Method and system for improving concentration efficiency based on mouse vibration prompt

Abstract

The invention relates to the technical field of computer input equipment, and discloses a method and system for improving concentration efficiency based on mouse vibration prompt, and the method comprises the steps: carrying out the behavior target analysis of a concentration time period parameter, and determining a concentration event of a target user; extracting operation track characteristics and interaction object attributes in the mouse operation behavior data, calculating an operation deviation degree corresponding to the target user, and analyzing an operation deviation event corresponding to the target user; setting a vibration strength gradient of using the mouse with respect to the operation deviation event; recording an operation timestamp and a trigger frequency when the target user executes the task, constructing a multi-dimensional efficiency evaluation matrix using a mouse, calculating a task immersion degree when the target user executes the task, and generating an efficiency analysis report of the target user; and creating a rest reminding mechanism according to the efficiency analysis report and the vibration feedback mechanism, and executing vibration reminding on the target user in real time. According to the invention, the concentration efficiency of the mouse in use can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for improving concentration efficiency based on mouse vibration prompts, and belongs to the technical field of computer input devices. Background Art

[0002] In today's fast-paced learning and working environments, people increasingly rely on computers to complete various tasks. However, during the long-term use of computers, distraction is extremely common, seriously affecting learning and working efficiency. How to help users concentrate and improve their focus has become a problem to be solved.

[0003] Currently, the methods for improving concentration efficiency mainly include three ways: time management software assistance, environment management, and reminder tools. Time management software helps users reasonably plan time by means of functions such as setting task time and making plans. However, such software cannot real-time perceive the user's behavior state and is difficult to make an immediate response to distraction behaviors. Environment management reduces external interference by creating a quiet and orderly space. However, this method requires a high level of personal self-discipline and cannot effectively prevent users from performing operations unrelated to tasks on the computer. Reminder tools usually use pop-ups, sounds, etc. for reminders, which not only easily distract the user's attention but also have a single reminder form and are difficult to continuously play a warning role. Therefore, a method for improving concentration efficiency is needed. Summary of the Invention

[0004] The present invention provides a method and system for improving concentration efficiency based on mouse vibration prompts, and its main purpose is to improve the concentration efficiency when using the mouse.

[0005] To achieve the above purpose, a method for improving concentration efficiency based on mouse vibration prompts provided by the present invention includes:

[0006] Obtain the concentration period parameters set by the target user and the use of the mouse, and combine with a preset task-related strategy library to perform behavior target analysis on the concentration period parameters to determine the concentration events of the target user;

[0007] Real-time monitor the mouse operation behavior data of the target user, extract the operation trajectory features and interaction object attributes in the mouse operation behavior data, combine the interaction object attributes and the concentration events, calculate the operation deviation degree corresponding to the target user, and analyze the operation deviation events corresponding to the target user based on the operation deviation degree;

[0008] Identify the event identifier corresponding to the operation deviation event, set the vibration intensity gradient of the used mouse for the operation deviation event according to the operation deviation degree and the event identifier, and construct a vibration feedback mechanism for the used mouse in combination with the operation trajectory features and the vibration intensity gradient;

[0009] After receiving the task execution instruction using the mouse, record the operation timestamp and trigger frequency of the target user during task execution. Combine the operation timestamp and the trigger frequency to construct a multi-dimensional efficiency evaluation matrix for using the mouse, and calculate the task immersion level of the target user during task execution. Generate an efficiency analysis report for the target user based on the multi-dimensional efficiency evaluation matrix and the task immersion level;

[0010] Create a rest reminder mechanism based on the efficiency analysis report and the vibration feedback mechanism, and perform real-time vibration reminders for the target user.

[0011] Optionally, combining with a preset task-related policy library, perform behavior target parsing on the focus period parameters to determine the focus events of the target user, including:

[0012] Analyze the task type corresponding to the focus period parameters, and query the main guiding policies associated with the task type from the preset task-related policy library;

[0013] Based on the main guiding policies, determine the behavior target parsing process corresponding to the focus period parameters;

[0014] Locate the key behavior nodes in the behavior target parsing process;

[0015] Based on the key behavior nodes, extract the behavior target parsing results from the focus period parameters;

[0016] Based on the behavior target parsing results, determine the focus events of the target user.

[0017] Optionally, the querying the main guiding policies associated with the task type from the preset task-related policy library includes:

[0018] Perform semantic parsing on the task type to obtain task type semantics, and screen out key semantics from the task type semantics;

[0019] Identify the policy tags in the preset task-related policy library, and perform semantic interpretation on the policy tags to obtain policy tag semantics;

[0020] Perform vectorization processing on the key semantics and the policy tag semantics respectively to obtain a first semantic vector and a second semantic vector;

[0021] Calculate the semantic similarity between the first semantic vector and the second semantic vector through the following formula:

[0022]

[0023] Among them, S represents the semantic similarity between the first semantic vector and the second vector semantics, E1 a represents the a-th vector in the first semantic vector, E2 b represents the b-th vector in the second semantic vector, a and b respectively represent the serial numbers corresponding to the first semantic vector and the second semantic vector, n and m respectively represent the quantities corresponding to the first semantic vector and the second semantic vector, and cos_sim represents the cosine similarity function;

[0024] Based on the semantic similarity, query the main guiding strategy associated with the task type from the preset task-related strategy library.

[0025] Optionally, the extraction of the operation trajectory features and interaction object attributes in the mouse operation behavior data includes:

[0026] Classify the mouse operation behavior data to obtain mouse movement trajectory data and mouse interaction object data;

[0027] Extract the trajectory description data in the mouse movement trajectory data, and based on the trajectory description data, draw the movement trajectory graph corresponding to the mouse movement trajectory data;

[0028] Extract the trajectory geometric features in the movement trajectory graph, and identify the trajectory movement direction in the movement trajectory graph;

[0029] Combine the trajectory geometric features and the trajectory movement direction to generate the operation trajectory features of the mouse operation behavior data;

[0030] Perform attribute parsing processing on the mouse interaction object data to obtain a data attribute set;

[0031] Perform screening processing on the data attribute set to obtain the interaction object attributes of the mouse operation behavior data.

[0032] Optionally, the calculation of the operation deviation degree corresponding to the target user by combining the interaction object attributes and the focus event includes:

[0033] Analyze the interaction object label corresponding to the interaction object attribute;

[0034] Extract the event features corresponding to the focus event, and calculate the association strength between the event features and the interaction object label;

[0035] Based on the association strength, construct an association matrix of the focus event and the interaction object attributes;

[0036] Statistically calculate the object operation duration corresponding to the interaction object attribute, and based on the object operation duration, assign the attribute weight corresponding to the interaction object attribute;

[0037] Calculate the comprehensive correlation degree between the focused event and the interactive object attribute by combining the correlation matrix and the attribute weight;

[0038] Calculate the operation deviation degree corresponding to the target user based on the comprehensive correlation degree.

[0039] Optionally, setting the vibration intensity gradient of the mouse regarding the operation deviation event according to the operation deviation degree and the event identifier includes:

[0040] Identify the deviation threshold in the operation deviation degree, where the deviation threshold includes a maximum deviation value and a minimum deviation value;

[0041] Query the vibration motor hardware and motor-related hardware corresponding to the mouse;

[0042] Test the vibration tolerance intensity corresponding to the vibration motor hardware and the motor-related hardware, where the vibration tolerance intensity includes a peak tolerance intensity and a valley tolerance intensity;

[0043] Combine the operation deviation degree, the maximum deviation value, the minimum deviation value, the peak tolerance intensity, and the valley tolerance intensity, and calculate the basic vibration intensity of the mouse regarding the operation deviation event through the following formula:

[0044]

[0045] where A represents the basic vibration intensity of the mouse regarding the operation deviation event, B represents the operation deviation degree, B max represents the maximum deviation value, B min represents the minimum deviation value, F max represents the peak tolerance intensity, F min represents the valley tolerance intensity;

[0046] Set the vibration intensity gradient of the mouse regarding the operation deviation event by combining the basic vibration intensity and the event identifier.

[0047] Optionally, constructing the vibration feedback mechanism of the mouse by combining the operation trajectory feature and the vibration intensity gradient includes:

[0048] Screen out the deviation trajectory feature from the operation trajectory feature, and perform element decomposition processing on the deviation trajectory feature to obtain the deviation element trajectory feature;

[0049] Determine the initial matching vibration parameters corresponding to the mouse by combining the deviation element trajectory feature and the vibration intensity gradient;

[0050] Evaluate the operation complexity corresponding to the operation trajectory features, and collect the target user behavior preference data;

[0051] Based on the user behavior preference data, analyze the behavior preference tendency corresponding to the target user;

[0052] Combine the operation complexity and the behavior preference tendency to optimize the initial matching vibration parameters to obtain optimized vibration parameters;

[0053] Based on the optimized vibration parameters, construct the vibration feedback mechanism for using the mouse.

[0054] Optionally, the constructing the multi-dimensional efficiency evaluation matrix for using the mouse by combining the operation timestamp and the trigger frequency includes:

[0055] Analyze the time distribution characteristics corresponding to the operation timestamp;

[0056] Based on the time distribution characteristics, extract the key operation periods corresponding to the target user from the operation timestamp;

[0057] Calculate the trigger coefficients of the trigger frequency in different time periods, and determine the representative trigger frequency in the trigger frequency according to the trigger coefficients;

[0058] Based on the key operation time periods, generate the efficiency evaluation dimensions for using the mouse;

[0059] Combine the efficiency evaluation dimensions and the representative trigger frequency to construct the multi-dimensional efficiency evaluation matrix for using the mouse.

[0060] Optionally, the calculating the task immersion degree of the target user when performing a task includes:

[0061] Record the operation log data and operation cycle data of the target user when performing the task;

[0062] Identify the operation step identifiers and step feedback information in the operation log data, and based on the step feedback information, count the effective operation frequency and the total operation frequency of the target user when performing the task;

[0063] Query the task operation process of the target user when performing the task, and combine the operation step identifiers and the task operation process to count the correct operation steps and the total operation steps of the target user when performing the task;

[0064] According to the operation cycle data, calculate the actual operation cycle of the target user when performing the task;

[0065] Combined with the effective operation frequency, the total operation frequency, the correct operation steps, the total operation steps, and the actual operation cycle, calculate the task immersion degree of the target user when performing a task through the following formula:

[0066]

[0067] Among them, H represents the task immersion degree of the target user when performing a task, G represents the effective operation frequency, G 总 represents the total operation frequency, L represents the correct operation steps, L 总 represents the total operation steps, t represents the actual operation cycle, and T represents the expected operation cycle.

[0068] To solve the above problems, the present invention also provides a system for improving focus efficiency based on mouse vibration prompts. The system includes:

[0069] A focus event determination module, configured to obtain the focus period parameter set by the target user and the use of the mouse, and combine with a preset task-related policy library to perform behavior target analysis on the focus period parameter to determine the focus event of the target user;

[0070] An operation deviation event analysis module, configured to monitor the mouse operation behavior data of the target user in real time, extract the operation trajectory features and interaction object attributes in the mouse operation behavior data, combine the interaction object attributes and the focus event, calculate the operation deviation degree corresponding to the target user, and based on the operation deviation degree, analyze the operation deviation event corresponding to the target user;

[0071] A vibration feedback mechanism construction module, configured to identify the event identifier corresponding to the operation deviation event, set the vibration intensity gradient of the used mouse for the operation deviation event according to the operation deviation degree and the event identifier, and combine the operation trajectory features and the vibration intensity gradient to construct the vibration feedback mechanism of the used mouse;

[0072] An efficiency analysis report generation module, configured to record the operation timestamp and trigger frequency of the target user when performing a task after responding to the task execution instruction of the used mouse, combine the operation timestamp and the trigger frequency to construct a multi-dimensional efficiency evaluation matrix of the used mouse, calculate the task immersion degree of the target user when performing a task, and generate an efficiency analysis report of the target user according to the multi-dimensional efficiency evaluation matrix and the task immersion degree;

[0073] A vibration reminder module, configured to create a rest reminder mechanism according to the efficiency analysis report and the vibration feedback mechanism, and perform real-time vibration reminder on the target user.

[0074] Compared with the problems described in the background art, the present invention combines a preset task-related policy library to perform behavioral target analysis on the focus period parameters to determine the focus events of the target user, and can convert the simple time parameters set by the user into specific executable focus behavioral targets, providing personalized focus guidance services for the user. According to the task characteristics and focus habits of the user, the guidance strategy is adjusted in real time to improve the user's focus efficiency. Further, the present invention can accurately characterize the user's operation habits and intentions by extracting the operation trajectory features and interaction object attributes in the mouse operation behavior data, providing an important basis for the calculation of the operation deviation degree corresponding to the subsequent target user. The present invention can accurately determine the deviation event type by identifying the event identifier corresponding to the operation deviation event. According to the operation deviation degree and the event identifier, the vibration intensity gradient of the mouse regarding the operation deviation event is set, and an appropriate vibration feedback intensity can be set for the target user. Further, the present invention constructs a multi-dimensional efficiency evaluation matrix of using the mouse by combining the operation timestamp and the trigger frequency, which can effectively quantify the efficiency of the user using the mouse to perform tasks, help to discover the advantages and disadvantages in the user's operation habits, and provide a strong basis for the performance optimization of the mouse and the improvement of the operation process. Further, the present invention creates a rest reminder mechanism according to the efficiency analysis report and the vibration feedback mechanism, and performs real-time vibration reminder on the target user, which can help the target user timely perceive fatigue and take the initiative to rest during the execution of high-intensity tasks, reduce the efficiency decline and health risks caused by continuous work, promote the combination of work and rest while improving work efficiency, and achieve a balance between sustainable work rhythm and physical and mental health. Therefore, the method and system for improving focus efficiency based on mouse vibration prompts provided by the embodiments of the present invention can improve the focus efficiency when using the mouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 FIG. is a schematic flow chart of a method for improving focus efficiency based on mouse vibration prompts provided by an embodiment of the present invention;

[0076] Figure 2 FIG. is a schematic diagram of a module for implementing the method for improving focus efficiency based on mouse vibration prompts provided by an embodiment of the present invention.

[0077] The implementation, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0079] An embodiment of the present application provides a method for improving concentration efficiency based on mouse vibration prompts. The execution subject of the method for improving concentration efficiency based on mouse vibration prompts includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for improving concentration efficiency based on mouse vibration prompts can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0080] Embodiment 1:

[0081] Referring to Figure 1 As shown, it is a flowchart of a method for improving concentration efficiency based on mouse vibration prompts provided by an embodiment of the present invention. In this embodiment, the method for improving concentration efficiency based on mouse vibration prompts includes:

[0082] S1. Obtain the concentration period parameters set by the target user and the use of the mouse, and combine with a preset task-related strategy library to perform behavioral target analysis on the concentration period parameters to determine the concentration events of the target user.

[0083] By combining with a preset task-related strategy library and performing behavioral target analysis on the concentration period parameters to determine the concentration events of the target user, the present invention can convert the simple time parameters set by the user into specific executable concentration behavioral targets, provide personalized concentration guidance services for the user, and adjust the guidance strategy in real time according to the user's task characteristics and concentration habits, so as to improve the user's concentration efficiency. Among them, the target user refers to those individuals who hope to improve their own concentration, are prone to distraction in learning, work and other scenarios, and are willing to use the system to help themselves concentrate on completing tasks. The concentration period parameters refer to the concentration time periods set by the user according to their own needs and task arrangements, such as a 25-minute concentration period set by using the Pomodoro Technique, or other duration concentration periods set according to the specific task difficulty and their own energy. The use of the mouse refers to the behavior of the user interacting with the mouse during the concentration task, including operations such as mouse movement, clicking, and scrolling. The preset task-related strategy library refers to a set of concentration guidance strategies pre-constructed by the system, which includes concentration guidance strategies for different types of tasks and concentration periods. The concentration event is a specific behavior or activity that the target user focuses on and is related to a specific task or interest within a specific time period.

[0084] As an embodiment of the present invention, the combination of the preset task-related strategy library to perform behavioral target analysis on the concentration period parameters to determine the concentration events of the target user includes:

[0085] Analyze the task type corresponding to the focus period parameter, and query the main guiding strategy associated with the task type from the preset task-related strategy library;

[0086] Based on the main guiding strategy, determine the behavior target analysis process corresponding to the focus period parameter;

[0087] Locate the key behavior nodes in the behavior target analysis process;

[0088] Based on the key behavior nodes, extract the behavior target analysis results in the focus period parameter;

[0089] Based on the behavior target analysis results, determine the focus event of the target user.

[0090] Among them, the task type refers to the category to which the tasks to be completed by the user during the focus period belong. For example, learning tasks (such as reading books, doing exercises, etc.), work tasks (such as writing documents, processing data, etc.), creative tasks (such as painting, writing, etc.). Different task types have different requirements and characteristics for focus. The main guiding strategy refers to the focus guiding strategy recommended for a specific task type in the preset task-related strategy library. For example, for learning tasks, the main guiding strategy may include reminding to rest regularly, setting learning progress goals, etc.; for work tasks, it may include restricting access to irrelevant web pages, giving phased task reminders, etc. The behavior target analysis process refers to a set of systematic analysis steps constructed according to the main guiding strategy for a specific task type and focus period. For example, when dealing with learning tasks, the behavior target analysis process can start from determining the learning content and goals, then analyzing the user's learning habits and abilities, and then formulating specific learning plans and reminder strategies, etc. The key behavior nodes refer to the key links or key behavior requirements in the behavior target analysis process, which play a decisive role in achieving the focus behavior goal. For example, in the behavior target analysis process of the above learning tasks, completing the learning progress on time and avoiding long-term distraction are key behavior nodes.

[0091] Furthermore, the analysis of the task type corresponding to the focus period parameter can be achieved through a natural language processing algorithm, such as using a text classification algorithm to analyze the task description input by the user to determine the task category to which it belongs. The query of the main guidance strategy associated with the task type can be achieved through a rule matching algorithm. The system searches for a matching guidance strategy in a preset task-related strategy library based on the task type. The determination of the behavioral goal parsing process corresponding to the focus period parameter can be achieved through a process construction method, such as constructing a preliminary process framework, and then using a machine learning algorithm to learn historical task data, optimizing and improving the initial process according to the task characteristics and focus period parameters, thereby determining a behavioral goal parsing process that fits the specific situation, and locating the key behavioral nodes in the behavioral goal parsing process. The point can be achieved through data analysis algorithms. By analyzing a large number of case data of successfully completing focus tasks, the behavioral nodes that play a key role in task completion can be identified. The extraction of behavioral target analysis results in the focus period parameters can be achieved through information extraction technology. A rule-based extraction method is adopted. According to the determined key behavioral nodes and behavioral target analysis process, structured information is extracted from relevant data to form the final behavioral target analysis results. Based on the behavioral target analysis results, the focus events of the target user are determined. For example, with the help of cluster analysis and sequence pattern mining technology, the user behavior data context is sorted out, and finally the focus events of the target user are obtained. For example, the user's behaviors around learning, work or leisure are identified, and coherent focus behavior segments such as continuous practice of questions, writing copy, chasing dramas and watching movies are divided.

[0092] Optionally, as an optional embodiment of the present invention, querying the main guidance strategy associated with the task type from the preset task-related strategy library includes:

[0093] Performing semantic analysis on the task type to obtain task type semantics, and screening out key semantics from the task type semantics;

[0094] Identify the policy tags in the preset task-related policy library, perform semantic interpretation on the policy tags, and obtain the policy tag semantics;

[0095] Performing vectorization processing on the key semantics and the policy label semantics respectively to obtain a first semantic vector and a second semantic vector;

[0096] The semantic similarity between the first semantic vector and the second semantic vector is calculated using the following formula:

[0097]

[0098] Among them, S represents the semantic similarity between the first semantic vector and the second semantic vector, E1 aDenote the a-th vector in the first semantic vector, E2 b Denote the b-th vector in the second semantic vector, where a and b represent the sequence numbers corresponding to the first semantic vector and the second semantic vector respectively, n and m represent the quantities corresponding to the first semantic vector and the second semantic vector respectively, and cos_sim represents the cosine similarity function;

[0099] Based on the semantic similarity, query the main guiding strategy associated with the task type from the preset task-related strategy library.

[0100] Among them, the task type semantics is the semantic description corresponding to the task type that can reflect the essential characteristics and connotations of the task; the key semantics is the most core semantic part in the task type semantics that plays a key role in completing the task; the strategy label is the label used to identify a specific task strategy in the preset task-related strategy library; the strategy label semantics is the semantic content represented by the strategy label that can explain the characteristics and functions of the strategy; the first semantic vector and the second semantic vector are the numerical vectors corresponding to the key semantics and the strategy label semantics respectively after vectorization processing, which can represent their semantic information in the vector space; the semantic similarity represents the similarity degree between the first semantic vector and the second semantic vector.

[0101] Furthermore, the task type can be semantically parsed through semantic analysis to obtain the task type semantics; the key semantics can be screened out from the task type semantics through the TF-IDF algorithm; the strategy labels in the preset task-related strategy library can be identified through an identification tool, and the identification tool is compiled by a scripting language; the strategy label semantics can be obtained by semantically interpreting the strategy labels through the above-mentioned semantic analysis method; the key semantics and the strategy label semantics can be vectorized respectively through the word2vec algorithm to obtain the first semantic vector and the second semantic vector; when the semantic similarity is greater than a preset threshold, the corresponding strategy in the preset task-related strategy library is used as the main guiding strategy associated with the task type, and the preset threshold can be set to 0.8 or can be set according to the actual application scenario.

[0102] S2. Real-time monitor the mouse operation behavior data of the target user, extract the operation trajectory features and interaction object attributes in the mouse operation behavior data, combine the interaction object attributes and the focus event, calculate the operation deviation degree corresponding to the target user, and analyze the operation deviation event corresponding to the target user based on the operation deviation degree.

[0103] By extracting the operation trajectory features and interaction object attributes in the mouse operation behavior data, the operation habits and intentions of the user can be accurately characterized, providing an important basis for the subsequent calculation of the operation deviation degree corresponding to the target user. Among them, the mouse operation behavior data refers to a series of data generated by the user during the use of the mouse, including but not limited to information such as the click position, click frequency, movement speed, and dragging behavior of the mouse. These data completely record the interaction process between the user and the device interface; the operation trajectory features refer to the feature information extracted from the mouse operation behavior data for describing the mouse movement path and rules, such as the length and curvature of the movement trajectory, and the direction change of the operation path; the interaction object attributes refer to the relevant attributes of the interface elements pointed to or acted on by the user through mouse operations, such as the function attributes of interface buttons, the content types of text boxes, and the categories of charts. Further, the real-time monitoring of the mouse operation behavior data of the target user can be achieved through the built-in sensors in the mouse device and the supporting monitoring software.

[0104] As an embodiment of the present invention, the extraction of the operation trajectory features and interaction object attributes in the mouse operation behavior data includes:

[0105] Classify the mouse operation behavior data to obtain mouse movement trajectory data and mouse interaction object data;

[0106] Extract the trajectory description data in the mouse movement trajectory data, and based on the trajectory description data, draw the movement trajectory diagram corresponding to the mouse movement trajectory data;

[0107] Extract the trajectory geometric features in the movement trajectory diagram, and identify the trajectory movement direction in the movement trajectory diagram;

[0108] Combine the trajectory geometric features and the trajectory movement direction to generate the operation trajectory features of the mouse operation behavior data;

[0109] Perform attribute parsing processing on the mouse interaction object data to obtain a data attribute set;

[0110] Perform screening processing on the data attribute set to obtain the interaction object attributes of the mouse operation behavior data.

[0111] Among them, the mouse movement trajectory data and the mouse interaction object data are respectively the relevant data in the mouse operation behavior data regarding the mouse movement path and the object that interacts with the mouse; the trajectory description data is the data in the mouse movement trajectory data used to describe the specific characteristics of the movement trajectory; the movement trajectory graph is an image that graphically displays the mouse movement trajectory corresponding to the mouse movement trajectory data; the trajectory geometric features are the geometric features such as the trajectory shape and length reflected in the movement trajectory graph; the trajectory movement direction is the direction in which the mouse moves in the movement trajectory graph; the data attribute set is the set of all attributes of the mouse interaction object data.

[0112] Furthermore, the mouse operation behavior data can be classified into mouse movement trajectory data and mouse interaction object data through event type discrimination; the trajectory description data in the mouse movement trajectory data can be extracted through coordinate and timestamp analysis; based on the trajectory description data, the movement trajectory graph corresponding to the mouse movement trajectory data can be drawn through a drawing algorithm; the trajectory geometric features in the movement trajectory graph, such as trajectory length and curvature, can be extracted through geometric calculation methods; the trajectory movement direction in the movement trajectory graph can be identified through vector analysis; by combining the trajectory geometric features and the trajectory movement direction, the operation trajectory features of the mouse operation behavior data can be generated through feature integration; the mouse interaction object data can be subjected to attribute parsing processing through a text parsing method to obtain a data attribute set; and the data attribute set can be filtered through key attribute screening to obtain the interaction object attributes of the mouse operation behavior data, such as screening the window name and the application to which it belongs for window - type objects, and screening the file name and file type for file - type objects, so as to construct a concise attribute system that can characterize the characteristics of the interaction object.

[0113] The present invention calculates the operation deviation degree corresponding to the target user by combining the interaction object attributes and the focus event, which can quantify the deviation degree between the user's operation and the focus task, providing an important basis for the subsequent analysis of the operation deviation event corresponding to the target user. Among them, the operation deviation degree is a quantitative index used to measure the deviation degree between the user's mouse operation behavior and the focus event. The larger the value, the higher the degree of deviation of the user's operation from the focus task; the smaller the value, the closer the user's operation is to the focus task.

[0114] As an embodiment of the present invention, the combination of the interaction object attributes and the focus event to calculate the operation deviation degree corresponding to the target user includes:

[0115] Analyze the interaction object label corresponding to the interaction object attribute;

[0116] Extract the event features corresponding to the focused event, and calculate the correlation strength between the event features and the interaction object label;

[0117] Based on the correlation strength, construct the correlation matrix of the focused event and the interaction object attribute;

[0118] Statistically analyze the object operation duration corresponding to the interaction object attribute, and based on the object operation duration, assign the attribute weight corresponding to the interaction object attribute;

[0119] Combine the correlation matrix and the attribute weight to calculate the comprehensive correlation degree between the focused event and the interaction object attribute;

[0120] Based on the comprehensive correlation degree, calculate the operation deviation degree corresponding to the target user.

[0121] Among them, the interaction object label is the classification identifier corresponding to the interaction object attribute, which is used to concisely summarize the type of the interaction object; the event feature is the key element corresponding to the focused event, which reflects the core characteristics of the focused event; the correlation strength represents the relevant tightness between the event feature and the interaction object label; the correlation matrix is a quantitative presentation of the relationship between the focused event and the interaction object attribute, showing their association; the object operation duration is the statistical operation time corresponding to the interaction object attribute, reflecting the time investment of the user in interacting with this object; the attribute weight is the importance coefficient corresponding to the interaction object attribute, measuring its role in the overall evaluation; the comprehensive correlation degree represents the overall correlation degree between the focused event and the interaction object attribute.

[0122] Furthermore, the interaction object label corresponding to the interaction object attribute can be analyzed through a classification model, such as a decision tree model; the event features corresponding to the focused event can be extracted through text mining techniques; the correlation strength between the event features and the interaction object label can be calculated through the Pearson correlation coefficient; based on the correlation strength, the correlation matrix of the focused event and the interaction object attribute can be constructed through the data matrix construction method; the object operation duration corresponding to the interaction object attribute can be statistically analyzed through timestamp analysis; based on the object operation duration, the attribute weight corresponding to the interaction object attribute can be assigned according to the duration ratio; by combining the correlation matrix and the attribute weight, the comprehensive correlation degree between the focused event and the interaction object attribute can be calculated through the weighted summation method; the operation deviation degree corresponding to the target user can be calculated by subtracting the comprehensive correlation degree from 1.

[0123] Based on the operation deviation degree, the present invention analyzes the operation deviation events corresponding to the target user, and can accurately identify the specific behaviors of the user deviating from the focused task during the operation. This helps to send a reminder to the user subsequently, guide the user to adjust the operation behavior, and improve the focus efficiency. Among them, the operation deviation event refers to the specific situation of deviating from the focused task caused by the user's mouse operation behavior. For example, when the operation deviation degree exceeds a certain threshold, it may indicate that the user has occurred operation deviation events such as switching to an entertainment website or opening an application unrelated to the task. Further, when the operation deviation degree reaches the preset alarm threshold, the system automatically triggers an analysis program. This program further analyzes the operation trajectory characteristics and the attributes of the interaction object, and combines the historical operation data and the common distraction behavior patterns to judge the type of operation deviation event that the user may occur. For example, if the operation trajectory frequently appears in the window of the video playback software and the attributes of the interaction object show an entertainment video file, it is judged that the user may be watching a video, which belongs to an operation deviation event.

[0124] S3. Identify the event identifier corresponding to the operation deviation event. According to the operation deviation degree and the event identifier, set the vibration intensity gradient of the mouse regarding the operation deviation event. Combine the operation trajectory characteristics and the vibration intensity gradient to construct the vibration feedback mechanism of the mouse.

[0125] By identifying the event identifier corresponding to the operation deviation event, the present invention can accurately determine the type of deviation event. According to the operation deviation degree and the event identifier, setting the vibration intensity gradient of the mouse regarding the operation deviation event can set an appropriate vibration feedback intensity for the target user. Among them, the event identifier is the unique identification label corresponding to the operation deviation event, which is used to define the type and nature of the operation deviation event; the vibration intensity gradient is the hierarchical vibration parameter setting of the mouse regarding the operation deviation event, which is determined based on the operation deviation degree and the event identifier to achieve differentiated vibration feedback. Further, the identification of the event identifier corresponding to the operation deviation event can be realized by using machine learning algorithms to extract and classify relevant features and other methods.

[0126] As an embodiment of the present invention, setting the vibration intensity gradient of the mouse regarding the operation deviation event according to the operation deviation degree and the event identifier includes:

[0127] Identify the deviation thresholds in the operation deviation degree. The deviation thresholds include the maximum deviation value and the minimum deviation value;

[0128] Query the vibration motor hardware and the motor-related hardware corresponding to the mouse;

[0129] Test the vibration tolerance of the vibration motor hardware and the motor-related hardware corresponding to the mouse, where the vibration tolerance includes the peak tolerance and the valley tolerance;

[0130] Combine the operation deviation degree, the maximum deviation value, the minimum deviation value, the peak tolerance, and the valley tolerance, and calculate the basic vibration intensity of the mouse with respect to the operation deviation event through the following formula:

[0131]

[0132] where A represents the basic vibration intensity of the mouse with respect to the operation deviation event, B represents the operation deviation degree, B max represents the maximum deviation value, B min represents the minimum deviation value, F max represents the peak tolerance, F min represents the valley tolerance;

[0133] Combine the basic vibration intensity and the event identifier, and set the vibration intensity gradient of the mouse with respect to the operation deviation event.

[0134] where the vibration motor hardware and the motor-related hardware are the vibration feedback components corresponding to the mouse; the vibration tolerance is the degree of vibration that the vibration motor hardware and the motor-related hardware can withstand and ensure normal operation.

[0135] Furthermore, the vibration motor hardware and the motor-related hardware corresponding to the mouse can be queried through the product manual, hardware detection tools, or disassembly analysis; different intensity vibrations can be simulated through vibration test equipment, and the vibration tolerance of the vibration motor hardware and the motor-related hardware can be tested with the help of sensors and performance monitoring tools; combine the basic vibration intensity and the event identifier, and adjust the basic vibration intensity according to the severity of the operation deviation event represented by the event identifier, while considering different ranges of the operation deviation degree, and set the vibration intensity gradient of the mouse with respect to the operation deviation event, such as matching a higher weighting coefficient for the identifier representing a serious deviation event, setting a relatively low vibration intensity gradient in the low operation deviation degree range, and setting a relatively high vibration intensity gradient in the high operation deviation degree range.

[0136] By combining the operation trajectory characteristics and the vibration intensity gradient, the present invention constructs a vibration feedback mechanism for the mouse, which allows users to intuitively and accurately perceive their own operation state through vibration, significantly improving the immersion and interaction efficiency of human-computer interaction. Among them, the vibration feedback mechanism is a mechanism adapted to the mouse to transmit information to users through vibration.

[0137] As an embodiment of the present invention, constructing the vibration feedback mechanism for using the mouse by combining the operation trajectory feature and the vibration intensity gradient includes:

[0138] Screen out the deviation trajectory features from the operation trajectory features, and perform element decomposition processing on the deviation trajectory features to obtain deviation element trajectory features;

[0139] Combine the deviation element trajectory features and the vibration intensity gradient to determine the initial matching vibration parameters corresponding to using the mouse;

[0140] Evaluate the operation complexity corresponding to the operation trajectory features, and collect the target user behavior preference data;

[0141] Based on the user behavior preference data, analyze the behavior preference tendency corresponding to the target user;

[0142] Combine the operation complexity and the behavior preference tendency to optimize the initial matching vibration parameters to obtain optimized vibration parameters;

[0143] Based on the optimized vibration parameters, construct the vibration feedback mechanism for using the mouse.

[0144] Among them, the deviation trajectory feature is the part of the operation trajectory feature that is different from the normal or expected trajectory; the deviation element trajectory feature is the specific constituent element obtained by decomposing and analyzing the deviation trajectory feature at the element level; the initial matching vibration parameter is the preliminary and adapted vibration-related parameter corresponding to using the mouse determined by combining the deviation element trajectory feature and the vibration intensity gradient; the operation complexity represents the difficulty and complexity of the operation process reflected by the operation trajectory feature; the behavior preference data is the data information about operation habits, vibration feedback preferences, etc. shown by the target user during the use of the mouse; the behavior preference tendency is the preference trend of the target user for specific operation methods, vibration feedback modes, etc. when using the mouse; the optimized vibration parameter is the more optimal vibration parameter obtained after adjusting and improving the initial matching vibration parameter by combining factors such as operation complexity, behavior preference data and tendency.

[0145] Furthermore, the deviated trajectory features can be screened out from the operation trajectory features by setting a normal trajectory range and making a comparison; the deviated trajectory features can be disassembled into elemental deviated trajectory features through data mining and feature extraction algorithms; combining the elemental deviated trajectory features and the vibration intensity gradient, the initial matching vibration parameters corresponding to the mouse usage can be determined by using a preset parameter matching model; the operation complexity corresponding to the operation trajectory features can be evaluated by analyzing indicators such as the number of operation steps, the operation difficulty coefficient, and the operation error tolerance rate; the target user's behavior preference data can be collected through channels such as user questionnaire surveys, analysis of operation behavior records, and integration of historical preference data; based on the user behavior preference data, methods such as cluster analysis and association rule mining can be used to analyze the behavior preference tendency corresponding to the target user; combining the operation complexity and the behavior preference tendency, the initial matching vibration parameters can be optimized according to the personalized adjustment strategy to obtain optimized vibration parameters; based on the optimized vibration parameters, a vibration feedback mechanism for the mouse usage can be constructed by using real-time feedback control technology and hardware driver adaptation. For example, a high-precision sensor is built into the mouse to collect operation trajectory data in real time, compare it with the optimized vibration parameters in real time, and accurately control the vibration intensity, frequency, and duration of the vibration motor through the hardware driver according to the comparison results; when a deviation in the operation trajectory is detected, the vibration feedback can be quickly adjusted according to the optimized vibration parameters to enable the user to promptly perceive the operation state; at the same time, through hardware driver adaptation, it is ensured that the vibration feedback can be stably and accurately implemented on different operating systems and devices. For example, in systems such as Windows and MacOS, a consistent and effective vibration feedback experience can be provided for users according to the optimized vibration parameters.

[0146] S4. After responding to the task execution instruction for the mouse usage, record the operation timestamp and trigger frequency of the target user during task execution. Combine the operation timestamp and the trigger frequency to construct a multi-dimensional efficiency evaluation matrix for the mouse usage, and calculate the task immersion degree of the target user during task execution. Generate an efficiency analysis report for the target user based on the multi-dimensional efficiency evaluation matrix and the task immersion degree.

[0147] The present invention constructs the multi-dimensional efficiency evaluation matrix of using the mouse by combining the operation timestamp and the trigger frequency, which can effectively quantify the efficiency of users using the mouse to perform tasks, help to discover the advantages and disadvantages of users' operation habits, and provide a strong basis for optimizing the performance of the mouse and improving the operation process, wherein the execution task instruction is the operation instruction based on which the mouse needs to be used to complete a specific task; the operation timestamp is the time record corresponding to each mouse operation of the target user when performing the task; the trigger frequency is the number of times the specific mouse operation occurs within a unit time when the target user performs the task; the multi-dimensional efficiency evaluation matrix is a comprehensive operation timestamp, trigger frequency, and the like. Frequency and other dimensions, a mathematical matrix is used to evaluate and analyze the efficiency of using the mouse to perform tasks. Furthermore, the operation timestamps and trigger frequencies of the target user when performing tasks can be recorded through third-party screen recording and analysis software. Using professional screen recording software, not only the screen image is recorded during the recording process, but also the mouse operation trajectory, click time and other information. After the recording is completed, the operation timestamps and trigger frequencies can be extracted from the recorded video through the software's built-in analysis function or other data analysis tools. Open source screen recording software like OBSStudio, combined with Python scripts for video analysis, can realize the extraction of mouse operation data.

[0148] As an embodiment of the present invention, the step of constructing the multi-dimensional efficiency evaluation matrix of using the mouse in combination with the operation timestamp and the trigger frequency includes:

[0149] Analyzing time distribution characteristics corresponding to the operation timestamp;

[0150] Extracting a key operation period corresponding to the target user from the operation timestamp based on the time distribution feature;

[0151] Calculating trigger coefficients of the trigger frequency in different time periods, and determining a representative trigger frequency in the trigger frequency according to the trigger coefficients;

[0152] generating an efficiency evaluation dimension of using the mouse based on the key operation time period;

[0153] The multi-dimensional efficiency evaluation matrix using the mouse is constructed by combining the efficiency evaluation dimension and the characterization trigger frequency.

[0154] Among them, the time distribution feature is the distribution law, central tendency, dispersion degree, etc. of the operation timestamps on the time axis; the key operation period is the time period corresponding to the operation timestamps of the target user with frequent operations and crucial for task execution; the trigger coefficient represents the quantitative coefficient of the relative importance, activity or influence of the trigger frequency in different time periods; the representative trigger frequency is the frequency value in the trigger frequency that can represent the typical characteristics, comprehensive level or core trend of the trigger frequency in the key operation period; the efficiency evaluation dimension is the specific index or angle for measuring operation efficiency from multiple aspects based on the key operation time period for using the mouse.

[0155] Furthermore, the time distribution feature corresponding to the operation timestamps can be analyzed by statistical methods (such as calculating the mean, median, standard deviation, drawing histograms, performing time series analysis, etc.); based on the time distribution feature, the key operation period corresponding to the target user can be extracted from the operation timestamps by using methods such as setting threshold screening, clustering analysis or combining business process judgment, etc.; the trigger coefficient of the trigger frequency in different time periods can be calculated by constructing a weight calculation model based on time characteristics, comparing operation frequencies in different time periods, etc.; according to the trigger coefficient, the representative trigger frequency in the trigger frequency can be determined by using the method of taking the average value; based on the key operation time period, the efficiency evaluation dimension for using the mouse can be generated by comprehensively considering factors such as operation behavior classification, operation index statistics and business requirements; combining the efficiency evaluation dimension and the representative trigger frequency, a matrix structure can be constructed with the representative trigger frequency as rows and the efficiency evaluation dimension as columns, and the corresponding data can be filled and standardized processing, etc. to construct the multi-dimensional efficiency evaluation matrix for using the mouse.

[0156] By calculating the task immersion degree of the target user when performing a task, the present invention can provide a quantitative basis for evaluating the target user's degree of focus and engagement state in the task, and provides an important basis for the generation of the subsequent efficiency analysis report of the target user, wherein the task immersion degree represents the degree to which the target user is fully immersed in the task when performing the task, focusing on task operations, time control and step execution, and ignoring external interferences.

[0157] As an embodiment of the present invention, calculating the task immersion degree of the target user when performing a task includes:

[0158] Recording the operation log data and operation cycle data of the target user when performing the task;

[0159] Identifying the operation step identifiers and step feedback information in the operation log data, and based on the step feedback information, counting the effective operation frequency and total operation frequency of the target user when performing the task;

[0160] Query the task operation process of the target user when performing a task, and combine the operation step identifier and the task operation process to count the correct operation steps and total operation steps of the target user when performing the task;

[0161] Calculate the actual operation cycle of the target user when performing the task according to the operation cycle data;

[0162] Combine the effective operation frequency, the total operation frequency, the correct operation steps, the total operation steps, and the actual operation cycle, and calculate the task immersion degree of the target user when performing the task through the following formula:

[0163]

[0164] Among them, H represents the task immersion degree of the target user when performing the task, G represents the effective operation frequency, G 总 represents the total operation frequency, L represents the correct operation steps, L 总 represents the total operation steps, t represents the actual operation cycle, and T represents the expected operation cycle.

[0165] Among them, the operation log data and the operation cycle data are the behavior data records of the target user when performing the task; the operation step identifier and the step feedback information are the key content elements in the operation log data; the effective operation frequency and the total operation frequency are the operation quantity statistical indicators of the target user when performing the task; the task operation process is the operation specification and sequence guidance of the target user when performing the task; the correct operation steps and the total operation steps are the reflections of the step completion situation of the target user when performing the task; the actual operation cycle is the time length actually spent by the target user when performing the task; the expected operation cycle is the time length that is preset or estimated based on experience for the target user to perform the task.

[0166] Furthermore, the operation log data and operation cycle data of the target user during task execution can be recorded by embedding a data collection module in the system. The embedded data collection module is compiled by JAVA language. The operation step identifiers and step feedback information in the operation log data can be identified through text parsing algorithms and identification matching mechanisms. Based on the step feedback information, the conditional counting function is used to count the effective operation frequency and total operation frequency of the target user during task execution. The task operation process of the target user during task execution can be queried by accessing a pre-constructed task process database. Combining the operation step identifiers and the task operation process, according to the step matching algorithm, the correct operation steps and total operation steps of the target user during task execution are counted. For example, if the task operation process is set as a standard template, the operation step identifiers are traversed, and each operation step identifier is compared one by one with the step sequence and content in the standard template. If they match, it is counted as a correct operation step, and at the same time, the number of all operation step identifiers is accumulated as the total operation steps. According to the operation cycle data, the actual operation cycle of the target user during task execution is calculated by using the time difference calculation method. The expected operation cycle can be obtained by analyzing historical task data or referring to task design standards.

[0167] The present invention generates an efficiency analysis report for the target user based on the multi-dimensional efficiency evaluation matrix and the task immersion degree, which can provide a comprehensive, in-depth, and intuitive task execution efficiency evaluation result for the target user, helping the user clearly understand their own advantages and disadvantages in the task execution process. Among them, the efficiency analysis report is a document or report that comprehensively reflects various efficiency indicators (such as operation efficiency, time efficiency, etc.) reflected by the multi-dimensional efficiency evaluation matrix and the degree of focus and input state reflected by the task immersion degree during the execution of a specific task by the target user, and quantitatively analyzes and summarizes the task execution efficiency. Further, based on the multi-dimensional efficiency evaluation matrix and the task immersion degree, visualization technology is used to integrate and sort out the analysis results according to a pre-set template structure and logic, so as to generate the efficiency analysis report of the target user.

[0168] S5. Create a rest reminder mechanism according to the efficiency analysis report and the vibration feedback mechanism, and perform vibration reminders for the target user in real time.

[0169] The present invention creates a rest reminder mechanism based on the efficiency analysis report and the vibration feedback mechanism, and performs real-time vibration reminders for the target user, which can help the target user timely perceive fatigue and take the initiative to rest during the execution of high-intensity tasks, reduce the decline in efficiency and health risks caused by continuous work, promote the combination of work and rest while improving work efficiency, and achieve a balance between sustainable work rhythm and physical and mental health. Among them, the rest reminder mechanism is an intelligent reminder system created based on the efficiency analysis report and the vibration feedback mechanism. Further, it analyzes key data such as task execution duration and efficiency fluctuations reflected in the efficiency analysis report in real time, and combines the hardware implementation method of the vibration feedback mechanism to set that when the user's efficiency significantly decreases and obvious signs of fatigue appear, a rest reminder is sent timely through vibration, so as to create a rest reminder mechanism, and finally perform real-time vibration reminders for the target user.

[0170] Compared with the problems described in the background art, the present invention combines a preset task-related strategy library to perform behavioral target analysis on the focus period parameters to determine the focus events of the target user, and can convert the simple time parameters set by the user into specific executable focus behavioral targets, providing personalized focus guidance services for the user. According to the task characteristics and focus habits of the user, the guidance strategy is adjusted in real time to improve the user's focus efficiency. Further, the present invention can accurately depict the user's operation habits and intentions by extracting the operation trajectory features and interaction object attributes in the mouse operation behavior data, providing an important basis for the subsequent calculation of the operation deviation degree corresponding to the target user. The present invention can accurately determine the deviation event type by identifying the event identifier corresponding to the operation deviation event. According to the operation deviation degree and the event identifier, a vibration intensity gradient of the mouse regarding the operation deviation event is set, and an appropriate vibration feedback intensity can be set for the target user. Further, the present invention constructs a multi-dimensional efficiency evaluation matrix of using the mouse by combining the operation timestamp and the trigger frequency, which can effectively quantify the efficiency of the user using the mouse to perform tasks, helps to discover the advantages and disadvantages in the user's operation habits, and provides a strong basis for the performance optimization of the mouse and the improvement of the operation process. Further, the present invention creates a rest reminder mechanism based on the efficiency analysis report and the vibration feedback mechanism, and performs real-time vibration reminders for the target user, which can help the target user timely perceive fatigue and take the initiative to rest during the execution of high-intensity tasks, reduce the decline in efficiency and health risks caused by continuous work, promote the combination of work and rest while improving work efficiency, and achieve a balance between sustainable work rhythm and physical and mental health. Therefore, the method and system for improving focus efficiency based on mouse vibration prompts provided by the embodiments of the present invention can improve the focus efficiency when using the mouse.

[0171] Embodiment 2:

[0172] AsFigure 2 As shown, it is a system functional module diagram of an invention that improves concentration efficiency based on mouse vibration prompts.

[0173] The system 200 for improving concentration efficiency based on mouse vibration prompts according to the present invention can be installed in an electronic device. According to the functions achieved, the system for improving concentration efficiency based on mouse vibration prompts can include a concentration event determination module 201, an operation deviation event analysis module 202, a vibration feedback mechanism construction module 203, an efficiency analysis report generation module 204, and a vibration reminder module 205. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0174] In an embodiment of the present invention, the functions of each module / unit are as follows:

[0175] The concentration event determination module 201 is used to obtain the concentration period parameters set by the target user and the use of the mouse, and combine with a preset task-related policy library to perform behavioral target analysis on the concentration period parameters to determine the concentration event of the target user;

[0176] The operation deviation event analysis module 202 is used to real-time monitor the mouse operation behavior data of the target user, extract the operation trajectory features and interaction object attributes in the mouse operation behavior data, combine the interaction object attributes and the concentration event, calculate the operation deviation degree corresponding to the target user, and based on the operation deviation degree, analyze the operation deviation event corresponding to the target user;

[0177] The vibration feedback mechanism construction module 203 is used to identify the event identifier corresponding to the operation deviation event, set the vibration intensity gradient of the mouse for the operation deviation event according to the operation deviation degree and the event identifier, and combine the operation trajectory features and the vibration intensity gradient to construct the vibration feedback mechanism of the mouse;

[0178] The efficiency analysis report generation module 204 is used to record the operation timestamp and trigger frequency of the target user when performing a task in response to the execution task instruction of the mouse, combine the operation timestamp and the trigger frequency to construct a multi-dimensional efficiency evaluation matrix of the mouse, calculate the task immersion degree of the target user when performing the task, and generate an efficiency analysis report of the target user according to the multi-dimensional efficiency evaluation matrix and the task immersion degree;

[0179] The vibration reminder module 205 is used to create a rest reminder mechanism according to the efficiency analysis report and the vibration feedback mechanism, and perform real-time vibration reminder on the target user.

[0180] Specifically, each module in the system 200 for improving concentration efficiency based on mouse vibration prompts in the embodiments of the present invention uses the same technical means as the method for improving concentration efficiency based on mouse vibration prompts described above Figure 1 and can produce the same technical effects, which will not be elaborated here.

[0181] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for improving concentration efficiency based on mouse vibration prompts, characterized in that, The method includes: Obtaining the focus period parameters set by the target user and the use of the mouse, and combining with a preset task-related policy library, performing behavior target analysis on the focus period parameters to determine the focus events of the target user; Real-time monitoring of the mouse operation behavior data of the target user, extracting the operation trajectory features and interaction object attributes in the mouse operation behavior data, combining the interaction object attributes and the focus events, calculating the operation deviation degree corresponding to the target user, and analyzing the operation deviation events corresponding to the target user based on the operation deviation degree; Identifying the event identifier corresponding to the operation deviation event, setting the vibration intensity gradient of the mouse for the operation deviation event according to the operation deviation degree and the event identifier, and constructing a vibration feedback mechanism for the mouse in combination with the operation trajectory features and the vibration intensity gradient; After responding to the execution task instruction of the mouse, recording the operation timestamp and trigger frequency of the target user during task execution, constructing a multi-dimensional efficiency evaluation matrix for the mouse in combination with the operation timestamp and the trigger frequency, calculating the task immersion degree of the target user during task execution, and generating an efficiency analysis report for the target user according to the multi-dimensional efficiency evaluation matrix and the task immersion degree; Creating a rest reminder mechanism according to the efficiency analysis report and the vibration feedback mechanism, and performing vibration reminder on the target user in real time.

2. The method for improving concentration efficiency based on mouse vibration prompts as claimed in claim 1, wherein The combining with a preset task-related policy library and performing behavior target analysis on the focus period parameters to determine the focus events of the target user includes: Analyzing the task type corresponding to the focus period parameters, and querying the main guiding policy associated with the task type from the preset task-related policy library; Based on the main guiding policy, determining the behavior target analysis process corresponding to the focus period parameters; Locating the key behavior nodes in the behavior target analysis process; Based on the key behavior nodes, extracting the behavior target analysis results in the focus period parameters; Based on the behavior target analysis results, determining the focus events of the target user.

3. The method for improving focus efficiency based on mouse vibration prompts according to claim 2, wherein The querying the main guiding policy associated with the task type from the preset task-related policy library includes: Performing semantic parsing on the task type to obtain the task type semantics, and screening out the key semantics from the task type semantics; Identifying the policy tags in the preset task-related policy library, performing semantic interpretation on the policy tags to obtain the policy tag semantics; Performing vectorization processing on the key semantics and the policy tag semantics respectively to obtain a first semantic vector and a second semantic vector; Calculating the semantic similarity between the first semantic vector and the second semantic vector through the following formula: Where S represents the semantic similarity between the first semantic vector and the second semantic vector, E1 a represents the a-th vector in the first semantic vector, and E2 b represents the b-th vector in the second semantic vector. a and b respectively represent the serial numbers corresponding to the first semantic vector and the second semantic vector, n and m respectively represent the quantities corresponding to the first semantic vector and the second semantic vector, and cos_sim represents the cosine similarity function; Based on the semantic similarity, querying the main guiding policy associated with the task type from the preset task-related policy library.

4. The method for improving concentration efficiency based on mouse vibration prompts according to claim 1, characterized in that, The extracting the operation trajectory features and interaction object attributes in the mouse operation behavior data includes: Performing data classification on the mouse operation behavior data to obtain mouse movement trajectory data and mouse interaction object data; Extract the trajectory description data from the mouse movement trajectory data, and draw a movement trajectory graph corresponding to the mouse movement trajectory data based on the trajectory description data; Extract the trajectory geometric features from the movement trajectory graph, and identify the trajectory movement direction in the movement trajectory graph; Combine the trajectory geometric features and the trajectory movement direction to generate the operation trajectory features of the mouse operation behavior data; Perform attribute parsing processing on the mouse interaction object data to obtain a data attribute set; Perform screening processing on the data attribute set to obtain the interaction object attributes of the mouse operation behavior data.

5. The method for improving concentration efficiency based on mouse vibration prompts as claimed in claim 1, wherein The calculating the operation deviation degree corresponding to the target user by combining the interaction object attributes and the focus event includes: Analyze the interaction object labels corresponding to the interaction object attributes; Extract the event features corresponding to the focus event, and calculate the correlation strength between the event features and the interaction object labels; Based on the correlation strength, construct a correlation matrix of the focus event and the interaction object attributes; Count the object operation duration corresponding to the interaction object attributes, and assign an attribute weight corresponding to the interaction object attributes based on the object operation duration; Combine the correlation matrix and the attribute weights to calculate the comprehensive correlation degree between the focus event and the interaction object attributes; Based on the comprehensive correlation degree, calculate the operation deviation degree corresponding to the target user.

6. The method for improving concentration efficiency based on mouse vibration prompts according to claim 1, characterized in that The setting the vibration intensity gradient of the mouse regarding the operation deviation event according to the operation deviation degree and the event identifier includes: Identify the deviation thresholds in the operation deviation degree, where the deviation thresholds include a maximum deviation value and a minimum deviation value; Query the vibration motor hardware and motor-related hardware corresponding to the mouse; Test the vibration tolerance intensity corresponding to the vibration motor hardware and the motor-related hardware, where the vibration tolerance intensity includes a peak tolerance intensity and a valley tolerance intensity; Combine the operation deviation degree, the maximum deviation value, the minimum deviation value, the peak tolerance intensity, and the valley tolerance intensity, and calculate the basic vibration intensity of the mouse regarding the operation deviation event through the following formula: Among them, A represents the basic vibration intensity of the mouse regarding the operation deviation event, B represents the operation deviation degree, B max represents the maximum deviation value, B min represents the minimum deviation value, F max represents the peak value of the bearing strength, F min represents the valley value of the bearing strength; Combine the basic vibration intensity and the event identifier to set the vibration intensity gradient of the mouse regarding the operation deviation event.

7. The method for improving focus efficiency based on mouse vibration prompts as claimed in claim 1, wherein The constructing the vibration feedback mechanism of the mouse by combining the operation trajectory features and the vibration intensity gradient includes: Screen out the deviation trajectory features from the operation trajectory features, and perform element decomposition processing on the deviation trajectory features to obtain deviation element trajectory features; Combine the deviation element trajectory features and the vibration intensity gradient to determine the initial matching vibration parameters corresponding to the mouse; Evaluate the operation complexity corresponding to the operation trajectory features, and collect the target user behavior preference data; Based on the user behavior preference data, analyze the behavior preference tendency corresponding to the target user; Combine the operation complexity and the behavior preference tendency to optimize the initial matching vibration parameters to obtain optimized vibration parameters; Based on the optimized vibration parameters, construct the vibration feedback mechanism for using the mouse.

8. The method for improving concentration efficiency based on mouse vibration prompt according to claim 1, wherein Combining the operation timestamp and the trigger frequency, construct the multi-dimensional efficiency evaluation matrix for using the mouse, including: Analyze the time distribution characteristics corresponding to the operation timestamp; Based on the time distribution characteristics, extract the key operation periods corresponding to the target user from the operation timestamp; Calculate the trigger coefficients of the trigger frequency in different time periods, and determine the representative trigger frequency in the trigger frequency according to the trigger coefficients; Based on the key operation time periods, generate the efficiency evaluation dimensions for using the mouse; Combine the efficiency evaluation dimensions and the representative trigger frequency to construct the multi-dimensional efficiency evaluation matrix for using the mouse.

9. The method for improving concentration efficiency based on mouse vibration prompts according to claim 1, wherein Calculating the task immersion degree of the target user when performing a task includes: Record the operation log data and operation cycle data of the target user when performing the task; Identify the operation step identifiers and step feedback information in the operation log data, and based on the step feedback information, count the effective operation frequency and total operation frequency of the target user when performing the task; Query the task operation process of the target user when performing the task, and combine the operation step identifiers and the task operation process to count the correct operation steps and total operation steps of the target user when performing the task; According to the operation cycle data, calculate the actual operation cycle of the target user when performing the task; Combining the effective operation frequency, the total operation frequency, the correct operation steps, the total operation steps, and the actual operation cycle, calculate the task immersion degree of the target user when performing the task through the following formula: Among them, H represents the task immersion degree of the target user when performing a task, G represents the effective operation frequency, and G 总 represents the total operation frequency, L represents the correct operation steps, and L 总 represents the total operation steps, t represents the actual operation cycle, and T represents the expected operation cycle.

10. A system for improving concentration efficiency based on mouse vibration prompts, characterized in that, The system includes: A focus event determination module, configured to obtain the focus period parameters set by the target user and use the mouse, and combine a preset task-related policy library to perform behavior target parsing on the focus period parameters to determine the focus event of the target user; An operation deviation event analysis module, configured to monitor the mouse operation behavior data of the target user in real time, extract the operation trajectory characteristics and interaction object attributes in the mouse operation behavior data, combine the interaction object attributes and the focus event, calculate the operation deviation degree corresponding to the target user, and based on the operation deviation degree, analyze the operation deviation event corresponding to the target user; A vibration feedback mechanism construction module, configured to identify the event identifier corresponding to the operation deviation event, set the vibration intensity gradient of the mouse for the operation deviation event according to the operation deviation degree and the event identifier, and combine the operation trajectory characteristics and the vibration intensity gradient to construct the vibration feedback mechanism for using the mouse; The efficiency analysis report generation module is used to record the operation timestamps and trigger frequencies of the target user when performing tasks in response to the execution task instruction using the mouse, construct a multi-dimensional efficiency evaluation matrix for using the mouse in combination with the operation timestamps and the trigger frequencies, calculate the task immersion degree of the target user when performing tasks, and generate an efficiency analysis report for the target user according to the multi-dimensional efficiency evaluation matrix and the task immersion degree; The vibration reminder module is used to create a rest reminder mechanism according to the efficiency analysis report and the vibration feedback mechanism, and perform real-time vibration reminders for the target user.

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