An analytical method for determining the responsible party of Internet web pages

Abstract

The present invention provides an analysis method for determining the responsible party of an Internet webpage, which relates to the field of network security technology. The method includes: processing webpage information to obtain processed data; treating each data point in the processed data as a particle and determining the initial position and velocity corresponding to each particle; determining a fitness function based on the initial velocity and position corresponding to each particle; updating the velocity and position of each particle based on the fitness function, and obtaining a final solution through multiple iterations; identifying a parameter set of the responsible party based on the final solution, and determining the responsible party of the webpage when a preset number of iterations is reached; and obtaining a responsible party information source based on the responsible party of the webpage. The present invention effectively improves the accuracy and efficiency of identifying the responsible party of a webpage through efficient data processing, while also enhancing the traceability of information.

Description

Technical Field

[0001] The present invention relates to the technical field of network security, and in particular to an analysis method for determining a responsible party for an Internet webpage. Background Art

[0002] Existing technical methods for determining responsible parties for web pages primarily rely on ICP filing information and WHOIS (Domain Name Lookup Protocol) registration information, both of which have limitations. Due to the widespread use of hosting services, the identification of responsible parties based on ICP filing information can lead to errors.

[0003] With the improvement of users' privacy awareness, a large number of sites currently use domain name privacy protection services to hide domain name owner information in order to protect personal information. Therefore, the technology based on WHOIS query cannot obtain accurate information about the responsible party. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an analysis method for determining the responsible parties of Internet web pages, thereby achieving accurate identification of the responsible parties of massive web pages.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] In a first aspect, an analysis method for determining the responsible party of an Internet webpage comprises:

[0007] Processing web page information to obtain processed data;

[0008] Treat each data point in the processed data as a particle and determine the initial position and velocity corresponding to each particle;

[0009] Determine the fitness function based on the initial velocity and position of each particle;

[0010] According to the fitness function, the speed and position of each particle are updated through multiple iterations to obtain the final solution;

[0011] Based on the final solution, a parameter set of the responsible party is identified. When the preset number of iterations is reached, the responsible party of the webpage is determined;

[0012] According to the responsible party of the web page, the responsible party information source is obtained.

[0013] Furthermore, the web page information is processed to obtain processed data, including:

[0014] Get web page information;

[0015] Parse the web page information to obtain the web page node tree, and extract attributes from the web page node tree content;

[0016] Perform field matching on text nodes in the node tree and extract the matching field content;

[0017] Filter and clean the field content to obtain processed data.

[0018] Furthermore, the web page information is parsed to obtain a web page node tree, and attributes are extracted from the web page node tree content, including:

[0019] Get the HTML source code of the web page;

[0020] Determine an HTML parsing library and import the HTML source code of the web page into the determined HTML parsing library;

[0021] Create a parser object using the specified parsing library and pass the HTML source code to the parser object;

[0022] The parser automatically converts HTML source code into a web page node tree. A web page node tree is a node tree that represents the document structure. Each HTML element becomes a node, and nodes are connected to each other through parent-child and sibling relationships.

[0023] Verify the obtained web page node tree to ensure that the web page node tree accurately reflects the structure of the original HTML document;

[0024] Traverse the web page node tree, starting from the root node, traverse downward layer by layer, visit the child nodes of each node, and extract attributes of the web page node tree content during the traversal process.

[0025] Furthermore, the formula of the fitness function f(X) is:

[0026]

[0027] Among them, X represents the parameter set of the responsible entity; α represents the weight coefficient; r i represents the relevance score; sim represents the similarity of the responsible parties; n represents the total number of data points; w a Indicates authoritative weight; pr x Indicates the authority score of the responsible entity; w b represents the consistency weight; p i Indicates the attribute value of the responsible subject; q i Indicates the attribute value of the related entity; m indicates the number of attributes; w c represents the complexity weight; c j represents the complexity coefficient; n jrepresents the number of component elements; k represents the number of components; w d represents risk weight; s represents risk value.

[0028] Furthermore, the speed update formula of each particle is:

[0029]

[0030] Among them, v i (t+1) represents the velocity of particle i at time t+1; ω i represents the inertia weight; v i (t) represents the current velocity of the particle; c1 and c1 represent acceleration coefficients; r1 and r1 represent random numbers between [0,1]; represents the speed update term based on the individual optimal position; represents the velocity update term based on the global optimal position; α represents the coefficient of the random perturbation term; x rand represents a random position; x i (t) represents the position of particle i at time t.

[0031] Furthermore, the position update formula of each particle is:

[0032] x i (t+1)=x i (t)+β(t)·v i (t+1)+γ·(x attract -x i (t))·exp(-δ·x i (t));

[0033] Among them, x i (t+1) represents the position of particle i at time t+1; x i (t) represents the position of particle i at time t; β(t) represents the coefficient that changes with time; γ represents the constant coefficient; x attract represents the attraction position; δ represents a positive coefficient; exp(-δ·x i (t)) represents a decay factor.

[0034] Furthermore, according to the responsible party of the webpage, the responsible party information source is obtained, including:

[0035] According to the responsible party of the webpage, confirm the identity of the responsible party;

[0036] According to the identity of the responsible party, obtain the responsible party's information and retrieve the public information corresponding to the responsible party;

[0037] Verify and authenticate the public information corresponding to the responsible party and the identity of the responsible party to determine the final responsible party information;

[0038] According to the final responsible party information, the information source of the responsible party can be obtained.

[0039] In a second aspect, an analysis system for determining the responsible party of an Internet webpage includes:

[0040] The acquisition module is used to process the web page information to obtain processed data; each data point in the processed data is regarded as a particle, and the initial position and velocity corresponding to each particle are determined; and the fitness function is determined according to the initial velocity and position corresponding to each particle;

[0041] The processing module is used to update the speed and position of each particle according to the fitness function, and obtain the final solution through multiple iterations; identify the parameter set of the responsible entity based on the final solution, and determine the responsible entity of the web page when the preset number of iterations is reached; and obtain the responsible entity information source based on the responsible entity of the web page.

[0042] According to a third aspect, a computing device includes:

[0043] one or more processors;

[0044] The storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0045] In a fourth aspect, a computer-readable storage medium stores a program, which implements the method when executed by a processor.

[0046] The above solution of the present invention includes at least the following beneficial effects:

[0047] By preprocessing webpage information, redundant and noisy data can be removed, making subsequent data analysis and responsible party identification more efficient and accurate. Treating each data point in the processed data as a particle and assigning it an initial position and velocity, this approach simulates the particle's dynamic search process. Using a fitness function to guide particle movement, the optimal solution can be found more quickly, thereby improving the speed and accuracy of responsible party identification. By iteratively updating the particle's velocity and position, the algorithm gradually approaches the optimal solution—the most likely set of responsible party parameters.

[0048] This iterative optimization method overcomes the shortcomings of existing technologies, such as the inability to identify responsible entities for sites using domain name privacy protection services and the misidentification of responsible entities based on ICP filings, thereby more accurately identifying responsible entities for web pages. By controlling the algorithm's termination condition through a preset number of iterations, the system ensures real-time performance and responsiveness while meeting certain accuracy requirements. The resulting identified responsible entity information can serve as a valuable reference for subsequent data analysis, network security monitoring, and market research. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of an analysis method for determining the responsible party of an Internet webpage provided by an embodiment of the present invention.

[0050] Figure 2 This is a schematic diagram of an analysis system for determining the responsible party of an Internet web page provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0052] like Figure 1 As shown, an embodiment of the present invention provides an analysis method for determining the responsible party of an Internet webpage, the method comprising the following steps:

[0053] Step 1: Process the web page information to obtain processed data;

[0054] Step 2: Treat each data point in the processed data as a particle and determine the initial position and velocity corresponding to each particle;

[0055] Step 3: Determine the fitness function based on the initial velocity and position of each particle.

[0056] Step 4: Update the velocity and position of each particle according to the fitness function, and obtain the final solution through multiple iterations;

[0057] Step 5: Based on the final solution, identify the parameter set of the responsible party. When the preset number of iterations is reached, the responsible party of the webpage is determined.

[0058] Step 6: Obtain the responsible party information source based on the responsible party of the webpage.

[0059] In an embodiment of the present invention, the system first extracts and processes the raw information from the webpage. This may include steps such as data cleaning, redundancy elimination, and key field extraction. The goal is to transform the unstructured webpage data into structured data suitable for subsequent analysis. Each data point in the processed data is considered a "particle." These particles represent possible solutions in the algorithm. Each particle is assigned an initial position and velocity, typically randomly generated, which serves as the starting point for the search process. The fitness function is a criterion for evaluating the quality of each particle (i.e., each possible solution). It calculates a fitness value based on the particle's position and velocity. This value reflects the degree to which the parameter set of the responsible entity represented by the particle matches the solution to the actual problem. Based on feedback from the fitness function, the algorithm continuously adjusts the velocity and position of each particle. This is an iterative process, and through multiple iterations, the particles gradually approach the optimal solution. In each iteration, the movement of the particle is influenced by both its own historical best position and the historical best position of the group. When the preset number of iterations is reached or other stopping conditions are met, the algorithm stops and selects the particle with the highest fitness as the final solution. This final solution represents the parameter set of the identified responsible entity for the webpage. Finally, based on the identified responsible party parameter set, the system can further obtain and integrate detailed information of the responsible party, which constitutes the responsible party information source.

[0060] By preprocessing and restructuring web page information, the subsequent data analysis process is simplified, making the identification of responsible parties more direct and efficient. The particle swarm optimization algorithm has the ability to search for the optimal solution globally, avoiding falling into local optimal situations, thereby improving the accuracy of responsible party identification. The speed and position of the particles are dynamically adjusted during the iteration process, allowing the algorithm to flexibly change the search strategy according to the current situation, enhancing the system's adaptability. By setting a reasonable number of iterations and stopping conditions, the algorithm can converge to the optimal solution in a relatively short period of time, ensuring real-time identification. The final identified responsible party information source contains not only the basic parameters of the responsible party, but also other related information, providing a rich data foundation for subsequent decision support.

[0061] In a preferred embodiment of the present invention, processing web page information to obtain processed data includes:

[0062] Get web page information;

[0063] Parse the web page information to obtain the web page node tree, and extract attributes from the web page node tree content;

[0064] Perform field matching on text nodes in the node tree and extract the matching field content;

[0065] Filter and clean the field content to obtain processed data.

[0066] In an embodiment of the present invention, the system first uses a web crawler or other data collection technology to crawl the target web page information from the internet. This information is typically stored in formats such as HTML and XML and contains the structure and content of the web page. The acquired web page information is then fed into a parser, such as a DOM parser, to construct a web page node tree. A node tree is a hierarchical data structure that clearly represents the relationships between various elements (such as titles, paragraphs, links, etc.) on a web page. After the node tree is generated, the system traverses each node in the tree and extracts the node's attribute information. These attributes include tag names, class names, IDs, styles, etc. The system then performs field matching operations on the text nodes in the node tree. Regular expressions are used to locate and extract specific information fields on the web page, such as product names, prices, and descriptions. The extracted field content may contain noise, duplicate data, or inconsistent formats. Therefore, the system filters and cleans this data to remove useless information, correct errors, and standardize the data format. The resulting processed data is cleaner and more standardized, facilitating subsequent analysis and use.

[0067] Detailed analysis and cleaning of web page information improves the accuracy and consistency of acquired data, providing a reliable foundation for subsequent data analysis. Automated field matching and extraction techniques improve the efficiency of quickly and accurately extracting required information from large numbers of web pages. The flexibility of field matching and cleaning rules enables the system to adapt to changes in the structure and content of different web pages, making it more widely applicable in various web data processing scenarios. The cleaning process removes redundant and erroneous data, ensuring the quality and usability of the final data and providing stronger data support for business decision-making. Automated web information processing and cleaning processes reduce the need for manual intervention, thereby saving labor and time costs.

[0068] In a preferred embodiment of the present invention, web page information is parsed to obtain a web page node tree, and attributes are extracted from the web page node tree content, including:

[0069] Get the HTML source code of the web page;

[0070] Determine an HTML parsing library and import the HTML source code of the web page into the determined HTML parsing library;

[0071] Create a parser object using the specified parsing library and pass the HTML source code to the parser object;

[0072] The parser automatically converts HTML source code into a web page node tree. A web page node tree is a node tree that represents the document structure. Each HTML element becomes a node, and nodes are connected to each other through parent-child and sibling relationships.

[0073] Verify the obtained web page node tree to ensure that the web page node tree accurately reflects the structure of the original HTML document;

[0074] Traverse the web page node tree, starting from the root node, traverse downward layer by layer, visit the child nodes of each node, and extract attributes of the web page node tree content during the traversal process.

[0075] In an embodiment of the present invention, first, the HTML source code of the target web page is obtained. This is typically achieved using web crawler technology, which sends a request to the web server and then receives and saves the returned HTML data. A suitable HTML parsing library is selected, such as BeautifulSoup or lxml in Python. These libraries provide the ability to convert the HTML source code into an easily manipulated node tree. Next, the obtained HTML source code is imported into the selected parsing library. A parser object is created using the selected parsing library, and the HTML source code is passed to this object as input. The parser is responsible for parsing the HTML source code and converting it into an internal data structure. The parser automatically converts the HTML source code into a web page node tree. In this tree structure, each HTML element becomes a node, and these nodes are connected to each other through parent-child and sibling relationships, forming a hierarchical structure that reflects the organization of the HTML document. After the conversion is complete, the resulting web page node tree is verified to ensure that it accurately reflects the structure of the original HTML document. This step is to check for errors or omissions during the parsing process. Finally, starting from the root node, the web page node tree is traversed downward layer by layer. During the traversal process, the child nodes of each node are visited, and the attribute information of each node is extracted, such as label name, attribute name-value pair, etc.

[0076] By parsing the HTML source code and converting it into a node tree, the originally unstructured web page data becomes structured, facilitating subsequent data manipulation and analysis. By traversing the node tree and extracting attributes, key information from the web page can be efficiently obtained without having to scan the entire HTML document line by line. Using a node tree to represent the web page structure provides great flexibility and scalability. Attribute information for specific parts can be selectively extracted as needed, or complex queries and operations can be performed on the entire web page structure. By verifying the accuracy of the node tree, the extracted attribute information can be ensured to be reliable and accurate, thus avoiding data deviations or omissions caused by parsing errors.

[0077] In a preferred embodiment of the present invention, the formula of the fitness function f(X) is:

[0078]

[0079] Among them, X represents the parameter set of the responsible entity; α represents the weight coefficient; r i represents the relevance score of the responsible party; sim represents the similarity of the responsible party; n represents the total number of data points; w a Indicates authoritative weight; pr x Indicates the authority score of the responsible entity; w b represents the consistency weight; p i Indicates the attribute value of the responsible subject; q i Indicates the attribute value of the related entity; m indicates the number of attributes; w c represents the complexity weight; c j represents the complexity coefficient; n j represents the number of component elements; k represents the number of components; w d represents risk weight; s represents risk value.

[0080] In the embodiment of the present invention, relevant data of the responsible party is collected, including the relevance score r i , authoritative rating pr x , attribute value p i , the complexity coefficient c of the component j and the number of elements n j Etc. Determine the total number of data points n, the number of attributes m, and the total number of components k. According to the specific scenario or needs, determine the similarity sim of the responsible party, the risk value s, and each weight coefficient (α, w a , w b , w c , w d ), calculate the weighted average of the relevance scores of all data points, that is, This reflects the overall relevance and similarity between the responsible subject and the data point, and the authority score pr x Add 1 and take the logarithm, that is, log(pr x +1) to smooth the score and reduce the impact of extreme values, which allows the model to perform effective evaluation even when the authority score is low. Calculate the consistency between the attributes of the responsible entity and the attributes of the related entities. By taking the sum of the absolute values ​​of the differences between the attribute values ​​and normalizing them, that is, To measure the degree of consistency between attributes, we add up the complexity contributions of each component, i.e. To assess the complexity of the responsible party, we directly use the risk value s as part of the assessment to reflect the potential risk or uncertainty associated with the responsible party. The calculation results of each part are weighted and summed according to the preset weight coefficients to obtain the final fitness function value f(X).

[0081] By comprehensively considering multiple evaluation criteria, it can more comprehensively reflect the comprehensive performance and potential risks of the responsible entity. a , w b , w c , w d ), the assessment focus can be customized based on specific needs and scenarios. The formula converts multiple qualitative indicators into quantitative scores, facilitating numerical comparison and ranking, helping to make more objective and accurate decisions. The formula's structure allows for the addition or modification of assessment items as needed to adapt to the assessment requirements of different fields or specific issues. By introducing risk values, the formula can factor potential risks into the assessment process, helping decision makers develop more robust strategies.

[0082] In a preferred embodiment of the present invention, the speed update formula of each particle is:

[0083]

[0084] Among them, v i (t+1) represents the velocity of particle i at time t+1; ω i represents the inertia weight; v i (t) represents the current velocity of the particle; c1 and c2 represent acceleration coefficients; r1 and r2 represent random numbers between [0,1]; The speed update term representing the optimal position of the individual; represents the speed update term of the global optimal position; α represents the coefficient of the random perturbation term; x rand represents a random position; x i (t) represents the position of particle i at time t.

[0085] In this embodiment of the present invention, each particle i is assigned an initial position and velocity. The velocity v of particle i at time t+1 is i (t+1) is based on its velocity v at time t i (t) Updated. Inertia weight ω i Determines the tendency of particles to maintain their original speed. A larger inertia weight will keep the particle at a higher speed, which is beneficial for global search; while a smaller inertia weight will slow down the particle speed, which is beneficial for local search and fine adjustment. Particles update their speed based on their own historical best position (individual best) and the historical best position of the entire particle group (global best). It is a velocity update term based on the individual optimal position, reflecting the tendency of the particle to approach its own historical optimal position. is the velocity update term based on the global optimal position, reflecting the tendency of the particle to approach the global optimal position. c1 and c2 are acceleration coefficients, respectively controlling the degree of influence of the individual optimum and the global optimum on the velocity update. r1 and r2 are random numbers between [0,1], which add randomness to the velocity update process and help avoid falling into the local optimal solution. α is the coefficient of the random perturbation term, which controls the influence of random perturbations on the velocity update. rand is a randomly selected position that provides the particle with an opportunity to escape the current search area, thereby enhancing the algorithm's global search capability. This process is repeated, with each iteration updating the particle's velocity and position until a termination condition is met (such as reaching the maximum number of iterations or finding a solution that meets the accuracy requirements).

[0086] By adjusting the inertia weight ω i The algorithm can find a balance between global search and local search. A larger inertia weight helps particles maintain their original speed and conduct a wider search, while a smaller inertia weight encourages particles to consider more local searches around their current position. and They respectively reflect the impact of the best historical position of the individual particle and the best historical position of the entire group on the particle velocity update. This combination allows particles to learn from their own experience and benefit from the wisdom of the group, thereby more effectively finding the global optimal solution. The introduction of random numbers r1 and r2 increases the randomness of the search process, helping particles to escape from the local optimum and avoid premature convergence to a non-global optimal solution. This randomness gives the algorithm stronger exploration capabilities and can handle complex optimization problems. By α·(x rand -x i The random perturbation term (t) introduces new possibilities into the particle search process. This random perturbation not only helps particles escape local optima but also enhances the algorithm's robustness, enabling it to cope with diverse optimization scenarios and problems. The velocity update formula of the particle swarm optimization algorithm is relatively simple, easy to program, and computationally efficient, making it widely applicable to solving various practical problems.

[0087] In a preferred embodiment of the present invention, the position update formula of each particle is:

[0088] x i (t+1)=x i (t)+β(t)·v i (t+1)+γ·(x attract -x i (t))·exp(-δ·x i (t));

[0089] Among them, x i (t+1) represents the position of particle i at time t+1; x i (t) represents the position of particle i at time t; β(t) represents the coefficient that changes with time; γ represents the constant coefficient; x attract represents the attraction position; δ represents a positive coefficient; exp(-δ·x i (t)) represents a decay factor.

[0090] In the embodiment of the present invention, at the beginning, each particle is initialized at a specific position x i (t), and has an initial velocity v i (t). The position x of particle i at time t+1 i (t+1) is based on its current position x i (t), updated speed v i (t+1), and an attractor x attract The relevant items are jointly determined. attract represents a global or local attractor, which can be the best position found by the entire particle group. The particles are pulled towards the attractor by a force whose magnitude depends on the distance between the particle's current position and the attractor. i (t)) is the attenuation factor, which is calculated based on the particle's current position x i (t) is used to adjust the pull of the attractor on the particle. δ is a constant that controls the decay rate of the exponential function. When the particle approaches the attractor, the value of the exponential function increases, reducing the effect of the pull and preventing excessive aggregation of particles. When the particle moves away from the attractor, the value of the exponential function decreases, increasing the effect of the pull and prompting the particle to move toward the attractor. The combined effect of these factors enables particles to explore new areas in the search space (through the velocity term and the adaptability of the exponential function) while also utilizing known excellent position information (by approaching the attractor). As the algorithm iterates, the particle swarm will gradually tend to find the global optimal solution or approach the preset attractor position, thereby achieving an optimal solution to the problem.

[0091] The new position x of the particle i (t+1) depends not only on its current position x i (t), and also considers the updated velocity v i (t+1). This cooperative update mechanism helps particles move more efficiently in the search space and avoid falling into local optimality. Introducing the attractor x attractThis allows particles to have a clear goal orientation during the search process. Particles are driven to approach the attractor, which helps to accelerate convergence to the global optimum or a specific target position. i (t)) introduces a nonlinear adjustment mechanism. This mechanism can adaptively adjust the influence of the attractor on the particle position update based on the particle's current position, thereby achieving a better balance and compromise between exploration and exploitation during the search process, helping particles to explore more widely in the search space and avoid premature convergence to the local optimal solution. By comprehensively considering the influence of speed, position, and attractors, particles can find a better solution on a global scale. It has strong versatility and flexibility and can be applied to a variety of optimization problems. By adjusting the parameters β(t), γ, and δ, a customized optimization strategy can be implemented for specific problems. Updating the particle position by comprehensively considering multiple factors helps to improve the convergence speed and accuracy of the algorithm. Particles can more effectively find the global optimal solution or approach the target position during the search process.

[0092] In a preferred embodiment of the present invention, obtaining the responsible party information source according to the responsible party of the webpage includes:

[0093] According to the responsible party of the webpage, confirm the identity of the responsible party;

[0094] According to the identity of the responsible party, obtain the responsible party's information and retrieve the public information corresponding to the responsible party;

[0095] Verify and authenticate the public information corresponding to the responsible party and the identity of the responsible party to determine the final responsible party information;

[0096] According to the final responsible party information, the information source of the responsible party can be obtained.

[0097] In an embodiment of the present invention, first, the identity of the responsible entity of the web page is preliminarily determined by analyzing the web page content, domain name registration information, information disclosed by the website operator, etc. This may include different types of entities such as companies, organizations, and individuals. After the identity of the responsible entity is preliminarily determined, more information about the responsible entity is retrieved and collected through various information channels (such as public databases, social media, news reports, etc.). The collected information needs to be verified and validated to ensure its accuracy and reliability. This may involve comparing multiple information sources, checking the logical consistency of information, verifying the source of information, etc. After verification and validation, the final responsible entity information can be determined, which includes the exact name of the responsible entity, contact information, scope of operations, etc. With the final responsible entity information, the original source of this information can be traced, such as official websites, government announcements, and business registration records.

[0098] Through multi-step verification and validation, the accuracy of the information obtained about responsible parties is improved, reducing the possibility of misjudgment and misleading information. Gathering information from multiple channels and cross-validating it ensures its reliability, providing a solid foundation for subsequent decisions or actions. Accurate and reliable information about responsible parties helps decision-makers make more informed and efficient decisions, reducing the risks and costs associated with inaccurate information. In the online environment, accurately identifying the responsible parties for web pages is crucial to protecting user rights and interests, helping users find the correct responsible parties for communication and rights protection when encountering problems.

[0099] like Figure 2 As shown, an embodiment of the present invention further provides an analysis system 20 for determining the responsible party of an Internet webpage, comprising:

[0100] The acquisition module 21 is used to process the web page information to obtain processed data; each data point in the processed data is regarded as a particle, and the initial position and velocity corresponding to each particle are determined; and the fitness function is determined based on the initial velocity and position corresponding to each particle;

[0101] Processing module 22 is used to update the speed and position of each particle according to the fitness function, and obtain the final solution through multiple iterations; identify the parameter set of the responsible entity based on the final solution, and when the preset number of iterations is reached, determine the responsible entity of the web page; and obtain the responsible entity information source based on the responsible entity of the web page.

[0102] It should be noted that this system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0103] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above-described method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0104] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the above-described method. All implementations in the above-described method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0105] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0106] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0108] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0109] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0110] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0111] In addition, it should be noted that, in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it will be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using basic programming skills after reading the description of the present invention.

[0112] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0113] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. An analytical method for determining the responsible party of an Internet webpage, characterized in that: The method comprises: Processing the webpage information to obtain processed data, where the processed data includes a plurality of data points, each data point representing a set of responsible entity parameters; Treat each data point in the processed data as a particle, and randomly determine the initial position and velocity corresponding to each particle; Determine the fitness function, fitness function The formula is: ； in, Represents a set of responsible subject parameters; represents the weight coefficient; represents the relevance score; sim represents the similarity of the responsible parties; n represents the total number of data points; Indicates authoritative weight; Indicates the authority score of the responsible entity; represents the consistency weight; Indicates the attribute value of the responsible subject; Indicates the attribute value of the related entity; m indicates the number of attributes; represents the complexity weight; represents the complexity coefficient; represents the number of component elements; k represents the number of components; represents the risk weight; represents the risk value; According to the fitness function, the speed and position of each particle are updated through multiple iterations to obtain the final solution; Based on the final solution, a parameter set of the responsible party is identified. When the preset number of iterations is reached, the responsible party of the webpage is determined; According to the responsible party of the web page, the responsible party information source is obtained.

2. The analysis method for determining the responsible party of an Internet webpage according to claim 1, characterized in that: Process web page information to obtain processed data, including: Get web page information; Parse the web page information to obtain the web page node tree, and extract attributes from the web page node tree content; Perform field matching on text nodes in the node tree and extract the matching field content; Filter and clean the field content to obtain processed data.

3. The analysis method for determining the responsible party of an Internet webpage according to claim 2, characterized in that: Parse the web page information to obtain the web page node tree and extract attributes from the web page node tree content, including: Get the HTML source code of the web page; Determine an HTML parsing library and import the HTML source code of the web page into the determined HTML parsing library; Create a parser object using the specified parsing library and pass the HTML source code to the parser object; The parser automatically converts HTML source code into a web page node tree. A web page node tree is a node tree that represents the document structure. Each HTML element becomes a node, and nodes are connected to each other through parent-child and sibling relationships. Verify the obtained web page node tree to ensure that the web page node tree accurately reflects the structure of the original HTML document; Traverse the web page node tree, starting from the root node, traverse downward layer by layer, visit the child nodes of each node, and extract attributes of the web page node tree content during the traversal process.

4. The analysis method for determining the responsible party of an Internet webpage according to claim 3, wherein the speed update formula of each particle is: ； in, represents the velocity of particle i at time t+1; represents the inertia weight; Indicates the current speed of the particle; and represents the acceleration factor; and Represents a random number between [0,1]; represents the speed update term based on the individual optimal position; represents the velocity update term based on the global optimal position; represents the coefficient of the random disturbance term; represents a random position; represents the position of particle i at time t.

5. The analysis method for determining the responsible party of an Internet webpage according to claim 4, characterized in that: The position update formula of each particle is: ； in, represents the position of particle i at time t+1; represents the position of particle i at time t; represents the coefficient of change over time; represents a constant coefficient; Indicates the attraction position; represents a positive coefficient; Represents a decay factor.

6. The analysis method for determining the responsible party of an Internet webpage according to claim 5, characterized in that: According to the responsible party of the webpage, the responsible party information source is obtained, including: According to the responsible party of the webpage, confirm the identity of the responsible party; According to the identity of the responsible party, obtain the responsible party's information and retrieve the public information corresponding to the responsible party; Verify and authenticate the public information corresponding to the responsible party and the identity of the responsible party to determine the final responsible party information; According to the final responsible party information, the information source of the responsible party can be obtained.

7. An analysis system for determining the responsible party of an Internet web page, characterized in that: include: An acquisition module is used to process web page information to obtain processed data, where the processed data includes multiple data points, each of which represents a set of responsible entity parameters; Treat each data point in the processed data as a particle, and randomly determine the initial position and speed corresponding to each particle; determine the fitness function, fitness function The formula is: ； in, Represents a set of responsible subject parameters; represents the weight coefficient; represents the relevance score; sim represents the similarity of the responsible parties; n represents the total number of data points; Indicates authoritative weight; Indicates the authority score of the responsible entity; represents the consistency weight; Indicates the attribute value of the responsible subject; Indicates the attribute value of the related entity; m indicates the number of attributes; represents the complexity weight; represents the complexity coefficient; represents the number of component elements; k represents the number of components; represents the risk weight; represents the risk value; The processing module is used to update the speed and position of each particle according to the fitness function, and obtain the final solution through multiple iterations; identify the parameter set of the responsible entity based on the final solution, and determine the responsible entity of the web page when the preset number of iterations is reached; and obtain the responsible entity information source based on the responsible entity of the web page.

8. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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