CN117349515A - Search processing methods, electronic devices and storage media - Google Patents

Search processing methods, electronic devices and storage media Download PDF

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CN117349515A
CN117349515A CN202311219863.3A CN202311219863A CN117349515A CN 117349515 A CN117349515 A CN 117349515A CN 202311219863 A CN202311219863 A CN 202311219863A CN 117349515 A CN117349515 A CN 117349515A
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search
search request
attribute
auxiliary information
commerce service
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丁瑞雪
陈博理
张延钊
龙定坤
刘楚
谢朋峻
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Hangzhou AliCloud Feitian Information Technology Co Ltd
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Priority to PCT/CN2024/103719 priority patent/WO2025060594A1/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
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    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/95Retrieval from the web
    • G06F16/953Querying, e.g. by the use of web search engines
    • G06F16/9535Search customisation based on user profiles and personalisation
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    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q30/06Buying, selling or leasing transactions
    • G06Q30/0601Electronic shopping [e-shopping]
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Abstract

本申请公开了一种搜索处理方法、电子设备和存储介质,涉及大模型技术、计算机技术领域。其中,该方法包括:获取搜索请求;对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。本申请解决了相关技术中难以将行业知识注入搜索处理过程导致搜索精确性较低的技术问题。

This application discloses a search processing method, electronic equipment and storage medium, and relates to the fields of large model technology and computer technology. The method includes: obtaining a search request; performing attribute feature analysis on the search request, and generating auxiliary information associated with the search request, where the auxiliary information is at least one target attribute feature of preset industry knowledge corresponding to the search request; based on the search request and Auxiliary information to obtain the search results corresponding to the search request. This application solves the technical problem in related technologies that it is difficult to inject industry knowledge into the search processing process, resulting in low search accuracy.

Description

搜索处理方法、电子设备和存储介质Search processing methods, electronic devices and storage media

技术领域Technical field

本申请涉及大模型技术、计算机技术领域,具体而言,涉及一种搜索处理方法、电子设备和存储介质。This application relates to the fields of large model technology and computer technology, specifically, to a search processing method, electronic equipment and storage media.

背景技术Background technique

近年来,基于人工智能模型的搜索引擎技术被越来越多的应用至各行各业。然而,传统的搜索引擎通常基于小模型和简单的统计算法来实现,难以适用于复杂场景。当前虽然也存在一些基于大模型的搜索引擎(如差分搜索索引,Differential Search Index,DSI),但是现有的基于大模型的搜索引擎往往完全依赖大模型的性能,难以将大模型与传统搜索引擎的行业定制和人工干预的特点相结合,这限制了现有的基于大模型的搜索引擎的搜索精确性。In recent years, search engine technology based on artificial intelligence models has been increasingly applied to all walks of life. However, traditional search engines are usually implemented based on small models and simple statistical algorithms, which are difficult to apply to complex scenarios. Although there are currently some search engines based on large models (such as Differential Search Index, DSI), existing search engines based on large models often rely entirely on the performance of large models, and it is difficult to integrate large models with traditional search engines. The combination of industry customization and manual intervention characteristics limits the search accuracy of existing large model-based search engines.

针对上述的问题,目前尚未提出有效的解决方案。In response to the above problems, no effective solution has yet been proposed.

发明内容Contents of the invention

本申请实施例提供了一种搜索处理方法、电子设备和存储介质,以至少解决相关技术中难以将行业知识注入搜索处理过程导致搜索精确性较低的技术问题。Embodiments of the present application provide a search processing method, electronic device, and storage medium to at least solve the technical problem in the related art that it is difficult to inject industry knowledge into the search processing process, resulting in low search accuracy.

根据本申请实施例的一个方面,提供了一种搜索处理方法,包括:获取搜索请求;对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。According to one aspect of the embodiment of the present application, a search processing method is provided, including: obtaining a search request; performing attribute feature analysis on the search request, and generating auxiliary information associated with the search request, where the auxiliary information is a preset corresponding to the search request At least one target attribute feature of industry knowledge; based on the search request and auxiliary information, obtain the search results corresponding to the search request.

根据本申请实施例的另一方面,还提供了另一种搜索处理方法,包括:获取搜索请求;采用差分搜索索引模型对搜索请求进行属性特征分析以生成搜索请求关联的辅助信息,以及对搜索请求与辅助信息进行行业知识推理以输出目标文档标识,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;基于目标文档标识获取搜索请求对应的搜索结果。According to another aspect of the embodiment of the present application, another search processing method is also provided, including: obtaining a search request; using a differential search index model to perform attribute feature analysis on the search request to generate auxiliary information associated with the search request; and Request industry knowledge reasoning with the auxiliary information to output a target document identifier, where the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request; and obtain the search results corresponding to the search request based on the target document identifier.

根据本申请实施例的另一方面,还提供了又一种搜索处理方法,包括:获取电商服务搜索请求;对电商服务搜索请求进行电商属性特征分析,生成电商服务搜索请求关联的电商服务辅助信息,其中,电商服务辅助信息为电商服务搜索请求对应的电商服务行业知识的目标电商属性特征组合;基于电商服务搜索请求与电商服务辅助信息,获取电商服务搜索请求对应的电商服务搜索结果。According to another aspect of the embodiment of the present application, another search processing method is also provided, including: obtaining an e-commerce service search request; performing e-commerce attribute feature analysis on the e-commerce service search request, and generating an e-commerce service search request associated with the e-commerce service search request. E-commerce service auxiliary information, where the e-commerce service auxiliary information is the target e-commerce attribute feature combination of the e-commerce service industry knowledge corresponding to the e-commerce service search request; based on the e-commerce service search request and the e-commerce service auxiliary information, the e-commerce service auxiliary information is obtained E-commerce service search results corresponding to the service search request.

根据本申请实施例的另一方面,还提供了一种电子设备,包括:存储器,存储有可执行程序;处理器,用于运行程序,其中,程序运行时执行任意一项上述的搜索处理方法。According to another aspect of the embodiment of the present application, an electronic device is also provided, including: a memory storing an executable program; a processor configured to run the program, wherein any one of the above search processing methods is executed when the program is running. .

根据本申请实施例的另一方面,还提供了一种计算机可读存储介质,上述计算机可读存储介质包括存储的程序,其中,在上述程序运行时控制上述计算机可读存储介质所在设备执行任意一项上述的搜索处理方法。According to another aspect of the embodiment of the present application, a computer-readable storage medium is also provided. The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any arbitrary One of the above search processing methods.

在本申请实施例中,首先获取搜索请求,通过对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,进一步基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。上述过程中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征,由此,本申请提供的搜索处理方法通过挖掘搜索请求的属性特征,将与预设行业知识相对应的属性特征引入搜索处理中,达到了针对预设行业知识进行搜索处理的目的,从而实现了提升针对特定行业知识进行搜索的精确性的技术效果,进而解决了相关技术中难以将行业知识注入搜索处理过程导致搜索精确性较低的技术问题。In the embodiment of the present application, the search request is first obtained, and auxiliary information associated with the search request is generated by analyzing the attribute characteristics of the search request. Further, based on the search request and the auxiliary information, the search results corresponding to the search request are obtained. In the above process, the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request. Therefore, the search processing method provided by this application mines the attribute features of the search request and combines the attributes corresponding to the preset industry knowledge. Features are introduced into search processing to achieve the purpose of search processing for preset industry knowledge, thereby achieving the technical effect of improving the accuracy of searching for specific industry knowledge, thereby solving the difficulty in injecting industry knowledge into the search processing process in related technologies. Technical issues resulting in less accurate searches.

容易注意到的是,上面的通用描述和后面的详细描述仅仅是为了对本申请进行举例和解释,并不构成对本申请的限定。It is easy to notice that the above general description and the following detailed description are only for illustrating and explaining the present application, and do not constitute a limitation of the present application.

附图说明Description of drawings

此处所说明的附图用来提供对本申请的进一步理解,构成本申请的一部分,本申请的示意性实施例及其说明用于解释本申请,并不构成对本申请的不当限定。在附图中:The drawings described here are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the attached picture:

图1是本申请中搜索处理方法的应用场景的示意图;Figure 1 is a schematic diagram of the application scenario of the search processing method in this application;

图2是根据本申请实施例1的一种搜索处理方法的流程图;Figure 2 is a flow chart of a search processing method according to Embodiment 1 of the present application;

图3是根据本申请实施例的一种可选的搜索处理过程的示意图;Figure 3 is a schematic diagram of an optional search processing process according to an embodiment of the present application;

图4是根据本申请实施例2的另一种搜索处理方法的流程图;Figure 4 is a flow chart of another search processing method according to Embodiment 2 of the present application;

图5是根据本申请实施例3的另一种搜索处理方法的流程图;Figure 5 is a flow chart of another search processing method according to Embodiment 3 of the present application;

图6是根据本申请实施例4的一种搜索处理装置的结构示意图;Figure 6 is a schematic structural diagram of a search processing device according to Embodiment 4 of the present application;

图7是根据本申请实施例4的一种可选的搜索处理装置的结构示意图;Figure 7 is a schematic structural diagram of an optional search processing device according to Embodiment 4 of the present application;

图8是根据本申请实施例4的另一种搜索处理装置的结构示意图;Figure 8 is a schematic structural diagram of another search processing device according to Embodiment 4 of the present application;

图9是根据本申请实施例4的又一种搜索处理装置的结构示意图;Figure 9 is a schematic structural diagram of yet another search processing device according to Embodiment 4 of the present application;

图10是根据本申请实施例5的一种计算机终端的结构框图。Figure 10 is a structural block diagram of a computer terminal according to Embodiment 5 of the present application.

具体实施方式Detailed ways

为了使本技术领域的人员更好地理解本申请方案,下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本申请一部分的实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都应当属于本申请保护的范围。In order to enable those in the technical field to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only These are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts should fall within the scope of protection of this application.

需要说明的是,本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象,而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换,以便这里描述的本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外,术语“包括”和“具有”以及他们的任何变形,意图在于覆盖不排他的包含,例如,包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元,而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。It should be noted that the terms "first", "second", etc. in the description and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the application described herein can be practiced in sequences other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

本申请提供的技术方案主要采用大模型技术实现,此处的大模型是指具有大规模模型参数的深度学习模型,通常可以包含上亿、上百亿、上千亿、上万亿甚至十万亿以上的模型参数。大模型又可以称为基石模型/基础模型(Foundation Model),通过大规模无标注的语料进行大模型的预训练,产出亿级以上参数的预训练模型,这种模型能适应广泛的下游任务,模型具有较好的泛化能力,例如大规模语言模型(Large Language Model,LLM)、多模态预训练模型(multi-modal pre-training model)等。The technical solution provided by this application is mainly implemented using large model technology. The large model here refers to a deep learning model with large-scale model parameters, which can usually include hundreds of millions, tens of billions, hundreds of billions, trillions or even hundreds of thousands. More than 100 million model parameters. The large model can also be called the cornerstone model/foundation model. It is pre-trained through large-scale unlabeled corpus to produce a pre-trained model with more than 100 million parameters. This model can adapt to a wide range of downstream tasks. , the model has good generalization ability, such as large-scale language model (Large Language Model, LLM), multi-modal pre-training model (multi-modal pre-training model), etc.

需要说明的是,大模型在实际应用时,可以通过少量样本对预训练模型进行微调,使得大模型可以应用于不同的任务中。例如,大模型可以广泛应用于自然语言处理(Natural Language Processing,NLP)、计算机视觉等领域,具体可以应用于如视觉问答(Visual Question Answering,VQA)、图像描述(Image Caption,IC)、图像生成等计算机视觉领域任务,也可以广泛应用于基于文本的情感分类、文本摘要生成、机器翻译等自然语言处理领域任务。因此,大模型主要的应用场景包括但不限于数字助理、智能机器人、搜索、在线教育、办公软件、电子商务、智能设计等。It should be noted that when a large model is actually used, the pre-trained model can be fine-tuned with a small number of samples, so that the large model can be applied to different tasks. For example, large models can be widely used in fields such as Natural Language Processing (NLP) and computer vision. Specifically, they can be used in areas such as Visual Question Answering (VQA), Image Caption (IC), and image generation. It can also be widely used in natural language processing tasks such as text-based sentiment classification, text summary generation, and machine translation. Therefore, the main application scenarios of large models include but are not limited to digital assistants, intelligent robots, search, online education, office software, e-commerce, intelligent design, etc.

首先,在对本申请实施例进行描述的过程中出现的部分名词或术语适用于如下解释。First, some nouns or terms that appear in the description of the embodiments of this application are applicable to the following explanations.

推理过程:模型在实际场景中经过数据输入产出对应输出的过程。Inference process: The process in which the model produces corresponding output through data input in actual scenarios.

大语言模型(LLM):是指具备一定程度的通用能力的人工智能模型。Large language model (LLM): refers to an artificial intelligence model with a certain degree of general capabilities.

差分搜索索引(Differential Search Index,DSI):是指使用大模型替代传统搜索引擎的一种搜索系统。Differential Search Index (DSI): refers to a search system that uses large models to replace traditional search engines.

命名实体识别(Named Entity Recognition,NER):是指一种用于从文本中提取命名实体的技术。Named Entity Recognition (NER): refers to a technology used to extract named entities from text.

文本片段(prompt):作为大模型的输入初始文本,用于引导大模型生成特定内容。Text fragment (prompt): used as the input initial text of the large model, used to guide the large model to generate specific content.

特征(feature):是指对查询(query)进行属性打标后得到的属性信息。Feature: refers to the attribute information obtained after attribute marking of the query.

实施例1Example 1

根据本申请实施例,提供了一种搜索处理方法,需要说明的是,在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行,并且,虽然在流程图中示出了逻辑顺序,但是在某些情况下,可以以不同于此处的顺序执行所示出或描述的步骤。According to an embodiment of the present application, a search processing method is provided. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the steps in the flow chart A logical order is shown, but in some cases the steps shown or described may be performed in a different order than herein.

考虑到大模型的模型参数量庞大,且移动终端的运算资源有限,本申请实施例提供的上述搜索处理方法可以应用于如图1所示的应用场景,但不仅限于此。在如图1所示的应用场景中,大模型部署在服务器10中,服务器10可以通过局域网连接、广域网连接、因特网连接,或者其他类型的数据网络,连接一个或多个客户端设备20,此处的客户端设备20可以包括但不限于:智能手机、平板电脑、笔记本电脑、掌上电脑、个人计算机、智能家居设备、车载设备等。客户端设备20可以通过图形用户界面与用户进行交互,实现对大模型的调用,进而实现本申请实施例所提供的方法。Considering that the large model has a large amount of model parameters and the computing resources of the mobile terminal are limited, the above search processing method provided by the embodiment of the present application can be applied to the application scenario as shown in Figure 1, but is not limited to this. In the application scenario shown in Figure 1, the large model is deployed in the server 10. The server 10 can connect one or more client devices 20 through a local area network connection, a wide area network connection, an Internet connection, or other types of data networks. This The client device 20 may include but is not limited to: smart phones, tablet computers, notebook computers, PDAs, personal computers, smart home devices, vehicle-mounted devices, etc. The client device 20 can interact with the user through a graphical user interface to call the large model, thereby implementing the method provided by the embodiment of the present application.

在上述运行环境下,本申请提供了如图2所示的搜索处理方法。图2是根据本申请实施例1的一种搜索处理方法的流程图,如图2所示,该搜索处理方法包括:Under the above operating environment, this application provides a search processing method as shown in Figure 2. Figure 2 is a flow chart of a search processing method according to Embodiment 1 of the present application. As shown in Figure 2, the search processing method includes:

步骤S21,获取搜索请求;Step S21, obtain the search request;

步骤S22,对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;Step S22, perform attribute feature analysis on the search request, and generate auxiliary information associated with the search request, where the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request;

步骤S23,基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。Step S23: Obtain the search results corresponding to the search request based on the search request and the auxiliary information.

上述搜索请求可以是目标应用场景中的查询(query)请求。上述目标应用场景可以但不限于是:电商、教育、医疗、会议、社交网络、金融产品、物流和导航等领域中涉及基于LLM进行搜索的场景。相对应地,预设行业知识为上述目标应用场景对应的行业知识。The above search request may be a query request in the target application scenario. The above target application scenarios can be, but are not limited to: scenarios involving LLM-based search in fields such as e-commerce, education, medical care, conferences, social networks, financial products, logistics and navigation. Correspondingly, the preset industry knowledge is the industry knowledge corresponding to the above target application scenario.

上述辅助信息由预设行业知识和至少一个目标属性特征得到,至少一个目标属性特征包括对搜索请求进行属性特征挖掘得到的多个维度上的属性特征。上述搜索请求和辅助信息的结合作为搜索模型(可以是LLM)的输入,由搜索模型的输出获取上述搜索结果。可见,上述针对搜索请求的搜索处理过程中考虑到的预设行业知识和搜索请求对应的多种属性特征,因此,上述搜索处理过程能够在预设行业的应用场景内具备良好的智能搜索性能,也即,能够得到精确的搜索结果,满足特定行业的个性化搜索需求。The above auxiliary information is obtained from preset industry knowledge and at least one target attribute feature. The at least one target attribute feature includes attribute features in multiple dimensions obtained by performing attribute feature mining on the search request. The combination of the above search request and the auxiliary information serves as the input of the search model (which may be LLM), and the above search results are obtained from the output of the search model. It can be seen that the above-mentioned search processing process for the search request takes into account the preset industry knowledge and the multiple attribute characteristics corresponding to the search request. Therefore, the above-mentioned search processing process can have good intelligent search performance in the application scenario of the preset industry. That is to say, accurate search results can be obtained to meet the personalized search needs of specific industries.

在本申请实施例中,首先获取搜索请求,通过对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,进一步基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。上述过程中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征,由此,本申请提供的搜索处理方法通过挖掘搜索请求的属性特征,将与预设行业知识相对应的属性特征引入搜索处理中,达到了针对预设行业知识进行搜索处理的目的,从而实现了提升针对特定行业知识进行搜索的精确性的技术效果,进而解决了相关技术中难以将行业知识注入搜索处理过程导致搜索精确性较低的技术问题。In the embodiment of the present application, the search request is first obtained, and auxiliary information associated with the search request is generated by analyzing the attribute characteristics of the search request. Further, based on the search request and the auxiliary information, the search results corresponding to the search request are obtained. In the above process, the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request. Therefore, the search processing method provided by this application mines the attribute features of the search request and combines the attributes corresponding to the preset industry knowledge. Features are introduced into search processing to achieve the purpose of search processing for preset industry knowledge, thereby achieving the technical effect of improving the accuracy of searching for specific industry knowledge, thereby solving the difficulty in injecting industry knowledge into the search processing process in related technologies. Technical issues resulting in less accurate searches.

在本申请实施例中,客户端设备和服务器构成的系统可以执行如下步骤:客户端设备执行向服务器发送搜索请求,服务器执行搜索处理方法对应的步骤,对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征,基于搜索请求与辅助信息,获取搜索请求对应的搜索结果,并将搜索结果返回至客户端(或者为客户端提供搜索处理功能的使用接口)。In the embodiment of this application, a system composed of a client device and a server can perform the following steps: the client device sends a search request to the server, the server performs steps corresponding to the search processing method, analyzes the attribute characteristics of the search request, and generates a search request. Associated auxiliary information, where the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request, based on the search request and the auxiliary information, obtain the search results corresponding to the search request, and return the search results to the client (or Provide the client with an interface for search processing functions).

需要说明的是,在客户端设备的运行资源能够满足大模型的部署和运行条件的情况下,本申请实施例可以在客户端设备中进行。It should be noted that, if the operating resources of the client device can meet the deployment and operating conditions of the large model, the embodiments of the present application can be performed on the client device.

在上述目标应用场景的一种具体实现方式中,根据本申请实施例提供的搜索处理方法,将目标应用场景对应的行业知识注入DSI的中间推理过程,以提升搜索结果的准确性以及提升搜索结果与对应行业的相关性。在上述具体实现方式中,搜索处理过程可以如图3所示,包括五个阶段:行业知识属性提取阶段、训练小模型阶段、特征选择阶段、思维链训练阶段和在线推理阶段。以下以该具体实现方式为例对本申请实施例提供的搜索处理方法进行进一步说明。In a specific implementation of the above target application scenario, according to the search processing method provided by the embodiment of the present application, the industry knowledge corresponding to the target application scenario is injected into the intermediate reasoning process of DSI to improve the accuracy of the search results and improve the search results. Relevance to corresponding industries. In the above specific implementation method, the search processing process can be shown in Figure 3, including five stages: industry knowledge attribute extraction stage, training small model stage, feature selection stage, thinking chain training stage and online reasoning stage. The search processing method provided by the embodiment of the present application will be further described below by taking this specific implementation as an example.

在一种可选的实施例中,在步骤S22中,对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,包括如下方法步骤:In an optional embodiment, in step S22, perform attribute feature analysis on the search request and generate auxiliary information associated with the search request, including the following method steps:

步骤S221,对搜索请求进行行业知识属性提取,得到属性提取结果;Step S221: Extract industry knowledge attributes from the search request to obtain attribute extraction results;

步骤S222,对属性提取结果进行属性标注,得到属性标注结果,其中,属性标注结果用于指示预设行业知识在多个维度上的知识属性;Step S222: Perform attribute annotation on the attribute extraction results to obtain an attribute annotation result, where the attribute annotation result is used to indicate the knowledge attributes of the preset industry knowledge in multiple dimensions;

步骤S223,对属性标注结果进行特征选择,得到辅助信息。Step S223: Perform feature selection on the attribute annotation results to obtain auxiliary information.

上述属性标注结果对应的知识属性至少包括:预设行业知识的地理属性、品牌属性、产品属性等。在上述可选的实施例中,在搜索处理中的行业知识属性提取阶段,对搜索请求进行行业知识属性提取,上述行业知识属性对应的多个维度由场景需求确定。在训练小模型阶段,对所提取的多个维度行业知识属性进行属性标注,得到属性标注结果,属性标注结果中可以包含多组标注数据,每组标注数据包括行业知识属性及其对应的标签。在特征选择阶段,对属性标注结果进行特征选择,所得到的辅助信息为多组标注数据中与搜索请求匹配的目标标注数据,在搜索处理过程中认为该目标标注数据为适合帮助得到搜索结果的行业知识。The knowledge attributes corresponding to the above attribute annotation results at least include: geographical attributes, brand attributes, product attributes, etc. of the preset industry knowledge. In the above optional embodiment, in the industry knowledge attribute extraction stage in the search process, the industry knowledge attributes are extracted from the search request, and the multiple dimensions corresponding to the industry knowledge attributes are determined by the scenario requirements. In the training small model stage, attribute labeling is performed on the extracted multi-dimensional industry knowledge attributes to obtain attribute labeling results. The attribute labeling results can contain multiple groups of labeling data, and each group of labeling data includes industry knowledge attributes and their corresponding labels. In the feature selection stage, feature selection is performed on the attribute annotation results. The obtained auxiliary information is the target annotation data matching the search request in multiple sets of annotation data. During the search processing, the target annotation data is considered to be suitable to help obtain the search results. Industry knowledge.

在一种可选的实施例中,在步骤S221中,对搜索请求进行行业知识属性提取,得到属性提取结果包括以下步骤中的至少之一:In an optional embodiment, in step S221, performing industry knowledge attribute extraction on the search request, and obtaining the attribute extraction result includes at least one of the following steps:

步骤S2211,基于预设行业知识,对搜索请求进行分词处理,得到第一属性提取结果;Step S2211: Based on the preset industry knowledge, perform word segmentation processing on the search request to obtain the first attribute extraction result;

步骤S2212,基于预设行业知识,对搜索请求进行命名实体识别,得到第二属性提取结果;Step S2212: Based on the preset industry knowledge, perform named entity recognition on the search request to obtain the second attribute extraction result;

步骤S2213,基于预设行业知识,对第一属性提取结果进行同义词扩展,得到第三属性提取结果;Step S2213: Based on the preset industry knowledge, perform synonym expansion on the first attribute extraction result to obtain the third attribute extraction result;

步骤S2214,基于预设行业知识,对搜索请求进行关键词提取,得到第四属性提取结果;Step S2214: Extract keywords from the search request based on the preset industry knowledge to obtain the fourth attribute extraction result;

步骤S2215,基于预设行业知识,对搜索请求进行表述形式改写,得到第五属性提取结果。Step S2215: Based on the preset industry knowledge, the expression form of the search request is rewritten to obtain the fifth attribute extraction result.

作为一种示例性的实施例,从预设行业知识(如电商知识、计算机技术知识等)中提取有助于实现搜索请求(如多个query)与目标答案文本之间链接的如下多种属性。As an exemplary embodiment, the following multiple types of information are extracted from preset industry knowledge (such as e-commerce knowledge, computer technology knowledge, etc.) that help to realize the link between the search request (such as multiple queries) and the target answer text. Attributes.

第一种,分词信息。将搜索请求按照语义划分为多个词语,得到分词信息。例如,针对查询语句中包含的“在南京市长江大桥上”,进行语义划分后得到的分词信息为:["在","南京","市","长江大桥","上"]。The first type is word segmentation information. Divide the search request into multiple words according to semantics to obtain word segmentation information. For example, for "on the Nanjing Yangtze River Bridge" contained in the query statement, the word segmentation information obtained after semantic division is: ["on", "Nanjing", "city", "Yangtze River Bridge", "on"].

第二种,命名实体识别(NER)信息。对搜索请求中包含的行业知识实体进行识别,得到NER信息。例如,针对查询语句中包含的“苹果手机壳”,进行行业知识实体识别得到的NER信息包括:["苹果":"品牌","手机壳":"产品"]。The second type is Named Entity Recognition (NER) information. Identify the industry knowledge entities included in the search request and obtain NER information. For example, for "Apple mobile phone case" included in the query statement, the NER information obtained by industry knowledge entity recognition includes: ["Apple":"brand", "mobile phone case":"product"].

第三种,同义词信息。对分词信息进行同义词扩展处理,得到搜索请求对应的同义词信息,以增加搜索的覆盖范围和准确性。The third type is synonym information. Perform synonym expansion processing on the word segmentation information to obtain synonym information corresponding to the search request, so as to increase the coverage and accuracy of the search.

第四种,关键词(keyword)信息。对搜索请求(如查询语句)中的关键词进行标识,以便于后续使用关键词。The fourth type is keyword information. Identify keywords in search requests (such as query statements) to facilitate subsequent use of the keywords.

第五种,改写信息。对搜索请求进行表述形式改写,得到改写信息,该改写信息包括同一个搜索请求对应的多种表述语句。The fifth type is to rewrite the information. Rewrite the expression form of the search request to obtain rewritten information, which includes multiple expression sentences corresponding to the same search request.

通过上述步骤S221、步骤S2211至步骤S2215,本申请实施例实现了在多个维度上对搜索请求的行业知识属性提取,提升了搜索处理过程中对搜索请求的机器理解,进而有助于提高搜索结果的准确性和相关性。Through the above-mentioned step S221, step S2211 to step S2215, the embodiment of the present application realizes the extraction of industry knowledge attributes of the search request in multiple dimensions, improves the machine understanding of the search request during the search processing process, and thereby helps to improve the search process. Accuracy and relevance of results.

在一种可选的实施例中,在步骤S222中,对属性提取结果进行属性标注,得到属性标注结果,包括如下方法步骤:In an optional embodiment, in step S222, performing attribute annotation on the attribute extraction result to obtain an attribute annotation result includes the following method steps:

步骤S2221,采用预设行业知识对应的标注模型,对属性提取结果进行属性标注,得到属性标注结果,其中,属性标注结果包括:多个属性特征组合。Step S2221: Use the annotation model corresponding to the preset industry knowledge to perform attribute annotation on the attribute extraction results to obtain an attribute annotation result, where the attribute annotation result includes: multiple attribute feature combinations.

作为一种示例性的实施例,上述预设行业知识对应的标注模型可以是行业定制的预训练小模型,例如,指定行业的分词模型、NER模型等。在搜索处理中的训练小模型阶段,采用上述预训练小模型对属性提取结果进行属性打标,得到多个属性特征组合。例如,采用指定行业的分词模型对该指定行业对应的搜索请求的分词结果进行属性打标,得到分词结果中n个词语对应的n组特征{f1,f2,...,fn},其中,每组特征可以包含至少一个特征,例如第i个词语对应的一组fi中包含多个特征。As an exemplary embodiment, the annotation model corresponding to the above-mentioned preset industry knowledge may be an industry-customized pre-trained small model, for example, a word segmentation model, a NER model for a specified industry, etc. In the training small model stage in the search process, the above-mentioned pre-trained small model is used to perform attribute marking on the attribute extraction results, and multiple attribute feature combinations are obtained. For example, the word segmentation model of a specified industry is used to attribute attribute mark the word segmentation results of search requests corresponding to the specified industry, and n sets of features {f1, f2,...,fn} corresponding to n words in the word segmentation results are obtained, where, Each set of features can contain at least one feature. For example, a set of fi corresponding to the i-th word contains multiple features.

在一种可选的实施例中,在步骤S223中,对属性标注结果进行特征选择,得到辅助信息,包括如下方法步骤:In an optional embodiment, in step S223, feature selection is performed on the attribute annotation results to obtain auxiliary information, including the following method steps:

步骤S2231,从属性标注结果中选取多个候选特征组合;Step S2231, select multiple candidate feature combinations from the attribute annotation results;

步骤S2232,计算多个候选特征组合对于预设结果的信息增益;Step S2232, calculate the information gain of multiple candidate feature combinations for the preset result;

步骤S2233,基于信息增益,从多个候选特征组合中选择至少一个目标属性特征,得到辅助信息。Step S2233: Based on the information gain, select at least one target attribute feature from multiple candidate feature combinations to obtain auxiliary information.

作为一种示例性的实施例,在搜索处理中的特征选择阶段,针对搜索请求(如多个query),从对应的属性标注结果中选取多个候选特征组合,例如,随机抽样得到m个特征组合。基于m个特征组合计算得到每个特征组合对预设结果的信息增益,其中,预设结果为搜索请求对应的预期答案。当搜索请求中包含多个query时,上述辅助信息包括每个query对应的至少一个目标属性特征F。As an exemplary embodiment, in the feature selection stage of the search process, for the search request (such as multiple queries), multiple candidate feature combinations are selected from the corresponding attribute annotation results, for example, m features are randomly sampled to obtain combination. Based on m feature combinations, the information gain of each feature combination on the preset result is calculated, where the preset result is the expected answer corresponding to the search request. When the search request contains multiple queries, the above auxiliary information includes at least one target attribute feature F corresponding to each query.

在一种可选的实施例中,在步骤S2232中,计算多个候选特征组合对于预设结果的信息增益,包括如下方法步骤:In an optional embodiment, in step S2232, calculating the information gain of multiple candidate feature combinations for the preset result includes the following method steps:

步骤S321,获取基于搜索请求与辅助信息预测得到预设结果的第一概率,以及获取基于搜索请求预测得到预设结果的第二概率;Step S321, obtain the first probability of predicting the preset result based on the search request and the auxiliary information, and obtain the second probability of predicting the preset result based on the search request;

步骤S322,利用第一概率与第二概率计算得到信息增益。Step S322: Calculate the information gain using the first probability and the second probability.

作为一种示例性的实施例,计算m个特征组合对预设结果的信息增益时,将m个特征组合记为F1至Fm,对于其中任一个特征组合Fi,计算第一概率P(doc|query,Fi)和第二概率P(doc|query),该第一概率为在有辅助信息的情况下响应搜索请求得到预设结果的概率,该第二概率为没有辅助信息的情况下响应搜索请求得到预设结果的概率。对任一个特征组合Fi,计算第一概率和第二概率的差值作为对应的信息增益Hi,也即,Hi=P(doc|query,Fi)-P(doc|query)。As an exemplary embodiment, when calculating the information gain of m feature combinations on the preset result, the m feature combinations are recorded as F1 to Fm, and for any one of the feature combinations Fi, the first probability P(doc| query, Fi) and the second probability P(doc|query). The first probability is the probability of obtaining the preset result in response to the search request with auxiliary information. The second probability is the probability of responding to the search without auxiliary information. Probability of requesting a preset result. For any feature combination Fi, the difference between the first probability and the second probability is calculated as the corresponding information gain Hi, that is, Hi=P(doc|query, Fi)-P(doc|query).

在一种可选的实施例中,在步骤S2233中,基于信息增益,从多个候选特征组合中选择至少一个目标属性特征,包括如下方法步骤:In an optional embodiment, in step S2233, selecting at least one target attribute feature from multiple candidate feature combinations based on information gain includes the following method steps:

步骤S331,从多个候选特征组合中选择信息增益最大的候选特征组合,得到至少一个目标属性特征。Step S331: Select the candidate feature combination with the largest information gain from multiple candidate feature combinations to obtain at least one target attribute feature.

作为一种示例性的实施例,基于m个特征组合对应的信息增益H1至Hm,从中选取信息增益最大值对应的特征组合作为至少一个目标属性特征F。例如,采用maxarg()函数,将maxarg(Hi)对应的特征组合选作F。As an exemplary embodiment, based on the information gains H1 to Hm corresponding to m feature combinations, the feature combination corresponding to the maximum information gain is selected as at least one target attribute feature F. For example, use the maxarg() function and select the feature combination corresponding to maxarg(Hi) as F.

在一种可选的实施例中,在步骤S23中,基于搜索请求与辅助信息,获取搜索请求对应的搜索结果,包括如下方法步骤:In an optional embodiment, in step S23, obtaining the search results corresponding to the search request based on the search request and the auxiliary information includes the following method steps:

步骤S231,采用差分搜索索引模型对搜索请求与辅助信息进行行业知识推理,得到目标文档标识,其中,差分搜索索引模型利用多组数据通过机器学习训练得到,多组数据为第一训练数据与第二训练数据的混合数据,第一训练数据包括:样本提示、样本文档标识,第二训练数据包括:样本提示、样本提示对应的至少一个目标属性特征和样本文档标识;Step S231: Use a differential search index model to perform industry knowledge inference on the search request and auxiliary information to obtain the target document identifier. The differential search index model is obtained through machine learning training using multiple sets of data. The multiple sets of data are the first training data and the second training data. Mixed data of two training data, the first training data includes: sample prompts and sample document identifiers, the second training data includes: sample prompts, at least one target attribute feature corresponding to the sample prompts and the sample document identifier;

步骤S232,基于目标文档标识获取搜索请求对应的搜索结果。Step S232: Obtain the search result corresponding to the search request based on the target document identifier.

作为一种示例性的实施例,用于训练差分搜索索引(DSI)模型的训练集包括第一训练数据(prompt,target)和第二训练数据(query,F_target),其中,F_target由query对应的至少一个目标属性特征F与样本文档标识target拼接得到。As an exemplary embodiment, the training set used to train the Differential Search Index (DSI) model includes first training data (prompt, target) and second training data (query, F_target), where F_target is represented by query corresponding to At least one target attribute feature F is obtained by splicing the sample document identifier target.

在搜索处理中的思维链训练阶段,将至少一个目标属性特征F作为思维链来训练DSI模型,这样DSI模型将会学习到行业知识的中间推理过程,使得搜索结果更具行业相关性。In the thinking chain training stage of search processing, at least one target attribute feature F is used as a thinking chain to train the DSI model. In this way, the DSI model will learn the intermediate reasoning process of industry knowledge, making the search results more industry relevant.

在搜索处理中的在线推理阶段,当用户通过训练完成的DSI模型进行搜索时,首先,将用户输入的搜索请求(query_user)作为样本提示prompt,采用DSI模型利用行业知识特征组合对query_user进行分析,得到该query_user对应的至少一个目标属性特征F_user;然后,再将query_user与至少一个目标属性特征F_user的拼接结果作为样本提示prompt,通过DSI模型得到该query_user对应的文档编号,该文档编号对应的文档内容作为query_user对应的搜索结果。至此完成了一次针对query_user的搜索查询过程。In the online reasoning stage of search processing, when a user searches through the trained DSI model, first, the search request (query_user) input by the user is used as a sample prompt, and the DSI model is used to analyze query_user using a combination of industry knowledge features. Obtain at least one target attribute feature F_user corresponding to the query_user; then, use the splicing result of query_user and at least one target attribute feature F_user as a sample prompt, and obtain the document number corresponding to the query_user and the document content corresponding to the document number through the DSI model As the search result corresponding to query_user. This completes a search query process for query_user.

容易注意到的是,通过本申请实施例提供的上述搜索处理方式,将预设行业知识注入DSI模型的中间推理过程,提升了DSI模型搜索结果的准确性和与指定行业的相关性。It is easy to notice that through the above search processing method provided by the embodiment of the present application, preset industry knowledge is injected into the intermediate reasoning process of the DSI model, which improves the accuracy and relevance of the DSI model search results to the specified industry.

在一种可选的实施例中,通过终端设备提供一图形用户界面,图形用户界面所显示的内容至少部分地包含一电商服务搜索对话框,搜索处理方法还包括如下方法步骤:In an optional embodiment, a graphical user interface is provided through the terminal device, and the content displayed by the graphical user interface at least partially includes an e-commerce service search dialog box. The search processing method also includes the following method steps:

步骤S241,响应对电商服务搜索对话框执行的输入操作,确定电商服务搜索请求;Step S241, respond to the input operation performed on the e-commerce service search dialog box and determine the e-commerce service search request;

步骤S242,响应对电商服务搜索对话框执行的发送操作,对电商服务搜索请求进行电商服务属性特征分析以生成电商服务辅助信息,以及基于电商服务搜索请求与电商服务辅助信息获取电商服务搜索结果;Step S242, in response to the sending operation performed on the e-commerce service search dialog box, perform e-commerce service attribute feature analysis on the e-commerce service search request to generate e-commerce service auxiliary information, and based on the e-commerce service search request and e-commerce service auxiliary information Get e-commerce service search results;

步骤S243,在电商服务搜索对话框内显示电商服务搜索结果。Step S243: Display the e-commerce service search results in the e-commerce service search dialog box.

在上述可选的实施例中,上述电商服务搜索对话框可以用于实现预设行业对应的专属电商服务搜索功能,例如预设行业的应用场景下设置的智能问答功能,该智能问答功能不限于文本问答,还可以将上述实施例与语音转文本、文本转语音、视频转文本、文本转视频等技术结合实现语音问答功能、视频问答功能或虚拟现实/增强现实问答功能。In the above optional embodiment, the above-mentioned e-commerce service search dialog box can be used to implement an exclusive e-commerce service search function corresponding to the preset industry, such as an intelligent question and answer function set in the application scenario of the preset industry. The intelligent question and answer function Not limited to text Q&A, the above embodiments can also be combined with speech-to-text, text-to-speech, video-to-text, text-to-video and other technologies to implement voice Q&A function, video Q&A function or virtual reality/augmented reality Q&A function.

当检测到作用于电商服务搜索对话框的输入操作,根据该电商服务搜索对话框内的输入内容确定电商服务搜索请求,该电商服务搜索请求可以包括至少一个查询(query)语句。When an input operation on the e-commerce service search dialog box is detected, an e-commerce service search request is determined based on the input content in the e-commerce service search dialog box. The e-commerce service search request may include at least one query statement.

当检测到作用于电商服务搜索对话框的发送操作,触发搜索事件,对电商服务搜索请求进行电商服务属性特征分析以生成电商服务辅助信息,以及基于电商服务搜索请求与电商服务辅助信息获取电商服务搜索结果,并自动触发在电商服务搜索对话框内显示电商服务搜索结果的事件。将电商服务搜索结果展示给用户的形式可以是:以文本形式或图片形式显示在图形用户界面内,转化成音频通过音频输出设备输出,转化成视频显示在图形用户界面内。When a send operation on the e-commerce service search dialog box is detected, a search event is triggered, the e-commerce service attribute characteristics are analyzed for the e-commerce service search request to generate e-commerce service auxiliary information, and the e-commerce service search request is compared with the e-commerce service based on the e-commerce service search request. The service auxiliary information obtains the e-commerce service search results and automatically triggers an event that displays the e-commerce service search results in the e-commerce service search dialog box. The form of displaying the e-commerce service search results to the user can be: displayed in the graphical user interface in the form of text or pictures, converted into audio and output through the audio output device, converted into video and displayed in the graphical user interface.

需要说明的是,上述输入操作和上述发送操作均可以是用户用手指接触上述终端设备的显示屏并触控该终端设备的操作。该触控操作可以包括单点触控、多点触控,其中,每个触控点的触控操作可以包括点击、长按、重按、划动等。上述输入操作和上述发送操作还可以是通过鼠标、键盘等输入设备实现的触控操作。It should be noted that both the above-mentioned input operation and the above-mentioned sending operation may be operations in which the user touches the display screen of the above-mentioned terminal device with his finger and touches the terminal device. The touch operation may include single-touch, multi-touch, where the touch operation of each touch point may include click, long press, re-press, swipe, etc. The above-mentioned input operation and the above-mentioned sending operation may also be touch operations implemented through input devices such as mouse and keyboard.

通过上述步骤S241至步骤S243,本申请实施例提供的搜索处理方法可以运行于客户端,体现一种可视化的交互方式以实现搜索处理功能,这种可视化的交互方式能够更加直观地将预设行业的问答场景通过客户端提供给用户,以便用户更加便捷地提出电商服务搜索请求并触发电商服务搜索结果的查询,这种方式对用户来说操作友好、便捷,用户体验好。Through the above steps S241 to S243, the search processing method provided by the embodiment of the present application can be run on the client, embodying a visual interaction method to realize the search processing function. This visual interaction method can more intuitively combine the preset industries. The question and answer scenario is provided to users through the client, so that users can more easily make e-commerce service search requests and trigger e-commerce service search result queries. This method is user-friendly, convenient and has a good user experience.

需要说明的是,本申请所涉及的用户信息(包括但不限于用户设备信息、用户个人信息等)和数据(包括但不限于用于分析的数据、存储的数据、展示的数据等),均为经用户授权或者经过各方充分授权的信息和数据,并且相关数据的收集、使用和处理需要遵守相关国家和地区的相关法律法规和标准,并提供有相应的操作入口,供用户选择授权或者拒绝。It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all It is information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with the relevant laws, regulations and standards of relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or reject.

需要说明的是,对于前述的各方法实施例,为了简单描述,故将其都表述为一系列的动作组合,但是本领域技术人员应该知悉,本申请并不受所描述的动作顺序的限制,因为依据本申请,某些步骤可以采用其他顺序或者同时进行。其次,本领域技术人员也应该知悉,说明书中所描述的实施例均属于优选实施例,所涉及的动作和模块并不一定是本申请所必须的。It should be noted that for the sake of simple description, the foregoing method embodiments are expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence. Because in accordance with this application, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily necessary for this application.

通过以上的实施方式的描述,本领域的技术人员可以清楚地了解到根据上述实施例的方法可借助软件加必需的通用硬件平台的方式来实现,当然也可以通过硬件。基于这样的理解,本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质(如只读存储器(Read-OnlyMemory,ROM)、随机存取器(Random Access Memory,RAM)、磁碟、光盘)中,包括若干指令用以使得一台终端设备(可以是手机,计算机,服务器,或者网络设备等)执行本申请各个实施例所述的方法。Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or that contributes to the existing technology. The computer software product is stored in a storage medium (such as a read-only memory (Read-Only Memory) , ROM), random access memory (Random Access Memory, RAM), magnetic disk, optical disk), including several instructions to cause a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute this application Methods described in various embodiments.

实施例2Example 2

在如实施例1中的运行环境下,本申请提供了如图4所示的另一种搜索处理方法。图4是根据本申请实施例2的另一种搜索处理方法的流程图,如图4所示,该搜索处理方法包括:In the operating environment as in Embodiment 1, this application provides another search processing method as shown in Figure 4. Figure 4 is a flow chart of another search processing method according to Embodiment 2 of the present application. As shown in Figure 4, the search processing method includes:

步骤S41,获取搜索请求;Step S41, obtain the search request;

步骤S42,采用差分搜索索引模型对搜索请求进行属性特征分析以生成搜索请求关联的辅助信息,以及对搜索请求与辅助信息进行行业知识推理以输出目标文档标识,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;Step S42: Use the differential search index model to analyze the attribute characteristics of the search request to generate auxiliary information associated with the search request, and perform industry knowledge reasoning on the search request and the auxiliary information to output the target document identification, where the auxiliary information is the search request corresponding Preset at least one target attribute characteristic of industry knowledge;

步骤S43,基于目标文档标识获取搜索请求对应的搜索结果。Step S43: Obtain search results corresponding to the search request based on the target document identifier.

上述搜索请求可以是目标应用场景中的查询(query)请求。上述目标应用场景可以但不限于是:电商、教育、医疗、会议、社交网络、金融产品、物流和导航等领域中涉及基于LLM进行搜索的场景。相对应地,预设行业知识为上述目标应用场景对应的行业知识。The above search request may be a query request in the target application scenario. The above target application scenarios can be, but are not limited to: scenarios involving LLM-based search in fields such as e-commerce, education, medical care, conferences, social networks, financial products, logistics and navigation. Correspondingly, the preset industry knowledge is the industry knowledge corresponding to the above target application scenario.

上述辅助信息由预设行业知识和至少一个目标属性特征得到,目标属性特征包括对搜索请求进行属性特征挖掘得到的多个属性特征。上述搜索请求和辅助信息的结合作为差分搜索索引(DSI)模型的输入,由DSI模型对搜索请求与辅助信息进行行业知识推理以输出目标文档标识,并将该目标文档标识以及对应的文档内容作为上述搜索请求对应的搜索结果。The above auxiliary information is obtained from preset industry knowledge and at least one target attribute feature. The target attribute feature includes multiple attribute features obtained by performing attribute feature mining on the search request. The combination of the above search request and auxiliary information serves as the input of the Differential Search Index (DSI) model. The DSI model performs industry knowledge reasoning on the search request and auxiliary information to output the target document identifier, and uses the target document identifier and the corresponding document content as Search results corresponding to the above search request.

可见,上述针对搜索请求的搜索处理过程中考虑到的预设行业知识和搜索请求对应的多种属性特征,因此,上述搜索处理过程能够在预设行业的应用场景内具备良好的智能搜索性能,也即,能够得到精确的搜索结果,满足特定行业的个性化搜索需求。It can be seen that the above-mentioned search processing process for the search request takes into account the preset industry knowledge and the multiple attribute characteristics corresponding to the search request. Therefore, the above-mentioned search processing process can have good intelligent search performance in the application scenario of the preset industry. That is to say, accurate search results can be obtained to meet the personalized search needs of specific industries.

在本申请实施例中,首先获取搜索请求,通过采用差分搜索索引模型对搜索请求进行属性特征分析以生成搜索请求关联的辅助信息,以及对搜索请求与辅助信息进行行业知识推理以输出目标文档标识,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征,进一步基于目标文档标识获取搜索请求对应的搜索结果。上述过程中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征,由此,本申请提供的搜索处理方法通过挖掘搜索请求的属性特征,将与预设行业知识相对应的属性特征引入搜索处理中,达到了针对预设行业知识进行搜索处理的目的,从而实现了提升针对特定行业知识进行搜索的精确性的技术效果,进而解决了相关技术中难以将行业知识注入搜索处理过程导致搜索精确性较低的技术问题。In the embodiment of this application, the search request is first obtained, the attribute characteristics of the search request are analyzed by using a differential search index model to generate auxiliary information associated with the search request, and industry knowledge reasoning is performed on the search request and the auxiliary information to output the target document identification , wherein the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request, and the search results corresponding to the search request are further obtained based on the target document identification. In the above process, the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request. Therefore, the search processing method provided by this application mines the attribute features of the search request and combines the attributes corresponding to the preset industry knowledge. Features are introduced into search processing to achieve the purpose of search processing for preset industry knowledge, thereby achieving the technical effect of improving the accuracy of searching for specific industry knowledge, thereby solving the difficulty in injecting industry knowledge into the search processing process in related technologies. Technical issues resulting in less accurate searches.

在本申请实施例中,客户端设备和服务器构成的系统可以执行如下步骤:客户端设备执行向服务器发送搜索请求,服务器执行搜索处理方法对应的步骤,采用差分搜索索引模型对搜索请求进行属性特征分析以生成搜索请求关联的辅助信息,以及对搜索请求与辅助信息进行行业知识推理以输出目标文档标识,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征,基于目标文档标识获取搜索请求对应的搜索结果,并将搜索结果返回至客户端(或者为客户端提供搜索处理功能的使用接口)。In this embodiment of the present application, a system composed of a client device and a server can perform the following steps: the client device sends a search request to the server, the server performs steps corresponding to the search processing method, and uses a differential search index model to attribute attributes to the search request. Analyze to generate auxiliary information associated with the search request, and perform industry knowledge reasoning on the search request and the auxiliary information to output a target document identification, where the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request, based on the target document The identifier obtains the search results corresponding to the search request and returns the search results to the client (or provides the client with an interface for the search processing function).

需要说明的是,在客户端设备的运行资源能够满足大模型的部署和运行条件的情况下,本申请实施例可以在客户端设备中进行。It should be noted that, if the operating resources of the client device can meet the deployment and operating conditions of the large model, the embodiments of the present application can be performed on the client device.

在一种可选的实施例中,在步骤S42中,采用差分搜索索引模型对搜索请求与辅助信息进行行业知识推理以输出目标文档标识,包括如下方法步骤:In an optional embodiment, in step S42, a differential search index model is used to perform industry knowledge reasoning on the search request and auxiliary information to output the target document identification, including the following method steps:

步骤S421,通过搜索请求确定初始提示内容,其中,初始提示内容基于预设提示模板进行配置;Step S421, determine the initial prompt content through the search request, where the initial prompt content is configured based on a preset prompt template;

步骤S422,对初始提示内容与辅助信息进行拼接,得到目标提示内容;Step S422, splice the initial prompt content and auxiliary information to obtain the target prompt content;

步骤S423,采用差分搜索索引模型对目标提示模板进行行业知识推理,得到目标文档标识。Step S423: Use the differential search index model to perform industry knowledge inference on the target prompt template to obtain the target document identification.

在应用场景中,通过所获取的搜索请求,确定该搜索请求(query)对应的初始提示内容,(该初始提示内容可以是文本片段prompt),将该初始提示内容与上述辅助信息进行拼接,得到目标提示内容,辅助信息由搜索请求对应的预设行业知识的至少一个目标属性特征(记为F)得到,由此,目标提示内容可以表示为query_F,采用训练完成的DSI模型,基于预设行业知识对该目标提示内容query_F进行行业知识推理,得到目标文档标识,进而得到搜索请求(query)对应的搜索结果。In the application scenario, the initial prompt content corresponding to the search request (query) is determined through the obtained search request (the initial prompt content can be a text fragment prompt), and the initial prompt content is spliced with the above auxiliary information to obtain Target prompt content and auxiliary information are obtained from at least one target attribute feature (marked as F) of the preset industry knowledge corresponding to the search request. Therefore, the target prompt content can be expressed as query_F, using the trained DSI model, based on the preset industry Knowledge performs industry knowledge reasoning on the target prompt content query_F, obtains the target document identifier, and then obtains the search results corresponding to the search request (query).

容易注意到的是,通过本申请实施例提供的上述搜索处理方式,将预设行业知识注入DSI模型的中间推理过程,提升了DSI模型搜索结果的准确性和与指定行业的相关性。It is easy to notice that through the above search processing method provided by the embodiment of the present application, preset industry knowledge is injected into the intermediate reasoning process of the DSI model, which improves the accuracy and relevance of the DSI model search results to the specified industry.

需要说明的是,本实施例的优选实施方式可以参见实施例1中的相关描述,此处不再赘述。It should be noted that for the preferred implementation of this embodiment, reference can be made to the relevant description in Embodiment 1, which will not be described again here.

实施例3Example 3

在如实施例1中的运行环境下,本申请提供了如图5所示的另一种搜索处理方法。图5是根据本申请实施例3的另一种搜索处理方法的流程图,如图5所示,该搜索处理方法包括:In the operating environment as in Embodiment 1, this application provides another search processing method as shown in Figure 5. Figure 5 is a flow chart of another search processing method according to Embodiment 3 of the present application. As shown in Figure 5, the search processing method includes:

步骤S51,获取电商服务搜索请求;Step S51, obtain an e-commerce service search request;

步骤S52,对电商服务搜索请求进行电商属性特征分析,生成电商服务搜索请求关联的电商服务辅助信息,其中,电商服务辅助信息为电商服务搜索请求对应的电商服务行业知识的至少一个目标电商属性特征;Step S52: Perform e-commerce attribute feature analysis on the e-commerce service search request, and generate e-commerce service auxiliary information associated with the e-commerce service search request, where the e-commerce service auxiliary information is the e-commerce service industry knowledge corresponding to the e-commerce service search request. At least one target e-commerce attribute characteristic;

步骤S53,基于电商服务搜索请求与电商服务辅助信息,获取电商服务搜索请求对应的电商服务搜索结果。Step S53: Obtain the e-commerce service search results corresponding to the e-commerce service search request based on the e-commerce service search request and the e-commerce service auxiliary information.

上述电商服务搜索请求可以是电商服务应用场景中的查询(query)请求。相对应地,电商服务行业知识为上述电商服务应用场景对应的行业知识,例如,对话问答知识、用户评论知识等。The above-mentioned e-commerce service search request may be a query request in an e-commerce service application scenario. Correspondingly, the e-commerce service industry knowledge is the industry knowledge corresponding to the above-mentioned e-commerce service application scenarios, such as dialogue question and answer knowledge, user review knowledge, etc.

上述电商服务辅助信息由电商服务行业知识和至少一个目标电商属性特征得到,目标属性特征包括对电商服务搜索请求进行属性特征挖掘得到的多个属性特征。上述电商服务搜索请求和电商服务辅助信息的结合作为搜索模型(可以是LLM)的输入,由搜索模型的输出获取上述电商服务搜索结果。可见,上述针对电商服务搜索请求的搜索处理过程中考虑到的电商服务行业知识和电商服务搜索请求对应的多种属性特征,因此,上述搜索处理过程能够在预设行业的应用场景内具备良好的智能搜索性能,也即,能够得到精确的电商服务搜索结果,满足电商服务行业的个性化搜索需求。The above-mentioned e-commerce service auxiliary information is obtained from e-commerce service industry knowledge and at least one target e-commerce attribute feature. The target attribute feature includes multiple attribute features obtained by performing attribute feature mining on the e-commerce service search request. The combination of the above-mentioned e-commerce service search request and the e-commerce service auxiliary information is used as the input of the search model (which may be LLM), and the above-mentioned e-commerce service search results are obtained from the output of the search model. It can be seen that the above search processing process for the e-commerce service search request takes into account the e-commerce service industry knowledge and the multiple attribute characteristics corresponding to the e-commerce service search request. Therefore, the above search processing process can be used within the application scenario of the preset industry. It has good intelligent search performance, that is, it can obtain accurate e-commerce service search results and meet the personalized search needs of the e-commerce service industry.

在本申请实施例中,首先获取电商服务搜索请求,通过对电商服务搜索请求进行电商属性特征分析,生成电商服务搜索请求关联的电商服务辅助信息,进一步基于电商服务搜索请求与电商服务辅助信息,获取电商服务搜索请求对应的电商服务搜索结果。上述过程中,电商服务辅助信息为电商服务搜索请求对应的电商服务行业知识的至少一个目标电商属性特征,由此,本申请提供的搜索处理方法通过挖掘电商服务搜索请求的属性特征,将与电商服务行业知识相对应的属性特征引入搜索处理中,达到了针对电商服务行业知识进行搜索处理的目的,从而实现了提升针对电商服务行业知识进行搜索的精确性的技术效果,进而解决了相关技术中难以将行业知识注入搜索处理过程导致搜索精确性较低的技术问题。In the embodiment of this application, the e-commerce service search request is first obtained, and the e-commerce service search request is analyzed by the e-commerce attribute characteristics to generate e-commerce service auxiliary information associated with the e-commerce service search request, and further based on the e-commerce service search request and e-commerce service auxiliary information to obtain e-commerce service search results corresponding to the e-commerce service search request. In the above process, the e-commerce service auxiliary information is at least one target e-commerce attribute feature of the e-commerce service industry knowledge corresponding to the e-commerce service search request. Therefore, the search processing method provided by this application mines the attributes of the e-commerce service search request. Features, attribute features corresponding to e-commerce service industry knowledge are introduced into search processing, achieving the purpose of search processing for e-commerce service industry knowledge, thereby realizing a technology that improves the accuracy of searching for e-commerce service industry knowledge effect, thereby solving the technical problem in related technologies that it is difficult to inject industry knowledge into the search processing process, resulting in low search accuracy.

在本申请实施例中,客户端设备和服务器构成的系统可以执行如下步骤:客户端设备执行向服务器发送电商服务搜索请求,服务器执行搜索处理方法对应的步骤,对电商服务搜索请求进行电商属性特征分析,生成电商服务搜索请求关联的电商服务辅助信息,其中,电商服务辅助信息为电商服务搜索请求对应的电商服务行业知识的至少一个目标电商属性特征,基于电商服务搜索请求与电商服务辅助信息,获取电商服务搜索请求对应的电商服务搜索结果,并将电商服务搜索结果返回至客户端(或者为客户端提供搜索处理功能的使用接口)。In the embodiment of this application, the system composed of a client device and a server can perform the following steps: the client device sends an e-commerce service search request to the server, the server executes the steps corresponding to the search processing method, and electronically processes the e-commerce service search request. Analysis of business attribute characteristics to generate e-commerce service auxiliary information associated with the e-commerce service search request, where the e-commerce service auxiliary information is at least one target e-commerce attribute feature of the e-commerce service industry knowledge corresponding to the e-commerce service search request, based on the e-commerce service search request The e-commerce service search request and the e-commerce service auxiliary information are obtained, the e-commerce service search results corresponding to the e-commerce service search request are obtained, and the e-commerce service search results are returned to the client (or a user interface for the search processing function is provided for the client).

需要说明的是,在客户端设备的运行资源能够满足大模型的部署和运行条件的情况下,本申请实施例可以在客户端设备中进行。It should be noted that, if the operating resources of the client device can meet the deployment and operating conditions of the large model, the embodiments of the present application can be performed on the client device.

此外,除了电商服务场景外,实施例1提供的搜索处理方法还可以扩展至法律问答场景、医疗服务场景、教育问答场景等。In addition, in addition to e-commerce service scenarios, the search processing method provided in Embodiment 1 can also be extended to legal question and answer scenarios, medical service scenarios, educational question and answer scenarios, etc.

在法律问答场景中,搜索处理方法包括:获取法律问答搜索请求;对法律问答搜索请求进行电商属性特征分析,生成法律问答搜索请求关联的法律问答辅助信息,其中,法律问答辅助信息为法律问答搜索请求对应的法律问答行业知识的至少一个目标电商属性特征;基于法律问答搜索请求与法律问答辅助信息,获取法律问答搜索请求对应的法律问答搜索结果。In the legal question and answer scenario, the search processing method includes: obtaining a legal question and answer search request; performing an e-commerce attribute feature analysis on the legal question and answer search request, and generating legal question and answer auxiliary information associated with the legal question and answer search request, where the legal question and answer auxiliary information is legal question and answer At least one target e-commerce attribute characteristic of the legal question and answer industry knowledge corresponding to the search request; based on the legal question and answer search request and the legal question and answer auxiliary information, obtain the legal question and answer search results corresponding to the legal question and answer search request.

在医疗服务场景中,搜索处理方法包括:获取医疗服务搜索请求;对医疗服务搜索请求进行电商属性特征分析,生成医疗服务搜索请求关联的医疗服务辅助信息,其中,医疗服务辅助信息为医疗服务搜索请求对应的医疗服务行业知识的至少一个目标电商属性特征;基于医疗服务搜索请求与医疗服务辅助信息,获取医疗服务搜索请求对应的医疗服务搜索结果。In the medical service scenario, the search processing method includes: obtaining a medical service search request; analyzing the e-commerce attribute characteristics of the medical service search request, and generating medical service auxiliary information associated with the medical service search request, where the medical service auxiliary information is medical service At least one target e-commerce attribute characteristic of the medical service industry knowledge corresponding to the search request; and based on the medical service search request and the medical service auxiliary information, obtain the medical service search results corresponding to the medical service search request.

在教育问答场景中,搜索处理方法包括:获取教育问答搜索请求;对教育问答搜索请求进行电商属性特征分析,生成教育问答搜索请求关联的教育问答辅助信息,其中,教育问答辅助信息为教育问答搜索请求对应的教育问答行业知识的至少一个目标电商属性特征;基于教育问答搜索请求与教育问答辅助信息,获取教育问答搜索请求对应的教育问答搜索结果。In the educational Q&A scenario, the search processing method includes: obtaining an educational Q&A search request; performing an e-commerce attribute feature analysis on the educational Q&A search request, and generating educational Q&A auxiliary information associated with the educational Q&A search request, where the educational Q&A auxiliary information is educational Q&A At least one target e-commerce attribute characteristic of the education Q&A industry knowledge corresponding to the search request; based on the education Q&A search request and the education Q&A auxiliary information, obtain the education Q&A search results corresponding to the education Q&A search request.

需要说明的是,本实施例的优选实施方式可以参见实施例1中的相关描述,此处不再赘述。It should be noted that for the preferred implementation of this embodiment, reference can be made to the relevant description in Embodiment 1, which will not be described again here.

实施例4Example 4

根据本申请实施例,还提供了一种用于实施上述搜索处理方法的装置实施例。图6是根据本申请实施例4的一种搜索处理装置的结构示意图,如图6所示,该装置包括:According to an embodiment of the present application, a device embodiment for implementing the above search processing method is also provided. Figure 6 is a schematic structural diagram of a search processing device according to Embodiment 4 of the present application. As shown in Figure 6, the device includes:

获取模块601,用于获取搜索请求;Obtain module 601, used to obtain search requests;

生成模块602,用于对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;The generation module 602 is configured to perform attribute feature analysis on the search request and generate auxiliary information associated with the search request, where the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request;

处理模块603,用于基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。The processing module 603 is used to obtain search results corresponding to the search request based on the search request and auxiliary information.

可选地,上述生成模块602还用于:对搜索请求进行行业知识属性提取,得到属性提取结果;对属性提取结果进行属性标注,得到属性标注结果,其中,属性标注结果用于指示预设行业知识在多个维度上的知识属性;对属性标注结果进行特征选择,得到辅助信息。Optionally, the above-mentioned generation module 602 is also used to: extract industry knowledge attributes from the search request to obtain attribute extraction results; perform attribute annotation on the attribute extraction results to obtain attribute annotation results, where the attribute annotation results are used to indicate the preset industry. Knowledge attributes of knowledge in multiple dimensions; feature selection is performed on the attribute annotation results to obtain auxiliary information.

可选地,上述生成模块602还用于:基于预设行业知识,对搜索请求进行分词处理,得到第一属性提取结果;基于预设行业知识,对搜索请求进行命名实体识别,得到第二属性提取结果;基于预设行业知识,对第一属性提取结果进行同义词扩展,得到第三属性提取结果;基于预设行业知识,对搜索请求进行关键词提取,得到第四属性提取结果;基于预设行业知识,对搜索请求进行表述形式改写,得到第五属性提取结果。Optionally, the above-mentioned generation module 602 is also used to: perform word segmentation processing on the search request based on the preset industry knowledge to obtain the first attribute extraction result; perform named entity recognition on the search request based on the preset industry knowledge to obtain the second attribute Extraction results; based on the preset industry knowledge, perform synonym expansion on the first attribute extraction result to obtain the third attribute extraction result; based on the preset industry knowledge, perform keyword extraction on the search request to obtain the fourth attribute extraction result; based on the preset Industry knowledge is used to rewrite the search request to obtain the fifth attribute extraction result.

可选地,上述生成模块602还用于:采用预设行业知识对应的标注模型,对属性提取结果进行属性标注,得到属性标注结果,其中,属性标注结果包括:多个属性特征组合。Optionally, the above-mentioned generation module 602 is also configured to use an annotation model corresponding to preset industry knowledge to perform attribute annotation on the attribute extraction results to obtain an attribute annotation result, where the attribute annotation result includes: multiple attribute feature combinations.

可选地,上述生成模块602还用于:从属性标注结果中选取多个候选特征组合;计算多个候选特征组合对于预设结果的信息增益;基于信息增益,从多个候选特征组合中选择至少一个目标属性特征,得到辅助信息。Optionally, the above-mentioned generation module 602 is also used to: select multiple candidate feature combinations from the attribute annotation results; calculate the information gain of the multiple candidate feature combinations for the preset result; and select from the multiple candidate feature combinations based on the information gain. At least one target attribute feature is used to obtain auxiliary information.

可选地,上述生成模块602还用于:获取基于搜索请求与辅助信息预测得到预设结果的第一概率,以及获取基于搜索请求预测得到预设结果的第二概率;利用第一概率与第二概率计算得到信息增益。Optionally, the above-mentioned generation module 602 is also configured to: obtain the first probability of predicting the preset result based on the search request and the auxiliary information, and obtain the second probability of predicting the preset result based on the search request; using the first probability and the third probability The information gain is obtained by calculating the second probability.

可选地,上述生成模块602还用于:从多个候选特征组合中选择信息增益最大的候选特征组合,得到至少一个目标属性特征。Optionally, the above-mentioned generation module 602 is also used to select a candidate feature combination with the largest information gain from multiple candidate feature combinations to obtain at least one target attribute feature.

可选地,上述处理模块603还用于:采用差分搜索索引模型对搜索请求与辅助信息进行行业知识推理,得到目标文档标识,其中,差分搜索索引模型利用多组数据通过机器学习训练得到,多组数据为第一训练数据与第二训练数据的混合数据,第一训练数据包括:样本提示、样本文档标识,第二训练数据包括:样本提示、样本提示对应的至少一个目标属性特征和样本文档标识;基于目标文档标识获取搜索请求对应的搜索结果。Optionally, the above processing module 603 is also used to: use a differential search index model to perform industry knowledge reasoning on the search request and auxiliary information to obtain the target document identification, wherein the differential search index model is obtained through machine learning training using multiple sets of data. The set of data is a mixture of first training data and second training data. The first training data includes: sample prompts and sample document identifiers. The second training data includes: sample prompts, at least one target attribute feature corresponding to the sample prompts, and the sample document. Identification; obtain the search results corresponding to the search request based on the target document identification.

可选地,图7是根据本申请实施例4的一种可选的搜索处理装置的结构示意图,如图7所示,该装置除包括图6所示的所有模块外,还包括:可视化模块604。通过终端设备提供一图形用户界面,图形用户界面所显示的内容至少部分地包含一电商服务搜索对话框,上述可视化模块604用于:响应对电商服务搜索对话框执行的输入操作,确定搜索请求;响应对电商服务搜索对话框执行的发送操作,对搜索请求进行属性特征分析以生成辅助信息,以及基于搜索请求与辅助信息获取搜索结果;在电商服务搜索对话框内显示搜索结果。Optionally, Figure 7 is a schematic structural diagram of an optional search processing device according to Embodiment 4 of the present application. As shown in Figure 7, in addition to all the modules shown in Figure 6, the device also includes: a visualization module 604. A graphical user interface is provided through the terminal device, and the content displayed by the graphical user interface at least partially includes an e-commerce service search dialog box. The above-mentioned visualization module 604 is used to: respond to the input operation performed on the e-commerce service search dialog box, determine the search Request; respond to the send operation performed on the e-commerce service search dialog box, perform attribute feature analysis on the search request to generate auxiliary information, and obtain search results based on the search request and the auxiliary information; display the search results in the e-commerce service search dialog box.

在本申请实施例中,首先通过获取模块,获取搜索请求,通过生成模块,对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,进一步采用处理模块,基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。上述过程中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征,由此,本申请提供的搜索处理方法通过挖掘搜索请求的属性特征,将与预设行业知识相对应的属性特征引入搜索处理中,达到了针对预设行业知识进行搜索处理的目的,从而实现了提升针对特定行业知识进行搜索的精确性的技术效果,进而解决了相关技术中难以将行业知识注入搜索处理过程导致搜索精确性较低的技术问题。In the embodiment of this application, the search request is first obtained through the acquisition module, the attribute characteristics of the search request are analyzed through the generation module, and the auxiliary information associated with the search request is generated. The processing module is further used to obtain the search request based on the search request and the auxiliary information. Request the corresponding search results. In the above process, the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request. Therefore, the search processing method provided by this application mines the attribute features of the search request and combines the attributes corresponding to the preset industry knowledge. Features are introduced into search processing to achieve the purpose of search processing for preset industry knowledge, thereby achieving the technical effect of improving the accuracy of searching for specific industry knowledge, thereby solving the difficulty in injecting industry knowledge into the search processing process in related technologies. Technical issues resulting in less accurate searches.

此处需要说明的是,上述获取模块601、生成模块602和处理模块603对应于实施例1中的步骤S21至步骤S23,三个模块与对应的步骤所实现的实例和应用场景相同,但不限于上述实施例1所公开的内容。需要说明的是,上述模块或单元可以是存储在存储器(例如,存储器104)中并由一个或多个处理器(例如,处理器102a,102b,……,102n)处理的硬件组件或软件组件,上述模块也可以作为装置的一部分可以运行在实施例1提供的计算机终端10中。It should be noted here that the above-mentioned acquisition module 601, generation module 602 and processing module 603 correspond to steps S21 to step S23 in Embodiment 1. The examples and application scenarios implemented by the three modules and the corresponding steps are the same, but they are not It is limited to the content disclosed in the above-mentioned Embodiment 1. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a memory (for example, the memory 104) and processed by one or more processors (for example, the processors 102a, 102b, ..., 102n) , the above module can also be run in the computer terminal 10 provided in Embodiment 1 as part of the device.

根据本申请实施例,还提供了另一种用于实施上述实施例2中的搜索处理方法的装置实施例。图8是根据本申请实施例4的另一种搜索处理装置的结构示意图,如图8所示,该装置包括:According to the embodiment of the present application, another device embodiment for implementing the search processing method in the above-mentioned Embodiment 2 is also provided. Figure 8 is a schematic structural diagram of another search processing device according to Embodiment 4 of the present application. As shown in Figure 8, the device includes:

获取模块801,用于获取搜索请求;Obtain module 801, used to obtain search requests;

生成模块802,用于采用差分搜索索引模型对搜索请求进行属性特征分析以生成搜索请求关联的辅助信息,以及对搜索请求与辅助信息进行行业知识推理以输出目标文档标识,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;The generation module 802 is used to perform attribute feature analysis on the search request using a differential search index model to generate auxiliary information associated with the search request, and perform industry knowledge reasoning on the search request and the auxiliary information to output a target document identifier, where the auxiliary information is the search request Request at least one target attribute characteristic of the corresponding preset industry knowledge;

处理模块803,用于基于目标文档标识获取搜索请求对应的搜索结果。The processing module 803 is used to obtain the search results corresponding to the search request based on the target document identifier.

可选地,上述生成模块802还用于:通过搜索请求确定初始提示内容,其中,初始提示内容基于预设提示模板进行配置;对初始提示内容与辅助信息进行拼接,得到目标提示模板;采用差分搜索索引模型对目标提示模板进行行业知识推理,得到目标文档标识。Optionally, the above-mentioned generation module 802 is also used to: determine the initial prompt content through a search request, wherein the initial prompt content is configured based on a preset prompt template; splice the initial prompt content and auxiliary information to obtain the target prompt template; use differential The search index model performs industry knowledge reasoning on the target prompt template to obtain the target document identification.

此处需要说明的是,上述获取模块801、生成模块802和处理模块803对应于实施例2中的步骤S41至步骤S43,三个模块与对应的步骤所实现的实例和应用场景相同,但不限于上述实施例2所公开的内容。需要说明的是,上述模块或单元可以是存储在存储器(例如,存储器104)中并由一个或多个处理器(例如,处理器102a,102b,……,102n)处理的硬件组件或软件组件,上述模块也可以作为装置的一部分可以运行在实施例1提供的计算机终端10中。It should be noted here that the above-mentioned acquisition module 801, generation module 802 and processing module 803 correspond to steps S41 to step S43 in Embodiment 2. The examples and application scenarios implemented by the three modules and the corresponding steps are the same, but they are not the same. It is limited to the content disclosed in the above-mentioned Embodiment 2. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a memory (for example, the memory 104) and processed by one or more processors (for example, the processors 102a, 102b, ..., 102n) , the above module can also be run in the computer terminal 10 provided in Embodiment 1 as part of the device.

根据本申请实施例,还提供了另一种用于实施上述实施例3中的搜索处理方法的装置实施例。图9是根据本申请实施例4的又一种搜索处理装置的结构示意图,如图9所示,该装置包括:According to the embodiment of the present application, another device embodiment for implementing the search processing method in the above-mentioned Embodiment 3 is also provided. Figure 9 is a schematic structural diagram of another search processing device according to Embodiment 4 of the present application. As shown in Figure 9, the device includes:

获取模块901,用于获取电商服务搜索请求;Acquisition module 901, used to obtain e-commerce service search requests;

生成模块902,用于对电商服务搜索请求进行电商属性特征分析,生成电商服务搜索请求关联的电商服务辅助信息,其中,电商服务辅助信息为电商服务搜索请求对应的电商服务行业知识的至少一个目标电商属性特征;The generation module 902 is used to perform e-commerce attribute feature analysis on the e-commerce service search request, and generate e-commerce service auxiliary information associated with the e-commerce service search request, where the e-commerce service auxiliary information is the e-commerce corresponding to the e-commerce service search request. At least one target e-commerce attribute characteristic of service industry knowledge;

获取模块903,用于基于电商服务搜索请求与电商服务辅助信息,获取电商服务搜索请求对应的电商服务搜索结果。The acquisition module 903 is configured to obtain the e-commerce service search results corresponding to the e-commerce service search request based on the e-commerce service search request and the e-commerce service auxiliary information.

此处需要说明的是,上述获取模块901、生成模块902和处理模块903对应于实施例3中的步骤S51至步骤S53,三个模块与对应的步骤所实现的实例和应用场景相同,但不限于上述实施例3所公开的内容。需要说明的是,上述模块或单元可以是存储在存储器(例如,存储器104)中并由一个或多个处理器(例如,处理器102a,102b,……,102n)处理的硬件组件或软件组件,上述模块也可以作为装置的一部分可以运行在实施例1提供的计算机终端10中。It should be noted here that the above-mentioned acquisition module 901, generation module 902 and processing module 903 correspond to steps S51 to step S53 in Embodiment 3. The examples and application scenarios implemented by the three modules and the corresponding steps are the same, but they are not It is limited to the content disclosed in the above-mentioned Embodiment 3. It should be noted that the above-mentioned modules or units may be hardware components or software components stored in a memory (for example, the memory 104) and processed by one or more processors (for example, the processors 102a, 102b, ..., 102n) , the above module can also be run in the computer terminal 10 provided in Embodiment 1 as part of the device.

需要说明的是,本实施例的优选实施方式可以参见实施例1、实施例2或实施例3中的相关描述,此处不再赘述。It should be noted that for the preferred implementation of this embodiment, reference can be made to the relevant descriptions in Embodiment 1, Embodiment 2, or Embodiment 3, which will not be described again here.

实施例5Example 5

根据本申请实施例,还提供了一种计算机终端,该计算机终端可以是计算机终端群中的任意一个计算机终端设备。可选地,在本实施例中,上述计算机终端也可以替换为移动终端等终端设备。According to an embodiment of the present application, a computer terminal is also provided, and the computer terminal may be any computer terminal device in a group of computer terminals. Optionally, in this embodiment, the above computer terminal can also be replaced by a terminal device such as a mobile terminal.

可选地,在本实施例中,上述计算机终端可以位于计算机网络的多个网络设备中的至少一个网络设备。Optionally, in this embodiment, the above-mentioned computer terminal may be located in at least one network device among multiple network devices of the computer network.

在本实施例中,上述计算机终端可以执行搜索处理方法中以下步骤的程序代码:获取搜索请求;对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the search processing method: obtain the search request; conduct attribute feature analysis on the search request, and generate auxiliary information associated with the search request, where the auxiliary information is the search request corresponding Preset at least one target attribute characteristic of industry knowledge; based on the search request and auxiliary information, obtain the search results corresponding to the search request.

可选地,图10是根据本申请实施例5的一种计算机终端的结构框图,如图10所示,该计算机终端100可以包括:一个或多个(图中仅示出一个)处理器1002、存储器1004、存储控制器1006、以及外设接口1008,其中,外设接口1008与射频模块、音频模块和显示器连接。Optionally, FIG. 10 is a structural block diagram of a computer terminal according to Embodiment 5 of the present application. As shown in FIG. 10 , the computer terminal 100 may include: one or more (only one is shown in the figure) processors 1002 , memory 1004, storage controller 1006, and peripheral interface 1008, where the peripheral interface 1008 is connected to the radio frequency module, the audio module and the display.

其中,存储器1004可用于存储软件程序以及模块,如本申请实施例中的搜索处理方法和装置对应的程序指令/模块,处理器通过运行存储在存储器内的软件程序以及模块,从而执行各种功能应用以及数据处理,即实现上述的搜索处理方法。存储器1004可包括高速随机存储器,还可以包括非易失性存储器,如一个或者多个磁性存储装置、闪存、或者其他非易失性固态存储器。在一些实例中,存储器1004可进一步包括相对于处理器远程设置的存储器,这些远程存储器可以通过网络连接至计算机终端100。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。Among them, the memory 1004 can be used to store software programs and modules, such as the program instructions/modules corresponding to the search processing method and device in the embodiment of the present application. The processor executes various functions by running the software programs and modules stored in the memory. Application and data processing, that is, implementing the above search processing method. Memory 1004 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 1004 may further include memory located remotely relative to the processor, and these remote memories may be connected to the computer terminal 100 through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

处理器1002可以通过传输装置调用存储器存储的信息及应用程序,以执行下述步骤:获取搜索请求;对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。The processor 1002 can call the information stored in the memory and the application program through the transmission device to perform the following steps: obtain the search request; perform attribute feature analysis on the search request, and generate auxiliary information associated with the search request, where the auxiliary information is the search request corresponding At least one target attribute feature of the preset industry knowledge; based on the search request and auxiliary information, obtain the search results corresponding to the search request.

可选地,上述处理器1002还可以执行如下步骤的程序代码:对搜索请求进行行业知识属性提取,得到属性提取结果;对属性提取结果进行属性标注,得到属性标注结果,其中,属性标注结果用于指示预设行业知识在多个维度上的知识属性;对属性标注结果进行特征选择,得到辅助信息。Optionally, the above-mentioned processor 1002 can also execute the program code of the following steps: extract industry knowledge attributes from the search request to obtain the attribute extraction results; perform attribute annotation on the attribute extraction results to obtain the attribute annotation results, where the attribute annotation results are expressed in It is used to indicate the knowledge attributes of preset industry knowledge in multiple dimensions; perform feature selection on the attribute annotation results to obtain auxiliary information.

可选地,上述处理器1002还可以执行如下步骤的程序代码:基于预设行业知识,对搜索请求进行分词处理,得到第一属性提取结果;基于预设行业知识,对搜索请求进行命名实体识别,得到第二属性提取结果;基于预设行业知识,对第一属性提取结果进行同义词扩展,得到第三属性提取结果;基于预设行业知识,对搜索请求进行关键词提取,得到第四属性提取结果;基于预设行业知识,对搜索请求进行表述形式改写,得到第五属性提取结果。Optionally, the above-mentioned processor 1002 can also execute the program code of the following steps: perform word segmentation processing on the search request based on the preset industry knowledge to obtain the first attribute extraction result; perform named entity recognition on the search request based on the preset industry knowledge. , obtain the second attribute extraction result; based on the preset industry knowledge, perform synonym expansion on the first attribute extraction result, and obtain the third attribute extraction result; based on the preset industry knowledge, perform keyword extraction on the search request, and obtain the fourth attribute extraction Result: Based on the preset industry knowledge, the expression form of the search request is rewritten to obtain the fifth attribute extraction result.

可选地,上述处理器1002还可以执行如下步骤的程序代码:采用预设行业知识对应的标注模型,对属性提取结果进行属性标注,得到属性标注结果,其中,属性标注结果包括:多个属性特征组合。Optionally, the above-mentioned processor 1002 can also execute the program code of the following steps: use an annotation model corresponding to the preset industry knowledge to perform attribute annotation on the attribute extraction results to obtain an attribute annotation result, where the attribute annotation result includes: multiple attributes Feature combination.

可选地,上述处理器1002还可以执行如下步骤的程序代码:从属性标注结果中选取多个候选特征组合;计算多个候选特征组合对于预设结果的信息增益;基于信息增益,从多个候选特征组合中选择至少一个目标属性特征,得到辅助信息。Optionally, the above-mentioned processor 1002 can also execute the program code of the following steps: select multiple candidate feature combinations from the attribute annotation results; calculate the information gain of the multiple candidate feature combinations for the preset result; based on the information gain, select from multiple candidate feature combinations. Select at least one target attribute feature from the candidate feature combination to obtain auxiliary information.

可选地,上述处理器1002还可以执行如下步骤的程序代码:获取基于搜索请求与辅助信息预测得到预设结果的第一概率,以及获取基于搜索请求预测得到预设结果的第二概率;利用第一概率与第二概率计算得到信息增益。Optionally, the above-mentioned processor 1002 can also execute the program code of the following steps: obtaining the first probability of predicting the preset result based on the search request and the auxiliary information, and obtaining the second probability of predicting the preset result based on the search request; using The first probability and the second probability are calculated to obtain the information gain.

可选地,上述处理器1002还可以执行如下步骤的程序代码:从多个候选特征组合中选择信息增益最大的候选特征组合,得到至少一个目标属性特征。Optionally, the above-mentioned processor 1002 can also execute the program code of the following steps: select the candidate feature combination with the largest information gain from multiple candidate feature combinations to obtain at least one target attribute feature.

可选地,上述处理器1002还可以执行如下步骤的程序代码:采用差分搜索索引模型对搜索请求与辅助信息进行行业知识推理,得到目标文档标识,其中,差分搜索索引模型利用多组数据通过机器学习训练得到,多组数据为第一训练数据与第二训练数据的混合数据,第一训练数据包括:样本提示、样本文档标识,第二训练数据包括:样本提示、样本提示对应的至少一个目标属性特征和样本文档标识;基于目标文档标识获取搜索请求对应的搜索结果。Optionally, the above-mentioned processor 1002 can also execute the program code of the following steps: using a differential search index model to perform industry knowledge reasoning on the search request and auxiliary information to obtain the target document identification, wherein the differential search index model uses multiple sets of data to pass through the machine Obtained from learning and training, multiple sets of data are mixed data of first training data and second training data. The first training data includes: sample prompts and sample document identifiers. The second training data includes: sample prompts and at least one target corresponding to the sample prompts. Attribute characteristics and sample document identification; obtain search results corresponding to the search request based on the target document identification.

可选地,上述处理器1002还可以执行如下步骤的程序代码:响应对电商服务搜索对话框执行的输入操作,确定搜索请求;响应对电商服务搜索对话框执行的发送操作,对搜索请求进行属性特征分析以生成辅助信息,以及基于搜索请求与辅助信息获取搜索结果;在电商服务搜索对话框内显示搜索结果。Optionally, the above-mentioned processor 1002 can also execute the program code of the following steps: respond to the input operation performed on the e-commerce service search dialog box, determine the search request; respond to the send operation performed on the e-commerce service search dialog box, respond to the search request Perform attribute feature analysis to generate auxiliary information, and obtain search results based on the search request and auxiliary information; display the search results in the e-commerce service search dialog box.

处理器1002可以通过传输装置调用存储器存储的信息及应用程序,以执行下述步骤:获取搜索请求;采用差分搜索索引模型对搜索请求进行属性特征分析以生成搜索请求关联的辅助信息,以及对搜索请求与辅助信息进行行业知识推理以输出目标文档标识,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;基于目标文档标识获取搜索请求对应的搜索结果。The processor 1002 can call the information stored in the memory and the application program through the transmission device to perform the following steps: obtain the search request; use the differential search index model to analyze the attribute characteristics of the search request to generate auxiliary information associated with the search request, and perform the search request Request industry knowledge reasoning with the auxiliary information to output a target document identifier, where the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request; and obtain the search results corresponding to the search request based on the target document identifier.

可选地,上述处理器1002还可以执行如下步骤的程序代码:通过搜索请求确定初始提示内容,其中,初始提示内容基于预设提示模板进行配置;对初始提示内容与辅助信息进行拼接,得到目标提示模板;采用差分搜索索引模型对目标提示模板进行行业知识推理,得到目标文档标识。Optionally, the above-mentioned processor 1002 can also execute the program code of the following steps: determine the initial prompt content through a search request, wherein the initial prompt content is configured based on a preset prompt template; splice the initial prompt content and auxiliary information to obtain the target Prompt template; use the differential search index model to perform industry knowledge reasoning on the target prompt template to obtain the target document identification.

处理器1002可以通过传输装置调用存储器存储的信息及应用程序,以执行下述步骤:获取电商服务搜索请求;对电商服务搜索请求进行电商属性特征分析,生成电商服务搜索请求关联的电商服务辅助信息,其中,电商服务辅助信息为电商服务搜索请求对应的电商服务行业知识的至少一个目标电商属性特征;基于电商服务搜索请求与电商服务辅助信息,获取电商服务搜索请求对应的电商服务搜索结果。The processor 1002 can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain the e-commerce service search request; analyze the e-commerce attribute characteristics of the e-commerce service search request, and generate the e-commerce service search request associated E-commerce service auxiliary information, wherein the e-commerce service auxiliary information is at least one target e-commerce attribute characteristic of the e-commerce service industry knowledge corresponding to the e-commerce service search request; based on the e-commerce service search request and the e-commerce service auxiliary information, the e-commerce service auxiliary information is obtained E-commerce service search results corresponding to the merchant service search request.

采用本申请实施例,提供了一种用于实现搜索处理方法的计算机终端的方案。首先获取搜索请求,通过对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,进一步基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。上述过程中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征,由此,本申请提供的搜索处理方法通过挖掘搜索请求的属性特征,将与预设行业知识相对应的属性特征引入搜索处理中,达到了针对预设行业知识进行搜索处理的目的,从而实现了提升针对特定行业知识进行搜索的精确性的技术效果,进而解决了相关技术中难以将行业知识注入搜索处理过程导致搜索精确性较低的技术问题。Using the embodiments of the present application, a solution for a computer terminal for implementing a search processing method is provided. First, the search request is obtained, and the auxiliary information associated with the search request is generated by analyzing the attribute characteristics of the search request. Further, based on the search request and the auxiliary information, the search results corresponding to the search request are obtained. In the above process, the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request. Therefore, the search processing method provided by this application mines the attribute features of the search request and combines the attributes corresponding to the preset industry knowledge. Features are introduced into search processing to achieve the purpose of search processing for preset industry knowledge, thereby achieving the technical effect of improving the accuracy of searching for specific industry knowledge, thereby solving the difficulty in injecting industry knowledge into the search processing process in related technologies. Technical issues resulting in less accurate searches.

本领域普通技术人员可以理解,图10所示的结构仅为示意,计算机终端也可以是智能手机(如Android手机、iOS手机等)、平板电脑、掌上电脑以及移动互联网设备(MobileInternet Devices,MID)等终端设备。图10其并不对上述计算机终端的结构造成限定。例如,计算机终端100还可包括比图10中所示更多或者更少的组件(如网络接口、显示装置等),或者具有与图10所示不同的配置。Those of ordinary skill in the art can understand that the structure shown in Figure 10 is only illustrative, and the computer terminal can also be a smart phone (such as an Android phone, iOS phone, etc.), a tablet computer, a handheld computer, and a mobile Internet device (Mobile Internet Devices, MID) and other terminal equipment. Figure 10 does not limit the structure of the above computer terminal. For example, the computer terminal 100 may also include more or less components (such as network interfaces, display devices, etc.) than shown in FIG. 10 , or have a different configuration than that shown in FIG. 10 .

本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令终端设备相关的硬件来完成,该程序可以存储于一计算机可读存储介质中,存储介质可以包括:闪存盘、ROM、RAM、磁盘或光盘等。Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program. The program can be stored in a computer-readable storage medium, and the storage medium can Including: flash disk, ROM, RAM, magnetic disk or optical disk, etc.

实施例6Example 6

根据本申请实施例,还提供了一种计算机可读存储介质。可选地,在本实施例中,上述存储介质可以用于保存上述实施例1、实施例2或实施例3所提供的搜索处理方法所执行的程序代码。According to an embodiment of the present application, a computer-readable storage medium is also provided. Optionally, in this embodiment, the above-mentioned storage medium can be used to save the program code executed by the search processing method provided in the above-mentioned Embodiment 1, Embodiment 2 or Embodiment 3.

可选地,在本实施例中,上述存储介质可以位于计算机网络中计算机终端群中的任意一个计算机终端中,或者位于移动终端群中的任意一个移动终端中。Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in the computer network, or in any mobile terminal in a mobile terminal group.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:获取搜索请求;对搜索请求进行属性特征分析,生成搜索请求关联的辅助信息,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;基于搜索请求与辅助信息,获取搜索请求对应的搜索结果。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: obtaining a search request; performing attribute feature analysis on the search request, and generating auxiliary information associated with the search request, wherein, The auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request; based on the search request and the auxiliary information, the search results corresponding to the search request are obtained.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:对搜索请求进行行业知识属性提取,得到属性提取结果;对属性提取结果进行属性标注,得到属性标注结果,其中,属性标注结果用于指示预设行业知识在多个维度上的知识属性;对属性标注结果进行特征选择,得到辅助信息。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: extracting industry knowledge attributes from the search request to obtain attribute extraction results; performing attribute annotation on the attribute extraction results, The attribute annotation results are obtained, where the attribute annotation results are used to indicate the knowledge attributes of the preset industry knowledge in multiple dimensions; feature selection is performed on the attribute annotation results to obtain auxiliary information.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:基于预设行业知识,对搜索请求进行分词处理,得到第一属性提取结果;基于预设行业知识,对搜索请求进行命名实体识别,得到第二属性提取结果;基于预设行业知识,对第一属性提取结果进行同义词扩展,得到第三属性提取结果;基于预设行业知识,对搜索请求进行关键词提取,得到第四属性提取结果;基于预设行业知识,对搜索请求进行表述形式改写,得到第五属性提取结果。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: performing word segmentation processing on the search request based on preset industry knowledge to obtain the first attribute extraction result; Assuming industry knowledge, perform named entity recognition on the search request to obtain the second attribute extraction result; based on the preset industry knowledge, perform synonym expansion on the first attribute extraction result to obtain the third attribute extraction result; based on the preset industry knowledge, perform search Request keyword extraction to obtain the fourth attribute extraction result; based on the preset industry knowledge, rewrite the search request's expression form to obtain the fifth attribute extraction result.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:采用预设行业知识对应的标注模型,对属性提取结果进行属性标注,得到属性标注结果,其中,属性标注结果包括:多个属性特征组合。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: using an annotation model corresponding to the preset industry knowledge, performing attribute annotation on the attribute extraction results, and obtaining an attribute annotation result. , where the attribute annotation results include: multiple attribute feature combinations.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:从属性标注结果中选取多个候选特征组合;计算多个候选特征组合对于预设结果的信息增益;基于信息增益,从多个候选特征组合中选择至少一个目标属性特征,得到辅助信息。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: selecting multiple candidate feature combinations from the attribute annotation results; calculating multiple candidate feature combinations for the preset result Information gain; based on the information gain, select at least one target attribute feature from multiple candidate feature combinations to obtain auxiliary information.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:获取基于搜索请求与辅助信息预测得到预设结果的第一概率,以及获取基于搜索请求预测得到预设结果的第二概率;利用第一概率与第二概率计算得到信息增益。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: obtaining the first probability of predicting the preset result based on the search request and the auxiliary information, and obtaining the first probability based on the search request. Predict the second probability of the preset result; calculate the information gain using the first probability and the second probability.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:从多个候选特征组合中选择信息增益最大的候选特征组合,得到至少一个目标属性特征。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: selecting a candidate feature combination with the largest information gain from multiple candidate feature combinations to obtain at least one target attribute feature .

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:采用差分搜索索引模型对搜索请求与辅助信息进行行业知识推理,得到目标文档标识,其中,差分搜索索引模型利用多组数据通过机器学习训练得到,多组数据为第一训练数据与第二训练数据的混合数据,第一训练数据包括:样本提示、样本文档标识,第二训练数据包括:样本提示、样本提示对应的至少一个目标属性特征和样本文档标识;基于目标文档标识获取搜索请求对应的搜索结果。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: using a differential search index model to perform industry knowledge reasoning on the search request and auxiliary information to obtain the target document identification, where , the differential search index model is obtained through machine learning training using multiple sets of data. The multiple sets of data are a mixture of first training data and second training data. The first training data includes: sample prompts and sample document identifiers. The second training data includes : The sample prompt, at least one target attribute feature corresponding to the sample prompt, and the sample document identifier; obtain the search results corresponding to the search request based on the target document identifier.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:响应对电商服务搜索对话框执行的输入操作,确定搜索请求;响应对电商服务搜索对话框执行的发送操作,对搜索请求进行属性特征分析以生成辅助信息,以及基于搜索请求与辅助信息获取搜索结果;在电商服务搜索对话框内显示搜索结果。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: responding to an input operation performed on the e-commerce service search dialog box, determining the search request; responding to the e-commerce service search dialog box The sending operation performed by the search dialog box performs attribute feature analysis on the search request to generate auxiliary information, and obtains search results based on the search request and auxiliary information; the search results are displayed in the e-commerce service search dialog box.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:获取搜索请求;采用差分搜索索引模型对搜索请求进行属性特征分析以生成搜索请求关联的辅助信息,以及对搜索请求与辅助信息进行行业知识推理以输出目标文档标识,其中,辅助信息为搜索请求对应的预设行业知识的至少一个目标属性特征;基于目标文档标识获取搜索请求对应的搜索结果。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: obtaining a search request; using a differential search index model to perform attribute feature analysis on the search request to generate a search request-related Auxiliary information, and perform industry knowledge inference on the search request and the auxiliary information to output a target document identifier, where the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request; obtain the search corresponding to the search request based on the target document identifier result.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:通过搜索请求确定初始提示内容,其中,初始提示内容基于预设提示模板进行配置;对初始提示内容与辅助信息进行拼接,得到目标提示模板;采用差分搜索索引模型对目标提示模板进行行业知识推理,得到目标文档标识。Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for performing the following steps: determining initial prompt content through a search request, wherein the initial prompt content is configured based on a preset prompt template; The initial prompt content and auxiliary information are spliced to obtain the target prompt template; a differential search index model is used to perform industry knowledge reasoning on the target prompt template to obtain the target document identification.

可选地,在本实施例中,计算机可读存储介质被设置为存储用于执行以下步骤的程序代码:获取电商服务搜索请求;对电商服务搜索请求进行电商属性特征分析,生成电商服务搜索请求关联的电商服务辅助信息,其中,电商服务辅助信息为电商服务搜索请求对应的电商服务行业知识的至少一个目标电商属性特征;基于电商服务搜索请求与电商服务辅助信息,获取电商服务搜索请求对应的电商服务搜索结果。Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: obtaining an e-commerce service search request; performing e-commerce attribute feature analysis on the e-commerce service search request, and generating an e-commerce service search request. E-commerce service auxiliary information associated with the e-commerce service search request, where the e-commerce service auxiliary information is at least one target e-commerce attribute feature of the e-commerce service industry knowledge corresponding to the e-commerce service search request; based on the e-commerce service search request and e-commerce Service auxiliary information, obtain the e-commerce service search results corresponding to the e-commerce service search request.

上述本申请实施例序号仅仅为了描述,不代表实施例的优劣。The above serial numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

在本申请的上述实施例中,对各个实施例的描述都各有侧重,某个实施例中没有详述的部分,可以参见其他实施例的相关描述。In the above-mentioned embodiments of the present application, each embodiment is described with its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

在本申请所提供的几个实施例中,应该理解到,所揭露的技术内容,可通过其它的方式实现。其中,以上所描述的装置实施例仅仅是示意性的,例如所述单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,例如多个单元或组件可以结合或者可以集成到另一个系统,或一些特征可以忽略,或不执行。另一点,所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口,单元或模块的间接耦合或通信连接,可以是电性或其它的形式。In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or may be Integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the units or modules may be in electrical or other forms.

所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。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 they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

另外,在本申请各个实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or software functional units.

所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。基于这样的理解,本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可为个人计算机、服务器或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括:U盘、ROM、RAM、移动硬盘、磁碟或者光盘等各种可以存储程序代码的介质。If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to cause a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage media include: U disk, ROM, RAM, mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

以上所述仅是本申请的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本申请原理的前提下,还可以做出若干改进和润饰,这些改进和润饰也应视为本申请的保护范围。The above are only the preferred embodiments of the present application. It should be pointed out that for those of ordinary skill in the art, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications can also be made. should be regarded as the scope of protection of this application.

Claims (14)

1.一种搜索处理方法,其特征在于,包括:1. A search processing method, characterized in that it includes: 获取搜索请求;Get search request; 对所述搜索请求进行属性特征分析,生成所述搜索请求关联的辅助信息,其中,所述辅助信息为所述搜索请求对应的预设行业知识的至少一个目标属性特征;Perform attribute feature analysis on the search request to generate auxiliary information associated with the search request, where the auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request; 基于所述搜索请求与所述辅助信息,获取所述搜索请求对应的搜索结果。Based on the search request and the auxiliary information, a search result corresponding to the search request is obtained. 2.根据权利要求1所述的搜索处理方法,其特征在于,对所述搜索请求进行属性特征分析,生成所述搜索请求关联的所述辅助信息包括:2. The search processing method according to claim 1, characterized in that performing attribute feature analysis on the search request and generating the auxiliary information associated with the search request includes: 对所述搜索请求进行行业知识属性提取,得到属性提取结果;Extract industry knowledge attributes from the search request to obtain attribute extraction results; 对所述属性提取结果进行属性标注,得到属性标注结果,其中,所述属性标注结果用于指示所述预设行业知识在多个维度上的知识属性;Perform attribute annotation on the attribute extraction results to obtain an attribute annotation result, where the attribute annotation result is used to indicate the knowledge attributes of the preset industry knowledge in multiple dimensions; 对所述属性标注结果进行特征选择,得到所述辅助信息。Feature selection is performed on the attribute annotation results to obtain the auxiliary information. 3.根据权利要求2所述的搜索处理方法,其特征在于,对所述搜索请求进行行业知识属性提取,得到所述属性提取结果包括以下一项或多项:3. The search processing method according to claim 2, wherein the search request is subjected to industry knowledge attribute extraction, and the attribute extraction result obtained includes one or more of the following: 基于所述预设行业知识,对所述搜索请求进行分词处理,得到第一属性提取结果;Based on the preset industry knowledge, perform word segmentation processing on the search request to obtain the first attribute extraction result; 基于所述预设行业知识,对所述搜索请求进行命名实体识别,得到第二属性提取结果;Based on the preset industry knowledge, perform named entity recognition on the search request to obtain a second attribute extraction result; 基于所述预设行业知识,对所述第一属性提取结果进行同义词扩展,得到第三属性提取结果;Based on the preset industry knowledge, perform synonym expansion on the first attribute extraction result to obtain a third attribute extraction result; 基于所述预设行业知识,对所述搜索请求进行关键词提取,得到第四属性提取结果;Based on the preset industry knowledge, perform keyword extraction on the search request to obtain a fourth attribute extraction result; 基于所述预设行业知识,对所述搜索请求进行表述形式改写,得到第五属性提取结果。Based on the preset industry knowledge, the expression form of the search request is rewritten to obtain a fifth attribute extraction result. 4.根据权利要求2所述的搜索处理方法,其特征在于,对所述属性提取结果进行属性标注,得到所述属性标注结果包括:4. The search processing method according to claim 2, characterized in that, performing attribute annotation on the attribute extraction result, and obtaining the attribute annotation result includes: 采用所述预设行业知识对应的标注模型,对所述属性提取结果进行属性标注,得到所述属性标注结果,其中,所述属性标注结果包括:多个属性特征组合。The annotation model corresponding to the preset industry knowledge is used to perform attribute annotation on the attribute extraction result to obtain the attribute annotation result, where the attribute annotation result includes: a combination of multiple attribute features. 5.根据权利要求2所述的搜索处理方法,其特征在于,对所述属性标注结果进行特征选择,得到所述辅助信息包括:5. The search processing method according to claim 2, characterized in that feature selection is performed on the attribute annotation results to obtain the auxiliary information including: 从所述属性标注结果中选取多个候选特征组合;Select multiple candidate feature combinations from the attribute annotation results; 计算所述多个候选特征组合对于预设结果的信息增益;Calculate the information gain of the multiple candidate feature combinations for the preset result; 基于所述信息增益,从所述多个候选特征组合中选择所述至少一个目标属性特征,得到所述辅助信息。Based on the information gain, the at least one target attribute feature is selected from the plurality of candidate feature combinations to obtain the auxiliary information. 6.根据权利要求5所述的搜索处理方法,其特征在于,计算所述多个候选特征组合对于所述预设结果的所述信息增益包括:6. The search processing method according to claim 5, wherein calculating the information gain of the plurality of candidate feature combinations for the preset result includes: 获取基于所述搜索请求与所述辅助信息预测得到所述预设结果的第一概率,以及获取基于所述搜索请求预测得到所述预设结果的第二概率;Obtain the first probability of predicting the preset result based on the search request and the auxiliary information, and obtain the second probability of predicting the preset result based on the search request; 利用所述第一概率与所述第二概率计算得到所述信息增益。The information gain is calculated using the first probability and the second probability. 7.根据权利要求5所述的搜索处理方法,其特征在于,基于所述信息增益,从所述多个候选特征组合中选择所述至少一个目标属性特征包括:7. The search processing method according to claim 5, characterized in that, based on the information gain, selecting the at least one target attribute feature from the plurality of candidate feature combinations includes: 从所述多个候选特征组合中选择所述信息增益最大的候选特征组合,得到所述至少一个目标属性特征。Select the candidate feature combination with the largest information gain from the plurality of candidate feature combinations to obtain the at least one target attribute feature. 8.根据权利要求1所述的搜索处理方法,其特征在于,基于所述搜索请求与所述辅助信息,获取所述搜索请求对应的搜索结果包括:8. The search processing method according to claim 1, characterized in that, based on the search request and the auxiliary information, obtaining the search results corresponding to the search request includes: 采用差分搜索索引模型对所述搜索请求与所述辅助信息进行行业知识推理,得到目标文档标识,其中,所述差分搜索索引模型利用多组数据通过机器学习训练得到,所述多组数据为第一训练数据与第二训练数据的混合数据,所述第一训练数据包括:样本提示、样本文档标识,所述第二训练数据包括:所述样本提示、所述样本提示对应的至少一个目标属性特征和所述样本文档标识;A differential search index model is used to perform industry knowledge inference on the search request and the auxiliary information to obtain the target document identification, wherein the differential search index model is obtained through machine learning training using multiple sets of data, and the multiple sets of data are the first A mixture of training data and second training data. The first training data includes: sample prompts and sample document identifiers. The second training data includes: the sample prompts and at least one target attribute corresponding to the sample prompts. Characteristics and identification of the sample document; 基于所述目标文档标识获取所述搜索请求对应的所述搜索结果。Obtain the search result corresponding to the search request based on the target document identifier. 9.根据权利要求1所述的搜索处理方法,其特征在于,通过终端设备提供一图形用户界面,所述图形用户界面所显示的内容至少部分地包含一电商服务搜索对话框,所述搜索处理方法还包括:9. The search processing method according to claim 1, characterized in that a graphical user interface is provided through the terminal device, and the content displayed by the graphical user interface at least partially includes an e-commerce service search dialog box, and the search Treatment methods also include: 响应对所述电商服务搜索对话框执行的输入操作,确定电商服务搜索请求;In response to the input operation performed on the e-commerce service search dialog box, determine the e-commerce service search request; 响应对所述电商服务搜索对话框执行的发送操作,对所述电商服务搜索请求进行电商服务属性特征分析以生成所述电商服务辅助信息,以及基于所述电商服务搜索请求与所述电商服务辅助信息获取所述电商服务搜索结果;In response to the sending operation performed on the e-commerce service search dialog box, perform e-commerce service attribute feature analysis on the e-commerce service search request to generate the e-commerce service auxiliary information, and based on the e-commerce service search request and The e-commerce service auxiliary information obtains the e-commerce service search results; 在所述电商服务搜索对话框内显示所述电商服务搜索结果。The e-commerce service search results are displayed in the e-commerce service search dialog box. 10.一种搜索处理方法,其特征在于,包括:10. A search processing method, characterized by comprising: 获取搜索请求;Get search request; 采用差分搜索索引模型对所述搜索请求进行属性特征分析以生成所述搜索请求关联的辅助信息,以及对所述搜索请求与所述辅助信息进行行业知识推理以输出目标文档标识,其中,所述辅助信息为所述搜索请求对应的预设行业知识的至少一个目标属性特征;A differential search index model is used to perform attribute feature analysis on the search request to generate auxiliary information associated with the search request, and industry knowledge reasoning is performed on the search request and the auxiliary information to output a target document identification, wherein: The auxiliary information is at least one target attribute feature of the preset industry knowledge corresponding to the search request; 基于所述目标文档标识获取所述搜索请求对应的搜索结果。Obtain search results corresponding to the search request based on the target document identifier. 11.根据权利要求10所述的搜索处理方法,其特征在于,采用所述差分搜索索引模型对所述搜索请求与所述辅助信息进行行业知识推理以输出所述目标文档标识包括:11. The search processing method according to claim 10, wherein using the differential search index model to perform industry knowledge inference on the search request and the auxiliary information to output the target document identification includes: 通过所述搜索请求确定初始提示内容,其中,所述初始提示内容基于预设提示模板进行配置;Determine initial prompt content through the search request, wherein the initial prompt content is configured based on a preset prompt template; 对所述初始提示内容与所述辅助信息进行拼接,得到目标提示内容;Splice the initial prompt content and the auxiliary information to obtain the target prompt content; 采用所述差分搜索索引模型对所述目标提示模板进行行业知识推理,得到所述目标文档标识。The differential search index model is used to perform industry knowledge reasoning on the target prompt template to obtain the target document identification. 12.一种搜索处理方法,其特征在于,包括:12. A search processing method, characterized by comprising: 获取电商服务搜索请求;Get e-commerce service search request; 对所述电商服务搜索请求进行电商属性特征分析,生成所述电商服务搜索请求关联的电商服务辅助信息,其中,所述电商服务辅助信息为所述电商服务搜索请求对应的电商服务行业知识的至少一个目标电商属性特征;Conduct an e-commerce attribute feature analysis on the e-commerce service search request to generate e-commerce service auxiliary information associated with the e-commerce service search request, where the e-commerce service auxiliary information is the e-commerce service search request corresponding to At least one target e-commerce attribute characteristic of e-commerce service industry knowledge; 基于所述电商服务搜索请求与所述电商服务辅助信息,获取所述电商服务搜索请求对应的电商服务搜索结果。Based on the e-commerce service search request and the e-commerce service auxiliary information, the e-commerce service search result corresponding to the e-commerce service search request is obtained. 13.一种电子设备,其特征在于,包括:13. An electronic device, characterized in that it includes: 存储器,存储有可执行程序;Memory, which stores executable programs; 处理器,用于运行所述程序,其中,所述程序运行时执行权利要求1至12中任意一项所述的搜索处理方法。A processor, configured to run the program, wherein when the program is run, the search processing method according to any one of claims 1 to 12 is executed. 14.一种计算机可读存储介质,其特征在于,所述计算机可读存储介质包括存储的可执行程序,其中,在所述可执行程序运行时控制所述计算机可读存储介质所在设备执行权利要求1至12中任意一项所述的搜索处理方法。14. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the right The search processing method according to any one of claims 1 to 12.
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