File searching method and device, electronic equipment and medium

Abstract

The present disclosure provides a file search method and device, electronic equipment and medium, relates to the technical field of computers, and particularly relates to the technical fields of information search, file search, intelligent network disk and cloud computing. The specific implementation scheme is as follows: determining a candidate file identifier of a candidate file in a target folder, performing word segmentation on the candidate file identifier to determine a candidate word segmentation result; determining an association relationship between the candidate word segmentation result and the candidate file, and determining a candidate file quantity of the candidate file associated with the candidate word segmentation result according to the association relationship; determining a file search word from the candidate word segmentation result according to the candidate file quantity, and displaying the file search word, so that a user can search for a file in the target folder according to the file search word. The present disclosure can shorten the time required by the user to search for a file in the target folder, and can also ensure the recall rate of file search.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, specifically to the fields of information search, file search, smart cloud storage and cloud computing, and particularly to a file search method, apparatus, electronic device and medium. Background Technology

[0002] With the advent of the digital information age, more and more data is stored in the form of files in folders on electronic devices, and often a large number of files are stored in the same folder.

[0003] When a user wants to search for files in a target folder, they usually need to manually search the target folder or enter search terms. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and medium for searching files, which reduces the time required for file searches and ensures the recall rate of file searches.

[0005] According to one aspect of this disclosure, a method for searching documents is provided, comprising:

[0006] Determine the candidate file identifiers of candidate files in the target folder, and perform word segmentation on the candidate file identifiers to determine the candidate word segmentation results;

[0007] Determine the association between the candidate word segmentation results and the candidate files, and determine the number of candidate files associated with the candidate word segmentation results based on the association.

[0008] Based on the number of candidate files, file search terms are determined from the candidate word segmentation results, and the file search terms are displayed so that users can search for files in the target folder based on the file search terms.

[0009] According to another aspect of this disclosure, a document search apparatus is provided, comprising:

[0010] The word segmentation module is used to determine the candidate file identifiers of candidate files in the target folder, and to segment the candidate file identifiers to determine the candidate word segmentation results;

[0011] The file quantity determination module is used to determine the association between the candidate word segmentation results and the candidate files, and to determine the number of candidate files associated with the candidate word segmentation results based on the association.

[0012] The file search term display module is used to determine file search terms from the candidate word segmentation results based on the number of candidate files, and display the file search terms so that users can search for files in the target folder based on the file search terms.

[0013] According to another aspect of this disclosure, an electronic device is provided, comprising:

[0014] At least one processor; and

[0015] A memory that is communicatively connected to at least one processor; wherein,

[0016] The memory stores instructions that can be executed by at least one processor to enable the at least one processor to perform any of the methods of this disclosure.

[0017] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to perform any of the methods of this disclosure.

[0018] According to another aspect of this disclosure, a computer program product is provided, including a computer program and a method for the computer program to be executed by a processor according to any of the methods disclosed herein.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0020] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0021] Figure 1A This is a flowchart of a method for searching some documents according to embodiments of this disclosure;

[0022] Figure 1B This is a schematic diagram of an interface for file searching according to some embodiments of this disclosure;

[0023] Figure 1C These are schematic diagrams of interfaces for file searching according to embodiments of this disclosure;

[0024] Figure 2 This is a flowchart of another document search method disclosed according to embodiments of this disclosure;

[0025] Figure 3 This is a flowchart of another document search method disclosed according to embodiments of this disclosure;

[0026] Figure 4A This is a flowchart of another document search method disclosed according to embodiments of this disclosure;

[0027] Figure 4B This is a flowchart illustrating the display of some search terms according to embodiments of this disclosure;

[0028] Figure 5 This is a schematic diagram of the structure of a document search device disclosed in some embodiments of this disclosure;

[0029] Figure 6 This is a block diagram of an electronic device used to implement the file search method disclosed in the embodiments of this disclosure. Detailed Implementation

[0030] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0031] When a user wants to search for files in a target folder, they usually need to manually search the target folder or enter search terms.

[0032] However, manual search usually requires users to flip through multiple pages in the target folder to find the file they want to search for, which is a very cumbersome process and takes a long time. While manually entering search terms can shorten the search time to some extent, it depends on the user having a general understanding of the file distribution in the target folder. If the user is accessing the target folder for the first time to search for files, the search terms they enter are likely to be invalid, resulting in a low recall rate for file searches.

[0033] To address the aforementioned issues, existing technologies offer some improvements, such as categorizing and filtering files in the target folder based on structured information in a database to facilitate quick browsing for users. However, this approach requires accumulating a large amount of structured data, resulting in a lengthy data preparation period and high implementation costs.

[0034] Figure 1A This is a flowchart illustrating some file search methods disclosed in embodiments of this disclosure. This embodiment can be applied to situations where users are assisted in searching for files in a target folder. The methods in this embodiment can be executed by the file search device disclosed in this disclosure. The device can be implemented in software and / or hardware and can be integrated into any electronic device with computing capabilities.

[0035] like Figure 1A As shown, the file search method disclosed in this embodiment may include:

[0036] S101. Determine the candidate file identifiers of the candidate files in the target folder, and perform word segmentation on the candidate file identifiers to determine the candidate word segmentation results.

[0037] The target folder refers to the folder that stores the candidate files. It can be a folder in the terminal device system, such as a folder in the terminal device system of a smartphone, smart tablet or personal computer, or a folder in the cloud device system, such as a folder in the cloud storage, cloud drive or cloud server.

[0038] A candidate file identifier is a unique identifier text for a candidate file. For example, a candidate file identifier can be the file name of the candidate file.

[0039] In one implementation, the number of candidate files stored in each candidate folder is determined, and the number of files in each candidate folder is compared with a file count threshold. Candidate folders with a file count greater than the threshold are then designated as target folders. For example, assuming the file count threshold is 10, candidate folders with a file count greater than 10 are designated as target folders. The file count threshold can be dynamically adjusted based on the maximum number of files that can be displayed on a single screen of the device containing the candidate folder. Essentially, the file count threshold is proportional to the maximum number of files; that is, the threshold is increased when the maximum number of files is large, and decreased when the maximum number of files is small. By dynamically adjusting the file count threshold based on the maximum number of files, the accuracy of target folder determination is improved, avoiding the problems of unreasonable determination and low accuracy that can occur when using a single, fixed file count threshold.

[0040] Determine the candidate file identifiers for each candidate file stored in the target folder, and use word segmentation algorithms to segment each candidate file identifier, including but not limited to the maximum matching word segmentation algorithm, the shortest path word segmentation algorithm, or the neural network word segmentation algorithm, to determine the candidate word segmentation results corresponding to each candidate file identifier.

[0041] For example, assuming the candidate file identifier of any candidate file is "007.Poetry and Story | 'Acacia' [Tang Dynasty, Wang Wei].mp3", then the candidate file identifier of the candidate file is segmented into words, and the resulting candidate word segmentation results include: "007", ".", "poetry", "story", "|", "'Acacia'", "[", "Tang Dynasty", "·", "Wang Wei", "]" and "mp3".

[0042] By identifying candidate file identifiers for candidate files in the target folder and performing word segmentation on these identifiers to determine candidate word segmentation results, the data preparation effect was achieved, laying a data foundation for subsequent determination of file search terms based on candidate word segmentation results.

[0043] S102. Determine the association between candidate word segmentation results and candidate files, and determine the number of candidate files associated with candidate word segmentation results based on the association.

[0044] In one implementation, the association between candidate files and candidate word segmentation results is determined based on the association between candidate files and candidate file identifiers, and the association between candidate file identifiers and candidate word segmentation results.

[0045] For example, suppose the candidate word segmentation results obtained by segmenting the candidate file identifier of file 1 are "mp3", "poetry", "story", "Xiangsi", and "Wang Wei"; the candidate word segmentation results obtained by segmenting the candidate file identifier of file 2 are "Tang poetry", "Li Bai", and "mp3"; the candidate word segmentation results obtained by segmenting the candidate file identifier of file 3 are "Song lyrics", "Qingyu'an·Yuanxi", "Xin Qiji", and "mp3"; and the candidate word segmentation results obtained by segmenting the candidate file identifier of file 4 are "poetry", "birdsong in the valley", "Wang Wei", and "mp3".

[0046] File 1 is associated with "mp3", "poetry", "story", "Acacia", and "Wang Wei"; File 2 is associated with "Tang poetry", "Li Bai", and "mp3"; File 3 is associated with "Song lyrics", "Green Jade Table - Lantern Festival", "Xin Qiji", and "mp3"; File 4 is associated with "poetry", "Birdsong Stream", "Wang Wei", and "mp3".

[0047] An inverted index is used to determine the candidate files associated with the candidate word segmentation results by identifying the relationships between candidate files and candidate word segmentation results.

[0048] For example, continuing with the above example, let's assume the relationship between candidate files and candidate word segmentation results is as follows:

[0049] "File 1" - "mp3", "Poetry", "Story", "Acacia" and "Wang Wei";

[0050] "File 2" - "Tang Poetry", "Li Bai", and "mp3";

[0051] "File 3" - "Song Ci", "Qing Yu An: Yuan Xi", "Xin Qiji" and "mp3";

[0052] "File 4" - "Poetry", "Birds Singing in the Gully", "Wang Wei", and "mp3".

[0053] Based on the above association relationships, perform an inverted index to determine the candidate files associated with each candidate word segmentation result:

[0054] "mp3" - [File 1, File 2, File 3, File 4]; "Poetry" - [File 1, File 4]; "Story" - [File 1]; "Thoughts of Love" - [File 1]; "Wang Wei" - [File 1, File 4]; "Tang Poetry" - [File 2]; "Li Bai" - [File 2]; "Song Ci" - [File 3]; "The Lantern Festival on the 15th Day of the First Month" - [File 3]; "Xin Qiji" - [File 3]; "Birds Singing in the Gully" - [File 4].

[0055] Based on the candidate files associated with each candidate word segmentation result, determine the number of candidate files associated with each candidate word segmentation result.

[0056] Exemplarily, continue to use the above example for explanation. The number of candidate files associated with "mp3" is "4"; the number of candidate files associated with "Poetry" is "2"; the number of candidate files associated with "Thoughts of Love" is "1"; the number of candidate files associated with "Wang Wei" is "2"; the number of candidate files associated with "Tang Poetry" is "1"; the number of candidate files associated with "Li Bai" is "1"; the number of candidate files associated with "Song Ci" is "1"; the number of candidate files associated with "The Lantern Festival on the 15th Day of the First Month" is "1"; the number of candidate files associated with "Xin Qiji" is "1"; the number of candidate files associated with "Birds Singing in the Gully" is "1".

[0057] By determining the association relationship between the candidate word segmentation results and the candidate files, and based on the association relationship, determining the number of candidate files associated with the candidate word segmentation results, it lays a data foundation for subsequently determining the file search terms from the candidate word segmentation results based on the number of candidate files.

[0058] S103. Determine the file search terms from the candidate word segmentation results according to the number of candidate files, and display the file search terms for the user to search for files in the target folder according to the file search terms.

[0059] In one implementation, compare the number of candidate files associated with each candidate word segmentation result with a preset quantity threshold, and use the candidate word segmentation results whose associated number of candidate files meets the quantity threshold as the file search terms.

[0060] The file search terms are visualized in the target folder interface, allowing users to select and specify target search terms based on their search needs, such as through touch, gesture, or voice selection.

[0061] In response to the user's selection, the system determines the target search term from the file search terms. Based on the previously established association between candidate word segmentation results and candidate files, it selects candidate files related to the target search term as target files and then visualizes them in the target folder's interface, such as displaying the file identifier or file icon. Users can then process the target files according to their needs, such as opening, deleting, or modifying the file identifier.

[0062] For example, suppose the target search term is "poetry", and in the pre-established association between candidate word segmentation results and candidate files, "poetry" is associated with "file1" and "file4". Therefore, "file1" and "file4" are used as target files and displayed in the interface of the target folder.

[0063] This disclosure identifies candidate file identifiers for candidate files in a target folder, segments these identifiers to determine candidate segmentation results, establishes the association between the segmentation results and candidate files, determines the number of candidate files associated with each segmentation result, and then identifies file search terms from the segmentation results based on this association. These search terms are then displayed, allowing users to directly select them for file searches within the target folder, eliminating the need for manual searching and reducing search time. Furthermore, since the search terms are derived from candidate file identifiers, displaying them helps users quickly understand the file distribution within the target folder, preventing low recall rates due to users blindly entering search terms and ensuring high recall. Moreover, this solution does not require the prior accumulation of large amounts of structured data, resulting in lower implementation costs and a better user experience.

[0064] Figure 1B These are schematic diagrams of some file search interfaces disclosed in embodiments of this disclosure, such as... Figure 1B As shown, 10 represents the interface of the target folder, while 11 represents the file search terms displayed to the user in the interface of the target folder.

[0065] Figure 1C These are schematic diagrams of other file search interfaces disclosed in embodiments of this disclosure, such as... Figure 1C As shown, 10 represents the interface of the target folder, 11 represents the file search term displayed to the user in the interface of the target folder, 12 represents the target search term "Tang poetry" selected by the user, and 13 represents the target file displayed to the user in the interface of the target folder based on the target search term "Tang poetry". The file identifier of the target file contains the characters "Tang poetry".

[0066] Figure 2 This is a flowchart of another document search method disclosed in the embodiments of this disclosure, which is further optimized and extended based on the above technical solutions, and can be combined with the above optional implementation methods.

[0067] like Figure 2 As shown, the file search method disclosed in this embodiment may include:

[0068] S201. Determine the candidate file identifiers of the candidate files in the target folder, and perform word segmentation on the candidate file identifiers to determine the candidate word segmentation results.

[0069] S202. Determine the association between candidate word segmentation results and candidate files, and determine the number of candidate files associated with candidate word segmentation results based on the association.

[0070] S203. The candidate word segmentation results with a number of candidate files greater than the first threshold and less than the second threshold are used as file search terms.

[0071] The first and second quantity thresholds can be set and adjusted according to actual business needs.

[0072] Optionally, a first quantity threshold can be set to "2", and a second quantity threshold can be set to 80% of the total number of files in the target folder. For example, assuming the total number of files is 10, the second quantity threshold can be set to "8", meaning that if the number of candidate files associated with any candidate word segmentation result is greater than "2" and less than "8", then that candidate word segmentation result will be used as a file search term.

[0073] By using candidate word segmentation results with a number of candidate files greater than a first threshold and less than a second threshold as file search terms, the frequency of file search terms appearing in candidate file identifiers is avoided from being too high or too low, thus losing their search value. This ensures the rationality and accuracy of file search terms and improves user satisfaction with file search results.

[0074] S204. The candidate files associated with the file search term are used as auxiliary files, and the first display priority of the file search term is determined according to the number of auxiliary files.

[0075] In one embodiment, according to the pre-established association relationship between the candidate word segmentation results and the candidate files, the candidate files associated with the file search terms are used as auxiliary files. The number of auxiliary files corresponding to each file search term is counted, and the first display priority of each file search term is determined according to the descending order of the number of auxiliary files.

[0076] Exemplarily, assume that the number of auxiliary files corresponding to file search term 1 is "3", the number of auxiliary files corresponding to file search term 2 is "4", and the number of auxiliary files corresponding to file search term 3 is "5". Then the sorting order of the first display priority is file search term 3 - file search term 2 - file search term 1, that is, "file search term 3" is displayed first, "file search term 2" is displayed next, and "file search term 1" is displayed last. In other words, the file search terms with more corresponding auxiliary files will be displayed first.

[0077] S205. Display the file search terms in sequence according to the first display priority, so that the user can search for files in the target folder according to the file search terms.

[0078] By using the candidate files associated with the file search terms as auxiliary files, determining the first display priority of the file search terms according to the number of auxiliary files of the auxiliary files, and displaying the file search terms in sequence according to the first display priority, it is ensured that the file search terms with more associated auxiliary files can be displayed first, thereby indirectly ensuring the recall rate of file search.

[0079] Optionally, after performing word segmentation on the candidate file identifiers to obtain the candidate word segmentation results, it further includes:

[0080] Taking the candidate word segmentation results with the word type of non-named entity as the to-be-optimized word segmentation results; removing the to-be-optimized word segmentation results with the word content of stop words.

[0081] Among them, the stop words include but are not limited to Chinese stop words.

[0082] Exemplarily, assume that the candidate word segmentation results include "007", ".", "poetry", "story", "丨", "Thoughts of Love", "[", "Tang", "·", "Wang Wei", "]", and "mp3". Among them, the candidate word segmentation results with the word type of named entity are: "poetry", "story", "Thoughts of Love", and "Wang Wei", and the candidate word segmentation results with the word type of non-named entity are: "007", ".", "丨", "[", "Tang", "·", "]", and "mp3". Then "007", ".", "丨", "[", "Tang", "·", "]", and "mp3" are taken as the to-be-optimized word segmentation results.

[0083] Among the candidate word segmentation results to be optimized, ".", "|", "[", "·", and "]" are all candidate word segmentations with stop words as their word contents. Therefore, ".", "|", "[", "·", and "]" are removed.

[0084] By taking the candidate word segmentation results with the word type of non-named entity as the candidate word segmentation results to be optimized, and removing the candidate word segmentation results with stop words as their word contents. Since stop words do not have actual word meanings, by removing the candidate word segmentation results with stop words as their word contents, the quality of the file search terms finally obtained can be ensured; moreover, since the importance of the candidate word segmentation results with the word type of named entity is higher than that of the candidate word segmentation results with the word type of non-named entity, only taking the candidate word segmentation results with the word type of non-named entity as the candidate word segmentation results to be optimized, rather than taking the candidate word segmentation results with the word type of named entity as the candidate word segmentation results to be optimized, can avoid the candidate word segmentation results with the word type of named entity from being accidentally removed, and can also ensure the quality of the file search terms finally obtained.

[0085] Optionally, after performing word segmentation on the candidate file identifiers to determine the candidate word segmentation results, it further includes:

[0086] Removing the candidate word segmentation results with the character count less than the third quantity threshold and / or the word content being digital text.

[0087] Among them, the third quantity threshold can be set and adjusted according to actual business requirements.

[0088] In one implementation, determine the character count and word content of each candidate word segmentation result, and remove the candidate word segmentation results with the character count less than the third quantity threshold or the word content being digital text. Among them, the third quantity threshold is optionally 2.

[0089] Since the candidate word segmentation results with too small character count or the word content being digital text have low search value as file search terms, by removing the candidate word segmentation results with the character count less than the third quantity threshold and / or the word content being digital text, the quality of the file search terms finally obtained can be ensured.

[0090] Figure 3 It is a flowchart of another file search method disclosed in the embodiments of the present disclosure, which is further optimized and extended based on the above technical solutions and can be combined with each of the above optional implementation manners.

[0091] As Figure 3 shown, the file search method disclosed in this embodiment may include:

[0092] S301. Determine the candidate file identifiers of the candidate files in the target folder, and perform word segmentation on the candidate file identifiers to determine the candidate word segmentation results.

[0093] S302. Determine the association between candidate word segmentation results and candidate files, and determine the number of candidate files associated with candidate word segmentation results based on the association.

[0094] S303. The candidate word segmentation results with a number of candidate files greater than the first threshold and less than the second threshold are used as file search terms.

[0095] S304. Determine the number of times a file search term is selected by the user in historical time periods, and determine the second display priority of the file search term based on the number of times it is selected by the user.

[0096] The number of times a user selects a user can be the number of times the current user selects a user, the number of times the current user selects a user, or the number of times the current user selects a user and other users together. This embodiment does not limit the specific concept of "user".

[0097] In one implementation, the number of times each file search term was selected by the user in historical time periods is determined, and the second display priority of each file search term is determined according to the reverse order of the number of times it was selected by the user.

[0098] For example, suppose that in a historical moment, file search term 1 was selected by the user 200 times, file search term 2 was selected 150 times, and file search term 3 was selected 300 times. Then the second display priority sorting order is file search term 3 - file search term 1 - file search term 2. That is, file search term 3 is displayed first, then file search term 1, and finally file search term 2. In other words, the file search term that is selected by the user more times will be displayed first.

[0099] S305. Display file search terms sequentially according to the second display priority, so that users can search for files in the target folder based on the file search terms.

[0100] By determining the number of times a file search term is selected by a user in a historical time period, and then determining the second display priority of the file search term based on the number of times it is selected by the user, the file search terms are displayed in order according to the second display priority. This ensures that file search terms with high search popularity are displayed first. Since file search terms with high search popularity are also very likely to be selected by the current user, the time required for file search can be further shortened.

[0101] Figure 4AThis is a flowchart of another document search method disclosed in the embodiments of this disclosure, which is further optimized and extended based on the above technical solutions, and can be combined with the above optional implementation methods.

[0102] like Figure 4A As shown, the file search method disclosed in this embodiment may include:

[0103] S401. Determine the candidate file identifiers of the candidate files in the target folder, and perform word segmentation on the candidate file identifiers to determine the candidate word segmentation results.

[0104] S402. Determine the association between candidate word segmentation results and candidate files, and determine the number of candidate files associated with candidate word segmentation results based on the association.

[0105] S403. The candidate word segmentation results with a number of candidate files greater than the first threshold and less than the second threshold are used as file search terms.

[0106] S404. Determine the term type of the file search term and determine the third display priority of the file search term based on the term type.

[0107] Among them, the third display priority of the first type of file search terms is higher than the third display priority of the second type of file search terms. The word type of the first type of file search terms is named entity, while the word type of the second type of file search terms is unnamed entity.

[0108] For example, suppose the file search terms include "poetry", "story", "007" and "mp3". Since "poetry" and "story" are named entities, while "007" and "mp3" are unnamed entities, "poetry" and "story" are set to the third display priority, which is higher than "007" and "mp3". That is, the two file search terms "poetry" and "story" are displayed first.

[0109] S405. Display file search terms sequentially according to the third display priority, allowing users to search for files in the target folder based on the file search terms.

[0110] Since file search terms with the term type "named entity" are more important than those with the term type "non-named entity", prioritizing the display of file search terms with the term type "named entity" allows users to quickly focus on more important file search terms, thus ensuring the quality of file search.

[0111] Optionally, this embodiment also provides a method for displaying file search terms, which is further optimized and expanded based on the above technical solution, and can be combined with the above optional implementation methods, including:

[0112] The system performs a weighted sum based on the number of auxiliary files, the number of times they are selected by the user, and the type of the term. The fourth display priority of the file search terms is determined based on the weighted sum. The file search terms are then displayed in order according to the fourth display priority.

[0113] In one implementation, the number of auxiliary files corresponding to each file search term, the number of times they are selected by the user, and the term type are determined. Furthermore, a first weight corresponding to the "number of auxiliary files," a second weight corresponding to the "number of times they are selected by the user," and a third weight corresponding to the "term type" are further determined. Based on the first, second, and third weights, the number of auxiliary files, the number of times they are selected by the user, and the term type are weighted and summed to determine the weighted score corresponding to each file search term. During the weighted summation process, the value of "term type" can be set to 1 for "named entities" and "0" for "non-named entities," etc.

[0114] Based on the reverse order of the weighted scores, the fourth display priority for each document's search terms is determined.

[0115] For example, assuming the weighted score for file search term 1 is "50", the number of times file search term 2 is selected by the user is "40", and the number of times file search term 3 is selected by the user is "30", then the order of the fourth display priority is file search term 1 - file search term 2 - file search term 3. That is, "file search term 1" is displayed first, then "file search term 2", and finally "file search term 3". In other words, the file search term with the higher weighted score will be displayed first.

[0116] By weighting and summing the number of auxiliary files, the number of times they are selected by users, and the type of the words, and determining the fourth display priority of the file search terms based on the weighted summation result, the file search terms are displayed in order according to the fourth display priority. This achieves the effect of determining the display priority of file search terms from multiple data dimensions, which improves the accuracy and reliability of determining the display priority of file search terms and avoids the problem of low accuracy and reliability when relying on a single data dimension to determine the display priority of file search terms.

[0117] Figure 4B This is a flowchart illustrating the display of some search terms according to embodiments of this disclosure, such as... Figure 4B As shown, it includes:

[0118] S410, Access the user file system.

[0119] S411. Determine whether the number of files is greater than the file number threshold.

[0120] Specifically, determine the number of candidate files stored in any candidate folder in the user file system, and determine whether the number of files is greater than the file number threshold. If not, execute S412; if so, execute S413.

[0121] S412, Do not enable the search term display function.

[0122] S413. Determine the candidate file identifier.

[0123] S414. Determine the candidate word segmentation results.

[0124] Specifically, the candidate document identifier is segmented to determine the candidate segmentation results, and candidate segmentation results with word type non-named entity and candidate segmentation results with word type named entity are identified.

[0125] S415. For candidate word segmentation results whose word type is non-named entity, stop words are removed.

[0126] S416. Remove candidate word segmentation results from all remaining candidate word segmentation results that have fewer than the third quantity threshold and / or whose word content is numeric text.

[0127] S417, Inverted Index.

[0128] Specifically, an inverted index is used to determine the candidate files associated with the candidate word segmentation results based on the relationship between the candidate files and the remaining candidate word segmentation results.

[0129] S418. The candidate word segmentation results with a number of candidate files greater than the first threshold and less than the second threshold are used as file search terms.

[0130] S419. Display the file search terms.

[0131] Specifically, the system displays file search terms, allowing users to search for files in the target folder based on those terms.

[0132] The specific execution methods of the above steps can be found in the descriptions of the above method embodiments of this disclosure, and will not be repeated here.

[0133] Figure 5 This is a schematic diagram of the structure of a file search device disclosed in some embodiments of this disclosure, which can be applied to assist users in searching for files in a target folder. The device in this embodiment can be implemented in software and / or hardware and can be integrated into any electronic device with computing capabilities.

[0134] like Figure 5As shown, the file search device 50 disclosed in this embodiment may include a word segmentation module 51, a file quantity determination module 52, and a file search term display module 53, wherein:

[0135] The word segmentation module 51 is used to determine the candidate file identifier of the candidate files in the target folder, and to segment the candidate file identifier to determine the candidate word segmentation result;

[0136] The file quantity determination module 52 is used to determine the association between the candidate word segmentation results and the candidate files, and to determine the number of candidate files associated with the candidate word segmentation results based on the association.

[0137] The file search term display module 53 is used to determine file search terms from the candidate word segmentation results based on the number of candidate files, and display the file search terms so that users can search for files in the target folder based on the file search terms.

[0138] Optionally, the file search term display module 53 is specifically used for:

[0139] Candidate files associated with the file search term are used as auxiliary files, and the first display priority of the file search term is determined based on the number of auxiliary files.

[0140] The file search terms are displayed sequentially according to the first display priority.

[0141] Optionally, the file search term display module 53 is further used for:

[0142] Determine the number of times the file search term was selected by the user in a historical time period, and determine a second display priority for the file search term based on the number of times it was selected by the user;

[0143] The file search terms are displayed sequentially according to the second display priority.

[0144] Optionally, the file search term display module 53 is further used for:

[0145] Determine the word type of the file search term, and determine the third display priority of the file search term based on the word type;

[0146] The file search terms are displayed sequentially according to the third display priority.

[0147] Among them, the third display priority of the first type of file search terms is higher than the third display priority of the second type of file search terms. The word type of the first type of file search terms is named entity, while the word type of the second type of file search terms is non-named entity.

[0148] Optionally, the file search term display module 53 is further used for:

[0149] The number of auxiliary files, the number of times the user selects them, and the word type are weighted and summed, and the fourth display priority of the file search terms is determined based on the weighted summation result.

[0150] The file search terms are displayed sequentially according to the fourth display priority.

[0151] Optionally, the file search term display module 53 is further used for:

[0152] Candidate word segmentation results whose number of candidate files is greater than a first threshold and less than a second threshold are used as the file search terms.

[0153] Optionally, the device further includes a first data cleaning module, specifically used for:

[0154] Candidate word segmentation results with word type non-named entity are used as word segmentation results to be optimized;

[0155] The word segmentation results to be optimized are removed if the word content is a stop word.

[0156] Optionally, the device further includes a second data cleaning module, specifically used for:

[0157] Candidate word segmentation results with a character count less than the third threshold and / or whose word content is digital text are eliminated.

[0158] The file search device 50 disclosed in this embodiment can execute the file search method disclosed in this embodiment, and has the corresponding functional modules and beneficial effects for executing the method. Content not described in detail in this embodiment can be referred to the description in the method embodiments of this disclosure.

[0159] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0160] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0161] Figure 6A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0162] like Figure 6 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0163] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0164] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the file search method. For example, in some embodiments, the file search method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the file search method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the file search method by any other suitable means (e.g., by means of firmware).

[0165] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0166] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0167] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0168] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0169] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0170] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0171] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0172] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for searching files, comprising: Determine the candidate file identifiers of candidate files in the target folder, and perform word segmentation on the candidate file identifiers to determine the candidate word segmentation results; wherein, the candidate file identifier represents the unique identity identifier text of the candidate file; Determine the association between the candidate word segmentation results and the candidate files, and determine the number of candidate files associated with the candidate word segmentation results based on the association. Based on the number of candidate files, file search terms are determined from the candidate word segmentation results, and the file search terms are displayed so that users can search for files in the target folder based on the file search terms. The step of determining the association between the candidate word segmentation results and the candidate files includes: The association between the candidate file and the candidate word segmentation result is determined based on the association between the candidate file and the candidate file identifier, and the association between the candidate file identifier and the candidate word segmentation result. The step of determining the file search term from the candidate word segmentation results based on the number of candidate files includes: Candidate word segmentation results whose number of candidate files is greater than a first threshold and less than a second threshold are used as the file search terms; After determining the candidate word segmentation results by segmenting the candidate file identifier, the method further includes: Candidate word segmentation results with word type non-named entity are used as word segmentation results to be optimized; The word segmentation results to be optimized are removed if the word content is a stop word.

2. The method according to claim 1, wherein, The step of displaying the file search terms includes: Candidate files associated with the file search term are used as auxiliary files, and the first display priority of the file search term is determined based on the number of auxiliary files. The file search terms are displayed sequentially according to the first display priority.

3. The method according to claim 2, wherein, The step of displaying the file search terms includes: Determine the number of times the file search term was selected by the user in a historical time period, and determine a second display priority for the file search term based on the number of times it was selected by the user; The file search terms are displayed sequentially according to the second display priority.

4. The method according to claim 3, wherein, The step of displaying the file search terms includes: Determine the word type of the file search term, and determine the third display priority of the file search term based on the word type; The file search terms are displayed sequentially according to the third display priority. Among them, the third display priority of the first type of file search terms is higher than the third display priority of the second type of file search terms. The word type of the first type of file search terms is named entity, while the word type of the second type of file search terms is non-named entity.

5. The method according to claim 4, wherein, The step of displaying the file search terms includes: The number of auxiliary files, the number of times the user selects them, and the word type are weighted and summed, and the fourth display priority of the file search terms is determined based on the weighted summation result. The file search terms are displayed sequentially according to the fourth display priority.

6. The method according to claim 1, after determining the candidate word segmentation result by segmenting the candidate document identifier, further comprising: Candidate word segmentation results with a character count less than the third threshold and / or whose word content is digital text are eliminated.

7. A file search device, comprising: The word segmentation module is used to determine the candidate file identifier of the candidate files in the target folder, and to segment the candidate file identifier to determine the candidate word segmentation result; wherein, the candidate file identifier represents the unique identity identification text of the candidate file; The file quantity determination module is used to determine the association between the candidate word segmentation results and the candidate files, and to determine the number of candidate files associated with the candidate word segmentation results based on the association. The file search term display module is used to determine file search terms from the candidate word segmentation results based on the number of candidate files, and display the file search terms so that users can search for files in the target folder based on the file search terms. The file quantity determination module is specifically used for: The association between the candidate file and the candidate word segmentation result is determined based on the association between the candidate file and the candidate file identifier, and the association between the candidate file identifier and the candidate word segmentation result. Specifically, the file search term display module is further used for: Candidate word segmentation results whose number of candidate files is greater than a first threshold and less than a second threshold are used as the file search terms; The device further includes a first data cleaning module, specifically used for: Candidate word segmentation results with word type non-named entity are used as word segmentation results to be optimized; The word segmentation results to be optimized are removed if the word content is a stop word.

8. The apparatus according to claim 7, wherein, The file search term display module is specifically used for: Candidate files associated with the file search term are used as auxiliary files, and the first display priority of the file search term is determined based on the number of auxiliary files. The file search terms are displayed sequentially according to the first display priority.

9. The apparatus according to claim 8, wherein, The file search term display module is further used for: Determine the number of times the file search term was selected by the user in a historical time period, and determine a second display priority for the file search term based on the number of times it was selected by the user; The file search terms are displayed sequentially according to the second display priority.

10. The apparatus according to claim 9, wherein, The file search term display module is further used for: Determine the word type of the file search term, and determine the third display priority of the file search term based on the word type; The file search terms are displayed sequentially according to the third display priority. Among them, the third display priority of the first type of file search terms is higher than the third display priority of the second type of file search terms. The word type of the first type of file search terms is named entity, while the word type of the second type of file search terms is non-named entity.

11. The apparatus according to claim 10, wherein, The file search term display module is further used for: The number of auxiliary files, the number of times the user selects them, and the word type are weighted and summed, and the fourth display priority of the file search terms is determined based on the weighted summation result. The file search terms are displayed sequentially according to the fourth display priority.

12. The apparatus according to claim 7, further comprising a second data cleaning module, specifically used for: Candidate word segmentation results with a character count less than the third threshold and / or whose word content is digital text are eliminated.

13. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

15. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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