Clipboard management method and system
By monitoring the clipboard service and user behavior recognition model, dynamically adjusting the recommendation list of clipboard data, the problem that clipboard cannot manage multiple contents in the existing technology is solved, and efficient clipboard management is realized that multi-task scenarios are adapted to.
Patent Information
- Application Number
- CN202411333282.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-24
- Publication Date
- 2025-01-03
AI Technical Summary
The existing clipboard functions cannot effectively manage and reuse multiple content, and cannot adapt to multitasking scenarios.
By monitoring the clipboard service, add clipboard data according to the cache capacity strategy, and calculate the recommendation score based on the usage frequency and recent use time to sort; identify user behavior feature vectors based on the user behavior recognition model, dynamically adjust the recommendation list, and provide personalized clipping Board management operations.
Improve user operation efficiency and user experience, enable the clipboard management function to adapt to diverse and multi-task scenarios, and support deletion, editing, clearing and filtering operations.
Smart Images
Figure CN119248842A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of information processing technology, and in particular to a clipboard management method and system. Background Art
[0002] The clipboard is a mechanism for temporarily storing data, which allows users to copy and paste data between different applications or different parts of the same application. The function of the clipboard is very basic but extremely important, and is widely used in various operating systems and software.
[0003] The existing clipboard function is limited to the storage and copying of single content, cannot effectively manage and reuse multiple contents, and cannot adapt to multi-tasking scenarios. Summary of the invention
[0004] The present invention provides a clipboard management method and system to solve the problem that the existing clipboard cannot effectively manage and reuse multiple contents and cannot adapt to multi-tasking scenarios.
[0005] In order to solve the above technical problems, an embodiment of the present invention provides a clipboard management method, including:
[0006] Listen to the clipboard service, and when new clipboard data is obtained, add the clipboard data to the clipboard cache according to the cache capacity strategy;
[0007] Calculating a recommendation score for each piece of clipboard data according to the usage frequency and the most recent usage time of the clipboard data, and sorting all the clipboard data in the clipboard cache according to the recommendation score to generate a first recommendation list;
[0008] Identifying a user behavior feature vector based on a user behavior recognition model, and matching a user behavior pattern according to the user behavior feature vector;
[0009] The first recommendation list is dynamically adjusted according to the user behavior pattern to generate a second recommendation list; and when the user calls the clipboard service, the second recommendation list is displayed to the user so that the user can perform clipboard management operations, including deletion, editing, clearing and filtering operations.
[0010] The present invention caches newly acquired clipboard data by monitoring clipboard services, and sorts the user's historical clipboard data according to usage frequency, recent usage time and user behavior patterns, recommending frequently used or recently used clipboard content to users to improve user operation efficiency. At the same time, through personalized display, users can perform operations such as deletion, editing, clearing and filtering, thereby improving user experience and enabling clipboard management functions to adapt to diverse and multi-tasking scenarios.
[0011] Furthermore, the clipboard monitoring service, when acquiring new clipboard data, adds the clipboard data to the clipboard cache according to the cache capacity strategy, specifically:
[0012] Obtain a clipboard service object, set a listener in the clipboard service object, and initialize a clipboard cache;
[0013] When new clipboard data is monitored, the new clipboard data is compared with the first clipboard data in the clipboard cache. When the new clipboard data is different from the first clipboard data in the clipboard cache, the new clipboard data is added to the clipboard cache; the first clipboard data is the clipboard data recorded last time.
[0014] Further, the adding the clipboard data to the clipboard cache according to the cache capacity strategy further includes:
[0015] When the total amount of clipboard data in the clipboard cache exceeds a preset threshold, the clipboard data cached the longest is deleted.
[0016] Furthermore, the calculation formula of the recommendation score is specifically:
[0017]
[0018] Among them, S is the recommendation score, f is the number of times used, t is the time interval between uses, and α and β are the preset weight coefficients.
[0019] Furthermore, before identifying the user behavior feature vector based on the user behavior recognition model and matching the user behavior pattern according to the user behavior feature vector, the method further includes:
[0020] Collecting a user portrait data set, building a user behavior recognition model based on machine learning, and training the user behavior recognition model based on the user portrait data set;
[0021] Input the user portrait data set into a trained user behavior recognition model to obtain a user behavior feature vector;
[0022] The behavior feature vectors are clustered according to a clustering algorithm, and the user's behavior feature vectors are divided into a number of user behavior patterns according to the clustering result.
[0023] In a second aspect, the present invention provides a clipboard management system, including: a cache module, a score calculation module, a behavior recognition module, and an optimization module;
[0024] The cache module is used to monitor the clipboard service and, when acquiring new clipboard data, add the clipboard data to the clipboard cache according to the cache capacity strategy;
[0025] The score calculation module is used to calculate the recommendation score of each clipboard data according to the usage frequency and the most recent usage time of the clipboard data, and sort all the clipboard data in the clipboard cache according to the recommendation score to generate a first recommendation list;
[0026] The behavior recognition module is used to identify the user behavior feature vector based on the user behavior recognition model, and match the user behavior pattern according to the user behavior feature vector;
[0027] The optimization module is used to dynamically adjust the first recommendation list according to the user behavior pattern to generate a second recommendation list; and when the user calls the clipboard service, the second recommendation list is displayed to the user so that the user can perform clipboard management operations, and the clipboard management operations include deletion, editing, clearing and filtering operations.
[0028] Furthermore, the cache module is specifically used for:
[0029] Obtain a clipboard service object, set a listener in the clipboard service object, and initialize a clipboard cache;
[0030] When new clipboard data is monitored, the new clipboard data is compared with the first clipboard data in the clipboard cache. When the new clipboard data is different from the first clipboard data in the clipboard cache, the new clipboard data is added to the clipboard cache; the first clipboard data is the clipboard data recorded last time.
[0031] Furthermore, the cache module is also used for:
[0032] When the total amount of clipboard data in the clipboard cache exceeds a preset threshold, the clipboard data cached the longest is deleted.
[0033] Furthermore, the calculation formula of the recommendation score is specifically:
[0034]
[0035] Among them, S is the recommendation score, f is the number of times used, t is the time interval between uses, and α and β are the preset weight coefficients.
[0036] Furthermore, the behavior recognition module is further used to:
[0037] Collecting a user portrait data set, building a user behavior recognition model based on machine learning, and training the user behavior recognition model based on the user portrait data set;
[0038] Input the user portrait data set into a trained user behavior recognition model to obtain a user behavior feature vector;
[0039] The behavior feature vectors are clustered according to a clustering algorithm, and the user's behavior feature vectors are divided into a number of user behavior patterns according to the clustering result. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] Figure 1 A schematic flow chart of a clipboard management method provided in one embodiment of the present invention;
[0041] Figure 2 A schematic diagram of the structure of a clipboard management system provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0042] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
[0043] The terms "first" and "second" and the like in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.
[0044] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.
[0045] Example 1
[0046] See also Figure 1 , Figure 1 1 is a flow chart of a clipboard management method provided in an embodiment of the present invention. The present invention provides a clipboard management method, including steps 101 to 104, which are as follows:
[0047] Step 101: monitoring the clipboard service, and when obtaining new clipboard data, adding the clipboard data to the clipboard cache according to the cache capacity strategy;
[0048] In this embodiment, the clipboard monitoring service, when acquiring new clipboard data, adds the clipboard data to the clipboard cache according to the cache capacity strategy, specifically:
[0049] Obtain a clipboard service object, set a listener in the clipboard service object, and initialize a clipboard cache;
[0050] When new clipboard data is monitored, the new clipboard data is compared with the first clipboard data in the clipboard cache. When the new clipboard data is different from the first clipboard data in the clipboard cache, the new clipboard data is added to the clipboard cache; the first clipboard data is the clipboard data recorded last time.
[0051] In this embodiment, the clipboard service object refers to a component or service in an operating system or application program that is responsible for managing clipboard operations. The clipboard service object processes operations related to the clipboard, such as copying, cutting, and pasting data.
[0052] In this embodiment, a listener is set in the clipboard service object, and a clipboard cache of the clipboard service object is initialized, and the clipboard cache is used to store records of historical clipboard data. The set listener is used to determine whether the clipboard service object obtains new clipboard data.
[0053] In this embodiment, the adding the clipboard data to the clipboard cache according to the cache capacity strategy further includes:
[0054] When the total amount of clipboard data in the clipboard cache exceeds a preset threshold, the clipboard data cached the longest is deleted.
[0055] In this embodiment, when a change in the clipboard content is monitored, new clipboard data is obtained, and the new clipboard data is added to the clipboard cache according to the cache capacity strategy. The cache capacity strategy stipulates that when new clipboard data is obtained, the new clipboard data is compared with the clipboard data recorded last time. If the contents of the two clipboard data are different, the new clipboard data is added to the clipboard cache, otherwise, it is not added. The cache capacity strategy also stipulates that when the amount of clipboard data in the clipboard cache exceeds a preset threshold, the clipboard data cached the longest is deleted.
[0056] In this embodiment, the clipboard data is cached through a cache capacity strategy to avoid the same clipboard data occupying the data memory. At the same time, the clipboard data that has been cached for too long is cleared in time to improve the memory usage.
[0057] Step 102: Calculate a recommendation score for each piece of clipboard data according to the usage frequency and the most recent usage time of the clipboard data, and sort all the clipboard data in the clipboard cache according to the recommendation score to generate a first recommendation list;
[0058] In this embodiment, basic information of each clipboard data is recorded, the basic information including usage frequency and most recent usage time, and a score of each clipboard data in the clipboard cache is calculated based on the usage frequency and most recent usage time of the clipboard data.
[0059] In this embodiment, the calculation formula of the recommendation score is specifically:
[0060]
[0061] Among them, S is the recommendation score, f is the number of times used, t is the time interval between uses, and α and β are preset weight coefficients.
[0062] In this embodiment, α is used to adjust the importance of usage frequency. β is used to adjust the importance of recent usage time. Usage frequency indicates the number of times the content is used. Usage time interval, i.e., recent usage time indicates the time interval (in seconds) from the last use to the current use.
[0063] In this embodiment, the score of each clipboard data is calculated according to the calculation formula of the recommendation score, and the clipboard data is sorted in descending order of the recommendation score, and the high-scoring clipboard data is obtained according to the preset recommendation threshold to generate the first recommendation list.
[0064] In another optional embodiment, the score of each clipboard data is calculated according to the calculation formula of the recommendation score, and the clipboard data is sorted in descending order of the recommendation score, and the clipboard data with a score greater than the preset score threshold is filtered out according to the preset score threshold, and the first recommendation table is generated with the filtered clipboard data.
[0065] Step 103: identifying a user behavior feature vector based on the user behavior recognition model, and matching a user behavior pattern according to the user behavior feature vector;
[0066] In this embodiment, before identifying the user behavior feature vector based on the user behavior recognition model and matching the user behavior pattern according to the user behavior feature vector, the method further includes:
[0067] Collecting a user portrait data set, building a user behavior recognition model based on machine learning, and training the user behavior recognition model based on the user portrait data set;
[0068] Input the user portrait data set into a trained user behavior recognition model to obtain a user behavior feature vector;
[0069] The behavior feature vectors are clustered according to a clustering algorithm, and the user's behavior feature vectors are divided into a number of user behavior patterns according to the clustering result.
[0070] In this embodiment, a user identification model is constructed based on machine learning, and the user identification model may be a decision tree model, a random forest model, or a long short-term memory model.
[0071] In this embodiment, a user behavior recognition model is constructed based on a decision tree model, and a user portrait is collected based on clipboard operation logs and user interaction data. The user portrait includes features such as timestamp, content type, operation frequency, content length, and content source;
[0072] In this embodiment, the time features in the user portrait data are normalized, the category features are one-hot encoded, and the text features are keyword extracted.
[0073] In this embodiment, the timestamp and operation frequency in the user portrait data are normalized, the content type is uniquely encoded, and the text content is extracted for keyword processing according to the TF-IDF (Term Frequency-Inverse Document Frequency) method. The TF-IDF method is a statistical method for evaluating the importance of words in a document collection. By calculating the frequency of each word in the text content, and calculating the occurrence rate of the word in the clipboard data recorded by all clipboard caches, that is, the inverse document frequency of the word, the word importance is calculated according to the frequency and inverse document frequency, and the keyword is confirmed according to the word importance.
[0074] In this embodiment, the frequency of the words is specifically:
[0075]
[0076] Wherein, m is the number of times the word appears in the current clipboard data, and n is the total number of words in the current clipboard data.
[0077] In this embodiment, the inverse document frequency of the term is specifically:
[0078]
[0079] Wherein, N is the total number of clipboard data in the clipboard cache, and M is the number of documents containing the term in all clipboard data in the clipboard cache.
[0080] In this embodiment, the importance of the words is specifically:
[0081] S TF-IDF =f TF ×f IDF (4)
[0082] In this embodiment, the word importance of each word in the clipboard data is calculated. When the word importance of a certain word is greater than a preset importance threshold, the word is considered a keyword of the clipboard data.
[0083] In this embodiment, the preprocessed user portrait data is divided into a training set and a test set, and the user behavior recognition model is optimized based on the training set and a model optimization algorithm, wherein the optimization algorithm includes a G ini coefficient, information gain, and a back propagation algorithm.
[0084] In this embodiment, the hyperparameters of the user behavior recognition model are adjusted through grid search or random search. Specifically, in the decision tree model, the maximum depth of the decision tree model is adjusted according to the grid search or random search, and the model performance is evaluated using k-fold cross validation. When the model performance reaches the preset conditions, the trained user behavior recognition model is obtained.
[0085] In this embodiment, the collected user portrait data set is identified according to the trained user behavior recognition model, and the user behavior feature vector corresponding to each user portrait data is obtained. The user behavior feature vector includes the frequently used content type, operation period, and content source of the clipboard data.
[0086] In this embodiment, the user behavior feature vectors obtained by identifying each clipboard data are clustered, and divided into several user behavior models according to the clustering results, and different recommendation strategies are specified for each user behavior model.
[0087] In this embodiment, a clustering algorithm such as K-Means or DBSCAN is used to cluster the user's behavior feature vectors, identify different user behavior patterns, and formulate personalized recommendation strategies based on these patterns.
[0088] Step 104: Dynamically adjust the first recommendation list according to the user behavior pattern to generate a second recommendation list; and when the user calls the clipboard service, display the second recommendation list to the user so that the user can perform clipboard management operations, which include deletion, editing, clearing and filtering operations.
[0089] In this embodiment, the first recommendation list is optimized according to the user behavior pattern and the user's behavior characteristics, and the recommended content is dynamically adjusted to generate the second recommendation list.
[0090] As a specific example in an embodiment of the present invention, when a frequently used content type in a user behavior feature vector is in the medical field, the user is divided into a frequently used field type during clustering, and the ranking of clipboard data with a medical field type in the clipboard cache is increased.
[0091] In this embodiment, user operations are collected in real time, and the user's behavior feature vector and user behavior pattern are updated in real time according to the user operations, so as to dynamically update the recommendation list.
[0092] In this embodiment, the recommendation model is dynamically updated by collecting the latest user operation data in real time, and the model parameters are adjusted in real time through an online learning algorithm, such as incremental learning, to maintain the efficiency and accuracy of the recommendation strategy.
[0093] In this embodiment, when the second recommendation list is displayed to the user, a clipboard management page is also provided to the user, which displays the history of clipboard data and provides the user with functions such as deletion, clearing, and filtering clipboard data by type.
[0094] Please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a clipboard management system provided by an embodiment of the present invention, comprising: a cache module 201, a score calculation module 202, a behavior recognition module 203, and an optimization module 204;
[0095] The cache module 201 is used to monitor the clipboard service, and when new clipboard data is obtained, the clipboard data is added to the clipboard cache according to the cache capacity strategy;
[0096] The score calculation module 202 is used to calculate the recommendation score of each clipboard data according to the usage frequency and the most recent usage time of the clipboard data, and sort all the clipboard data in the clipboard cache according to the recommendation score to generate a first recommendation list;
[0097] The behavior recognition module 203 is used to recognize a user behavior feature vector based on a user behavior recognition model, and match a user behavior pattern according to the user behavior feature vector;
[0098] The optimization module 204 is used to dynamically adjust the first recommendation list according to the user behavior pattern to generate a second recommendation list; and when the user calls the clipboard service, the second recommendation list is displayed to the user so that the user can perform clipboard management operations, and the clipboard management operations include deletion, editing, clearing and filtering operations.
[0099] In this embodiment, the cache module is specifically used for:
[0100] Obtain a clipboard service object, set a listener in the clipboard service object, and initialize a clipboard cache;
[0101] When new clipboard data is monitored, the new clipboard data is compared with the first clipboard data in the clipboard cache. When the new clipboard data is different from the first clipboard data in the clipboard cache, the new clipboard data is added to the clipboard cache; the first clipboard data is the clipboard data recorded last time.
[0102] In this embodiment, the cache module is further used for:
[0103] When the total amount of clipboard data in the clipboard cache exceeds a preset threshold, the clipboard data cached the longest is deleted.
[0104] In this embodiment, the calculation formula of the recommendation score is specifically:
[0105]
[0106] Among them, S is the recommendation score, f is the number of times used, t is the time interval between uses, and α and β are the preset weight coefficients.
[0107] In this embodiment, the behavior recognition module is further used to:
[0108] Collecting a user portrait data set, building a user behavior recognition model based on machine learning, and training the user behavior recognition model based on the user portrait data set;
[0109] Input the user portrait data set into a trained user behavior recognition model to obtain a user behavior feature vector;
[0110] The behavior feature vectors are clustered according to a clustering algorithm, and the user's behavior feature vectors are divided into a number of user behavior patterns according to the clustering result.
[0111] In this embodiment, the user accesses the clipboard management system through the tablet interface. The system provides a clipboard history display interface, where the user can browse, delete, clear or filter records by type. The system monitors the changes in the clipboard content in real time in the background, and analyzes it through a machine learning model in combination with the user's operation records, and intelligently recommends the most relevant clipboard content. After the user selects the clipboard content to be used, the system copies it to the current clipboard for the user to paste and use. The system continuously updates and optimizes the recommendation strategy based on the user's selection behavior to improve the user experience.
[0112] In this embodiment, by monitoring the clipboard service, the newly acquired clipboard data is cached, and the user's historical clipboard data is sorted according to the frequency of use, the last usage time and the user behavior pattern, and the frequently used or recently used clipboard content is recommended to the user to improve the user's operating efficiency. At the same time, through personalized display, the user can perform operations such as deletion, editing, clearing and filtering, thereby improving the user's usage experience and enabling the clipboard management function to adapt to diverse and multi-tasking scenarios.
[0113] In an embodiment of the present invention, a multi-device access platform processing device is also provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above-mentioned multi-device access platform processing method is implemented.
[0114] In an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned multi-device access platform processing method.
[0115] Exemplarily, the computer program may be divided into one or more modules, one or more modules are stored in a memory and executed by a processor to implement the present invention. One or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the multi-device access platform processing device.
[0116] The multi-device access platform processing device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The multi-device access platform processing device may include, but is not limited to, a processor, a memory, and a display. Those skilled in the art will appreciate that the above components are merely examples of the multi-device access platform processing device and do not constitute a limitation on the multi-device access platform processing device. It may include more or fewer components than the components, or a combination of certain components, or different components. For example, the multi-device access platform processing device may also include input and output devices, network access devices, buses, and the like.
[0117] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the multi-device access platform processing device, and uses various interfaces and lines to connect various parts of the entire multi-device access platform processing device.
[0118] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the multi-device access platform processing device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, a text conversion function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.
[0119] Among them, if the module based on the processing of the multi-device access platform is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. Ordinary technicians in this field can understand and implement it without paying creative labor.
[0120] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for 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 protection of the present invention.
Claims
1. A clipboard management method, characterized in that: include: Monitor the clipboard service, and when new clipboard data is obtained, add the clipboard data to the clipboard cache according to the cache capacity strategy; Calculating a recommendation score for each piece of clipboard data according to the usage frequency and the most recent usage time of the clipboard data, and sorting all the clipboard data in the clipboard cache according to the recommendation score to generate a first recommendation list; Identifying a user behavior feature vector based on a user behavior recognition model, and matching a user behavior pattern according to the user behavior feature vector; The first recommendation list is dynamically adjusted according to the user behavior pattern to generate a second recommendation list; and when the user calls the clipboard service, the second recommendation list is displayed to the user so that the user can perform clipboard management operations, including deletion, editing, clearing and filtering operations.
2. A clipboard management method as claimed in claim 1, characterized in that: The monitoring clipboard service, when acquiring new clipboard data, adds the clipboard data to the clipboard cache according to the cache capacity strategy, specifically: Obtain a clipboard service object, set a listener in the clipboard service object, and initialize a clipboard cache; When new clipboard data is monitored, the new clipboard data is compared with the first clipboard data in the clipboard cache. When the new clipboard data is different from the first clipboard data in the clipboard cache, the new clipboard data is added to the clipboard cache; the first clipboard data is the clipboard data recorded last time.
3. A clipboard management method as claimed in claim 2, characterized in that: The adding the clipboard data to the clipboard cache according to the cache capacity strategy also includes: When the total amount of clipboard data in the clipboard cache exceeds a preset threshold, the clipboard data cached the longest is deleted.
4. A clipboard management method as claimed in claim 1, characterized in that: The calculation formula of the recommendation score is specifically: Among them, S is the recommendation score, f is the number of times used, t is the time interval between uses, and α and β are preset weight coefficients.
5. A clipboard management method as claimed in claim 1, characterized in that: Before identifying the user behavior feature vector based on the user behavior recognition model and matching the user behavior pattern according to the user behavior feature vector, the method further includes: Collecting a user portrait data set, building a user behavior recognition model based on machine learning, and training the user behavior recognition model based on the user portrait data set; Input the user portrait data set into a trained user behavior recognition model to obtain a user behavior feature vector; The behavior feature vectors are clustered according to a clustering algorithm, and the user's behavior feature vectors are divided into a number of user behavior patterns according to the clustering result.
6. A clipboard management system, characterized in that: include: Cache module, score calculation module, behavior recognition module, optimization module; The cache module is used to monitor the clipboard service and, when acquiring new clipboard data, add the clipboard data to the clipboard cache according to the cache capacity strategy; The score calculation module is used to calculate the recommendation score of each clipboard data according to the usage frequency and the most recent usage time of the clipboard data, and sort all the clipboard data in the clipboard cache according to the recommendation score to generate a first recommendation list; The behavior recognition module is used to identify the user behavior feature vector based on the user behavior recognition model, and match the user behavior pattern according to the user behavior feature vector; The optimization module is used to dynamically adjust the first recommendation list according to the user behavior pattern to generate a second recommendation list; When the user calls the clipboard service, the second recommendation list is displayed to the user so that the user can perform clipboard management operations, and the clipboard management operations include deletion, editing, clearing and filtering operations.
7. A clipboard management system as claimed in claim 6, characterized in that: The cache module is specifically used for: Obtain a clipboard service object, set a listener in the clipboard service object, and initialize a clipboard cache; When new clipboard data is monitored, the new clipboard data is compared with the first clipboard data in the clipboard cache. When the new clipboard data is different from the first clipboard data in the clipboard cache, the new clipboard data is added to the clipboard cache; the first clipboard data is the clipboard data recorded last time.
8. A clipboard management system as claimed in claim 7, characterized in that: The cache module is further used for: When the total amount of clipboard data in the clipboard cache exceeds a preset threshold, the clipboard data cached the longest is deleted.
9. A clipboard management system as claimed in claim 6, characterized in that: The calculation formula of the recommendation score is specifically: Among them, S is the recommendation score, f is the number of times used, t is the time interval between uses, and α and β are preset weight coefficients.
10. A clipboard management system as claimed in claim 6, characterized in that: The behavior recognition module is further specifically used for: Collecting a user portrait data set, building a user behavior recognition model based on machine learning, and training the user behavior recognition model based on the user portrait data set; Input the user portrait data set into a trained user behavior recognition model to obtain a user behavior feature vector; The behavior feature vectors are clustered according to a clustering algorithm, and the user's behavior feature vectors are divided into a number of user behavior patterns according to the clustering result.