Enterprise qualification certificate multi-dimensional intelligent retrieval method and system

Abstract

The invention belongs to the technical field of information retrieval, and particularly relates to a multi-dimensional intelligent retrieval method and system for enterprise qualification certificates, and the method comprises the following steps: S1, data collection and preprocessing: collecting original data of the enterprise qualification certificates; s2, multi-dimensional data extraction and classification: extracting multi-dimensional key information from the preprocessed data, and performing classified storage on the information; s3, user retrieval request analysis: receiving a retrieval request input by a user, and performing natural language processing on the retrieval request; s4, intelligent retrieval and matching: performing intelligent retrieval in the multi-dimensional index data according to the analyzed retrieval conditions; and S5, displaying and feeding back a retrieval result: displaying the retrieved qualification certificate information meeting the condition to the user in a visual mode. According to the method, multi-dimensional retrieval of the enterprise qualification certificate is realized, a user can perform comprehensive retrieval according to a plurality of conditions, complex retrieval requirements of an enterprise are met, and the flexibility and practicability of retrieval are improved.

Description

Technical Field

[0001] The present invention belongs to the field of information retrieval technology, and in particular relates to a multi-dimensional intelligent retrieval method and system for enterprise qualification certificates. Background Art

[0002] As companies continue to grow and expand their businesses, the number of qualification certificates they hold is increasing. These certificates contain a wealth of information, such as the certificate name, issuing authority, validity period, and business scope. In practical applications, companies need to quickly and accurately retrieve specific qualification certificates and related information to meet various needs, such as business cooperation, project applications, and qualification reviews. However, existing methods for searching for enterprise qualification certificates are often limited to simple searches based on certificate name or number, making them inefficient and difficult to meet the multi-dimensional, complex search requirements of enterprises. When enterprises need to comprehensively screen qualification certificates based on multiple criteria (such as specific issuing authority, validity period, and business scope), traditional search methods are inefficient and difficult to guarantee the accuracy and comprehensiveness of search results, severely impacting work efficiency and business development.

[0003] Therefore, there is an urgent need for a method and system that can realize multi-dimensional intelligent retrieval of enterprise qualification certificates. Summary of the Invention

[0004] The purpose of the present invention is to address the problems raised in the above background technology and provide a method and system for multi-dimensional intelligent retrieval of enterprise qualification certificates that can realize multi-dimensional retrieval of enterprise qualification certificates.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A multi-dimensional intelligent retrieval method for enterprise qualification certificates includes the following steps: S1. Data collection and preprocessing: Collect the original data of enterprise qualification certificates through interface docking or manual input, including but not limited to certificate name, certificate number, issuing authority, issuance date, validity period, business scope, grade, approval department, etc. S2. Multi-dimensional data extraction and classification: Extract key information of multiple dimensions from the pre-processed data and classify and store this information; S3. User search request parsing: Receive the search request input by the user, perform natural language processing on the search request, and identify the keywords, logical relationships, and search conditions in the search request; S4, Intelligent Retrieval and Matching: Based on the parsed retrieval conditions, intelligent retrieval is performed in the multi-dimensional index data; S5. Search result display and feedback: The retrieved qualified qualification certificate information will be displayed to the user in an intuitive manner, including the basic information, detailed information and related attachments of the certificate.

[0006] Preferably, in said S1, the collected raw data is cleaned to remove duplicate, erroneous and invalid data, and the data is standardized to uniformly convert the same type of data in different formats into a prescribed format.

[0007] Preferably, in S2, the certificate name, certificate number, etc. are used as basic information dimensions; the issuing agency, approval department, etc. are used as management agency dimensions; the issuance date, validity period, etc. are used as time dimensions; the business scope, level, etc. are used as business attribute dimensions. At the same time, an index is established for the information under each dimension for quick positioning and retrieval.

[0008] Preferably, in S3, the user inputs "query all first-level qualification certificates issued by XX institution and valid after January 1, 2024", and the system can recognize keywords such as "XX institution", "after January 1, 2024", "first-level", and the logical relationship of "and", and convert them into search conditions that the system can recognize.

[0009] Preferably, in said S4, a search algorithm based on semantic understanding is adopted, which not only matches keywords but also understands the semantics and contextual relationships of keywords, thereby improving the accuracy of the search. For complex search conditions, combined queries are performed according to logical relationships.

[0010] Preferably, in S5, a feedback function for the search results is provided, and users can evaluate the search results, such as whether the search results are accurate and whether they meet their needs. The system optimizes and adjusts the search algorithm and weights based on the user's feedback information to continuously improve the accuracy of the search and user satisfaction.

[0011] A multi-dimensional intelligent retrieval system for enterprise qualification certificates, including: Data collection module: used to collect the original data of enterprise qualification certificates through interface docking or manual input, and to clean and standardize the original data; Data processing and storage module: responsible for extracting multi-dimensional key information from pre-processed data, classifying and storing it, and creating an index for the information in each dimension; Search request parsing module: receives the search request input by the user, parses the search request using natural language processing technology, and converts it into search conditions that the system can recognize; Intelligent retrieval module: Based on the parsed retrieval conditions, it conducts intelligent retrieval in multi-dimensional index data, adopts a retrieval algorithm based on semantic understanding, combines queries according to logical relationships, and sorts the retrieval results according to information weight; Result display and feedback module: displays the search results to users in an intuitive manner and collects user feedback on the search results for optimizing the search algorithm and weight.

[0012] Preferably, the data acquisition module has the function of automatically monitoring data updates from external data sources when performing data acquisition. When data updates are detected, the data acquisition process can be triggered in a timely manner to ensure the real-time nature of the original data of the enterprise qualification certificate collected; The data processing and storage module adopts a dynamic index update mechanism when establishing an index. When data is added, modified or deleted, the index of the corresponding dimension can be automatically and synchronously updated to ensure the consistency of the index data with the actual stored data. When performing a search, the intelligent search module can adaptively adjust the information weight according to the user's historical search records and behavior habits, further improving the matching degree between the search results and the user's needs; In addition to supporting users to evaluate and provide feedback on search results, the result display and feedback module also has a data visualization display function.

[0013] Compared with existing technologies, the advantages of this multi-dimensional intelligent retrieval method and system for enterprise qualification certificates are: 1. Multi-dimensional search: The present invention realizes multi-dimensional search of enterprise qualification certificates. Users can conduct comprehensive searches based on multiple conditions, meeting the complex search needs of enterprises and improving the flexibility and practicality of searches.

[0014] 2. Intelligent retrieval: By adopting a retrieval algorithm based on semantic understanding, it can understand the semantics and contextual relationships of user search requests, not only matching keywords but also performing semantic matching, greatly improving the accuracy and comprehensiveness of retrieval.

[0015] 3. Efficiency: Data preprocessing and indexing, as well as reasonable retrieval algorithm design, make the retrieval process more efficient, quickly respond to user retrieval requests, and improve the company's work efficiency.

[0016] 4. Optimization and adjustment: By collecting user feedback on search results, we optimize and adjust the search algorithm and weights to continuously adapt to the company's business changes and user needs, and improve the system's search performance and user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1This is a workflow diagram of a multi-dimensional intelligent retrieval method and system for enterprise qualification certificates provided by the present invention. DETAILED DESCRIPTION

[0018] The following examples are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0019] Example: Refer to Figure 1 , a multi-dimensional intelligent retrieval method for enterprise qualification certificates, comprising the following steps: S1. Data collection and preprocessing: Collect the original data of enterprise qualification certificates through interface docking or manual input, including but not limited to certificate name, certificate number, issuing authority, issuance date, validity period, business scope, grade, approval department, etc. Specifically, after collecting the raw data, data cleaning is crucial. By writing a specialized data cleaning program and utilizing a data duplication detection algorithm, duplicate data can be identified and removed to avoid data redundancy. For erroneous data, judgments and corrections are made based on the data's logical rules and business experience. For example, checks are made to determine whether the date format is correct and whether the certificate number complies with the specifications. Invalid data is directly eliminated. After cleaning, the data is standardized, and data of the same type in different formats is uniformly converted to the specified format. For example, all date data is uniformly converted to the "YYYY-MM-DD" format. This lays a good foundation for subsequent data processing and retrieval.

[0020] S2. Multi-dimensional data extraction and classification: Extract key information of multiple dimensions from the pre-processed data and classify and store this information; Specifically, in S2, the certificate name, certificate number, etc. are used as basic information dimensions; the issuing agency, approval department, etc. are used as management agency dimensions; the issuance date, validity period, etc. are used as time dimensions; the business scope, level, etc. are used as business attribute dimensions. At the same time, an index is established for the information under each dimension for quick positioning and retrieval.

[0021] After dividing the dimensions, index the information within each dimension. Use efficient indexing algorithms, such as B-tree indexes or hash indexes, based on the characteristics of the data in each dimension. This significantly increases data search speed and reduces search time during subsequent searches.

[0022] S3. User search request parsing: Receive the search request input by the user, perform natural language processing on the search request, and identify the keywords, logical relationships, and search conditions in the search request; Specifically, in S3, the user inputs "query all first-level qualification certificates issued by XX institution and valid after January 1, 2024", and the system can recognize keywords such as "XX institution", "after January 1, 2024", "first-level", and the logical relationship of "and", and convert them into search conditions that the system can recognize.

[0023] Next, the logical relationships between keywords are analyzed to clarify the association between the various conditions. Finally, the identified keywords and logical relationships are converted into search conditions that the system can recognize, preparing for subsequent search operations.

[0024] S4, Intelligent Retrieval and Matching: Based on the parsed retrieval conditions, intelligent retrieval is performed in the multi-dimensional index data; Specifically, in S4, a search algorithm based on semantic understanding is used, which not only matches keywords but also understands the semantics and contextual relationships of keywords, thereby improving the accuracy of the search. For complex search conditions, combined queries are performed according to logical relationships. For example, for an "and" relationship, multiple conditions need to be met simultaneously; for an "or" relationship, it is sufficient to meet one of the conditions. During the search process, the search results are sorted according to the weights of information in different dimensions. The weights can be set according to actual business needs. For example, a higher weight can be set for the business scope dimension to highlight business-related qualifications.

[0025] S5. Search result display and feedback: The retrieved qualified qualification certificate information will be displayed to the user in an intuitive manner, including the basic information, detailed information and related attachments of the certificate.

[0026] Specifically, a feedback function for search results is provided. A review portal is provided on the display interface, allowing users to evaluate the search results, such as whether the search results are accurate and meet their needs. The system collects user feedback and optimizes the search algorithm and weighting through data analysis algorithms. For example, if a large number of users report that certain search results are inaccurate, the system will analyze the reasons and adjust the parameters or weighting settings in the search algorithm to continuously improve search accuracy and user satisfaction.

[0027] A multi-dimensional intelligent retrieval system for enterprise qualification certificates, including: Data Collection Module: This module is responsible for collecting raw data from enterprise qualification certificates through interface integration or manual entry. For interface integration, it supports multiple data interface protocols, such as HTTP and FTP, to accommodate different external data sources. For manual entry, it provides a user-friendly interface and allows for data validation rules to be configured, ensuring real-time verification of the format and content of user-entered data to prevent incorrect data entry.

[0028] After data collection is complete, the raw data is cleaned and standardized. During the cleaning process, various algorithms from the data cleaning algorithm library, such as rule-based and statistical-based cleaning algorithms, are applied to remove duplicate, erroneous, and invalid data. During standardization, data of the same type in different formats is converted to a standardized format based on pre-defined data format specifications.

[0029] Furthermore, the data collection module automatically monitors external data sources for updates during data collection. This functionality is achieved through periodic polling or message subscriptions. When data updates are detected, the data collection process is triggered immediately, ensuring the real-time nature of the collected raw data from enterprise qualification certificates.

[0030] Data Processing and Storage Module: This module is responsible for extracting multi-dimensional key information from preprocessed data and categorizing and storing it. This information is extracted using data parsing technology, following pre-defined dimensional rules to accurately extract all aspects of the certificate. This categorized storage utilizes a database management system, creating corresponding data tables based on different dimensions, such as basic information tables, management organization tables, time dimension tables, and business attribute tables.

[0031] A dynamic index update mechanism is used to index information for each dimension. When data is added, modified, or deleted, database triggers automatically capture these changes and synchronously update the index for the corresponding dimension, ensuring consistency between the indexed data and the actual stored data, ensuring accurate and efficient retrieval.

[0032] Search Request Parsing Module: This module receives search requests entered by users and supports multiple input methods, such as text box input and voice input. It utilizes natural language processing technology to parse search requests, including word segmentation, part-of-speech tagging, named entity recognition, and syntactic analysis. Through these operations, it identifies keywords, logical relationships, and search conditions in the search request and converts them into an internal representation recognizable by the system, providing accurate instructions for subsequent intelligent retrieval.

[0033] Intelligent Retrieval Module: Based on parsed search criteria, intelligent retrieval is performed within multi-dimensional indexed data. This module utilizes a semantic understanding-based retrieval algorithm, combined with deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to provide semantic understanding of search criteria. It combines queries based on logical relationships, enabling accurate processing of complex search criteria.

[0034] During the search process, the information weight is adaptively adjusted based on the user's historical search records and behavioral habits. By analyzing the user's search history and understanding the user's attention to information of different dimensions, the weight is dynamically adjusted to further improve the matching of search results with user needs.

[0035] Results Display and Feedback Module: This module presents search results to users in an intuitive manner, offering a variety of display views, such as list and card views, to meet diverse user needs. Displays include basic certificate information, detailed information, and related attachments. In addition to supporting user feedback on search results, it also features data visualization.

[0036] Relevant statistical information about search results can be displayed in the form of charts (such as bar charts and line graphs), such as the distribution of certificates issued by different issuing authorities and the proportion of certificates in different business scopes, so that users can more intuitively understand the overall situation of corporate qualification certificates. By collecting user feedback information, it is passed to the intelligent search module and data processing and storage module to optimize the search algorithm and weighting, thereby improving system performance and user experience.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-dimensional intelligent retrieval method for enterprise qualification certificates, characterized by: The following steps are involved: S1. Data collection and preprocessing: Collect the original data of enterprise qualification certificates through interface docking or manual input, including but not limited to certificate name, certificate number, issuing authority, issuance date, validity period, business scope, grade, and approval department; S2. Multi-dimensional data extraction and classification: Extract key information of multiple dimensions from the pre-processed data and classify and store this information; S3. User search request parsing: Receive the search request input by the user, perform natural language processing on the search request, and identify the keywords, logical relationships, and search conditions in the search request; S4, Intelligent Retrieval and Matching: Based on the parsed retrieval conditions, intelligent retrieval is performed in the multi-dimensional index data; S5. Search result display and feedback: The retrieved qualified qualification certificate information will be displayed to the user in an intuitive manner, including the basic information, detailed information and related attachments of the certificate.

2. The multi-dimensional intelligent retrieval method for enterprise qualification certificates according to claim 1 is characterized in that: In S1, the collected raw data is cleaned to remove duplicate, erroneous and invalid data, and the data is standardized to uniformly convert the same type of data in different formats into a prescribed format.

3. The multi-dimensional intelligent retrieval method for enterprise qualification certificates according to claim 1 is characterized in that: In S2, the certificate name, certificate number, etc. are used as basic information dimensions; the issuing agency, approval department, etc. are used as management agency dimensions; the issuance date, validity period, etc. are used as time dimensions; the business scope, level, etc. are used as business attribute dimensions. At the same time, an index is established for the information under each dimension for quick positioning and retrieval.

4. The multi-dimensional intelligent retrieval method for enterprise qualification certificates according to claim 1 is characterized in that: In S3, the user inputs "query all first-level qualification certificates issued by XX institution and valid after January 1, 2024". The system can recognize keywords such as "XX institution", "after January 1, 2024", "first-level", and the logical relationship of "and", and convert them into search conditions that the system can recognize.

5. The multi-dimensional intelligent retrieval method for enterprise qualification certificates according to claim 1 is characterized in that: In the above S4, a search algorithm based on semantic understanding is used, which not only matches keywords but also understands the semantics and contextual relationships of keywords, thereby improving the accuracy of the search. For complex search conditions, combined queries are performed according to logical relationships.

6. The multi-dimensional intelligent retrieval method for enterprise qualification certificates according to claim 1 is characterized in that: In S5, a feedback function for search results is provided, and users can evaluate the search results, such as whether the search results are accurate and whether they meet their needs. The system optimizes and adjusts the search algorithm and weights based on the user's feedback information to continuously improve the search accuracy and user satisfaction.

7. A multi-dimensional intelligent retrieval system for enterprise qualification certificates, characterized by: include: Data collection module: used to collect the original data of enterprise qualification certificates through interface docking or manual input, and to clean and standardize the original data; Data processing and storage module: responsible for extracting multi-dimensional key information from pre-processed data, classifying and storing it, and creating an index for the information in each dimension; Search request parsing module: receives the search request input by the user, parses the search request using natural language processing technology, and converts it into search conditions that the system can recognize; Intelligent retrieval module: Based on the parsed retrieval conditions, it conducts intelligent retrieval in multi-dimensional index data, adopts a retrieval algorithm based on semantic understanding, combines queries according to logical relationships, and sorts the retrieval results according to information weight; Result display and feedback module: displays the search results to users in an intuitive manner and collects user feedback on the search results for optimizing the search algorithm and weight.

8. The multi-dimensional intelligent retrieval system for enterprise qualification certificates according to claim 7 is characterized in that: The data acquisition module has the function of automatically monitoring the data update of the external data source when performing data acquisition. When the data update is detected, the data acquisition process can be triggered in time to ensure the real-time nature of the original data of the enterprise qualification certificate collected; The data processing and storage module adopts a dynamic index update mechanism when establishing an index. When data is added, modified or deleted, the index of the corresponding dimension can be automatically and synchronously updated to ensure the consistency of the index data with the actual stored data. When performing a search, the intelligent search module can adaptively adjust the information weight according to the user's historical search records and behavior habits, further improving the matching degree between the search results and the user's needs; In addition to supporting users to evaluate and provide feedback on search results, the result display and feedback module also has a data visualization display function.

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