CN111951113A - A big data-based investment service method and device - Google Patents
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Abstract
本发明提供了一种基于大数据的投资服务方法和装置，该方法包括：获取用户需求信息；将用户需求信息进行特征提取，确定用户需求特征，传递至业务后台系统；根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案；建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；根据用户投资风险承受能力，确定投资服务方案。本发明实现投资风险控制，提供了安全高效的投资服务。
The present invention provides a big data-based investment service method and device. The method includes: acquiring user demand information; extracting features from the user demand information, determining the user demand characteristics, and transmitting them to a business back-end system; The database of the business back-end system is matched using big data technology to determine the matching investment plan; an artificial intelligence learning model is established, and the matching investment plan is used as input data to train the artificial intelligence learning model until the artificial intelligence learning model converges, and the artificial intelligence learning model after training is determined. model; according to the artificial intelligence learning model after training and user demand information, determine the user's investment risk tolerance; according to the user's investment risk tolerance, determine the investment service plan. The invention realizes investment risk control and provides safe and efficient investment services.
Description
技术领域technical field
本发明涉及计算机数据处理技术领域，尤其涉及一种基于大数据的投资服务方法和装置。The invention relates to the technical field of computer data processing, in particular to a big data-based investment service method and device.
背景技术Background technique
本部分旨在为权利要求书中陈述的本发明的实施方式提供背景或上下文。此处的描述不因为包括在本部分中就承认是现有技术。This section is intended to provide a background or context for the embodiments of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section.
银行的高净值客户的子女境外求学带来的境外财富管理需求以及全球视野带来的全球资产配置需求，为境外金融投资领域的发展提供了催化剂，配套的跨境咨询服务也应运而生。客户会通过多渠道咨询跨境服务和了解跨境投资项目。用户通过商业咨询服务机构咨询签约、整理翻译项目资料，均需通过人工处理，实则繁琐费时，且由于客户资料保密及风险要求，境外投资案例具有参考价值高且难以获取的特点，造成投资服务风险控制难以保障。The demand for overseas wealth management brought about by the children of high-net-worth customers of banks studying abroad and the demand for global asset allocation brought about by a global perspective have provided a catalyst for the development of overseas financial investment, and supporting cross-border consulting services have also emerged. Customers will consult cross-border services and understand cross-border investment projects through multiple channels. Users through business consulting service agencies consult and sign contracts and organize translation project materials, all of which need to be processed manually, which is cumbersome and time-consuming. In addition, due to the confidentiality of customer information and risk requirements, overseas investment cases have the characteristics of high reference value and difficult to obtain, resulting in investment service risks. Control is difficult to guarantee.
因此，如何提供一种新的方案，其能够解决上述技术问题是本领域亟待解决的技术难题。Therefore, how to provide a new solution that can solve the above-mentioned technical problems is a technical problem to be solved urgently in the art.
发明内容SUMMARY OF THE INVENTION
本发明实施例提供一种基于大数据的投资服务方法，实现投资风险控制，提供了安全高效的投资服务，该方法包括：The embodiment of the present invention provides a big data-based investment service method, realizes investment risk control, and provides safe and efficient investment services, and the method includes:
获取用户需求信息；Obtain user demand information;
将用户需求信息进行特征提取，确定用户需求特征，传递至业务后台系统；Feature extraction of user demand information, determine user demand characteristics, and transfer it to the business background system;
根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案；According to the characteristics of user needs, the database of the business back-office system uses big data technology to match, and determine the matching investment plan;
建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；Establish an artificial intelligence learning model, use the matching investment plan as input data, train the artificial intelligence learning model until the artificial intelligence learning model converges, and determine the artificial intelligence learning model after training;
根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；According to the artificial intelligence learning model after training and user demand information, determine the user's investment risk tolerance;
根据用户投资风险承受能力，确定投资服务方案。According to the user's investment risk tolerance, determine the investment service plan.
本发明实施例还提供一种基于大数据的投资服务装置，包括：The embodiment of the present invention also provides a big data-based investment service device, including:
用户需求信息获取模块，用于获取用户需求信息；The user demand information acquisition module is used to obtain the user demand information;
特征提取模块，用于将用户需求信息进行特征提取，确定用户需求特征，传递至业务后台系统；The feature extraction module is used to perform feature extraction on the user demand information, determine the user demand characteristics, and transmit it to the business background system;
大数据匹配模块，用于根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案；The big data matching module is used to match the database of the business back-end system using big data technology according to the characteristics of user needs, and determine the matching investment plan;
人工智能学习模型训练模块，用于建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；The artificial intelligence learning model training module is used to establish an artificial intelligence learning model, and use the matching investment plan as input data to train the artificial intelligence learning model until the artificial intelligence learning model converges, and determine the artificial intelligence learning model after training;
用户投资风险承受能力确定模块，用于根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；The user's investment risk tolerance determination module is used to determine the user's investment risk tolerance according to the artificial intelligence learning model after training and user demand information;
投资服务方案确定模块，用于根据用户投资风险承受能力，确定投资服务方案。The investment service plan determination module is used to determine the investment service plan according to the user's investment risk tolerance.
本发明实施例还提供一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述一种基于大数据的投资服务方法。An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the above-mentioned big data-based computer program when the processor executes the computer program. Investment Services Method.
本发明实施例还提供一种计算机可读存储介质，所述计算机可读存储介质存储有执行上述一种基于大数据的投资服务方法的计算机程序。An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for executing the above-mentioned big data-based investment service method.
本发明实施例提供的一种基于大数据的投资服务方法和装置，首先获取用户需求信息；将用户需求信息进行特征提取，确定用户需求特征，传递至业务后台系统；根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案；建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；根据用户投资风险承受能力，确定投资服务方案。本发明实施例通过将用户需求信息进行特征提取，确定用户需求特征，保证了用户在进行咨询时信息不泄露，保障投资服务的保密性；然后在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案，建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；利用已有的匹配投资方案训练人工智能学习模型，不会将已有的投资方案泄漏，保障了数据库中已有匹配投资方案的安全性，同时通过训练人工智能学习模型，可以利用相似的投资方案来预测本次用户咨询服务的风险情况，接着根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；根据用户投资风险承受能力，确定投资服务方案。本发明实施例通过大数据技术和人工智能学习模型，将用户投资服务方案自动化输出，实现了投资风险控制，提供了安全高效的投资服务。The big data-based investment service method and device provided by the embodiment of the present invention firstly obtains user demand information; performs feature extraction on the user demand information, determines the user demand characteristics, and transmits them to the business background system; according to the user demand characteristics, in the business The database of the back-end system uses big data technology for matching to determine the matching investment plan; establishes an artificial intelligence learning model, takes the matching investment plan as input data, and trains the artificial intelligence learning model until the artificial intelligence learning model converges, and determines the artificial intelligence learning model after training. ; Determine the user's investment risk tolerance according to the artificial intelligence learning model after training and user demand information; determine the investment service plan according to the user's investment risk tolerance. In the embodiment of the present invention, the user demand information is extracted to determine the characteristics of the user demand, which ensures that the information is not leaked when the user conducts consultation, and the confidentiality of the investment service is guaranteed; Determine the matching investment plan, establish an artificial intelligence learning model, use the matching investment plan as input data, train the artificial intelligence learning model until the artificial intelligence learning model converges, determine the artificial intelligence learning model after training; use the existing matching investment plan to train the artificial intelligence The learning model will not leak the existing investment plans, which ensures the security of the existing matching investment plans in the database. At the same time, by training the artificial intelligence learning model, similar investment plans can be used to predict the risk of this user consulting service. , and then determine the user's investment risk tolerance according to the artificial intelligence learning model after training and user demand information; determine the investment service plan according to the user's investment risk tolerance. The embodiment of the present invention automatically outputs the user's investment service plan through big data technology and artificial intelligence learning model, realizes investment risk control, and provides safe and efficient investment services.
附图说明Description of drawings
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。在附图中：In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts. In the attached image:
图1为本发明实施例一种基于大数据的投资服务方法示意图。FIG. 1 is a schematic diagram of a big data-based investment service method according to an embodiment of the present invention.
图2为一种利用本发明实施例提供的一种基于大数据的投资服务方法进行投资服务的步骤主流程示意图。FIG. 2 is a schematic diagram of a main flow of steps for performing an investment service using a big data-based investment service method provided by an embodiment of the present invention.
图3为一种利用本发明实施例提供的一种基于大数据的投资服务方法进行投资服务的步骤详细过程示意图。FIG. 3 is a schematic diagram of a detailed process of steps for performing an investment service by using a big data-based investment service method provided by an embodiment of the present invention.
图4为运行本发明实施的一种基于大数据的投资服务方法的计算机装置示意图。FIG. 4 is a schematic diagram of a computer device running a big data-based investment service method implemented by the present invention.
图5为本发明实施例一种基于大数据的投资服务装置示意图。FIG. 5 is a schematic diagram of a big data-based investment service device according to an embodiment of the present invention.
具体实施方式Detailed ways
为使本发明实施例的目的、技术方案和优点更加清楚明白，下面结合附图对本发明实施例做进一步详细说明。在此，本发明的示意性实施例及其说明用于解释本发明，但并不作为对本发明的限定。In order to make the purposes, technical solutions and advantages of the embodiments of the present invention more clearly understood, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention.
图1为本发明实施例一种基于大数据的投资服务方法示意图，如图1所示，本发明实施例提供一种基于大数据的投资服务方法，实现投资风险控制，提供了安全高效的投资服务，该方法包括：FIG. 1 is a schematic diagram of a big data-based investment service method according to an embodiment of the present invention. As shown in FIG. 1 , an embodiment of the present invention provides a big data-based investment service method, which realizes investment risk control and provides safe and efficient investment. service, the method includes:
步骤101：获取用户需求信息；Step 101: Obtain user demand information;
步骤102：将用户需求信息进行特征提取，确定用户需求特征，传递至业务后台系统；Step 102: Feature extraction is performed on the user demand information, the user demand characteristics are determined, and the feature is transmitted to the business background system;
步骤103：根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案；Step 103: According to the characteristics of user needs, use big data technology to perform matching in the database of the business background system, and determine the matching investment plan;
步骤104：建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；Step 104: establish an artificial intelligence learning model, use the matching investment plan as input data, train the artificial intelligence learning model until the artificial intelligence learning model converges, and determine the artificial intelligence learning model after training;
步骤105：根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；Step 105: Determine the user's investment risk tolerance according to the artificial intelligence learning model after training and user demand information;
步骤106：根据用户投资风险承受能力，确定投资服务方案。Step 106: Determine an investment service plan according to the user's investment risk tolerance.
本发明实施例提供的一种基于大数据的投资服务方法，首先获取用户需求信息；将用户需求信息进行特征提取，确定用户需求特征，传递至业务后台系统；根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案；建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；根据用户投资风险承受能力，确定投资服务方案。本发明实施例通过将用户需求信息进行特征提取，确定用户需求特征，保证了用户在进行咨询时信息不泄露，保障投资服务的保密性；然后在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案，建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；利用已有的匹配投资方案训练人工智能学习模型，不会将已有的投资方案泄漏，保障了数据库中已有匹配投资方案的安全性，同时通过训练人工智能学习模型，可以利用相似的投资方案来预测本次用户咨询服务的风险情况，接着根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；根据用户投资风险承受能力，确定投资服务方案。本发明实施例通过大数据技术和人工智能学习模型，将用户投资服务方案自动化输出，实现了投资风险控制，提供了安全高效的投资服务。A big data-based investment service method provided by an embodiment of the present invention first obtains user demand information; performs feature extraction on the user demand information, determines the user demand characteristics, and transmits them to the business background system; according to the user demand characteristics, the business background system The database uses big data technology for matching to determine the matching investment plan; establish an artificial intelligence learning model, use the matching investment plan as input data, train the artificial intelligence learning model until the artificial intelligence learning model converges, and determine the artificial intelligence learning model after training; After training, the artificial intelligence learns the model and user demand information to determine the user's investment risk tolerance; according to the user's investment risk tolerance, the investment service plan is determined. In the embodiment of the present invention, the user demand information is extracted to determine the characteristics of the user demand, which ensures that the information is not leaked when the user conducts consultation, and the confidentiality of the investment service is guaranteed; Determine the matching investment plan, establish an artificial intelligence learning model, use the matching investment plan as input data, train the artificial intelligence learning model until the artificial intelligence learning model converges, determine the artificial intelligence learning model after training; use the existing matching investment plan to train the artificial intelligence The learning model will not leak the existing investment plans, which ensures the security of the existing matching investment plans in the database. At the same time, by training the artificial intelligence learning model, similar investment plans can be used to predict the risk of this user consulting service. , and then determine the user's investment risk tolerance according to the artificial intelligence learning model after training and user demand information; determine the investment service plan according to the user's investment risk tolerance. The embodiment of the present invention automatically outputs the user's investment service plan through big data technology and artificial intelligence learning model, realizes investment risk control, and provides safe and efficient investment services.
本发明实施例提供的一种基于大数据的投资服务方法，具体实施时可以包括：A big data-based investment service method provided by the embodiment of the present invention may include:
获取用户需求信息；将用户需求信息进行特征提取，确定用户需求特征，传递至业务后台系统；根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案；建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；根据用户投资风险承受能力，确定投资服务方案。Obtain user demand information; extract the user demand information, determine the user demand characteristics, and transmit it to the business back-end system; according to the user demand characteristics, use big data technology to match in the database of the business back-end system to determine the matching investment plan; establish artificial intelligence Learning model, take the matching investment plan as input data, train the artificial intelligence learning model until the artificial intelligence learning model converges, and determine the artificial intelligence learning model after training; determine the user's investment risk tolerance according to the artificial intelligence learning model after training and user demand information ; According to the user's investment risk tolerance, determine the investment service plan.
本发明实施例中，大数据技术，是指一种规模大到在获取、存储、管理、分析方面大大超出了传统数据库软件工具能力范围的数据集合，具有海量的数据规模、快速的数据流转、多样的数据类型和价值密度低四大特征。聚类算法，是指将大量数据进行处理，根据它们的相似性对数据进行聚类。In the embodiments of the present invention, big data technology refers to a data collection whose scale is so large that its acquisition, storage, management, and analysis greatly exceed the capabilities of traditional database software tools, and has massive data scale, rapid data flow, Diverse data types and low value density are four characteristics. Clustering algorithm refers to processing a large amount of data and clustering the data according to their similarity.
具体实施本发明实施例提供的一种基于大数据的投资服务方法时，在一个实施例中，前述的获取用户需求信息，至少包括：获取用户主观风险承受能力、用户投资产品喜好、用户信用信息和用户投资方式。When implementing a big data-based investment service method provided by an embodiment of the present invention, in one embodiment, the aforementioned acquisition of user demand information includes at least: acquiring the user's subjective risk tolerance, user investment product preferences, and user credit information and user investment methods.
实施例中，用户通过跨境投资场景平台向商业咨询服务机构提出咨询请求，商业咨询服务机构将咨询请求细化为用户主观风险承受能力、用户投资产品喜好、用户信用信息和用户投资方式，从而获取用户需求信息。In the embodiment, the user submits a consultation request to the business consulting service agency through the cross-border investment scenario platform, and the business consulting service agency refines the consultation request into the user's subjective risk tolerance, the user's investment product preference, the user's credit information, and the user's investment method, thereby Obtain user demand information.
具体实施本发明实施例提供的一种基于大数据的投资服务方法时，在一个实施例中，前述的将用户需求信息进行特征提取，确定用户需求特征，包括：When implementing a big data-based investment service method provided by an embodiment of the present invention, in one embodiment, the aforementioned feature extraction of user demand information to determine user demand characteristics includes:
将用户需求信息进行特征提取，确定风险类型、收益率、安全因素、风险因素和财务风险；Extract features from user demand information to determine risk types, rates of return, safety factors, risk factors and financial risks;
将风险类型、收益率、安全因素、风险因素和财务风险进行信息整合，确定用户需求特征。Integrate information on risk type, rate of return, safety factor, risk factor and financial risk to determine the characteristics of user needs.
实施例中，将用户需求信息进行特征提取，确定用户需求特征，可以包括：将用户需求信息进行特征提取，确定风险类型、收益率、安全因素、风险因素和财务风险；将风险类型、收益率、安全因素、风险因素和财务风险进行信息整合，确定用户需求特征。前述的特征提取，保证了用户在进行咨询时信息不泄露，保障投资服务的保密性。In the embodiment, feature extraction is performed on the user demand information to determine the user demand feature, which may include: performing feature extraction on the user demand information to determine the risk type, rate of return, safety factor, risk factor and financial risk; , security factors, risk factors and financial risks for information integration to determine the characteristics of user needs. The aforementioned feature extraction ensures that the user's information is not leaked during consultation, and the confidentiality of investment services is guaranteed.
具体实施本发明实施例提供的一种基于大数据的投资服务方法时，在一个实施例中，前述的根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案，包括：When implementing the big data-based investment service method provided by the embodiment of the present invention, in one embodiment, according to the characteristics of the user's needs, the database of the business back-end system uses big data technology to perform matching to determine the matching investment plan, include:
根据用户需求特征，利用大数据技术的决策树算法和聚类算法，在业务后台系统的数据库中与存储的投资方案集合进行匹配，将与用户需求特征匹配的投资方案从数据库中取出，确定为匹配投资方案。According to the characteristics of user needs, the decision tree algorithm and clustering algorithm of big data technology are used to match the set of investment plans stored in the database of the business background system, and the investment plans matching the characteristics of user needs are taken out from the database and determined as Match the investment plan.
实施例中，银行的业务后台系统的数据库中，存储有大量的投资方案构成的投资方案集合，但鉴于数据保密与用户隐私保护以及投资策略等方面的考虑，这些大量的投资方案是不能被其所属人之外的任何人获取；但在银行内部，可以利用投资方案进行特征分析，提取出非敏感信息，用作新的投资方案的参考；因此，可以根据用户需求特征，利用大数据技术的决策树算法和聚类算法，在业务后台系统的数据库中与存储的投资方案集合进行匹配，将与用户需求特征匹配的投资方案从数据库中取出，确定为匹配投资方案。所述匹配投资方案，是指与本次用户需求信息存在较高相似度的投资方案，具备较高的参考价值。In the embodiment, the database of the bank's business back-office system stores a large number of investment proposal sets consisting of investment proposals, but in view of data confidentiality, user privacy protection and investment strategy considerations, these large numbers of investment proposals cannot be used by other investment proposals. Anyone other than the owner can obtain it; however, within the bank, the investment plan can be used for feature analysis to extract non-sensitive information and use it as a reference for new investment plans; therefore, according to the characteristics of user needs, the use of big data technology The decision tree algorithm and clustering algorithm match the set of investment plans stored in the database of the business background system, and take out the investment plan that matches the user's demand characteristics from the database and determine it as the matching investment plan. The matching investment plan refers to an investment plan that has a high degree of similarity with the current user demand information, and has a high reference value.
具体实施本发明实施例提供的一种基于大数据的投资服务方法时，在一个实施例中，前述的建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型，包括：When specifically implementing the big data-based investment service method provided by the embodiment of the present invention, in one embodiment, the aforementioned artificial intelligence learning model is established, and the matching investment plan is used as input data, and the artificial intelligence learning model is trained until the artificial intelligence The learning model converges, and the artificial intelligence learning model after training is determined, including:
建立人工智能学习模型，将匹配投资方案划分为训练集和验证集；Build an artificial intelligence learning model and divide matching investment plans into training sets and validation sets;
将训练集作为输入数据，训练人工智能学习模型，根据训练结果修正人工智能学习模型的参数，并利用验证集进行验证，直至验证集的准确率的结果收敛，保存此时的人工智能学习模型为训练后人工智能学习模型。Use the training set as input data to train the artificial intelligence learning model, correct the parameters of the artificial intelligence learning model according to the training results, and use the verification set for verification until the accuracy of the verification set converges. Save the artificial intelligence learning model at this time as The artificial intelligence learns the model after training.
利用已有的匹配投资方案训练人工智能学习模型，不会将已有的投资方案泄漏，保障了数据库中已有匹配投资方案的安全性，同时通过训练人工智能学习模型，可以利用相似的投资方案来预测本次用户咨询服务的风险情况，因此，建立人工智能学习模型，将匹配投资方案划分为训练集和验证集；将训练集作为输入数据，训练人工智能学习模型，根据训练结果修正人工智能学习模型的参数，并利用验证集进行验证，在训练的过程中一直检测验证集的准确率，直至验证集的准确率的结果收敛，保存此时的人工智能学习模型为训练后人工智能学习模型。Using the existing matching investment plan to train the artificial intelligence learning model will not leak the existing investment plan, which ensures the security of the existing matching investment plan in the database. At the same time, by training the artificial intelligence learning model, similar investment plans can be used. To predict the risk situation of this user consulting service, therefore, an artificial intelligence learning model is established, and the matching investment plan is divided into training set and verification set; the training set is used as input data, the artificial intelligence learning model is trained, and the artificial intelligence is revised according to the training results. Learn the parameters of the model and use the validation set for verification. During the training process, check the accuracy of the validation set until the accuracy of the validation set converges. Save the AI learning model at this time as the post-training AI learning model .
本发明实施例通过大数据技术和人工智能学习模型，将用户投资服务方案自动化输出，实现了投资风险控制，提供了安全高效的投资服务。The embodiment of the present invention automatically outputs the user's investment service plan through big data technology and artificial intelligence learning model, realizes investment risk control, and provides safe and efficient investment services.
具体实施本发明实施例提供的一种基于大数据的投资服务方法时，在一个实施例中，前述的根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力，包括：将用户需求信息输入到训练后人工智能学习模型和，得到用户投资风险承受能力，前述的户投资风险承受能力，分为高风向承受能力和低风险承受能力。When specifically implementing the big data-based investment service method provided by the embodiment of the present invention, in one embodiment, the aforementioned determination of the user's investment risk tolerance according to the artificial intelligence learning model after training and user demand information includes: The demand information is input into the artificial intelligence learning model after training, and the user's investment risk tolerance is obtained. The aforementioned user's investment risk tolerance is divided into high wind direction tolerance and low risk tolerance.
具体实施本发明实施例提供的一种基于大数据的投资服务方法时，在一个实施例中，前述的根据用户投资风险承受能力，确定投资服务方案，包括：根据用户投资风险承受能力，确定投资服务方案；投资服务方案，包括：高风险投资策略和低风险投资策略；当用户投资风险承受能力为高风向承受能力时，推荐高风险投资策略；当用户投资风险承受能力为低风向承受能力时，推荐低风险投资策略；高风险投资策略将会分析企业的业绩或项目的估值，评估企业或项目未来价值，进而判断合理区间，计算出低估价位点。低风险投资策略将尽可能的量化不同投资市场之间的差异如人口基数，消费能力等，计算出与本地市场之间的投资差异空间。When specifically implementing the big data-based investment service method provided by the embodiment of the present invention, in one embodiment, determining the investment service plan according to the user's investment risk tolerance includes: determining the investment according to the user's investment risk tolerance Service plan; investment service plan, including: high-risk investment strategy and low-risk investment strategy; when the user's investment risk tolerance is high wind direction tolerance, high-risk investment strategy is recommended; when the user's investment risk tolerance is low wind direction tolerance , recommend low-risk investment strategies; high-risk investment strategies will analyze the performance of the company or the valuation of the project, evaluate the future value of the company or project, and then determine the reasonable range and calculate the undervaluation point. The low-risk investment strategy will try to quantify the differences between different investment markets such as population base, consumption capacity, etc., and calculate the investment difference space between the local market and the local market.
图2为一种利用本发明实施例提供的一种基于大数据的投资服务方法进行投资服务的步骤主流程示意图；图3为一种利用本发明实施例提供的一种基于大数据的投资服务方法进行投资服务的步骤详细过程示意图。如图2和图3所示，还提供一种利用本发明实施例提供的一种基于大数据的投资服务方法进行投资服务的步骤，包括：FIG. 2 is a schematic diagram of the main flow of the steps of performing an investment service using a big data-based investment service method provided by an embodiment of the present invention; FIG. 3 is a big data-based investment service provided by an embodiment of the present invention. A schematic diagram of the detailed process of the steps of the method for investment services. As shown in FIG. 2 and FIG. 3 , there is also provided a step of using a big data-based investment service method provided by an embodiment of the present invention to provide investment services, including:
a)用户通过跨境投资场景平台向商业咨询服务机构提出咨询请求。a) Users make consultation requests to business consulting service agencies through the cross-border investment scenario platform.
b)根据用户的需求，跨境投资场景平台利用大数据方法和人工智能算法，可以精准推算出每个用户的投资风险和偏好；b) According to the needs of users, the cross-border investment scenario platform uses big data methods and artificial intelligence algorithms to accurately calculate the investment risks and preferences of each user;
主要有：根据用户主观填写风险承受能力，产品喜好等内容，结合该用户信用情况、日常投资方式情况等多维度用户数据分析，综合判定该用户的风险承受能力，推荐更为合适的项目方案。通过算法判断预推的项目方案的可行性情况，并剔除相对较差的项目方案。跨境投资场景平台利用大数据的决策树算法和聚类算法，以及人工智能的列举法和归纳法，推算出用户的投资风险和偏好。大数据和人工智能的算法将根据用户提供的风险类型、收益率、安全因素、政治风险因素、财务风险等数据进行归类，试图找到数据的内在结构。通过深度学习和模型训练，在原先的预测模型里面输入需要预测的数据，最终得到推荐的项目方案。Mainly include: according to the user's subjective filling in risk tolerance, product preferences, etc., combined with multi-dimensional user data analysis such as the user's credit situation, daily investment methods, etc., comprehensively determine the user's risk tolerance, and recommend more appropriate project plans. The feasibility of the pre-projected project plan is judged by the algorithm, and the relatively poor project plan is eliminated. The cross-border investment scenario platform uses the decision tree algorithm and clustering algorithm of big data, as well as the enumeration method and induction method of artificial intelligence, to calculate the investment risk and preference of users. Algorithms of big data and artificial intelligence will classify the data provided by users according to the type of risk, rate of return, security factors, political risk factors, financial risks, etc., trying to find the internal structure of the data. Through deep learning and model training, input the data to be predicted in the original prediction model, and finally get the recommended project plan.
c)商业咨询服务机构向用户提供项目相关文件以及投资策略；c) Business consulting service institutions provide users with project-related documents and investment strategies;
主要有：高风险投资策略将会分析企业的业绩或项目的估值，评估企业或项目未来价值，进而判断合理区间，计算出低估价位点。低风险投资策略将尽可能的量化不同投资市场之间的差异如人口基数，消费能力等，计算出与本地市场之间的投资差异空间。The main ones are: high-risk investment strategy will analyze the performance of the company or the valuation of the project, evaluate the future value of the company or project, and then judge the reasonable range and calculate the undervaluation point. The low-risk investment strategy will try to quantify the differences between different investment markets such as population base, consumption capacity, etc., and calculate the investment difference space between the local market and the local market.
d)用户支付咨询费用后，咨询服务机构根据用户要求对项目方进行尽职调查。d) After the user pays the consulting fee, the consulting service agency will conduct due diligence on the project party according to the user's requirements.
e)调查结束后，咨询服务机构将签约协议相关信息翻译反馈给用户。e) After the investigation, the consulting service agency will translate the relevant information of the signed agreement and feed it back to the user.
f)用户签订合同后，支付费用。f) After the user signs the contract, pay the fee.
图4为运行本发明实施的一种基于大数据的投资服务方法的计算机装置示意图，如图4所示，本发明实施例还提供一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述一种基于大数据的投资服务方法。FIG. 4 is a schematic diagram of a computer device running a big data-based investment service method implemented by the present invention. As shown in FIG. 4 , an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer stored on the memory and stored in the memory. A computer program that can be run on a processor, when the processor executes the computer program, the above-mentioned big data-based investment service method is implemented.
本发明实施例还提供一种计算机可读存储介质，所述计算机可读存储介质存储有执行实现上述一种基于大数据的投资服务方法的计算机程序。Embodiments of the present invention further provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program for implementing the above-mentioned method for an investment service based on big data.
本发明实施例中还提供了一种基于大数据的投资服务装置，如下面的实施例所述。由于该装置解决问题的原理与一种基于大数据的投资服务方法相似，因此该装置的实施可以参见一种基于大数据的投资服务方法的实施，重复之处不再赘述。Embodiments of the present invention also provide an investment service device based on big data, as described in the following embodiments. Since the principle of the device for solving the problem is similar to a big data-based investment service method, the implementation of the device can refer to the implementation of a big data-based investment service method, and the repetition will not be repeated.
图5为本发明实施例一种基于大数据的投资服务装置示意图，如图5所示，本发明实施例还提供一种基于大数据的投资服务装置，具体实施时可以包括：FIG. 5 is a schematic diagram of a big data-based investment service device according to an embodiment of the present invention. As shown in FIG. 5 , an embodiment of the present invention further provides a big data-based investment service device, which may include:
用户需求信息获取模块501，用于获取用户需求信息；User demand
特征提取模块502，用于将用户需求信息进行特征提取，确定用户需求特征，传递至业务后台系统；The
大数据匹配模块503，用于根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案；The big
人工智能学习模型训练模块504，用于建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；The artificial intelligence learning
用户投资风险承受能力确定模块505，用于根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；The user's investment risk
投资服务方案确定模块506，用于根据用户投资风险承受能力，确定投资服务方案。The investment service
具体实施本发明实施例提供的一种基于大数据的投资服务装置时，在一个实施例中，前述的用户需求信息获取模块，具体用于：获取用户主观风险承受能力、用户投资产品喜好、用户信用信息和用户投资方式。When implementing a big data-based investment service device provided by an embodiment of the present invention, in one embodiment, the aforementioned user demand information acquisition module is specifically used to: acquire the user's subjective risk tolerance, user investment product preferences, user Credit information and user investment methods.
具体实施本发明实施例提供的一种基于大数据的投资服务装置时，在一个实施例中，前述的特征提取模块，具体用于：When implementing a big data-based investment service device provided by an embodiment of the present invention, in one embodiment, the aforementioned feature extraction module is specifically used for:
将用户需求信息进行特征提取，确定风险类型、收益率、安全因素、风险因素和财务风险；Extract features from user demand information to determine risk types, rates of return, safety factors, risk factors and financial risks;
将风险类型、收益率、安全因素、风险因素和财务风险进行信息整合，确定用户需求特征。Integrate information on risk type, rate of return, safety factor, risk factor and financial risk to determine the characteristics of user needs.
具体实施本发明实施例提供的一种基于大数据的投资服务装置时，在一个实施例中，前述的大数据匹配模块，具体用于：When implementing a big data-based investment service device provided by an embodiment of the present invention, in one embodiment, the aforementioned big data matching module is specifically used for:
根据用户需求特征，利用大数据技术的决策树算法和聚类算法，在业务后台系统的数据库中与存储的投资方案集合进行匹配，将与用户需求特征匹配的投资方案从数据库中取出，确定为匹配投资方案。According to the characteristics of user needs, the decision tree algorithm and clustering algorithm of big data technology are used to match the set of investment plans stored in the database of the business background system, and the investment plans matching the characteristics of user needs are taken out from the database and determined as Match the investment plan.
具体实施本发明实施例提供的一种基于大数据的投资服务装置时，在一个实施例中，前述的人工智能学习模型训练模块，具体用于：When implementing the big data-based investment service device provided by the embodiment of the present invention, in one embodiment, the aforementioned artificial intelligence learning model training module is specifically used for:
建立人工智能学习模型，将匹配投资方案划分为训练集和验证集；Build an artificial intelligence learning model and divide matching investment plans into training sets and validation sets;
将训练集作为输入数据，训练人工智能学习模型，根据训练结果修正人工智能学习模型的参数，并利用验证集进行验证，直至验证集的准确率的结果收敛，保存此时的人工智能学习模型为训练后人工智能学习模型。Use the training set as input data to train the artificial intelligence learning model, correct the parameters of the artificial intelligence learning model according to the training results, and use the verification set for verification until the accuracy of the verification set converges. Save the artificial intelligence learning model at this time as The artificial intelligence learns the model after training.
综上，本发明实施例提供的一种基于大数据的投资服务方法和装置，首先获取用户需求信息；将用户需求信息进行特征提取，确定用户需求特征，传递至业务后台系统；根据用户需求特征，在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案；建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；根据用户投资风险承受能力，确定投资服务方案。To sum up, the big data-based investment service method and device provided by the embodiments of the present invention firstly obtain user demand information; perform feature extraction on the user demand information, determine the user demand characteristics, and transmit them to the business background system; according to the user demand characteristics , in the database of the business back-end system, use big data technology to match, and determine the matching investment plan; establish an artificial intelligence learning model, use the matching investment plan as input data, train the artificial intelligence learning model, until the artificial intelligence learning model converges, determine the artificial intelligence after training. Intelligent learning model; according to the artificial intelligence learning model after training and user demand information, determine the user's investment risk tolerance; according to the user's investment risk tolerance, determine the investment service plan.
本发明实施例通过将用户需求信息进行特征提取，确定用户需求特征，保证了用户在进行咨询时信息不泄露，保障投资服务的保密性；然后在业务后台系统的数据库利用大数据技术进行匹配，确定匹配投资方案，建立人工智能学习模型，将匹配投资方案作为输入数据，训练人工智能学习模型，直至人工智能学习模型收敛，确定训练后人工智能学习模型；利用已有的匹配投资方案训练人工智能学习模型，不会将已有的投资方案泄漏，保障了数据库中已有匹配投资方案的安全性，同时通过训练人工智能学习模型，可以利用相似的投资方案来预测本次用户咨询服务的风险情况，接着根据训练后人工智能学习模型和用户需求信息，确定用户投资风险承受能力；根据用户投资风险承受能力，确定投资服务方案。本发明实施例通过大数据技术和人工智能学习模型，将用户投资服务方案自动化输出，实现了投资风险控制，提供了安全高效的投资服务。In the embodiment of the present invention, the user demand information is extracted to determine the characteristics of the user demand, which ensures that the information is not leaked when the user conducts consultation, and the confidentiality of the investment service is guaranteed; Determine the matching investment plan, establish an artificial intelligence learning model, use the matching investment plan as input data, train the artificial intelligence learning model until the artificial intelligence learning model converges, determine the artificial intelligence learning model after training; use the existing matching investment plan to train the artificial intelligence The learning model will not leak the existing investment plans, which ensures the security of the existing matching investment plans in the database. At the same time, by training the artificial intelligence learning model, similar investment plans can be used to predict the risk of this user consulting service. , and then determine the user's investment risk tolerance according to the artificial intelligence learning model after training and user demand information; determine the investment service plan according to the user's investment risk tolerance. The embodiment of the present invention automatically outputs the user's investment service plan through big data technology and artificial intelligence learning model, realizes investment risk control, and provides safe and efficient investment services.
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flows of the flowcharts and/or the block or blocks of the block diagrams.
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.
以上所述的具体实施例，对本发明的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本发明的具体实施例而已，并不用于限定本发明的保护范围，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above-mentioned specific embodiments are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798608A (en) * | 2017-10-19 | 2018-03-13 | 深圳市耐飞科技有限公司 | A kind of investment product combined recommendation method and system |
| CN109727136A (en) * | 2018-04-12 | 2019-05-07 | 平安证券股份有限公司 | The configuration method and device of financial asset |
| CN109785140A (en) * | 2018-12-05 | 2019-05-21 | 上海拍拍贷金融信息服务有限公司 | A kind of financing investment planning method and device based on intelligent sound |
| CN110276668A (en) * | 2019-07-01 | 2019-09-24 | 中国工商银行股份有限公司 | The method and system that finance product intelligently pushing, matching degree determine |
| CN110400185A (en) * | 2019-07-31 | 2019-11-01 | 中国工商银行股份有限公司 | Products Show method and system |
| CN111178767A (en) * | 2019-12-31 | 2020-05-19 | 中国银行股份有限公司 | Risk control method and system, computer device and computer-readable storage medium |
-
2020
- 2020-08-17 CN CN202010825581.8A patent/CN111951113A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798608A (en) * | 2017-10-19 | 2018-03-13 | 深圳市耐飞科技有限公司 | A kind of investment product combined recommendation method and system |
| CN109727136A (en) * | 2018-04-12 | 2019-05-07 | 平安证券股份有限公司 | The configuration method and device of financial asset |
| CN109785140A (en) * | 2018-12-05 | 2019-05-21 | 上海拍拍贷金融信息服务有限公司 | A kind of financing investment planning method and device based on intelligent sound |
| CN110276668A (en) * | 2019-07-01 | 2019-09-24 | 中国工商银行股份有限公司 | The method and system that finance product intelligently pushing, matching degree determine |
| CN110400185A (en) * | 2019-07-31 | 2019-11-01 | 中国工商银行股份有限公司 | Products Show method and system |
| CN111178767A (en) * | 2019-12-31 | 2020-05-19 | 中国银行股份有限公司 | Risk control method and system, computer device and computer-readable storage medium |
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