Screening system based on intelligent data matching

Abstract

The invention discloses a screening system based on intelligent data matching, and the system comprises a multi-modal data fusion module, a dynamic weight adjustment module, a matching engine, an adaptive learning module, a data preprocessing module, a feature extraction module, a result display module, and a feedback collection and analysis module. The output end of the data preprocessing module is connected with the input end of the feature extraction module, the output end of the feature extraction module is connected with the input end of the multi-modal data fusion module, and the output end of the multi-modal data fusion module is connected with the input end of the matching engine. The matching engine is connected with the adaptive learning module and the dynamic weight adjustment module, the output end of the matching engine is connected with the input end of the result display module, and the result display module is connected with the feedback collection and analysis module. The invention provides an efficient and accurate data matching and screening system which is suitable for a rapid information retrieval task in a big data environment.

Description

Technical Field

[0001] The present invention relates to the technical field of data screening, and in particular to a screening system based on intelligent data matching. Background Art

[0002] With the rapid development of Internet technology, the amount of information has shown an explosive growth trend. In this context, how to quickly and accurately obtain the required information from massive data has become a major challenge in the research and application fields. Traditional data matching methods mainly rely on keyword matching or simple rule engines. Although they can meet basic search needs to a certain extent, they also have many limitations. First, these methods can usually only process a single type of data (such as plain text), and lack effective integration and processing capabilities for multiple types of data such as text, images, and audio, resulting in low information utilization efficiency. Secondly, traditional methods often use fixed feature weights and cannot be flexibly adjusted according to the user's specific query conditions, resulting in a low match between search results and user needs, affecting user experience. In addition, due to the lack of an effective adaptive learning mechanism, these systems are difficult to self-optimize based on user feedback information, and their performance may gradually decline over time. Finally, in the face of large-scale concurrent requests and real-time response requirements in a big data environment, the processing speed and concurrency of traditional methods are also stretched, and it is difficult to meet the requirements of modern applications for efficiency. Summary of the invention

[0003] 1. Technical Problems Solved

[0004] The technical problems to be solved by the present invention are the various problems mentioned in the above background technology, and a screening system based on intelligent data matching is provided.

[0005] 2. Technical Solution

[0006] To solve the above technical problems, the technical solution provided by the present invention is: a screening system based on intelligent data matching, comprising a multimodal data fusion module, a dynamic weight adjustment module, a matching engine, an adaptive learning module, a data preprocessing module, a feature extraction module, a result display module and a feedback collection and analysis module, wherein the output end of the data preprocessing module is connected to the input end of the feature extraction module, the output end of the feature extraction module is connected to the input end of the multimodal data fusion module, the output end of the multimodal data fusion module is connected to the input end of the matching engine, the matching engine is connected to the adaptive learning module and the dynamic weight adjustment module, the output end of the matching engine is connected to the input end of the result display module, and the result display module is connected to the feedback collection and analysis module:

[0007] Multimodal data fusion module, used to process and integrate multimedia information from different sources and types;

[0008] Dynamic weight adjustment module, used to automatically adjust the importance of each feature according to the user's specific query conditions;

[0009] The matching engine, as a core component, integrates multimodal data fusion, dynamic weight adjustment, and adaptive learning functions to achieve efficient and accurate data matching;

[0010] Adaptive learning module, used to continuously improve the system's matching strategy through user feedback;

[0011] Data preprocessing module, used to convert raw data into a unified format and perform preliminary cleaning and labeling;

[0012] Feature extraction module, which uses natural language processing, computer vision and other technologies to extract key features from preprocessed data;

[0013] The result display module is used to present the final matching results in a user-friendly manner;

[0014] The feedback collection and analysis module is used to collect users' evaluation of search results for subsequent optimization.

[0015] As an improvement, the data preprocessing module specifically includes:

[0016] Data cleaning unit: remove irrelevant or erroneous information, such as deleting duplicate records, filling missing values, etc.;

[0017] Data standardization unit: convert data into a unified format and standard, such as date format, numerical range, etc.;

[0018] Data annotation unit: Label the data to help subsequent modules better understand and use the data.

[0019] As an improvement, the feature extraction module specifically includes:

[0020] Text feature extraction unit: uses TF-IDF, word embedding and other technologies to extract semantic features from text data;

[0021] Image feature extraction unit: uses deep learning methods such as convolutional neural networks (CNN) to extract visual features from images;

[0022] Audio feature extraction unit: Extracts sound features from audio data through Fourier transform, Mel-frequency cepstral coefficient (MFCC) and other technologies.

[0023] As an improvement, the multimodal data fusion module specifically includes:

[0024] Feature Mapping Unit: maps different types of features into the same space, ensuring that they can be directly compared and combined;

[0025] Feature fusion unit: It uses weighted averaging, stacking and other methods to merge the features of multiple modes into a comprehensive feature vector.

[0026] As an improvement, the matching engine specifically includes:

[0027] Index building unit: Create efficient data indexes to speed up searches.

[0028] Similarity calculation unit: calculates the similarity score between the query and the entries in the database.

[0029] Sorting unit: sorts candidate results according to similarity scores.

[0030] Filtering unit: removes results that do not meet the conditions and improves matching accuracy.

[0031] As an improvement, the result display module supports multiple forms of result output including lists and charts, and can flexibly adjust the display style according to the design of the user interface.

[0032] As an improvement, the feedback collection and analysis module can collect user feedback through online questionnaires, scoring, etc., and convert it into a basis for system optimization.

[0033] 3. Beneficial Effects

[0034] The advantages of the present invention compared with the prior art are:

[0035] The present invention provides a screening system based on intelligent data matching, which realizes efficient and accurate matching of massive data by integrating advanced technologies such as multimodal data fusion, dynamic weight adjustment, adaptive learning and modular design. Specifically, it can process various types of data such as text, image, audio, etc., realize effective fusion of cross-modal data through deep learning technology, enhance the correlation and complementarity between data, and improve data utilization efficiency; according to the user's specific query conditions, the system can automatically adjust the importance weights of different features to ensure that the search results are closer to the user's actual needs and improve the user experience; using the reinforcement learning algorithm, the system can continuously optimize its matching strategy based on user feedback, gradually improve the search quality and system performance, and realize intelligent service upgrades; through distributed computing technology and hardware acceleration means, the system supports large-scale concurrent requests and real-time responses, significantly improves data processing speed and concurrency, and is suitable for fast information retrieval tasks in a big data environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1It is a structural diagram of a screening system based on intelligent data matching of the present invention. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0038] Embodiment 1

[0039] like Figure 1 As shown, a screening system based on intelligent data matching includes a multimodal data fusion module, a dynamic weight adjustment module, an adaptive learning module, a data preprocessing module, a feature extraction module, a result display module and a feedback collection and analysis module, wherein the output end of the data preprocessing module is connected to the input end of the feature extraction module, the output end of the feature extraction module is connected to the input end of the multimodal data fusion module, the output end of the multimodal data fusion module is connected to the input end of the matching engine, the matching engine is connected to the adaptive learning module and the dynamic weight adjustment module, the output end of the matching engine is connected to the input end of the result display module, and the result display module is connected to the feedback collection and analysis module:

[0040] Multimodal data fusion module, used to process and integrate multimedia information from different sources and types;

[0041] Dynamic weight adjustment module, used to automatically adjust the importance of each feature according to the user's specific query conditions;

[0042] The matching engine, as a core component, integrates multimodal data fusion, dynamic weight adjustment, and adaptive learning functions to achieve efficient and accurate data matching;

[0043] Adaptive learning module, used to continuously improve the system's matching strategy through user feedback;

[0044] Data preprocessing module, used to convert raw data into a unified format and perform preliminary cleaning and labeling;

[0045] Feature extraction module, which uses natural language processing, computer vision and other technologies to extract key features from preprocessed data;

[0046] The result display module is used to present the final matching results in a user-friendly manner;

[0047] The feedback collection and analysis module is used to collect users' evaluation of search results for subsequent optimization.

[0048] In the specific implementation process, the data preprocessing module converts the format, cleans and annotates the raw data, and then passes it to the feature extraction module to extract useful feature information. The feature extraction module passes the key features extracted from various types of data to the multimodal data fusion module, which is responsible for integrating these feature information to form a comprehensive data representation. At the same time, the multimodal data fusion module may need to feedback certain specific data requirements to the feature extraction module in order to better optimize the feature extraction process. The comprehensive data representation after multimodal data fusion is passed to the matching engine as the basis for matching.

[0049] During the matching process, the matching engine interacts with the dynamic weight adjustment module to dynamically adjust the importance weights of each feature according to the user's query conditions. The matching engine also interacts with the adaptive learning module to continuously optimize the matching strategy and algorithm by receiving user feedback information. After the matching engine completes the matching, it passes the most relevant data items to the result display module, which presents them in a user-friendly manner. Users can give feedback through the interface provided by the result display module, and this feedback information will be collected and analyzed by the feedback collection and analysis module to further optimize system performance.

[0050] The data preprocessing module specifically includes:

[0051] Data cleaning unit: remove irrelevant or erroneous information, such as deleting duplicate records, filling missing values, etc.;

[0052] Data standardization unit: convert data into a unified format and standard, such as date format, numerical range, etc.;

[0053] Data annotation unit: Label the data to help subsequent modules better understand and use the data.

[0054] The feature extraction module specifically includes:

[0055] Text feature extraction unit: uses TF-IDF, word embedding and other technologies to extract semantic features from text data;

[0056] Image feature extraction unit: uses deep learning methods such as convolutional neural networks (CNN) to extract visual features from images;

[0057] Audio feature extraction unit: Extracts sound features from audio data through Fourier transform, Mel-frequency cepstral coefficient (MFCC) and other technologies.

[0058] The multimodal data fusion module specifically includes:

[0059] Feature Mapping Unit: maps different types of features into the same space, ensuring that they can be directly compared and combined;

[0060] Feature fusion unit: It uses weighted averaging, stacking and other methods to merge the features of multiple modes into a comprehensive feature vector.

[0061] The matching engine specifically includes:

[0062] Index building unit: Create efficient data indexes to speed up searches.

[0063] Similarity calculation unit: calculates the similarity score between the query and the entries in the database.

[0064] Sorting unit: sorts candidate results according to similarity scores.

[0065] Filtering unit: removes results that do not meet the conditions and improves matching accuracy.

[0066] The result display module supports multiple forms of result output including lists and charts, and can flexibly adjust the display style according to the design of the user interface. The feedback collection and analysis module can collect user feedback through online questionnaires, scoring, etc., and convert it into a basis for system optimization.

[0067] The specific implementation steps of the present invention are as follows:

[0068] Data preparation: The data preprocessing module cleans, standardizes and labels the raw data to ensure data quality and consistency.

[0069] Feature extraction: The feature extraction module extracts key features from the preprocessed data, including text, image, and audio features.

[0070] Data fusion: The multimodal data fusion module maps different types of features into the same space and uses weighted averaging or stacking methods to perform feature fusion.

[0071] Dynamic weight adjustment: The dynamic weight adjustment module automatically adjusts the importance weight of each feature based on the user's query conditions and historical feedback information.

[0072] Intelligent matching: The matching engine uses the fused feature vectors and the dynamic weight adjustment results to perform efficient matching operations and generate candidate results.

[0073] Result display: The result display module presents the matching results in a user-friendly manner and supports multiple forms of output, such as lists, charts, etc.

[0074] User feedback: Users give feedback through the interface provided by the result display module, and the feedback information is collected and analyzed by the feedback collection and analysis module.

[0075] System optimization: The adaptive learning module continuously optimizes matching strategies and algorithm parameters based on user feedback to improve the overall performance of the system.

[0076] Through the above specific implementation methods, the present invention provides an efficient and accurate data matching and screening system that can process various types of data, dynamically adjust matching strategies according to user needs, and continuously optimize system performance through adaptive learning, which is suitable for fast information retrieval tasks in a big data environment.

[0077] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0078] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0079] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A screening system based on intelligent data matching, characterized in that: It includes a multimodal data fusion module, a dynamic weight adjustment module, an adaptive learning module, a data preprocessing module, a feature extraction module, a result display module and a feedback collection and analysis module. The output end of the data preprocessing module is connected to the input end of the feature extraction module, the output end of the feature extraction module is connected to the input end of the multimodal data fusion module, the output end of the multimodal data fusion module is connected to the input end of the matching engine, the matching engine is connected to the adaptive learning module and the dynamic weight adjustment module, the output end of the matching engine is connected to the input end of the result display module, and the result display module is connected to the feedback collection and analysis module: Multimodal data fusion module, used to process and integrate multimedia information from different sources and types; Dynamic weight adjustment module, used to automatically adjust the importance of each feature according to the user's specific query conditions; The matching engine, as a core component, integrates multimodal data fusion, dynamic weight adjustment, and adaptive learning functions to achieve efficient and accurate data matching; Adaptive learning module, used to continuously improve the system's matching strategy through user feedback; Data preprocessing module, used to convert raw data into a unified format and perform preliminary cleaning and labeling; Feature extraction module, which uses natural language processing, computer vision and other technologies to extract key features from preprocessed data; The result display module is used to present the final matching results in a user-friendly manner; The feedback collection and analysis module is used to collect users' evaluation of search results for subsequent optimization.

2. A screening system based on intelligent data matching according to claim 1, characterized in that: The data preprocessing module specifically includes: Data cleaning unit: remove irrelevant or erroneous information, such as deleting duplicate records, filling missing values, etc.; Data standardization unit: convert data into a unified format and standard, such as date format, numerical range, etc.; Data annotation unit: Label the data to help subsequent modules better understand and use the data.

3. A screening system based on intelligent data matching according to claim 1, characterized in that: The feature extraction module specifically includes: Text feature extraction unit: uses TF-IDF, word embedding and other technologies to extract semantic features from text data; Image feature extraction unit: uses deep learning methods such as convolutional neural networks (CNN) to extract visual features from images; Audio feature extraction unit: Extracts sound features from audio data through Fourier transform, Mel-frequency cepstral coefficient (MFCC) and other technologies.

4. A screening system based on intelligent data matching according to claim 1, characterized in that: The multimodal data fusion module specifically includes: Feature Mapping Unit: maps different types of features into the same space, ensuring that they can be directly compared and combined; Feature fusion unit: It uses weighted averaging, stacking and other methods to merge the features of multiple modes into a comprehensive feature vector.

5. The screening system based on intelligent data matching according to claim 1, characterized in that: The matching engine specifically includes: Index building unit: Create efficient data indexes to speed up searches. Similarity calculation unit: calculates the similarity score between the query and the entries in the database. Sorting unit: sorts candidate results according to similarity scores. Filtering unit: removes results that do not meet the conditions and improves matching accuracy.

6. The screening system based on intelligent data matching according to claim 1, characterized in that: The result display module supports multiple forms of result output including lists and charts, and can flexibly adjust the display style according to the design of the user interface.

7. The screening system based on intelligent data matching according to claim 1, characterized in that: The feedback collection and analysis module can collect user feedback through online questionnaires, scoring, etc., and convert it into a basis for system optimization.

Images