Network identifier-based website association relationship mining and dynamic monitoring method, device, equipment and medium

Abstract

This application discloses a method, apparatus, device, and medium for mining and dynamically monitoring website association relationships based on network identifiers, relating to the field of computer technology. The method includes: acquiring webpage information from several web pages and updating a target website association graph based on the identifier information and website domain names contained therein; if a target monitoring task corresponding to a target identifier is received from a browser client, the newly acquired webpage information is matched with the target identifier; if the match is successful, the newly acquired webpage information is determined to be a false alarm based on the historical and current list of associated websites corresponding to the target identifier, determined through the target website association graph; if it is not a false alarm, an association website intelligence event is generated and pushed to the browser client. Thus, associated websites corresponding to identifiers can be identified, and the number of websites corresponding to identifiers can be monitored in real time through intelligence event pushes.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, device, and medium for mining and dynamically monitoring website associations based on network identifiers. Background Technology

[0002] In the fields of overseas internet expansion, SEO (Search Engine Optimization), and advertising analysis, acquiring competitive intelligence is crucial. Analysts typically need to assess the traffic value of a website, determine whether it has a mature operational team behind it (website cluster model), and monitor competitors' advertising trends.

[0003] Currently, website relationships typically require manual analysis by relevant personnel, frequently switching between different tools. This approach is not only inefficient but also makes it difficult to uncover implicit connections between websites. For example, a seemingly independent blog might actually be part of a large network of 100 websites. Without linking them together using Shared IDs, analysts cannot gain a comprehensive understanding of their competitors. Furthermore, current methods lag in detecting changes in the number of websites, failing to determine the number of competitor websites in real time. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, device, and medium for mining and dynamically monitoring website associations based on network identifiers. This method can identify associated websites corresponding to identifiers and monitor the changes in the number of websites corresponding to identifiers in real time through intelligence event push notifications. The specific solution is as follows: In a first aspect, this application discloses a method for mining and dynamically monitoring website relationships based on network identifiers, applied to a backend server, including: The webpage information of several network pages is obtained by a preset crawler cluster, and the target website association graph is updated according to the identifier information in the webpage information and the corresponding website domain name of the several network pages. If a target monitoring task corresponding to a target identifier is received from a browser client, the newly added web page information obtained through the preset crawler cluster is matched with the target identifier to obtain the corresponding matching result. If the matching result indicates a successful match, then based on the target website association graph, determine the historical association website list and the current association website list corresponding to the target identifier, and based on the historical association website list and the current association website list, determine whether the newly added webpage information is a false alarm. If the newly added webpage information is not a false alarm, a related website intelligence event is generated and pushed to the browser client.

[0005] Optionally, the step of obtaining webpage information of several web pages through a preset crawler cluster, and updating the target website association graph according to the identifier information in the webpage information and the corresponding website domain names of the several web pages, includes: The webpage information of several web pages is obtained by a preset crawler cluster, and the webpage source code in the webpage information is scanned by a preset regular expression to extract the identifier information in the webpage information; the preset crawler cluster is a listening script injected into the several webpages based on a preset script injection rule; the preset script injection rule is to inject a listening script into the several webpages if the webpage address of the several webpages is in a preset webpage list. The identifier information is analyzed to identify common identifiers in the identifier information, and the common identifiers are removed from the identifier information to obtain the target identifier information; The target website association graph is updated based on the target identifier information and the corresponding website domains of the several web pages.

[0006] Optionally, updating the target website association graph based on the target identifier information and the corresponding website domains of the plurality of network pages includes: The target identifier information and the corresponding website domain names of the plurality of network pages are used as nodes in the target website association graph; If the webpage source code contains the target identifier information and / or the website domain name, then an edge is constructed between the network page corresponding to the webpage source code and the node to complete the update of the target website association graph.

[0007] Optionally, after obtaining webpage information of several web pages through a preset crawler cluster, and updating the target website association graph according to the identifier information in the webpage information and the corresponding website domains of the several web pages, the method further includes: If a webpage information retrieval request is received from the browser client, the corresponding webpage data to be fed back is retrieved from the target website association graph based on several query identifiers in the webpage information retrieval request. The webpage data to be fed back is sent to the browser client so that the browser client can render the information page based on the webpage data to be fed back.

[0008] Optionally, if a target monitoring task corresponding to the target identifier is received from a browser client, the newly added webpage information obtained through the preset crawler cluster is matched with the target identifier to obtain the corresponding matching result, including: If a target monitoring task corresponding to the target identifier is received from a browser client, it is matched with the newly added web page information of the several network pages obtained by the preset crawler cluster. If the target identifier is included in the source code of the newly added webpage information, a first matching result representing a successful match is generated. If the target identifier is not included in the source code of the newly added webpage information, a second matching result representing a failed match is generated.

[0009] Optionally, determining whether the newly added webpage information is a false alarm based on the historical associated website list and the current associated website list includes: Perform a difference operation on the historical list of associated websites and the current list of associated websites to obtain the corresponding difference result; If the difference result is not empty, the newly added webpage information is determined to be a false alarm; if the difference result is empty, the newly added webpage information is determined to be a false alarm.

[0010] Optionally, if the newly added webpage information is not a false alarm, then generating a related website intelligence event and pushing the related website intelligence event to the browser client includes: If the newly added webpage information is not a false alarm, then a related website intelligence event is generated based on a preset data structure, and the related website intelligence event is pushed to a preset message center so that the preset message center can push a red dot notification to the browser client based on the related website intelligence event.

[0011] Secondly, this application discloses a method for mining and dynamically monitoring website relationships based on network identifiers, applied to a backend server, including: The graph update module is used to obtain web page information of several network pages through a preset crawler cluster, and update the target website association graph according to the identifier information in the web page information and the corresponding website domain name of the several network pages. The identifier matching module is used to match the newly added web page information obtained by the preset crawler cluster with the target identifier if a target monitoring task corresponding to the target identifier is received by the browser client, so as to obtain the corresponding matching result. The false alarm information confirmation module is used to determine the historical associated website list and the current associated website list corresponding to the target identifier based on the target website association graph if the matching result indicates a successful match, and to determine whether the newly added web page information is a false alarm information based on the historical associated website list and the current associated website list. The intelligence event push module is used to generate a related website intelligence event and push the related website intelligence event to the browser client if the newly added web page information is not a false alarm.

[0012] Thirdly, this application discloses an electronic device, including: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned method for mining and dynamically monitoring website associations based on network identifiers.

[0013] Fourthly, this application discloses a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned method for mining and dynamically monitoring website associations based on network identifiers.

[0014] In this application, webpage information of several web pages can be obtained through a preset crawler cluster, and the target website association graph can be updated according to the identifier information in the webpage information and the corresponding website domain names of the several web pages. If a target monitoring task corresponding to the target identifier is received by a browser client, the newly added webpage information obtained by the preset crawler cluster is matched with the target identifier to obtain the corresponding matching result. If the matching result indicates a successful match, the historical association website list and the current association website list corresponding to the target identifier are determined based on the target website association graph, and it is determined whether the newly added webpage information is a false alarm based on the historical association website list and the current association website list. If the newly added webpage information is not a false alarm, an association website intelligence event is generated and the association website intelligence event is pushed to the browser client.

[0015] Therefore, the method described in this application can acquire web page information through a pre-set crawler cluster, and then update the target website association graph using the identifier information in the web page information and the corresponding website domain name. If a target monitoring task corresponding to the target identifier is received from a browser client, the newly acquired web page information is matched with the target identifier. If the match is successful, it is determined whether the newly acquired web page information is a false alarm based on the historical and current list of associated websites corresponding to the target identifier determined through the target website association graph. If it is not a false alarm, an associated website intelligence event is generated and pushed to the browser client. In this way, scattered and seemingly unrelated websites can be linked into a network, thereby identifying the user's associated websites and providing deeper intelligence value. Furthermore, timely early warning intelligence pushes can monitor the changes in the number of websites a user visits in real time. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for mining and dynamically monitoring website associations based on network identifiers disclosed in this application; Figure 2 This is a schematic diagram of a browser client rendering process disclosed in this application; Figure 3 This is a schematic diagram of a website monitoring and intelligence push process disclosed in this application; Figure 4 This is a schematic diagram of the architecture of a website association mining and dynamic monitoring method based on network identifiers disclosed in this application; Figure 5 This is a schematic diagram of a website association mining and dynamic monitoring device based on network identifiers disclosed in this application; Figure 6 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Currently, website relationships usually require manual investigation by relevant personnel, which involves frequently switching between different tools, resulting in low efficiency. Furthermore, current methods are lagging in perceiving changes in the number of websites and cannot determine the number of competitors' websites in real time.

[0020] See Figure 1 As shown, this embodiment of the invention discloses a method for mining and dynamically monitoring website associations based on network identifiers, applied to a backend server, including: Step S11: Obtain webpage information of several web pages through a preset crawler cluster, and update the target website association graph according to the identifier information in the webpage information and the corresponding website domain names of the several web pages.

[0021] In this embodiment, it is necessary to obtain webpage information of several network pages through a preset crawler cluster, and update the target website association graph based on the identifier information in the intelligence event webpage information and the corresponding website domains of the intelligence event webpages. Specifically, it is necessary to obtain webpage information of several network pages through a preset crawler cluster, and scan the webpage source code in the webpage information using preset regular expressions to extract the identifier information in the webpage information. It should be noted that after the crawler accesses the target URL (Uniform Resource Locator), it not only downloads the HTML (HyperText Markup Language) source code, but also intercepts network requests. The system uses regular expressions to scan the page source code and the ads.txt file to extract key features, namely target identifier information (Publisher ID and advertiser information). Among them, the Publisher ID matches the formats pub-\d{16} (AdSense), G-\w+ (Analytics 4), UA-\d+ (Universal Analytics), etc. The advertiser information requires parsing the source address of the embedded ad iframe on the webpage, tracing the redirection chain, and identifying the actual advertiser domain of the ad. It should be further explained that the default crawler cluster consists of listening scripts injected into several webpages based on default script injection rules; the default script injection rules are that if the webpage address of several webpages is in a default list of webpages, then a listening script is injected into those webpages.

[0022] Furthermore, the identifier information needs to be analyzed to identify common identifiers and remove them from the identifier information to obtain the target identifier information. That is, frequency analysis needs to be performed on the extracted IDs (IdentityDocument) to remove common IDs, such as statistical IDs commonly used by some website building platforms, and retain private IDs with unique identification significance.

[0023] Finally, the target website association graph needs to be updated based on the target identifier information and the corresponding website domains of several web pages. It should be noted that the target identifier information and the corresponding website domains of several web pages need to be used as nodes in the target website association graph. That is, when constructing the graph, each website domain (Website, Publisher ID) and advertisement (Advertiser) needs to be abstracted as a node in the graph, and then the graph is constructed. Furthermore, if the web page source code contains the target identifier information and / or website domain, then the edges between the corresponding web page and the node are constructed to update the target website association graph. For example, when the source code of website A contains ID X, the edge is established: Website_A - [HAS_ID] -> ID_X. When advertiser Y places an advertisement on website A, the edge is established: Advertiser_Y - [TARGETS] -> Website_A. In this way, with this structure, when a user queries a certain ID, the system only needs to traverse all the incoming edges of the ID node to obtain the list of all associated websites in O(1) time complexity, regardless of whether the number is tens or tens of thousands, which effectively improves the processing efficiency.

[0024] It should be noted that in some cases, browser clients need to perform data injection, such as... Figure 2 As shown, context awareness and SERP (Search Engine Results Page) monitoring are required first. The browser's background scripts need to continuously listen for browser tab update events (chrome.tabs.onUpdated). Furthermore, the system has a built-in table of supported search engine URL rules, i.e., a pre-defined list of web pages, such as ^https: / / www.google.[az.]+ / search. If the current URL matches the rules, the script is automatically injected. Then, DOM (Document Object Model) structure parsing and target location are performed. After the script is injected, it doesn't directly manipulate the page; instead, it first uses MutationObserver to monitor changes in the DOM tree to accommodate the infinite scrolling or asynchronous loading characteristics of search engines. For example, the system identifies the container elements of the search results (such as div.g or div.srg in Google) within each container, parses out the hyperlinks (anchor tags) of the target landing page, and extracts their Host portion as query keywords.

[0025] Then, asynchronous deduplication and data aggregation are required. The script adds all extracted domains within the visible area to a queue and uses a debounce mechanism to merge multiple domains within a short period into a single API request sent to the backend server. The backend returns a JSON (JavaScript Object Notation) object containing each domain's average monthly traffic, main traffic sources, core Publisher ID, and the number of other websites associated with that ID. Finally, Shadow DOM isolation rendering is performed. After obtaining the data, the script dynamically creates a new DOM node at the bottom of each search result entry. To prevent the search engine's original CSS (Cascading Style Sheets) styles (such as global font and line height) from affecting the display of the intelligence panel, and to prevent the intelligence panel's styles from disrupting the original page layout, this application uses the Shadow DOM method. Ultimately, the system creates a shadow-root and encapsulates the intelligence panel's HTML structure and independent CSS stylesheets within it. The panel contains interactive elements; for example, clicking on "Publisher ID" triggers the sidebar to expand, displaying a list of all associated websites under that ID.

[0026] However, it's important to clarify that the above description is from the browser client's perspective. From the backend server's perspective, it receives webpage information retrieval requests from the browser client, queries the target website's relational graph based on several query identifiers in the request, and then sends the corresponding webpage data back to the browser client so that the browser can render the information page based on it. In this way, by directly injecting data into the SERP, users can determine the website's scale and value without clicking through to the site. This reduces the cumbersome "click-redirect-query-return" process to a convenient mouse-based operation, significantly improving the efficiency of information filtering.

[0027] Step S12: If a target monitoring task corresponding to the target identifier is received from a browser client, the newly added web page information obtained through the preset crawler cluster is matched with the target identifier to obtain the corresponding matching result.

[0028] In this embodiment, if a target monitoring task corresponding to a target identifier is received from a browser client, the newly added webpage information obtained by a preset crawler cluster is matched with the target identifier to obtain the corresponding matching result. Specifically, the processing flow is as follows: Figure 3As shown, if a target monitoring task corresponding to a target identifier is received from a browser client, it is matched using newly added web page information obtained from several web pages by a preset crawler cluster. It should be noted that if a user clicks to monitor this ID on the front-end interface, the system back-end receives an instruction to add the target object, such as pub-1234567890123456, to a high-frequency scanning queue. The system no longer treats this ID as ordinary data but as a monitoring anchor point, therefore a monitoring task needs to be generated.

[0029] If the backend receives a monitoring task, it performs an incremental scan of the reverse index, that is, it matches the newly added webpage information obtained by the preset crawler cluster with the target identifier to obtain the corresponding matching results. Specifically, if a new domain, new-site.com, is crawled and its source code contains pub-1234567890123456 from the monitoring list, the system immediately marks the matching event. For advertisers, a headless browser is used to simulate accessing the new site. If a delivery request from the target advertiser is captured, a matching is also triggered. If the source code of the newly added webpage information includes the target identifier, a first matching result indicating a successful match is generated; if the source code of the newly added webpage information does not include the target identifier, a second matching result indicating a failed match is generated.

[0030] Step S13: If the matching result indicates a successful match, then based on the target website association graph, determine the historical association website list and the current association website list corresponding to the target identifier, and based on the historical association website list and the current association website list, determine whether the newly added webpage information is a false alarm.

[0031] In this embodiment, it is necessary to determine whether the newly added webpage information is a false alarm based on the historical list of associated websites and the current list of associated websites. Figure 3 As shown, time slice comparison and event generation are required. This involves performing a difference operation on the historical and current lists of associated websites to obtain the corresponding difference result. Specifically, it's necessary to determine the historical list of associated websites (historical snapshot) and the current list of associated websites (current snapshot) corresponding to the target identifier. It should be noted that the system database stores the historical state sequence of the monitored objects: State_T0, State_T1, ..., State_Tn. The comparison logic is as follows: This involves performing a difference operation. If the difference result is not empty, the newly added webpage information is determined to be a genuine false alarm; if the difference result is empty, the newly added webpage information is determined to be a false alarm.

[0032] Step S14: If the newly added webpage information is not a false alarm, then generate an associated website intelligence event and push the associated website intelligence event to the browser client.

[0033] In this embodiment, if the newly added webpage information is not a false alarm, a related website intelligence event is generated and pushed to the browser client. Specifically, if the newly added webpage information is not a false alarm, a related website intelligence event is generated based on a preset data structure. The generation of the related website intelligence event based on the preset data structure is as follows: ; Next, the related website intelligence events need to be pushed to a preset message center so that the preset message center can push red dot notifications to browser clients based on the related website intelligence events. Specifically, the generated events are pushed to the message center. Depending on the user settings, the system pushes red dot notifications to online browser extensions via WebSocket or sends daily reports via email service to inform users. In this way, through automated differential comparison, the system can push information to users at the early stages of competitor actions, such as building a new website or launching new advertisements, ensuring that users are aware of changes in the number of competitor websites in real time.

[0034] In this embodiment, web information of web pages can be obtained through a preset crawler cluster. Then, the target website association graph is updated using the identifier information in the web page information and the corresponding website domain name of the web page. If a target monitoring task corresponding to the target identifier is received by the browser client, the newly acquired web page information is matched with the target identifier. If the match is successful, it is determined whether the newly acquired web page information is a false alarm based on the historical and current list of associated websites corresponding to the target identifier determined by the target website association graph. If it is not a false alarm, an associated website intelligence event is generated and pushed to the browser client. In this way, scattered and seemingly unrelated websites can be linked into a network, thereby identifying websites associated with the user and providing deeper intelligence value. Furthermore, timely early warning intelligence pushes can monitor the changes in the number of related websites in real time.

[0035] As a preferred embodiment, such as Figure 4The diagram illustrates the architecture of the website association mining and dynamic monitoring method based on network identifiers proposed in this application. This includes web crawler-based data collection, database-based data storage, and interaction between the browser client and the backend server. Specifically, a web crawler cluster is used to obtain webpage information, such as HTML source code. The constructed target website association graph needs to be stored in the database. It should be noted that the database in this application uses a graph database (such as Neo4j) to store the data, rather than a traditional relational database. The browser client can create corresponding information pages for search entries, displaying relevant information based on the search results. The backend server then mines and dynamically monitors website associations to provide users with real-time website association intelligence.

[0036] See Figure 5 As shown, this embodiment of the invention discloses a method for mining and dynamically monitoring website associations based on network identifiers, applied to a backend server, including: The graph update module 11 is used to obtain web page information of several network pages through a preset crawler cluster, and update the target website association graph according to the identifier information in the web page information and the corresponding website domain name of the several network pages. The identifier matching module 12 is used to match the newly added web page information obtained by the preset crawler cluster with the target identifier if a target monitoring task corresponding to the target identifier is received by the browser client, so as to obtain the corresponding matching result. The false alarm information confirmation module 13 is used to determine the historical associated website list and the current associated website list corresponding to the target identifier based on the target website association graph if the matching result indicates a successful match, and to determine whether the newly added web page information is a false alarm information based on the historical associated website list and the current associated website list. The intelligence event push module 14 is used to generate an associated website intelligence event and push the associated website intelligence event to the browser client if the newly added web page information is not a false alarm.

[0037] In this embodiment, web information of web pages can be obtained through a preset crawler cluster. Then, the target website association graph is updated using the identifier information in the web page information and the corresponding website domain name. If a target monitoring task corresponding to the target identifier is received from the browser client, the newly acquired web page information is matched with the target identifier. If the match is successful, it is determined whether the newly acquired web page information is a false alarm based on the historical and current list of associated websites corresponding to the target identifier determined through the target website association graph. If it is not a false alarm, an associated website intelligence event is generated and pushed to the browser client. In this way, scattered and seemingly unrelated websites can be linked into a network, thereby identifying the websites associated with the user and providing deeper intelligence value. Furthermore, timely early warning intelligence pushes can monitor the changes in the number of websites a user visits in real time.

[0038] In some embodiments, the map update module 11 may specifically include: The identifier extraction submodule is used to obtain web page information of several web pages through a preset crawler cluster, and to scan the web page source code in the web page information using a preset regular expression to extract identifier information from the web page information; the preset crawler cluster is a listening script injected into the several web pages based on a preset script injection rule; the preset script injection rule is to inject a listening script into the several web pages if the web page address of the several web pages is in a preset web page list. The identifier removal submodule is used to analyze the identifier information to identify common identifiers in the identifier information and remove the common identifiers from the identifier information to obtain the target identifier information; The graph update submodule is used to update the target website association graph based on the target identifier information and the corresponding website domains of the several network pages.

[0039] In some embodiments, the map update submodule may specifically include: A node determination unit is used to use the target identifier information and the corresponding website domain names of the plurality of network pages as nodes in the target website association graph; The graph update unit is used to construct an edge between the network page corresponding to the web page source code and the node if the web page source code contains the target identifier information and / or the website domain name, so as to complete the update of the target website association graph.

[0040] In some embodiments, the website association mining and dynamic monitoring device based on network identifiers may further include: The data lookup unit is used to, if it receives a webpage information retrieval request from the browser client, query the corresponding webpage data to be fed back from the target website association graph based on several query identifiers in the webpage information retrieval request; The data feedback unit is used to feed back the webpage data to be fed back to the browser client, so that the browser client can render the information page based on the webpage data to be fed back.

[0041] In some embodiments, the identifier matching module 12 may specifically include: The information matching unit is used to match the newly added web page information of the plurality of network pages obtained by the preset crawler cluster if it receives a target monitoring task corresponding to the target identifier created by the browser client. The first result generation unit is used to generate a first matching result indicating a successful match if the source code of the webpage in the newly added webpage information includes the target identifier. The second result generation unit is used to generate a second matching result indicating a matching failure if the target identifier is not included in the source code of the newly added web page information.

[0042] In some embodiments, the false alarm information confirmation module 13 may specifically include: The difference operation unit is used to perform a difference operation on the historical associated website list and the current associated website list to obtain the corresponding difference result; The false alarm information determination unit is used to determine that the newly added webpage information is not a false alarm if the difference set result is not empty, and to determine that the newly added webpage information is a false alarm if the difference set result is empty.

[0043] In some embodiments, the intelligence event push module 14 may specifically include: The intelligence event push unit is used to generate a related website intelligence event based on a preset data structure if the newly added web page information is not a false alarm, and push the related website intelligence event to a preset message center so that the preset message center can push red dot notifications to the browser client based on the related website intelligence event.

[0044] Furthermore, embodiments of this application also disclose an electronic device, Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0045] Figure 6This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the website association mining and dynamic monitoring method based on network identifiers disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be a computer.

[0046] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0047] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0048] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the website association mining and dynamic monitoring method based on network identifiers executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0049] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for mining and dynamically monitoring website associations based on network identifiers. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0050] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0051] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0052] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0053] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0054] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for mining and dynamically monitoring website relationships based on network identifiers, characterized in that, Applied to backend servers, including: The webpage information of several network pages is obtained by a preset crawler cluster, and the target website association graph is updated according to the identifier information in the webpage information and the corresponding website domain name of the several network pages. If a target monitoring task corresponding to a target identifier is received from a browser client, the newly added web page information obtained through the preset crawler cluster is matched with the target identifier to obtain the corresponding matching result. If the matching result indicates a successful match, then based on the target website association graph, determine the historical association website list and the current association website list corresponding to the target identifier, and based on the historical association website list and the current association website list, determine whether the newly added webpage information is a false alarm. If the newly added webpage information is not a false alarm, a related website intelligence event is generated and pushed to the browser client.

2. The method for website association mining and dynamic monitoring based on network identifiers according to claim 1, characterized in that, The step of obtaining webpage information of several web pages through a preset crawler cluster, and updating the target website association graph based on the identifier information in the webpage information and the corresponding website domains of the several web pages, includes: The webpage information of several web pages is obtained by a preset crawler cluster, and the webpage source code in the webpage information is scanned by a preset regular expression to extract the identifier information in the webpage information; the preset crawler cluster is a listening script injected into the several webpages based on a preset script injection rule; the preset script injection rule is to inject a listening script into the several webpages if the webpage address of the several webpages is in a preset webpage list. The identifier information is analyzed to identify common identifiers in the identifier information, and the common identifiers are removed from the identifier information to obtain the target identifier information; The target website association graph is updated based on the target identifier information and the corresponding website domains of the several web pages.

3. The method for website association mining and dynamic monitoring based on network identifiers according to claim 2, characterized in that, The process of updating the target website association graph based on the target identifier information and the corresponding website domains of the plurality of network pages includes: The target identifier information and the corresponding website domain names of the plurality of network pages are used as nodes in the target website association graph; If the webpage source code contains the target identifier information and / or the website domain name, then an edge is constructed between the network page corresponding to the webpage source code and the node to complete the update of the target website association graph.

4. The method for website association mining and dynamic monitoring based on network identifiers according to claim 2, characterized in that, After obtaining webpage information of several web pages through a preset crawler cluster, and updating the target website association graph based on the identifier information in the webpage information and the corresponding website domains of the several web pages, the method further includes: If a webpage information retrieval request is received from the browser client, the corresponding webpage data to be fed back is retrieved from the target website association graph based on several query identifiers in the webpage information retrieval request. The webpage data to be fed back is sent to the browser client so that the browser client can render the information page based on the webpage data to be fed back.

5. The method for website association mining and dynamic monitoring based on network identifiers according to claim 1, characterized in that, If a target monitoring task corresponding to a target identifier is received from a browser client, the newly added webpage information obtained through the preset crawler cluster is matched with the target identifier to obtain the corresponding matching result, including: If a target monitoring task corresponding to the target identifier is received from a browser client, it is matched with the newly added web page information of the several network pages obtained by the preset crawler cluster. If the target identifier is included in the source code of the newly added webpage information, a first matching result representing a successful match is generated. If the target identifier is not included in the source code of the newly added webpage information, a second matching result representing a failed match is generated.

6. The method for website association mining and dynamic monitoring based on network identifiers according to claim 1, characterized in that, The step of determining whether the newly added webpage information is a false alarm based on the historical and current list of associated websites includes: Perform a difference operation on the historical list of associated websites and the current list of associated websites to obtain the corresponding difference result; If the difference result is not empty, the newly added webpage information is determined to be a false alarm; if the difference result is empty, the newly added webpage information is determined to be a false alarm.

7. The method for website association mining and dynamic monitoring based on network identifiers according to any one of claims 1 to 6, characterized in that, If the newly added webpage information is not a false alarm, then a related website intelligence event is generated, and the related website intelligence event is pushed to the browser client, including: If the newly added webpage information is not a false alarm, then a related website intelligence event is generated based on a preset data structure, and the related website intelligence event is pushed to a preset message center so that the preset message center can push a red dot notification to the browser client based on the related website intelligence event.

8. A method for mining and dynamically monitoring website relationships based on network identifiers, characterized in that, Applied to backend servers, including: The graph update module is used to obtain web page information of several network pages through a preset crawler cluster, and update the target website association graph according to the identifier information in the web page information and the corresponding website domain name of the several network pages. The identifier matching module is used to match the newly added web page information obtained by the preset crawler cluster with the target identifier if a target monitoring task corresponding to the target identifier is received by the browser client, so as to obtain the corresponding matching result. The false alarm information confirmation module is used to determine the historical associated website list and the current associated website list corresponding to the target identifier based on the target website association graph if the matching result indicates a successful match, and to determine whether the newly added web page information is a false alarm information based on the historical associated website list and the current associated website list. The intelligence event push module is used to generate a related website intelligence event and push the related website intelligence event to the browser client if the newly added web page information is not a false alarm.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the website association mining and dynamic monitoring method based on network identifiers as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer programs, wherein the computer programs, when executed by a processor, implement the website association mining and dynamic monitoring method based on network identifiers as described in any one of claims 1 to 7.

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