Multi-dimensional domain name evaluation method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a multi-dimensional domain name evaluation method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: carrying out the data set extraction of a multi-dimensional data source, and obtaining a multi-dimensional domain name data set; performing feature extraction on the domain name data set to obtain multi-dimensional domain name features; analyzing the multi-dimensional domain name features through an analytic hierarchy model to obtain domain name feature weights corresponding to the multi-dimensional domain name features; and obtaining a multi-dimensional domain name evaluation result based on the multi-dimensional domain name feature and the domain name feature weight. According to the invention, the efficiency and objectivity of domain name evaluation can be remarkably improved, and the method has the capability of processing massive domain name data.

Description

Technical Field

[0001] The present disclosure relates to the technical field of Internet data processing, and in particular to a multi-dimensional domain name evaluation method, device, electronic device, storage medium and program product. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the disclosure that are recited in the claims. No description herein is admitted to be prior art by inclusion in this section.

[0003] The multi-dimensional domain name evaluation method is a comprehensive analysis method that comprehensively analyzes the domain name from multiple key dimensions (such as technical status, SEO friendliness, brand influence, legal compliance, user trust, etc.) to accurately measure its current value and potential value-added ability.

[0004] However, in related technologies, the evaluation of domain names is mostly done manually, which is not only inefficient but also easily affected by subjective factors. It is also difficult to cope with the processing needs of large-scale domain name data. Summary of the invention

[0005] In view of this, the purpose of the present disclosure is to propose a multi-dimensional domain name evaluation method, device, electronic device, storage medium and program product, which at least to a certain extent solve one of the technical problems in the related art.

[0006] Based on the above purpose, the first aspect of the exemplary embodiment of the present disclosure provides a multi-dimensional domain name evaluation method, which is applied to a server, and the method includes:

[0007] Extracting data sets from multidimensional data sources to obtain multidimensional domain name data sets;

[0008] Extracting features from the domain name dataset to obtain multi-dimensional domain name features;

[0009] Analyze the multidimensional domain name features through a hierarchical analysis model to obtain domain name feature weights corresponding to the multidimensional domain name features;

[0010] Based on the multi-dimensional domain name features and the domain name feature weights, a multi-dimensional domain name evaluation result is obtained.

[0011] Based on the same inventive concept, the second aspect of the exemplary embodiment of the present disclosure provides a multi-dimensional domain name evaluation device, including:

[0012] A data set determination module is configured to extract data sets from the multidimensional data source to obtain a multidimensional domain name data set;

[0013] A feature determination module is configured to extract features from the domain name data set to obtain multi-dimensional domain name features;

[0014] A weight determination module is configured to analyze the multi-dimensional domain name features through a hierarchical analysis model to obtain domain name feature weights corresponding to the multi-dimensional domain name features;

[0015] The evaluation result determination module is configured to obtain a multi-dimensional domain name evaluation result based on the multi-dimensional domain name feature and the domain name feature weight.

[0016] Based on the same inventive concept, a third aspect of the exemplary embodiment of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0017] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of the present disclosure provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method described in the first aspect.

[0018] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of the present disclosure provides a computer program product, including computer program instructions. When the computer program instructions are executed on a computer, the computer executes the method described in the first aspect.

[0019] From the above, it can be seen that the multidimensional domain name evaluation method, device, electronic device, storage medium and program product provided by the embodiment of the present disclosure include: extracting a data set from a multidimensional data source to obtain a multidimensional domain name data set; extracting features from the domain name data set to obtain multidimensional domain name features; analyzing the multidimensional domain name features through a hierarchical analysis model to obtain domain name feature weights corresponding to the multidimensional domain name features; and obtaining multidimensional domain name evaluation results based on the multidimensional domain name features and the domain name feature weights. The present disclosure can significantly improve the efficiency and objectivity of domain name evaluation, and at the same time has the ability to process massive domain name data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A schematic diagram of an application scenario of the multi-dimensional domain name evaluation method provided by an exemplary embodiment of the present disclosure;

[0022] Figure 2A flowchart of a multi-dimensional domain name evaluation method provided by an exemplary embodiment of the present disclosure;

[0023] Figure 3 A schematic diagram of a structure of a multi-dimensional domain name evaluation device provided by an exemplary embodiment of the present disclosure;

[0024] Figure 4 A schematic diagram of the hardware structure of an electronic device provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] It is understandable that before using the technical solutions disclosed in the embodiments of this application, the type, scope of use, usage scenarios, etc. of the personal information involved in this application should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0026] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present application according to the prompt message.

[0027] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0028] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation method of the present application. Other methods that meet the relevant laws and regulations may also be applied to the implementation method of the present application.

[0029] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0030] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, the principles and spirit of the present disclosure will be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0031] It should be understood herein that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

[0032] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connecting" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "one" or "a" before an element does not exclude the existence of multiple such elements.

[0033] The principle and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure.

[0034] As described in the background technology, in the related art, the evaluation of domain names is mostly carried out manually. This method is not only inefficient, but also easily affected by subjective factors, and it is difficult to cope with the processing needs of large-scale domain name data. Specifically, in traditional domain name evaluation, manual evaluation methods have occupied a dominant position. This method mainly relies on the experience and professional knowledge of the evaluator to make subjective judgments on various dimensions of the domain name. However, this method has many limitations. First, the efficiency of manual evaluation is extremely low, because the evaluator needs to analyze multiple dimensions such as the registration information, traffic data, page content, etc. of the domain name one by one, which makes it almost impossible to process large-scale domain name data. For example, when faced with tens of thousands or even more domain names, manual evaluation is not only time-consuming and labor-intensive, but also difficult to complete the task in a short time.

[0035] Secondly, the manual evaluation process is highly susceptible to subjective factors. The knowledge background, experience level, and personal preferences of different evaluators may significantly affect the evaluation results. For example, for the same domain name, different evaluators may give completely different scores based on their understanding of the domain suffix type, page content richness, or traffic indicators. This subjectivity not only reduces the reliability of the evaluation results, but may also lead to misjudgments, especially in the identification of high-value domain names and the screening of risky domain names.

[0036] In addition, with the development of the Internet, the amount of domain name data has exploded, and the characteristic dimensions of domain names have become increasingly complex. Manual evaluation methods are difficult to cope with such large-scale and complex data processing needs. For example, modern domain name evaluation not only needs to consider the basic registration information of the domain name, but also needs to analyze its historical activity, page structure, number of inbound links and other multi-dimensional data. Manual methods are prone to omissions or errors when processing these complex data, and it is difficult to ensure the comprehensiveness and accuracy of the evaluation.

[0037] In order to solve the above problems, the present disclosure provides a multi-dimensional domain name evaluation method, device, electronic device, storage medium and program product solution, the method comprising:

[0038] Extracting data sets from multidimensional data sources to obtain multidimensional domain name data sets; extracting features from the domain name data sets to obtain multidimensional domain name features; analyzing the multidimensional domain name features through a hierarchical analysis model to obtain domain name feature weights corresponding to the multidimensional domain name features; obtaining multidimensional domain name evaluation results based on the multidimensional domain name features and the domain name feature weights. The present invention discloses a multidimensional domain name evaluation method based on a hierarchical analysis model, which introduces multidimensional domain name features, such as domain name quality, authority, traffic, active age, and health status, and uses a hierarchical analysis model for weighted calculation, thereby achieving automated domain name evaluation; based on this, the present invention can significantly improve the efficiency and objectivity of domain name evaluation, while having the ability to process massive domain name data.

[0039] After introducing the basic principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.

[0040] refer to Figure 1 , which is a schematic diagram of an application scenario of the multi-dimensional domain name evaluation method provided by the exemplary embodiment of the present disclosure.

[0041] This application scenario includes a terminal device 101 and a server 102. The terminal device 101 and the server 102 may be connected via a wired or wireless communication network to achieve data interaction.

[0042] The terminal device 101 may be an electronic device with data transmission and multimedia input / output functions close to the user side, including but not limited to a desktop computer, a mobile phone, a mobile computer, a tablet computer, a media player, a smart wearable device, a personal digital assistant (PDA) or other electronic devices capable of realizing the above functions. The electronic device may include a processor and a display screen with a touch input function, the display screen is used to present a graphical user interface, the graphical user interface can display an application interface, and the processor is used to process application data, generate a graphical user interface, and control the display of the graphical user interface on the display screen.

[0043] Server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0044] In some exemplary embodiments, the multi-dimensional domain name evaluation method may be run on the terminal device 101 or the server 102 .

[0045] When the multi-dimensional domain name evaluation method is run on the server 102 , the server 102 is used to provide a multi-dimensional domain name evaluation service to the user of the terminal device 101 .

[0046] The server 102 extracts a data set from the multidimensional data source to obtain a multidimensional domain name data set;

[0047] The server 102 extracts features from the domain name dataset to obtain multi-dimensional domain name features;

[0048] The server 102 analyzes the multi-dimensional domain name features through a hierarchical analysis model to obtain domain name feature weights corresponding to the multi-dimensional domain name features;

[0049] After the server 102 obtains the multi-dimensional domain name evaluation result based on the multi-dimensional domain name feature and the domain name feature weight, the server 102 transmits the multi-dimensional domain name evaluation result to the terminal device 101 .

[0050] It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principle of the present disclosure, and the embodiments of the present disclosure are not limited in this respect. On the contrary, the embodiments of the present disclosure can be applied to any applicable scenario.

[0051] refer to Figure 2 , a multi-dimensional domain name evaluation method, the method comprising the following steps:

[0052] Step S210: extracting a data set from a multidimensional data source to obtain a multidimensional domain name data set.

[0053] In this exemplary embodiment, a data set is extracted from a multidimensional data source to obtain a multidimensional domain name data set, including:

[0054] Query the multidimensional data source to obtain the basic information of the multidimensional domain name;

[0055] The domain name registration information is captured to obtain the multi-dimensional domain name data set.

[0056] In specific implementation, the method of querying the multi-dimensional data source to obtain the basic information of the multi-dimensional domain name is as follows:

[0057] The system obtains basic information of domain names from multiple data sources, mainly including whitelist and blacklist data sets. The data of whitelist domain names comes from public and trusted ranking data (such as Alexa global ranking), which usually represents higher-quality domain names. Blacklist domain names come from the list of marked domain names provided by network security agencies, which contain potential risk domain names or malicious domain names that have been banned. In addition, the system obtains important data such as DNS server information, registration time, update time, and expiration time of the domain name through DNS query tools. At the same time, the system uses historical snapshot tools such as Archive.org to collect the activity history of the domain name to understand its long-term activity and change records. Among them, the important data such as the DNS server information, registration time, update time, expiration time of the domain name and the long-term activity and change records of the domain name are multi-dimensional domain name basic information.

[0058] In a specific implementation, the domain name registration information is captured to obtain the multi-dimensional domain name data set:

[0059] The system deploys web crawler technology to capture the HTML code, number of images, link structure, inbound links and other information of the domain name page, and ultimately form a complete domain name data set.

[0060] Step S220: extract features from the domain name dataset to obtain multi-dimensional domain name features.

[0061] In this exemplary embodiment, the multi-dimensional domain name features include: domain name length, domain name suffix, page HTML code amount, page image quantity, inbound link quantity and traffic data.

[0062] In specific implementation, the domain name length refers to:

[0063] The number of characters in a domain name. Generally, shorter domain names are easier to remember and have more market value.

[0064] In specific implementation, the domain name suffix refers to:

[0065] Different suffixes (such as .com, .org, .net, etc.) have different influence and recognition in the field.

[0066] In specific implementation, the amount of HTML code on a page refers to:

[0067] By analyzing the amount of Hypertext Markup Language (HTML) code on the domain name page, the complexity of the page and the richness of the content are evaluated.

[0068] In specific implementation, the number of page images refers to:

[0069] The number of images included in a domain page usually reflects the visual expression and interactivity of the page.

[0070] In practice, the number of inbound links refers to:

[0071] The number of external links pointing to a domain name (Backlinks). This indicator usually represents the authority of the domain name and its influence in search engines.

[0072] In specific implementation, traffic data refers to:

[0073] The system extracts traffic indicators of domain names through tools such as Alexa or SimilarWeb to estimate the popularity of domain names and the size of their visits. Each feature is expressed in a quantitative way to ensure that it can be processed and calculated uniformly in subsequent evaluations.

[0074] In the above exemplary embodiment, a multi-dimensional domain name feature is introduced. The following describes a method for obtaining the multi-dimensional domain name feature:

[0075] In this exemplary embodiment, extracting features from the domain name dataset to obtain multi-dimensional domain name features includes:

[0076] Feature extraction is performed on the domain name data set to obtain the domain name length, the domain name suffix, the page hypertext markup language code amount, the page image number, the external link number and the traffic data.

[0077] In a specific implementation, the domain name data set is subjected to feature extraction to obtain the domain name length, the domain name suffix, the amount of page hypertext markup language code, the number of page images, the number of external links and the traffic data:

[0078] The system first calculates the length of the domain name, that is, the number of characters in the domain name string, to evaluate its simplicity and memorability. Secondly, identify the suffix type of the domain name (such as .com, .org, .net, etc.) and assign corresponding weights based on the versatility and influence of the suffix in the market. Next, analyze the amount of HTML code on the domain name page to evaluate the complexity and content richness of the page; count the number of pictures on the page to reflect the visual expression and user experience of the page. In addition, the system also calculates the number of inbound links pointing to the domain name as an indicator of the domain name's authority and search engine influence. Finally, use traffic analysis tools (such as Alexa or SimilarWeb) to obtain traffic data for the domain name to evaluate its popularity and market value. Through these steps, the system is able to quantify the multi-dimensional characteristics of the domain name and provide data support for subsequent comprehensive evaluations.

[0079] In the above exemplary embodiment, a method for obtaining the domain name length, the domain name suffix, the page hypertext markup language code amount, the page image number, the external link number and the traffic data is introduced. Next, a method for making adjustments in response to data anomalies and / or environmental anomalies when extracting data sets from a multidimensional data source and / or extracting features from the domain name data set is introduced:

[0080] In this exemplary embodiment, when extracting a data set from a multidimensional data source and / or extracting features from the domain name data set, in response to the existence of a data anomaly and / or an environmental anomaly, a record is made based on the data anomaly and / or the environmental anomaly to obtain an error log, and a recovery retry is performed to determine whether the recovery retry is successful. In response to the failure of the recovery retry, an early warning message is generated;

[0081] The error log and warning information are sent to the terminal device, adjustment information sent from the terminal device is received, and adjustment is performed based on the adjustment information and the error log.

[0082] In specific implementation, data anomaly refers to:

[0083] Various abnormal situations that occur during the domain name data collection, feature extraction and calculation process may lead to data collection failure, processing interruption or inaccurate results. Specifically, they include data transmission interruption caused by network fluctuations, insufficient permissions to access certain data sources, missing or incorrect data formats, and logical errors in the feature extraction process.

[0084] In specific implementation, environmental abnormalities refer to:

[0085] During the operation of the domain name evaluation system, unexpected situations may occur due to changes in the external operating environment or internal system conditions. These anomalies may include server hardware failure, unstable network connection, incompatible operating system or database versions, insufficient resources (such as memory or storage space), software vulnerabilities, and external attacks (such as DDoS attacks).

[0086] In a specific implementation, when a data set is extracted from a multidimensional data source and / or a feature is extracted from the domain name data set, in response to the existence of a data anomaly and / or an environmental anomaly, a record is made based on the data anomaly and / or the environmental anomaly, an error log is obtained, and a recovery retry is performed, and whether the recovery retry is successful is determined. In response to an unsuccessful recovery retry, an early warning message is generated; the error log and the early warning message are sent to a terminal device, and adjustment information sent from the terminal device is received. The adjustment is performed based on the adjustment information and the error log:

[0087] During data collection, feature extraction or calculation, if there are abnormal situations such as network fluctuations, restricted permissions or missing data, the system will automatically record detailed error logs and start a retry mechanism to try to recover. If the retry fails, the system will send an alert to the administrator's terminal device. The administrator can manually adjust system parameters or restart related processes based on the error log to ensure the smooth progress of the entire evaluation process and the integrity of the data.

[0088] Step S230: Analyze the multi-dimensional domain name features through a hierarchical analysis model to obtain domain name feature weights corresponding to the multi-dimensional domain name features.

[0089] In this exemplary embodiment, the hierarchical analysis model is constructed in the following manner:

[0090] Determine several key dimensions of the multi-dimensional domain name features, compare every two of the key dimensions, and construct a judgment matrix;

[0091] The key dimensions are calculated by the judgment matrix to obtain the relative weight corresponding to each key dimension;

[0092] Performing a consistency check on the judgment matrix, and obtaining a consistency judgment matrix in response to a consistency ratio of the judgment matrix being less than a preset threshold;

[0093] Dynamically adjusting the relative weight based on the application scenario to obtain an adjusted relative weight;

[0094] The adjusted relative weights are stored in the consistency judgment matrix to obtain the hierarchical analysis model.

[0095] In a specific implementation, several key dimensions of the multi-dimensional domain name feature are determined, and every two of the key dimensions are compared to construct a judgment matrix:

[0096] First, the system will comprehensively consider multiple dimensions of domain name value assessment, such as domain name quality, authority, traffic, active age, health status, etc. These dimensions are selected based on industry experience and actual needs of domain name value assessment, and can fully reflect the comprehensive value of the domain name. After determining the key dimensions, the system will use the paired comparison method in the hierarchical analysis method to construct a judgment matrix. Specifically, the system compares each two key dimensions one by one to evaluate their relative importance in the domain name value assessment. This comparison can be completed through expert scoring. Experts will quantify the relative importance between each two dimensions based on their experience and expertise. For example, if traffic is considered more important than domain name length, experts may give a higher weight value to represent this relative relationship. In this way, the system can obtain a complete judgment matrix in which each element reflects the relative importance between two key dimensions.

[0097] As a specific embodiment, the judgment matrix is ​​a square matrix whose elements represent the relative importance between every two key dimensions. For example, if the element a in the judgment matrix is ij represents the importance of dimension i relative to dimension j, then a ij >1 means dimension i is more important than dimension j, and vice versa. The results of this pairwise comparison form the basis of the judgment matrix.

[0098] In a specific implementation, the key dimensions are calculated by the judgment matrix to obtain the relative weight corresponding to each key dimension:

[0099] Calculate the maximum eigenvalue λ of the judgment matrix max and its corresponding eigenvector. After normalization, the eigenvector can be used as the relative weight of each key dimension. The purpose of normalization is to ensure that the sum of all weights is 1, so that the weights are comparable. For example, assuming that the judgment matrix is ​​A and the eigenvector corresponding to its maximum eigenvalue is W, then W is the relative weight vector of each key dimension after normalization.

[0100] In a specific implementation, a consistency check is performed on the judgment matrix, and in response to the consistency ratio of the judgment matrix being less than a preset threshold, a consistency judgment matrix is ​​obtained:

[0101] The consistency test determines whether the judgment matrix is ​​reasonable by calculating the consistency index (CI) and the consistency ratio (CR). If the consistency ratio CR is less than or equal to 0.1, the judgment matrix is ​​considered to have satisfactory consistency, and the calculated weight is reliable; if the CR is greater than 0.1, the judgment matrix needs to be readjusted and recalculated. Through this process, this method can scientifically determine the relative weight of each key dimension.

[0102] In specific implementation, the relative weight is dynamically adjusted based on the application scenario to obtain the adjusted relative weight:

[0103] The importance of each key dimension is re-evaluated according to the characteristics and goals of the application scenario. For example, in the domain name evaluation in the financial field, the authority and traffic of the domain name may be more important, while in network security monitoring, the health status and active age of the domain name may be more valuable. Therefore, the system will adjust the weight distribution of each key dimension according to the specific needs of these application scenarios.

[0104] Step S240: Obtain a multi-dimensional domain name evaluation result based on the multi-dimensional domain name feature and the domain name feature weight.

[0105] In this exemplary embodiment, based on the multi-dimensional domain name features and the domain name feature weights, a multi-dimensional domain name evaluation result is obtained, including:

[0106] Performing weighted calculation based on the domain name feature weight and the multi-dimensional domain name feature to obtain a comprehensive score corresponding to the multi-dimensional domain name feature;

[0107] Normalizing the comprehensive score to obtain a normalized comprehensive score;

[0108] Based on the normalized comprehensive score, the multi-dimensional domain name evaluation result is obtained.

[0109] In a specific implementation, a weighted calculation is performed based on the domain name feature weight and the multi-dimensional domain name feature to obtain a comprehensive score corresponding to the multi-dimensional domain name feature:

[0110] The system performs weighted calculations based on domain name feature weights and multi-dimensional domain name features. Specifically, the extracted multi-dimensional domain name features (such as domain name length, suffix type, page HTML code volume, number of images, number of inbound links, and traffic data, etc.) are normalized and multiplied by the corresponding weights to obtain the weighted value of each feature. These weighted values ​​reflect the contribution of each feature in the domain name value assessment. The system adds up the weighted values ​​of all features and finally obtains the comprehensive score corresponding to the multi-dimensional domain name features, which preliminarily quantifies the comprehensive value of the domain name.

[0111] In specific implementation, the comprehensive score is normalized to obtain a normalized comprehensive score:

[0112] The system normalizes the comprehensive score. Normalization is to map the comprehensive score to a unified interval (such as 0 to 1 or 0 to 100) to eliminate the impact of different domain names due to differences in feature dimensions or different score ranges.

[0113] In specific implementation, the method of obtaining the multi-dimensional domain name evaluation result based on the normalized comprehensive score is as follows:

[0114] The normalized comprehensive score is divided into different grade intervals (e.g., excellent, good, average, poor, and bad), each of which corresponds to a specific domain value level. The system determines the final evaluation result of the domain name based on the interval to which the normalized comprehensive score belongs.

[0115] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0116] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, 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.

[0117] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a multi-dimensional domain name evaluation device.

[0118] refer to Figure 3 , the multi-dimensional domain name evaluation device comprises:

[0119] The data set determination module 310 is configured to extract data sets from the multidimensional data source to obtain a multidimensional domain name data set;

[0120] The feature determination module 320 is configured to extract features from the domain name dataset to obtain multi-dimensional domain name features;

[0121] The weight determination module 330 is configured to analyze the multi-dimensional domain name feature through a hierarchical analysis model to obtain a domain name feature weight corresponding to the multi-dimensional domain name feature;

[0122] The evaluation result determination module 340 is configured to obtain a multi-dimensional domain name evaluation result based on the multi-dimensional domain name feature and the domain name feature weight.

[0123] In this exemplary embodiment, the data set determination module 310 is specifically configured to:

[0124] Query the multidimensional data source to obtain the multidimensional domain name basic information; crawl the domain name registration information to obtain the multidimensional domain name data set; in response to the existence of data anomalies and / or environmental anomalies, record based on the data anomalies and / or the environmental anomalies to obtain an error log, and perform a recovery retry, determine whether the recovery retry is successful, and generate an early warning message in response to an unsuccessful recovery retry; send the error log and the early warning message to the terminal device, receive the adjustment information sent from the terminal device, and make adjustments based on the adjustment information and the error log.

[0125] In this exemplary embodiment, the feature determination module 320 is specifically configured as follows:

[0126] Perform feature extraction on the domain name data set to obtain domain name length, domain name suffix, page hypertext markup language code amount, page image number, external link number and traffic data; in response to the existence of data anomalies and / or environmental anomalies, record based on the data anomalies and / or the environmental anomalies to obtain an error log, and perform recovery retries to determine whether the recovery retry is successful, and generate an early warning message in response to an unsuccessful recovery retry; send the error log and the early warning message to the terminal device, receive adjustment information sent from the terminal device, and make adjustments based on the adjustment information and the error log.

[0127] In this exemplary embodiment, the weight determination module 330 is specifically configured as follows:

[0128] The multidimensional domain name feature is analyzed by a hierarchical analysis model to obtain the domain name feature weight corresponding to the multidimensional domain name feature; the hierarchical analysis model is constructed in the following manner: several key dimensions of the multidimensional domain name feature are determined, and every two of the key dimensions are compared to construct a judgment matrix; the key dimensions are calculated by the judgment matrix to obtain the relative weight corresponding to each of the key dimensions; the judgment matrix is ​​subjected to a consistency check, and in response to the consistency ratio of the judgment matrix being less than a preset threshold, a consistency judgment matrix is ​​obtained; the relative weights are dynamically adjusted based on application scenarios to obtain adjusted relative weights; the adjusted relative weights are stored in the consistency judgment matrix to obtain the hierarchical analysis model.

[0129] In this exemplary embodiment, the evaluation result determination module 340 is specifically configured as follows:

[0130] A weighted calculation is performed based on the domain name feature weight and the multi-dimensional domain name feature to obtain a comprehensive score corresponding to the multi-dimensional domain name feature; the comprehensive score is normalized to obtain a normalized comprehensive score; and the multi-dimensional domain name evaluation result is obtained based on the normalized comprehensive score.

[0131] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0132] The device of the above embodiment is used to implement the corresponding multi-dimensional domain name evaluation method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0133] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the multi-dimensional domain name evaluation method described in any of the above embodiments is implemented.

[0134] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0135] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0136] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0137] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0138] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0139] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0140] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0141] The electronic device of the above embodiment is used to implement the corresponding multi-dimensional domain name evaluation method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0142] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the multi-dimensional domain name evaluation method as described in any of the above embodiments.

[0143] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0144] The above-mentioned non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0145] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the multi-dimensional domain name evaluation method described in any embodiment in the above exemplary method part, and have the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0146] Based on the same inventive concept, corresponding to the multidimensional domain name evaluation method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor execute the multidimensional domain name evaluation method. Corresponding to the execution subject corresponding to each step in each embodiment of the multidimensional domain name evaluation method, the processor that executes the corresponding step may belong to the corresponding execution subject.

[0147] The computer program product of the above embodiment is used to enable the computer and / or the processor to execute the multi-dimensional domain name evaluation method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0148] Those skilled in the art will appreciate that the embodiments of the present disclosure may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." In addition, in some embodiments, the present disclosure may also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code.

[0149] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive examples) of computer-readable storage media may include, for example: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0150] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0151] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0152] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0153] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified in the boxes in the flowchart and / or block diagram.

[0154] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable medium produce a product that includes an instruction device that implements the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0155] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable device to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0156] In addition, although the operations of the disclosed method are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0157] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0158] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0159] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0160] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0161] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0162] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

[0163] Although the spirit and principle of the present disclosure have been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A multi-dimensional domain name evaluation method, characterized in that: include: Extracting data sets from multidimensional data sources to obtain multidimensional domain name data sets; Extracting features from the domain name dataset to obtain multi-dimensional domain name features; Analyze the multidimensional domain name features through a hierarchical analysis model to obtain domain name feature weights corresponding to the multidimensional domain name features; Based on the multi-dimensional domain name features and the domain name feature weights, a multi-dimensional domain name evaluation result is obtained.

2. The method according to claim 1, characterized in that The step of extracting a data set from a multidimensional data source to obtain a multidimensional domain name data set includes: Query the multidimensional data source to obtain the basic information of the multidimensional domain name; The domain name registration information is captured to obtain the multi-dimensional domain name data set.

3. The method according to claim 1, characterized in that The multi-dimensional domain name features include: domain name length, domain name suffix, page HTML code volume, page image quantity, inbound link quantity and traffic data; The extracting features of the domain name dataset to obtain multi-dimensional domain name features includes: Feature extraction is performed on the domain name data set to obtain the domain name length, the domain name suffix, the amount of hypertext markup language code on the page, the number of page images, the number of inbound links and the traffic data.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: In response to the existence of data anomalies and / or environmental anomalies, recording is performed based on the data anomalies and / or the environmental anomalies to obtain an error log, and recovery retry is performed to determine whether the recovery retry is successful, and in response to the recovery retry being unsuccessful, an early warning message is generated; The error log and warning information are sent to the terminal device, adjustment information sent from the terminal device is received, and adjustment is performed based on the adjustment information and the error log.

5. The method according to claim 1, characterized in that The hierarchical analysis model is constructed in the following way: Determine several key dimensions of the multi-dimensional domain name features, compare every two of the key dimensions, and construct a judgment matrix; The key dimensions are calculated by the judgment matrix to obtain the relative weight corresponding to each key dimension; Performing a consistency check on the judgment matrix, and obtaining a consistency judgment matrix in response to a consistency ratio of the judgment matrix being less than a preset threshold; Dynamically adjusting the relative weight based on the application scenario to obtain an adjusted relative weight; The adjusted relative weights are stored in the consistency judgment matrix to obtain the hierarchical analysis model.

6. The method according to claim 1, characterized in that The obtaining of a multi-dimensional domain name evaluation result based on the multi-dimensional domain name feature and the domain name feature weight includes: Performing weighted calculation based on the domain name feature weight and the multi-dimensional domain name feature to obtain a comprehensive score corresponding to the multi-dimensional domain name feature; Normalizing the comprehensive score to obtain a normalized comprehensive score; Based on the normalized comprehensive score, the multi-dimensional domain name evaluation result is obtained.

7. A multi-dimensional domain name evaluation device, characterized in that: include: A data set determination module is configured to extract data sets from the multidimensional data source to obtain a multidimensional domain name data set; A feature determination module is configured to extract features from the domain name data set to obtain multi-dimensional domain name features; A weight determination module is configured to analyze the multi-dimensional domain name features through a hierarchical analysis model to obtain domain name feature weights corresponding to the multi-dimensional domain name features; The evaluation result determination module is configured to obtain a multi-dimensional domain name evaluation result based on the multi-dimensional domain name feature and the domain name feature weight.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer program instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

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