技术领域Technical Field
本申请涉及计算机及网络技术领域，特别涉及一种汽车网站的指标框架生成方法、装置、电子设备及存储介质。The present application relates to the field of computer and network technology, and in particular to a method, device, electronic device and storage medium for generating an indicator framework of an automobile website.
背景技术Background Art
在汽车公司经营活动中，品牌官网和经销商网站是消费者获取该品牌汽车信息的主要渠道，因此对网站表现的衡量在汽车品牌线上运营中占据重要地位，相关部门需要对汽车官网运营现状进行监测和报告。In the business activities of automobile companies, the brand's official website and dealer websites are the main channels for consumers to obtain information about the brand's automobiles. Therefore, the measurement of website performance occupies an important position in the online operations of automobile brands. Relevant departments need to monitor and report on the current operating status of automobile official websites.
相关技术中，通常依据商业常识来人为界定汽车网站的指标框架，并转化漏斗以应用于报表中。In related technologies, the indicator framework of the automobile website is usually manually defined based on business common sense, and the conversion funnel is applied to the report.
但是，实际运营中消费者个体差异大、网站数据变化繁多，依靠直觉人为界定主观性强，界定结果容易与实际情况不符，不能满足实际网站运营中监测的需要。However, in actual operations, consumers vary greatly from one individual to another and website data changes a lot. Manual definition based on intuition is highly subjective and the definition results are easily inconsistent with the actual situation, which cannot meet the monitoring needs in actual website operations.
发明内容Summary of the invention
本申请提供一种汽车网站的指标框架生成方法、装置、电子设备及存储介质，以解决通常采用人工界定方式生成指标框架，效率较低，且最终生成的指标框架合理性较差，无法满足报表开发及监测需求等问题。The present application provides a method, device, electronic device and storage medium for generating an indicator framework for an automobile website, so as to solve the problems that the indicator framework is usually generated by manual definition, which is inefficient and the finally generated indicator framework is not rational enough and cannot meet the needs of report development and monitoring.
本申请第一方面实施例提供一种汽车网站的指标框架生成方法，包括以下步骤：从相关汽车网站中爬取目标汽车的价值数据和多个网站行为指标特征；根据所述价值数据和所述多个网站行为指标特征计算每个指标特征与价值之间的相关性等级，基于所述相关性等级匹配每个指标特征在指标特征框架中的实际位置；对所述多个网站行为指标特征进行指标特征分簇，得到分簇结果，根据所述分簇结果确定所述指标特征框架的实际层数，并根据所述每个指标特征在指标特征框架中的实际位置和所述实际层数生成所述汽车网站中所述目标汽车的指标特征框架。A first aspect of the present application provides an indicator framework generation method for an automobile website, comprising the following steps: crawling value data and multiple website behavior indicator features of a target automobile from relevant automobile websites; calculating the correlation level between each indicator feature and the value based on the value data and the multiple website behavior indicator features, and matching the actual position of each indicator feature in an indicator feature framework based on the correlation level; clustering the multiple website behavior indicator features to obtain clustering results, determining the actual number of layers of the indicator feature framework based on the clustering results, and generating the indicator feature framework of the target automobile in the automobile website based on the actual position of each indicator feature in the indicator feature framework and the actual number of layers.
可选地，所述对所述多个网站行为指标特征进行指标特征分簇，得到分簇结果，包括：对所述多个网站行为指标特征进行层次聚类，得到聚类结果；对所述多个网站行为指标特征进行因子分析，得到分析结果；根据所述聚类结果和所述分析结果进行指标特征分簇，得到所述分簇结果。Optionally, clustering the multiple website behavior indicator features to obtain clustering results includes: performing hierarchical clustering on the multiple website behavior indicator features to obtain clustering results; performing factor analysis on the multiple website behavior indicator features to obtain analysis results; and clustering the indicator features according to the clustering results and the analysis results to obtain the clustering results.
可选地，所述对所述多个网站行为指标特征进行层次聚类，得到聚类结果，包括：计算所述多个网站行为指标特征中任意指标特征之间的欧氏距离；将所述欧氏距离小于预设距离的指标特征组合，得到第一新指标特征；计算所述第一新指标特征与所述多个网站行为指标特征中剩余特征之间的欧氏距离，直到所有特征之间的欧氏距离大于或等于所述预设距离，生成特征的树形结构。Optionally, the hierarchical clustering of the multiple website behavior indicator features to obtain a clustering result includes: calculating the Euclidean distance between any indicator features among the multiple website behavior indicator features; combining the indicator features whose Euclidean distance is less than a preset distance to obtain a first new indicator feature; calculating the Euclidean distance between the first new indicator feature and the remaining features among the multiple website behavior indicator features until the Euclidean distance between all features is greater than or equal to the preset distance, thereby generating a tree structure of features.
可选地，所述对所述多个网站行为指标特征进行因子分析，得到分析结果，包括：计算所述多个网站行为指标特征中每个指标特征在多个目标因子上的载荷系数；将每个目标因子上大于预设系数的载荷系数作为所述每个目标因子的目标载荷系数；根据所述每个目标因子的目标载荷系数生成因子载荷矩阵。Optionally, the factor analysis is performed on the multiple website behavior indicator features to obtain analysis results, including: calculating the load coefficient of each indicator feature in the multiple website behavior indicator features on multiple target factors; taking the load coefficient on each target factor that is greater than a preset coefficient as the target load coefficient of each target factor; and generating a factor load matrix based on the target load coefficient of each target factor.
可选地，在根据所述价值数据和所述多个网站行为指标特征计算每个指标特征与价值之间的相关性等级之前，还包括：对所述价值数据和所述多个网站行为指标特征进行数据清洗处理，得到处理后的指标特征和价值数据。Optionally, before calculating the correlation level between each indicator feature and the value based on the value data and the multiple website behavior indicator features, it also includes: performing data cleaning processing on the value data and the multiple website behavior indicator features to obtain processed indicator features and value data.
可选地，在根据所述价值数据和所述多个网站行为指标特征计算每个指标特征与价值之间的相关性等级之前，还包括：根据每个处理后的指标特征和延迟算子生成多个第二新指标特征。Optionally, before calculating the correlation level between each indicator feature and the value according to the value data and the multiple website behavior indicator features, it also includes: generating multiple second new indicator features according to each processed indicator feature and the delay operator.
可选地，根据所述价值数据和所述多个网站行为指标特征计算每个指标特征与价值之间的相关性等级，包括：计算每个第二新指标特征与价值之间的Pearson相关系数；根据所述Pearson相关系数生成相关系数矩阵，并将所述相关系数矩阵中数值按照预设顺序渲染，生成相关系数热力图；识别所述相关系数热力图，得到所述每个指标特征与价值之间的相关性等级。Optionally, the correlation level between each indicator feature and the value is calculated based on the value data and the multiple website behavior indicator features, including: calculating the Pearson correlation coefficient between each second new indicator feature and the value; generating a correlation coefficient matrix based on the Pearson correlation coefficient, and rendering the values in the correlation coefficient matrix in a preset order to generate a correlation coefficient heat map; identifying the correlation coefficient heat map to obtain the correlation level between each indicator feature and the value.
本申请第二方面实施例提供一种汽车网站的指标框架生成装置，包括：爬取模块，用于从汽车网站中爬取目标汽车的价值数据和多个网站行为指标特征；匹配模块，用于根据所述价值数据和所述多个网站行为指标特征计算每个指标特征与价值之间的相关性等级，基于所述相关性等级匹配每个指标特征在指标特征框架中的实际位置；生成模块，用于对所述多个网站行为指标特征进行指标特征分簇，得到分簇结果，根据所述分簇结果确定所述指标特征框架的实际层数，根据所述每个指标特征在指标特征框架中的实际位置和所述实际层数生成所述汽车网站中所述目标汽车的指标特征框架。The second aspect of the present application provides an indicator framework generation device for an automobile website, including: a crawling module, used to crawl value data and multiple website behavior indicator features of a target automobile from the automobile website; a matching module, used to calculate the correlation level between each indicator feature and the value based on the value data and the multiple website behavior indicator features, and match the actual position of each indicator feature in the indicator feature framework based on the correlation level; a generation module, used to cluster the multiple website behavior indicator features to obtain clustering results, determine the actual number of layers of the indicator feature framework based on the clustering results, and generate the indicator feature framework of the target automobile in the automobile website based on the actual position of each indicator feature in the indicator feature framework and the actual number of layers.
本申请第三方面实施例提供一种电子设备，包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述程序，以实现如上述实施例所述的汽车网站的指标框架生成方法。The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for generating an indicator framework for an automobile website as described in the above embodiment.
本申请第四方面实施例提供一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行，以用于实现如上述实施例所述的汽车网站的指标框架生成方法。A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the method for generating an indicator framework for an automobile website as described in the above embodiment.
由此，本申请至少具有如下有益效果：Therefore, this application has at least the following beneficial effects:
可以基于数据统计方式自动生成汽车网站中汽车的指标框架，提升框架的生成效率，避免人为因素对于指标框架界定的影响，提升框架界定的科学性，使得指标框架的构成更加合理，满足报表开发及监测需求。由此，解决了通常采用人工界定方式生成指标框架，效率较低，且最终生成的指标框架合理性较差，无法满足报表开发及监测需求等技术问题。The indicator framework of automobiles in automobile websites can be automatically generated based on data statistics, which improves the efficiency of framework generation, avoids the influence of human factors on the definition of indicator framework, improves the scientific nature of framework definition, makes the structure of indicator framework more reasonable, and meets the needs of report development and monitoring. Therefore, it solves the technical problems that the indicator framework is usually generated by manual definition, which is inefficient, and the indicator framework generated is not reasonable and cannot meet the needs of report development and monitoring.
本申请附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本申请的实践了解到。Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
本申请上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：The above and/or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:
图1为根据本申请实施例提供的一种汽车网站的指标框架生成方法的流程图；FIG1 is a flow chart of a method for generating an indicator framework of an automobile website according to an embodiment of the present application;
图2为根据本申请实施例提供的输出示例图；FIG2 is an output example diagram provided according to an embodiment of the present application;
图3为根据本申请实施例提供的热力图输出示例图；FIG3 is an example diagram of a thermal map output according to an embodiment of the present application;
图4为根据本申请实施例提供的层次聚类的一个欧式距离矩阵示例图；FIG4 is an example diagram of a Euclidean distance matrix of hierarchical clustering provided according to an embodiment of the present application;
图5为根据本申请实施例提供的层次聚类的另一个欧式距离矩阵示例图；FIG5 is another example diagram of a Euclidean distance matrix for hierarchical clustering according to an embodiment of the present application;
图6为根据本申请实施例提供的层次聚类输出示例图；FIG6 is a diagram showing an example of hierarchical clustering output according to an embodiment of the present application;
图7为根据本申请实施例提供的汽车网站的指标框架生成方法的工作流程图；FIG7 is a flowchart of a method for generating an indicator framework for an automobile website according to an embodiment of the present application;
图8为根据本申请实施例提供的因子分析输出示例图；FIG8 is a diagram showing an example of factor analysis output according to an embodiment of the present application;
图9为根据本申请实施例提供的汽车网站的指标框架生成装置的示例图；FIG9 is an exemplary diagram of an indicator framework generating device for an automobile website according to an embodiment of the present application;
图10为根据本申请实施例提供的电子设备的结构示意图。FIG. 10 is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.
具体实施方式DETAILED DESCRIPTION
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本申请，而不能理解为对本申请的限制。Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.
下面参考附图描述本申请实施例的汽车网站的指标框架生成方法、装置、电子设备及存储介质。针对上述背景技术中提到的通常采用人工界定方式生成指标框架，效率较低，且最终生成的指标框架合理性较差，无法满足报表开发及监测需求的问题，本申请提供了一种汽车网站的指标框架生成方法，在该方法中，可以基于数据统计方式自动生成汽车网站中汽车的指标框架，提升框架的生成效率，避免人为因素对于指标框架界定的影响，提升框架界定的科学性，使得指标框架的构成更加合理，满足报表开发及监测需求。由此，解决了通常采用人工界定方式生成指标框架，效率较低，且最终生成的指标框架合理性较差，无法满足报表开发及监测需求等问题。The following describes the indicator framework generation method, device, electronic device and storage medium of the automobile website of the embodiment of the present application with reference to the accompanying drawings. In view of the problem mentioned in the above background technology that the indicator framework is usually generated by manual definition, which is inefficient, and the rationality of the indicator framework finally generated is poor, and it cannot meet the needs of report development and monitoring, the present application provides an indicator framework generation method for an automobile website. In this method, the indicator framework of the automobile in the automobile website can be automatically generated based on data statistics, the efficiency of framework generation is improved, the influence of human factors on the definition of the indicator framework is avoided, the scientific nature of the framework definition is improved, and the composition of the indicator framework is more reasonable to meet the needs of report development and monitoring. As a result, the problem that the indicator framework is usually generated by manual definition, which is inefficient, and the rationality of the indicator framework finally generated is poor, and it cannot meet the needs of report development and monitoring is solved.
具体而言，图1为本申请实施例所提供的一种汽车的指标框架生成方法的流程示意图。Specifically, FIG1 is a flow chart of a method for generating an indicator framework of an automobile provided in an embodiment of the present application.
如图1所示，该汽车网站的指标框架生成方法包括以下步骤：As shown in Figure 1, the indicator framework generation method of the automobile website includes the following steps:
在步骤S101中，从相关汽车网站中爬取目标汽车的价值数据和多个网站行为指标特征。In step S101, the value data of the target car and multiple website behavior indicator features are crawled from relevant automobile websites.
其中，相关汽车网站可以是目标汽车的品牌官网或第三方网站等；价值数据是指车辆的具体价值，比如可以是指车辆的销量等；网站行为指标特征是指用于引导进入网站的用户进行浏览的相关特征，例如图2中每一层即为一个网站行为指标特征；目标汽车可以根据实际情况具体设置或选择，对此不作具体限定。Among them, the relevant automobile website can be the brand official website of the target automobile or a third-party website, etc.; the value data refers to the specific value of the vehicle, such as the sales volume of the vehicle, etc.; the website behavior indicator feature refers to the relevant features used to guide users entering the website to browse, for example, each layer in Figure 2 is a website behavior indicator feature; the target automobile can be specifically set or selected according to the actual situation, and there is no specific limitation on this.
可以理解的是，当获取用于生成指标架构的数据时，本申请实施例可以采用爬取的方式从汽车网站得到相关数据，且可以通过多种方式实现爬取操作，例如Excel、Python等。It is understandable that when obtaining data for generating an indicator architecture, the embodiment of the present application can obtain relevant data from the automobile website by crawling, and the crawling operation can be implemented in a variety of ways, such as Excel, Python, etc.
在具体应用时，本申请的实施例可以获取汽车品牌网站每周会话级别统计数据指标x1，x2…,xn以及该品牌的销量数据y，最终数据为x1，x2…,xn,y，即共有m行(n+1)列数据，其中一行为一周数据，一列为一个指标特征。In specific applications, the embodiments of the present application can obtain weekly session-level statistical data indicators x1 , x2 …, xn of a car brand website and the brand's sales data y. The final data is x1 , x2 …, xn , y, that is, there are m rows (n+1) columns of data, one row is one week's data, and one column is one indicator feature.
在步骤S102中，根据价值数据和多个网站行为指标特征计算每个指标特征与价值之间的相关性等级，基于相关性等级匹配每个指标特征在指标特征框架中的实际位置。In step S102, the correlation level between each indicator feature and the value is calculated based on the value data and multiple website behavior indicator features, and the actual position of each indicator feature in the indicator feature framework is matched based on the correlation level.
其中，相关性等级可以是指每个指标特征与价值之间相关程度的大小；相关性等级越高，指标特征与价值之间相关性就越强；相关性等级越低，指标特征与价值之间相关性就越弱。Among them, the correlation level can refer to the degree of correlation between each indicator feature and the value; the higher the correlation level, the stronger the correlation between the indicator feature and the value; the lower the correlation level, the weaker the correlation between the indicator feature and the value.
可以理解的是，本申请实施例可以根据每个指标特征与价值之间的相关性等级确定指标特征与价值之间的相关性程度，并根据相关性程度的大小确定每个指标特征的实际位置。It can be understood that the embodiments of the present application can determine the degree of correlation between the indicator feature and the value according to the correlation level between each indicator feature and the value, and determine the actual position of each indicator feature according to the magnitude of the correlation degree.
在本申请实施例中，根据价值数据和多个网站行为指标特征计算每个指标特征与价值之间的相关性等级，包括：计算每个第二新指标特征与价值之间的Pearson相关系数；根据Pearson相关系数生成相关系数矩阵，并将相关系数矩阵中数值按照预设顺序渲染，生成相关系数热力图；识别相关系数热力图，得到每个指标特征与价值之间的相关性等级。In an embodiment of the present application, the correlation level between each indicator feature and the value is calculated based on the value data and multiple website behavior indicator features, including: calculating the Pearson correlation coefficient between each second new indicator feature and the value; generating a correlation coefficient matrix based on the Pearson correlation coefficient, and rendering the values in the correlation coefficient matrix in a preset order to generate a correlation coefficient heat map; identifying the correlation coefficient heat map to obtain the correlation level between each indicator feature and the value.
其中，预设顺序可以是如数值由高到低或由低到高等的顺序，可以根据实际情况进行设置等，对此不作具体限定。The preset order may be an order of values from high to low or from low to high, etc., and may be set according to actual conditions, and is not specifically limited thereto.
可以理解的是，本申请实施例可以通过对指标特征与价值之间的相关性的计算，得到Pearson相关系数并生成系数矩阵，由矩阵相关数值渲染得到相关系数热力图，以读图的方式对每个指标特征与价值之间的相关性等级进行确定。It can be understood that the embodiments of the present application can obtain the Pearson correlation coefficient and generate a coefficient matrix by calculating the correlation between the indicator characteristics and the value, and obtain a correlation coefficient heat map by rendering the matrix related numerical values, so as to determine the correlation level between each indicator characteristic and the value by reading the map.
举例而言，以“经销商查询”为例，加上1阶延迟算子，可以是代表一星期，并创建一个新列：经销商查询_lagl。将处理后的数据进行热力图相关系数计算，生成相关系数矩阵，列对应1～13阶延迟算子xi_lag1,xi_lag2,xi_lag3…,xi_lag13，如“经销商查询”行，第三列为该特征应用3阶延迟算子后与销量数据的Pearson相关系数；在得到相关系数矩阵后按数值高低进行涂色，最终得到相关系数热力图，热力图示例可以如图3所示。For example, taking "dealer query" as an example, adding a 1st-order delay operator can represent one week, and create a new column: dealer query_lagl. The processed data is subjected to heat map correlation coefficient calculation to generate a correlation coefficient matrix, and the columns correspond to 1st to 13th-order delay operatorsxi_lag1 ,xi_lag2 ,xi_lag3 ...,xi_lag13 . For example, in the "dealer query" row, the third column is the Pearson correlation coefficient between the feature and the sales data after applying the 3rd-order delay operator; after obtaining the correlation coefficient matrix, color it according to the value, and finally obtain the correlation coefficient heat map. The heat map example can be shown in Figure 3.
需要说明的是，Pearson相关系数在计算时不仅可以直接利用爬取得到多个网站行为指标特征，而且还可以利用对爬取得到的多个网站行为指标特征处理得到第二新指标特征，对此不作具体限定。其中，第二新指标特征将在后续实施例进行详细阐述，在此不做过多描述。It should be noted that when calculating the Pearson correlation coefficient, not only can multiple website behavior indicator features obtained by crawling be directly used, but also multiple website behavior indicator features obtained by crawling can be processed to obtain a second new indicator feature, which is not specifically limited. Among them, the second new indicator feature will be described in detail in subsequent embodiments, and will not be described in detail here.
下面在计算Pearson相关系数时，以利用第二新指标特征为例进行阐述，具体如下：When calculating the Pearson correlation coefficient, the second new indicator feature is used as an example to illustrate the details as follows:
设各个第二新指标特征为xi，价值为y，Pearson相关系数的计算公式为：Assuming that the characteristics of each second new indicator are xi and the value is y, the calculation formula of Pearson correlation coefficient is:
其中，系数取值范围可以是[-1,1]，若系数值越接近1，则说明该第二新指标特征与价值之间的正相关性越强；越接近-1，则说明两者之间负相关性越强；越接近0则说明两者之间相关性越弱。in, The coefficient value range can be [-1,1]. If the coefficient value is closer to 1, the positive correlation between the second new indicator feature and the value is stronger; the closer it is to -1, the stronger the negative correlation between the two; the closer it is to 0, the weaker the correlation between the two.
在本申请实施例中，在根据价值数据和多个网站行为指标特征计算每个指标特征与价值之间的相关性等级之前，本申请实施例可以通过多种方式对爬取得到的多个网站行为指标特征进行处理，生成第二新指标特征，对此不作具体限定。In an embodiment of the present application, before calculating the correlation level between each indicator feature and the value based on the value data and multiple website behavior indicator features, the embodiment of the present application can process the multiple website behavior indicator features obtained by crawling in a variety of ways to generate a second new indicator feature, and no specific limitation is made to this.
作为一种可能实现的方式，根据爬取得到的多个网站行为指标特征和延迟算子生成多个第二新指标特征。可以理解的是，本申请实施例可以对输入的原始特征即网站行为指标{X1,X2,…,Xn}与延迟算子Β结合，创建多个特征组合{Xi_lag1,Xi_lag2,Xi_lag3,…,Xi_lagg}，其中，Xi_lagj代表Xi应用m阶延迟算子后得到的新特征；As a possible implementation method, multiple second new indicator features are generated based on multiple website behavior indicator features and delay operators obtained by crawling. It can be understood that the embodiment of the present application can combine the input original features, namely the website behavior indicators {X1 ,X2 ,…,Xn } with the delay operator Β to create multiple feature combinations {Xi_lag1 ,Xi_lag2 ,Xi_lag3 ,…,Xi_lagg }, whereXi_lagj represents the new feature obtained afterXi applies the m-order delay operator;
作为另一种可能实现的方式，对价值数据和多个网站行为指标特征进行数据清洗处理，得到处理后的指标特征和价值数据；根据每个处理后的指标特征和延迟算子生成多个第二新指标特征。As another possible implementation method, data cleaning processing is performed on the value data and multiple website behavior indicator features to obtain processed indicator features and value data; and multiple second new indicator features are generated according to each processed indicator feature and delay operator.
其中，延迟算子可以是对时间函数进行的某一延迟操作。The delay operator may be a delay operation performed on a time function.
其中，数据清洗处理可以是指对爬取得到的杂乱无章的原始数据进行提取处理，包括剔除异常值、空值，比如一些埋点造成的数据异常等；本申请实施例可以通过floor&capping等至少一种方法实现数据清洗，对此不作具体限定。Among them, data cleaning processing can refer to the extraction and processing of the disordered original data obtained by crawling, including the elimination of abnormal values and null values, such as data anomalies caused by some buried points; the embodiment of the present application can achieve data cleaning through at least one method such as floor&capping, and there is no specific limitation on this.
可以理解的是，本申请实施例可以先对爬取得到的原始价值数据和网站行为指标特征进行清洗，得到剔除异常值等后的价值数据和指标特征，根据处理后的指标特征和延迟算子生成第二新指标特征。It is understandable that the embodiments of the present application can first clean the original value data and website behavior indicator characteristics obtained by crawling, obtain the value data and indicator characteristics after eliminating outliers, and generate a second new indicator characteristic based on the processed indicator characteristics and delay operator.
下面在进行数据清洗时，以floor&capping方法为例进行阐述，具体如下：When performing data cleaning, the floor&capping method is used as an example to illustrate the details as follows:
对任意一个指标特征xi，认定95％的数据点应该在区间内，其中分别为指标特征xi的平均值和标准差，对于任意的j∈[1,m]有：For any indicator feature xi , it is assumed that 95% of the data points should be in In the range, are the mean and standard deviation of the indicator featurexi respectively. For any j∈[1,m], we have:
对x1，x2…,xn,y进行上述的floor&capping操作，完成数据清洗，并对于对出去价值数据以外的各个特征xi加上延迟算子Bk创建出新特征。The above floor & capping operations are performed on x1 , x2 …, xn , y to complete data cleaning, and a delay operator Bk is added to each featurexi except for the valuable data to create a new feature.
在步骤S103中，对多个网站行为指标特征进行指标特征分簇，得到分簇结果，根据分簇结果确定指标特征框架的实际层数，并根据每个指标特征在指标特征框架中的实际位置和实际层数生成汽车网站中目标汽车的指标特征框架。In step S103, the indicator features of multiple website behavior indicators are clustered to obtain clustering results, the actual number of layers of the indicator feature framework is determined according to the clustering results, and the indicator feature framework of the target car in the car website is generated according to the actual position and actual number of layers of each indicator feature in the indicator feature framework.
可以理解的是，本申请实施例需要对指标框架进行细致划分，可以通过对网站原始指标特征进行分簇的方式，确定指标特征框架的实际层数；以每个指标特征的实际位置和实际层数为坐标，在指标特征框架中具体定位指标特征，以生成目标汽车的汽车网站指标特征框架。It can be understood that the embodiment of the present application requires a detailed division of the indicator framework, and the actual number of layers of the indicator feature framework can be determined by clustering the original indicator features of the website; the indicator features are specifically located in the indicator feature framework with the actual position and actual number of layers of each indicator feature as coordinates to generate the automobile website indicator feature framework of the target automobile.
在本申请实施例中，对多个网站行为指标特征进行指标特征分簇，得到分簇结果，包括：对多个网站行为指标特征进行层次聚类，得到聚类结果；对多个网站行为指标特征进行因子分析，得到分析结果；根据聚类结果和分析结果进行指标特征分簇，得到分簇结果。In an embodiment of the present application, indicator feature clustering is performed on multiple website behavior indicator features to obtain clustering results, including: hierarchical clustering is performed on multiple website behavior indicator features to obtain clustering results; factor analysis is performed on multiple website behavior indicator features to obtain analysis results; indicator feature clustering is performed according to the clustering results and the analysis results to obtain clustering results.
其中，聚类可以是指将物理或抽象对象的集合分成由类似对象组成的多个类的过程；因子分析可以是指研究从变量群中提取共性因子的统计技术。Among them, clustering can refer to the process of dividing a collection of physical or abstract objects into multiple classes consisting of similar objects; factor analysis can refer to the statistical technique of studying the extraction of common factors from a group of variables.
可以理解的是，本申请实施例需要对多个网站行为特征指标特征分簇时，为了进一步分簇，可以分别对多个网站行为指标特征进行层次聚类和因子分析，并对层次聚类和因子分析后分别得到的结果整合并再次进行分簇，得到最终的分簇结果，以提升指示框架生成的可靠性和科学性。It can be understood that when the embodiment of the present application needs to cluster multiple website behavior feature indicator features, in order to further cluster, hierarchical clustering and factor analysis can be performed on the multiple website behavior indicator features respectively, and the results obtained after hierarchical clustering and factor analysis can be integrated and clustered again to obtain the final clustering result, so as to improve the reliability and scientificity of the generated indicator framework.
在本申请实施例中，对多个网站行为指标特征进行层次聚类，得到聚类结果，包括：计算多个网站行为指标特征中任意指标特征之间的欧氏距离；将欧氏距离小于预设距离的指标特征组合，得到第一新指标特征；计算第一新指标特征与多个网站行为指标特征中剩余特征之间的欧氏距离，直到所有特征之间的欧氏距离大于或等于预设距离，生成特征的树形结构。In an embodiment of the present application, hierarchical clustering is performed on multiple website behavior indicator features to obtain a clustering result, including: calculating the Euclidean distance between any indicator features in the multiple website behavior indicator features; combining indicator features whose Euclidean distance is less than a preset distance to obtain a first new indicator feature; calculating the Euclidean distance between the first new indicator feature and the remaining features in the multiple website behavior indicator features, until the Euclidean distance between all features is greater than or equal to the preset distance, thereby generating a tree structure of features.
其中，对于某两个特征xi与xj，欧氏距离Among them, for two features xi and xj , the Euclidean distance
其中，预设距离可以用于对网站行为指标中任意特征值之间的欧氏距离的划分，预设距离可以根据实际情况进行具体设置等，对此不作具体限定。Among them, the preset distance can be used to divide the Euclidean distance between any characteristic values in the website behavior index. The preset distance can be specifically set according to actual conditions, and no specific limitation is made to this.
可以理解的是，本申请实施例可以通过对指标特征进行层次聚类处理，将多个指标特征按相似特征划分成不同类，即根据指定的聚类层次将不同指标特征分配到不同的簇中，得到对多个网站行为指标特征层次聚类的分簇结果。It can be understood that the embodiments of the present application can divide multiple indicator features into different categories according to similar features by performing hierarchical clustering processing on the indicator features, that is, assigning different indicator features to different clusters according to the specified clustering level, and obtaining the clustering results of hierarchical clustering of multiple website behavior indicator features.
举例而言，以“经销商查询”为例，加上1阶延迟算子，可以是代表一星期，并创建一个新列：经销商查询_lagl。将数据进行层次聚类，计算各个指标特征两两之间的欧氏距离，得到相似度矩阵，如图4所示；将欧氏距离最近的两个特征组合成一个新的特征，即第一新指标特征，对剩余特征与第一新指标特征继续进行计算操作，得到相似度矩阵，如图5所示；自底向下组合成一个树状结构，该步骤可以由Excel等至少一个统计软件完成，对此不作具体限制，其中，示例图可以如图6所示。For example, taking "dealer query" as an example, adding a 1st-order delay operator can represent one week, and create a new column: dealer query_lagl. Perform hierarchical clustering on the data, calculate the Euclidean distance between each indicator feature, and obtain a similarity matrix, as shown in Figure 4; combine the two features with the closest Euclidean distance into a new feature, that is, the first new indicator feature, and continue to calculate the remaining features and the first new indicator feature to obtain a similarity matrix, as shown in Figure 5; combine from bottom to bottom into a tree structure, this step can be completed by at least one statistical software such as Excel, and there is no specific limitation on this, where the example diagram can be shown in Figure 6.
在本申请实施例中，对多个网站行为指标特征进行因子分析，得到分析结果，包括：计算多个网站行为指标特征中每个指标特征在多个目标因子上的载荷系数；将每个目标因子上大于预设系数的载荷系数作为每个目标因子的目标载荷系数；根据每个目标因子的目标载荷系数生成因子载荷矩阵。In an embodiment of the present application, factor analysis is performed on multiple website behavior indicator features to obtain analysis results, including: calculating the load coefficient of each indicator feature in the multiple website behavior indicator features on multiple target factors; taking the load coefficient on each target factor that is greater than a preset coefficient as the target load coefficient of each target factor; and generating a factor load matrix based on the target load coefficient of each target factor.
其中，因子分析可以在许多变量中找出隐藏的具有代表性的因子，且将相同本质的变量归入一个因子可以减少变量项目，载荷系数可以是为方便在实际因子分析中对多个目标因子计算所引入的系数；预设系数可以根据实际情况进行具体设置等，对此不作具体限定。Among them, factor analysis can find hidden representative factors among many variables, and classifying variables of the same nature into one factor can reduce the variable items. The loading coefficient can be a coefficient introduced to facilitate the calculation of multiple target factors in actual factor analysis; the preset coefficient can be specifically set according to actual conditions, etc., and there is no specific limitation on this.
可以理解的是，本申请实施例可以通过因子分析，将多个本质相同或具有共性因子的指标特征归入一个因子进行分类，检验变量间的关系的假设，得到对多个网站行为特征指标特征进行因子分析的分簇结果。It can be understood that the embodiments of the present application can classify multiple indicator features that are essentially the same or have common factors into one factor through factor analysis, test the hypothesis of the relationship between variables, and obtain the clustering results of factor analysis of multiple website behavior feature indicator features.
举例而言，以“经销商查询”为例，加上1阶延迟算子，可以是代表一星期，并创建一个新列：经销商查询_lagl。将数据进行因子分析，利用统计软件对除销量以外的不应用延迟算子特征的原始量进行因子分析，即对x1，x2…,xn进行因子分析；计算出因子载荷矩阵，如图5，矩阵中每个数值即为对应的xi在因子i上的载荷系数；对各因子找到载荷系数较大的变量，如在图4中，对于因子1，变量Q7，Q8，Q9被认为在因子1上载荷系数较大，即因子1为这三个变量背后的主要因子。For example, taking "dealer query" as an example, adding a 1st-order delay operator can represent one week, and create a new column: dealer query_lagl. Perform factor analysis on the data, and use statistical software to perform factor analysis on the original quantity other than sales volume without the delay operator feature, that is, perform factor analysis onx1 ,x2 ...,xn ; calculate the factor loading matrix, as shown in Figure 5, each value in the matrix is the corresponding load coefficient of xi on factor i; find the variable with a larger load coefficient for each factor, as shown in Figure 4, for factor 1, variables Q7, Q8, and Q9 are considered to have a larger load coefficient on factor 1, that is, factor 1 is the main factor behind these three variables.
综上，本申请实施例可以在获取目标汽车的价值数据和多个网站行为指标特征后，应用延迟算子及相关系数，帮助确定不同指标在框架中的不同位置；基于因子分析和层次聚类对指标分簇，找出框架中位置相近的指标簇；结合分析，生成汽车网站的指标框架。In summary, after obtaining the value data of the target car and the characteristics of multiple website behavior indicators, the embodiment of the present application can apply the delay operator and the correlation coefficient to help determine the different positions of different indicators in the framework; cluster the indicators based on factor analysis and hierarchical clustering to find the indicator clusters with similar positions in the framework; and combine the analysis to generate the indicator framework of the automobile website.
下面将通过一个具体实施例对本申请实施例的汽车网站的指标框架生成方法进行阐述，如图7所示，具体步骤如下：The following will describe the index framework generation method of the automobile website of the embodiment of the present application through a specific embodiment, as shown in FIG7 , the specific steps are as follows:
1、获取汽车品牌网站每周会话级别统计数据指标x1，x2…,xn以及该品牌的销量数据y，最终数据为x1，x2…,xn,y，即共有m行(n+1)列数据，其中一行为一周数据，一列为一个指标特征。以x15为例，该数字为指标特征x1的第5周数据。1. Get the weekly session-level statistical data indicators x1 , x2 ..., xn and the sales data y of the car brand website. The final data is x1 , x2 ..., xn , y, that is, there are m rows (n+1) columns of data, one row is a week of data, and one column is an indicator feature. Take x15 as an example, this number is the 5th week data of the indicator feature x1 .
2、数据清洗，并对除去销量以外的各个特征xi加上延迟算子Bk创建出新特征。2. Data cleaning, and adding delay operatorBk to each feature xi except sales volume to create new features.
其中，数据清洗可以使用floor&capping的方法：即对任意一个指标特征xi，认定95％的数据点应该在区间内，其中分别为指标特征xi的平均值和标准差，对于任意的j∈[1,m]有：Among them, data cleaning can use the floor & capping method: that is, for any indicator feature xi , it is determined that 95% of the data points should be in In the range, are the mean and standard deviation of the indicator featurexi, respectively. For any j∈[1,m], we have:
对x1，x2…,xn,y进行上述的floor&capping操作，完成数据清洗。其中，k为延迟算子的阶数，本申请实施例可以加上1～13阶延迟算子。Perform the above floor & capping operations on x1 , x2 , ..., xn , y to complete data cleaning. Wherein, k is the order of the delay operator, and the embodiment of the present application can add 1 to 13 order delay operators.
3、对于“经销商查询”加上1阶延迟算子，即将该特征每周对应时间加上数据集的数据间隔，在本申请实施例中为一个星期，并创建一个新列：经销商查询_lag1；并将处理后的数据分别进行热力图相关系数计算并绘图操作、层次聚类操作和因子分析操作。3. For "dealer query", add a first-order lag operator, that is, add the corresponding time of this feature every week to the data interval of the data set, which is one week in the embodiment of the present application, and create a new column: dealer query_lag1; and perform heat map correlation coefficient calculation and drawing operations, hierarchical clustering operations and factor analysis operations on the processed data.
在计算热力图相关系数并绘图时：When calculating the heat map correlation coefficient and plotting:
(1)、利用统计软件计算各阶延迟算子下各个特征xi与销量y之间的Pearson相关系数，计算式为：(1) Use statistical software to calculate the Pearson correlation coefficient between each featurexi and sales volume y under each order of delay operator. The calculation formula is:
其中，该系数取值范围为[-1,1]，若系数值越接近1，则说明该特征与销量之间的正相关关系越强，越接近-1，则说明该特征与销量之间的负相关关系越强，越接近0则说明两者之间相关关系越弱；in, The coefficient value range is [-1,1]. If the coefficient value is closer to 1, the positive correlation between the feature and sales volume is stronger. If it is closer to -1, the negative correlation between the feature and sales volume is stronger. If it is closer to 0, the correlation between the two is weaker.
(2)、生成相关系数矩阵，即行为各个特征，列为对应1～13阶延迟算子xi_lag1,xi_lag2,xi_lag3…,xi_lag13。例如，对于“经销商查询”行，第三列为该特征应用3阶延迟算子后与销量数据的Pearson相关系数；(2) Generate a correlation coefficient matrix, that is, the rows are for each feature, and the columns are for the corresponding 1st to 13th order delay operators xi_lag1 , xi_lag2 , xi_lag3 …, xi_lag13 . For example, for the "dealer query" row, the third column is the Pearson correlation coefficient between the feature and the sales data after applying the 3rd order delay operator;
(3)、在得到相关系数矩阵后按数值高低进行涂色，最终得到相关系数热力图，如图3所示，具体如下：(3) After obtaining the correlation coefficient matrix, color it according to the value, and finally obtain the correlation coefficient heat map, as shown in Figure 3. The details are as follows:
在进行层次聚类时：When performing hierarchical clustering:
(1)、对于某两个特征xi与xj，欧氏距离对各个特征计算它们两两之间的欧式距离，得到相似度矩阵，如图4所示；(1) For two features xi and xj , the Euclidean distance The Euclidean distance between each feature is calculated to obtain the similarity matrix, as shown in Figure 4;
(2)、将欧式距离最近的两个特征组合成一个新的特征，对剩余特征与该特征继续进行(1)中的操作，如图5所示；(2) Combine the two features with the closest Euclidean distance into a new feature, and continue the operation in (1) for the remaining features and the new feature, as shown in Figure 5;
(3)、由统计软件最终自底向下组合成一个树形结构，如图6所示。(3) The statistical software finally combines the data from bottom to bottom into a tree structure, as shown in Figure 6.
在进行因子分析时：When performing factor analysis:
(1)、利用统计软件对除销量以外的不应用延迟算子特征的原始量进行因子分析，即对x1，x2…,xn进行因子分析；(1) Use statistical software to perform factor analysis on the original quantities other than sales volume that do not use the delay operator feature, that is, perform factor analysis on x1 , x2 …, xn ;
(2)、计算出因子载荷矩阵，如图5所示，矩阵中每个数值即为对应的xi在因子i上的载荷系数；(2) Calculate the factor loading matrix, as shown in Figure 5. Each value in the matrix is the loading coefficient of the correspondingxi on factor i.
(3)、对各因子，找到载荷系数较大的变量，如在图5中，对因子1，变量Q7，Q8，Q9被认为在因子1上载荷系数较大，即因子1为这三个变量背后的主要因子。(3) For each factor, find the variable with a larger loading coefficient. For example, in Figure 5, for factor 1, variables Q7, Q8, and Q9 are considered to have a larger loading coefficient on factor 1, that is, factor 1 is the main factor behind these three variables.
4、对相关系数热力图、因子载荷矩阵、层次聚类的结果进行分析。4. Analyze the results of correlation coefficient heat map, factor loading matrix, and hierarchical clustering.
(1)、如图3所示，相关系数热力图中，x1，x2，x4在一阶延迟算子上与销量的相关系数最高，而x5在三阶上与销量的相关系数最高；即：对x1，x2，x4，上周的值与本周的销量有强相关系数，所以这三个变量更适合放在转化漏斗的底端；而对x5，三周前的值与本周的销量有强相关性，所以这个变量更适合放在转化漏斗的上部分；(1) As shown in Figure 3, in the correlation coefficient heat map, x1, x2, and x4 have the highest correlation coefficient with sales at the first-order delay operator, while x5 has the highest correlation coefficient with sales at the third order; that is, for x1, x2, and x4, last week's values have a strong correlation coefficient with this week's sales, so these three variables are more suitable to be placed at the bottom of the conversion funnel; and for x5, the value three weeks ago has a strong correlation with this week's sales, so this variable is more suitable to be placed in the upper part of the conversion funnel;
(2)、如图6所示，变量A和F，B和C在树状图中关系接近，所以可以认为变量A和F，B和C处于转化漏斗的同阶段或比较接近的阶段；(2) As shown in Figure 6, variables A and F, B and C are close in the tree diagram, so it can be considered that variables A and F, B and C are at the same stage or relatively close stages of the conversion funnel;
(3)、如图8所示，变量Q7，Q8，Q9在因子1上载荷系数较大，变量Q1，Q2，Q3在因子4上载荷系数较大，所以Q7，Q8，Q9被认为处于转化漏斗的同阶段或比较接近的阶段，而变量Q1，Q2，Q3被认为处于转化漏斗的同阶段或比较接近的阶段；(3) As shown in Figure 8, variables Q7, Q8, and Q9 have larger loading coefficients on factor 1, and variables Q1, Q2, and Q3 have larger loading coefficients on factor 4, so Q7, Q8, and Q9 are considered to be at the same stage or a relatively close stage of the conversion funnel, while variables Q1, Q2, and Q3 are considered to be at the same stage or a relatively close stage of the conversion funnel;
(4)、根据业务常识判断，如图2所示，以汽车网站为例，预约试驾阶段基本处于购买前比较有意向的阶段，所以放在转化漏斗的底端。而对于网站访问这一指标，更多属于购买前的大略浏览，因此可以放在转化漏斗的上部分。(4) Based on business common sense, as shown in Figure 2, taking the automobile website as an example, the test drive booking stage is basically a stage of intention before purchase, so it is placed at the bottom of the conversion funnel. As for the website visit indicator, it is more of a rough browse before purchase, so it can be placed in the upper part of the conversion funnel.
5、根据上述步骤结果，生成最终汽车网站的指标框架。5. Based on the results of the above steps, generate the indicator framework of the final automobile website.
根据本申请实施例提出的汽车网站的指标框架生成方法，可以基于数据统计方式自动生成汽车网站中汽车的指标框架，提升框架的生成效率，避免人为因素对于指标框架界定的影响，提升框架界定的科学性，使得指标框架的构成更加合理，满足报表开发及监测需求。According to the indicator framework generation method for an automobile website proposed in the embodiment of the present application, the indicator framework of automobiles in the automobile website can be automatically generated based on data statistics, thereby improving the efficiency of framework generation, avoiding the influence of human factors on the definition of the indicator framework, improving the scientific nature of the framework definition, and making the structure of the indicator framework more reasonable to meet report development and monitoring needs.
其次参照附图描述根据本申请实施例提出的汽车网站的指标框架生成装置。Next, the indicator framework generating device for the automobile website proposed in the embodiment of the present application is described with reference to the accompanying drawings.
图9是本申请实施例的汽车网站的指标框架生成装置的方框示意图。FIG9 is a block diagram of an indicator framework generating device for an automobile website according to an embodiment of the present application.
如图9所示，该汽车网站的指标框架生成装置10包括：爬取模块100、匹配模块200和生成模块300。As shown in FIG. 9 , the indicator framework generating device 10 of the automobile website includes: a crawling module 100 , a matching module 200 and a generating module 300 .
在本申请实施例中，爬取模块100用于从汽车网站中爬取目标汽车的价值数据和多个网站行为指标特征；匹配模块200用于根据价值数据和多个网站行为指标特征计算每个指标特征与价值之间的相关性等级，基于相关性等级匹配每个指标特征在指标特征框架中的实际位置；生成模块300用于对多个网站行为指标特征进行指标特征分簇，得到分簇结果，根据分簇结果确定指标特征框架的实际层数，根据每个指标特征在指标特征框架中的实际位置和实际层数生成汽车网站中目标汽车的指标特征框架。In an embodiment of the present application, a crawling module 100 is used to crawl value data and multiple website behavior indicator features of a target car from a car website; a matching module 200 is used to calculate the correlation level between each indicator feature and the value based on the value data and multiple website behavior indicator features, and match the actual position of each indicator feature in the indicator feature framework based on the correlation level; a generation module 300 is used to perform indicator feature clustering on multiple website behavior indicator features to obtain clustering results, determine the actual number of layers of the indicator feature framework based on the clustering results, and generate the indicator feature framework of the target car in the car website based on the actual position and actual number of layers of each indicator feature in the indicator feature framework.
需要说明的是，前述对汽车网站的指标框架生成方法实施例的解释说明也适用于该实施例的汽车网站的指标框架生成装置，此处不多赘述。It should be noted that the above explanation of the embodiment of the method for generating an indicator framework for an automobile website is also applicable to the device for generating an indicator framework for an automobile website of this embodiment, and will not be elaborated herein.
根据本申请实施例提出的汽车网站的指标框架生成装置，可以基于数据统计方式自动生成汽车网站中汽车的指标框架，提升框架的生成效率，避免人为因素对于指标框架界定的影响，提升框架界定的科学性，使得指标框架的构成更加合理，满足报表开发及监测需求。According to the indicator framework generation device for the automobile website proposed in the embodiment of the present application, the indicator framework of the automobile in the automobile website can be automatically generated based on data statistics, thereby improving the efficiency of framework generation, avoiding the influence of human factors on the definition of the indicator framework, improving the scientific nature of the framework definition, and making the structure of the indicator framework more reasonable to meet the needs of report development and monitoring.
图10为本申请实施例提供的电子设备的结构示意图。该电子设备可以包括：FIG10 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:
存储器1001、处理器1002及存储在存储器1001上并可在处理器1002上运行的计算机程序。A memory 1001 , a processor 1002 , and a computer program stored in the memory 1001 and executable on the processor 1002 .
处理器1002执行程序时实现上述实施例中提供的汽车网站的指标框架生成方法。When the processor 1002 executes the program, the indicator framework generation method for the automobile website provided in the above embodiment is implemented.
进一步地，电子设备还包括：Furthermore, the electronic device further comprises:
通信接口1003，用于存储器1001和处理器1002之间的通信。The communication interface 1003 is used for communication between the memory 1001 and the processor 1002 .
存储器1001，用于存放可在处理器1002上运行的计算机程序。The memory 1001 is used to store computer programs that can be executed on the processor 1002 .
存储器1001可能包含高速RAM(Random Access Memory，随机存取存储器)存储器，也可能还包括非易失性存储器，例如至少一个磁盘存储器。The memory 1001 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.
如果存储器1001、处理器1002和通信接口1003独立实现，则通信接口1003、存储器1001和处理器1002可以通过总线相互连接并完成相互间的通信。总线可以是ISA(IndustryStandard Architecture，工业标准体系结构)总线、PCI(Peripheral Component，外部设备互连)总线或EISA(Extended Industry Standard Architecture，扩展工业标准体系结构)总线等。总线可以分为地址总线、数据总线、控制总线等。为便于表示，图10中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。If the memory 1001, the processor 1002 and the communication interface 1003 are implemented independently, the communication interface 1003, the memory 1001 and the processor 1002 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in FIG10, but it does not mean that there is only one bus or one type of bus.
可选的，在具体实现上，如果存储器1001、处理器1002及通信接口1003，集成在一块芯片上实现，则存储器1001、处理器1002及通信接口1003可以通过内部接口完成相互间的通信。Optionally, in a specific implementation, if the memory 1001, the processor 1002 and the communication interface 1003 are integrated on a chip, the memory 1001, the processor 1002 and the communication interface 1003 can communicate with each other through an internal interface.
处理器1002可能是一个CPU(Central Processing Unit，中央处理器)，或者是ASIC(Application Specific Integrated Circuit，特定集成电路)，或者是被配置成实施本申请实施例的一个或多个集成电路。The processor 1002 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.
本申请实施例还提供一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如上的汽车网站的指标框架生成方法。An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for generating an indicator framework for an automobile website.
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不是必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或N个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本申请的描述中，“N个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更N个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本申请的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本申请的实施例所属技术领域的技术人员所理解。Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.
应当理解，本申请的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，N个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。如，如果用硬件来实现和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列，现场可编程门阵列等。It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.
本技术领域的普通技术人员可以理解实现上述实施例方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210435497.4ACN114757709B (en) | 2022-04-24 | 2022-04-24 | Index frame generation method, device and equipment of automobile website and storage medium |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210435497.4ACN114757709B (en) | 2022-04-24 | 2022-04-24 | Index frame generation method, device and equipment of automobile website and storage medium |
| Publication Number | Publication Date |
|---|---|
| CN114757709A CN114757709A (en) | 2022-07-15 |
| CN114757709Btrue CN114757709B (en) | 2024-11-01 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210435497.4AActiveCN114757709B (en) | 2022-04-24 | 2022-04-24 | Index frame generation method, device and equipment of automobile website and storage medium |
| Country | Link |
|---|---|
| CN (1) | CN114757709B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480187A (en)* | 2017-07-10 | 2017-12-15 | 北京京东尚科信息技术有限公司 | User's value category method and apparatus based on cluster analysis |
| CN107832937A (en)* | 2017-09-28 | 2018-03-23 | 杭州摩西信息科技有限公司 | Financial technology Central exponent analysis method, storage medium and equipment |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104462156B (en)* | 2013-09-25 | 2018-12-28 | 阿里巴巴集团控股有限公司 | A kind of feature extraction based on user behavior, personalized recommendation method and system |
| CN107944060B (en)* | 2018-01-02 | 2020-07-31 | 天津大学 | Product information retrieval method oriented to automobile vertical website |
| WO2020024206A1 (en)* | 2018-08-02 | 2020-02-06 | 深圳先进技术研究院 | Dcgan-based parking data repairing method and apparatus, and device and storage medium |
| CN112465533A (en)* | 2019-09-09 | 2021-03-09 | 中国移动通信集团河北有限公司 | Intelligent product selection method and device and computing equipment |
| CN111832289B (en)* | 2020-07-13 | 2023-08-11 | 重庆大学 | Service discovery method based on clustering and Gaussian LDA |
| CN113326412B (en)* | 2021-05-11 | 2024-06-14 | 雄狮汽车科技(南京)有限公司 | Method and device for identifying network data of automobile host factory |
| CN113988672A (en)* | 2021-11-02 | 2022-01-28 | 广东电网有限责任公司广州供电局 | Power distribution network equipment risk level assessment method, device, equipment and medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480187A (en)* | 2017-07-10 | 2017-12-15 | 北京京东尚科信息技术有限公司 | User's value category method and apparatus based on cluster analysis |
| CN107832937A (en)* | 2017-09-28 | 2018-03-23 | 杭州摩西信息科技有限公司 | Financial technology Central exponent analysis method, storage medium and equipment |
| Publication number | Publication date |
|---|---|
| CN114757709A (en) | 2022-07-15 |
| Publication | Publication Date | Title |
|---|---|---|
| Chien et al. | Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement | |
| CN111666275B (en) | Data processing method and device, electronic equipment and storage medium | |
| CN112365070B (en) | A method, device, equipment and readable storage medium for predicting power load | |
| US20070299835A1 (en) | Search engine for software components and a search program for software components | |
| CN115147092A (en) | Resource approval method, training method and device of random forest model | |
| CN116501979B (en) | Information recommendation method, information recommendation device, computer equipment and computer readable storage medium | |
| CN115422205A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN113434436A (en) | Test case generation method and device, electronic equipment and storage medium | |
| CN116187675A (en) | Task allocation method, device, equipment and storage medium | |
| CN112862621A (en) | Vehicle insurance risk assessment method and device and computer equipment | |
| CN105989066A (en) | Information processing method and device | |
| US20070239679A1 (en) | Master pattern generation and utilization | |
| CN117951186A (en) | Method and device for generating insight data | |
| CN114757709B (en) | Index frame generation method, device and equipment of automobile website and storage medium | |
| JP2021185469A (en) | Performance analyzer and performance analysis method | |
| CN110060088A (en) | Competitive product monitoring method, device and storage medium, computer equipment | |
| CN117290420A (en) | Enterprise financial management method and system for financial data extraction | |
| CN112632392A (en) | Method, apparatus, device, medium and program product for determining candidate get-off location | |
| CN114358325A (en) | Maintenance scheme recommendation method and device | |
| CN117221134B (en) | State analysis method and system based on Internet | |
| CN118013948B (en) | Text processing method and system integrating multi-mode interaction | |
| JP7710939B2 (en) | causal search device | |
| JPH11316773A (en) | Method for estimating layout area of semiconductor integrated circuit, and cad device therefor | |
| CN115114283B (en) | Data processing method, device, computer readable medium and electronic device | |
| EP4538902A1 (en) | Recommendation method and apparatus, and electronic device |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |