complaint ticket may include the location information of the terminal.

[0060] S202: Based on the complaint ticket, determine the target root cause that causes the performance of the terminal to be in a state to be optimized.

[0061] Specifically, the server stores multiple root causes corresponding to terminal performance issues. Accordingly, after receiving the complaint ticket, the server can extract information related to terminal performance (such as network connection interruption) from the complaint ticket. Afterwards, the server can determine the root cause corresponding to the information related to terminal performance from the root causes corresponding to the multiple stored terminal performance issues based on the extracted information, and use the root cause as the target root cause that causes the terminal performance to be in a state to be optimized.

[0062] In some embodiments, the above-mentioned determination of the target root cause causing the performance of the terminal to be in a state to be optimized based on the complaint work order may specifically include: determining the network device associated with the performance of the terminal, and then performing fault detection on the network device to obtain the detection result, and when the detection result indicates that the network device has a fault, determining the root cause of the fault of the network device, and using the root cause of the fault as the target root cause causing the performance of the terminal to be optimized.

[0063] The embodiments of the present application do not limit the type of root cause of the fault. For example, the root cause of the fault may be a hardware failure of the network device, and the hardware failure may include a power failure, a hard disk failure, an antenna failure, or a physical connection (such as a network cable, optical fiber, etc.) of the network device. For another example, the root cause of the fault may be a software failure of the network device, and the software failure may include system conflicts, compatibility issues, data corruption, or external interference such as network attacks and virus infections.

[0064] Specifically, the server can extract the location information of the terminal and information related to the terminal performance from the complaint ticket. Afterwards, the server can query the network devices (such as base stations, switches, etc.) connected to the terminal in the network topology based on the location information of the terminal. Afterwards, the server can determine the target network device associated with the performance of the terminal among the network devices connected to the terminal based on the information related to the terminal performance, and determine the detection scheme for the target network device (such as detecting whether the signal transmission power of the base station is stable). Finally, the server can detect the target network device and obtain the detection result.

[0065] In the case where the detection result indicates that the target network device has failed, the server can determine the root cause of the failure that caused the target network device to fail based on the detection result, and use the root cause of the failure as the target root cause that causes the terminal's performance to be optimized. In the case where the detection result indicates that the target network device has not failed, the server can also monitor and analyze the network traffic through a network traffic monitoring tool to determine whether the network is congested. In the case of congestion, the server can determine the cause of the network congestion based on the monitoring and analysis results, and use the cause as the target root cause that causes the terminal's performance to be optimized.

[0066] Optionally, after determining the detection scheme for the target network device, the server may also send the information of the target network device and the detection scheme to an operation terminal held by a professional. After the professional obtains the information of the target network device and the detection scheme through the operation terminal, he can then use a professional testing tool to perform fault detection on the target network device according to the detection scheme, obtain the detection result, and then send the detection result to the server through the operation terminal.

[0067] S203: Generate a target optimization solution based on the target root cause.

[0068] Among them, the target optimization solution is used to improve the performance of the terminal.

[0069] Specifically, the server stores optimization solutions corresponding to multiple root causes, including the target root cause. Accordingly, the server can determine at least one optimization solution corresponding to the target root cause from the optimization solutions corresponding to the multiple root causes stored. Afterwards, the server can select an optimization solution from the at least one optimization solution corresponding to the target root cause as the target optimization solution.

[0070] The embodiment of the present application does not limit the way in which the server stores the optimization solutions corresponding to the multiple root causes. For example, the server can store the optimization solutions corresponding to the multiple root causes in a database, which can include a graph database, a relational database, and a non-relational database. For another example, the server can store the optimization solutions corresponding to the multiple root causes in a file system.

[0071] For example, the optimization solutions corresponding to multiple root causes are stored in a graph database. Accordingly, the server can search the graph database based on the target root cause to obtain at least one optimization solution corresponding to the target root cause. Afterwards, the server can select an optimization solution from the at least one optimization solution corresponding to the target root cause as the target optimization solution.

[0072] In some embodiments, the root cause of the fault may indicate that a first component of the network device fails, and the target optimization solution may be to repair the first component.

[0073] For example, taking the root cause of the fault being corrosion on the surface of the base station antenna as an example, the target optimization solution generated by the server can clean the corroded part of the antenna and apply anti-corrosion paint on the surface of the antenna.

[0074] Through the above technical solution, the server can locate the network equipment associated with the performance of the terminal based on the information in the complaint ticket, and perform fault detection on the network equipment, and then determine the root cause of the terminal's performance being in a state to be optimized based on the detection results, thereby improving the accuracy of fault detection and providing a basis for the subsequent generation of optimization solutions. Afterwards, the server can generate an optimization solution that can improve the performance of the terminal based on the root cause that causes the terminal to be in a state to be optimized. In this way, a refined and targeted optimization solution can be generated to provide a basis for subsequent optimization work, thereby improving the efficiency of network optimization work and meeting users' needs for high-quality mobile network services.

[0075] In some embodiments, an information extraction model is deployed in the server. Based on this, in the above S203, the optimization solutions corresponding to the multiple root causes stored in the server can be obtained in the following manner: the server can obtain text class information, and then extract the optimization solution corresponding to each root cause from the text class information through the information extraction model.

[0076] The text information includes multiple root causes and an optimization solution corresponding to each of the multiple root causes.

[0077] The embodiment of the present application does not limit the type of text information. For example, the text information may be an optimization guide, an optimization case solved in history, or experience information of network optimization experts in handling network problems.

[0078] Specifically, the server stores text information. Accordingly, the server can input the internally stored text information into the information extraction model, and the information extraction model can extract information from the text information through natural language processing technology, and finally obtain an optimization solution corresponding to each root cause output by the information extraction model.

[0079] Through the above solution, the server can extract the optimization solution corresponding to each root cause in various text information through an intelligent information extraction process, thereby providing accurate and effective decision support for network optimization work.

[0080] In some embodiments, after extracting the optimization solution corresponding to each root cause from the text information through the information extraction model, the method in which the server stores the optimization solutions corresponding to the multiple root causes in the graph database includes the following steps:

[0081] (1) Construct the first knowledge graph.

[0082] Specifically, Figure 3 As shown, the server can use natural language processing technology to identify the entities in the optimization solution corresponding to each root cause and extract the relationship between the entities. Then, the server can use the entities as vertices and the relationship between the entities as edges to obtain the first knowledge graph.

[0083] In an embodiment of the present application, a method for constructing a first knowledge graph can be constructed based on a knowledge graph construction framework, and the first knowledge graph construction framework includes two modules: a visual editing module and a knowledge graph supplement module based on multi-round dialogues. Among them, the visual editing module can be used to display the relationship between entities in the first knowledge graph, and to provide operations such as editing, updating, and deleting. The knowledge graph supplement module based on multi-round dialogues can be used to conduct multi-round communication dialogues with users, automatically extract the relationship between entities in the text input by the user, and store the extracted relationship between entities in the first knowledge graph.

[0084] (2) Storing entities and relationships between entities in the first knowledge graph in a graph database.

[0085] The embodiment of the present application does not limit the type of graph database. For example, the graph database may be a Neo4j graph database, a Redis Graph graph database, or a Janus Graph graph database.

[0086] In some embodiments, the server may store information of network devices related to the root cause in a graph database, specifically including the following steps:

[0087] (1) Obtain basic information about network devices.

[0088] The embodiments of the present application do not limit the basic information of the network device. For example, the basic information of the network device may include the type of the network device, the location information of the network device, and key indicators of the network device, such as performance management (PM) indicators and device configuration management (CM) indicators.

[0089] Specifically, the network device may send basic information to the server at preset intervals, and correspondingly, the server may receive the basic information sent by the network device.

[0090] (2) Preprocess the basic information of the network device to obtain preprocessed basic information.

[0091] The embodiment of the present application does not limit the steps for preprocessing the basic information of the network device. For example, the server can perform integrity detection on the basic information of the network device. For another example, the server can perform outlier processing on the basic information of the network device. For another example, the server can perform regularization and normalization processing on the basic information of the network device. For another example, the server can perform labeling and bucketing processing on the basic information of the network device.

[0092] Exemplarily, taking the example that the types of base stations include macro base stations and micro base stations, the server may label the types of base stations, such as using 0 as the label of the macro base station and using 1 as the label of the micro base station.

[0093] For example, the basic information is the traffic through the base station in a week. The server can count the traffic through the base station every day in a week, and then bucket and label the traffic after counting, such as dividing the traffic data with a daily total traffic in the range of 0-10 gigabytes (GB) into the first bucket and using 1 as its label, and dividing the traffic data with a daily total traffic in the range of 10-20GB into the second bucket and using 2 as its label.

[0094] (3) Construct a second knowledge graph and store the entities and relationships between entities in the second knowledge graph in a graph database.

[0095] Specifically, the server can refer to the above-mentioned method for constructing the first knowledge graph to construct the second knowledge graph, and store the entities and the relationships between the entities in the second knowledge graph in the graph database.

[0096] In some embodiments, after obtaining the first knowledge graph and the second knowledge graph, Figure 3 As shown, the server can use entity alignment technology to fuse the first knowledge graph and the second knowledge graph to obtain a fused optimized knowledge graph.

[0097] Figure 4 The entities contained in the optimization knowledge graph and the relationships between them are shown, where the entities include root cause, optimization solution, reason, key indicator, work order (i.e. complaint work order), antenna, cell, low perception, base station, and front-line action.

[0098] The relationships between entities include optimization plan-solution-root cause, which means that the optimization plan can solve the problem caused by the root cause; work order-corresponding action-first-line action, which means the first-line action corresponding to the complaint content in the work order (such as adjusting the base station antenna); work order-corresponding indicator-key indicator, which means the key indicator corresponding to the complaint content in the work order (such as network delay); work order-corresponding root cause-root cause, which means the root cause corresponding to the complaint content in the work order; first-line action-solution-root cause, which means the first-line action can solve the problem caused by the root cause; antenna-service-cell, which means the antenna can provide wireless communication service for the corresponding cell; base station-coverage-cell, which means the signal of the base station can cover the area where the cell is located; root cause-cause-reason, which means the root cause (such as base station antenna damage) leads to the direct cause (such as poor signal quality); cause-cause-low perception, which means the cause (such as poor signal quality) leads to the user's perception of network service quality decline; work order-occurrence cell-cell, which means the complaint content in the work order occurred in the corresponding cell.

[0099] In some embodiments, the server may train the information extraction model in the following manner, which may specifically include: obtaining training data identifying optimization solutions corresponding to different root causes, and using the training data to train a large language model to obtain an information extraction model.

[0100] The embodiment of the present application does not limit the type of large language model. For example, Figure 5 As shown, the large language model can be an LLaMA model, a chatGLM model, or a Baichuan model.

[0101] In the implementation of this application, the training data may be the same data as the above-mentioned text information, and is not limited here.

[0102] Specifically, take the LLaMA model as an example. The server can convert the format of the training data and convert the training data into a data form that meets the format requirements of the LLaMA model. Afterwards, the server can mark the training data after the format conversion to obtain the marked training data, and the marked training data is the data marked with the optimization solutions corresponding to different root causes. Then, Figure 6 As shown, the server can input the training data with optimization solutions corresponding to different root causes into the LLaMA model, train the LLaMA model, and obtain the trained LLaMA model. Afterwards, the server can optimize the trained LLaMA model through the artificial feedback reinforcement learning method to obtain the final information extraction model.

[0103] Through the above method, an information extraction model suitable for the field of network optimization can be obtained to provide support for network optimization work.

[0104] In some embodiments, a root cause determination model and a solution generation model are deployed in the server. The root cause determination model is used to determine the target root cause that causes the performance of the terminal to be optimized based on the complaint ticket. The solution generation model is used to generate a target optimization solution based on the target root cause.

[0105] It should be noted that the solution generation model and the information extraction model can be separate models or integrated into one model (hereinafter referred to as the network optimization model). The training method of the solution generation model can refer to the training method of the information extraction model mentioned above, which will not be introduced here.

[0106] It should be noted that the root cause location model is a lightweight traditional model. The embodiment of the present application does not limit the type of root cause location model. For example, Figure 7 As shown, the root cause localization model can be a graph neural network model, a convolutional neural network model, or a recurrent neural network model.

[0107] Specifically, taking the interaction between the root cause determination model and the network optimization model as an example, the above Figure 2 S201-S203 may specifically include: Figure 8 As shown, the server 102 can input the complaint ticket into the network optimization model, and the network optimization model can extract the information related to the terminal performance in the complaint ticket, and can generate a call instruction based on the information related to the terminal performance, and input the call instruction into the root cause location model, and the call instruction is used to instruct the root cause location model to perform the target task. Afterwards, the root cause location model can perform the target task based on the call instruction, and obtain the target root cause output by the root cause location model that causes the terminal performance to be in a state to be optimized. Finally, the root cause location model can input the target root cause into the network optimization model to obtain the target optimization solution output by the network optimization model.

[0108] The present application embodiment does not limit the type of target task. Figure 8 As shown, the target tasks may include but are not limited to: generating codes, searching databases, detecting network failures, analyzing root causes of poor quality, generating texts, and compressing alarms.

[0109] Fig. 9 A schematic diagram of a structure of an optimization scheme determination device provided in an embodiment of the present application, such as Fig. 9 As shown, the device comprises:

[0110] The transceiver unit 901 is used to obtain the complaint ticket sent by the terminal.

[0111] The determination unit 902 is used to determine the target root cause that causes the performance of the terminal to be in a state to be optimized based on the complaint ticket.

[0112] The processing unit 903 is used to generate a target optimization solution based on the target root cause.

[0113] Optionally, the determining unit 902 is further configured to determine a network device associated with the performance of the terminal.

[0114] Optionally, the processing unit 903 is further configured to perform fault detection on the network device to obtain a detection result.

[0115] Optionally, the determination unit 902 is further configured to, when the detection result indicates that a network device has failed, determine a root cause of the failure of the network device, and use the root cause of the failure as a target root cause causing the performance of the terminal to be in a state to be optimized.

[0116] Optionally, the determination unit 902 is further configured to determine at least one optimization solution corresponding to the target root cause.

[0117] Optionally, the determination unit 902 is further configured to determine a target optimization solution based on at least one optimization solution corresponding to the target root cause.

[0118] Optionally, the processing unit 903 is further configured to extract an optimization solution corresponding to each root cause from the text class information through an information extraction model.

[0119] Optionally, the processing unit 903 is further configured to train the large language model using the training data to obtain an information extraction model.

[0120] Fig.10 Another possible structural diagram of the optimization scheme determination device involved in the above embodiment is shown. The optimization scheme determination device includes: a processor 1001 and a communication interface 1002. The processor 1001 is used to control and manage the actions of the optimization scheme determination device, and the communication interface 1002 is used to support the communication between the optimization scheme determination device and other network entities. The optimization scheme determination device may also include a memory 1003 and a bus 1004, and the memory 1003 is used to store program codes and data of the optimization scheme determination device.

[0121] Among them, the memory 1003 can be a memory in an optimization scheme determination device, etc. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid-state drive; the memory may also include a combination of the above types of memory.

[0122] The processor 1001 may be a processor that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0123] The bus 1004 may be an extended industry standard architecture (EISA) bus, etc. The bus 1004 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0124] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0125] An embodiment of the present application provides a computer program product comprising instructions. When the computer program product is run on a computer, the computer is enabled to execute the optimization solution determination method in the above method embodiment.

[0126] An embodiment of the present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a computer, the computer executes the optimization solution determination method in the method flow shown in the above method embodiment.

[0127] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). In the embodiments of the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0128] An embodiment of the present invention provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the optimization solution determination method described in the embodiment of the present application.

[0129] Since the optimization scheme determination device, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above-mentioned method, the technical effects that can be obtained can also refer to the above-mentioned method embodiments, and the embodiments of the present invention will not be repeated here.

[0130] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0131] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0132] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0133] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining an optimization scheme, characterized in that: Applied to a server, the method comprises: Obtaining a complaint ticket sent by the terminal; the complaint ticket is used to indicate that the performance of the terminal is in a state to be optimized; Based on the complaint ticket, determining a target root cause causing the performance of the terminal to be in a state to be optimized; Based on the target root cause, a target optimization solution is generated; the target optimization solution is used to improve the performance of the terminal.

2. The method according to claim 1, characterized in that The determining, based on the complaint work order, a target root cause causing the performance of the terminal to be in a state to be optimized includes: determining a network device associated with the performance of the terminal; Performing fault detection on the network device to obtain a detection result; In the case where the detection result indicates that the network device has a fault, determining a root cause of the fault of the network device; The root cause of the fault is used as a target root cause causing the performance of the terminal to be in a state to be optimized.

3. The method according to claim 2, characterized in that The root cause of the fault indicates that a first component of the network device fails; and the target optimization solution is to repair the first component.

4. The method according to any one of claims 1 to 3, characterized in that The server stores optimization solutions corresponding to multiple root causes; The multiple root causes include the target root cause; The generating a target optimization solution based on the target root cause includes: Determine at least one optimization solution corresponding to the target root cause; The target optimization solution is determined based on at least one optimization solution corresponding to the target root cause.

5. The method according to claim 4, characterized in that The server is deployed with an information extraction model, and the method further comprises: The optimization solutions corresponding to the multiple root causes are obtained by: Acquire text information; the text information includes the multiple root causes and the optimization solution corresponding to each of the multiple root causes; The optimization solution corresponding to each root cause is extracted from the text information through the information extraction model.

6. The method according to claim 5, characterized in that The method further comprises: The information extraction model is obtained by training in the following manner: Obtain training data with optimization solutions corresponding to different root causes; The large language model is trained using the training data to obtain the information extraction model.

7. The method according to claim 1, characterized in that The server is deployed with a root cause determination model and a solution generation model; the root cause determination model is used to determine the target root cause that causes the performance of the terminal to be in a state to be optimized based on the complaint ticket; The solution generation model is used to generate a target optimization solution based on the target root cause.

8. An optimization scheme determination device, characterized in that: The device comprises: A transceiver unit, used to obtain a complaint ticket sent by a terminal; the complaint ticket is used to indicate that the performance of the terminal is in a state to be optimized; A determination unit, configured to determine, based on the complaint ticket, a target root cause causing the performance of the terminal to be in a state to be optimized; A processing unit is used to generate a target optimization solution based on the target root cause; the target optimization solution is used to improve the performance of the terminal.

9. An optimization scheme determination device, characterized in that: include: A processor and a communication interface; the communication interface is coupled to the processor, and the processor is used to run a computer program or instruction to implement the optimization solution determination method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions. When a computer executes the instructions, the computer executes the optimization solution determination method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The computer program product comprises computer instructions, and when the computer instructions are executed on a computer, the computer is caused to execute the optimization solution determination method according to any one of claims 1 to 7.

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