Personal data retrieval method and device based on deep neural network

Abstract

The present invention discloses a deep neural network-based personal data retrieval method and device, which relates to the field of data retrieval technology. The method and device specifically include the following steps: S1. Data preprocessing and index construction; S2. Deep neural network model design; S3. Retrieval strategy and fragmentation processing; S4. Display of retrieval results; S5. Feedback and optimization. By learning the semantic relationships between file names through a deep neural network, intelligent retrieval capabilities surpass those of traditional keyword matching, accurately understanding user intent, and finding relevant files even when file names do not fully match, are achieved. If full file name retrieval fails, a fragmentation processing strategy, combined with deep neural network similarity calculation, effectively expands the search scope, improves retrieval recall and accuracy, and reduces the user input burden.

Description

Technical Field

[0001] The present invention relates to the field of data retrieval technology, and specifically to a personal data retrieval method and device based on a deep neural network. Background Art

[0002] With the rapid development of the internet and information technology, global data volumes are exploding, and the storage and management of personal data are becoming increasingly important. Personal data retrieval technology, as a key means of processing this massive amount of data, is crucial for improving data utilization efficiency, protecting personal privacy, and promoting data security. Search engine technology is at the core of personal data retrieval. It utilizes techniques such as indexing and inverted indexing to enable rapid retrieval of massive amounts of data. By optimizing search algorithms, establishing efficient index structures, and providing query optimization capabilities, search engine technology can significantly improve the efficiency and accuracy of data retrieval.

[0003] Currently, when retrieving data, retrieval systems generally use indexes to identify and retrieve input file names. However, in daily use, it is often the case that a file cannot be retrieved by entering a complete file name, but a file can be retrieved by entering partial characters. The reason for this is usually that the index is damaged, resulting in the inability to properly identify the file name and correctly retrieve the corresponding file, which in turn affects the efficiency of the search. If the user does not try to search for a partial file name, he or she will think that there is no corresponding file and waste time, affecting work. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a personal data retrieval method and device based on deep neural network, which solves the problems existing in existing data retrieval.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a personal data retrieval method based on a deep neural network, specifically comprising the following steps:

[0006] S1. Data Preprocessing and Index Construction: Collect personal data from various data sources and clean the data; build an index for each file, including the full file name and content summary;

[0007] S2. Deep Neural Network Model Design: Use the deep neural network model for training to learn the semantic relationship between file names, and prepare file name pairs as training data; define the encoding function E(x) and similarity calculation function sim(x,y) for query and file name;

[0008] S3. Search strategy and fragmentation processing: First, the full file name entered by the user is directly searched in the index. If the full file name search fails, the file name is split into long, medium, and short fragments, and the results are searched and merged separately. The similarity is then calculated using a deep neural network model to filter and sort the candidate sets. The decision to update the index is confirmed through similarity judgment or manual determination, and feedback is recorded for optimization.

[0009] S4. Display search results: Display the sorted candidate file list to the user, including file name, modification time, and preview image information;

[0010] S5. Feedback and Optimization: Collect user feedback to adjust model parameters and indexing strategies, and use user-confirmed search queries and file names as new training samples to construct new training data or adjust indexing strategies to cover these queries.

[0011] Preferably, S1 data preprocessing and index construction specifically include:

[0012] S1.1 Data collection and cleaning:

[0013] Collect personal data from various data sources, clean the data, remove invalid or duplicate items, and standardize file formats and naming;

[0014] S1.2 Filename index construction:

[0015] Build an index for each file, including the full file name and file content summary, and use hash tables or inverted index technology to speed up the retrieval process.

[0016] Preferably, the S2 deep neural network model design specifically includes:

[0017] S2.1 Model selection and training:

[0018] Select deep neural network model for text similarity calculation;

[0019] The model is trained to learn the semantic relationship between file names, and a large number of file name pairs are used as training data, including similar and dissimilar file names;

[0020] S2.2 File name parameter setting:

[0021] q: query the complete file name entered by the user;

[0022] di: the file name of the i-th file in the dataset;

[0023] E(x): encoder function, encodes the file name x into a vector;

[0024] sim(x, y): A function that calculates the similarity between two vectors x and y, then:

[0025]

[0026] Preferably, the S3 search strategy and fragmentation processing specifically include:

[0027] S3.1 Full file name retrieval: directly use the full file name q entered by the user to search in the index;

[0028] S3.2 If the full file name search fails, perform a fragment search:

[0029] S3.2.1 Define segment types: Based on the length and content of the file name q, it is divided into three types of segments: long, medium, and short. Each type of segment contains at least two sub-segments:

[0030] Long segment: contains the continuous part of most characters. The length is determined by the total length of q, and it is guaranteed to have at least two different parts as sub-segments of the long segment.

[0031] Medium fragments: The length is between long fragments and short fragments, and are obtained by further splitting long fragments;

[0032] Short segments: split into multiple segments according to word form;

[0033] Splitting process:

[0034] Long segment splitting: First, identify complete words, phrases, or long continuous character sequences in q as long segments, ensuring that there are at least two;

[0035] Mid-segment splitting: generate mid-segments based on further splitting of long segments, and also ensure that there are at least two;

[0036] Short fragment splitting: extract or generate short fragments from long and medium fragments, and ensure that there are at least two;

[0037] S3.2.2 Fragment set construction: The split long, medium and short fragments are placed into the set Q respectively L ={q L1 ,q L2 ,...q Lx}, Q M ={q M1 ,q M2 ,...q My}, Q S ={q S1 ,q S2 ,...q Sz};

[0038] S3.2.3 Segment retrieval and merging:

[0039] For each segment set Q type (Q L , Q M , Q S ) to retrieve the fragments and obtain the set R of all file names containing the fragments type，i ;

[0040] Merge all R type，i The results are removed and duplicates are obtained to obtain the preliminary candidate set R′;

[0041] S3.2.4 File name extraction and similarity calculation:

[0042] Extract the file names from R′ and use the deep neural network model to calculate each file name d j The similarity sim(E(q), E(d j ));

[0043] S3.2.5 Similarity threshold judgment and sorting:

[0044] Set a similarity threshold τ (e.g. 0.7) and filter out file names with similarity greater than τ to form the final candidate set C;

[0045] Sort the file names in C by the last modification time of the file. The earlier the modification time of the file, the later it is sorted.

[0046] Assume that file d j The last modification time is t j , the sorting is based on:

[0047] sort_key(d j )=t j ;

[0048] The sort function will sort the elements in C in ascending order of sort_key.

[0049] Preferably, after the files are retrieved and sorted, step S3.2.6 is performed to automatically update the index and verify, specifically including:

[0050] Maximum correlation judgment:

[0051] After the user confirms that the correct file has been found through the fragment search (for example, the user clicks on a file in the search results and confirms it), the system sets the file name to d f And calculate the similarity sim(E(q), E(d f ));

[0052] Update index conditions:

[0053] If sim(E(q), E(d f )) is greater than or equal to the preset update threshold τ update , and τ update >τ, then the system thinks d f Highly correlated with q, the index should be updated;

[0054] Index Update:

[0055] Enhanced index: d f Associating the file name and its related attributes with the query q or a fragment of q, possibly by updating a hash table or an inverted index;

[0056] Record feedback: Record the successful search and update in the indexing system for future optimization and model training;

[0057] Manual selection confirmation options:

[0058] If the user is not sure which file in the search results is correct, the system provides a confirmation button or drop-down menu to allow the user to select the correct file from the search results; the user's selection will trigger the automatic update logic to update the index.

[0059] Preferably, S5 feedback and optimization specifically include:

[0060] When collecting user feedback, mark search results that are confirmed by users after searching through snippets;

[0061] Use the user-confirmed search query and the corresponding file name as new training samples to train the model and improve accuracy;

[0062] Analyze user queries for which documents were not found and build new training data or adjust your indexing strategy to cover these queries.

[0063] Preferably, the specific algorithm processing method in the S5 feedback and optimization step includes:

[0064] S5.1 Quality assessment and weight allocation of feedback data:

[0065] Introducing a feedback quality evaluation parameter: For user-confirmed search results, a feedback quality evaluation parameter p is introduced. The parameter is dynamically calculated based on user behavior, including clicks, dwell time, and subsequent actions, and is estimated using the following formula:

[0066]

[0067] Among them, α, β, and γ are weight coefficients adjusted according to the business scenario, that is, the reasonable proportion of different factors in the evaluation;

[0068] S5.2 Parameter adjustment of deep neural network model:

[0069] Use feedback data with quality assessment to adjust the weights of the deep neural network; let the model weight be W, the loss function be L(W,D), where D is the training data set; introduce feedback data D feedback After that, the new training objective is expressed as:

[0070]

[0071] Among them, L feedback is a specific loss function for feedback data, p is the feedback quality evaluation parameter, and λ is the weight coefficient that balances the original training data and feedback data;

[0072] S5.3 Optimization of indexing strategy:

[0073] Index update strategy: For the index strategy, based on the query Q where the user does not find the document miss To adjust; first, analyze the query Q miss The degree of match with the existing index, and then build new index items or adjust the weight of existing index items;

[0074] Index item weight adjustment:

[0075] Assume that the original weight of index item i is w i , for query q∈Q miss , if index item i has a high similarity with q but is not selected, increase the weight of i:

[0076] w i ′=w i +δ·sim(q,i);

[0077] Among them, δ is the adjustment step size, sim(q,i) is the similarity function between query q and index item i;

[0078] S5.4 Construction of new training data: For user queries where no files are found, new file names or descriptions related to these queries are generated through natural language processing technology as new training data, which is implemented through the conditional text generation model;

[0079] S5.5 Continuous Monitoring and Feedback Loop: Establish a monitoring system to continuously track model performance and indexing efficiency, and iteratively optimize based on new user feedback; this includes regularly evaluating the model's performance in specific query scenarios and adjusting indexing strategies to cope with new query patterns.

[0080] The present invention also discloses a personal data retrieval device based on a deep neural network, comprising:

[0081] A data collection and preprocessing module and an index building module, for implementing step S1;

[0082] A deep neural network model design module, used to implement step S2;

[0083] A retrieval strategy and fragmentation processing module, used to implement step S3;

[0084] A result display module, used to implement step S4;

[0085] The feedback and optimization module is used to implement step S5.

[0086] The present invention provides a method and device for personal data retrieval based on a deep neural network. Compared with the existing technology, it has the following advantages:

[0087] 1. This deep neural network-based personal data retrieval method and device uses deep neural networks to learn the semantic relationship between file names, achieving intelligent retrieval capabilities that surpass traditional keyword matching. It can accurately understand user intent and find relevant files even if the file name does not fully match. When the full file name retrieval fails, a fragmentation processing strategy is adopted, combined with deep neural network similarity calculation, to effectively expand the search scope, improve the recall rate and accuracy of the search, and reduce the user input burden. The introduction of a user feedback mechanism not only adjusts the search results in real time, but also continuously optimizes the model parameters and indexing strategies based on user-confirmed search queries and file names, forming a closed-loop optimization system, continuously improving retrieval performance, and ensuring long-term adaptability and accuracy.

[0088] 2. This deep neural network-based personal data retrieval method and device learns the semantic relationship between file names through a deep neural network model, achieving high-precision understanding and matching of file names. Even if the query entered by the user is not completely consistent with the file name, relevant files can be accurately retrieved. When the full file name retrieval fails, an innovative fragmentation processing technology is used to split the file name into fragments of different lengths for retrieval. By merging the results and calculating similarities, the search scope is effectively expanded, and the recall rate and efficiency of the search are improved. By collecting user feedback, the model parameters and indexing strategies are continuously adjusted to form a closed-loop optimization mechanism, ensuring that the retrieval system can adapt to and optimize user query needs in the long term, thereby improving the user experience.

[0089] 3. This deep neural network-based personal data retrieval method and device achieves intelligent index updates by calculating the similarity between user-confirmed files and the original query and setting an update threshold. This mechanism automatically captures user intent, optimizes the index structure, and improves the accuracy and efficiency of future retrieval. A manual selection confirmation option allows users to participate in the index update process, which not only enhances user participation and satisfaction but also ensures the accuracy of index updates. Simultaneously, by recording user feedback, valuable data support is provided for continuous system optimization. This system closely integrates user feedback with index updates and model training, forming a closed-loop optimization system. This system can continuously adapt to changing user needs, improve retrieval performance, and provide users with more intelligent and personalized data retrieval services.

[0090] 4. This deep neural network-based personal data retrieval method and device, by introducing a quality assessment and weight distribution mechanism for feedback data, can accurately utilize user feedback to optimize the deep neural network model and indexing strategy, thereby improving the model's ability to understand user intent and retrieval accuracy; automatically analyze user queries for which no files were found, construct new training data or adjust indexing strategies, and form an intelligent iterative optimization mechanism, which can not only quickly respond to changes in user needs, but also continuously improve the overall performance of the system; establish a continuous monitoring and feedback loop to ensure that the system can adapt to and optimize user query needs in the long term, and through regular evaluation and adjustment, ensure that the system always maintains efficient and accurate retrieval capabilities, providing users with more intelligent and personalized data retrieval services. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 Schematic diagram of the method flow of the present invention;

[0092] Figure 2 This is a schematic diagram of file name segment splitting according to the present invention;

[0093] Figure 3 This is a system principle block diagram of the present invention. DETAILED DESCRIPTION

[0094] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0095] See Figure 1-Figure 2 , the present invention discloses a personal data retrieval method based on deep neural network and provides the following four technical solutions:

[0096] The first implementation method specifically includes the following steps:

[0097] S1. Data Preprocessing and Index Construction: Collect personal data from various data sources and clean the data; build an index for each file, including the full file name and content summary;

[0098] S2. Deep Neural Network Model Design: Use the deep neural network model for training to learn the semantic relationship between file names, and prepare file name pairs as training data; define the encoding function E(x) and similarity calculation function sim(x,y) for query and file name;

[0099] S3. Search strategy and fragmentation processing: First, the full file name entered by the user is directly searched in the index. If the full file name search fails, the file name is split into long, medium, and short fragments, and the results are searched and merged separately. The similarity is then calculated using a deep neural network model to filter and sort the candidate sets. The decision to update the index is confirmed through similarity judgment or manual determination, and feedback is recorded for optimization.

[0100] S4. Display search results: Display the sorted candidate file list to the user, including file name, modification time, and preview image information;

[0101] S5. Feedback and Optimization: Collect user feedback to adjust model parameters and indexing strategies, and use user-confirmed search queries and file names as new training samples to construct new training data or adjust indexing strategies to cover these queries.

[0102] By learning the semantic relationship between file names through deep neural networks, it achieves intelligent retrieval capabilities that go beyond traditional keyword matching, can accurately understand user intent, and can find relevant files even if the file name does not fully match; when the full file name retrieval fails, a fragmentation processing strategy is adopted, combined with deep neural network similarity calculation, which effectively expands the retrieval scope, improves the recall rate and accuracy of the retrieval, and reduces the user input burden; the introduction of a user feedback mechanism not only adjusts the retrieval results in real time, but also continuously optimizes the model parameters and indexing strategies through user-confirmed search queries and file names, forming a closed-loop optimization system, continuously improving retrieval performance, and ensuring long-term adaptability and accuracy.

[0103] The second implementation method differs from the first implementation method in that S1 data preprocessing and index construction specifically include:

[0104] S1.1 Data collection and cleaning:

[0105] Collect personal data from various data sources (such as hard drives, cloud storage, etc.), clean the data, remove invalid or duplicate items, and standardize file formats and naming;

[0106] S1.2 Filename index construction:

[0107] Build an index for each file, including the full file name and file content summary, and use hash tables or inverted index technology to speed up the retrieval process.

[0108] The S2 deep neural network model design specifically includes:

[0109] S2.1 Model selection and training:

[0110] Select deep neural network models (such as BERT, Transformer, etc.) for text similarity calculation;

[0111] The model is trained to learn the semantic relationship between file names, and a large number of file name pairs are used as training data, including similar and dissimilar file names;

[0112] S2.2 File name parameter setting:

[0113] q: query the complete file name entered by the user;

[0114] di: the file name of the i-th file in the dataset;

[0115] E(x): encoder function, encodes the file name x into a vector;

[0116] sim(x, y): A function that calculates the similarity between two vectors x and y, then:

[0117]

[0118] S3 retrieval strategy and fragmentation processing specifically include:

[0119] S3.1 Full file name retrieval: directly use the full file name q entered by the user to search in the index;

[0120] S3.2 If the full file name search fails, perform a fragment search:

[0121] S3.2.1 Define segment types: Based on the length and content of the file name q, it is divided into three types of segments: long, medium, and short. Each type of segment contains at least two sub-segments:

[0122] Long segment: contains the continuous part of most characters. The length is determined by the total length of q, and it is guaranteed to have at least two different parts as sub-segments of the long segment.

[0123] Medium fragments: The length is between long fragments and short fragments, and are obtained by further splitting long fragments;

[0124] Short segments: split into multiple segments according to word form;

[0125] Splitting process:

[0126] Long segment splitting: First, identify complete words, phrases, or long continuous character sequences in q as long segments, ensuring that there are at least two;

[0127] Mid-segment splitting: generate mid-segments based on further splitting of long segments, and also ensure that there are at least two;

[0128] Short segment splitting: Extract or generate short segments from long and medium segments, such as initials, abbreviations, etc., and ensure that there are at least two;

[0129] S3.2.2 Fragment set construction: The split long, medium and short fragments are placed into the set Q respectively L ={q L1 ,q L2 ,...q Lx}, Q M ={q M1 ,q M2 ,...q My}, Q S ={q S1 ,q S2 ,...q Sz};

[0130] S3.2.3 Segment retrieval and merging:

[0131] For each segment set Q type (Q L , Q M , Q S ) to retrieve the fragments and obtain the set R of all file names containing the fragments type，i ;

[0132] Merge all R type，i The results are removed and duplicates are obtained to obtain the preliminary candidate set R′;

[0133] S3.2.4 File name extraction and similarity calculation:

[0134] Extract the file names from R′ and use the deep neural network model to calculate each file name d j The similarity sim(E(q), E(d j ));

[0135] S3.2.5 Similarity threshold judgment and sorting:

[0136] Set a similarity threshold τ (e.g. 0.7) and filter out file names with similarity greater than τ to form the final candidate set C;

[0137] Sort the file names in C by the last modification time of the file. The earlier the modification time of the file, the later it is sorted.

[0138] Assume that file d j The last modification time is t j , the sorting is based on:

[0139] sort_key(d j )=t j ;

[0140] The sort function will sort the elements in C in ascending order of sort_key.

[0141] By learning the semantic relationship between file names through a deep neural network model, high-precision understanding and matching of file names is achieved. Even if the query entered by the user is not completely consistent with the file name, relevant files can be accurately retrieved. When the retrieval of the complete file name fails, an innovative fragmentation processing technology is used to split the file name into fragments of different lengths for retrieval. By merging the results and calculating the similarity, the retrieval scope is effectively expanded, and the recall rate and efficiency of the retrieval are improved. By collecting user feedback, the model parameters and indexing strategies are continuously adjusted to form a closed-loop optimization mechanism to ensure that the retrieval system can adapt to and optimize user query needs in the long term, thereby improving the user experience.

[0142] The third embodiment differs from the second embodiment mainly in that after the files are retrieved and sorted, step S3.2.6 is performed to automatically update the index and verify it, specifically including:

[0143] Maximum correlation judgment:

[0144] After the user confirms that the correct file has been found through the fragment search (for example, the user clicks on a file in the search results and confirms it), the system sets the file name to d f And calculate the similarity sim(E(q), E(d f ));

[0145] Update index conditions:

[0146] If sim(E(q), E(d f )) is greater than or equal to the preset update threshold τ update , and τ update >τ, then the system thinks d f Highly correlated with q, the index should be updated;

[0147] Index Update:

[0148] Enhanced index: d fThe file name and its related attributes (such as content summary, tags, etc.) are associated with the query q or a fragment of q, which may be achieved by updating a hash table or an inverted index;

[0149] Record feedback: Record the successful search and update in the indexing system for future optimization and model training;

[0150] Manual selection confirmation options:

[0151] If the user is not sure which file in the search results is correct, the system provides a confirmation button or drop-down menu to allow the user to select the correct file from the search results; the user's selection will trigger the automatic update logic to update the index.

[0152] By calculating the similarity between user-confirmed files and the original query and setting an update threshold, the index is intelligently updated. This mechanism automatically captures user intent, optimizes the index structure, and improves the accuracy and efficiency of future searches. Providing a manual confirmation option allows users to participate in the index update process, which not only enhances user participation and satisfaction but also ensures the accuracy of index updates. At the same time, by recording user feedback, valuable data support is provided for continuous system optimization. Closely integrating user feedback with index updates and model training forms a closed-loop optimization system. This system can continuously adapt to changes in user needs, improve retrieval performance, and provide users with more intelligent and personalized data retrieval services.

[0153] The fourth implementation mode differs from the third implementation mode mainly in that S5 feedback and optimization specifically include:

[0154] When collecting user feedback, we mark search results confirmed by users after searching through snippets. This feedback is used not only to adjust the parameters of the deep neural network model, but also to update the indexing strategy to ensure that the index accurately reflects the relationship between user intent and file content.

[0155] Use the user-confirmed search query and the corresponding file name as new training samples to train the model and improve accuracy;

[0156] Analyze user queries for which documents were not found and build new training data or adjust your indexing strategy to cover these queries.

[0157] The specific algorithm processing methods in the S5 feedback and optimization step include:

[0158] S5.1 Quality assessment and weight allocation of feedback data:

[0159] Introducing a feedback quality evaluation parameter: For user-confirmed search results, a feedback quality evaluation parameter p can be introduced. This parameter is dynamically calculated based on user behavior, including clicks, dwell time, and subsequent actions, and is estimated using the following formula:

[0160]

[0161] Among them, α, β, and γ are weight coefficients adjusted according to the business scenario, that is, the reasonable proportion of different factors in the evaluation;

[0162] S5.2 Parameter adjustment of deep neural network model:

[0163] Use feedback data with quality assessment to adjust the weights of deep neural networks (such as BERT, Transformer, etc.); let the model weight be W and the loss function be L(W, D), where D is the training data set; introduce feedback data D feedback After that, the new training objective is expressed as:

[0164]

[0165] Among them, L feedback is a specific loss function for feedback data (such as cross entropy loss), p is the feedback quality evaluation parameter, and λ is the weight coefficient that balances the original training data and feedback data;

[0166] S5.3 Optimization of indexing strategy:

[0167] Index update strategy: For the index strategy, based on the query Q where the user does not find the document miss To adjust; first, analyze the query Q miss The degree of match with the existing index, and then build new index items or adjust the weight of existing index items;

[0168] Index item weight adjustment:

[0169] Assume that the original weight of index item i is w i , for query q∈Q miss , if index item i has a high similarity with q but is not selected, increase the weight of i:

[0170] w i ′=w i +δ·sim(q,i);

[0171] Among them, δ is the adjustment step size, sim(q,i) is the similarity function between query q and index item i;

[0172] S5.4 Construction of New Training Data: For user queries where no files were found, natural language processing techniques (such as text generation models) are used to generate new file names or descriptions related to these queries where no files were found. These files are then used as new training data. This is achieved using conditional text generation models, such as the GPT series models.

[0173] S5.5 Continuous Monitoring and Feedback Loop: Establish a monitoring system to continuously track model performance and indexing efficiency, and iteratively optimize based on new user feedback; this includes regularly evaluating the model's performance in specific query scenarios and adjusting indexing strategies to cope with new query patterns.

[0174] By introducing a quality assessment and weight distribution mechanism for feedback data, we can accurately utilize user feedback to optimize deep neural network models and indexing strategies, improving the model's ability to understand user intent and retrieval accuracy; automatically analyze user queries for files that have not been found, build new training data or adjust indexing strategies, and form an intelligent iterative optimization mechanism, which can not only quickly respond to changes in user needs, but also continuously improve the overall performance of the system; establish a continuous monitoring and feedback loop to ensure that the system can adapt to and optimize user query needs in the long term, and through regular evaluation and adjustment, ensure that the system always maintains efficient and accurate retrieval capabilities, providing users with more intelligent and personalized data retrieval services.

[0175] See Figure 3 The present invention also discloses a personal data retrieval device based on a deep neural network, comprising:

[0176] A data collection and preprocessing module and an index building module, for implementing step S1;

[0177] A deep neural network model design module, used to implement step S2;

[0178] A retrieval strategy and fragmentation processing module, used to implement step S3;

[0179] A result display module, used to implement step S4;

[0180] The feedback and optimization module is used to implement step S5.

[0181] At the same time, the contents not described in detail in this specification belong to the existing technology well known to those skilled in the art.

[0182] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0183] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A personal data retrieval method based on a deep neural network, characterized in that: The specific steps include: S1. Data Preprocessing and Index Construction: Collect personal data from various data sources and clean the data; build an index for each file, including the full file name and content summary; S2. Deep Neural Network Model Design: Use the deep neural network model for training to learn the semantic relationship between file names, and prepare file name pairs as training data; define the encoding function E(x) and similarity calculation function sim(x,y) for query and file name; S3. Search strategy and fragmentation processing: First, the full file name entered by the user is directly searched in the index. If the full file name search fails, the file name is split into long, medium, and short fragments, and the results are searched and merged separately. The similarity is then calculated using a deep neural network model to filter and sort the candidate sets. The decision to update the index is confirmed through similarity judgment or manual determination, and feedback is recorded for optimization. Long segment: contains the continuous part of most characters. The length is determined by the total length of q, and it is guaranteed to have at least two different parts as sub-segments of the long segment. Medium fragments: The length is between long fragments and short fragments, and are obtained by further splitting long fragments; Short segments: split into multiple segments according to word form; Splitting process: Long segment splitting: First, identify complete words, phrases, or long continuous character sequences in q as long segments, ensuring that there are at least two; Mid-segment splitting: generate mid-segments based on further splitting of long segments, and also ensure that there are at least two; Short fragment splitting: extract or generate short fragments from long and medium fragments, and ensure that there are at least two; S4. Display search results: Display the sorted candidate file list to the user, including file name, modification time, and preview image information; S5. Feedback and Optimization: Collect user feedback to adjust model parameters and indexing strategies. Mark search results confirmed by users after fragment search. Use user-confirmed search queries and file names as new training samples to train the model and improve accuracy. Analyze user queries for which files were not found, build new training data, or adjust indexing strategies to cover these queries.

2. The personal data retrieval method based on deep neural network according to claim 1, characterized in that: S1 data preprocessing and index construction specifically include: S1.1 Data collection and cleaning: Collect personal data from various data sources, clean the data, remove invalid or duplicate items, and standardize file formats and naming; S1.2 Filename index construction: Build an index for each file, including the full file name and file content summary, and use hash tables or inverted index technology to speed up the retrieval process.

3. The personal data retrieval method based on deep neural network according to claim 1, characterized in that: The S2 deep neural network model design specifically includes: S2.1 Model selection and training: Select deep neural network model for text similarity calculation; The model is trained to learn the semantic relationship between file names, and a large number of file name pairs are used as training data, including similar and dissimilar file names; S2.2 File name parameter setting: q: query the complete file name entered by the user; di: the file name of the i-th file in the dataset; E(x): encoder function, encodes the file name x into a vector; sim(x, y): A function that calculates the similarity between two vectors x and y, then:

4. The personal data retrieval method based on deep neural network according to claim 3, characterized in that: S3 retrieval strategy and fragmentation processing specifically include: S3.1 Full file name retrieval: directly use the full file name q entered by the user to search in the index; S3.2 If the full file name search fails, perform a fragment search: S3.2.1 Define segment types: Based on the length and content of the file name q, it is divided into three types of segments: long, medium, and short. Each type of segment contains at least two sub-segments: S3.2.2 Fragment set construction: The split long, medium and short fragments are placed into the set Q respectively L ={q L1 , q L2 ,...q Lx },Q M ={q M1 ,q M2 ,...q My },Q S ={q S1 ,q S2 ,...q Sz }; S3.2.3 Segment retrieval and merging: For each segment set Q type (Q L , Q M , Q S ) to retrieve the fragments and obtain the set R of all file names containing the fragments type，i ; Merge all R type，i The results are removed and duplicates are obtained to obtain the preliminary candidate set R′; S3.2.4 File name extraction and similarity calculation: Extract the file names from R′ and use the deep neural network model to calculate each file name d j The similarity sim(E(q), E(d j )); S3.2.5 Similarity threshold judgment and sorting: Set a similarity threshold τ and filter out the file names with similarity greater than τ to form the final candidate set C; Sort the file names in C by the last modification time of the file. The earlier the modification time of the file, the later it is sorted. Assume that file d j The last modification time is t j , the sorting is based on: sort_key(d j )=t j ; The sort function will sort the elements in C in ascending order of sort_key.

5. The personal data retrieval method based on deep neural network according to claim 4, characterized in that: After the files are retrieved and sorted, step S3.2.6 is performed to automatically update the index and verify it, which specifically includes: Maximum correlation judgment: After the user confirms that the correct file has been found through the fragment search, the system sets the file name to d f And calculate the similarity sim(E(q), E(d f )); Update index conditions: If sim(E(q), E(d f )) is greater than or equal to the preset update threshold τ update , and τ update >τ, then the system thinks d f Highly correlated with q, the index should be updated; Index Update: Enhanced index: d f Associating the file name and its related attributes with the query q or a fragment of q, possibly by updating a hash table or an inverted index; Record feedback: Record the successful search and update in the indexing system for future optimization and model training; Manual selection confirmation options: If the user is not sure which file in the search results is correct, the system provides a confirmation button or drop-down menu to allow the user to select the correct file from the search results; the user's selection will trigger the automatic update logic to update the index.

6. A personal data retrieval device based on a deep neural network, using the personal data retrieval method based on a deep neural network according to any one of claims 1 to 5 for retrieval, characterized in that: include: A data collection and preprocessing module and an index building module, for implementing step S1; A deep neural network model design module, used to implement step S2; A retrieval strategy and fragmentation processing module, used to implement step S3; A result display module, used to implement step S4; The feedback and optimization module is used to implement step S5.

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