Information technology-based remote resource organization service method and system

Abstract

The application discloses a remote resource arrangement service method and system based on information technology, relates to the technical field of information technology resource arrangement, and solves the problems that resources are dispersed in different places, information resources are relatively disordered, classification and archiving of a large number of remote resources are a complex task, there are problems of unclear classification standards and unclear resource ownership, and the arrangement result is not accurate and consistent enough. The application arranges information, divides keywords into different levels, specifies a classification tree, allows insertion, deletion and merging of nodes at any time according to different requirements, provides a more flexible and expandable data management mode, classifies and manages data through the classification tree, makes data more organized, and thus better protects the security and integrity of data, avoids storage of repeated data, improves archiving efficiency and storage utilization, in addition, permission control is performed to limit access of specific data, and sensitive data is ensured not to be leaked.

Description

Technical Field

[0001] This invention relates to the field of information technology resource organization technology, specifically to a method and system for remote resource organization services based on information technology. Background Technology

[0002] Information technology resource organization is a system for organizing and managing remote resources. This system can collect, classify, organize, store, and retrieve remote resources through the Internet or other communication networks, so that users can easily access and utilize these resources.

[0003] In response, Chinese patent application number CN114449125A discloses an archival organization and digitization system based on archival information resource planning. This system includes an archival information resource platform, an organization system, and a recording system. The output of the archival information resource platform is connected to the inputs of the organization and recording systems. The organization system includes a central processing unit, a retrieval unit, a scanning device, a photographic device, a classification unit, an archival database unit, an information display unit, a text recognition unit, an archival storage unit, an archival retrieval unit, a feedback unit, and an image recognition unit. This invention relates to the field of archival management technology. This archival organization and digitization system based on archival information resource planning photographs and preserves the archives during scanning, ensuring the original information is preserved and avoiding information loss. It solves the problem that existing processing methods easily cause significant damage to paper archives and result in information loss.

[0004] In response, Chinese patent application number CN114817644A discloses a method and system for classifying and intelligently searching government information resources based on Elasticsearch. Its main functions include: realizing the classification and intelligent search of government information resources by topic; the method includes collecting government data, classifying government information resources according to their inherent relevance and topics closely related to the public, forming a multi-level classification tree; building an Elasticsearch cluster, optimizing the underlying S1 data based on Elasticsearch, and asynchronously accessing government information resource data through RabbitMQ message middleware; accepting user query requests and responding to them; obtaining user feedback and storing it in an evaluation list, and updating the multi-level classification tree based on the evaluation list. The system is constructed based on the above method and consists of a data collection and cleaning unit, a storage unit, a service unit, an interaction unit, and a display unit.

[0005] In this era of information explosion, resources are scattered in different places. In some enterprise-level systems, information resources are quite messy, making it very difficult for users to search. Organizing, collecting, and managing resources is very complex and prone to errors. Classifying and archiving a large number of remote resources is a complex task, which may have problems such as unclear classification standards and unclear resource ownership, resulting in inaccurate and inconsistent organization results.

[0006] To address the aforementioned issues, a remote resource organization service method and system based on information technology is proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for remote resource organization services based on information technology, which solves the problem of messy remote resource archiving in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a remote resource organization service system based on information technology, including remote resource collection, using web crawlers to automatically acquire remote resources, including web pages, documents, images, and videos, parsing the collected data, and extracting key information;

[0009] Remote resources are categorized by subject and type, and then organized and archived.

[0010] Clear target classification in archiving; machine learning and natural language processing technologies are used to automatically classify and archive remote resources.

[0011] Train the model, provide feedback and suggestions, remote resource labeling and classification, and optimize the classification and archiving process;

[0012] A multi-level classification system and tagging system are used to divide the hierarchy using main classification and sub-classification, and a tagging system is introduced to further describe the characteristics and attributes of resources;

[0013] Remote resource retrieval provides a user interface for searching remote resources using keywords;

[0014] Remote resource recommendation: User browsing history and interests are stored, and recommendation algorithms recommend relevant remote resources.

[0015] Remote resource access control allows you to authorize users to access remote resources.

[0016] Preferably, the web crawler acquires remote resources, determines the target website and page structure, identifies the specific target website and page based on the target information, and determines the HTML tags or CSS selectors where the information to be crawled is located. The crawled data needs to be stored in a database or file. The appropriate storage method is selected as needed, and the corresponding storage code is written.

[0017] Preferably, the process involves collecting and parsing data, constructing a DOM tree, finding and extracting target data, locating target elements based on tag names, attribute names, or CSS selectors during traversal, and extracting the required structured data. CSS styles are parsed to assist in parsing HTML web pages, and the style information of elements with similar structured information, such as fonts and colors, is obtained by parsing the CSS stylesheet. A custom parser is written to parse and extract data from the HTML web page. The custom parser combines regular expressions, CSS selector techniques, and the characteristics of HTML tag attributes and tag names to extract specific structured data.

[0018] Preferably, the OpenCV library is used for image processing and template matching to obtain the required information from the target image.

[0019] Preferably, a keyword library is constructed for remote resource classification. Keywords are words, phrases and expressions related to the classification. Remote resources match their titles, descriptions or other relevant information with the keyword library. String matching algorithms and regular expressions are used to determine whether the resources contain keywords. A score is defined for each keyword that is matched.

[0020] Preferably, based on the keyword matching results, resources are sequentially classified into corresponding subcategories. After classifying the resources, the node information of the classification tree is updated, and the number of resources and keyword matching degree information under each category are recorded. For each subcategory, the same algorithm is recursively applied, and resources continue to undergo keyword matching and hierarchical division. All resources are processed and classified, and the classification ends.

[0021] Preferably, the subclasses are processed recursively to obtain a list of subclasses for the current category. The list of subclasses is traversed, and after the recursive processing is completed, the final category tree is returned.

[0022] Remote resource data classification calculation formula:

[0023] Remote resource = ∑((resource importance x classification weight) / resource distance);

[0024] Resource importance represents the importance of the resource, category weight represents the degree of user attention to the category, and resource distance represents the distance between the resource and the user's focus.

[0025] Resource recursive keyword matching and hierarchical partitioning, proposed algorithm formula:

[0026] Matching degree = ∑(keyword weight x matching coefficient) / total keyword weight;

[0027] Keyword weight represents the importance of the keyword, while the matching coefficient represents the degree of matching of the keyword within that level.

[0028] Preferably, the training dataset of the classification system is updated regularly, new resources are added and outdated resources are deleted, and the algorithm performance of the classification system is evaluated regularly, including accuracy and recall metrics.

[0029] Preferably, different classification strategies are adopted for different types of resources: text resources use text classification algorithms, and image resources use image recognition algorithms.

[0030] This information technology-based remote resource organization service system allows users to add resources to the system by uploading files or sharing links. The system then collects resources from the internet using web crawling technology. It automatically categorizes resources based on type, keywords, and tags, assigning them to appropriate categories. Tags and descriptions are added to resources to facilitate better searching and identification. Keyword extraction and text summarization techniques are used to generate tags and descriptions. The system provides a resource search function, allowing users to quickly find the resources they need using keywords and tags. Search engine technology enables efficient resource searching. Users can then share the organized resources with other users by generating sharing links and sending resources to specific users.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] 1. The remote resource organization service method and system based on information technology provided by this invention classifies and archives data after data parsing. During the archiving process, the system algorithm organizes the information by matching keywords with string algorithms and hierarchically dividing the keywords into a classification tree. This allows for the insertion, deletion, and merging of nodes at any time according to different needs, providing a more flexible and scalable data management method. The classification tree classifies and manages the data, making it more organized and thus better protecting the security and integrity of the data. It avoids the storage of duplicate data, improves archiving efficiency and storage utilization, and also allows for access control, restricting access to specific data to ensure that sensitive data is not leaked. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0034] Figure 2 This is a schematic diagram of the remote resource classification process of the present invention; Detailed Implementation

[0035] 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.

[0036] To further understand the content of this invention, a detailed description of the invention will be provided in conjunction with the accompanying drawings.

[0037] Combination Figures 1-2 The present invention provides a remote resource organization service system based on information technology, comprising remote resource acquisition, using web crawlers to automatically acquire remote resources, including web pages, documents, images, and videos. The web crawler acquires remote resources, determines the target website and page structure, and, based on the target information, determines the specific target website and page, as well as the HTML tags or CSS selectors containing the information to be crawled. The crawled data needs to be stored in a database or file. The appropriate storage method is selected as needed, and the corresponding storage code is written. First, the database connection library of Python is used to connect to the database where the data is to be stored. According to the data type and structure to be stored, the corresponding table is created in the database, and the column names, data types, lengths, and constraint attributes of the table are defined. Then, the data is processed. After the data is obtained in the crawler program, the data needs to be cleaned and transformed, useless information is deleted, and the data is formatted. Python's string processing and regular expression tools are used to preprocess the data to make it conform to the table design. The processed data is then inserted into the database table.

[0038] The process involves collecting and parsing data, extracting key information, constructing a DOM tree, finding and extracting target data, locating target elements based on tag names, attribute names, or CSS selectors during traversal, and extracting the required structured data. It also uses CSS style parsing to assist in parsing HTML web pages, obtaining style information for elements with similar structured information, such as fonts and colors. A custom parser is written to parse and extract data from the HTML web page, combining regular expressions, CSS selector techniques, and HTML tag attributes and name features to extract specific structured data. The OpenCV library is used for image processing and template matching to obtain the required information from the target image.

[0039] Remote resources are categorized by subject and type. The categorized remote resources are then organized and archived. A keyword library is built for each category. Keywords are words, phrases, and expressions related to the category. The titles, descriptions, or other relevant information of remote resources are matched against the keyword library. String matching algorithms and regular expressions are used to determine whether the resources contain keywords. A score is defined for each matched keyword.

[0040] Based on the keyword matching results, resources are sequentially categorized into their corresponding subcategories. After categorizing the resources, the node information of the categorization tree is updated, recording the number of resources and keyword matching degree information under each category.

[0041] Traverse all nodes of the classification tree, classify the child nodes of each node, and calculate the score of the child nodes. Update the number and score of the child nodes of each node, and calculate the score of the current node (e.g., weighted average) based on the number and score of the child nodes. Recursively update the score and number information of the parent nodes upwards until the root node. Finally, the score and number information of the root node is the score and number information of the entire classification tree. For each subclass, recursively apply the same algorithm, recursively process the subclasses, obtain the list of subclasses of the current class, traverse the list of subclasses, and after the recursive processing is completed, return the final classification tree.

[0042] Remote resource data classification calculation formula:

[0043] Remote resource = ∑((resource importance x classification weight) / resource distance);

[0044] Resource importance represents the importance of the resource, category weight represents the degree of user attention to the category, and resource distance represents the distance between the resource and the user's focus.

[0045] Resource recursive keyword matching and hierarchical partitioning, proposed algorithm formula:

[0046] Matching degree = ∑(keyword weight x matching coefficient) / total keyword weight;

[0047] Keyword weight represents the importance of the keyword, and the matching coefficient represents the degree of matching of the keyword at that level.

[0048] The resources continue to be matched with keywords and divided into levels. All resources are processed and categorized, and the categorization is completed.

[0049] Clear target classification in archiving; machine learning and natural language processing technologies are used to automatically classify and archive remote resources.

[0050] Train the model, provide feedback and suggestions, label and classify remote resources, optimize the classification and archiving process, regularly update the training dataset of the classification system, add new resources and delete outdated resources, and regularly evaluate the algorithm performance of the classification system, including accuracy and recall metrics.

[0051] A multi-level classification and labeling system is implemented, employing a primary and sub-classification approach for hierarchical division. A labeling system is introduced to further describe the characteristics and attributes of resources. Different classification strategies are used for different types of resources: text resources utilize text classification algorithms, while image resources use image recognition algorithms. The text classification algorithm requires preprocessing and cleaning the acquired data, removing invalid characters, punctuation marks, and stop words, performing word segmentation and stemming, and extracting text features. Based on specific needs, an appropriate classifier is selected and trained. The classifier model is then deployed to a server, and its performance is evaluated. Evaluation metrics include accuracy, precision, and recall.

[0052] Remote resource retrieval provides a user interface for searching remote resources using keywords;

[0053] Remote resource recommendation: User browsing history and interests are stored, and recommendation algorithms recommend relevant remote resources.

[0054] Remote resource access control allows users to authorize access to remote resources. Users must first enter their identity information, which the system verifies. If verification is successful, the user is allowed access. Users then select the remote resources they wish to access through the system interface, performing operations such as viewing, modifying, uploading, downloading, and backing up. After an operation is performed, the system sends the operation request to the server, which executes the corresponding operation and returns the result to the client.

[0055] This information technology-based remote resource organization service system allows users to add resources to the system by uploading files or sharing links. The system then collects resources from the internet using web crawling technology. It automatically categorizes resources based on type, keywords, and tags, assigning them to appropriate categories. Tags and descriptions are added to resources to facilitate better searching and identification by users. Keyword extraction and text summarization are used to generate tags and descriptions. The system provides a resource search function, allowing users to quickly find the resources they need using keywords and tags. Search engine technology enables efficient resource searching. Users can then share the organized resources with other users by generating sharing links and sending resources to specific users.

[0056] 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 process, method, article, or apparatus.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A remote resource organization service system based on information technology, characterized in that: This includes remote resource collection, which utilizes web crawlers to automatically acquire remote resources such as web pages, documents, images, and videos, and then analyzes the collected data to extract key information. Remote resources are categorized by subject and type, and then organized and archived. Using machine learning and natural language processing technologies, remote resources are automatically classified and archived; Train the model, provide feedback and suggestions, remote resource labeling and classification, and optimize the classification and archiving process; A multi-level classification system and tagging system are used to divide the hierarchy using main classification and sub-classification, and a tagging system is introduced to further describe the characteristics and attributes of resources; Remote resource retrieval provides a user interface for searching remote resources using keywords; Remote resource recommendation: User browsing history and interests are stored, and recommendation algorithms recommend relevant remote resources. Remote resource access control: This involves managing access permissions for remote resources and authorizing user access. Based on the keyword matching results, the resources are classified into the corresponding subcategories in sequence. After classifying the resources, the node information of the classification tree is updated, and the number of resources and keyword matching degree information under each category are recorded. The same algorithm is applied recursively to each subcategory, and the resources continue to be matched with keywords and divided into levels. All resources are processed and classified, and the classification ends. Recursively process subcategories to obtain a list of subcategories for the current category, traverse the list of subcategories, and return the final category tree after recursive processing is complete; Remote resource data classification calculation formula: Remote resource = ∑ ((resource importance x classification weight) / resource distance); Resource importance represents the importance of the resource, category weight represents the degree of user attention to the category, and resource distance represents the distance between the resource and the user's focus. Resource recursive keyword matching and hierarchical partitioning, proposed algorithm formula: Matching degree = ∑(keyword weight x matching coefficient) / total keyword weight; Keyword weight represents the importance of the keyword, while the matching coefficient represents the degree of matching of the keyword within that level.

2. The remote resource organization service system based on information technology according to claim 1, characterized in that: Web crawlers acquire remote resources, determine the target website and page structure, identify the specific target website and page based on the target information, and determine the HTML tags or CSS selectors containing the information to be crawled. The crawled data needs to be stored in a database or file. The appropriate storage method is selected as needed, and the corresponding storage code is written.

3. The remote resource organization service system based on information technology according to claim 1, characterized in that: The process involves collecting and parsing data, constructing a DOM tree, finding and extracting target data, locating target elements based on tag names, attribute names, or CSS selectors during traversal, and extracting the required structured data. It also uses CSS style parsing to assist in parsing HTML web pages, obtaining style information for elements with similar structured information, including fonts and colors. A custom parser is written to parse and extract data from the HTML web page, combining regular expressions, CSS selector techniques, and HTML tag attributes and name characteristics to extract specific structured data.

4. The remote resource organization service system based on information technology according to claim 3, characterized in that: The OpenCV library is used for image processing and template matching to obtain the required information from the target image.

5. The remote resource organization service system based on information technology according to claim 1, characterized in that: Remote resource categorization constructs a keyword library for each category. Keywords are words, phrases, and expressions related to the category. Remote resources match their titles, descriptions, or other relevant information against the keyword library. String matching algorithms and regular expressions are used to determine whether a resource contains keywords. Once a keyword is matched, a score is defined for each keyword.

6. The remote resource organization service system based on information technology according to claim 1, characterized in that: Regularly update the training dataset of the classification system, adding new resources and deleting outdated resources, and regularly evaluate the algorithm performance of the classification system, including accuracy and recall metrics.

7. The remote resource organization service system based on information technology according to claim 1, characterized in that: Different classification strategies are used for different types of resources: text resources use text classification algorithms, and image resources use image recognition algorithms.

8. The method of the remote resource organization service system based on information technology according to any one of claims 1-7, characterized in that: Users add resources to the system by uploading files and sharing links. The system collects resources from the internet using web crawling technology, automatically categorizes them based on type, keywords, and tags, and assigns them to appropriate categories. The system adds tags or descriptions to resources to help users better search and identify them, using keyword extraction and text summarization techniques to generate tags or descriptions. The system provides a resource search function, allowing users to quickly find the resources they need using keywords and tags, and uses search engine technology to achieve efficient resource searching. Users can then share the organized resources with other users by generating share links and sending resources to specific users.

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