技术领域Technical Field
本发明涉及流量数据处理技术领域，具体涉及一种信息安全数据存储方法、系统及电子设备。The present invention relates to the technical field of flow data processing, and in particular to an information security data storage method, system and electronic equipment.
背景技术Background Art
HTTP（Hypertext Transfer Protocol）是一种用于传输超文本数据的协议，它是互联网上最常用的协议之一，用于在客户端和服务器之间传输网页、图像、视频、音频和其他数据；HTTP流量是一种常见的网络信息安全数据，HTTP流量指的是通过HTTP协议传输的数据流量，当用户通过浏览器访问网页、下载文件、发送表单数据等操作时，都会产生HTTP流量，通过对HTTP流量进行合理存储分析，可以识别性能瓶颈，并采取措施优化系统的性能和响应速度。HTTP (Hypertext Transfer Protocol) is a protocol for transmitting hypertext data. It is one of the most commonly used protocols on the Internet and is used to transmit web pages, images, videos, audio and other data between clients and servers. HTTP traffic is a common type of network information security data. HTTP traffic refers to data traffic transmitted through the HTTP protocol. When users access web pages, download files, send form data, etc. through browsers, HTTP traffic is generated. By performing reasonable storage analysis on HTTP traffic, performance bottlenecks can be identified and measures can be taken to optimize system performance and response speed.
现有技术中，采用传统的数据存储算法对HTTP流量数据进行存储；实际情况下，系统会将所有数据存储请求视为同等重要，对所有的HTTP流量数据进行一致性的存储，且HTTP流量数据产生速度较快，数据量庞大，具有较多的冗余信息，因此，若将冗余信息数据和重要信息数据视为同等重要，可能会导致资源的不合理分配和性能的不稳定，重要的数据存储请求可能会被延迟处理，从而影响系统的响应时间和性能表现，降低了数据存储的存储效率。In the prior art, traditional data storage algorithms are used to store HTTP traffic data. In actual situations, the system will regard all data storage requests as equally important and store all HTTP traffic data consistently. Moreover, HTTP traffic data is generated at a fast speed, with a large amount of data and more redundant information. Therefore, if redundant information data and important information data are regarded as equally important, it may lead to unreasonable allocation of resources and unstable performance. Important data storage requests may be delayed, thereby affecting the response time and performance of the system and reducing the storage efficiency of data storage.
发明内容Summary of the invention
为了解决将冗余信息数据和重要信息数据视为同等重要，重要的数据存储请求可能会被延迟处理的技术问题，本发明的目的在于提供一种信息安全数据存储方法、系统及电子设备，所采用的技术方案具体如下：In order to solve the technical problem that redundant information data and important information data are considered equally important, and important data storage requests may be delayed, the purpose of the present invention is to provide an information security data storage method, system and electronic device, and the technical solution adopted is as follows:
本发明提出了一种信息安全数据存储方法，所述方法包括：The present invention proposes a method for storing information security data, the method comprising:
获取目标终端服务平台在预设时间范围内每个时刻的HTTP流量数据；所述HTTP流量数据包括IP地址访问频率数据和IP地址响应时间数据；Obtaining HTTP traffic data of the target terminal service platform at each moment within a preset time range; the HTTP traffic data includes IP address access frequency data and IP address response time data;
根据每个HTTP流量数据中数据的幅值变化特征，获得每个HTTP流量数据的信息重要属性值；根据每个HTTP流量数据中不同IP地址访问频率的变化趋势和所述信息重要属性值，获得每个HTTP流量数据的初始信息重要特征值；根据每个HTTP流量数据中IP地址响应时间特征对所述初始信息重要特征值进行调整，获得每个HTTP流量数据的最终信息重要特征值；According to the amplitude variation characteristics of the data in each HTTP traffic data, the information important attribute value of each HTTP traffic data is obtained; according to the variation trend of the access frequency of different IP addresses in each HTTP traffic data and the information important attribute value, the initial information important characteristic value of each HTTP traffic data is obtained; according to the IP address response time characteristics in each HTTP traffic data, the initial information important characteristic value is adjusted to obtain the final information important characteristic value of each HTTP traffic data;
根据每个HTTP流量数据的所述最终信息重要特征值，对所有HTTP流量数据进行聚类，获得数据聚类簇；根据数据聚类簇中数据的数量筛选出参考聚类簇；根据每个HTTP流量数据和参考聚类簇中所有HTTP流量数据之间的最终信息重要特征值分布，获得每个HTTP流量数据的存储必要性；According to the final important characteristic value of each HTTP traffic data, all HTTP traffic data are clustered to obtain data clusters; according to the number of data in the data clusters, reference clusters are selected; according to the distribution of the final important characteristic value of the information between each HTTP traffic data and all HTTP traffic data in the reference clusters, the necessity of storing each HTTP traffic data is obtained;
根据所述存储必要性获得每个HTTP流量数据的存储优先级。The storage priority of each HTTP traffic data is obtained according to the storage necessity.
进一步地，所述信息重要属性值的获取方法包括：Furthermore, the method for obtaining the important attribute value of the information includes:
将每个HTTP流量数据中IP地址访问频率最大值和IP地址响应时间最大值融合，作为对应HTTP流量数据的幅值；The maximum IP address access frequency and the maximum IP address response time in each HTTP traffic data are merged as the amplitude of the corresponding HTTP traffic data;
获得所有HTTP流量数据的幅值均值作为整体幅值水平；Get the amplitude mean of all HTTP traffic data as the overall amplitude level;
根据每个HTTP流量数据的幅值相对于所述整体幅值水平的偏差程度，获得HTTP流量数据的信息重要属性值。According to the degree of deviation of the amplitude of each HTTP traffic data relative to the overall amplitude level, the information importance attribute value of the HTTP traffic data is obtained.
进一步地，所述初始信息重要特征值的获取方法包括：Furthermore, the method for obtaining the important characteristic values of the initial information includes:
根据每个HTTP流量数据中IP地址访问频率最大值与其他每个IP地址访问频率之间的变化趋势和所述信息重要属性值，获得每个HTTP流量数据的初始信息重要特征值，IP地址访问频率最大值与其他每个IP地址访问频率之间的变化趋势和所述信息重要属性值均与所述信息重要特征值呈正相关。The initial important information characteristic value of each HTTP traffic data is obtained according to the changing trend between the maximum IP address access frequency and each other IP address access frequency in each HTTP traffic data and the important information attribute value. The changing trend between the maximum IP address access frequency and each other IP address access frequency and the important information attribute value are both positively correlated with the important information characteristic value.
进一步地，所述最终信息重要特征值的获取方法包括：Furthermore, the method for obtaining the important characteristic values of the final information includes:
计算每个HTTP流量数据中所有IP地址响应时间的均值，作为局部响应时间水平；计算所有HTTP流量数据的局部响应时间水平的均值，作为整体响应时间水平；Calculate the average response time of all IP addresses in each HTTP traffic data as the local response time level; calculate the average local response time level of all HTTP traffic data as the overall response time level;
根据每个HTTP流量数据的所述局部响应时间水平与所述整体响应时间水平之间的变化情况和所述初始信息重要特征值，获得每个HTTP流量数据的最终信息重要特征值，局部响应时间水平与所述整体响应时间水平之间的变化情况和所述初始信息重要特征值均与最终信息重要特征值呈正相关。According to the change between the local response time level and the overall response time level of each HTTP traffic data and the initial information important characteristic value, the final information important characteristic value of each HTTP traffic data is obtained, and the change between the local response time level and the overall response time level and the initial information important characteristic value are positively correlated with the final information important characteristic value.
进一步地，所述存储必要性的获取方法包括：Furthermore, the method for obtaining the storage necessity includes:
获得参考聚类簇中所有HTTP流量数据的最终信息重要特征值的均值，作为平均最终信息重要特征值；Obtain the average of the final information important feature values of all HTTP traffic data in the reference cluster as the average final information important feature value;
计算每个HTTP流量数据的所述最终信息重要特征值和所述平均最终信息重要特征值之间的差值，并进行归一化映射，获得每个HTTP流量数据的存储必要性。The difference between the final information important feature value of each HTTP traffic data and the average final information important feature value is calculated, and normalized mapping is performed to obtain the storage necessity of each HTTP traffic data.
进一步地，所述存储优先级的获取方法包括：Furthermore, the method for obtaining the storage priority includes:
获取预设存储优先级对应的存储必要性范围；Obtaining a storage necessity range corresponding to a preset storage priority;
若每个HTTP流量数据的所述存储必要性存在预设存储优先级对应的存储必要性范围，对应的存储优先级作为每个HTTP流量数据的存储优先级。If the storage necessity of each HTTP traffic data falls within a storage necessity range corresponding to a preset storage priority, the corresponding storage priority is used as the storage priority of each HTTP traffic data.
进一步地，所述数据聚类簇的获取方法包括：Furthermore, the method for obtaining the data clustering clusters includes:
根据每个HTTP流量数据的所述最终信息重要特征值，对所有HTTP流量数据进行K-means聚类，获得数据聚类簇。According to the important characteristic value of the final information of each HTTP traffic data, K-means clustering is performed on all HTTP traffic data to obtain data clustering clusters.
进一步地，所述参考聚类簇的获取方法包括：Furthermore, the method for obtaining the reference cluster includes:
选取所有数据聚类簇中HTTP流量数据最多对应的数据聚类簇，作为参考聚类簇。The data cluster with the most HTTP traffic data among all data clusters is selected as the reference cluster.
本发明一个实施例又提供了一种信息安全数据存储系统，所述系统包括：An embodiment of the present invention further provides an information security data storage system, the system comprising:
数据获取模块：获取目标终端服务平台在每个时刻的HTTP流量数据；所述HTTP流量数据包括IP地址访问频率数据和IP地址响应时间数据；Data acquisition module: acquires HTTP traffic data of the target terminal service platform at each moment; the HTTP traffic data includes IP address access frequency data and IP address response time data;
信息重要性修正模块：根据每个HTTP流量数据中数据的幅值变化特征，获得每个HTTP流量数据的信息重要属性值；根据每个HTTP流量数据中不同IP地址访问频率的变化趋势和所述信息重要属性值，获得每个HTTP流量数据的初始信息重要特征值；根据每个HTTP流量数据中IP地址响应时间特征对所述初始信息重要特征值进行调整，获得每个HTTP流量数据的最终信息重要特征值；Information importance correction module: according to the amplitude change characteristics of the data in each HTTP traffic data, obtain the information importance attribute value of each HTTP traffic data; according to the change trend of the access frequency of different IP addresses in each HTTP traffic data and the information importance attribute value, obtain the initial information importance characteristic value of each HTTP traffic data; according to the IP address response time characteristics in each HTTP traffic data, adjust the initial information importance characteristic value to obtain the final information importance characteristic value of each HTTP traffic data;
存储必要性分析模块：根据每个HTTP流量数据的所述最终信息重要特征值，对所有HTTP流量数据进行聚类，获得数据聚类簇；根据数据聚类簇中数据的数量筛选出参考聚类簇；根据每个HTTP流量数据和参考聚类簇中所有HTTP流量数据之间的最终信息重要特征值分布，获得每个HTTP流量数据的存储必要性；Storage necessity analysis module: cluster all HTTP traffic data according to the final information important characteristic value of each HTTP traffic data to obtain data clustering clusters; select reference clustering clusters according to the number of data in the data clustering clusters; obtain the storage necessity of each HTTP traffic data according to the distribution of the final information important characteristic value between each HTTP traffic data and all HTTP traffic data in the reference clustering clusters;
存储优先级划分模块：根据所述存储必要性获得每个HTTP流量数据的存储优先级。Storage priority division module: obtains the storage priority of each HTTP traffic data according to the storage necessity.
本发明还提出了一种电子设备，包括存储器、处理器以及存储在所述存储器中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计算机程序时，实现任意一项所述一种信息安全数据存储方法的步骤。The present invention also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of any one of the information security data storage methods are implemented.
本发明具有如下有益效果：The present invention has the following beneficial effects:
本发明根据每个HTTP流量数据中数据的幅值变化特征，获得每个HTTP流量数据的信息重要属性值，幅值变化特征反映了网络行为的变化模式，有助于评估数据的重要性；根据每个HTTP流量数据中不同IP地址访问频率的变化趋势和信息重要属性值，获得每个HTTP流量数据的初始信息重要特征值，初始识别出流量数据包含关键特征和重要信息的程度；根据每个HTTP流量数据中IP地址响应时间特征对初始信息重要特征值进行调整，获得每个HTTP流量数据的最终信息重要特征值，通过调整特征值，使其更全面地反映数据的真实情况；根据每个HTTP流量数据的最终信息重要特征值，对所有HTTP流量数据进行聚类，获得数据聚类簇，通过聚类，可以将具有相似特征的HTTP流量数据聚集在一起；根据数据聚类簇中数据的数量筛选出参考聚类簇，将包含大量冗余信息的流量数据筛选出来，便于后续流量数据重要性的分析；根据每个HTTP流量数据和参考聚类簇中所有HTTP流量数据之间的最终信息重要特征值分布，获得每个HTTP流量数据的存储必要性，反映了HTTP流量数据包含的信息重要程度；获得每个HTTP流量数据的存储优先级。本发明通过分析每个HTTP流量数据准确的存储必要性，合理划分存储优先级，提高数据存储的效率。The present invention obtains the information important attribute value of each HTTP traffic data according to the amplitude change characteristics of the data in each HTTP traffic data. The amplitude change characteristics reflect the change pattern of network behavior and are helpful to evaluate the importance of data. According to the change trend of the access frequency of different IP addresses in each HTTP traffic data and the information important attribute value, the initial information important characteristic value of each HTTP traffic data is obtained, and the degree to which the traffic data contains key features and important information is initially identified. According to the IP address response time characteristics in each HTTP traffic data, the initial information important characteristic value is adjusted to obtain the final information important characteristic value of each HTTP traffic data. By adjusting the characteristic value, it can more comprehensively reflect the data. The real situation; according to the final information important characteristic value of each HTTP traffic data, all HTTP traffic data are clustered to obtain data clustering clusters. Through clustering, HTTP traffic data with similar characteristics can be clustered together; according to the number of data in the data clustering cluster, a reference clustering cluster is screened out, and traffic data containing a large amount of redundant information is screened out, which is convenient for the subsequent analysis of the importance of traffic data; according to the distribution of the final information important characteristic value between each HTTP traffic data and all HTTP traffic data in the reference clustering cluster, the storage necessity of each HTTP traffic data is obtained, which reflects the importance of the information contained in the HTTP traffic data; and the storage priority of each HTTP traffic data is obtained. The present invention analyzes the accurate storage necessity of each HTTP traffic data, reasonably divides the storage priority, and improves the efficiency of data storage.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施例或现有技术中的技术方案和优点，下面将对实施例或现有技术描述中所需要使用的附图作简单的介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它附图。In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
图1为本发明一个实施例所提供的一种信息安全数据存储方法的流程图；FIG1 is a flow chart of a method for secure information data storage provided by an embodiment of the present invention;
图2为本发明一个实施例所提供的一种信息重要属性值的获取方法流程图；FIG2 is a flow chart of a method for obtaining important attribute values of information provided by an embodiment of the present invention;
图3为本发明一个实施例所提供的一种信息安全数据存储系统的结构框图；FIG3 is a structural block diagram of an information security data storage system provided by an embodiment of the present invention;
图4为本发明一个实施例所提供的一种电子设备的结构框图。FIG. 4 is a structural block diagram of an electronic device provided by an embodiment of the present invention.
具体实施方式DETAILED DESCRIPTION
为了更进一步阐述本发明为达成预定发明目的所采取的技术手段及功效，以下结合附图及较佳实施例，对依据本发明提出的一种信息安全数据存储方法、系统及电子设备，其具体实施方式、结构、特征及其功效，详细说明如下。在下述说明中，不同的“一个实施例”或“另一个实施例”指的不一定是同一实施例。此外，一或多个实施例中的特定特征、结构或特点可由任何合适形式组合。In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the information security data storage method, system and electronic device proposed by the present invention, its specific implementation method, structure, features and effects in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.
除非另有定义，本文所使用的所有的技术和科学术语与属于本发明的技术领域的技术人员通常理解的含义相同。Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
下面结合附图具体的说明本发明所提供的一种信息安全数据存储方法、系统及电子设备的具体方案。The following describes in detail a specific scheme of an information security data storage method, system and electronic device provided by the present invention in conjunction with the accompanying drawings.
请参阅图1，其示出了本发明一个实施例提供的一种信息安全数据存储方法的流程图，具体方法包括：Please refer to FIG. 1 , which shows a flow chart of a method for storing information security data provided by an embodiment of the present invention. The specific method includes:
步骤S1：获取目标终端服务平台在预设时间范围内每个时刻的HTTP流量数据，HTTP流量数据包括IP地址访问频率数据和IP地址响应时间数据。Step S1: Obtain HTTP traffic data of the target terminal service platform at each moment within a preset time range, where the HTTP traffic data includes IP address access frequency data and IP address response time data.
在本发明的实施例中，由于HTTP流量数据产生速度较快，导致HTTP流量数据的数据量庞大，具有较多的冗余信息，进而导致HTTP流量数据的存储效率较低，通过对HTTP流量进行合理存储分析，可以识别性能瓶颈，并采取措施优化系统的性能和响应速度；首先，对目标终端服务平台的网络接口进行配置，选择适合的网络流量分析和采集工具，常用的工具包括Wireshark、tcpdump、tshark以及Bro等，对每个IP(Internet Protocol，互联网协议)地址的流量数据进行捕获，进而获取目标终端服务平台在预设时间范围内每个时刻的HTTP流量数据，HTTP流量数据包括IP地址访问频率数据和IP地址响应时间数据，即在一个时刻下，各个用户对不同的IP地址进行访问生成了IP地址访问频率数据和IP地址响应时间数据，这些数据共同构成了一个多维的HTTP流量数据。In an embodiment of the present invention, due to the high speed at which HTTP traffic data is generated, the amount of HTTP traffic data is huge and has more redundant information, which in turn leads to low storage efficiency of HTTP traffic data. By performing reasonable storage analysis on HTTP traffic, performance bottlenecks can be identified and measures can be taken to optimize the performance and response speed of the system. First, the network interface of the target terminal service platform is configured, and a suitable network traffic analysis and collection tool is selected. Commonly used tools include Wireshark, tcpdump, tshark, and Bro, etc. The traffic data of each IP (Internet Protocol) address is captured, and then the HTTP traffic data of the target terminal service platform at each moment within a preset time range is obtained. The HTTP traffic data includes IP address access frequency data and IP address response time data, that is, at a moment, each user accesses different IP addresses to generate IP address access frequency data and IP address response time data, and these data together constitute a multi-dimensional HTTP traffic data.
需要说明的是，在本发明的实施例中，为了便于后续数据的处理，对IP地址访问频率数据和IP地址响应时间数据进行标准化，消除数据上的量纲，使得不同单位或数量级的指标能够进行综合分析和比较。标准化可采用Z-score标准化、最大最小标准化等现有方法，具体手段为本领域技术人员熟知的技术手段，在此不做赘述。It should be noted that, in the embodiments of the present invention, in order to facilitate the subsequent data processing, the IP address access frequency data and the IP address response time data are standardized to eliminate the dimension of the data so that indicators of different units or orders of magnitude can be comprehensively analyzed and compared. Standardization can adopt existing methods such as Z-score standardization and maximum and minimum standardization. The specific means are technical means well known to those skilled in the art and will not be described in detail here.
需要说明的是，在本发明的一个实施例中，预设时间范围为一个月，按照时序连续不断的收集数据；在本发明的其他实施例中，预设时间范围大小可根据具体情况具体设置，在此不做限定及赘述。It should be noted that, in one embodiment of the present invention, the preset time range is one month, and data is collected continuously in chronological order; in other embodiments of the present invention, the size of the preset time range can be set according to the specific circumstances, and is not limited or elaborated here.
步骤S2：根据每个HTTP流量数据中数据的幅值变化特征，获得每个HTTP流量数据的信息重要属性值；根据每个HTTP流量数据中不同IP地址访问频率的变化趋势和信息重要属性值，获得每个HTTP流量数据的初始信息重要特征值；根据每个HTTP流量数据中IP地址响应时间特征对初始信息重要特征值进行调整，获得每个HTTP流量数据的最终信息重要特征值。Step S2: Obtain the important information attribute value of each HTTP traffic data according to the amplitude change characteristics of the data in each HTTP traffic data; obtain the initial important information feature value of each HTTP traffic data according to the change trend of the access frequency of different IP addresses in each HTTP traffic data and the important information attribute value; adjust the initial important information feature value according to the IP address response time characteristics in each HTTP traffic data to obtain the final important information feature value of each HTTP traffic data.
当用户通过浏览器访问网页、下载文件、发送表单数据等操作时，都会产生HTTP流量数据，对于短期热点资讯或用户主流偏好的内容，用户端会进行更多地访问，会造成相应IP地址发送的请求量增加，进而IP地址访问频率越大；当某个IP地址发送的请求量增加过快，服务器负载可能会增加，此时如果处理器、内存或网络带宽等服务器资源有限，过多的请求可能会导致IP地址响应时间增加；所以后续通过分析IP地址访问频率和IP地址响应时间，对HTTP流量数据的重要程度进行评估。When users access web pages, download files, send form data, and perform other operations through browsers, HTTP traffic data will be generated. For short-term hot information or content that users prefer, users will visit more frequently, which will increase the number of requests sent by the corresponding IP address, and thus the IP address access frequency will increase. When the number of requests sent by a certain IP address increases too quickly, the server load may increase. At this time, if server resources such as processors, memory, or network bandwidth are limited, too many requests may cause the IP address response time to increase. Therefore, the importance of HTTP traffic data is evaluated by analyzing the IP address access frequency and IP address response time.
HTTP流量数据的幅值特征可以了解网络流量的整体活动水平；低幅值的HTTP流量数据表示用户活动较少，如网站访问量较低或用户在线行为不活跃；高幅值的HTTP流量数据通常表示用户活动较为频繁，如大量用户同时访问网站或进行在线操作，对应的信息重要程度越高；所以根据每个HTTP流量数据中数据的幅值变化特征，获得每个HTTP流量数据的信息重要属性值。The amplitude characteristics of HTTP traffic data can help us understand the overall activity level of network traffic. Low-amplitude HTTP traffic data indicates less user activity, such as low website visits or inactive user online behavior. High-amplitude HTTP traffic data usually indicates more frequent user activity, such as a large number of users visiting the website or performing online operations at the same time, and the corresponding information is more important. Therefore, the information importance attribute value of each HTTP traffic data is obtained based on the amplitude change characteristics of the data in each HTTP traffic data.
优选地，在本发明的一个实施例中，信息重要属性值的获取方法请参阅图2，其示出了本发明一个实施例所提供的一种信息重要属性值的获取方法流程图，该方法包括：Preferably, in an embodiment of the present invention, the method for obtaining the important attribute value of information is shown in FIG2 , which shows a flow chart of a method for obtaining the important attribute value of information provided by an embodiment of the present invention. The method includes:
步骤S201：将每个HTTP流量数据中IP地址访问频率最大值和IP地址响应时间最大值融合，作为对应HTTP流量数据的幅值。Step S201: The maximum IP address access frequency and the maximum IP address response time in each HTTP traffic data are merged to serve as the amplitude of the corresponding HTTP traffic data.
对于短期热点资讯或用户主流偏好的内容，用户端会进行更多地访问，使得IP地址访问频率和IP地址响应时间越大，呈现出更多的HTTP流量数据。For short-term hot information or content that users prefer, users will visit more frequently, resulting in a higher IP address access frequency and IP address response time, and more HTTP traffic data.
在本发明的一些实施方式中，可采用相乘或相加的方式将HTTP流量数据中IP地址访问频率最大值和IP地址响应时间最大值进行融合，获得对应HTTP流量数据的幅值。In some embodiments of the present invention, the maximum IP address access frequency and the maximum IP address response time in the HTTP traffic data may be merged by multiplication or addition to obtain the amplitude of the corresponding HTTP traffic data.
步骤S202：获得所有HTTP流量数据的幅值均值作为整体幅值水平。Step S202: Obtain the amplitude mean of all HTTP traffic data as the overall amplitude level.
为了便于后续数据对比，通过求平均的方法量化一个代表整体流量数据幅值水平的指标，反映出流量数据的普遍情况，从而更准确地把握网络流量的整体趋势和特征。In order to facilitate subsequent data comparison, an indicator representing the amplitude level of the overall traffic data is quantified by averaging to reflect the general situation of the traffic data, so as to more accurately grasp the overall trend and characteristics of network traffic.
步骤S203：根据每个HTTP流量数据的幅值相对于整体幅值水平的偏差程度，获得HTTP流量数据的信息重要属性值。Step S203: Obtaining information important attribute values of HTTP traffic data according to the degree of deviation of the amplitude of each HTTP traffic data relative to the overall amplitude level.
为了进一步量化HTTP流量数据的特异性，通过分析任一HTTP流量数据的幅值水平相对于整体HTTP流量数据幅值水平的偏差程度，偏差程度越大，说明任一HTTP流量数据的幅值水平比整体HTTP流量数据的幅值水平高的越多，更体现出HTTP流量数据的幅值相对于整体幅值水平的异常性或突出性，存在短期热点资讯或用户主流偏好内容的可能性越高，信息重要程度越大。In order to further quantify the specificity of HTTP traffic data, the degree of deviation of the amplitude level of any HTTP traffic data relative to the amplitude level of the overall HTTP traffic data is analyzed. The greater the deviation, the higher the amplitude level of any HTTP traffic data is than the amplitude level of the overall HTTP traffic data, which better reflects the abnormality or prominence of the amplitude of the HTTP traffic data relative to the overall amplitude level. The higher the possibility of short-term hot information or mainstream user preference content, the greater the importance of the information.
需要说明的是，在本发明的一些实施方式中，可以通过计算HTTP流量数据的幅值相对于整体幅值水平之间的差值或者比值来表现偏差程度，HTTP流量数据的幅值越大于整体幅值水平，偏差程度越大，越可能包含短期热点资讯或用户主流偏好内容，信息重要程度越大。It should be noted that, in some embodiments of the present invention, the degree of deviation can be expressed by calculating the difference or ratio between the amplitude of the HTTP traffic data and the overall amplitude level. The larger the amplitude of the HTTP traffic data is than the overall amplitude level, the greater the degree of deviation, the more likely it is that it contains short-term hot information or mainstream user preference content, and the greater the importance of the information.
在本发明的一个实施例中，信息重要属性值的公式表示为：In one embodiment of the present invention, the formula for the important attribute value of information is expressed as:
； ;
其中，表示第个HTTP流量数据的信息重要属性值；表示第个HTTP流量数据的幅值；表示第个HTTP流量数据的幅值；表示所有HTTP流量数据的数量；表示归一化函数。in, Indicates Important attribute values of HTTP traffic data; Indicates The amplitude of HTTP traffic data; Indicates The amplitude of HTTP traffic data; Indicates the amount of all HTTP traffic data; Represents the normalization function.
在信息重要属性值的公式中，表示所有HTTP流量数据的幅值均值，即整体幅值水平，表示计算第个HTTP流量数据的幅值与整体幅值水平的差值，差值越大，第个HTTP流量数据的幅值相较于整体幅值水平越大，对应HTTP流量数据包含IP地址访问频率最大值和IP地址响应时间最大值越大，说明存在短期热点资讯或用户主流偏好内容的可能性越高，HTTP流量数据的重要程度越高；相反地，差值越小，第个HTTP流量数据的幅值相较于整体幅值水平越小，说明HTTP流量数据包含IP地址访问频率最大值和IP地址响应时间最大值越小，重要程度越差。In the formula for the important attribute value of information, Indicates the mean amplitude of all HTTP traffic data, that is, the overall amplitude level. Indicates the calculation The difference between the amplitude of the HTTP traffic data and the overall amplitude level. The larger the difference, the The larger the amplitude of the individual HTTP traffic data is compared to the overall amplitude level, the larger the corresponding HTTP traffic data includes the maximum IP address access frequency and the maximum IP address response time, indicating that the possibility of short-term hot information or mainstream user preference content is higher, and the importance of the HTTP traffic data is higher; on the contrary, the smaller the difference, the higher the probability of short-term hot information or mainstream user preference content. The smaller the amplitude of individual HTTP traffic data is compared to the overall amplitude level, the smaller the maximum IP address access frequency and the maximum IP address response time contained in the HTTP traffic data are, and the less important they are.
IP地址访问频率越高，用户对于IP地址的请求操作越多，越可能是短期热点资讯或用户主流偏好内容，产生越多的流量数据；信息重要属性值可以反映用户的行为模式、偏好和需求等，信息重要属性值越高，信息的重要程度的可信度越高，初始信息重要特征值越高；所以根据每个HTTP流量数据中不同IP地址访问频率的变化趋势和信息重要属性值，获得每个HTTP流量数据的初始信息重要特征值。The higher the IP address access frequency, the more user request operations on the IP address, the more likely it is that it is short-term hot information or user mainstream preferred content, and the more traffic data is generated; the important attribute value of information can reflect the user's behavior patterns, preferences and needs, etc. The higher the important attribute value of information, the higher the credibility of the importance of information, and the higher the initial important characteristic value of information; therefore, according to the changing trend of the access frequency of different IP addresses in each HTTP traffic data and the important attribute value of information, the initial important characteristic value of information of each HTTP traffic data is obtained.
优选地，在本发明的一个实施例中，初始信息重要特征值的获取方法包括：Preferably, in one embodiment of the present invention, the method for obtaining important characteristic values of initial information includes:
根据每个HTTP流量数据中IP地址访问频率最大值与其他每个IP地址访问频率之间的变化趋势和信息重要属性值，获得每个HTTP流量数据的初始信息重要特征值，IP地址访问频率最大值与其他每个IP地址访问频率之间的变化趋势和信息重要属性值均与信息重要特征值呈正相关。其中，正相关关系表示因变量会随着自变量的增大而增大，因变量会随着自变量的减小而减小，具体关系可以为相乘关系、相加关系、指数函数的幂等，由实际应用进行确定。According to the change trend and information important attribute value between the maximum IP address access frequency and each other IP address access frequency in each HTTP traffic data, the initial information important characteristic value of each HTTP traffic data is obtained. The change trend and information important attribute value between the maximum IP address access frequency and each other IP address access frequency are positively correlated with the information important characteristic value. Among them, the positive correlation relationship means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. The specific relationship can be a multiplication relationship, an addition relationship, and the idempotence of an exponential function, which is determined by actual application.
在本发明的一个实施例中，信息重要特征值的公式表示为：In one embodiment of the present invention, the formula for the important characteristic value of information is expressed as:
； ;
其中，表示第个HTTP流量数据的初始信息重要特征值；表示第个HTTP流量数据的信息重要属性值；表示第个HTTP流量数据中IP地址访问频率最大值；表示第个HTTP流量数据中除IP地址访问频率最大值之外的所有其他IP地址访问频率的数量；表示第个HTTP流量数据中除IP地址访问频率最大值之外的第个其他IP地址访问频率。in, Indicates The important characteristic values of the initial information of HTTP traffic data; Indicates Important attribute values of HTTP traffic data; Indicates The maximum IP address access frequency in HTTP traffic data; Indicates The number of all other IP address access frequencies except the maximum IP address access frequency in the HTTP traffic data; Indicates HTTP traffic data except for the maximum IP address access frequency The frequency of access from other IP addresses.
在信息重要特征值的公式中，表示第个HTTP流量数据的IP地址访问频率最大值与第个其他IP地址访问频率之间的差值，即差值越大，第个HTTP流量数据的IP地址访问频率最大值相对于第个其他IP地址访问频率的变化趋势越大，是短期热点资讯或用户主流偏好内容的概率越高，相反地，差值越小，第个HTTP流量数据的IP地址访问频率最大值相对于第个其他IP地址访问频率的变化趋势越小，是短期热点资讯或用户主流偏好内容的概率越低；表示第个HTTP流量数据的IP地址访问频率最大值与所有其他IP地址访问频率之间差值的均值，均值越小，说明IP地址访问频率最大值相对于其他IP地址访问频率的变化趋势越小，访问频率越接近，相反地，均值越大，说明IP地址访问频率最大值相对于其他IP地址访问频率的变化趋势越大，访问频率越存在明显差异，信息重要属性值越大，初始信息重要特征值越大，存在短期热点资讯或用户主流偏好内容的概率越高。In the formula for the information-significant eigenvalue, Indicates The maximum value of the IP address access frequency of HTTP traffic data is The difference between the access frequencies of other IP addresses, that is, the larger the difference, the The maximum IP address access frequency of HTTP traffic data is relative to The greater the change trend of the access frequency of other IP addresses, the higher the probability that it is short-term hot information or content preferred by users. On the contrary, the smaller the difference, the higher the probability that it is short-term hot information or content preferred by users. The maximum IP address access frequency of HTTP traffic data is relative to The smaller the change trend of the access frequency of other IP addresses, the lower the probability that it is short-term hot information or content preferred by users; Indicates The mean of the difference between the maximum IP address access frequency of HTTP traffic data and the access frequencies of all other IP addresses. The smaller the mean, the smaller the change trend of the maximum IP address access frequency relative to the access frequencies of other IP addresses, and the closer the access frequencies. On the contrary, the larger the mean, the greater the change trend of the maximum IP address access frequency relative to the access frequencies of other IP addresses, the more obvious the difference in access frequencies, the greater the value of the important attribute of the information, the greater the important characteristic value of the initial information, and the higher the probability of the existence of short-term hot information or mainstream user preference content.
需要说明的是，在本发明的其他实施例中，也可通过将差值均值和信息重要属性值相加等其他基础数学运算方法构建均值越大，信息重要属性值越大，初始信息重要特征值越大的相关关系，具体手段为本领域技术人员熟知的技术手段，在此不做赘述。It should be noted that in other embodiments of the present invention, other basic mathematical operations such as adding the difference mean and the information important attribute value can be used to construct a correlation that the larger the mean, the larger the information important attribute value, and the larger the initial information important eigenvalue. The specific means are technical means well known to those skilled in the art and will not be elaborated here.
IP地址的响应时间反映了网络性能和用户请求的处理速度，响应时间越长，说明网络延迟越高或服务器处理请求的能力越差，说明当前存在较多的请求量使得服务器的负载增加，初始信息重要特征值越大，访问某短期热点资讯或用户主流偏好的内容的可能性越大，最终信息重要特征值越大；引入IP地址响应时间特征并调整初始信息重要特征值，有助于更准确地评估对应信息的实际重要程度；所以根据每个HTTP流量数据中IP地址响应时间特征对初始信息重要特征值进行调整，获得每个HTTP流量数据的最终信息重要特征值。The response time of the IP address reflects the network performance and the processing speed of user requests. The longer the response time, the higher the network delay or the worse the server's ability to process requests, which means that there are currently a large number of requests, which increases the server load. The larger the initial information important feature value, the greater the possibility of accessing certain short-term hot information or content that the user's mainstream preference prefers, and the larger the final information important feature value. Introducing the IP address response time feature and adjusting the initial information important feature value can help to more accurately evaluate the actual importance of the corresponding information. Therefore, the initial information important feature value is adjusted according to the IP address response time feature in each HTTP traffic data to obtain the final information important feature value of each HTTP traffic data.
优选地，在本发明的一个实施例中，最终信息重要特征值的获取方法包括：Preferably, in one embodiment of the present invention, the method for obtaining the important characteristic value of the final information includes:
计算每个HTTP流量数据中所有IP地址响应时间的均值，作为局部响应时间水平；计算所有HTTP流量数据的局部响应时间水平的均值，作为整体响应时间水平；Calculate the average response time of all IP addresses in each HTTP traffic data as the local response time level; calculate the average local response time level of all HTTP traffic data as the overall response time level;
根据每个HTTP流量数据的局部响应时间水平与整体响应时间水平之间的变化情况和初始信息重要特征值，获得每个HTTP流量数据的最终信息重要特征值，局部响应时间水平与整体响应时间水平之间的变化情况和初始信息重要特征值均与最终信息重要特征值呈正相关。其中，正相关关系表示因变量会随着自变量的增大而增大，因变量会随着自变量的减小而减小，具体关系可以为相乘关系、相加关系、指数函数的幂等，由实际应用进行确定。According to the change between the local response time level and the overall response time level of each HTTP traffic data and the initial information important characteristic value, the final information important characteristic value of each HTTP traffic data is obtained, and the change between the local response time level and the overall response time level and the initial information important characteristic value are positively correlated with the final information important characteristic value. Among them, the positive correlation relationship means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. The specific relationship can be a multiplication relationship, an addition relationship, and the idempotence of an exponential function, which is determined by actual applications.
在本发明的一个实施例中，最终信息重要特征值的公式表示为：In one embodiment of the present invention, the formula for the final important characteristic value of the information is expressed as:
； ;
其中，表示第个HTTP流量数据的最终信息重要特征值；表示第个HTTP流量数据的局部响应时间水平；表示所有时刻的HTTP流量数据的整体响应时间水平；表示第个HTTP流量数据的初始信息重要特征值；表示归一化函数。in, Indicates The final important characteristic value of HTTP traffic data; Indicates The local response time level of HTTP traffic data; Indicates the overall response time level of HTTP traffic data at all times; Indicates The important characteristic values of the initial information of HTTP traffic data; Represents the normalization function.
在最终信息重要特征值的公式中，表示计算对于第个HTTP流量数据的局部响应时间水平与整体响应时间水平之间的差值，即差值越大，第个HTTP流量数据的局部响应时间水平相对于整体响应时间水平的变化情况越大，第个HTTP流量数据的局部响应时间水平越显著，包含短期热点资讯或用户直流偏好内容的可能性越高，相反地，差值越小，第个HTTP流量数据的局部响应时间水平相对于整体响应时间水平的变化情况越小，第个HTTP流量数据的局部响应时间水平越不突出，包含短期热点资讯或用户直流偏好内容的可能性越小；因此，差值越大，对于HTTP流量数据的初始信息重要特征值的可信任程度越大，越需要调大初始信息重要特征值，最终信息重要特征值越大。In the formula for the final information-significant eigenvalue, Indicates the calculation for the The difference between the local response time level and the overall response time level of HTTP traffic data, that is, the larger the difference, the The greater the change in the local response time level of HTTP traffic data relative to the overall response time level, the The more significant the local response time level of HTTP traffic data is, the more likely it is to contain short-term hot information or user direct preference content. On the contrary, the smaller the difference is, the higher the probability is. The smaller the change in the local response time level of HTTP traffic data relative to the overall response time level, the The less prominent the local response time level of an HTTP traffic data is, the less likely it is that it contains short-term hot information or user direct preference content; therefore, the larger the difference is, the greater the degree of trust in the initial information important characteristic value of the HTTP traffic data is, the more it is necessary to increase the initial information important characteristic value, and the larger the final information important characteristic value is.
需要说明的是，在本发明的其他实施例中，也可将归一化后差值结果和初始信息重要特征值相加等基础数学运算方法构建差值越大，初始信息重要特征值越大，最终信息重要特征值越大的相关关系；也可采用作商的方法来表现变化情况，构建HTTP流量数据的局部响应时间水平越大，整体响应时间水平越小，变化情况越大的相关关系；将具体手段为本领域技术人员熟知的技术手段，在此不做赘述。It should be noted that, in other embodiments of the present invention, It is also possible to construct a correlation that the larger the difference, the larger the initial information important eigenvalue, and the larger the final information important eigenvalue by adding the normalized difference result and the initial information important eigenvalue; The quotient method can also be used to express the changes, and a correlation is established that the greater the local response time level of HTTP traffic data, the smaller the overall response time level, and the greater the change; the specific means are technical means well known to technical personnel in this field and will not be repeated here.
步骤S3：根据每个HTTP流量数据的最终信息重要特征值，对所有HTTP流量数据进行聚类，获得数据聚类簇；根据数据聚类簇中数据的数量筛选出参考聚类簇；根据每个HTTP流量数据和参考聚类簇中所有HTTP流量数据之间的最终信息重要特征值分布，获得每个HTTP流量数据的存储必要性。Step S3: according to the final important characteristic value of the information of each HTTP traffic data, all HTTP traffic data are clustered to obtain data clustering clusters; according to the number of data in the data clustering clusters, reference clustering clusters are screened out; according to the distribution of the final important characteristic value of the information between each HTTP traffic data and all HTTP traffic data in the reference clustering clusters, the necessity of storing each HTTP traffic data is obtained.
由于HTTP流量数据产生速度较快，导致HTTP流量数据的数据量庞大，具有较多的冗余信息，使得大部分对应的HTTP流量数据的重要程度都较差，即最终信息重要特征值较小，通过聚类可以将具有相似重要程度的HTTP流量数据分组，所以根据每个HTTP流量数据的最终信息重要特征值，对所有HTTP流量数据进行聚类，获得数据聚类簇。Since HTTP traffic data is generated at a fast speed, the amount of HTTP traffic data is huge and has a lot of redundant information, which makes the importance of most corresponding HTTP traffic data poor, that is, the final important feature value of the information is small. Through clustering, HTTP traffic data with similar importance can be grouped. Therefore, according to the final important feature value of each HTTP traffic data, all HTTP traffic data are clustered to obtain data clustering clusters.
优选地，在本发明的一个实施例中，数据聚类簇的获取方法包括：Preferably, in one embodiment of the present invention, the method for obtaining data clusters includes:
根据每个HTTP流量数据的最终信息重要特征值，对所有HTTP流量数据进行K-means聚类，获得数据聚类簇。According to the important characteristic values of the final information of each HTTP traffic data, K-means clustering is performed on all HTTP traffic data to obtain data clustering clusters.
K-means聚类算法是将多个聚类对象依据聚类对象之间的相似性聚集到指定的K个聚类簇中，每个聚类对象属于且仅属于一个其到数据聚类簇中心距离最小的聚类簇中。需要说明的是，在本发明的一个实施例中，K值的获取方法为采用肘部法则确定K值。具体K-means聚类算法和肘部法则为本领域技术人员熟知的技术手段，在此不做赘述。The K-means clustering algorithm is to cluster multiple clustering objects into specified K clusters based on the similarity between the clustering objects. Each clustering object belongs to and only belongs to one clustering cluster with the smallest distance to the center of the data clustering cluster. It should be noted that, in one embodiment of the present invention, the method for obtaining the K value is to determine the K value using the elbow rule. The specific K-means clustering algorithm and the elbow rule are technical means well known to those skilled in the art and will not be described in detail here.
通过数据的数量可以识别出包含不同重要程度的数据聚类簇；由于HTTP流量数据中存在大量的冗余信息，且最终信息重要特征值均处于相似的低水平状态，对应数量越多的数据越是包含冗余信息的聚类簇，可以作为参考聚类簇，用于评估其他HTTP流量数据的重要程度。所以根据数据聚类簇中数据的数量筛选出参考聚类簇。The number of data can be used to identify data clusters with different degrees of importance; since there is a large amount of redundant information in HTTP traffic data, and the final important feature values of the information are all at a similar low level, the more data there are, the more redundant information the cluster contains, and it can be used as a reference cluster to evaluate the importance of other HTTP traffic data. Therefore, the reference cluster is selected based on the number of data in the data cluster.
优选地，在本发明的一个实施例中，参考聚类簇的获取方法包括：Preferably, in one embodiment of the present invention, the method for obtaining the reference cluster includes:
选取所有数据聚类簇中HTTP流量数据数量最多对应的数据聚类簇，作为参考聚类簇。The data cluster with the largest amount of HTTP traffic data among all data clusters is selected as the reference cluster.
通过分析HTTP流量数据的最终信息重要特征值分布，可以理解每个HTTP流量数据相对于冗余信息数据的重要性水平之间的关系，量化HTTP流量数据的存储必要性；所以根据每个HTTP流量数据和参考聚类簇中所有HTTP流量数据之间的最终信息重要特征值分布，获得每个HTTP流量数据的存储必要性。By analyzing the distribution of the final information important characteristic values of HTTP traffic data, we can understand the relationship between the importance level of each HTTP traffic data relative to the redundant information data and quantify the necessity of storing HTTP traffic data; therefore, based on the distribution of the final information important characteristic values between each HTTP traffic data and all HTTP traffic data in the reference cluster, the necessity of storing each HTTP traffic data is obtained.
优选地，在本发明的一个实施例中，存储必要性的获取方法包括：Preferably, in one embodiment of the present invention, the method for acquiring storage necessity includes:
获得参考聚类簇中所有HTTP流量数据的最终信息重要特征值的均值，作为平均最终信息重要特征值；Obtain the average of the final information important feature values of all HTTP traffic data in the reference cluster as the average final information important feature value;
计算每个HTTP流量数据的最终信息重要特征值和平均最终信息重要特征值之间的差值，并进行归一化映射，获得每个HTTP流量数据的存储必要性。The difference between the final information important characteristic value of each HTTP traffic data and the average final information important characteristic value is calculated, and normalized mapping is performed to obtain the storage necessity of each HTTP traffic data.
在本发明的一个实施例中，存储必要性的公式为：In one embodiment of the present invention, the formula for storage necessity is:
； ;
其中，表示第个HTTP流量数据的存储必要性；表示参考聚类簇中HTTP流量数据的数量；表示第个HTTP流量数据的最终信息重要特征值；表示第个HTTP流量数据的最终信息重要特征值；表示归一化函数。in, Indicates The necessity of storing HTTP traffic data; Indicates the number of HTTP traffic data in the reference cluster; Indicates The final important characteristic value of HTTP traffic data; Indicates The final important characteristic value of HTTP traffic data; Represents the normalization function.
在存储必要性的公式中，表示计算参考聚类簇中所有HTTP流量数据的最终信息重要特征值的均值，即平均最终信息重要特征值；表示第个HTTP流量数据的最终信息重要特征值和平均最终信息重要特征值之间的差值，差值越大，相较于冗余信息的最终信息重要特征值越大，第个HTTP流量数据的重要程度越高，存储必要性越高。In the formula for storage necessity, It means calculating the mean of the final important characteristic values of all HTTP traffic data in the reference cluster, that is, the average final important characteristic value of information; Indicates The difference between the final information important characteristic value of HTTP traffic data and the average final information important characteristic value. The larger the difference, the larger the final information important characteristic value compared to the redundant information. The more important the HTTP traffic data is, the more necessary it is to store it.
步骤S4：根据存储必要性获得每个HTTP流量数据的存储优先级。Step S4: Obtain the storage priority of each HTTP traffic data according to the storage necessity.
存储必要性反映了HTTP流量数据包含的信息重要程度，通过设定存储优先级，可以优先存储必要性更高的数据，而将必要性较低的数据存储在成本较低的存储介质上或进行压缩、归档等处理，从而降低整体存储成本。根据存储必要性获得每个HTTP流量数据的存储优先级。Storage necessity reflects the importance of the information contained in HTTP traffic data. By setting storage priority, data with higher necessity can be stored first, and data with lower necessity can be stored on storage media with lower cost or compressed, archived, etc., thereby reducing the overall storage cost. The storage priority of each HTTP traffic data is obtained according to storage necessity.
优选地，在本发明的一个实施例中，存储优先级的获取方法包括：Preferably, in one embodiment of the present invention, the method for acquiring the storage priority includes:
获取预设存储优先级对应的存储必要性范围；若每个HTTP流量数据的存储必要性存在预设存储优先级对应的存储必要性范围，对应的存储优先级作为每个HTTP流量数据的存储优先级。Obtain a storage necessity range corresponding to a preset storage priority; if the storage necessity of each HTTP traffic data exists within a storage necessity range corresponding to a preset storage priority, the corresponding storage priority is used as the storage priority of each HTTP traffic data.
需要说明的是，在本发明的一个实施例中，预设存储优先级为1、2、3、4、5，对应的存储必要性范围依次为、、、、；在本发明的其他实施例中，预设存储优先级以及对应的存储必要性范围可根据具体情况具体设置，在此不做限定及赘述。It should be noted that, in one embodiment of the present invention, the preset storage priorities are 1, 2, 3, 4, and 5, and the corresponding storage necessity ranges are: , , , , In other embodiments of the present invention, the preset storage priority and the corresponding storage necessity range can be set according to the specific situation, and are not limited or elaborated here.
将存储优先级按照从大到小的顺序进行存储，对于存储优先级越高的HTTP流量数据在存储数据时，存储在高性能的存储介质上，可以提高数据的访问速度和响应性能；而对于存储优先级较低的HTTP流量数据，可以适当选择更经济的存储方案，从而平衡性能和成本之间的关系，提高了存储资源的利用率。The storage priority is stored in order from large to small. When storing HTTP traffic data with higher storage priority, it is stored on high-performance storage media, which can improve data access speed and response performance. For HTTP traffic data with lower storage priority, a more economical storage solution can be appropriately selected to balance the relationship between performance and cost and improve the utilization of storage resources.
综上所述，本发明根据每个HTTP流量数据中数据的幅值变化特征，获得每个HTTP流量数据的信息重要属性值；根据每个HTTP流量数据中不同IP地址访问频率的变化趋势和信息重要属性值，获得每个HTTP流量数据的初始信息重要特征值；结合HTTP流量数据中IP地址响应时间特征，获得最终信息重要特征值；获得所有HTTP流量数据的数据聚类簇；进而筛选出参考聚类簇；根据每个HTTP流量数据和参考聚类簇中所有HTTP流量数据之间的最终信息重要特征值分布，获得每个HTTP流量数据的存储必要性；获得每个HTTP流量数据的存储优先级。本发明通过分析每个HTTP流量数据准确的存储必要性，合理划分存储优先级，提高数据存储的效率。In summary, the present invention obtains the important information attribute value of each HTTP traffic data according to the amplitude change characteristics of the data in each HTTP traffic data; obtains the initial important information characteristic value of each HTTP traffic data according to the change trend of the access frequency of different IP addresses in each HTTP traffic data and the important information attribute value; obtains the final important information characteristic value in combination with the IP address response time characteristics in the HTTP traffic data; obtains the data clustering cluster of all HTTP traffic data; and then screens out the reference clustering cluster; obtains the storage necessity of each HTTP traffic data according to the distribution of the final important information characteristic values between each HTTP traffic data and all HTTP traffic data in the reference clustering cluster; and obtains the storage priority of each HTTP traffic data. The present invention improves the efficiency of data storage by analyzing the accurate storage necessity of each HTTP traffic data and reasonably dividing the storage priority.
基于与本申请实施例提供的一种信息安全数据存储方法相同的申请构思，本实施例还提出了一种信息安全数据存储系统，如图3所示，系统包括：数据获取模块301、信息重要性修正模块302、存储必要性分析模块303和存储优先级划分模块304：Based on the same application concept as the information security data storage method provided in the embodiment of the present application, the present embodiment also proposes an information security data storage system. As shown in FIG3 , the system includes: a data acquisition module 301, an information importance correction module 302, a storage necessity analysis module 303 and a storage priority division module 304:
数据获取模块301：获取目标终端服务平台在每个时刻的HTTP流量数据；HTTP流量数据包括IP地址访问频率数据和IP地址响应时间数据；Data acquisition module 301: acquires HTTP traffic data of the target terminal service platform at each moment; HTTP traffic data includes IP address access frequency data and IP address response time data;
信息重要性修正模块302：根据每个HTTP流量数据中数据的幅值变化特征，获得每个HTTP流量数据的信息重要属性值；根据每个HTTP流量数据中不同IP地址访问频率的变化趋势和信息重要属性值，获得每个HTTP流量数据的初始信息重要特征值；根据每个HTTP流量数据中IP地址响应时间特征对初始信息重要特征值进行调整，获得每个HTTP流量数据的最终信息重要特征值；The information importance correction module 302: obtains the information importance attribute value of each HTTP traffic data according to the amplitude change characteristics of the data in each HTTP traffic data; obtains the initial information importance characteristic value of each HTTP traffic data according to the change trend of the access frequency of different IP addresses in each HTTP traffic data and the information importance attribute value; adjusts the initial information importance characteristic value according to the IP address response time characteristics in each HTTP traffic data to obtain the final information importance characteristic value of each HTTP traffic data;
存储必要性分析模块303：根据每个HTTP流量数据的最终信息重要特征值，对所有HTTP流量数据进行聚类，获得数据聚类簇；根据数据聚类簇中数据的数量筛选出参考聚类簇；根据每个HTTP流量数据和参考聚类簇中所有HTTP流量数据之间的最终信息重要特征值分布，获得每个HTTP流量数据的存储必要性；Storage necessity analysis module 303: clustering all HTTP traffic data according to the final information important characteristic value of each HTTP traffic data to obtain data clustering clusters; screening out reference clustering clusters according to the number of data in the data clustering clusters; obtaining the storage necessity of each HTTP traffic data according to the distribution of the final information important characteristic value between each HTTP traffic data and all HTTP traffic data in the reference clustering clusters;
存储优先级划分模块304：根据存储必要性获得每个HTTP流量数据的存储优先级。The storage priority division module 304 obtains the storage priority of each HTTP traffic data according to the storage necessity.
需要说明的是，上述实施例提供的系统，仅以上述各功能模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能模块完成，即将计算机设备的内部结构划分成不同的功能模块，以完成以上描述的全部或者部分功能。It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above.
应理解，本实施例提供的系统用于执行上述一种信息安全数据存储方法，因此具有与其存储的应用程序所采用、运行或实现的方法相同的有益效果。It should be understood that the system provided in this embodiment is used to execute the above-mentioned information security data storage method, and therefore has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.
本发明还提出了一种电子设备，如图4，包括存储器401、处理器402以及存储在存储器中并可在处理器上运行的计算机程序403，处理器402执行计算机程序403时，实现任意一项一种信息安全数据存储方法的步骤。The present invention also proposes an electronic device, as shown in Figure 4, including a memory 401, a processor 402, and a computer program 403 stored in the memory and executable on the processor. When the processor 402 executes the computer program 403, it implements any one of the steps of a method for secure information data storage.
需要说明的是：上述本发明实施例先后顺序仅仅为了描述，不代表实施例的优劣。在附图中描绘的过程不一定要求示出的特定顺序或者连续顺序才能实现期望的结果。在某些实施方式中，多任务处理和并行处理也是可以的或者可能是有利的。It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
本说明书中的各个实施例均采用递进的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.
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Assignor:LINYI University Contract record no.:X2025980017023 Denomination of invention:An information security data storage method, system, and electronic device Granted publication date:20241025 License type:Common License Record date:20250812 Application publication date:20240802 Assignee:SHANDONG JINXIANG ALUMINUM Co.,Ltd. Assignor:LINYI University Contract record no.:X2025980017026 Denomination of invention:An information security data storage method, system, and electronic device Granted publication date:20241025 License type:Common License Record date:20250812 Application publication date:20240802 Assignee:Shandong Xinhang Zhilian Technology Development Co.,Ltd. Assignor:LINYI University Contract record no.:X2025980017063 Denomination of invention:An information security data storage method, system, and electronic device Granted publication date:20241025 License type:Common License Record date:20250812 Application publication date:20240802 Assignee:SHANDONG ZHONGMAI CABLE Co.,Ltd. Assignor:LINYI University Contract record no.:X2025980017162 Denomination of invention:An information security data storage method, system, and electronic device Granted publication date:20241025 License type:Common License Record date:20250813 Application publication date:20240802 Assignee:Linyi Hongyuan Thermal Power Co.,Ltd. Assignor:LINYI University Contract record no.:X2025980017169 Denomination of invention:An information security data storage method, system, and electronic device Granted publication date:20241025 License type:Common License Record date:20250813 Application publication date:20240802 Assignee:SHANDONG HUALIN NEW ENERGY EQUIPMENT CO.,LTD. Assignor:LINYI University Contract record no.:X2025980017171 Denomination of invention:An information security data storage method, system, and electronic device Granted publication date:20241025 License type:Common License Record date:20250813 Application publication date:20240802 Assignee:Tancheng Pengcheng Printing Co.,Ltd. Assignor:LINYI University Contract record no.:X2025980017172 Denomination of invention:An information security data storage method, system, and electronic device Granted publication date:20241025 License type:Common License Record date:20250813 Application publication date:20240802 Assignee:SHANDONG CHISHENG ELECTRIC POWER ENGINEERING Co.,Ltd. Assignor:LINYI University Contract record no.:X2025980017190 Denomination of invention:An information security data storage method, system, and electronic device Granted publication date:20241025 License type:Common License Record date:20250813 Application publication date:20240802 Assignee:Linyi Dima block chain network technology Co.,Ltd. Assignor:LINYI University Contract record no.:X2025980016786 Denomination of invention:An information security data storage method, system, and electronic device Granted publication date:20241025 License type:Common License Record date:20250808 | |
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