CN116016122A - Prediction method, device, equipment and storage medium for network failure solution - Google Patents
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Abstract
Description
技术领域technical field
本申请涉及数据处理技术,尤其涉及一种网络故障解决方案的预测方法、装置、设备及存储介质。The present application relates to data processing technology, and in particular to a network failure solution prediction method, device, equipment and storage medium.
背景技术Background technique
随着社会发展,用户对网络的需求量不断增加的同时也加大了网络在使用过程中出现故障的概率,为了能够及时处理网络故障,运营商为此也做了不少努力。With the development of society, users' demand for the network is increasing, and the probability of network failures during use is also increased. In order to deal with network failures in a timely manner, operators have also made a lot of efforts.
现有技术中当用户在生活或工作中遇到网络故障时,用户及时将网络故障情况告知相应的运维人员,接着由运维人员通过网络故障情况确定出网络故障对应的解决方案,然后告知用户如何解决。其中,对于复杂的网络故障情况,运维人员可能需要通过线下勘察的方式确定出对应的解决方案。In the prior art, when a user encounters a network failure in life or work, the user promptly informs the corresponding operation and maintenance personnel of the network failure, and then the operation and maintenance personnel determine the corresponding solution to the network failure through the network failure situation, and then inform How users solve it. Among them, for complex network failure situations, operation and maintenance personnel may need to determine the corresponding solution through offline survey.
由此可见,现有技术中当用户遇到网络故障,用户依赖运维人员的专业性找到解决方案,然后再做出相应处理,所以使得用户对运维人员有很大的依赖性;同时,由于运维人员在找解决方案的时候需要花费大量时间,因此效率较低,所以也会降低用户使用网络的体验感。It can be seen that in the prior art, when a user encounters a network failure, the user relies on the professionalism of the operation and maintenance personnel to find a solution, and then makes corresponding processing, so that the user has a great dependence on the operation and maintenance personnel; at the same time, Since the operation and maintenance personnel need to spend a lot of time looking for a solution, the efficiency is low, and the user's experience of using the network will also be reduced.
发明内容Contents of the invention
本申请提供一种网络故障解决方案的预测方法、装置、设备及存储介质,用以解决用户对运维人员有很大的依赖性,以及运维人员在找解决方案的时候需要花费大量时间,效率较低的问题。This application provides a prediction method, device, equipment, and storage medium for network failure solutions to solve the problem that users have a great dependence on operation and maintenance personnel, and that operation and maintenance personnel need to spend a lot of time looking for solutions. The problem of low efficiency.
第一方面,本申请提供一种网络故障解决方案的预测方法,所述方法包括:In a first aspect, the present application provides a method for predicting a network failure solution, the method comprising:
接收用户终端发送的目标网络对应的目标网络故障数据;receiving target network fault data corresponding to the target network sent by the user terminal;
采用已训练至收敛的解决方案预测模型根据所述目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案;所述已训练至收敛的解决方案预测模型是预先构建的多个孪生变换器的双向编码器表征量Bert模型的融合模型;The solution prediction model trained to convergence is used to predict the solution of the target network according to the fault data of the target network to obtain the target solution; the solution prediction model trained to convergence is a plurality of pre-built twins A fusion model of the bidirectional encoder characterization quantity Bert model of the converter;
将所述目标解决方案发送至用户终端,以指示用户终端显示目标解决方案。sending the target solution to the user terminal to instruct the user terminal to display the target solution.
在一种方式中,所述已训练至收敛的解决方案预测模型中包含多个已训练至收敛的孪生Bert模型;不同孪生Bert模型具有不同层次配置参数的;所述配置参数是Bert模型关于对语义粒度的抓取特征的参数;In one way, the solution prediction model that has been trained to convergence includes a plurality of twin Bert models that have been trained to convergence; different twin Bert models have different levels of configuration parameters; Semantic granularity capture feature parameters;
所述采用已训练至收敛的解决方案预测模型根据所述目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案,包括:The solution prediction model trained to convergence is used to predict the solution of the target network according to the fault data of the target network to obtain the target solution, including:
将所述目标网络故障数据分别输入至各所述已训练至收敛的孪生Bert模型中;Input the target network failure data into each of the twin Bert models that have been trained to converge;
分别采用各所述已训练至收敛的孪生Bert模型对所述目标网络故障数据进行对应语义粒度的特征提取,以获得对应的网络故障特征向量;Using each of the twin Bert models that have been trained to converge to perform feature extraction corresponding to the semantic granularity of the target network fault data, so as to obtain a corresponding network fault feature vector;
根据多个所述网络故障特征向量对目标网络的解决方案进行预测,以获得目标解决方案。The solution of the target network is predicted according to the plurality of network fault feature vectors, so as to obtain the target solution.
在一种方式中,所述已训练至收敛的解决方案预测模型中还包括线性层;所述线性层包括预设的预测标签公式;In one manner, the solution prediction model that has been trained to convergence further includes a linear layer; the linear layer includes a preset prediction label formula;
所述根据多个所述网络故障特征向量对目标网络的解决方案进行预测,以获得目标解决方案,包括:The predicting the solution of the target network according to the plurality of network fault feature vectors to obtain the target solution includes:
基于网络故障权重公式及多个所述网络故障特征向量计算出网络故障融合特征向量;Calculate the network fault fusion feature vector based on the network fault weight formula and a plurality of the network fault feature vectors;
将所述网络故障融合特征向量输入到所述预设的预测标签公式中计算出目标预测标签值;inputting the network fault fusion feature vector into the preset prediction label formula to calculate a target prediction label value;
基于所述目标预测标签值确定出对应的目标预测标签范围;determining a corresponding target prediction label range based on the target prediction label value;
基于所述目标预测标签范围确定出对应的目标解决方案。A corresponding target solution is determined based on the target predicted label range.
在一种方式中,所述基于所述目标预测标签范围确定出对应的目标解决方案,包括:In one manner, the determining the corresponding target solution based on the target prediction label range includes:
获取预先存储的预设的预测标签范围及其对应的解决方案的映射关系;Obtain the mapping relationship between the pre-stored preset prediction label range and its corresponding solution;
响应于所述目标预测标签范围与所述映射关系中任一预设的预测标签范围一致,将一致的预设的预测标签范围对应的解决方案确定为目标解决方案。In response to the target predicted label range being consistent with any preset predicted label range in the mapping relationship, a solution corresponding to the consistent preset predicted label range is determined as the target solution.
在一种方式中,所述采用已训练至收敛的解决方案预测模型根据所述目标网络故障数据对目标网络的解决方案进行预测之前,还包括:In one manner, before using the solution prediction model that has been trained to convergence to predict the solution of the target network according to the fault data of the target network, it further includes:
获取训练样本集;所述训练样本集中的网络故障数据来源于用户终端发送的历史网络故障数据以及专家经验知识库中的网络故障数据;所述训练样本集包含多个网络故障数据以及各网络故障数据对应的解决方案的预测标签值;Obtain a training sample set; the network fault data in the training sample set comes from the historical network fault data sent by the user terminal and the network fault data in the expert experience knowledge base; the training sample set includes a plurality of network fault data and each network fault The predicted label value of the solution corresponding to the data;
采用所述训练样本集对多个孪生的预设Bert模型进行训练;Using the training sample set to train a plurality of twin preset Bert models;
将多个满足预设训练收敛条件的孪生Bert模型确定为多个已训练至收敛的孪生Bert模型。Determining multiple twin Bert models satisfying preset training convergence conditions as multiple twin Bert models trained to convergence.
在一种方式中,所述将多个满足预设训练收敛条件的孪生Bert模型确定为多个已训练至收敛的孪生Bert模型之后,还包括:In one manner, after the multiple twin Bert models that meet the preset training convergence conditions are determined as multiple twin Bert models that have been trained to convergence, it also includes:
基于多个所述已训练至收敛的孪生Bert模型以及线性层构建出多个孪生Bert模型的融合模型;Construct a fusion model of multiple twin Bert models based on multiple twin Bert models that have been trained to convergence and a linear layer;
将所述多个孪生Bert模型的融合模型确定为已训练至收敛的解决方案预测模型。The fusion model of the plurality of twin Bert models is determined as the solution prediction model that has been trained to convergence.
在一种方式中,所述方法还包括:In one mode, the method also includes:
获取网络故障更新数据集;所述网络故障更新数据集中的网络故障更新数据来源于用户终端发送的网络故障更新数据以及专家经验知识库中的网络故障更新数据;所述网络故障更新数据集包含多个网络故障更新数据以及各网络故障更新数据对应的解决方案的预测标签值;Obtain a network fault update data set; the network fault update data in the network fault update data set comes from the network fault update data sent by the user terminal and the network fault update data in the expert experience knowledge base; the network fault update data set contains multiple Each network fault update data and the predicted label value of the solution corresponding to each network fault update data;
采用所述网络故障更新数据集对多个已训练至收敛的孪生Bert模型进行更新训练;A plurality of twin Bert models that have been trained to convergence are updated and trained using the network fault update data set;
将多个满足预设更新训练条件的孪生Bert模型确定为多个更新后的已训练至收敛的孪生Bert模型;Determining multiple twin Bert models that meet the preset update training conditions as multiple updated twin Bert models that have been trained to converge;
基于多个所述更新后的已训练至收敛的孪生Bert模型以及线性层构建出多个更新后的孪生Bert模型的融合模型;Construct a fusion model of multiple updated twin Bert models based on multiple updated twin Bert models trained to convergence and a linear layer;
将所述多个更新后的孪生Bert模型的融合模型确定为最新的已训练至收敛的解决方案预测模型。The fusion model of the multiple updated twin Bert models is determined as the latest solution prediction model that has been trained to convergence.
第二方面,本申请提供一种网络故障解决方案的预测装置,所述装置包括:In a second aspect, the present application provides a device for predicting network failure solutions, the device comprising:
接收模块,用于接收用户终端发送的目标网络对应的目标网络故障数据;A receiving module, configured to receive target network fault data corresponding to the target network sent by the user terminal;
预测模块,用于采用已训练至收敛的解决方案预测模型根据所述目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案;所述已训练至收敛的解决方案预测模型是预先构建的多个孪生变换器的双向编码器表征量Bert模型的融合模型;The prediction module is used to use the solution prediction model trained to convergence to predict the solution of the target network according to the fault data of the target network to obtain the target solution; the solution prediction model trained to convergence is pre- The fusion model of the two-way encoder characterization Bert model of multiple twin transformers constructed;
发送模块,用于将所述目标解决方案发送至用户终端,以指示用户终端显示目标解决方案。A sending module, configured to send the target solution to the user terminal, so as to instruct the user terminal to display the target solution.
第三方面,本申请提供一种电子设备,包括:处理器,以及与所述处理器通信连接的存储器;In a third aspect, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;
所述存储器存储计算机执行指令;the memory stores computer-executable instructions;
所述处理器执行所述存储器存储的计算机执行指令,以实现如第一方面或任一种方式所述的方法。The processor executes the computer-executed instructions stored in the memory, so as to implement the method as described in the first aspect or any one manner.
第四方面,本申请提供一种计算机可读存储介质,所述计算机可读存储介质中存储有计算机执行指令,所述计算机执行指令被处理器执行时用于实现如第一方面或任一种方式所述的方法。In a fourth aspect, the present application provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, they are used to implement the first aspect or any one of method described in the method.
本申请提供的一种网络故障解决方案的预测方法、装置、设备及存储介质,具体包括:接收用户终端发送的目标网络对应的目标网络故障数据;采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案;已训练至收敛的解决方案预测模型是预先构建的多个孪生变换器的双向编码器表征量Bert模型的融合模型;将目标解决方案发送至用户终端,以指示用户终端显示目标解决方案。本申请网络故障解决方案的预测装置(以下简称预测装置)首先接收用户终端发送的关于目标网络对应的目标网络故障数据,接着预测装置将采用已训练至收敛的解决方案预测模型对目标网络解决方案进行预测,由于解决方案预测模型是预先训练至收敛的,所以是一个较优的预测模型,同时,由于已训练至收敛的解决方案预测模型是多个孪生Bert模型的融合模型,因此目标网络故障数据基于多个孪生Bert模型进行预测,所以最终预测得到的目标解决方案较准确;由于本申请相较于现有技术不再依赖于运维人员找到目标网络故障数据的解决方案,所以减少了对运维人员的依赖性;进一步的,运维人员在找目标解决方案的时,需要花费大量时间,所以效率低,而本申请不依赖运维人员,借助已训练至收敛的解决方案预测模型就能预测出目标解决方案,因此能够解决时间,提高找到解决方案的效率,且本申请用户能够自己直接获得目标解决方案,进一步提高用户体验感。The present application provides a network failure solution prediction method, device, equipment, and storage medium, specifically including: receiving the target network failure data corresponding to the target network sent by the user terminal; using the solution prediction model that has been trained to converge according to the target The network fault data predicts the solution of the target network to obtain the target solution; the solution prediction model that has been trained to converge is a fusion model of the bidirectional encoder characterization Bert model of multiple twin transformers built in advance; the target The solution is sent to the user terminal to instruct the user terminal to display the target solution. The prediction device for the network fault solution of the present application (hereinafter referred to as the prediction device) first receives the target network fault data corresponding to the target network sent by the user terminal, and then the prediction device will use the solution prediction model that has been trained to converge to analyze the target network solution For prediction, since the solution prediction model is pre-trained to convergence, it is a better prediction model. At the same time, since the solution prediction model that has been trained to convergence is a fusion model of multiple twin Bert models, the target network failure The data is predicted based on multiple twin Bert models, so the final predicted target solution is more accurate; compared with the existing technology, this application no longer relies on the operation and maintenance personnel to find the solution to the target network fault data, so it reduces the need for Dependency of operation and maintenance personnel; further, operation and maintenance personnel need to spend a lot of time to find the target solution, so the efficiency is low, and this application does not rely on operation and maintenance personnel, with the help of the solution prediction model that has been trained to converge The target solution can be predicted, so it can reduce the time and improve the efficiency of finding the solution, and the user of this application can directly obtain the target solution by himself, further improving the user experience.
附图说明Description of drawings
此处的附图被并入说明书中并构成本说明书的一部分,示出了符合本申请的实施例,并与说明书一起用于解释本申请的原理。The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description serve to explain the principles of the application.
图1为本申请提供的一种网络故障解决方案的预测方法的应用场景图;FIG. 1 is an application scenario diagram of a prediction method for a network failure solution provided by the present application;
图2为本申请实施例一提供的一种网络故障解决方案的预测方法的流程示意图;FIG. 2 is a schematic flowchart of a method for predicting a network fault solution provided in Embodiment 1 of the present application;
图3为本申请实施例二提供的一种网络故障解决方案的预测方法的流程示意图;FIG. 3 is a schematic flowchart of a method for predicting a network fault solution provided in Embodiment 2 of the present application;
图4为本申请实施例三提供的一种网络故障解决方案的预测方法的流程示意图;FIG. 4 is a schematic flowchart of a method for predicting a network failure solution provided in Embodiment 3 of the present application;
图5为本申请实施例四提供的一种网络故障解决方案的预测方法的流程示意图;FIG. 5 is a schematic flowchart of a method for predicting a network fault solution provided in Embodiment 4 of the present application;
图6为本申请实施例四提供的一种已训练至收敛的解决方案预测模型示意图;FIG. 6 is a schematic diagram of a solution prediction model that has been trained to convergence provided in Embodiment 4 of the present application;
图7为本申请实施例五提供的一种网络故障解决方案的预测方法的流程示意图;FIG. 7 is a schematic flowchart of a method for predicting a network fault solution provided in Embodiment 5 of the present application;
图8为本申请实施例六提供的一种网络故障解决方案的预测装置示意图;FIG. 8 is a schematic diagram of a prediction device for a network fault solution provided in Embodiment 6 of the present application;
图9为本申请实施例七提供的一种电子设备的结构示意图。FIG. 9 is a schematic structural diagram of an electronic device provided in Embodiment 7 of the present application.
通过上述附图,已示出本申请明确的实施例,后文中将有更详细的描述。这些附图和文字描述并不是为了通过任何方式限制本申请构思的范围,而是通过参考特定实施例为本领域技术人员说明本申请的概念。By means of the above drawings, specific embodiments of the present application have been shown, which will be described in more detail hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of the application in any way, but to illustrate the concept of the application for those skilled in the art by referring to specific embodiments.
具体实施方式Detailed ways
这里将详细地对示例性实施例进行说明,其示例表示在附图中。下面的描述涉及附图时,除非另有表示,不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本申请相一致的所有实施方式。相反,它们仅是与如所附权利要求书中所详述的、本申请的一些方面相一致的装置和方法的例子。Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present application as recited in the appended claims.
首先对本申请所涉及的名词进行解释:First, the nouns involved in this application are explained:
变换器的双向编码器表征量(Bidirectional Encoder Representation fromTransformers,简称Bert):是指一个面向自然语言处理任务的无监督预训练语言模型。Bidirectional Encoder Representation from Transformers (Bert for short): refers to an unsupervised pre-trained language model for natural language processing tasks.
现有技术中当用户在生活或工作中遇到网络故障时,用户及时将网络故障情况告知相应的运维人员,接着由运维人员通过网络故障情况确定出网络故障对应的解决方案,然后告知用户如何解决。其中,对于复杂的网络故障情况,运维人员可能需要通过线下勘察的方式确定出对应的解决方案。In the prior art, when a user encounters a network failure in life or work, the user promptly informs the corresponding operation and maintenance personnel of the network failure, and then the operation and maintenance personnel determine the corresponding solution to the network failure through the network failure situation, and then inform How users solve it. Among them, for complex network failure situations, operation and maintenance personnel may need to determine the corresponding solution through offline survey.
由此可见,现有技术中当用户遇到网络故障,用户依赖运维人员找到解决方案,然后再做出相应处理,所以使得用户对运维人员有很大的依赖性;同时,由于运维人员在找解决方案的时候需要花费大量时间,因此效率较低,所以也会降低用户使用网络的体验感。It can be seen that in the prior art, when a user encounters a network failure, the user relies on the operation and maintenance personnel to find a solution and then make corresponding processing, so the user has a great dependence on the operation and maintenance personnel; at the same time, due to the operation and maintenance personnel It takes a lot of time for personnel to find a solution, so the efficiency is low, so it will also reduce the user's experience of using the network.
为了解决现有技术的缺陷,本方案发明人经过创造性研究,设计一种新的方案。本方案提供一种网络故障解决方案的预测方法,为了解决解决用户对运维人员有很大的依赖性的问题,本方案预测装置接收用户终端发送的目标网络故障数据,接着通过已训练至收敛的解决方案预测模型根据目标网络故障数据进行预测,得到目标网络的解决方案,由于本方案是采用已训练至收敛的解决方案预测模型得到解决方案的,所以不依赖运维人员去找目标解决方案,因此降低了对运维人员的依赖性;为了解决运维人员在找目标解决方案时需要花费打开时间,效率较低的问题,本方案通过机器而非人工去获得目标解决方案,所以能够节约时间,提高获得目标解决方案的效率,本方案将获得的目标解决方案直接发送至用户终端,从而用户能够基于用户终端显示的目标解决方案做出相应处理,由于目标解决方案可以通过用户终端显示,所以用户能直接获得,进而能够提高用户的体验感。In order to solve the defects of the prior art, the inventor of the present solution designs a new solution through creative research. This solution provides a prediction method for network failure solutions. In order to solve the problem that users have a great dependence on operation and maintenance personnel, the prediction device of this solution receives the target network failure data sent by the user terminal, and then passes the training to converge The solution prediction model of the target network predicts the target network fault data to obtain the solution of the target network. Since this solution is obtained by using the solution prediction model that has been trained to converge, it does not rely on the operation and maintenance personnel to find the target solution. , thus reducing the dependence on the operation and maintenance personnel; in order to solve the problem that the operation and maintenance personnel need to spend opening time and low efficiency when looking for the target solution, this solution obtains the target solution through machines instead of manual labor, so it can save time to improve the efficiency of obtaining the target solution. This solution directly sends the obtained target solution to the user terminal, so that the user can make corresponding processing based on the target solution displayed on the user terminal. Since the target solution can be displayed through the user terminal, Therefore, the user can obtain it directly, which in turn can improve the user experience.
下面对本申请提供一种网络故障解决方案的预测方法、装置、设备及存储介质的应用场景进行介绍。The application scenarios of a network failure solution prediction method, device, equipment, and storage medium provided by the present application are introduced below.
图1为本申请提供的一种网络故障解决方案的预测方法的应用场景图。如图1所示,该应用场景图包括用户终端101以及电子设备102,其中,电子设备102中包含网络故障解决方案的预测装置(以下简称预测装置)103,预测装置103中包含已训练至收敛的解决方案的预测模型104。FIG. 1 is an application scenario diagram of a network failure solution prediction method provided by the present application. As shown in Figure 1, the application scenario diagram includes a
其中,用户终端101与电子设备102通信连接,其中,通信连接可以是有线连接,也可以是无线连接。Wherein, the
具体的,用户通过用户终端101输入目标网络对应的目标网络故障数据,然后用户终端101向电子设备102发送目标网络故障数据,电子设备102将目标网络故障数据传输至预测装置103中。Specifically, the user inputs the target network fault data corresponding to the target network through the
进一步的,预测装置103将目标网络故障数据输入至已训练至收敛的解决方案预测模型104中,接着采用已训练至收敛的解决方案的预测模型104对目标故障数据进行预测,得到目标解决方案。Further, the
进一步的,预测装置103向用户终端101发送目标解决方案,以指示用户终端101显示目标解决方案。Further, the
本申请提供的网络故障解决方案的预测的方法,旨在解决现有技术的如上技术问题。The method for predicting network fault solutions provided in this application aims to solve the above technical problems in the prior art.
下面以具体地实施例对本申请的技术方案以及本申请的技术方案如何解决上述技术问题进行详细说明。下面这几条具体的实施例可以相互结合,对于相同或相似的概念或过程可能在某些实施例中不再赘述。下面将结合附图,对本申请的实施例进行描述。The technical solution of the present application and how the technical solution of the present application solves the above technical problems will be described in detail below with specific embodiments. The following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below in conjunction with the accompanying drawings.
实施例一Embodiment one
图2为本申请实施例一提供的一种网络故障解决方案的预测方法的流程示意图。本实施例方法的执行主体为网络故障解决方案的预测装置(以下简称预测装置),如图2所示,具体步骤如下。FIG. 2 is a schematic flowchart of a method for predicting a network fault solution provided in Embodiment 1 of the present application. The execution subject of the method in this embodiment is a predicting device for network failure solutions (hereinafter referred to as the predicting device), as shown in FIG. 2 , and the specific steps are as follows.
S201,接收用户终端发送的目标网络对应的目标网络故障数据。S201. Receive target network failure data corresponding to the target network sent by the user terminal.
其中,用户终端是用户使用的设备,可以是手机或者电脑等,此处不做限制。Wherein, the user terminal is a device used by the user, which may be a mobile phone or a computer, and is not limited here.
其中,目标网络是用户社会或工作中使用的网络。Wherein, the target network is a network used by the user in society or work.
其中,目标网络故障数据是目标网络出现故障的情况描述。Wherein, the target network failure data is a description of the failure of the target network.
具体的,用户在用户终端中输入目标网络故障数据,然后将目标网络故障数据发送至预测装置,由预测装置接收目标网络故障数据。Specifically, the user inputs the target network fault data in the user terminal, and then sends the target network fault data to the predicting device, and the predicting device receives the target network fault data.
其中,用户终端可以装载一种网络故障应用软件,用户打开网络故障应用软件输入目标网络故障数据。Wherein, the user terminal may be loaded with a network fault application software, and the user opens the network fault application software to input target network fault data.
S202,采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案;已训练至收敛的解决方案预测模型是预先构建的多个孪生变换器的双向编码器表征量Bert模型的融合模型。S202, using the solution prediction model that has been trained to convergence to predict the solution of the target network according to the fault data of the target network to obtain the target solution; the solution prediction model that has been trained to convergence is a plurality of twin transformers pre-built A Fusion Model of the Bidirectional Encoder Representation Bert Model.
其中,已训练至收敛的解决方案预测模型是预先构建的多个孪生Bert模型的融合模型。Among them, the solution prediction model that has been trained to convergence is a fusion model of multiple twin Bert models built in advance.
具体的,将目标网络故障数据输入至已训练至收敛的解决方案预测模型,接着已训练至收敛的解决方案预测模型通过对目标网络故障数据的语义描述预测出目标解决方案。Specifically, the target network fault data is input into the solution prediction model that has been trained to convergence, and then the solution prediction model that has been trained to convergence predicts the target solution through the semantic description of the target network fault data.
S203,将目标解决方案发送至用户终端,以指示用户终端显示目标解决方案。S203. Send the target solution to the user terminal to instruct the user terminal to display the target solution.
具体的,预测装置将目标解决方案通过有线或无线方式发送至用户终端,从而在用户终端中将显示目标解决方案。Specifically, the prediction device sends the target solution to the user terminal in a wired or wireless manner, so that the target solution will be displayed in the user terminal.
在一种方式中,当目标解决方案发送至用户终端时,也会生成一则提醒消息,从而提醒用户通过用户终端查看目标解决方案。In one manner, when the target solution is sent to the user terminal, a reminder message is also generated, thereby reminding the user to check the target solution through the user terminal.
本实施例提供一种网络故障解决方案的预测方法,具体包括:接收用户终端发送的目标网络对应的目标网络故障数据;采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案;已训练至收敛的解决方案预测模型是预先构建的多个孪生变换器的双向编码器表征量Bert模型的融合模型;将目标解决方案发送至用户终端,以指示用户终端显示目标解决方案。本实施例网络故障解决方案的预测装置(以下简称预测装置)首先接收用户终端发送的关于目标网络对应的目标网络故障数据,接着预测装置将采用已训练至收敛的解决方案预测模型对目标网络解决方案进行预测,由于解决方案预测模型是预先训练至收敛的,所以是一个较优的预测模型,同时,由于已训练至收敛的解决方案预测模型是多个孪生Bert模型的融合模型,因此目标网络故障数据基于多个孪生Bert模型进行预测,所以最终预测得到的目标解决方案较准确;由于本申请相较于现有技术不再依赖于运维人员找到目标网络故障数据的解决方案,所以减少了对运维人员的依赖性;进一步的,运维人员在找目标解决方案的时,需要花费大量时间,所以效率低,而本申请不依赖运维人员,借助已训练至收敛的解决方案预测模型就能预测出目标解决方案,因此能够解决时间,提高找到解决方案的效率,且本申请用户能够自己直接获得目标解决方案,进一步提高用户体验感。This embodiment provides a method for predicting a network fault solution, which specifically includes: receiving target network fault data corresponding to the target network sent by a user terminal; The solution is predicted to obtain the target solution; the solution prediction model that has been trained to convergence is the fusion model of the bidirectional encoder characterization Bert model of multiple twin transformers pre-built; the target solution is sent to the user terminal, to instruct the user terminal to display the target solution. The prediction device of the network fault solution in this embodiment (hereinafter referred to as the prediction device) first receives the target network fault data corresponding to the target network sent by the user terminal, and then the prediction device will use the solution prediction model that has been trained to converge to solve the problem of the target network. The solution prediction model is pre-trained to convergence, so it is a better prediction model. At the same time, since the solution prediction model that has been trained to convergence is a fusion model of multiple twin Bert models, the target network The fault data is predicted based on multiple twin Bert models, so the final predicted target solution is more accurate; compared with the existing technology, this application no longer relies on the operation and maintenance personnel to find the solution to the target network fault data, so it reduces Dependence on operation and maintenance personnel; further, operation and maintenance personnel need to spend a lot of time to find the target solution, so the efficiency is low, and this application does not rely on operation and maintenance personnel, with the help of the solution prediction model that has been trained to convergence The target solution can be predicted, so it can save time and improve the efficiency of finding the solution, and the user of this application can directly obtain the target solution by himself, further improving the user experience.
实施例二Embodiment two
本实施例是上述实施例一的进一步细化,本实施例已训练至收敛的解决方案预测模型中包含多个已训练至收敛的孪生Bert模型;不同孪生Bert模型具有不同层次配置参数的;配置参数是Bert模型关于对语义粒度的抓取特征的参数。This embodiment is a further refinement of the first embodiment above. The solution prediction model that has been trained to convergence in this embodiment includes multiple twin Bert models that have been trained to convergence; different twin Bert models have different levels of configuration parameters; configuration The parameters are the parameters of the Bert model about the features of semantic granularity.
其中,配置参数可以是不同层数的Transformer以及隐藏层个数。Among them, the configuration parameters can be Transformers with different layers and the number of hidden layers.
图3为本申请实施例二提供的一种网络故障解决方案的预测方法的流程示意图。本实施例是采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案的一种可选方式,如图3所示,具体步骤如下。FIG. 3 is a schematic flowchart of a method for predicting a network fault solution provided in Embodiment 2 of the present application. In this embodiment, a solution prediction model that has been trained to convergence is used to predict the solution of the target network according to the fault data of the target network, so as to obtain the target solution, as shown in FIG. 3 , and the specific steps are as follows.
S301,将目标网络故障数据分别输入至各已训练至收敛的孪生Bert模型中。S301. Input target network fault data into respective twin Bert models that have been trained to convergence.
具体的,预测装置将目标网络故障数据并行输入至以训练至收敛的孪生Bert模型中。Specifically, the prediction device inputs the target network fault data into the twin Bert model for training to convergence in parallel.
S302,分别采用各已训练至收敛的孪生Bert模型对目标网络故障数据进行对应语义粒度的特征提取,以获得对应的网络故障特征向量。S302. Using each of the twin Bert models that have been trained to convergence to perform feature extraction corresponding to the semantic granularity of the target network fault data, so as to obtain corresponding network fault feature vectors.
其中,语义粒度是指对文本是否分词,以词或以字来表示一个句子的输入特征。Among them, the semantic granularity refers to whether the text is divided into words, and the input features of a sentence are represented by words or characters.
其中,网络故障特征向量是一个固定长度的向量。Among them, the network fault feature vector is a fixed-length vector.
具体的,各已训练至收敛的孪生Bert模型对同一网络故障数据进行对应语义粒度的特征提取,由于语义粒度不一致,所以获得的网络故障特征向量也不一致,进而分别得到各自对应的网络故障特征向量。Specifically, the twin Bert models that have been trained to convergence perform feature extraction corresponding to the semantic granularity of the same network fault data. Since the semantic granularity is inconsistent, the obtained network fault feature vectors are also inconsistent, and then the corresponding network fault feature vectors are respectively obtained. .
S303,根据多个网络故障特征向量对目标网络的解决方案进行预测,以获得目标解决方案。S303. Predict the solution of the target network according to the plurality of network fault feature vectors, so as to obtain the target solution.
具体的,根据多个网络故障特征向量的特点预测出目标解决方案。Specifically, the target solution is predicted according to the characteristics of multiple network fault feature vectors.
本实施例提供一种网络故障解决方案的预测方法,本实施例中已训练至收敛的解决方案预测模型中包含多个已训练至收敛的孪生Bert模型;不同孪生Bert模型具有不同层次配置参数的;配置参数是Bert模型关于对语义粒度的抓取特征的参数。在采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案时,具体包括:将目标网络故障数据分别输入至各已训练至收敛的孪生Bert模型中;分别采用各已训练至收敛的孪生Bert模型对目标网络故障数据进行对应语义粒度的特征提取,以获得对应的网络故障特征向量;根据多个网络故障特征向量对目标网络的解决方案进行预测,以获得目标解决方案。本实施例采用多个已训练至收敛的孪生Bert模型对网络故障数据进行预测,由于Bert模型是已训练至收敛的,因此是一个较优的模型,同时,本实施例采用多个已训练至收敛的孪生Bert模型,不同孪生Bert模型具有不同层次配置参数,由于配置参数是Bert模型关于对语义粒度的抓取特征参数,从而采用多个已训练至收敛的孪生Bert模型可以得到各自对应的网络故障特征向量,该网络故障特征向量即准确,且从多个配置参数考虑,所以更加全面,接着基于多个网络故障特征向量预测出目标解决方案,由于网络故障特征向量即准确又全面,所以得到的目标解决方案也准确。This embodiment provides a prediction method for a network fault solution. In this embodiment, the solution prediction model that has been trained to convergence includes multiple twin Bert models that have been trained to convergence; different twin Bert models have different levels of configuration parameters. ; Configuration parameters are the parameters of the Bert model about the features of semantic granularity. When using the solution prediction model that has been trained to convergence to predict the solution of the target network based on the target network fault data to obtain the target solution, it specifically includes: inputting the target network fault data into each of the twins that have been trained to convergence In the Bert model, the twin Bert models that have been trained to convergence are used to extract features corresponding to the semantic granularity of the target network fault data to obtain the corresponding network fault feature vectors; the solution to the target network based on multiple network fault feature vectors Predictions are made to obtain target solutions. This embodiment uses multiple twin Bert models that have been trained to converge to predict network fault data. Since the Bert model has been trained to converge, it is a better model. At the same time, this embodiment uses multiple twin Bert models that have been trained to Convergent twin Bert models. Different twin Bert models have different levels of configuration parameters. Since the configuration parameters are the Bert model’s capture feature parameters for semantic granularity, multiple twin Bert models that have been trained to converge can be used to obtain their corresponding networks. Fault eigenvector, the network fault eigenvector is accurate, and considering multiple configuration parameters, it is more comprehensive, and then based on multiple network fault eigenvectors to predict the target solution, because the network fault eigenvector is accurate and comprehensive, so get The target solution is also accurate.
实施例三Embodiment Three
本实施例是上述任一实施例的进一步细化,本实施例已训练至收敛的解决方案预测模型中还包括线性层;线性层包括预设的预测标签公式。This embodiment is a further refinement of any of the above embodiments. In this embodiment, the solution prediction model that has been trained to convergence also includes a linear layer; the linear layer includes a preset prediction label formula.
其中,预设的预测标签公式可以是一个Softmax函数。预设的预测标签公式是经过训练之后得到的已训练至收敛的预测标签公式。在训练过程中,对预测标签公式中的参数进行训练,达到收敛条件后,就得到最终的预设的预测标签公式。Wherein, the preset prediction label formula may be a Softmax function. The preset prediction label formula is a prediction label formula that has been trained to convergence and obtained after training. During the training process, the parameters in the predicted label formula are trained, and after the convergence condition is reached, the final preset predicted label formula is obtained.
图4为本申请实施例三提供的一种网络故障解决方案的预测方法的流程示意图。本实施例是上述任一实施例的进一步细化,本实施例是根据多个网络故障特征向量对目标网络的解决方案进行预测,以获得目标解决方案的一种可选方式,如图4所示,具体步骤如下。FIG. 4 is a schematic flowchart of a method for predicting a network fault solution provided in Embodiment 3 of the present application. This embodiment is a further refinement of any of the above-mentioned embodiments. This embodiment is an optional way to predict the solution of the target network according to multiple network fault feature vectors to obtain the target solution, as shown in FIG. 4 The specific steps are as follows.
S401,基于网络故障权重公式及多个网络故障特征向量计算出网络故障融合特征向量。S401. Calculate a network fault fusion feature vector based on a network fault weight formula and multiple network fault feature vectors.
其中,网络故障权重公式是预先设定的,其中,权重可以根据自身需求或各网络故障特征向量的重要性进行配置,此处不做限制。Wherein, the network fault weight formula is preset, wherein the weight can be configured according to its own requirements or the importance of each network fault feature vector, which is not limited here.
在一种方式中,网络故障权重公式可以如(1)式所示:In one manner, the network failure weight formula can be shown in formula (1):
(1)V=ω1V1+ω2V2+……+ωiVi (1) V=ω 1 V 1 +ω 2 V 2 +...+ω i V i
其中,V表示网络故障融合特征向量,Vi表示各网络故障特征向量,i表示孪生Bert模型的个数,ωi表示各自对应的权重参数。Among them, V represents the network fault fusion feature vector, Vi represents the network fault feature vector, i represents the number of twin Bert models, and ω i represents the corresponding weight parameters.
具体的,预测装置将多个网络故障特征向量输入至网络故障权重公式中,从而计算出网络故障融合特征向量。其中,网络故障融合特征向量是一个固定长度向量。Specifically, the predicting device inputs a plurality of network fault feature vectors into the network fault weight formula, so as to calculate the network fault fusion feature vector. Among them, the network fault fusion feature vector is a fixed-length vector.
S402,将网络故障融合特征向量输入到预设的预测标签公式中计算出目标预测标签值。S402. Input the network fault fusion feature vector into a preset prediction label formula to calculate a target prediction label value.
在一种方式中,预设的预测标签公式可以如(2)式所示:In one manner, the preset predictive label formula can be shown in formula (2):
(2)Y=f(WV+b)(2) Y=f(WV+b)
其中,f(V)表示激活函数,可以是Softmax函数,V表示网络故障融合特征向量,b表示预设的偏差,W表示线性层的权重矩阵,Y表示线性层的输出,是一个固定长度向量,即Y=[Y1,...Ym]T,其中,Ym是Y的元素,m是元素的个数。Among them, f(V) represents the activation function, which can be a Softmax function, V represents the network fault fusion feature vector, b represents the preset deviation, W represents the weight matrix of the linear layer, and Y represents the output of the linear layer, which is a fixed-length vector , that is, Y=[Y 1 ,...Y m ] T , where Y m is an element of Y, and m is the number of elements.
具体的,将网络故障融合特征向量V输入至(2)式中,从而得到线性层的输出Y,接着从Y的多个元素中确定一个最大的元素Ymax,将其作为目标预测标签值。Specifically, the network fault fusion feature vector V is input into (2) to obtain the output Y of the linear layer, and then a maximum element Y max is determined from multiple elements of Y, and it is used as the target prediction label value.
S403,基于目标预测标签值确定出对应的目标预测标签范围。S403. Determine a corresponding target predicted label range based on the target predicted label value.
其中,目标预测标签范围是目标预测标签值所属范围,预测标签范围可以根据预设解决方案的总数来确定。Wherein, the target prediction label range is a range to which the target prediction label value belongs, and the prediction label range may be determined according to the total number of preset solutions.
具体的,Y是Softmax函数的输出,Ym的取值在[0,1]之间,Y中所有元素都进行归一化处理,所有元素加起来等于1。假设预设解决方案的总数为n,则将区间[0,1]分成n个区间范围,每个区间范围就是预测标签范围,每个预测标签范围对应所属的预设解决方案。Specifically, Y is the output of the Softmax function, the value of Y m is between [0,1], all elements in Y are normalized, and the sum of all elements is equal to 1. Assuming that the total number of preset solutions is n, the interval [0,1] is divided into n interval ranges, each interval range is a predicted label range, and each predicted label range corresponds to the preset solution to which it belongs.
示例性的,假设预设解决方案的总数为5,则将区间[0,1]分成5个区间范围,具体是,[0,0.2]、(0.2,0.4]、(0.4,0.6]、(0.6,0.8]以及(0.8,1]。同时,各预测标签范围对应一个预设解决方案。Exemplarily, assuming that the total number of preset solutions is 5, the interval [0,1] is divided into 5 interval ranges, specifically, [0,0.2], (0.2,0.4], (0.4,0.6], ( 0.6,0.8] and (0.8,1]. At the same time, each predicted label range corresponds to a preset solution.
进一步的,根据目标预测标签值确定对应的目标预测标签范围,根据上述示例性例子,假设目标预测标签值为0.88,则确定出目标预测标签范围为(0.8,1]。Further, the corresponding target predicted label range is determined according to the target predicted label value. According to the above exemplary example, assuming that the target predicted label value is 0.88, the target predicted label range is determined to be (0.8, 1].
S404,基于目标预测标签范围确定出对应的目标解决方案。S404. Determine a corresponding target solution based on the target prediction label range.
示例性的,在映射关系中目标预测标签范围(0.8,1]对应预设解决方案10,则将预设解决方案10确定为目标预测标签范围对应的目标解决方案。Exemplarily, in the mapping relationship, the target prediction label range (0.8, 1] corresponds to the preset solution 10, then the preset solution 10 is determined as the target solution corresponding to the target prediction label range.
本实施例提供一种网络故障解决方案的预测方法,在根据多个网络故障特征向量对目标网络的解决方案进行预测,以获得目标解决方案时,具体包括:基于网络故障权重公式及多个网络故障特征向量计算出网络故障融合特征向量;将网络故障融合特征向量输入到预设的预测标签公式中计算出目标预测标签值;基于目标预测标签值确定出对应的目标预测标签范围;基于目标预测标签范围确定出对应的目标解决方案。本实施例预测装置首先基于网络故障权重公式以及多个网络故障特征向量计算出网络故障融合特征向量,接着将网络故障融合特征向量输入到预设的预测标签公式中计算出目标预测标签值,由于目标预测标签值是属于某个目标预测标签范围的,而每一个目标预测标签范围对应有解决方案,因此,预测装置将根据目标预测标签值确定出目标预测标签范围,从而能够准确地确定出目标解决方案。This embodiment provides a method for predicting network failure solutions. When predicting the solution of the target network according to multiple network failure feature vectors to obtain the target solution, it specifically includes: based on the network failure weight formula and multiple network failures Calculate the network fault fusion feature vector from the fault feature vector; input the network fault fusion feature vector into the preset prediction label formula to calculate the target prediction label value; determine the corresponding target prediction label range based on the target prediction label value; Label ranges determine the corresponding target solutions. The prediction device in this embodiment first calculates the network fault fusion feature vector based on the network fault weight formula and multiple network fault feature vectors, and then inputs the network fault fusion feature vector into the preset prediction label formula to calculate the target prediction label value, because The target prediction label value belongs to a certain target prediction label range, and each target prediction label range corresponds to a solution. Therefore, the prediction device will determine the target prediction label range according to the target prediction label value, so that the target can be accurately determined. solution.
在一种方式中,本方式是基于目标预测标签范围确定出对应的目标解决方案的一种可选方式,具体步骤如下。In one way, this way is an optional way to determine the corresponding target solution based on the target prediction label range, and the specific steps are as follows.
获取预先存储的预设的预测标签范围及其对应的解决方案的映射关系。Obtain the mapping relationship between pre-stored preset predicted label ranges and their corresponding solutions.
其中,对应的解决方案可以是由经验专家确定出的。Wherein, the corresponding solutions may be determined by experienced experts.
其中,映射关系可以用表格形式表现出来,或者其他方式,此处不做限制。Wherein, the mapping relationship may be expressed in a table form, or in other ways, which is not limited here.
具体的,预测装置从自身存储区域中获取预设的预测标签范围及其对应的解决方案的映射关系,在该映射关系中可包含多个预测标签范围和对应解决方案。Specifically, the predicting device obtains a preset mapping relationship between a predicted label range and its corresponding solution from its own storage area, and the mapping relationship may include multiple predicted label ranges and corresponding solutions.
示例性的,假设预设解决方案的总数为n,则映射关系如下表1所示。Exemplarily, assuming that the total number of preset solutions is n, the mapping relationship is shown in Table 1 below.
表1:预设的预测标签范围及其对应的解决方案的映射关系Table 1: The mapping relationship between the preset prediction label range and its corresponding solution
响应于目标预测标签范围与映射关系中任一预设的预测标签范围一致,将一致的预设的预测标签范围对应的解决方案确定为目标解决方案。In response to that the target predicted label range is consistent with any preset predicted label range in the mapping relationship, a solution corresponding to the consistent preset predicted label range is determined as the target solution.
根据上述示例性的例子,预测装置将目标预测标签范围与表1中的预设的预测标签范围进行对比,在表1中找到任一个预设的预测标签范围与目标预测标签范围一致,例如,目标预测标签范围为(3/n,4/n],则对应表1中预设的预测标签范围为(3/n,4/n],进而确定出预设的预测标签范围(3/n,4/n]对应的解决方案为解决方案3,从而将解决方案3确定为目标解决方案。According to the above illustrative example, the prediction device compares the target predicted label range with the preset predicted label range in Table 1, and finds that any preset predicted label range in Table 1 is consistent with the target predicted label range, for example, The target prediction label range is (3/n,4/n], then the corresponding preset prediction label range in Table 1 is (3/n,4/n], and then the preset prediction label range (3/n ,4/n] corresponds to solution 3, so that solution 3 is determined as the target solution.
本方式在基于目标预测标签范围确定出对应的目标解决方案时,具体包括:获取预先存储的预设的预测标签范围及其对应的解决方案的映射关系;响应于目标预测标签范围与映射关系中任一预设的预测标签范围一致,将一致的预设的预测标签范围对应的解决方案确定为目标解决方案。本实施例预测装置获取预设的预测标签范围及其对应的解决方案的映射关系,由于映射关系能准确反映预设的预测标签范围对应的解决方案,进而响应于目标预测标签范围与映射关系中的预设的预测标签范围一致,接着将该一致的预设的预测标签范围对应的解决方案确定为目标解决方案,从而确定出的目标解决方案准确。When the method determines the corresponding target solution based on the target predicted label range, it specifically includes: obtaining the mapping relationship between the pre-stored preset predicted label range and its corresponding solution; responding to the target predicted label range and the mapping relationship Any preset predicted label range is consistent, and the solution corresponding to the consistent preset predicted label range is determined as the target solution. The prediction device in this embodiment obtains the mapping relationship between the preset prediction label range and its corresponding solution. Since the mapping relationship can accurately reflect the solution corresponding to the preset prediction label range, it responds to the target prediction label range and the mapping relationship. The preset prediction label ranges are consistent, and then the solution corresponding to the consistent preset prediction label range is determined as the target solution, so that the determined target solution is accurate.
实施例四Embodiment Four
图5为本申请实施例四提供的一种网络故障解决方案的预测方法的流程示意图。本实施例是上述任一实施例的进一步细化,本实施例是采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测之前的一种可选方式,如图5所示,具体步骤如下。FIG. 5 is a schematic flowchart of a method for predicting a network failure solution provided in Embodiment 4 of the present application. This embodiment is a further refinement of any of the above-mentioned embodiments. This embodiment is an optional method before using a solution prediction model that has been trained to converge to predict the solution of the target network according to the fault data of the target network, such as As shown in Figure 5, the specific steps are as follows.
S501,获取训练样本集;训练样本集中的网络故障数据来源于用户终端发送的历史网络故障数据以及专家经验知识库中的网络故障数据;训练样本集包含多个网络故障数据以及各网络故障数据对应的解决方案的预测标签值。S501. Obtain a training sample set; the network fault data in the training sample set comes from the historical network fault data sent by the user terminal and the network fault data in the expert experience knowledge base; the training sample set includes multiple network fault data and the corresponding network fault data The predicted label value for the solution of .
其中,历史网络故障数据是指用户终端之前发送的数据。专家经验知识库是运营商的专家总结出来的数据。Wherein, the historical network fault data refers to the data sent by the user terminal before. The expert experience knowledge base is the data summed up by the operator's experts.
其中,训练样本集包含的各网络故障数据对应的解决方案的预测标签值可以经过预先处理人为设定的标签值。其中,网络故障数据对应的解决方案的预测标签值能够反映网络故障数据的解决方案。Wherein, the predicted label value of the solution corresponding to each network fault data included in the training sample set may be pre-processed and manually set. Wherein, the predicted label value of the solution corresponding to the network fault data can reflect the solution of the network fault data.
具体的,预测装置从用户终端以及专家经验知识库中获取网络故障数据,接着预测装置通过显示器向专家显示网络故障数据,由专家输入设定的解决方案的预测标签值并发送至预测装置,进而网络故障数据集包含多个网络故障数据以及各网络故障数据对应的解决方案的预测标签值。Specifically, the prediction device acquires network fault data from the user terminal and the expert experience knowledge base, and then the prediction device displays the network fault data to the expert through the display, and the expert inputs the predicted label value of the set solution and sends it to the prediction device, and then The network fault data set includes a plurality of network fault data and a predicted label value of a solution corresponding to each network fault data.
S502,采用训练样本集对多个孪生的预设Bert模型进行训练。S502. Using the training sample set to train multiple twin preset Bert models.
具体的,将训练样本集中所有网络故障数据以及各网络故障数据对应的解决方案的预测标签值输入至多个孪生的预设Bert模型进行训练,从而预设Bert模型基于训练样本集对模型参数进行优化。Specifically, input all the network fault data in the training sample set and the predicted label values of the solutions corresponding to each network fault data to multiple twin preset Bert models for training, so that the preset Bert model can optimize the model parameters based on the training sample set .
S503,将多个满足预设训练收敛条件的孪生Bert模型确定为多个已训练至收敛的孪生Bert模型。S503. Determine multiple twin Bert models satisfying preset training convergence conditions as multiple twin Bert models trained to convergence.
当满足预设训练收敛条件后得到多个已训练至收敛的孪生Bert模型。When the preset training convergence conditions are met, multiple twin Bert models that have been trained to convergence are obtained.
本实施例提供一种网络故障解决方案的预测方法,在采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测之前,具体包括:获取训练样本集;训练样本集中的网络故障数据来源于用户终端发送的历史网络故障数据以及专家经验知识库中的网络故障数据;训练样本集包含多个网络故障数据以及各网络故障数据对应的解决方案的预测标签值;采用训练样本集对多个孪生的预设Bert模型进行训练;将多个满足预设训练收敛条件的孪生Bert模型确定为多个已训练至收敛的孪生Bert模型。本实施例预测装置首先获取训练样本集,然后基于训练样本集对多个孪生的预设Bert模型进行训练,接着将满足训练收敛条件的孪生Bert模型确定为多个已训练至收敛的孪生Bert模型,由于训练样本集中网络故障数据来源于用户终端发送是历史网络故障数据以及专家经验知识库中的网络故障数据,且训练样本集还包含各网络故障数据对应的解决方案的预测标签值,因此训练样本集来源广泛、全面,数据符合用户的实际情况,所以得到的已训练至收敛的孪生Bert模型较优,不仅专业,还符合用户的实际情况This embodiment provides a method for predicting a network fault solution. Before using the solution prediction model that has been trained to converge to predict the solution of the target network according to the target network fault data, it specifically includes: obtaining a training sample set; The centralized network fault data comes from the historical network fault data sent by the user terminal and the network fault data in the expert experience knowledge base; the training sample set contains multiple network fault data and the predicted label value of the solution corresponding to each network fault data; The training sample set trains multiple twin preset Bert models; multiple twin Bert models satisfying preset training convergence conditions are determined as multiple twin Bert models that have been trained to convergence. The prediction device in this embodiment first obtains a training sample set, then trains multiple twin preset Bert models based on the training sample set, and then determines the twin Bert models that meet the training convergence conditions as multiple twin Bert models that have been trained to convergence , because the network fault data in the training sample set comes from the historical network fault data sent by the user terminal and the network fault data in the expert experience knowledge base, and the training sample set also contains the predicted label value of the solution corresponding to each network fault data, so the training The source of the sample set is wide and comprehensive, and the data conforms to the actual situation of the user. Therefore, the twin Bert model that has been trained to convergence is better, not only professional, but also in line with the actual situation of the user
在一种方式中,本方式是将多个满足预设训练收敛条件的孪生Bert模型确定为多个已训练至收敛的孪生Bert模型之后的一种可选方式,具体内容如下。In one manner, this manner is an optional manner after determining multiple twin Bert models that meet the preset training convergence conditions as multiple twin Bert models that have been trained to convergence, and the specific content is as follows.
基于多个已训练至收敛的孪生Bert模型以及线性层构建出多个孪生Bert模型的融合模型。Construct a fusion model of multiple twin Bert models based on multiple twin Bert models that have been trained to convergence and linear layers.
将多个孪生Bert模型的融合模型确定为已训练至收敛的解决方案预测模型。A fusion model of multiple Siamese Bert models is determined as the solution prediction model trained to convergence.
图6为本申请实施例四提供的一种已训练至收敛的解决方案预测模型示意图。如图6所示,多个已训练至收敛的孪生Bert模型分别为Bert模型1、Bert模型2、Bert模型3以及Bert模型4,多个已训练至收敛的孪生Bert模型以及线性层构建出一个融合模型,即为已训练至收敛的解决方案预测模型。其中,多个已训练至收敛的孪生Bert模型的配置参数不同。FIG. 6 is a schematic diagram of a solution prediction model trained to convergence provided in Embodiment 4 of the present application. As shown in Figure 6, multiple twin Bert models that have been trained to convergence are Bert model 1, Bert model 2, Bert model 3, and Bert model 4. Multiple twin Bert models that have been trained to convergence and a linear layer construct a A fused model is a solution prediction model that has been trained to convergence. Among them, the configuration parameters of multiple twin Bert models that have been trained to convergence are different.
本方式在将多个满足预设训练收敛条件的孪生Bert模型确定为多个已训练至收敛的孪生Bert模型之后,具体包括:基于多个已训练至收敛的孪生Bert模型以及线性层构建出多个孪生Bert模型的融合模型;将多个孪生Bert模型的融合模型确定为已训练至收敛的解决方案预测模型。预测装置将多个已训练至收敛的孪生Bert模型以及线性层构建出多个孪生Bert模型的融合模型,该融合模型为已训练至收敛的解决方案预测模型,由于已训练至收敛的解决方案预测模型中包含的多个已训练至收敛的孪生Bert模型以及线性层,所以解决方案预测模型是一个较优的模型。In this method, after determining multiple twin Bert models that meet the preset training convergence conditions as multiple twin Bert models that have been trained to convergence, it specifically includes: constructing multiple A fusion model of twin Bert models; the fusion model of multiple twin Bert models is determined as a solution prediction model that has been trained to convergence. The prediction device builds a fusion model of multiple twin Bert models that have been trained to convergence and a linear layer. This fusion model is a solution prediction model that has been trained to convergence. Because the solution prediction that has been trained to convergence The model contains multiple twin Bert models and linear layers that have been trained to convergence, so the solution prediction model is a better model.
实施例五Embodiment five
图7为本申请实施例五提供的一种网络故障解决方案的预测方法的流程示意图。本实施例是上述任一实施例的进一步细化,如图7所示,具体步骤如下。FIG. 7 is a schematic flowchart of a method for predicting a network failure solution provided in Embodiment 5 of the present application. This embodiment is a further refinement of any of the above embodiments, as shown in FIG. 7 , and the specific steps are as follows.
S701,获取网络故障更新数据集;网络故障更新数据集中的网络故障更新数据来源于用户终端发送的网络故障更新数据以及专家经验知识库中的网络故障更新数据;网络故障更新数据集包含多个网络故障更新数据以及各网络故障更新数据对应的解决方案的预测标签值。S701. Obtain a network fault update data set; the network fault update data in the network fault update data set comes from the network fault update data sent by the user terminal and the network fault update data in the expert experience knowledge base; the network fault update data set includes multiple network faults The fault update data and the predicted label value of the solution corresponding to each network fault update data.
具体的,预测装置从用户终端以及专家经验知识库中获取网络故障更新数据,接着预测装置通过显示器向专家显示网络故障更新数据,由专家输入设定的解决方案的预测标签值并发送至预测装置,进而网络故障更新数据集包含多个网络故障更新数据以及各网络故障更新数据对应的解决方案的预测标签值。Specifically, the forecasting device obtains the network fault update data from the user terminal and the expert experience knowledge base, and then the forecasting device displays the network fault update data to the expert through the display, and the expert inputs the predicted label value of the set solution and sends it to the predicting device , and further the network fault update data set includes a plurality of network fault update data and the predicted label value of the solution corresponding to each network fault update data.
S702,采用网络故障更新数据集对多个已训练至收敛的孪生Bert模型进行更新训练。S702. Perform update training on multiple twin Bert models that have been trained to convergence by using the network fault update data set.
具体的,将网络故障更新数据集中所有网络故障更新数据以及各网络故障更新数据对应的解决方案的预测标签值输入至已训练至收敛的孪生Bert模型中进行更新训练,从而将已训练至收敛的孪生Bert模型中的模型参数进行进一步优化。Specifically, input all network fault update data in the network fault update data set and the predicted label value of the solution corresponding to each network fault update data into the twin Bert model that has been trained to convergence for update training, so that the trained to convergent The model parameters in the twin Bert model are further optimized.
S703,将多个满足预设更新训练条件的孪生Bert模型确定为多个更新后的已训练至收敛的孪生Bert模型。S703. Determine multiple twin Bert models satisfying preset update training conditions as multiple updated twin Bert models that have been trained to convergence.
其中,预设更新训练条件是专门为更新训练预先设定的条件。Wherein, the preset update training condition is a pre-set condition specially for the update training.
具体的,当多个孪生Bert模型满足预设更新训练条件时,已训练至收敛的孪生Bert模型将会更新,其中包含的模型参数也会更加优化。Specifically, when multiple twin Bert models meet the preset update training conditions, the twin Bert models that have been trained to convergence will be updated, and the model parameters contained in them will be more optimized.
进一步的,预测装置将多个满足预设更新训练条件的孪生Bert模型确定为多个更新后的已训练至收敛的孪生Bert模型。Further, the predicting device determines the multiple twin Bert models satisfying the preset updating training conditions as multiple updated twin Bert models that have been trained to convergence.
S704,基于多个更新后的已训练至收敛的孪生Bert模型以及线性层构建出多个更新后的孪生Bert模型的融合模型。S704. Construct a fusion model of multiple updated twin Bert models based on the multiple updated twin Bert models trained to convergence and the linear layer.
具体的,预测装置将重新选取多个更新后的已训练至收敛的孪生Bert模型以及线性层构建出多个更新后的孪生Bert模型的融合模型。所以最终得到的更新后的孪生Bert模型的融合模型是最近的,且更加符合实际需求。Specifically, the prediction device will reselect multiple updated twin Bert models that have been trained to convergence and the linear layer to construct a fusion model of multiple updated twin Bert models. Therefore, the final fusion model of the updated twin Bert model is the latest and more in line with actual needs.
在一种方式中,预测装置也能够对线性层进行更新训练,进而获得更优的预设的预测标签公式。In one manner, the prediction device can also update and train the linear layer, so as to obtain a better preset prediction label formula.
S705,将多个更新后的孪生Bert模型的融合模型确定为最新的已训练至收敛的解决方案预测模型。S705. Determine the fusion model of multiple updated twin Bert models as the latest solution prediction model that has been trained to convergence.
本实施例提供一种网络故障解决方案的预测方法,具体还包括:获取网络故障更新数据集;网络故障更新数据集中的网络故障更新数据来源于用户终端发送的网络故障更新数据以及专家经验知识库中的网络故障更新数据;网络故障更新数据集包含多个网络故障更新数据以及各网络故障更新数据对应的解决方案的预测标签值;采用网络故障更新数据集对多个已训练至收敛的孪生Bert模型进行更新训练;将多个满足预设更新训练条件的孪生Bert模型确定为多个更新后的已训练至收敛的孪生Bert模型;基于多个更新后的已训练至收敛的孪生Bert模型以及线性层构建出多个更新后的孪生Bert模型的融合模型;将多个更新后的孪生Bert模型的融合模型确定为最新的已训练至收敛的解决方案预测模型。本实施例预测装置能够获取网络故障更新数据集,从而基于网络更新数据集中包含的网络故障更新数据以及各网络故障更新数据对应的解决方案的预测标签值进行更新训练,进而能够构建出最新的已训练至收敛的解决方案预测模型,由于进行了更新训练,所以能够获得最新的已训练至收敛的解决方案预测模型,进而更加符合实际情况;同时,由于网络故障更新数据集中网络故障数据来源于用户终端发送的网络故障更新数据以及专家经验知识库中的网络故障更新数据,所以最新的已训练至收敛的解决方案预测模型也更加全面。This embodiment provides a prediction method for a network fault solution, which specifically further includes: obtaining a network fault update data set; the network fault update data in the network fault update data set is derived from the network fault update data sent by the user terminal and the expert experience knowledge base The network fault update data in ; the network fault update data set contains multiple network fault update data and the predicted label value of the solution corresponding to each network fault update data; the network fault update data set is used to train multiple twin Berts that have been trained to converge The model is updated and trained; multiple twin Bert models that meet the preset update training conditions are determined as multiple updated twin Bert models that have been trained to convergence; based on multiple updated twin Bert models that have been trained to convergence and linear The fusion model of multiple updated twin Bert models is constructed in the layer; the fusion model of multiple updated twin Bert models is determined as the latest solution prediction model that has been trained to convergence. The prediction device in this embodiment can obtain the network fault update data set, so as to perform update training based on the network fault update data contained in the network update data set and the predicted label value of the solution corresponding to each network fault update data, and then can construct the latest existing The solution prediction model trained to convergence, due to the updated training, can obtain the latest solution prediction model that has been trained to convergence, which is more in line with the actual situation; at the same time, due to the network fault update data set, the network fault data comes from the user The network fault update data sent by the terminal and the network fault update data in the expert experience knowledge base, so the latest solution prediction model that has been trained to convergence is also more comprehensive.
实施例六Embodiment six
下面是本申请的装置实施例,图8为本申请实施例六提供的一种网络故障解决方案的预测装置示意图,如图8所示,该装置800包括以下模块。The following are device embodiments of the present application. FIG. 8 is a schematic diagram of a prediction device for a network failure solution provided in Embodiment 6 of the present application. As shown in FIG. 8 , the
接收模块801,用于接收用户终端发送的目标网络对应的目标网络故障数据;The receiving
预测模块802,用于采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案;已训练至收敛的解决方案预测模型是预先构建的多个孪生变换器的双向编码器表征量Bert模型的融合模型;The
发送模块803,用于将目标解决方案发送至用户终端,以指示用户终端显示目标解决方案。The sending
在一种方式中,已训练至收敛的解决方案预测模型中包含多个已训练至收敛的孪生Bert模型;不同孪生Bert模型具有不同层次配置参数的;配置参数是Bert模型关于对语义粒度的抓取特征的参数;预测模块802,在采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测,以获得目标解决方案时,具体用于:In one way, the solution prediction model that has been trained to convergence contains multiple twin Bert models that have been trained to convergence; different twin Bert models have different levels of configuration parameters; configuration parameters are the Bert model's grasp of semantic granularity Get the parameters of the feature; the
将目标网络故障数据分别输入至各已训练至收敛的孪生Bert模型中;分别采用各已训练至收敛的孪生Bert模型对目标网络故障数据进行对应语义粒度的特征提取,以获得对应的网络故障特征向量;根据多个网络故障特征向量对目标网络的解决方案进行预测,以获得目标解决方案。Input the target network fault data into the twin Bert models that have been trained to convergence; respectively use the twin Bert models that have been trained to convergence to extract the features of the corresponding semantic granularity of the target network fault data to obtain the corresponding network fault features vector; the solution of the target network is predicted based on multiple network fault feature vectors to obtain the target solution.
在一种方式中,已训练至收敛的解决方案预测模型中还包括线性层;线性层包括预设的预测标签公式;预测模块802,在根据多个网络故障特征向量对目标网络的解决方案进行预测,以获得目标解决方案时,具体用于:In one manner, the solution prediction model that has been trained to converge also includes a linear layer; the linear layer includes a preset prediction label formula; the
基于网络故障权重公式及多个网络故障特征向量计算出网络故障融合特征向量;将网络故障融合特征向量输入到预设的预测标签公式中计算出目标预测标签值;Calculate the network fault fusion feature vector based on the network fault weight formula and multiple network fault feature vectors; input the network fault fusion feature vector into the preset prediction label formula to calculate the target prediction label value;
基于目标预测标签值确定出对应的目标预测标签范围;基于目标预测标签范围确定出对应的目标解决方案。A corresponding target prediction label range is determined based on the target prediction label value; a corresponding target solution is determined based on the target prediction label range.
在一种方式中,预测模块802,在基于目标预测标签范围确定出对应的目标解决方案时,具体用于:In one manner, the
获取预先存储的预设的预测标签范围及其对应的解决方案的映射关系;响应于目标预测标签范围与映射关系中任一预设的预测标签范围一致,将一致的预设的预测标签范围对应的解决方案确定为目标解决方案。Acquiring a pre-stored mapping relationship between a preset predicted label range and its corresponding solution; in response to the target predicted label range being consistent with any preset predicted label range in the mapping relationship, corresponding to the consistent preset predicted label range The solution of is identified as the target solution.
在一种方式中,在采用已训练至收敛的解决方案预测模型根据目标网络故障数据对目标网络的解决方案进行预测之前,本实施例提供一种网络故障解决方案的预测装置,还包括:获取模块、训练模块以及确定模块。In one manner, before using the solution prediction model that has been trained to converge to predict the solution of the target network according to the target network fault data, this embodiment provides a network fault solution prediction device, which further includes: obtaining module, training module, and determination module.
其中,获取模块,用于获取训练样本集;训练样本集中的网络故障数据来源于用户终端发送的历史网络故障数据以及专家经验知识库中的网络故障数据;训练样本集包含多个网络故障数据以及各网络故障数据对应的解决方案的预测标签值;训练模块,用于采用训练样本集对多个孪生的预设Bert模型进行训练;确定模块,用于将多个满足预设训练收敛条件的孪生Bert模型确定为多个已训练至收敛的孪生Bert模型。Among them, the obtaining module is used to obtain the training sample set; the network fault data in the training sample set comes from the historical network fault data sent by the user terminal and the network fault data in the expert experience knowledge base; the training sample set contains multiple network fault data and The predicted label value of the solution corresponding to each network fault data; the training module is used to train the preset Bert model of multiple twins using the training sample set; the determination module is used to combine multiple twins that meet the preset training convergence conditions The Bert model is determined to be multiple twin Bert models that have been trained to convergence.
在一种方式中,在将多个满足预设训练收敛条件的孪生Bert模型确定为多个已训练至收敛的孪生Bert模型之后,本实施例提供一种网络故障解决方案的预测装置,还包括:构建模块。In one manner, after determining multiple twin Bert models that meet the preset training convergence conditions as multiple twin Bert models that have been trained to converge, this embodiment provides a network fault solution prediction device, which also includes : Building blocks.
其中,构建模块,用于基于多个已训练至收敛的孪生Bert模型以及线性层构建出多个孪生Bert模型的融合模型;确定模块,还用于将多个孪生Bert模型的融合模型确定为已训练至收敛的解决方案预测模型。Among them, the construction module is used to construct the fusion model of multiple twin Bert models based on multiple twin Bert models trained to convergence and the linear layer; the determination module is also used to determine the fusion model of multiple twin Bert models as already Solution prediction model trained to convergence.
在一种方式中,本实施例提供一种网络故障解决方案的预测装置。In one manner, this embodiment provides an apparatus for predicting network fault solutions.
其中,获取模块,还用于获取网络故障更新数据集;网络故障更新数据集中的网络故障更新数据来源于用户终端发送的网络故障更新数据以及专家经验知识库中的网络故障更新数据;网络故障更新数据集包含多个网络故障更新数据以及各网络故障更新数据对应的解决方案的预测标签值;训练模块,还用于采用网络故障更新数据集对多个已训练至收敛的孪生Bert模型进行更新训练;确定模块,还用于将多个满足预设更新训练条件的孪生Bert模型确定为多个更新后的已训练至收敛的孪生Bert模型;构建模块,还用于基于多个更新后的已训练至收敛的孪生Bert模型以及线性层构建出多个更新后的孪生Bert模型的融合模型;确定模块,还用于将多个更新后的孪生Bert模型的融合模型确定为最新的已训练至收敛的解决方案预测模型。Wherein, the obtaining module is also used to obtain the network fault update data set; the network fault update data in the network fault update data set comes from the network fault update data sent by the user terminal and the network fault update data in the expert experience knowledge base; the network fault update The data set contains multiple network fault update data and the predicted label value of the solution corresponding to each network fault update data; the training module is also used to update and train multiple twin Bert models that have been trained to convergence using the network fault update data set The determination module is also used to determine multiple twin Bert models that meet the preset update training conditions as multiple updated twin Bert models that have been trained to converge; the building block is also used to train based on multiple updated Convergent twin Bert models and linear layers construct a fusion model of multiple updated twin Bert models; the determination module is also used to determine the fusion model of multiple updated twin Bert models as the latest one that has been trained to convergence Solution prediction model.
实施例七Embodiment seven
图9为本申请实施例七提供的一种电子设备的结构示意图。如图9所示,该电子设备900可以包括:处理器901,以及与处理器901通信连接的存储器902。其中,存储器902存储计算机执行指令;处理器901执行存储器902存储的计算机执行指令,以实现如上述实施例一至实施例五任一个方法实施例,具体实现方式和技术效果类似,这里不再赘述。FIG. 9 is a schematic structural diagram of an electronic device provided in Embodiment 7 of the present application. As shown in FIG. 9 , the
其中,本实施例中,存储器902和处理器901通过总线连接。总线可以是工业标准体系结构(Industry Standard Architecture,简称为ISA)总线、外部设备互连(PeripheralComponent Interconnect,简称为PCI)总线或扩展工业标准体系结构(Extended IndustryStandard Architecture,简称为EISA)总线等。总线可以分为地址总线、数据总线、控制总线等。为便于表示,图9中仅用一条粗线表示,但并不表示仅有一根总线或一种类型的总线。Wherein, in this embodiment, the
实施例八Embodiment Eight
本申请提供一种计算机可读存储介质,计算机可读存储介质中存储有计算机执行指令,计算机执行指令被处理器执行时用于实现如上述实施例一至实施例五任一个方法实施例,具体实现方式和技术效果类似,这里不再赘述。The present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement any method embodiment as in the above-mentioned embodiment 1 to embodiment 5, and the specific implementation The method and technical effect are similar and will not be repeated here.
本领域技术人员在考虑说明书及实践这里公开的发明后,将容易想到本申请的其它实施方案。本申请旨在涵盖本申请的任何变型、用途或者适应性变化,这些变型、用途或者适应性变化遵循本申请的一般性原理并包括本申请未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的,本申请的真正范围和精神由下面的权利要求书指出。Other embodiments of the present application will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the application, these modifications, uses or adaptations follow the general principles of the application and include common knowledge or conventional technical means in the technical field not disclosed in the application . The specification and examples are to be considered exemplary only, with a true scope and spirit of the application indicated by the following claims.
应当理解的是,本申请并不局限于上面已经描述并在附图中示出的精确结构,并且可以在不脱离其范围进行各种修改和改变。本申请的范围仅由所附的权利要求书来限制。It should be understood that the present application is not limited to the precise constructions which have been described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
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