CN113778951B - File adding method, device, equipment and storage medium - Google Patents
File adding method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN113778951B CN113778951B CN202111087221.3A CN202111087221A CN113778951B CN 113778951 B CN113778951 B CN 113778951B CN 202111087221 A CN202111087221 A CN 202111087221A CN 113778951 B CN113778951 B CN 113778951B
- Authority
- CN
- China
- Prior art keywords
- file
- additional
- link
- trained
- score
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000012544 monitoring process Methods 0.000 claims abstract description 26
- 239000013598 vector Substances 0.000 claims description 49
- 230000006870 function Effects 0.000 claims description 25
- 238000012549 training Methods 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 13
- 230000000007 visual effect Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000013473 artificial intelligence Methods 0.000 abstract description 7
- 238000005516 engineering process Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 241000219357 Cactaceae Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 150000001875 compounds Chemical group 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/14—Details of searching files based on file metadata
- G06F16/148—File search processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/16—File or folder operations, e.g. details of user interfaces specifically adapted to file systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
- G06F3/0482—Interaction with lists of selectable items, e.g. menus
Abstract
The invention relates to the field of artificial intelligence, and provides a file adding method, device and equipment and a storage medium, wherein the method comprises the following steps: acquiring unique identification information of a user, monitoring the use condition of each file by the user in real time according to the unique identification information, inputting the use condition of each file into a pre-trained additional judgment model, obtaining a first additional score of each file, and forming a link for a target file according to the first additional score. Therefore, corresponding links are generated for some common or important files, so that efficient use and viewing can be performed, and user experience is improved.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method, an apparatus, a device, and a storage medium for adding files.
Background
In daily office work and chat, a large amount of note information needs to be recorded, various note information is stored in different files respectively, when the note information needs to be used or checked, the note information needs to be searched from various folders, and some files which are commonly used or important cannot be used or checked quickly and efficiently, so that an additional method for the files is needed.
Disclosure of Invention
The invention mainly aims to provide a file adding method, device, equipment and storage medium, which aim to solve the problem that some commonly used or important files cannot be used or checked quickly and efficiently.
The invention provides a file adding method, which comprises the following steps:
Acquiring unique identification information of a user;
monitoring the first use condition of each first file by the user in real time according to the unique identification information;
inputting the first use condition of each first file into a pre-trained additional judgment model to obtain a first additional score of each first file;
taking a first file with the first additional score larger than a preset additional score in each first file as a target file, and extracting the file name, the suffix and the storage position of the target file;
forming a first link based on the storage location, and generating a name of the first link based on the file name and the suffix;
the first link and the name are added to a quick directory.
Further, before the step of inputting the first usage of each first file into the pre-trained additional judgment model to obtain the first additional score of each first file, the method further includes:
Acquiring second use conditions of corresponding second files of each second link in the quick catalogs of the plurality of users and corresponding second additional scores;
Converting the second use condition into a multidimensional vector according to a preset dimension; wherein the multidimensional vector is X j＝(x1j,x2j…xij…xnj),Xj which represents the multidimensional vector of the second use condition of the j-th second file, and X ij which represents the i-th vector of the second use condition of the j-th second file;
Inputting each multidimensional vector and the corresponding second additional score into an additional judgment initial model for training to obtain the pre-trained additional judgment model; wherein the additional determination initial model is hw(x)＝w0+w1x1+w2x2+wixi...+wnxn,, where h w (x) is the second additional score, w 0,w1,...,wn is the parameter value to be trained, and x i represents the ith dimension vector of the multidimensional vectors.
Further, after the step of inputting each multidimensional vector and the corresponding second additional score into an additional judgment initial model to train and obtaining the pre-trained additional judgment model, the method comprises the following steps:
Acquiring actual additional scores of second use cases of the second files and predicted additional scores of the pre-trained additional judgment model;
calculating a loss value of the pre-trained additional judgment model according to a loss function formula; wherein the loss function formula is:
y j represents the actual additional score corresponding to the second use case of j second files, h w(xij) represents the predicted additional score obtained by inputting the second use case of the j second files into the pre-trained additional judgment model, n represents the dimension of the multidimensional vector, Representing preset parameter values,/>A loss value representing the pre-trained additional judgment model;
judging whether the loss value is larger than a preset loss value or not;
if the loss value is larger than the preset loss value, continuing training the pre-trained additional judgment model until a pre-trained additional judgment model with the loss value smaller than the preset loss value is obtained.
Further, after the step of adding the first link and the name to the quick directory, the method further comprises:
detecting whether unused time of each first link in the quick catalog reaches preset time or not;
and clearing the first link reaching the preset time from the quick catalog.
Further, before the step of acquiring the first usage condition of each first file by the user according to the unique identifier information in real time, the method further includes:
judging whether each first file has established a first link in the quick directory;
Monitoring of the first file for which the first link has been established in the quick directory is stopped.
Further, before the step of detecting whether the unused time of each first link in the quick directory reaches the preset time, the method further includes:
extracting a first additional score of the target file;
Calculating the preset time corresponding to the first additional score of the target file according to the formula t=f (x i) +; wherein t represents a preset time, f (x i) represents a function of the additional score as a function of the corresponding time, b represents a minimum value of the preset time, and x i represents a first additional score of the ith first file.
Further, before the step of adding the first link and the name to the quick directory, the method further includes:
establishing a target folder of the quick directory; the target folder is used for storing various first links;
Putting the link mode of the target folder into a right mouse button menu;
And designing a visual click link corresponding to the link mode in a UI layer in a right mouse button menu.
The invention also provides a file adding device, which comprises:
The acquisition module is used for acquiring the unique identification information of the user;
the monitoring module is used for monitoring the first use condition of the user on each first file in real time according to the unique identification information;
the input module is used for inputting the first use condition of each first file into a pre-trained additional judgment model to obtain a first additional score of each first file;
the extraction module is used for taking a first file with the first additional score larger than a preset additional score in the first files as a target file, and extracting the file name, the suffix and the storage position of the target file;
the generation module is used for forming a first link based on the storage position and generating the name of the first link based on the file name and the suffix;
and the additional module is used for adding the first link and the name into the quick directory.
The invention also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of any of the preceding claims.
The invention has the beneficial effects that: acquiring unique identification information of a user, monitoring the use condition of each file by the user in real time according to the unique identification information, inputting the use condition of each file into a pre-trained additional judgment model, obtaining additional scores of each file, and forming links for target files according to the additional scores. Therefore, corresponding links are generated for some common or important files, so that efficient use and viewing can be performed, and user experience is improved.
Drawings
FIG. 1 is a flow chart illustrating a method for appending a file according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a file adding apparatus according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a computer device according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, in the embodiments of the present invention, all directional indicators (such as up, down, left, right, front, and back) are merely used to explain the relative positional relationship, movement conditions, and the like between the components in a specific posture (as shown in the drawings), if the specific posture is changed, the directional indicators correspondingly change, and the connection may be a direct connection or an indirect connection.
The term "and/or" is herein merely an association relation describing an associated object, meaning that there may be three relations, e.g., a and B, may represent: a exists alone, A and B exist together, and B exists alone.
Furthermore, descriptions such as those referred to as "first," "second," and the like, are provided for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implying an order of magnitude of the indicated technical features in the present disclosure. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Referring to fig. 1, the present invention proposes a method for adding a first file, including:
s1: acquiring unique identification information of a user;
s2: monitoring the first use condition of the user on each first file in real time according to the unique identification information;
S3: inputting the first use condition of each first file into a pre-trained additional judgment model to obtain additional scores of each first file;
S4: taking a first file with the first additional score larger than a preset additional score in each first file as a target file, and extracting the file name, the suffix and the storage position of the target file;
s5: forming a first link based on the storage location, and generating a name of the first link based on the file name and the suffix;
S6: the first link and the name are added to a quick directory.
As described in step S1, unique identification information of the user is obtained, where the unique identification information at least includes one or more of account information, identity information, and telephone of the user. Because each user has its corresponding unique identifying information, such as identity information, IP address, telephone, etc., the unique identifying information can be obtained to identify the corresponding user, so as to facilitate rapid detection of the quick directory corresponding to the user.
As described in the step S2, the first usage condition of each first file by the user is monitored in real time according to the unique identification information; the first use condition at least comprises the times that the user opens each first file respectively. The first use condition of the user on each first file can be obtained based on the unique identification information, if the first use condition is an APP, the first use condition of the user on each first file can be called in the APP, or the first use condition of the user on each first file can be monitored in a computer system or a monitoring computer system, specifically, a real-time monitoring mode can be realized through a corresponding operation and maintenance monitoring module, for example Zabbix, nagios, cacti and the like.
As described in step S3, the first usage of each first file is input into a pre-trained additional judgment model, and a first additional score of each first file is obtained. The pre-trained additional judgment model is trained according to the use condition of the file and the corresponding additional score, different use conditions correspond to different additional scores, different additional scores can be set by related personnel, and the model is trained as a training set, so that the corresponding pre-trained additional judgment model is obtained.
As described in step S4, a first file with the first additional score greater than a preset additional score among the first files is used as a target file, and the file name, the suffix and the storage location of the target file are extracted. The target file with the first additional score being larger than the preset additional score is considered as a first file which is required to be added by the user, and then the file name, the suffix and the storage position are extracted based on the requirement, so that the directory of the first file can be quickly found out in the follow-up formation, namely, the corresponding first link is generated in the directory.
As described in step S5 above, a first link is formed based on the storage location, and a name of the first link is generated based on the file name and the suffix. Specifically, a corresponding file path is generated based on the storage location, and the file name and the suffix of the file path are used as the naming of the link, so that the user can conveniently identify the content in the first link.
As described in step S6, the first link and the name are added to the quick directory, that is, the first link and the name are sent to the quick directory, where the quick directory is a quick open mode of the user, and may be set in any directory, for example, may be set under a right button menu of a mouse or may be set on a desktop. In a preferred embodiment, the link may be set in any one of the hierarchical menus of an APP, for example, the hierarchical menu of the APP, where the link is added to a storage location in the hierarchical menu, and then the UI layer is set accordingly to display the link.
In one embodiment, before the step S3 of inputting the first usage of each first file into the pre-trained additional judgment model to obtain the first additional score of each first file, the method includes:
s201: acquiring second use conditions of corresponding second files of each second link in the quick catalogs of the plurality of users and corresponding second additional scores;
S202: converting the second use condition into a multidimensional vector according to a preset dimension; wherein the multidimensional vector is X j＝(x1j,x2j…xij…xnj),Xj which represents the multidimensional vector of the second use condition of the j-th second file, and X ij which represents the i-th vector of the second use condition of the j-th second file;
S203: inputting each multidimensional vector and the corresponding second additional score into an additional judgment initial model for training to obtain the pre-trained additional judgment model; wherein the additional determination initial model is hw(x)＝w0+w1x1+w2x2+wixi...+wnxn,, where h w (x) is the second additional score, w 0,w1,...,wn is the parameter value to be trained, and x i represents the ith dimension vector of the multidimensional vectors.
As described in the above steps S201 to S203, training of the pre-trained additional judgment model is achieved.
In step S201, a second usage condition of the corresponding second file and a corresponding second additional score of each link are obtained, where the second usage condition may be monitored by a corresponding operation and maintenance monitoring system, for example, the operation and maintenance monitoring system may be any one of Zabbix, cacti, hyperic, and the corresponding second additional score may be obtained by self scoring of the corresponding user or may be uniformly scored by related personnel, so as to form a corresponding training set.
In step S202, the corresponding second usage situation is converted into a multidimensional vector, where the dimensions of the multidimensional vector may include the number of uses, the time of use, the degree of correlation with the user' S work, and so on.
In step S203, each multi-dimensional vector is input into the additional judgment initial model to perform training, so as to obtain a pre-trained additional judgment model, so that the calculated result can be close to the result of the user' S own additional of the second file, in addition, the function of the additional judgment initial model can be hw(x)＝w0+w1x1+w2x2+wixi...+wnxn,, then the parameter value w 0,w1,...,wn is calculated through the linear regression algorithm and each multi-dimensional vector, and the calculated parameter value is then added to the function, so as to obtain the pre-trained additional judgment model, in addition, the obtained second use condition can be converted into the corresponding additional duration according to the corresponding relationship between the second use condition and the additional time.
In one embodiment, the step S203 of inputting each of the multidimensional vectors and the corresponding second additional score into an additional judgment initial model to perform training to obtain the pre-trained additional judgment model includes:
S2031: acquiring actual additional scores of second use cases of the second files and predicted additional scores of the pre-trained additional judgment model;
s2032: calculating a loss value of the pre-trained additional judgment model according to a loss function formula; wherein the loss function formula is:
y j represents the actual additional score corresponding to the second use cases of j second files, h w(xij) represents the predicted additional score obtained by inputting the second use cases of j second files into the pre-trained additional judgment model, n represents the dimension of the multidimensional vector, Representing preset parameter values,/>A loss value representing the pre-trained additional judgment model;
S2033: judging whether the loss value is larger than a preset loss value or not;
S2034: if the loss value is larger than the preset loss value, continuing training the pre-trained additional judgment model until a pre-trained additional judgment model with the loss value smaller than the preset loss value is obtained.
As described in the above steps S2031 to S2034, the parameter detection of the pre-trained additional judgment model is realized. Calculating a loss value by acquiring an actual use score of a second use condition of the user and inputting the second use condition into a predicted additional score obtained in the pre-trained additional judgment model, wherein the calculated loss function is that
It should be noted that w i is a parameter value corresponding to the i-th dimension in the pre-trained additional judgment model, and then, judging whether the pre-trained additional judgment model meets the requirement according to the calculated loss value, if not, continuing to train until the loss value of the pre-trained additional judgment model is smaller than the preset loss value. Therefore, the training of the precision of the pre-trained additional judgment model is realized, and the obtained pre-trained additional judgment model can accurately predict whether to add the first file based on the first use condition.
In one embodiment, after the step S6 of adding the first link and the name to the quick directory, the method further includes:
s701: detecting whether unused time of each first link in the quick catalog reaches preset time or not;
s702: and clearing the first link reaching the preset time from the quick catalog.
As described in the above steps S601-S602, updating the first link in the quick directory in real time is achieved. The method comprises the steps of periodically detecting whether a time tag corresponding to a first link of the quick directory reaches a preset time, moving out the corresponding first link when the corresponding failure time is reached, namely indicating that the first link is out of date, so that the first link needs to be moved out of the quick directory. Specifically, the method for detecting whether the time label of each first link in the quick directory reaches the preset time may be that all the first links in the quick directory are traversed periodically, the corresponding dead time of each first link is detected, then the current time is obtained, and if the current time reaches or exceeds the preset time, it is indicated that the first link can be moved out of the quick directory.
In one embodiment, before the step S2 of obtaining the first usage situation of the user on each first file according to the unique identification information in real time, the method further includes:
s101: judging whether each first file has established a link in the quick directory;
S102: monitoring of the first file for which the first link has been established in the quick directory is stopped.
In the above steps S101-S102, monitoring of the first files that have already established links in the quick directory is reduced, repeated monitoring of the first files is avoided, and monitoring resources are wasted, i.e. it is determined whether each of the first files has established a first link in the quick directory. The first file with the link established can be marked, for example, special characters can be set in the file name of the first file for marking, or other ways can be used for marking, and then monitoring of the first file is stopped when the mark is detected, so that monitoring resources are saved, and the use performance of monitoring is improved.
In one embodiment, before the step S701 of detecting whether the unused time of each of the first links in the quick directory reaches a preset time, the method further includes:
s7001: extracting a first additional score of the target first file;
S7002: calculating the preset time corresponding to the first additional score of the target file according to the formula t=f (x i) +b; wherein t represents a preset time, f (x i) represents a function of the first additional score and the corresponding time, b represents a minimum value of the preset time, and x i represents the first additional score of the ith first file.
As described in the above steps S7001-S7002, it is realized that a preset time is set for each target file, that is, the first additional score obtained by the foregoing pre-trained additional judgment model is extracted, and then a preset time corresponding to the first additional score of each target file is calculated based on the formula t=f (x i) +b, where the value range of x i in the f (x i) function should be set to be greater than a certain value, that is, the first additional score needs to be greater than the preset additional score, and in addition, the f (x i) function may be a primary function, a secondary function, or a compound function.
In one embodiment, before the step S6 of adding the link and the name to the quick directory, the method further includes:
S501: establishing a target folder of the quick directory; the target folder is used for storing various first links;
s502: putting the link mode of the target folder into a right mouse button menu;
S503: and designing a visual click link corresponding to the link mode in a UI layer in a right mouse button menu.
Visual addition to the links is achieved as described in steps S501-S503 above. The corresponding target folder is established in the quick directory, or the corresponding target folder is selected, then the link in the target folder is copied to the forbidden zone, then the link mode of the target folder is put into a right-click menu of the mouse, namely the link mode of the corresponding target folder is stored in the right-click menu of the mouse, then the corresponding visual click link is designed in the UI layer, and then, when a user searches for the corresponding first file, the user only needs to click the right button, then the corresponding target folder is opened, and the target file can be found, so that the quick search of the target file is realized.
Referring to fig. 2, the present invention further provides an adding device for a file, including:
an acquisition module 10, configured to acquire unique identification information of a user;
The monitoring module 20 is configured to monitor, in real time, a first usage condition of each first file by the user according to the unique identifier information;
An input module 30, configured to input a first usage of each first file into a pre-trained additional determination model, to obtain a first additional score of each first file;
an extracting module 40, configured to take, as a target file, a first file, of the first files, where the first additional score is greater than a preset additional score, and extract a file name, a suffix, and a storage location of the target file;
A generating module 50, configured to form a first link based on the storage location, and generate a name of the first link based on the file name and the suffix;
An append module 60 for adding the first link and the name to a quick directory.
In one embodiment, the adding device of the first file further includes:
The additional score acquisition module is used for acquiring second use conditions of corresponding second files of each second link in the quick catalogue of the plurality of users and corresponding second additional scores;
The vector conversion module is used for converting the second use condition into a multidimensional vector according to a preset dimension; wherein the multidimensional vector is X j＝(x1j,x2j…xij…xnj),Xj which represents the multidimensional vector of the second use condition of the j-th second file, and X ij which represents the i-th vector of the second use condition of the j-th second file;
The model training module is used for inputting each multidimensional vector and the corresponding second additional score into an additional judgment initial model for training to obtain the pre-trained additional judgment model; wherein the additional determination initial model is hw(x)＝w0+w1x1+w2x2+wixi...+wnxn,, where h w (x) is the second additional score, w o,w1,...,wn is the parameter value to be trained, and x i represents the ith dimension vector of the multidimensional vectors.
In one embodiment, the adding device of the first file further includes:
a predictive additional score obtaining module, configured to obtain an actual additional score of the second usage situation of each second file and a predictive additional score of the pre-trained additional judgment model;
a loss value calculation module for
Calculating a loss value of the pre-trained additional judgment model according to a loss function formula; wherein the loss function formula is:
y j represents the actual additional score corresponding to the second use case of j second files, h w(xij) represents the predicted additional score obtained by inputting the second use case of the j second files into the pre-trained additional judgment model, n represents the dimension of the multidimensional vector, Representing preset parameter values,/>A loss value representing the pre-trained additional judgment model;
The loss value judging module is used for judging whether the loss value is larger than a preset loss value or not;
and the continuous training module is used for continuously training the pre-trained additional judgment model if the loss value is larger than the preset loss value until the pre-trained additional judgment model with the loss value smaller than the preset loss value is obtained.
In one embodiment, the adding device of the first file further includes:
the time detection module is used for detecting whether unused time of each first link in the quick catalog reaches preset time;
And the link clearing module is used for clearing the first link reaching the preset time from the quick catalogue.
In one embodiment, the adding device of the first file further includes:
the first file judging module is used for judging whether each first file has established a first link in the quick directory;
and the stopping module is used for stopping monitoring the first file with the first link established in the quick directory.
In one embodiment, the adding device of the first file further includes:
an additional score extraction module for extracting a first additional score of the target file;
A preset time calculation module, configured to calculate the preset time corresponding to the first additional score of the target file according to a formula t=f (x i) +b; wherein t represents a preset time, f (x i) represents a function of the additional score as a function of the corresponding time, b represents a minimum value of the preset time, and x i represents a first additional score of the ith first file.
In one embodiment, the adding device of the first file further includes:
The target first folder establishing module is used for establishing a target folder of the quick directory; the target folder is used for storing various first links;
The placing module is used for placing the link mode of the target folder into a right mouse button menu;
And the visual click link design module is used for designing visual click links corresponding to the link modes in the UI layer in the right mouse button menu.
The invention has the beneficial effects that: acquiring unique identification information of a user, monitoring first use conditions of the user on each first file in real time according to the unique identification information, inputting the first use conditions of each first file into a pre-trained additional judgment model, obtaining first additional scores of each first file, and forming links for target first files according to the first additional scores. Therefore, corresponding links are generated for some common or important first files, so that efficient use and viewing can be performed, and user experience is improved.
Referring to fig. 3, in an embodiment of the present application, there is further provided a computer device, which may be a server, and an internal structure thereof may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing various additional first files and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The method for adding the first file according to any of the above embodiments can be implemented when the computer program is executed by a processor.
It will be appreciated by those skilled in the art that the architecture shown in fig. 3 is merely a block diagram of a portion of the architecture in connection with the present inventive arrangements and is not intended to limit the computer devices to which the present inventive arrangements are applicable.
The embodiment of the present application also provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor can implement the method for adding a first file according to any of the foregoing embodiments.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by hardware associated with a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided by the present application and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that comprises the element.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (8)
1. A method for adding a file, comprising:
Acquiring unique identification information of a user;
monitoring the first use condition of each first file by the user in real time according to the unique identification information;
inputting the first use condition of each first file into a pre-trained additional judgment model to obtain a first additional score of each first file;
taking a first file with the first additional score larger than a preset additional score in each first file as a target file, and extracting the file name, the suffix and the storage position of the target file;
forming a first link based on the storage location, and generating a name of the first link based on the file name and the suffix;
adding the first link and the name to a quick directory;
before the step of inputting the first usage condition of each first file into the pre-trained additional judgment model to obtain the first additional score of each first file, the method further comprises:
Acquiring second use conditions of corresponding second files of each second link in the quick catalogs of the plurality of users and corresponding second additional scores;
Converting the second use condition into a multidimensional vector according to a preset dimension; wherein the multidimensional vector is X j＝(x1j,x2j…xij…xnj),Xj which represents the multidimensional vector of the second use condition of the j-th second file, and X ij which represents the i-th vector of the second use condition of the j-th second file;
Inputting each multidimensional vector and the corresponding second additional score into an additional judgment initial model for training to obtain the pre-trained additional judgment model; wherein the additional judgment initial model is hw(x)＝w0+w1x1+w2x2+wixi…+wnxn,, wherein h w (x) is the second additional score, w 0,w1,…,wn is a parameter value to be trained, x i represents an ith dimension vector in the multi-dimension vectors, and i represents 1 to n;
after the step of inputting each multidimensional vector and the corresponding second additional score into an additional judgment initial model to train and obtaining the pre-trained additional judgment model, the method comprises the following steps:
acquiring actual additional scores of second use cases of the second files and predicted additional scores of the pre-trained additional judgment model;
calculating a loss value of the pre-trained additional judgment model according to a loss function formula; wherein the loss function formula is:
y j represents the actual additional score corresponding to the second use case of j second files, h w(xij) represents the predicted additional score obtained by inputting the second use case of the j second files into the pre-trained additional judgment model, n represents the dimension of the multidimensional vector, Representing preset parameter values,/>A loss value representing the pre-trained additional judgment model;
judging whether the loss value is larger than a preset loss value or not;
if the loss value is larger than the preset loss value, continuing training the pre-trained additional judgment model until a pre-trained additional judgment model with the loss value smaller than the preset loss value is obtained.
2. The method of adding a file according to claim 1, wherein after the step of adding the first link and the name to a quick directory, the method further comprises:
detecting whether unused time of each first link in the quick catalog reaches preset time or not;
and clearing the first link reaching the preset time from the quick catalog.
3. The method for adding files according to claim 1, wherein before the step of acquiring the first usage of each first file by the user in real time based on the unique identification information, the method further comprises:
judging whether each first file has established a first link in the quick directory;
Monitoring of the first file for which the first link has been established in the quick directory is stopped.
4. The method of adding a file according to claim 2, wherein before the step of detecting whether the unused time of each of the first links in the quick directory reaches a preset time, further comprising:
extracting a first additional score of the target file;
Calculating the preset time corresponding to the first additional score of the target file according to the formula t=f (z i) +b; wherein t represents a preset time, f (z i) represents a function of the additional score as a function of the corresponding time, b represents a minimum value of the preset time, and z i represents a first additional score of the ith first file.
5. The method of appending the file according to claim 1, wherein before the step of adding the first link and the name to the quick directory, further comprising:
establishing a target folder of the quick directory; the target folder is used for storing various first links;
Putting the link mode of the target folder into a right mouse button menu;
And designing a visual click link corresponding to the link mode in a UI layer in a right mouse button menu.
6. An adding device for a file, comprising:
The acquisition module is used for acquiring the unique identification information of the user;
the monitoring module is used for monitoring the first use condition of the user on each first file in real time according to the unique identification information;
the input module is used for inputting the first use condition of each first file into a pre-trained additional judgment model to obtain a first additional score of each first file;
the extraction module is used for taking a first file with the first additional score larger than a preset additional score in the first files as a target file, and extracting the file name, the suffix and the storage position of the target file;
the generation module is used for forming a first link based on the storage position and generating the name of the first link based on the file name and the suffix;
The adding module is used for adding the first link and the name into a quick directory;
The additional score acquisition module is used for acquiring second use conditions of corresponding second files of each second link in the quick catalogue of the plurality of users and corresponding second additional scores;
The vector conversion module is used for converting the second use condition into a multidimensional vector according to a preset dimension; wherein the multidimensional vector is X j＝(x1j,x2j…xij…xnj),Xj which represents the multidimensional vector of the second use condition of the j-th second file, and X ij which represents the i-th vector of the second use condition of the j-th second file;
The model training module is used for inputting each multidimensional vector and the corresponding second additional score into an additional judgment initial model for training to obtain the pre-trained additional judgment model; wherein the additional judgment initial model is hw(x)＝w0+w1x1+w2x2+wixi…+wnxn,, wherein h w (x) is the second additional score, w 0,w1,…,wn is a parameter value to be trained, x i represents an ith dimension vector in the multi-dimension vectors, and i represents 1 to n;
a predictive additional score obtaining module, configured to obtain an actual additional score of the second usage situation of each second file and a predictive additional score of the pre-trained additional judgment model;
a loss value calculation module for
Calculating a loss value of the pre-trained additional judgment model according to a loss function formula; wherein the loss function formula is:
y j represents the actual additional score corresponding to the second use case of j second files, h w(xij) represents the predicted additional score obtained by inputting the second use case of the j second files into the pre-trained additional judgment model, n represents the dimension of the multidimensional vector, Representing preset parameter values,/>A loss value representing the pre-trained additional judgment model;
The loss value judging module is used for judging whether the loss value is larger than a preset loss value or not;
and the continuous training module is used for continuously training the pre-trained additional judgment model if the loss value is larger than the preset loss value until the pre-trained additional judgment model with the loss value smaller than the preset loss value is obtained.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111087221.3A CN113778951B (en) | 2021-09-16 | 2021-09-16 | File adding method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111087221.3A CN113778951B (en) | 2021-09-16 | 2021-09-16 | File adding method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113778951A CN113778951A (en) | 2021-12-10 |
| CN113778951B true CN113778951B (en) | 2024-04-26 |
Family
ID=78851419
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111087221.3A Active CN113778951B (en) | 2021-09-16 | 2021-09-16 | File adding method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113778951B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011028716A (en) * | 2009-06-26 | 2011-02-10 | Victor Co Of Japan Ltd | Backup device and backup method |
| CN104185834A (en) * | 2012-03-28 | 2014-12-03 | 宇龙计算机通信科技(深圳)有限公司 | Error correction method for operation objects and communication terminal |
| CN108319602A (en) * | 2017-01-17 | 2018-07-24 | 广州市动景计算机科技有限公司 | Data base management method and Database Systems |
| CN108898588A (en) * | 2018-06-22 | 2018-11-27 | 中山仰视科技有限公司 | Therapeutic effect appraisal procedure based on time series, electronic equipment |
| CN111104831A (en) * | 2018-10-29 | 2020-05-05 | 香港城市大学深圳研究院 | Visual tracking method, device, computer equipment and medium |
| CN111400261A (en) * | 2020-01-21 | 2020-07-10 | 行星算力(深圳)科技有限公司 | Method for rapidly adding or deleting folders by IPFS (Internet protocol file system) |
| CN112749131A (en) * | 2020-06-11 | 2021-05-04 | 腾讯科技（上海）有限公司 | Information duplicate elimination processing method and device and computer readable storage medium |
| CN112925753A (en) * | 2021-03-25 | 2021-06-08 | 平安科技（深圳）有限公司 | File additional writing method and device, electronic equipment and storage medium |
-
2021
- 2021-09-16 CN CN202111087221.3A patent/CN113778951B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011028716A (en) * | 2009-06-26 | 2011-02-10 | Victor Co Of Japan Ltd | Backup device and backup method |
| CN104185834A (en) * | 2012-03-28 | 2014-12-03 | 宇龙计算机通信科技(深圳)有限公司 | Error correction method for operation objects and communication terminal |
| CN108319602A (en) * | 2017-01-17 | 2018-07-24 | 广州市动景计算机科技有限公司 | Data base management method and Database Systems |
| CN108898588A (en) * | 2018-06-22 | 2018-11-27 | 中山仰视科技有限公司 | Therapeutic effect appraisal procedure based on time series, electronic equipment |
| CN111104831A (en) * | 2018-10-29 | 2020-05-05 | 香港城市大学深圳研究院 | Visual tracking method, device, computer equipment and medium |
| CN111400261A (en) * | 2020-01-21 | 2020-07-10 | 行星算力(深圳)科技有限公司 | Method for rapidly adding or deleting folders by IPFS (Internet protocol file system) |
| CN112749131A (en) * | 2020-06-11 | 2021-05-04 | 腾讯科技（上海）有限公司 | Information duplicate elimination processing method and device and computer readable storage medium |
| CN112925753A (en) * | 2021-03-25 | 2021-06-08 | 平安科技（深圳）有限公司 | File additional writing method and device, electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于自注意力机制的多域卷积神经网络的视觉追踪;李生武;张选德;;计算机应用(08);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113778951A (en) | 2021-12-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109933785B (en) | Method, apparatus, device and medium for entity association | |
| EP3805950A1 (en) | Patent knowledge base construction method, apparatus, computer device, and storage medium | |
| CN112182383B (en) | Recommendation method and device for second post and computer equipment | |
| CN110427612B (en) | Entity disambiguation method, device, equipment and storage medium based on multiple languages | |
| CN111192025A (en) | Occupational information matching method and device, computer equipment and storage medium | |
| CN111666766B (en) | Data processing method, device and equipment | |
| CN106778878B (en) | Character relation classification method and device | |
| CN111460131A (en) | Method, device and equipment for extracting official document abstract and computer readable storage medium | |
| CN110019703B (en) | Data marking method and device and intelligent question-answering method and system | |
| CN112685475A (en) | Report query method and device, computer equipment and storage medium | |
| CN113704436A (en) | User portrait label mining method and device based on session scene | |
| CN112632258A (en) | Text data processing method and device, computer equipment and storage medium | |
| CN112836061A (en) | Intelligent recommendation method and device and computer equipment | |
| CN112580329B (en) | Text noise data identification method, device, computer equipment and storage medium | |
| CN115659226A (en) | Data processing system for acquiring APP label | |
| US20230088128A1 (en) | System and method for determining an experience match between job candidates and open positions or projects | |
| CN113778951B (en) | File adding method, device, equipment and storage medium | |
| CN109660621A (en) | A kind of content delivery method and service equipment | |
| CN112084776A (en) | Similar article detection method, device, server and computer storage medium | |
| CN116821285A (en) | Text processing method, device, equipment and medium based on artificial intelligence | |
| CN116521938A (en) | Video data retrieval method, device, computer equipment and computer storage medium | |
| CN113902354B (en) | Travel evaluation data processing method and device and computer equipment | |
| CN113986245A (en) | Object code generation method, device, equipment and medium based on HALO platform | |
| CN115345166A (en) | Method, device, equipment and storage medium for identifying disease diagnosis name of medical text | |
| CN114842299A (en) | Training method, device, equipment and medium for image description information generation model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |