Data storage service system based on artificial intelligence

Abstract

The invention discloses a data storage service method based on artificial intelligence, and the method comprises the steps: obtaining a data storage task, extracting pre-stored file information in the data storage task, obtaining a feature value of a file accessed by a user, obtaining a file type storage rule in cloud storage, and obtaining a path of a file stored by the user last time; analyzing the pre-stored file information and a file type storage path rule in the cloud storage, analyzing the characteristics of the pre-stored file, providing a recommended storage path, monitoring the storage behavior characteristics of a user, predicting the storage demand of the user, dynamically adjusting the characteristics of the cloud storage, and storing the pre-stored file on the basis of the storage behavior characteristics of the user. According to the method, indexes of different dimensions are constructed for storage files in the cloud storage for visual display, links are added to visual charts, file distribution is further refined, the problem of single path recommendation provided when a user stores the files is solved, and the use experience of the user is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage services, and particularly to an artificial intelligence-based data storage service system. Background Art

[0002] In the digital economy era, the importance of data storage has become increasingly prominent. It is not only a key support for enterprise decision-making and personal files, but also the cornerstone of innovation and development. However, the rapid increase in the amount of data has brought challenges to storage management. The accumulation of massive amounts of data often leads to information redundancy and chaos, which not only increases storage costs but also may reduce the speed and accuracy of data retrieval. When facing the existing system during file storage operations, it often adopts the default storage path or the path used during the previous storage, lacking a reasonable analysis of the files to be stored and recommending a reasonable storage path. Instead, it requires the user to click on the storage path themselves. Especially when users lack effective storage planning, over time, it will cause chaos in cloud storage file management, which to a certain extent reduces the intelligence of the system and affects the user experience. In addition, during the intelligent recommendation analysis process, it is also possible to reasonably allocate the storage resources of files and improve the user access speed. Therefore, it is necessary to design and provide an artificial intelligence-based data storage service system with an intelligent storage path recommendation function and an optimized user experience. Summary of the Invention

[0003] The purpose of the present invention is to provide an artificial intelligence-based data storage service system to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solution: An artificial intelligence-based data storage service system that executes a storage service method based on cloud computing. The running steps of the method include:

[0005] Step S1: Obtain a data storage task, extract the pre-stored file information in the data storage task, obtain the characteristic values of the files accessed by the user, obtain the storage rules of file types in cloud storage, and obtain the path of the file stored by the user last time;

[0006] Step S2: Analyze the pre-stored file information and the storage path rules of file types in cloud storage, analyze the characteristics of the pre-stored files, and provide a recommended storage path;

[0007] Step S3: Monitor the storage behavior characteristics of the user, predict the storage needs of the user, and dynamically adjust the cloud storage characteristics;

[0008] Step S4: Based on the storage behavior characteristics of the user, visually display the stored files in the cloud storage.

[0009] According to the above technical solution, step S11: The stored file information includes: the extension name of the file, the size of the file, the name of the file, and the IP of the storage device. The storage law of the file type includes: storage path, directory structure, and file type distribution;

[0010] Step S12: Obtain the array index added before the storage of the stored files on the disk, sort the frequencies of the index words obtained in each folder from high to low in descending order, and add a hot index word array to each folder;

[0011] Step S13: Based on the file access module, identify the unadded array index, clean the unnecessary files on the disk. The unnecessary files include: empty files, corrupted files, and temporary files.

[0012] According to the above technical solution, step S2 further includes the following steps:

[0013] Step S21: Based on the file access module, count the index words of each layer of storage paths on the disk, and sort the frequencies of the statistical results from high to low in descending order to obtain the storage file characteristics of each layer of storage paths;

[0014] Step S22: Based on the analysis of the pre-stored file information, add a feature index to the pre-stored file. Identify and add an index to the file through the extension name of the pre-stored file, identify the storage capacity of the pre-stored file, analyze the name of the pre-stored file and add an index to the pre-stored file, identify and add an index to the IP address of the device used by the pre-stored file, and add a related one-dimensional array index to the pre-stored file;

[0015] Step S23: Based on the pre-stored file name and the one-dimensional array index, filter out the files with the same index as the pre-stored file index, match the names of the filtered files with the pre-stored file name. When the matching result is consistent, check whether the hash values of the matched files and the pre-stored file are the same. When the matching is consistent, pop up a prompt box to notify the user that the pre-matched file already exists on the disk, and inform the user of the path of the matched file, and ask the user whether to continue saving, and operate according to the user's instruction. When the matching is inconsistent, do not execute the step of asking for the user's opinion;

[0016] Step S24: Based on the comparison between the analysis result of the stored file index and the file index in the path of the user's last stored file, when the similarity of the comparison result is greater than the set threshold, set the path of the last stored file as the optimal storage path. Otherwise, match by obtaining the hot index words of each folder. When one matching folder is selected, recommend this folder as the optimal storage path. When multiple matching folders are selected, calculate the optimal storage path of these matching folders through the index words. The formula for the optimal storage path is as follows:

[0017] R i = w 1 * t + w 2 * f + w 3 * size + w 4 * ip

[0018] p * = argmax i R i

[0019] In the formula, R i represents the i-th folder among the multiple matching folders, t represents the recent time of the user's opening time, w 1 represents the weight coefficient of t, f represents the opening frequency of the user, w 2 represents the weight coefficient of the user's opening frequency, size represents the storage size of the pre-stored file, w 3 represents the weight coefficient of the storage size of the pre-stored file, ip represents the IP address where the user stores the pre-stored file, w 4 represents the weight coefficient of the IP address, p * represents the optimal storage path, argmax i represents the maximum value of R among all the multiple matching folders.

[0020] According to the above technical solution, step S22 further includes the following steps:

[0021] Step S221: Add an index by identifying the extension name of the pre-stored file. When the extension name is.docx\.pdf\.txt, add a document index. When the extension name is.xlsx\.csv, add a table index. In addition, for files including.exe\.dmg, etc., add a software installation package index, and so on;

[0022] Step S222: Compare by identifying the storage capacity of the pre-stored file with two set thresholds. If the storage capacity of the pre-stored file is less than the two thresholds, add a small file index. If the storage capacity of the pre-stored file is between the two thresholds, add a medium file index. If the storage capacity of the pre-stored file is greater than the two thresholds, add a large file index;

[0023] Step S223: By identifying keywords and related words in the pre-stored file names, and according to the file features in the cloud storage, assign a broad category index to the pre-stored files and add an index to the stored files;

[0024] Step S224: The one-dimensional array index sorting of the pre-stored files performs type matching according to the index sorting result of Step S12. The index words ranked higher will be located closer to the front in the array index.

[0025] According to the above technical solution, Step S3 further includes the following steps:

[0026] Step S31: According to the user's access situation, adjust the time index and access frequency index of the files opened by the user stored in the cloud storage in real time;

[0027] Step S32: Aggregate and analyze the indexes, analyze the access patterns of different files in the cloud storage by users, predict the future storage access and extraction requirements of users, and dynamically adjust the cloud storage resources. The calculation formula for predicting the storage access and extraction requirements of users is:

[0028]

[0029] In the formula, P j represents the probability that the jth file in the cloud storage needs to be accessed and extracted by the user. Among them, P j ∈(0, 1), α 0 、α 1 、α 2 、α 3 、α 4 represent the weight coefficients of the corresponding indexes;

[0030] Step S33: Compare each P j with a threshold set by the system. When the access frequency of a certain file in the cloud storage is greater than the threshold, it is classified as a high-frequency access file. When it is lower than the threshold and greater than 20% of the threshold, it is classified as a medium-frequency access file. When it is lower than 20% of the threshold, it is classified as a low-frequency access file. Among them, the high-frequency access files are stored in a distributed manner, the medium-frequency access files are stored in the standard performance storage layer, and the low-frequency access files are stored in the low-cost storage layer. Among them, for the files that need to perform big data analysis, they are all stored in the data lake.

[0031] According to the above technical solution, Step S4 further includes the following steps:

[0032] Step S41: Based on the one-dimensional array indexes of each file in the disk and the characteristics of the user's access to the disk information, visually display the files in different dimensions based on different indexes. The index type can be selected through the client interface button to display the corresponding visual chart. Frequently accessed files are displayed in a brighter color, moderately accessed files are displayed in a normal color, and infrequently accessed files are displayed in a darker color;

[0033] Step S42: Accumulate the user's button click behaviors and record the corresponding file types accessed by the user. Analyze the user's preferences and habits in visual index selection. When obtaining the user's access operation instruction, identify the type of file the current user is looking for and automatically recommend a visual chart that matches the user's past habits;

[0034] Step S43: In the visual chart display, each module is equipped with a link index. The user can click on one module to understand the file information in it in more detail.

[0035] According to the above technical solution, the system includes a user instruction receiving module, a stored file index module, an optimal path recommendation module, and a dynamic monitoring module:

[0036] The user instruction receiving module is used to receive the user's operation instructions on the client;

[0037] The stored file index module is used to add indexes to the stored files in the disk and the pre-stored files;

[0038] The optimal path recommendation module is used to match the indexes of the pre-stored files and the files in the disk, and recommend the optimal storage path of the current pre-stored files for the user;

[0039] The dynamic monitoring module is used to monitor the user's storage behavior. The system analyzes the user's storage behavior, reasonably stratifies the disk, and recommends a suitable visual table.

[0040] According to the above technical solution, the stored file index module includes an extension name index module, a capacity size index module, a file name index module, and an IP address index module:

[0041] The extension name index module is used to add indexes to files by the extension names of the stored files;

[0042] The capacity size index module is used to add indexes of three types: large, medium, and small to the storage capacities of files;

[0043] The file name index module is used to extract keywords from the names of the stored files, assign associated keywords, and add a broad category index;

[0044] The IP address indexing module is used to obtain the IP address of the user when storing files, so as to analyze the storage characteristics of the user under different IP addresses later.

[0045] According to the above technical solution, the system includes a duplicate reduction matching module and an optimal path analysis module:

[0046] The duplicate reduction matching module is used to match the pre-stored file with the files in the disk to determine whether the pre-stored file already exists in the disk;

[0047] The optimal path analysis module is used to match the index of the files in the disk with the index of the data storage files, and calculate the optimal storage path of the pre-stored file.

[0048] According to the above technical solution, the system includes a user storage feature module, a dynamic storage adjustment module, and a visualization display module:

[0049] The user storage feature module is used to record the operation behavior of the user on the client side and analyze the storage habits of the user;

[0050] The dynamic storage adjustment module is used to reasonably divide the files in the disk;

[0051] The visualization display module is used to divide the files in the disk through different indexes and display them in the form of charts.

[0052] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: Through the input of the user's instruction to store files, according to the information of the files to be stored, an index array is added to the pre-stored files to mark the pre-stored files, the indexes of the stored files in each file in the disk are retrieved, first, the broad range nouns of the folders at each folder node are matched, the folders with successful matches are further matched with each file in the folder for a more refined match with the pre-stored file, the optimal matching path is selected and recommended to the user, the storage habits of the user are analyzed, the step of the user selecting a path during storage is omitted, a better disk storage management is provided for the files in the user's disk. In addition, the system dynamically adjusts the distribution of the files in the disk according to the user's storage behavior, and performs visual analysis of the files in different dimensions according to the indexes of the files in the disk, thereby improving the function of providing intelligent storage path recommendations and optimizing the user experience. Description of the Drawings

[0053] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0054] Figure 1 It is a schematic diagram of the module composition of a data storage service system based on artificial intelligence provided in the second embodiment of the present invention. Specific implementation manners

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] Embodiment 1: This embodiment can be applied to the scenario of data storage services. This method can be executed by a data storage service system based on artificial intelligence provided in this embodiment. The method specifically includes the following steps:

[0057] Step S1: Obtain a data storage task, extract the pre-stored file information in the data storage task, obtain the feature values of the files accessed by the user, obtain the storage rules of file types in cloud storage, and obtain the path of the file stored by the user last time;

[0058] Step S2: Analyze the pre-stored file information and the storage path rules of file types in cloud storage, analyze the features of the pre-stored files, and provide a recommended storage path;

[0059] Step S3: Monitor the storage behavior characteristics of the user, predict the storage needs of the user, and dynamically adjust the cloud storage characteristics;

[0060] Step S4: Based on the storage behavior characteristics of the user, visually display the stored files in the cloud storage.

[0061] In the embodiment of the present invention, step S1 further includes the following steps:

[0062] Step S11: The stored file information includes: file extension, file size, file name, storage device ip. The storage rules of file types include: storage path, directory structure, file type distribution;

[0063] Step S12: Obtain the array index added before the stored files on the disk, sort the frequencies of the index words obtained in each folder from high to low in descending order, and add a hot index word array to each folder;

[0064] Step S13: Based on the file access module, identify the unadded array index, clean the unnecessary files on the disk. The unnecessary files include: empty files, damaged files, and temporary files.

[0065] In an embodiment of the present invention, step S2 further includes the following steps:

[0066] Step S21: Based on the file access module, count the index words of each layer of storage paths in the disk, and sort the frequencies of the statistical results in descending order from high to low to obtain the storage file features of each layer of storage paths;

[0067] Step S22: Analyze based on the pre-stored file information, add a feature index to the pre-stored file, add an index by identifying the file through the pre-stored file extension, identify the storage capacity of the pre-stored file, analyze the pre-stored file name and add an index to the pre-stored file, identify the device IP address used by the pre-stored file and add an index, and add a related one-dimensional array index to the pre-stored file;

[0068] Step S23: Based on the pre-stored file name and the one-dimensional array index, filter out the files with the same index as the pre-prepared file index, match the names of the filtered files with the pre-stored file name. When the matching result is consistent, check whether the hash values of the matched file and the pre-stored file are the same by calculation. When the matching is consistent, a prompt box will pop up to notify the user that the pre-matched file already exists in the disk, and inform the user of the path of the matched file, and ask the user whether to continue saving, and operate according to the user's instruction. When the matching is inconsistent, the step of asking for the user's opinion will not be executed;

[0069] Step S24: Based on the comparison between the analysis result of the storage file index and the file index in the path of the user's last stored file, when the similarity of the comparison result is greater than the set threshold, set the path of the last stored file as the optimal storage path. Otherwise, match by obtaining the hot index words of each folder. When a matching folder is filtered out, recommend this folder as the optimal storage path. When multiple matching folders are filtered out, calculate the optimal storage path of these matching folders through the index words. The formula for the optimal storage path is:

[0070] R i =w 1 *t+w 2 *f+w 3 *size+w 4 *ip

[0071] p * =argmax i R i

[0072] Wherein, R irepresents the i-th folder among the multiple matching folders, t represents the recent time of the user's opening time, w 1 represents the weight coefficient of t, f represents the opening frequency of the user, w 2 represents the weight coefficient of the user's opening frequency, size represents the storage size of the pre-stored file, w 3 represents the weight coefficient of the storage size of the pre-stored file, ip represents the IP address where the user stores the pre-stored file, w 4 represents the weight coefficient of the IP address, p * represents the optimal storage path, argmax i represents the maximum value of R among all the multiple matching folders.

[0073] In an embodiment of the present invention, the step S22 further includes the following steps:

[0074] Step S221: Add an index by identifying the extension name of the pre-stored file described in the text. When the extension name is.docx\.pdf\.txt, add a document index. When the extension name is.xlsx\.csv, add a table index. In addition, for files including.exe\.dmg, etc., add a software installation package index, and so on;

[0075] Step S222: Compare the storage capacity of the pre-stored file with two set thresholds by identification. If the storage capacity of the pre-stored file is less than the two thresholds, add a small file index. If the storage capacity of the pre-stored file is between the two thresholds, add a medium file index. If the storage capacity of the pre-stored file is greater than the two thresholds, add a large file index;

[0076] Step S223: Identify the keywords and related words in the name of the pre-stored file, and allocate a broad category index to the pre-stored file according to the file characteristics in the cloud storage, and add an index to the stored file;

[0077] Step S224: The one-dimensional array index sorting of the pre-stored file performs type matching according to the index sorting result of step S12. For the index words ranked higher, their positions in the array index will be more forward.

[0078] In an embodiment of the present invention, the step S3 further includes the following steps:

[0079] Step S31: According to the user's access situation, adjust the user's opening time index and access frequency index of the stored files in the cloud storage in real time;

[0080] Step S32: Aggregate and analyze the index, analyze the access patterns of different files in the cloud storage by the user, predict the user's future storage access and extraction requirements, and dynamically adjust the cloud storage resources. The calculation formula for predicting the user's storage access and extraction requirements is as follows:

[0081]

[0082] In the formula, P j represents the probability that the user needs to access and extract the j-th file in the cloud storage, where P j ∈(0, 1), and α 0 , α 1 , α 2 , α 3 , α 4 represent the weight coefficients corresponding to the indexes;

[0083] Step S33: Compare each P j with a threshold set by the system. When the access frequency of a certain file in the cloud storage is greater than the threshold, it is classified as a high-frequency access file; when it is lower than the threshold and greater than 20% of the threshold, it is classified as a medium-frequency access file; when it is lower than 20% of the threshold, it is classified as a low-frequency access file. Among them, the high-frequency access files are stored in a distributed manner, the medium-frequency access files are stored in the standard performance storage layer, and the low-frequency access files are stored in the low-cost storage layer. Among them, for the files that need to perform big data analysis, they are all stored in the data lake.

[0084] In the embodiment of the present invention, step S4 further includes the following steps:

[0085] Step S41: Based on the one-dimensional array indexes of each file in the disk and the characteristics of the user's access to the disk information, perform visual displays of the files in different dimensions based on different indexes. The index type can be selected through the client interface button to display the corresponding visual chart. The high-frequency access files are displayed in a brighter color, the medium-frequency access files are displayed in a normal color, and the low-frequency access files are displayed in a darker color;

[0086] Step S42: Accumulate the user's button click behaviors and record the corresponding file types accessed by the user, analyze the user's preferences and habits in visual index selection. When obtaining the user's access operation instruction, identify the type of file currently searched by the user, and automatically recommend a visual chart that matches the user's past habits;

[0087] Step S43: In the visual chart display, each module is equipped with a link index. The user can click on one of the modules to understand the file information in it in more detail.

[0088] Example 2: Example 2 of the present invention provides an artificial intelligence-based data storage service system. Figure 1 It is a schematic diagram of the module composition of an artificial intelligence-based data storage service system provided by Example 2 of the present invention. As Figure 1 shown, the system includes a user instruction receiving module, a stored file indexing module, an optimal path recommendation module, and a dynamic monitoring module:

[0089] The user instruction receiving module is used to receive the operation instructions of the user on the client.

[0090] The stored file indexing module is used to add indexes to the stored files in the disk and the pre-stored files.

[0091] The optimal path recommendation module is used to match the indexes of the pre-stored files and the files in the disk, and recommend the optimal storage path of the current pre-stored file to the user.

[0092] The dynamic monitoring module is used to monitor the storage behavior of the user. The system analyzes the user's storage behavior, reasonably stratifies the disk, and recommends a suitable visualization table.

[0093] In some embodiments of the present invention, the stored file indexing module includes an extension name indexing module, a capacity size indexing module, a file name indexing module, and an IP address indexing module:

[0094] The extension name indexing module is used to add indexes to files by the extension names of the stored files.

[0095] The capacity size indexing module is used to add indexes of three types, large, medium, and small, to the storage capacities of the files.

[0096] The file name indexing module is used to extract keywords from the names of the stored files, assign associated keywords, and add a broad category index.

[0097] The IP address indexing module is used to obtain the IP address of the user when storing files, for analyzing the storage characteristics of the user under different IP addresses later.

[0098] In some embodiments of the present invention, it includes a duplicate reduction matching module and an optimal path analysis module:

[0099] The duplicate reduction matching module is used to match the pre-stored files and the files in the disk, and determine whether the pre-stored files already exist in the disk.

[0100] The optimal path analysis module is used to match the index of the files in the disk with the index of the data storage files, and calculate the optimal storage path of the pre-stored files.

[0101] In some embodiments of the present invention, the system includes a user storage feature module, a dynamic adjustment storage module, and a visualization display module:

[0102] The user storage feature module is used to record the operation behaviors of the user on the client side and analyze the storage habits of the user;

[0103] The dynamic adjustment storage module is used to reasonably partition the files in the disk;

[0104] The visualization display module is used to partition the files in the disk through different indexes and display them in the form of charts.

[0105] It should be noted that in this article, relational terms such as first and second are only used 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 term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0106] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A data storage service method based on artificial intelligence, characterized in that: The operating steps of the method include: Step S1: Acquire a data storage task, extract pre-stored file information in the data storage task, obtain a feature value of a user accessing a file, obtain a file type storage rule in the cloud storage, and obtain a path where the user last stored a file; Step S2: analyzing the pre-stored file information and the file type storage path rules in the cloud storage, analyzing the features of the pre-stored file, and providing a recommended storage path; Step S3: monitoring user storage behavior characteristics, predicting user storage needs, and dynamically adjusting the cloud storage characteristics; Step S4: Based on the user's storage behavior characteristics, the storage files in the cloud storage are visually displayed.

2. The data storage service method based on artificial intelligence according to claim 1, characterized in that: Step S11: the stored file information includes: file extension, file size, file name, storage device IP, and the file type storage rule includes: storage path, directory structure, file type distribution; Step S12: obtaining the array index added to the storage file of the disk before storage, and sorting the frequencies of occurrence of the index words obtained in each folder in descending order from high to low, and adding a hot index word array to each folder; Step S13: Based on the file access module, the unadded array index is identified, and the unnecessary files in the disk are cleaned, and the unnecessary files include: empty files, damaged files, and temporary files.

3. The data storage service method based on artificial intelligence according to claim 2, characterized in that: The step S2 further comprises the following steps: Step S21: Based on the file access module, the index words of each layer of storage path in the disk are counted, and the frequency of occurrence of the statistical results is sorted in descending order from high to low to obtain the storage file characteristics of each layer of storage path; Step S22: Analyze based on the pre-stored file information, add a feature index to the pre-stored file, identify and add an index to the file by the pre-stored file extension, identify the storage capacity of the pre-stored file, analyze the pre-stored file name and add an index to the pre-stored file, identify and add an index to the device IP address used by the pre-stored file, and add a related one-dimensional array index to the pre-stored file; Step S23: based on the pre-stored file name and the one-dimensional array index, filter out the file with the same index as the prepared file, match the filter out file name with the pre-stored file name, and when the matching results are consistent, calculate the hash value of the matching file and the pre-stored file to see whether they are consistent. When the matching is consistent, a prompt box will pop up to inform the user that the pre-matched file already exists in the disk, and inform the user of the file path of the matching consistency, and ask the user whether to continue saving. The operation is performed according to the user's instructions. When the matching is inconsistent, the step of asking the user for opinion is not executed; Step S24: Based on the comparison of the stored file index analysis result with the file index in the path where the user last stored the file, when the similarity of the comparison result is greater than the set threshold, the path where the file was last stored is set as the optimal storage path. Otherwise, the hot index words of each folder are obtained for matching. When a matching folder is screened out, the folder is recommended as the optimal storage path. When multiple matching folders are screened out, the optimal storage paths of these matching folders are calculated by the index words. The optimal storage path calculation formula is: R i =w1*t+w2*f+w3*size+w4*ip p * =argmax i R i In the formula, R i represents the i-th folder in the multiple matching folders, t represents the recent time when the user opens the folder, w1 represents the weight coefficient of t, f represents the frequency of opening by the user, w2 represents the weight coefficient of the frequency of opening by the user, size represents the storage size of the pre-stored file, w3 represents the weight coefficient of the storage size of the pre-stored file, ip represents the IP address where the user stores the pre-stored file, w4 represents the weight coefficient of the IP address, p * represents the optimal storage path, argmax i Represents the maximum value of R among all the multiple matching folders.

4. The data storage service method based on artificial intelligence according to claim 3, characterized in that: The step S22 further comprises the following steps: Step S221: adding an index by identifying the extension of the pre-stored file described in the text, adding a document index when the extension is .docx\.pdf\.txt, adding a table index when the extension is .xlsx\.csv, and adding a software installation package index when the extension is .exe\.dmg, etc., and so on; Step S222: by identifying the storage capacity of the pre-stored file and comparing it with two set thresholds, if the storage capacity of the pre-stored file is less than the two thresholds, a small file index is added; if the storage capacity of the pre-stored file is between the two thresholds, a medium file index is added; if the storage capacity of the pre-stored file is greater than the two thresholds, a large file index is added; Step S223: by identifying the keywords and associated words in the name of the pre-stored file and according to the file features in the cloud storage, assigning a broad category index to the pre-stored file, and adding an index to the stored file; Step S224: the one-dimensional array index of the pre-stored file is sorted and type matched according to the index sorting result of step S12, and the index words with higher sorting will be positioned higher in the array index.

5. The data storage service method based on artificial intelligence according to claim 4, characterized in that: The step S3 further comprises the following steps: Step S31: adjusting the time index and access frequency index of the user opening the file stored in the cloud storage in real time according to the user's access situation; Step S32: aggregate and analyze the indexes, analyze the user's access patterns to different files in the cloud storage, predict the user's future storage access and retrieval requirements, and dynamically adjust the cloud storage resources. The calculation formula for predicting the user's storage access and retrieval requirements is: Where P j represents the probability that the user needs to access and extract the jth file in the cloud storage, where P j ∈(0,1), α0, α1, α2, α3, α4 represent the weight coefficients of the corresponding indexes; Step S33: Each P j A threshold set by the system is set for comparison. When the access frequency of a file in the cloud storage is greater than the threshold, it is classified as a high-frequency access file; when it is lower than the threshold and greater than 20% of the threshold, it is classified as a medium-frequency access file; when it is lower than 20% of the threshold, it is classified as a low-frequency access file. The high-frequency access files are stored in distributed storage, the medium-frequency access files are stored in the standard performance storage layer, and the low-frequency access files are stored in the low-cost storage layer. Among them, files that need to be analyzed by big data are stored in the data lake.

6. The data storage service method based on artificial intelligence according to claim 5, characterized in that: The step S4 further comprises the following steps: Step S41: Based on the one-dimensional array index of each file in the disk and the characteristics of the user accessing the disk information, the files are visualized in different dimensions based on different indexes. The index type can be selected through the button on the client interface to display the corresponding visualization chart. The frequently accessed files are displayed in brighter colors, the medium-frequency accessed files are displayed in ordinary colors, and the low-frequency accessed files are displayed in darker colors. Step S42: accumulating the user's button click behavior and the corresponding record of the file types accessed by the user, analyzing the user's preferences and habits in visual index selection, and when obtaining the user's access operation instruction, identifying the type of file the current user is looking for, and automatically recommending a visual chart that matches the user's past habits; Step S43: In the visual chart display, each module is equipped with a link index, and the user can click on one of the modules to understand the file information in more detail.

7. An artificial intelligence-based data storage service system, characterized in that: The system includes a user instruction receiving module, a storage file index module, an optimal path recommendation module, and a dynamic monitoring module: The user instruction receiving module is used to receive the user's operation instructions at the client end; The storage file index module is used to add indexes to the storage files in the disk and the pre-stored files; The optimal path recommendation module is used to match the index of the pre-stored file with the index of the file in the disk, and recommend the optimal storage path of the current pre-stored file to the user; The dynamic monitoring module is used to monitor the user's storage behavior. The system analyzes the user's storage behavior, reasonably layers the disk, and recommends a suitable visualization table.

8. The data storage service system based on artificial intelligence according to claim 7, characterized in that: The storage file index module includes an extension index module, a capacity index module, a file name index module, and an IP address index module: The extension index module is used to add indexes to files by storing their extensions; The capacity indexing module is used to add indexes of three types: large, medium and small for the storage capacity of files; The file name index module is used to extract keywords from the names of stored files, assign associated keywords and add a broad category index; The IP address indexing module is used to obtain the IP address of the user when storing the file, so as to analyze the storage characteristics of the user under different IP addresses later.

9. The data storage service system based on artificial intelligence according to claim 8, characterized in that: The module includes a weight reduction matching module and an optimal path analysis module: The duplication reduction matching module is used to match the pre-stored file with the file in the disk, and determine whether the pre-stored file already exists in the disk; The optimal path analysis module is used to match the index of the file in the disk with the index of the data storage file, and calculate the optimal storage path of the pre-stored file.

10. The data storage service system based on artificial intelligence according to claim 9, characterized in that: The module includes a user storage feature module, a dynamic adjustment storage module, and a visual display module: The user storage feature module is used to record the user's operation behavior on the client side and analyze the user's storage habits; The dynamic adjustment storage module is used to reasonably divide the files in the disk; The visual display module is used to divide the files in the disk into different indexes and display them in the form of charts.

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