CN117112628A - Logistics data updating method and system - Google Patents

Logistics data updating method and system Download PDF

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CN117112628A
CN117112628A CN202311159558.XA CN202311159558A CN117112628A CN 117112628 A CN117112628 A CN 117112628A CN 202311159558 A CN202311159558 A CN 202311159558A CN 117112628 A CN117112628 A CN 117112628A
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CN117112628B (en
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张艳峰
李扬
冯攀
杨芳
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Shaanxi Xiaoshennong Digital Technology Group Co ltd
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Langfang Jungle Technology Co ltd
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Abstract

The invention provides a method and a system for updating logistics data, and relates to the technical field of data processing. In the invention, key information mining processing is carried out on the historical logistics data to be processed so as to output historical key information characteristic representation corresponding to the historical logistics data; carrying out key information mining processing on target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data; performing matching degree calculation processing on the historical key information feature representation and the target key information feature representation to output corresponding feature representation matching degree; and based on the characteristic representation matching degree, carrying out data updating management and control on the historical logistics data and the target logistics data so as to at least save the target logistics data. Based on the method, the reliability of the update of the logistics data can be improved.

Description

一种物流数据的更新方法及系统A method and system for updating logistics data

技术领域Technical field

本发明涉及数据处理技术领域,具体而言,涉及一种物流数据的更新方法及系统。The present invention relates to the field of data processing technology, and specifically to a method and system for updating logistics data.

背景技术Background technique

物流数据的应用场景较多,应用价值也较高,因此,会对物流数据进行采集并存储,但是,物流数据的数据量也是非常大的,因而,也需要对存储的数据进行更新,如将不需要的物流数据丢弃,仅保存部分物流数据等。例如,在现有技术中,在进行物流数据的更新管控的过程中,一般是基于物流数据的形成时间进行处理,具体来说,一般是将形成时间较早的直接予以丢弃,并保留当前时间形成的物流数据,以实现物流数据的新旧更新,如此,就容易出现物流数据的更新管控的可靠度不佳的问题。Logistics data has many application scenarios and high application value. Therefore, logistics data will be collected and stored. However, the amount of logistics data is also very large. Therefore, the stored data also needs to be updated, such as Unnecessary logistics data is discarded, and only part of the logistics data is saved. For example, in the existing technology, in the process of updating and controlling logistics data, the logistics data is generally processed based on the formation time. Specifically, the data with an earlier formation time is generally directly discarded and the current time is retained. Logistics data formed in order to realize the updating of old and new logistics data. In this way, it is easy to have the problem of poor reliability in the update and control of logistics data.

发明内容Contents of the invention

有鉴于此,本发明的目的在于提供一种物流数据的更新方法及系统,以提高物流数据更新的可靠度。In view of this, the object of the present invention is to provide a logistics data updating method and system to improve the reliability of logistics data updating.

为实现上述目的,本发明实施例采用如下技术方案:In order to achieve the above objects, the embodiments of the present invention adopt the following technical solutions:

一种物流数据的更新方法,包括:A method for updating logistics data, including:

对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示,所述历史物流数据包括历史时间段内的每一条历史物流信息,一条所述历史物流信息对应于一个物流货物;Perform key information mining processing on the historical logistics data to be processed to output the historical key information feature representation corresponding to the historical logistics data. The historical logistics data includes each piece of historical logistics information within the historical time period, and one piece of the historical logistics information Corresponds to a logistics cargo;

对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示,所述目标物流数据包括当前时间段内的每一条目标物流信息,一条所述目标物流信息对应于一个物流货物;Perform key information mining processing on the target logistics data to be processed to output the target key information feature representation corresponding to the target logistics data. The target logistics data includes each piece of target logistics information in the current time period, one piece of the target logistics information. Corresponds to a logistics cargo;

对所述历史关键信息特征表示和所述目标关键信息特征表示进行匹配度计算处理,以输出对应的特征表示匹配度;Perform matching degree calculation processing on the historical key information feature representation and the target key information feature representation to output the corresponding feature representation matching degree;

基于所述特征表示匹配度,对所述历史物流数据和所述目标物流数据进行数据更新管控,以至少将所述目标物流数据进行保存。Based on the matching degree of the feature representation, data update management is performed on the historical logistics data and the target logistics data, so as to at least save the target logistics data.

在一些优选的实施例中,在上述物流数据的更新方法中,所述对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示的步骤,包括:In some preferred embodiments, in the above method for updating logistics data, the step of performing key information mining processing on the historical logistics data to be processed to output the feature representation of historical key information corresponding to the historical logistics data includes:

利用优化关键信息挖掘网络,对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示;Utilize the optimized key information mining network to perform key information mining processing on the historical logistics data to be processed to output the historical key information feature representation corresponding to the historical logistics data;

所述对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示的步骤,包括:The step of performing key information mining processing on the target logistics data to be processed to output target key information feature representation corresponding to the target logistics data includes:

利用所述优化关键信息挖掘网络,对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示。The optimized key information mining network is used to perform key information mining processing on the target logistics data to be processed, so as to output the target key information feature representation corresponding to the target logistics data.

在一些优选的实施例中,在上述物流数据的更新方法中,所述更新方法还包括:进行网络优化处理,形成优化关键信息挖掘网络;In some preferred embodiments, in the above-mentioned updating method of logistics data, the updating method further includes: performing network optimization processing to form an optimized key information mining network;

所述进行网络优化处理,形成优化关键信息挖掘网络的步骤,包括:The steps of performing network optimization processing to optimize the key information mining network include:

提取到典型物流数据组合,以及,分析出所述典型物流数据组合对应的多维度物流表征数据,所述典型物流数据组合至少包括第一典型物流数据和第二典型物流数据,所述第二典型物流数据与所述第一典型物流数据之间的数据匹配关系符合预设数据匹配关系,所述多维度物流表征数据包括至少两个维度的物流表征数据;Extract typical logistics data combinations, and analyze multi-dimensional logistics characterization data corresponding to the typical logistics data combinations. The typical logistics data combinations include at least first typical logistics data and second typical logistics data. The second typical logistics data combinations are The data matching relationship between the logistics data and the first typical logistics data conforms to the preset data matching relationship, and the multi-dimensional logistics characterization data includes at least two dimensions of logistics characterization data;

将所述多维度物流表征数据中第一维度的物流表征数据进行隐藏操作,以输出对应的隐藏后的多维度物流表征数据;Perform a hiding operation on the logistics characterization data of the first dimension in the multi-dimensional logistics characterization data to output the corresponding hidden multi-dimensional logistics characterization data;

通过初始关键信息挖掘网络,将所述隐藏后的多维度物流表征数据进行关键信息挖掘处理,以输出所述典型物流数据组合对应的典型关键信息特征表示和隐藏数据还原特征表示;Through the initial key information mining network, the hidden multi-dimensional logistics representation data is subjected to key information mining processing to output typical key information feature representations and hidden data restoration feature representations corresponding to the typical logistics data combinations;

基于所述第一维度的物流表征数据、所述隐藏数据还原特征表示和所述典型关键信息特征表示,将所述初始关键信息挖掘网络进行学习代价值的确定操作,以输出所述初始关键信息挖掘网络对应的还原学习代价值和对应的关键信息学习代价值;Based on the first dimension of logistics characterization data, the hidden data restoration feature representation and the typical key information feature representation, the initial key information mining network is used to determine the learning cost value to output the initial key information Mining the corresponding restoration learning cost value and the corresponding key information learning cost value of the network;

依据所述还原学习代价值和所述关键信息学习代价值,将所述初始关键信息挖掘网络进行优化处理,以形成优化关键信息挖掘网络。According to the restored learning cost value and the key information learning cost value, the initial key information mining network is optimized to form an optimized key information mining network.

在一些优选的实施例中,在上述物流数据的更新方法中,所述基于所述第一维度的物流表征数据、所述隐藏数据还原特征表示和所述典型关键信息特征表示,将所述初始关键信息挖掘网络进行学习代价值的确定操作,以输出所述初始关键信息挖掘网络对应的还原学习代价值和对应的关键信息学习代价值的步骤,包括:In some preferred embodiments, in the above method for updating logistics data, based on the logistics characterization data of the first dimension, the hidden data reduction feature representation and the typical key information feature representation, the initial The key information mining network determines the learning cost value to output the restored learning cost value and the corresponding key information learning cost value corresponding to the initial key information mining network, including:

基于所述第一维度的物流表征数据和所述隐藏数据还原特征表示,将所述初始关键信息挖掘网络进行第一学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的还原学习代价值;Based on the first dimension of logistics representation data and the hidden data restoration feature representation, the initial key information mining network is performed to determine the first learning cost value, and the restoration learning code corresponding to the initial key information mining network is output. value;

依据所述典型物流数据组合对应的典型关键信息特征表示,将所述初始关键信息挖掘网络进行第二学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的关键信息学习代价值。Based on the characteristic representation of typical key information corresponding to the typical logistics data combination, the initial key information mining network is used to determine the second learning cost value, and the key information learning cost value corresponding to the initial key information mining network is output.

在一些优选的实施例中,在上述物流数据的更新方法中,所述依据所述典型物流数据组合对应的典型关键信息特征表示,将所述初始关键信息挖掘网络进行第二学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的关键信息学习代价值的步骤,包括:In some preferred embodiments, in the above logistics data updating method, the initial key information mining network is used to determine the second learning cost value based on the typical key information feature representation corresponding to the typical logistics data combination. Operation, the step of outputting the key information learning cost value corresponding to the initial key information mining network includes:

从所述典型物流数据组合包括的第二典型物流数据中,确定出相关典型物流数据和非相关典型物流数据,所述相关典型物流数据和所述第一典型物流数据具有匹配关系,所述非相关典型物流数据和所述第一典型物流数据不具有匹配关系;以及,依据所述典型关键信息特征表示,分析出所述第一典型物流数据和所述相关典型物流数据之间的匹配度,以输出对应的相关维度匹配度,以及,分析出所述第一典型物流数据和所述非相关典型物流数据之间的匹配度,以输出对应的非相关维度匹配度;以及,基于所述相关维度匹配度和非相关维度匹配度,分析出所述初始关键信息挖掘网络对应的关键信息学习代价值;From the second typical logistics data included in the typical logistics data combination, relevant typical logistics data and non-related typical logistics data are determined, the relevant typical logistics data and the first typical logistics data have a matching relationship, and the non-related typical logistics data The relevant typical logistics data and the first typical logistics data do not have a matching relationship; and, based on the characteristic representation of the typical key information, the matching degree between the first typical logistics data and the relevant typical logistics data is analyzed, to output the corresponding relevant dimension matching degree, and to analyze the matching degree between the first typical logistics data and the non-relevant typical logistics data to output the corresponding non-relevant dimension matching degree; and, based on the correlation Dimension matching degree and non-relevant dimension matching degree, analyze the key information learning cost value corresponding to the initial key information mining network;

所述基于所述第一维度的物流表征数据和所述隐藏数据还原特征表示,将所述初始关键信息挖掘网络进行第一学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的还原学习代价值的步骤,包括:Based on the first dimension of logistics characterization data and the hidden data restoration feature representation, the initial key information mining network is used to determine the first learning cost value, and the corresponding restoration of the initial key information mining network is output. Steps to learn value include:

基于所述隐藏数据还原特征表示进行表征数据的还原处理,以输出对应的还原物流表征数据;以及,依据所述第一维度的物流表征数据和所述还原物流表征数据,将所述初始关键信息挖掘网络进行第一学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的还原学习代价值。Reduction processing of characterization data is performed based on the hidden data reduction feature representation to output corresponding reduced logistics characterization data; and, based on the first-dimensional logistics characterization data and the reduced logistics characterization data, the initial key information is The mining network determines the first learning cost value and outputs the restored learning cost value corresponding to the initial key information mining network.

在一些优选的实施例中,在上述物流数据的更新方法中,所述通过初始关键信息挖掘网络,将所述隐藏后的多维度物流表征数据进行关键信息挖掘处理,以输出所述典型物流数据组合对应的典型关键信息特征表示和隐藏数据还原特征表示的步骤,包括:In some preferred embodiments, in the above method for updating logistics data, the hidden multi-dimensional logistics representation data is subjected to key information mining processing through an initial key information mining network to output the typical logistics data. The steps of combining the corresponding typical key information feature representation and the hidden data restoration feature representation include:

将所述隐藏后的多维度物流表征数据进行分布域的整合处理,以输出位于同一分布域的每一个维度对应的初始特征表示;The hidden multi-dimensional logistics representation data is integrated into the distribution domain to output the initial feature representation corresponding to each dimension located in the same distribution domain;

通过初始关键信息挖掘网络,将所述初始特征表示进行聚合处理,以输出所述典型物流数据组合中每一个典型物流数据对应的典型关键信息特征表示;Through the initial key information mining network, the initial feature representation is aggregated to output the typical key information feature representation corresponding to each typical logistics data in the typical logistics data combination;

依据所述典型关键信息特征表示,将所述隐藏后的多维度物流表征数据中的隐藏数据进行还原处理,输出隐藏数据还原特征表示。According to the characteristic representation of typical key information, the hidden data in the hidden multi-dimensional logistics representation data is restored, and the restored characteristic representation of the hidden data is output.

在一些优选的实施例中,在上述物流数据的更新方法中,所述基于所述特征表示匹配度,对所述历史物流数据和所述目标物流数据进行数据更新管控,以至少将所述目标物流数据进行保存的步骤,包括:In some preferred embodiments, in the above method for updating logistics data, the historical logistics data and the target logistics data are subject to data update control based on the feature representation matching degree, so as to at least update the target logistics data. The steps to save logistics data include:

提取到预先配置的参考匹配度;Extract the pre-configured reference matching degree;

将所述参考匹配度和所述特征表示匹配度进行大小比较处理;Perform size comparison processing on the reference matching degree and the feature representation matching degree;

在所述特征表示匹配度大于或等于所述参考匹配度的情况下,将所述历史物流数据和所述目标物流数据都进行保存;When the characteristic representation matching degree is greater than or equal to the reference matching degree, both the historical logistics data and the target logistics data are saved;

在所述特征表示匹配度小于所述参考匹配度的情况下,将所述历史物流数据丢弃,并将所述目标物流数据进行保存。When the feature representation matching degree is less than the reference matching degree, the historical logistics data is discarded and the target logistics data is saved.

本发明实施例还提供一种物流数据的更新系统,包括:Embodiments of the present invention also provide a logistics data updating system, including:

第一关键信息挖掘模块,用于对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示,所述历史物流数据包括历史时间段内的每一条历史物流信息,一条所述历史物流信息对应于一个物流货物;The first key information mining module is used to perform key information mining processing on the historical logistics data to be processed to output the historical key information feature representation corresponding to the historical logistics data. The historical logistics data includes each piece of history within the historical time period. Logistics information, one piece of historical logistics information corresponds to one logistics cargo;

第二关键信息挖掘模块,用于对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示,所述目标物流数据包括当前时间段内的每一条目标物流信息,一条所述目标物流信息对应于一个物流货物;The second key information mining module is used to perform key information mining processing on the target logistics data to be processed, so as to output the target key information feature representation corresponding to the target logistics data. The target logistics data includes each target in the current time period. Logistics information, one piece of target logistics information corresponds to one logistics cargo;

匹配度计算模块,用于对所述历史关键信息特征表示和所述目标关键信息特征表示进行匹配度计算处理,以输出对应的特征表示匹配度;A matching degree calculation module, configured to perform matching degree calculation processing on the historical key information feature representation and the target key information feature representation, to output the corresponding feature representation matching degree;

数据更新管控模块,用于基于所述特征表示匹配度,对所述历史物流数据和所述目标物流数据进行数据更新管控,以至少将所述目标物流数据进行保存。A data update management and control module is configured to perform data update management and control on the historical logistics data and the target logistics data based on the characteristic representation matching degree, so as to at least save the target logistics data.

在一些优选的实施例中,在上述物流数据的更新系统中,所述第一关键信息挖掘模块具体用于:In some preferred embodiments, in the above-mentioned logistics data updating system, the first key information mining module is specifically used to:

利用优化关键信息挖掘网络,对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示;Utilize the optimized key information mining network to perform key information mining processing on the historical logistics data to be processed to output the historical key information feature representation corresponding to the historical logistics data;

所述第二关键信息挖掘模块具体用于:The second key information mining module is specifically used for:

利用所述优化关键信息挖掘网络,对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示。The optimized key information mining network is used to perform key information mining processing on the target logistics data to be processed, so as to output the target key information feature representation corresponding to the target logistics data.

在一些优选的实施例中,在上述物流数据的更新系统中,所述数据更新管控模块具体用于:In some preferred embodiments, in the above-mentioned logistics data update system, the data update management and control module is specifically used to:

提取到预先配置的参考匹配度;Extract the pre-configured reference matching degree;

将所述参考匹配度和所述特征表示匹配度进行大小比较处理;Perform size comparison processing on the reference matching degree and the feature representation matching degree;

在所述特征表示匹配度大于或等于所述参考匹配度的情况下,将所述历史物流数据和所述目标物流数据都进行保存;When the characteristic representation matching degree is greater than or equal to the reference matching degree, both the historical logistics data and the target logistics data are saved;

在所述特征表示匹配度小于所述参考匹配度的情况下,将所述历史物流数据丢弃,并将所述目标物流数据进行保存。When the feature representation matching degree is less than the reference matching degree, the historical logistics data is discarded and the target logistics data is saved.

本发明实施例提供的一种物流数据的更新方法及系统,可以对待处理的历史物流数据进行关键信息挖掘处理,以输出历史物流数据对应的历史关键信息特征表示;对待处理的目标物流数据进行关键信息挖掘处理,以输出目标物流数据对应的目标关键信息特征表示;对历史关键信息特征表示和目标关键信息特征表示进行匹配度计算处理,以输出对应的特征表示匹配度;基于特征表示匹配度,对历史物流数据和目标物流数据进行数据更新管控,以至少将目标物流数据进行保存。基于上述的内容,由于是将历史物流数据和目标物流数据进行特征表示匹配度的计算,使得可以基于得到的特征表示匹配度对历史物流数据和目标物流数据进行数据更新管控,如此,相较于直接基于形成时间进行数据更新管控的常规技术方案,可以提高物流数据更新的可靠度,从而改善现有技术中的不足。The logistics data updating method and system provided by the embodiments of the present invention can perform key information mining processing on the historical logistics data to be processed to output the historical key information feature representation corresponding to the historical logistics data; perform key information mining on the target logistics data to be processed. The information mining process is to output the target key information feature representation corresponding to the target logistics data; the matching degree calculation process is performed on the historical key information feature representation and the target key information feature representation to output the corresponding features to represent the matching degree; based on the feature representation matching degree, Perform data update control on historical logistics data and target logistics data to at least save the target logistics data. Based on the above content, since the feature representation matching degree of historical logistics data and target logistics data is calculated, data update management and control of historical logistics data and target logistics data can be carried out based on the obtained feature representation matching degree. In this way, compared with Conventional technical solutions for data update management and control based directly on formation time can improve the reliability of logistics data updates, thus improving the shortcomings of existing technologies.

为使本发明的上述目的、特征和优点能更明显易懂,下文特举较佳实施例,并配合所附附图,作详细说明如下。In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, preferred embodiments are given below and described in detail with reference to the accompanying drawings.

附图说明Description of drawings

图1为本发明实施例提供的物流数据的更新平台的结构框图。Figure 1 is a structural block diagram of a logistics data update platform provided by an embodiment of the present invention.

图2为本发明实施例提供的物流数据的更新方法包括的各步骤的流程示意图。FIG. 2 is a schematic flowchart of the steps included in the logistics data updating method provided by the embodiment of the present invention.

图3为本发明实施例提供的物流数据的更新系统包括的各模块的示意图。Figure 3 is a schematic diagram of each module included in the logistics data update system provided by the 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,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例只是本发明的一部分实施例,而不是全部的实施例。通常在此处附图中描述和示出的本发明实施例的组件可以以各种不同的配置来布置和设计。In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments These are only some of the embodiments of the present invention, not all of them. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

因此,以下对在附图中提供的本发明的实施例的详细描述并非旨在限制要求保护的本发明的范围,而是仅仅表示本发明的选定实施例。基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。Therefore, the following detailed description of the embodiments of the invention provided in the appended drawings is not intended to limit the scope of the claimed invention, but rather to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

如图1所示,本发明实施例提供了一种物流数据的更新平台。其中,所述物流数据的更新平台可以包括存储器和处理器。As shown in Figure 1, an embodiment of the present invention provides a logistics data update platform. Wherein, the logistics data updating platform may include a memory and a processor.

详细地,所述存储器和处理器之间直接或间接地电性连接,以实现数据的传输或交互。例如,相互之间可通过一条或多条通讯总线或信号线实现电性连接。所述存储器中可以存储有至少一个可以以软件或固件(firmware)的形式,存在的软件功能模块(计算机程序)。所述处理器可以用于执行所述存储器中存储的可执行的计算机程序,从而实现本发明实施例(如后文所述)提供的物流数据的更新方法。In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they can be electrically connected to each other through one or more communication buses or signal lines. The memory may store at least one software function module (computer program) that may exist in the form of software or firmware. The processor may be used to execute an executable computer program stored in the memory, thereby implementing the logistics data updating method provided by embodiments of the present invention (as described later).

可以选择的是,在一些实施方式中,所述存储器可以是,但不限于,随机存取存储器(Random Access Memory,RAM),只读存储器(Read Only Memory,ROM),可编程只读存储器(Programmable Read-Only Memory,PROM),可擦除只读存储器(Erasable ProgrammableRead-Only Memory,EPROM),电可擦除只读存储器(Electric Erasable ProgrammableRead-Only Memory,EEPROM)等。Alternatively, in some implementations, the memory may be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read only memory ( Programmable Read-Only Memory (PROM), Erasable ProgrammableRead-Only Memory (EPROM), Electrically Erasable ProgrammableRead-Only Memory (EEPROM), etc.

可以选择的是,在一些实施方式中,所述处理器可以是一种通用处理器,包括中央处理器(Central Processing Unit,CPU)、网络处理器(Network Processor,NP)、片上系统(System on Chip,SoC)等;还可以是数字信号处理器(DSP)、专用集成电路(ASIC)、现场可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。Alternatively, in some implementations, the processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a system on a chip (System on Chip). Chip, SoC), etc.; it can also be a digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

可以选择的是,在一些实施方式中,所述物流数据的更新平台可以是一种具备数据处理能力的服务器。Alternatively, in some implementations, the logistics data updating platform may be a server with data processing capabilities.

结合图2,本发明实施例还提供一种物流数据的更新方法,可应用于上述物流数据的更新平台。其中,所述物流数据的更新方法有关的流程所定义的方法步骤,可以由所述物流数据的更新平台实现。In conjunction with Figure 2, an embodiment of the present invention also provides a method for updating logistics data, which can be applied to the above-mentioned logistics data update platform. Wherein, the method steps defined in the process related to the logistics data updating method can be implemented by the logistics data updating platform.

下面将对图2所示的具体流程,进行详细阐述。The specific process shown in Figure 2 will be elaborated below.

步骤S110,对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示。Step S110: Perform key information mining processing on the historical logistics data to be processed to output the historical key information feature representation corresponding to the historical logistics data.

在本发明实施例中,所述物流数据的更新平台可以对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示。所述历史物流数据包括历史时间段内的每一条历史物流信息,如历史物流信息1、历史物流信息2、历史物流信息3、历史物流信息4、历史物流信息5、历史物流信息6、历史物流信息7、历史物流信息8、历史物流信息9等,一条所述历史物流信息对应于一个物流货物。示例性地,所述历史物流信息可以包括所述物流货物的货物描述信息、物流发送地信息、物流接收地信息、对应的用户信息等。In the embodiment of the present invention, the logistics data update platform can perform key information mining processing on the historical logistics data to be processed, so as to output the historical key information feature representation corresponding to the historical logistics data. The historical logistics data includes each piece of historical logistics information within the historical time period, such as historical logistics information 1, historical logistics information 2, historical logistics information 3, historical logistics information 4, historical logistics information 5, historical logistics information 6, historical logistics Information 7, historical logistics information 8, historical logistics information 9, etc., one piece of historical logistics information corresponds to one logistics cargo. For example, the historical logistics information may include cargo description information of the logistics goods, logistics sending place information, logistics receiving place information, corresponding user information, etc.

步骤S120,对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示。Step S120: Perform key information mining processing on the target logistics data to be processed to output target key information feature representation corresponding to the target logistics data.

在本发明实施例中,所述物流数据的更新平台可以对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示。所述目标物流数据包括当前时间段内的每一条目标物流信息,如目标物流信息1、目标物流信息2、目标物流信息3、目标物流信息4、目标物流信息5、目标物流信息6、目标物流信息7、目标物流信息8、目标物流信息9等,一条所述目标物流信息对应于一个物流货物。示例性地,所述目标物流信息可以包括所述物流货物的货物描述信息、物流发送地信息、物流接收地信息、对应的用户信息等。In the embodiment of the present invention, the logistics data update platform can perform key information mining processing on the target logistics data to be processed, so as to output the target key information feature representation corresponding to the target logistics data. The target logistics data includes each piece of target logistics information in the current time period, such as target logistics information 1, target logistics information 2, target logistics information 3, target logistics information 4, target logistics information 5, target logistics information 6, target logistics Information 7, target logistics information 8, target logistics information 9, etc., one piece of target logistics information corresponds to one logistics cargo. For example, the target logistics information may include cargo description information of the logistics goods, logistics sending location information, logistics receiving location information, corresponding user information, etc.

步骤S130,对所述历史关键信息特征表示和所述目标关键信息特征表示进行匹配度计算处理,以输出对应的特征表示匹配度。Step S130: Perform matching degree calculation processing on the historical key information feature representation and the target key information feature representation to output a corresponding feature representation matching degree.

在本发明实施例中,所述物流数据的更新平台可以对所述历史关键信息特征表示和所述目标关键信息特征表示进行匹配度计算处理,如余弦相似度的计算,以输出对应的特征表示匹配度。In the embodiment of the present invention, the logistics data update platform can perform matching degree calculation processing, such as cosine similarity calculation, on the historical key information feature representation and the target key information feature representation to output the corresponding feature representation. suitability.

步骤S140,基于所述特征表示匹配度,对所述历史物流数据和所述目标物流数据进行数据更新管控,以至少将所述目标物流数据进行保存。Step S140: Based on the feature representation matching degree, perform data update management and control on the historical logistics data and the target logistics data, so as to at least save the target logistics data.

在本发明实施例中,所述物流数据的更新平台可以基于所述特征表示匹配度,对所述历史物流数据和所述目标物流数据进行数据更新管控,以至少将所述目标物流数据进行保存。In an embodiment of the present invention, the logistics data update platform can perform data update management and control on the historical logistics data and the target logistics data based on the characteristic representation matching degree, so as to at least save the target logistics data. .

基于上述的内容,即步骤S110、步骤S120、步骤S130和步骤S140,由于是将历史物流数据和目标物流数据进行特征表示匹配度的计算,使得可以基于得到的特征表示匹配度对历史物流数据和目标物流数据进行数据更新管控,如此,相较于直接基于形成时间进行数据更新管控的常规技术方案,可以提高物流数据更新的可靠度,从而改善现有技术中的不足。Based on the above content, that is, step S110, step S120, step S130 and step S140, since the historical logistics data and the target logistics data are used to calculate the feature representation matching degree, the historical logistics data and the target logistics data can be compared based on the obtained feature representation matching degree. The target logistics data is subject to data update management and control. In this way, compared with the conventional technical solution of data update control based directly on the formation time, the reliability of logistics data update can be improved, thereby improving the deficiencies in the existing technology.

可以选择的是,在一些实施方式中,上述的步骤S110,即所述对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示的步骤,可以进一步包括以下的一些内容:Optionally, in some embodiments, the above-mentioned step S110, that is, the step of performing key information mining processing on the historical logistics data to be processed to output the historical key information feature representation corresponding to the historical logistics data, may be further Includes some of the following:

利用优化关键信息挖掘网络,对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示。The optimized key information mining network is used to perform key information mining processing on the historical logistics data to be processed, so as to output the historical key information feature representation corresponding to the historical logistics data.

可以选择的是,在一些实施方式中,上述的步骤S120,即所述对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示的步骤,可以进一步包括以下的一些内容:Optionally, in some embodiments, the above-mentioned step S120, that is, the step of performing key information mining processing on the target logistics data to be processed to output the target key information feature representation corresponding to the target logistics data, may be further Includes some of the following:

利用所述优化关键信息挖掘网络,对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示。The optimized key information mining network is used to perform key information mining processing on the target logistics data to be processed, so as to output the target key information feature representation corresponding to the target logistics data.

可以选择的是,在一些实施方式中,所述物流数据的更新方法还可以包括以下步骤,如进行网络优化处理,形成优化关键信息挖掘网络。基于此,所述进行网络优化处理,形成优化关键信息挖掘网络的步骤,可以进一步包括以下的一些内容:Optionally, in some embodiments, the logistics data updating method may also include the following steps, such as performing network optimization processing to form an optimized key information mining network. Based on this, the steps of performing network optimization processing to form an optimized key information mining network may further include the following contents:

提取到典型物流数据组合,以及,分析出所述典型物流数据组合对应的多维度物流表征数据,所述典型物流数据组合至少包括第一典型物流数据和第二典型物流数据,所述第二典型物流数据与所述第一典型物流数据之间的数据匹配关系符合预设数据匹配关系,所述多维度物流表征数据包括至少两个维度的物流表征数据,如图像维度和文本维度等;Extract typical logistics data combinations, and analyze multi-dimensional logistics characterization data corresponding to the typical logistics data combinations. The typical logistics data combinations include at least first typical logistics data and second typical logistics data. The second typical logistics data combinations are The data matching relationship between the logistics data and the first typical logistics data conforms to the preset data matching relationship, and the multi-dimensional logistics characterization data includes at least two dimensions of logistics characterization data, such as image dimensions and text dimensions;

将所述多维度物流表征数据中第一维度的物流表征数据进行隐藏操作,以输出对应的隐藏后的多维度物流表征数据,所述第一维度可以是所述至少两个维度中的任意一个维度,对此不做具体的限定,如所述第一维度属于图像维度时,可以将所述第一维度的物流表征数据对应的部分图像进行隐藏,如通过一帧全白的图像进行替换等;Perform a hiding operation on the logistics characterization data of the first dimension in the multi-dimensional logistics characterization data to output the corresponding hidden multi-dimensional logistics characterization data. The first dimension may be any one of the at least two dimensions. Dimension, there is no specific limit on this. For example, when the first dimension belongs to the image dimension, part of the image corresponding to the logistics representation data of the first dimension can be hidden, such as replacing it with a frame of all-white image, etc. ;

通过初始关键信息挖掘网络,将所述隐藏后的多维度物流表征数据进行关键信息挖掘处理,以输出所述典型物流数据组合对应的典型关键信息特征表示和隐藏数据还原特征表示;Through the initial key information mining network, the hidden multi-dimensional logistics representation data is subjected to key information mining processing to output typical key information feature representations and hidden data restoration feature representations corresponding to the typical logistics data combinations;

基于所述第一维度的物流表征数据、所述隐藏数据还原特征表示和所述典型关键信息特征表示,将所述初始关键信息挖掘网络进行学习代价值的确定操作,以输出所述初始关键信息挖掘网络对应的还原学习代价值和对应的关键信息学习代价值;Based on the first dimension of logistics characterization data, the hidden data restoration feature representation and the typical key information feature representation, the initial key information mining network is used to determine the learning cost value to output the initial key information Mining the corresponding restoration learning cost value and the corresponding key information learning cost value of the network;

依据所述还原学习代价值和所述关键信息学习代价值,将所述初始关键信息挖掘网络进行优化处理,以形成优化关键信息挖掘网络,示例性地,可以对所述还原学习代价值和所述关键信息学习代价值进行加权求和,以输出总的学习代价值,然后,再基于所述总的学习代价值对所述初始关键信息挖掘网络进行优化处理,以形成优化关键信息挖掘网络。According to the restored learning cost value and the key information learning cost value, the initial key information mining network is optimized to form an optimized key information mining network. For example, the restored learning cost value and the key information learning cost value can be The key information learning cost values are weighted and summed to output a total learning cost value, and then the initial key information mining network is optimized based on the total learning cost value to form an optimized key information mining network.

可以选择的是,在一些实施方式中,所述通过初始关键信息挖掘网络,将所述隐藏后的多维度物流表征数据进行关键信息挖掘处理,以输出所述典型物流数据组合对应的典型关键信息特征表示和隐藏数据还原特征表示的步骤,可以进一步包括以下的一些内容:Optionally, in some embodiments, the hidden multi-dimensional logistics representation data is subjected to key information mining processing through an initial key information mining network to output typical key information corresponding to the typical logistics data combination. Feature representation and hidden data The steps to restore feature representation can further include the following:

将所述隐藏后的多维度物流表征数据进行分布域的整合处理,以输出位于同一分布域的每一个维度对应的初始特征表示,示例性地,可以将所述隐藏后的多维度物流表征数据分别映射到同一个分布域中,如此,可以得到位于同一分布域的每一个维度对应的初始特征表示,所述分布域也可以理解为特征空间或数据集等;The hidden multi-dimensional logistics characterization data is integrated into the distribution domain to output an initial feature representation corresponding to each dimension in the same distribution domain. For example, the hidden multi-dimensional logistics characterization data can be They are respectively mapped to the same distribution domain. In this way, the initial feature representation corresponding to each dimension in the same distribution domain can be obtained. The distribution domain can also be understood as a feature space or a data set, etc.;

通过初始关键信息挖掘网络,将所述初始特征表示进行聚合处理,以输出所述典型物流数据组合中每一个典型物流数据对应的典型关键信息特征表示;Through the initial key information mining network, the initial feature representation is aggregated to output the typical key information feature representation corresponding to each typical logistics data in the typical logistics data combination;

依据所述典型关键信息特征表示,将所述隐藏后的多维度物流表征数据中的隐藏数据进行还原处理,输出隐藏数据还原特征表示,也就是说,可以基于所述典型关键信息特征表示,对所述第一维度对应的特征表示进行关联分析预测,以输出对应的隐藏数据还原特征表示。According to the typical key information feature representation, the hidden data in the hidden multi-dimensional logistics representation data is restored, and the hidden data restoration feature representation is output. That is to say, based on the typical key information feature representation, the The feature representation corresponding to the first dimension is subjected to correlation analysis and prediction to output the corresponding hidden data to restore the feature representation.

其中,可以选择的是,在一些实施方式中,所述通过初始关键信息挖掘网络,将所述初始特征表示进行聚合处理,以输出所述典型物流数据组合中每一个典型物流数据对应的典型关键信息特征表示的步骤,可以进一步包括以下的一些内容:Optionally, in some embodiments, the initial feature representation is aggregated through an initial key information mining network to output typical keys corresponding to each typical logistics data in the typical logistics data combination. The steps of information feature representation may further include the following:

搭建初始关键信息挖掘网络,所述初始关键信息挖掘网络包括第一关键信息挖掘单元和第二关键信息挖掘单元;Build an initial key information mining network, the initial key information mining network including a first key information mining unit and a second key information mining unit;

通过所述第一关键信息挖掘单元,对所述初始特征表示进行级联组合,以输出对应的级联初始特征表示(如初始特征表示1-初始特征表示2-初始特征表示3-初始特征表示4),再将所述级联初始特征表示进行关键信息挖掘处理,如进行卷积运算或滤波处理,输出第一典型关键信息特征表示;Through the first key information mining unit, the initial feature representation is cascaded and combined to output the corresponding cascaded initial feature representation (such as initial feature representation 1-initial feature representation 2-initial feature representation 3-initial feature representation 4), then perform key information mining processing on the cascaded initial feature representation, such as convolution operation or filtering processing, and output the first typical key information feature representation;

通过所述第二关键信息挖掘单元,将各所述初始特征表示分别进行关键信息挖掘处理,如进行卷积运算或滤波处理,输出局部关键信息特征表示,再对各所述局部关键信息特征表示进行级联组合(如局部关键信息特征表示1-局部关键信息特征表示2-局部关键信息特征表示3-局部关键信息特征表示4),输出第二典型关键信息特征表示;Through the second key information mining unit, each of the initial feature representations is subjected to key information mining processing, such as convolution operation or filtering processing, and a local key information feature representation is output, and then each of the local key information feature representations is Perform cascade combination (such as local key information feature representation 1-local key information feature representation 2-local key information feature representation 3-local key information feature representation 4), and output the second typical key information feature representation;

对所述第一典型关键信息特征表示和所述第二典型关键信息特征表示进行级联组合(如第一典型关键信息特征表示-第二典型关键信息特征表示),以输出所述典型物流数据组合对应的典型关键信息特征表示。Perform a cascade combination of the first typical key information feature representation and the second typical key information feature representation (such as the first typical key information feature representation - the second typical key information feature representation) to output the typical logistics data Typical key information feature representation corresponding to the combination.

可以选择的是,在一些实施方式中,所述基于所述第一维度的物流表征数据、所述隐藏数据还原特征表示和所述典型关键信息特征表示,将所述初始关键信息挖掘网络进行学习代价值的确定操作,以输出所述初始关键信息挖掘网络对应的还原学习代价值和对应的关键信息学习代价值的步骤,可以进一步包括以下的一些内容:Optionally, in some embodiments, the initial key information mining network is learned based on the first dimension of logistics characterization data, the hidden data restoration feature representation and the typical key information feature representation. The step of determining the cost value to output the restoration learning cost value and the corresponding key information learning cost value corresponding to the initial key information mining network may further include the following contents:

基于所述第一维度的物流表征数据和所述隐藏数据还原特征表示,将所述初始关键信息挖掘网络进行第一学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的还原学习代价值;Based on the first dimension of logistics representation data and the hidden data restoration feature representation, the initial key information mining network is performed to determine the first learning cost value, and the restoration learning code corresponding to the initial key information mining network is output. value;

依据所述典型物流数据组合对应的典型关键信息特征表示,将所述初始关键信息挖掘网络进行第二学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的关键信息学习代价值。Based on the characteristic representation of typical key information corresponding to the typical logistics data combination, the initial key information mining network is used to determine the second learning cost value, and the key information learning cost value corresponding to the initial key information mining network is output.

可以选择的是,在一些实施方式中,所述依据所述典型物流数据组合对应的典型关键信息特征表示,将所述初始关键信息挖掘网络进行第二学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的关键信息学习代价值的步骤,可以进一步包括以下的一些内容:Optionally, in some implementations, based on the characteristic representation of typical key information corresponding to the typical logistics data combination, the initial key information mining network is used to determine the second learning cost value, and the initial key information mining network is outputted. The steps of learning the value of key information corresponding to the key information mining network can further include the following contents:

从所述典型物流数据组合包括的第二典型物流数据中,确定出相关典型物流数据和非相关典型物流数据,所述相关典型物流数据和所述第一典型物流数据具有匹配关系(如数据之间的匹配度大于第一预设匹配度,所述第一预设匹配度可以为0.6、0.8、0.9等),所述非相关典型物流数据和所述第一典型物流数据不具有匹配关系(如数据之间的匹配度小于第二预设匹配度,所述第二预设匹配度可以为0.2、0.3、0.4等);From the second typical logistics data included in the typical logistics data combination, relevant typical logistics data and non-related typical logistics data are determined, and the relevant typical logistics data and the first typical logistics data have a matching relationship (such as between the data The matching degree between is greater than the first preset matching degree, the first preset matching degree can be 0.6, 0.8, 0.9, etc.), the non-correlated typical logistics data and the first typical logistics data do not have a matching relationship ( If the matching degree between data is less than the second preset matching degree, the second preset matching degree may be 0.2, 0.3, 0.4, etc.);

依据所述典型关键信息特征表示,分析出所述第一典型物流数据和所述相关典型物流数据之间的匹配度,以输出对应的相关维度匹配度,以及,分析出所述第一典型物流数据和所述非相关典型物流数据之间的匹配度,以输出对应的非相关维度匹配度;According to the typical key information feature representation, the matching degree between the first typical logistics data and the related typical logistics data is analyzed to output the corresponding related dimension matching degree, and the first typical logistics data is analyzed The matching degree between the data and the non-related typical logistics data to output the corresponding non-related dimension matching degree;

基于所述相关维度匹配度和所述非相关维度匹配度,分析出所述初始关键信息挖掘网络对应的关键信息学习代价值(示例性地,可以将所述相关维度匹配度和对应的实际匹配度进行误差计算,以及,将所述非相关维度匹配度和对应的实际匹配度进行误差计算,然后,再融合两个维度的误差,以得到所述关键信息学习代价值,或者,再不具有实际匹配度的情况下,可以将所述相关维度匹配度和1之间的绝对差值的正相关值作为对应的误差,将所述非相关维度匹配度和0之间的绝对差值的正相关值作为对应的误差,然后,再融合两个维度的误差,得到所述关键信息学习代价值)。Based on the relevant dimension matching degree and the non-relevant dimension matching degree, the key information learning cost corresponding to the initial key information mining network is analyzed (for example, the relevant dimension matching degree and the corresponding actual matching can be degree to calculate the error, and calculate the error between the non-correlated dimension matching degree and the corresponding actual matching degree, and then fuse the errors of the two dimensions to obtain the key information learning cost, or no longer have actual matching degree In the case of matching degree, the positive correlation value of the absolute difference between the relevant dimension matching degree and 1 can be used as the corresponding error, and the positive correlation value of the absolute difference between the non-correlated dimension matching degree and 0 can be used as the corresponding error. value as the corresponding error, and then fuse the errors in the two dimensions to obtain the key information learning cost value).

可以选择的是,在一些实施方式中,所述基于所述第一维度的物流表征数据和所述隐藏数据还原特征表示,将所述初始关键信息挖掘网络进行第一学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的还原学习代价值的步骤,可以进一步包括以下的一些内容:Optionally, in some embodiments, based on the first dimension of logistics characterization data and the hidden data restoration feature representation, the initial key information mining network is used to determine the first learning cost value, The step of outputting the restoration learning cost value corresponding to the initial key information mining network may further include the following content:

基于所述隐藏数据还原特征表示进行表征数据的还原处理,如关键信息挖掘的过程可以相反,以输出对应的还原物流表征数据;Perform restoration processing of characterization data based on the hidden data restoration feature representation. For example, the process of key information mining can be reversed to output corresponding restored logistics characterization data;

依据所述第一维度的物流表征数据和所述还原物流表征数据,将所述初始关键信息挖掘网络进行第一学习代价值的确定操作,输出所述初始关键信息挖掘网络对应的还原学习代价值;也就是说,可以计算所述第一维度的物流表征数据和所述还原物流表征数据之间的差异,以得到所述初始关键信息挖掘网络对应的还原学习代价值。According to the first-dimensional logistics characterization data and the restored logistics characterization data, the initial key information mining network is used to determine the first learning cost value, and the restored learning cost value corresponding to the initial key information mining network is output. ; That is to say, the difference between the first dimension of logistics representation data and the reduced logistics representation data can be calculated to obtain the reduction learning cost value corresponding to the initial key information mining network.

可以选择的是,在一些实施方式中,上述的步骤S140,即所述基于所述特征表示匹配度,对所述历史物流数据和所述目标物流数据进行数据更新管控,以至少将所述目标物流数据进行保存的步骤,可以进一步包括以下的一些内容:Optionally, in some embodiments, the above-mentioned step S140, that is, performing data update management and control on the historical logistics data and the target logistics data based on the characteristic representation matching degree, so as to at least update the target logistics data. The steps for saving logistics data can further include the following:

提取到预先配置的参考匹配度,所述参考匹配度的具体数值不受限制,可以根据实际需求进行选择配置,如0.3、0.5、0.7等;Extract the pre-configured reference matching degree. The specific value of the reference matching degree is not limited and can be selected and configured according to actual needs, such as 0.3, 0.5, 0.7, etc.;

将所述参考匹配度和所述特征表示匹配度进行大小比较处理;Perform size comparison processing on the reference matching degree and the feature representation matching degree;

在所述特征表示匹配度大于或等于所述参考匹配度的情况下,将所述历史物流数据和所述目标物流数据都进行保存;When the characteristic representation matching degree is greater than or equal to the reference matching degree, both the historical logistics data and the target logistics data are saved;

在所述特征表示匹配度小于所述参考匹配度的情况下,将所述历史物流数据丢弃,并将所述目标物流数据进行保存。When the feature representation matching degree is less than the reference matching degree, the historical logistics data is discarded and the target logistics data is saved.

其中,可以选择的是,在另一些实施方式中,上述的步骤S140,即所述基于所述特征表示匹配度,对所述历史物流数据和所述目标物流数据进行数据更新管控,以至少将所述目标物流数据进行保存的步骤,可以进一步包括以下的一些内容:Optionally, in other embodiments, the above-mentioned step S140, that is, performing data update management and control on the historical logistics data and the target logistics data based on the characteristic representation matching degree, so as to at least update the historical logistics data and the target logistics data. The step of saving the target logistics data may further include the following contents:

提取到预先配置的参考匹配度,所述参考匹配度的具体数值不受限制,可以根据实际需求进行选择配置,如0.3、0.5、0.7等;Extract the pre-configured reference matching degree. The specific value of the reference matching degree is not limited and can be selected and configured according to actual needs, such as 0.3, 0.5, 0.7, etc.;

将所述参考匹配度和所述特征表示匹配度进行大小比较处理;Perform size comparison processing on the reference matching degree and the feature representation matching degree;

在所述特征表示匹配度大于或等于所述参考匹配度的情况下,将所述历史物流数据和所述目标物流数据都进行保存;When the characteristic representation matching degree is greater than or equal to the reference matching degree, both the historical logistics data and the target logistics data are saved;

在所述特征表示匹配度小于所述参考匹配度的情况下,将所述历史物流数据中的部分数据丢弃,并将未被丢弃的部分历史物流数据保存,以及,将所述目标物流数据进行保存。When the matching degree of the feature representation is less than the reference matching degree, part of the data in the historical logistics data is discarded, part of the historical logistics data that has not been discarded is saved, and the target logistics data is processed save.

结合图3,本发明实施例还提供一种物流数据的更新系统,可应用于上述物流数据的更新平台。其中,所述物流数据的更新系统可以包括:In conjunction with Figure 3, an embodiment of the present invention also provides a logistics data update system, which can be applied to the above-mentioned logistics data update platform. Wherein, the logistics data updating system may include:

第一关键信息挖掘模块,用于对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示,所述历史物流数据包括历史时间段内的每一条历史物流信息,一条所述历史物流信息对应于一个物流货物;The first key information mining module is used to perform key information mining processing on the historical logistics data to be processed to output the historical key information feature representation corresponding to the historical logistics data. The historical logistics data includes each piece of history within the historical time period. Logistics information, one piece of historical logistics information corresponds to one logistics cargo;

第二关键信息挖掘模块,用于对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示,所述目标物流数据包括当前时间段内的每一条目标物流信息,一条所述目标物流信息对应于一个物流货物;The second key information mining module is used to perform key information mining processing on the target logistics data to be processed, so as to output the target key information feature representation corresponding to the target logistics data. The target logistics data includes each target in the current time period. Logistics information, one piece of target logistics information corresponds to one logistics cargo;

匹配度计算模块,用于对所述历史关键信息特征表示和所述目标关键信息特征表示进行匹配度计算处理,以输出对应的特征表示匹配度;A matching degree calculation module, configured to perform matching degree calculation processing on the historical key information feature representation and the target key information feature representation, to output the corresponding feature representation matching degree;

数据更新管控模块,用于基于所述特征表示匹配度,对所述历史物流数据和所述目标物流数据进行数据更新管控,以至少将所述目标物流数据进行保存。A data update management and control module is configured to perform data update management and control on the historical logistics data and the target logistics data based on the characteristic representation matching degree, so as to at least save the target logistics data.

可以选择的是,在一些实施方式中,所述第一关键信息挖掘模块具体用于:利用优化关键信息挖掘网络,对待处理的历史物流数据进行关键信息挖掘处理,以输出所述历史物流数据对应的历史关键信息特征表示;Optionally, in some implementations, the first key information mining module is specifically configured to use an optimized key information mining network to perform key information mining processing on the historical logistics data to be processed, so as to output the historical logistics data corresponding to Representation of historical key information features;

可以选择的是,在一些实施方式中,所述第二关键信息挖掘模块具体用于:利用所述优化关键信息挖掘网络,对待处理的目标物流数据进行关键信息挖掘处理,以输出所述目标物流数据对应的目标关键信息特征表示。Optionally, in some implementations, the second key information mining module is specifically configured to: use the optimized key information mining network to perform key information mining processing on the target logistics data to be processed, so as to output the target logistics The target key information features corresponding to the data are represented.

可以选择的是,在一些实施方式中,所述物流数据的更新系统还可以包括其它的软件功能模块,该其它的软件功能模块可以用于:进行网络优化处理,形成优化关键信息挖掘网络。Optionally, in some implementations, the logistics data updating system may also include other software functional modules, and the other software functional modules may be used to: perform network optimization processing to form an optimized key information mining network.

可以选择的是,在一些实施方式中,所述其它的软件功能模块具体可以用于:提取到典型物流数据组合,以及,分析出所述典型物流数据组合对应的多维度物流表征数据,所述典型物流数据组合至少包括第一典型物流数据和第二典型物流数据,所述第二典型物流数据与所述第一典型物流数据之间的数据匹配关系符合预设数据匹配关系,所述多维度物流表征数据包括至少两个维度的物流表征数据;将所述多维度物流表征数据中第一维度的物流表征数据进行隐藏操作,以输出对应的隐藏后的多维度物流表征数据;通过初始关键信息挖掘网络,将所述隐藏后的多维度物流表征数据进行关键信息挖掘处理,以输出所述典型物流数据组合对应的典型关键信息特征表示和隐藏数据还原特征表示;基于所述第一维度的物流表征数据、所述隐藏数据还原特征表示和所述典型关键信息特征表示,将所述初始关键信息挖掘网络进行学习代价值的确定操作,以输出所述初始关键信息挖掘网络对应的还原学习代价值和对应的关键信息学习代价值;依据所述还原学习代价值和所述关键信息学习代价值,将所述初始关键信息挖掘网络进行优化处理,以形成优化关键信息挖掘网络。Optionally, in some implementations, the other software function modules can be specifically used to: extract typical logistics data combinations, and analyze the multi-dimensional logistics characterization data corresponding to the typical logistics data combinations. The typical logistics data combination at least includes first typical logistics data and second typical logistics data. The data matching relationship between the second typical logistics data and the first typical logistics data conforms to the preset data matching relationship. The multi-dimensional The logistics characterization data includes at least two dimensions of logistics characterization data; the first dimension of the logistics characterization data in the multi-dimensional logistics characterization data is hidden to output the corresponding hidden multi-dimensional logistics characterization data; through the initial key information Mining the network, the hidden multi-dimensional logistics representation data is subjected to key information mining processing to output the typical key information feature representation corresponding to the typical logistics data combination and the hidden data restoration feature representation; logistics based on the first dimension The characterization data, the hidden data restoration feature representation and the typical key information feature representation are used to determine the learning cost value of the initial key information mining network to output the restoration learning cost value corresponding to the initial key information mining network. and the corresponding key information learning cost value; based on the restored learning cost value and the key information learning cost value, the initial key information mining network is optimized to form an optimized key information mining network.

可以选择的是,在一些实施方式中,所述数据更新管控模块具体用于:Optionally, in some implementations, the data update management and control module is specifically used to:

提取到预先配置的参考匹配度;将所述参考匹配度和所述特征表示匹配度进行大小比较处理;在所述特征表示匹配度大于或等于所述参考匹配度的情况下,将所述历史物流数据和所述目标物流数据都进行保存;在所述特征表示匹配度小于所述参考匹配度的情况下,将所述历史物流数据丢弃,并将所述目标物流数据进行保存。Extract a preconfigured reference matching degree; compare the reference matching degree and the feature representation matching degree; when the feature representation matching degree is greater than or equal to the reference matching degree, compare the history Both the logistics data and the target logistics data are saved; when the characteristic representation matching degree is less than the reference matching degree, the historical logistics data is discarded and the target logistics data is saved.

综上所述,本发明提供的一种物流数据的更新方法及系统,可以对待处理的历史物流数据进行关键信息挖掘处理,以输出历史物流数据对应的历史关键信息特征表示;对待处理的目标物流数据进行关键信息挖掘处理,以输出目标物流数据对应的目标关键信息特征表示;对历史关键信息特征表示和目标关键信息特征表示进行匹配度计算处理,以输出对应的特征表示匹配度;基于特征表示匹配度,对历史物流数据和目标物流数据进行数据更新管控,以至少将目标物流数据进行保存。基于上述的内容,由于是将历史物流数据和目标物流数据进行特征表示匹配度的计算,使得可以基于得到的特征表示匹配度对历史物流数据和目标物流数据进行数据更新管控,如此,相较于直接基于形成时间进行数据更新管控的常规技术方案,可以提高物流数据更新的可靠度,从而改善现有技术中的不足。In summary, the present invention provides a method and system for updating logistics data, which can perform key information mining processing on the historical logistics data to be processed, so as to output the historical key information feature representation corresponding to the historical logistics data; the target logistics to be processed The data is processed for key information mining to output the target key information feature representation corresponding to the target logistics data; the matching degree calculation process is performed on the historical key information feature representation and the target key information feature representation to output the corresponding features to represent the matching degree; based on the feature representation Matching degree, perform data update control on historical logistics data and target logistics data, so as to at least save the target logistics data. Based on the above content, since the feature representation matching degree of historical logistics data and target logistics data is calculated, data update management and control of historical logistics data and target logistics data can be carried out based on the obtained feature representation matching degree. In this way, compared with Conventional technical solutions for data update management and control based directly on formation time can improve the reliability of logistics data updates, thus improving the shortcomings of existing technologies.

在本发明实施例所提供的几个实施例中,应该理解到,所揭露的装置和方法,也可以通过其它的方式实现。以上所描述的装置和方法实施例仅仅是示意性的,例如,附图中的流程图和框图显示了根据本发明的多个实施例的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上,流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分,所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意,在有些作为替换的实现方式中,方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如,两个连续的方框实际上可以基本并行地执行,它们有时也可以按相反的顺序执行,这依所涉及的功能而定。也要注意的是,框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合,可以用执行规定的功能或动作的专用的基于硬件的系统来实现,或者可以用专用硬件与计算机指令的组合来实现。In the several embodiments provided by the embodiments of the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device and method embodiments described above are only illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show possible implementation architectures of the devices, methods and computer program products according to multiple embodiments of the present invention. Functionality and operation. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more components for implementing the specified logical function(s). Executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two consecutive blocks may actually execute substantially in parallel, or they may sometimes execute in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts. , or can be implemented using a combination of specialized hardware and computer instructions.

另外,在本发明各个实施例中的各功能模块可以集成在一起形成一个独立的部分,也可以是各个模块单独存在,也可以两个或两个以上模块集成形成一个独立的部分。In addition, each functional module in various embodiments of the present invention can be integrated together to form an independent part, each module can exist alone, or two or more modules can be integrated to form an independent part.

所述功能如果以软件功能模块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,本发明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(可以是个人计算机,电子设备,或者网络设备等)执行本发明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、移动硬盘、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。需要说明的是,在本文中,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. . It should be noted that, as used herein, the terms "include", "comprises" or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or device that includes a series of elements not only includes those elements, It also includes other elements not expressly listed or inherent in the process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or apparatus that includes the stated element.

以上所述仅为本发明的优选实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明可以有各种更改和变化。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims (10)

1. A method for updating logistic data, comprising:
performing key information mining processing on historical logistics data to be processed so as to output historical key information characteristic representation corresponding to the historical logistics data, wherein the historical logistics data comprises each piece of historical logistics information in a historical time period, and one piece of historical logistics information corresponds to one logistics cargo;
performing key information mining processing on target logistics data to be processed to output target key information characteristic representation corresponding to the target logistics data, wherein the target logistics data comprises target logistics information of each item in the current time period, and one piece of target logistics information corresponds to one logistics cargo;
performing matching degree calculation processing on the historical key information feature representation and the target key information feature representation to output corresponding feature representation matching degree;
And based on the characteristic representation matching degree, carrying out data updating management and control on the historical logistics data and the target logistics data so as to at least save the target logistics data.
2. The method for updating logistics data according to claim 1, wherein the step of performing key information mining processing on the historical logistics data to be processed to output a historical key information feature representation corresponding to the historical logistics data comprises the steps of:
performing key information mining processing on the historical logistics data to be processed by using an optimized key information mining network so as to output a historical key information characteristic representation corresponding to the historical logistics data;
the step of performing key information mining processing on target logistics data to be processed to output target key information characteristic representation corresponding to the target logistics data comprises the following steps:
and carrying out key information mining processing on target logistics data to be processed by utilizing the optimized key information mining network so as to output target key information characteristic representation corresponding to the target logistics data.
3. The method for updating physical distribution data according to claim 2, wherein the method for updating further comprises: performing network optimization processing to form an optimized key information mining network;
The step of performing network optimization processing to form an optimized key information mining network comprises the following steps:
extracting a typical logistics data combination, and analyzing multi-dimensional logistics characterization data corresponding to the typical logistics data combination, wherein the typical logistics data combination at least comprises first typical logistics data and second typical logistics data, a data matching relationship between the second typical logistics data and the first typical logistics data accords with a preset data matching relationship, and the multi-dimensional logistics characterization data comprises logistics characterization data with at least two dimensions;
hiding the logistics characterization data of the first dimension in the multi-dimensional logistics characterization data to output corresponding hidden multi-dimensional logistics characterization data;
performing key information mining processing on the hidden multidimensional logistics characterization data through an initial key information mining network to output typical key information characteristic representation and hidden data restoration characteristic representation corresponding to the typical logistics data combination;
based on the logistics characterization data, the hidden data reduction feature representation and the typical key information feature representation of the first dimension, determining a learning cost value of the initial key information mining network so as to output a reduction learning cost value corresponding to the initial key information mining network and a corresponding key information learning cost value;
And carrying out optimization processing on the initial key information mining network according to the reduction learning cost value and the key information learning cost value so as to form an optimized key information mining network.
4. The method for updating logistics data according to claim 3, wherein the step of performing a learning cost value determining operation on the initial key information mining network based on the logistics characterization data, the hidden data restoration feature representation and the typical key information feature representation in the first dimension to output a restoration learning cost value corresponding to the initial key information mining network and a corresponding key information learning cost value comprises:
based on the logistics characterization data of the first dimension and the hidden data restoration characteristic representation, carrying out first learning cost value determination operation on the initial key information mining network, and outputting restoration learning cost values corresponding to the initial key information mining network;
and according to the typical key information characteristic representation corresponding to the typical logistics data combination, carrying out the second learning cost value determining operation on the initial key information mining network, and outputting the key information learning cost value corresponding to the initial key information mining network.
5. The method for updating physical distribution data according to claim 4, wherein the step of performing a second learning cost value determination operation on the initial critical information mining network according to the representative critical information feature representation corresponding to the representative physical distribution data combination, and outputting the critical information learning cost value corresponding to the initial critical information mining network includes:
determining relevant typical logistics data and irrelevant typical logistics data from second typical logistics data included in the typical logistics data combination, wherein the relevant typical logistics data and the first typical logistics data have a matching relationship, and the irrelevant typical logistics data and the first typical logistics data do not have a matching relationship; according to the typical key information characteristic representation, analyzing the matching degree between the first typical logistics data and the related typical logistics data to output corresponding related dimension matching degree, and analyzing the matching degree between the first typical logistics data and the non-related typical logistics data to output corresponding non-related dimension matching degree; based on the relevant dimension matching degree and the irrelevant dimension matching degree, analyzing the key information learning cost value corresponding to the initial key information mining network;
The step of restoring the characteristic representation based on the logistics characterization data and the hidden data in the first dimension, performing a first learning cost value determining operation on the initial key information mining network, and outputting a restoring learning cost value corresponding to the initial key information mining network includes:
performing restoration processing of the characterization data based on the hidden data restoration characteristic representation to output corresponding restored logistics characterization data; and according to the logistics characterization data and the restored logistics characterization data of the first dimension, determining a first learning cost value of the initial key information mining network, and outputting a restored learning cost value corresponding to the initial key information mining network.
6. The method for updating logistics data according to claim 3, wherein the step of performing key information mining processing on the hidden multidimensional logistics characterization data through an initial key information mining network to output a typical key information feature representation and a hidden data restoration feature representation corresponding to the typical logistics data combination comprises the steps of:
carrying out integration processing of the distribution domain on the hidden multidimensional stream characterization data so as to output initial feature representations corresponding to each dimension in the same distribution domain;
The initial characteristic representation is subjected to aggregation processing through an initial key information mining network so as to output a typical key information characteristic representation corresponding to each typical logistics data in the typical logistics data combination;
and according to the typical key information characteristic representation, carrying out reduction processing on hidden data in the hidden multidimensional stream characterization data, and outputting hidden data reduction characteristic representation.
7. The method for updating physical distribution data according to any one of claims 1 to 6, wherein the step of performing data updating management on the historical physical distribution data and the target physical distribution data based on the feature representation matching degree to save at least the target physical distribution data comprises the steps of:
extracting a pre-configured reference matching degree;
comparing the reference matching degree with the characteristic representation matching degree;
storing the historical logistics data and the target logistics data under the condition that the characteristic representation matching degree is larger than or equal to the reference matching degree;
and discarding the historical logistics data and storing the target logistics data under the condition that the characteristic representation matching degree is smaller than the reference matching degree.
8. A system for updating logistic data, comprising:
the first key information mining module is used for carrying out key information mining processing on historical logistics data to be processed so as to output historical key information characteristic representation corresponding to the historical logistics data, wherein the historical logistics data comprises each piece of historical logistics information in a historical time period, and one piece of historical logistics information corresponds to one logistics cargo;
the second key information mining module is used for carrying out key information mining processing on target logistics data to be processed so as to output target key information characteristic representation corresponding to the target logistics data, wherein the target logistics data comprises each item of target logistics information in the current time period, and one piece of target logistics information corresponds to one piece of logistics goods;
the matching degree calculation module is used for carrying out matching degree calculation processing on the historical key information feature representation and the target key information feature representation so as to output corresponding feature representation matching degree;
and the data updating management and control module is used for carrying out data updating management and control on the historical logistics data and the target logistics data based on the characteristic representation matching degree so as to at least save the target logistics data.
9. The system for updating logistics data according to claim 8, wherein the first key information mining module is specifically configured to:
performing key information mining processing on the historical logistics data to be processed by using an optimized key information mining network so as to output a historical key information characteristic representation corresponding to the historical logistics data;
the second key information mining module is specifically configured to:
and carrying out key information mining processing on target logistics data to be processed by utilizing the optimized key information mining network so as to output target key information characteristic representation corresponding to the target logistics data.
10. The system for updating physical distribution data according to claim 8, wherein the data updating management module is specifically configured to:
extracting a pre-configured reference matching degree;
comparing the reference matching degree with the characteristic representation matching degree;
storing the historical logistics data and the target logistics data under the condition that the characteristic representation matching degree is larger than or equal to the reference matching degree;
and discarding the historical logistics data and storing the target logistics data under the condition that the characteristic representation matching degree is smaller than the reference matching degree.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117807377A (en) * 2024-03-01 2024-04-02 深圳市快金数据技术服务有限公司 Multidimensional logistics data mining and predicting method and system
CN117910906A (en) * 2024-02-19 2024-04-19 广东康利达物联科技有限公司 Data visualization method and system applied to intelligent logistics

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106547740A (en) * 2016-11-24 2017-03-29 四川无声信息技术有限公司 Text message processing method and device
CN109597881A (en) * 2018-12-17 2019-04-09 北京百度网讯科技有限公司 Matching degree determines method, apparatus, equipment and medium
CN110609958A (en) * 2019-09-19 2019-12-24 Oppo广东移动通信有限公司 Data push method, device, electronic device and storage medium
CN112182295A (en) * 2019-07-05 2021-01-05 浙江宇视科技有限公司 Business processing method and device based on behavior prediction and electronic equipment
WO2021139146A1 (en) * 2020-01-09 2021-07-15 平安国际智慧城市科技股份有限公司 Information recommendation method, device, computer-readable storage medium, and apparatus
CN114693409A (en) * 2022-04-24 2022-07-01 中国工商银行股份有限公司 Product matching method, device, computer equipment, storage medium and program product
CN115049446A (en) * 2021-03-09 2022-09-13 腾讯科技(深圳)有限公司 Merchant identification method and device, electronic equipment and computer readable medium
CN115544214A (en) * 2022-12-02 2022-12-30 广州数说故事信息科技有限公司 Event processing method and device and computer readable storage medium
CN116361440A (en) * 2023-04-06 2023-06-30 江苏擎虎智能科技有限公司 Digital financial product session interaction method and system based on artificial intelligence
CN116467606A (en) * 2023-03-03 2023-07-21 苏州凌云光工业智能技术有限公司 Determination method, device, equipment and medium of decision suggestion information

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106547740A (en) * 2016-11-24 2017-03-29 四川无声信息技术有限公司 Text message processing method and device
CN109597881A (en) * 2018-12-17 2019-04-09 北京百度网讯科技有限公司 Matching degree determines method, apparatus, equipment and medium
CN112182295A (en) * 2019-07-05 2021-01-05 浙江宇视科技有限公司 Business processing method and device based on behavior prediction and electronic equipment
CN110609958A (en) * 2019-09-19 2019-12-24 Oppo广东移动通信有限公司 Data push method, device, electronic device and storage medium
WO2021139146A1 (en) * 2020-01-09 2021-07-15 平安国际智慧城市科技股份有限公司 Information recommendation method, device, computer-readable storage medium, and apparatus
CN115049446A (en) * 2021-03-09 2022-09-13 腾讯科技(深圳)有限公司 Merchant identification method and device, electronic equipment and computer readable medium
CN114693409A (en) * 2022-04-24 2022-07-01 中国工商银行股份有限公司 Product matching method, device, computer equipment, storage medium and program product
CN115544214A (en) * 2022-12-02 2022-12-30 广州数说故事信息科技有限公司 Event processing method and device and computer readable storage medium
CN116467606A (en) * 2023-03-03 2023-07-21 苏州凌云光工业智能技术有限公司 Determination method, device, equipment and medium of decision suggestion information
CN116361440A (en) * 2023-04-06 2023-06-30 江苏擎虎智能科技有限公司 Digital financial product session interaction method and system based on artificial intelligence

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HUI GAO, YU-YU WU: "Research on the Development of Personalized Recommendation", 2021 INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SCIENCE AND ARTIFICIAL INTELLIGENCE (CISAI), 28 February 2022 (2022-02-28) *
董庆兴;李赛;张大斌;李延晖;: "基于匹配属性相似度的应急决策方案推荐方法", 控制与决策, no. 07, 27 April 2016 (2016-04-27) *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117910906A (en) * 2024-02-19 2024-04-19 广东康利达物联科技有限公司 Data visualization method and system applied to intelligent logistics
CN117807377A (en) * 2024-03-01 2024-04-02 深圳市快金数据技术服务有限公司 Multidimensional logistics data mining and predicting method and system
CN117807377B (en) * 2024-03-01 2024-05-14 深圳市快金数据技术服务有限公司 Multidimensional logistics data mining and predicting method and system

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