Intelligent mouse management method integrated with heart rate detection function and integrated system

Abstract

The invention relates to the technical field of man-machine interaction, and discloses an intelligent mouse management method and integrated system integrated with a heart rate detection function, and the method comprises the steps: transmitting light with a specific wavelength to hand contact skin, and receiving reflected light of the light with the specific wavelength; converting the reflected light into a light electric signal, analyzing ambient light interference noise and motion artifact noise in the light electric signal, and denoising the light electric signal to obtain a denoised light electric signal; calculating a signal peak value of the de-noised light electric signal, marking a sequence heart rate value of a target user, collecting user behavior data of the target user in a time period corresponding to the sequence heart rate value, analyzing a mouse operation frequency and a mouse operation type of the target user, and analyzing an application scene of the target user; and analyzing the scene state of the target user, and constructing a mouse management report of the heart rate detection mouse. The user experience of the user using the intelligent mouse can be improved.

Description

Technical Field

[0001] The present invention relates to an intelligent mouse management method and an integrated system integrating a heart rate detection function, belonging to the technical field of human-computer interaction. Background Art

[0002] Intelligent mouse management refers to using advanced technical means and intelligent algorithms to optimize and personalize the use of the mouse, so as to improve the user's work efficiency, comfort and overall use experience. The goal of intelligent mouse management is to make the mouse not only an input device through the intelligent upgrade of the mouse function, but an intelligent tool that can improve work efficiency, pay attention to user health and provide a personalized experience.

[0003] Traditional intelligent mouse management methods are usually limited to basic user settings, such as adjusting DPI (dots per inch) sensitivity, scroll wheel speed and button functions for management. This method cannot automatically adjust the settings according to the actual needs and physiological states of users, ignoring the analysis of user behavior patterns. Therefore, it cannot provide customized usage suggestions according to different work or game scenarios, and thus cannot achieve a deeper optimization of the user experience and an improvement in work efficiency. Summary of the Invention

[0004] The present invention provides an intelligent mouse management method and an integrated system integrating a heart rate detection function, and its main purpose is to improve the user experience of users using intelligent mice.

[0005] To achieve the above object, an intelligent mouse management method integrating a heart rate detection function provided by the present invention includes:

[0006] Determine the hand contact skin of the heart rate detection mouse and the target user, use the corresponding PPG sensor of the heart rate detection mouse to emit specific wavelength light to the hand contact skin, and use a preset photodiode to receive the reflected light of the specific wavelength light;

[0007] Convert the reflected light into a light electrical signal, analyze the ambient light interference noise and motion artifact noise in the light electrical signal, and denoise the light electrical signal based on the ambient light interference noise and motion artifact noise to obtain a denoised light electrical signal;

[0008] Calculate the signal peak value of the denoised light electrical signal, mark the sequence heart rate value of the target user according to the signal peak value, and collect the user behavior data of the target user in the time period corresponding to the sequence heart rate value, where the user behavior data includes mouse behavior data and computer screen operation data;

[0009] Based on the user behavior data, analyze the mouse operation frequency and mouse operation type of the target user, and based on the computer screen operation data, analyze the application scenarios of the target user;

[0010] Based on the sequence heart rate values, the mouse operation frequency, the mouse operation type, and the application scenarios, use a preset state intelligent analysis algorithm to analyze the scenario state of the target user, and based on the scenario state, construct a mouse management report for the heart rate detection mouse.

[0011] Optionally, the step of using the PPG sensor of the heart rate detection mouse to emit specific wavelength light to the hand contact skin includes:

[0012] Analyze the dirt index of the hand contact skin;

[0013] Based on the dirt index, determine the coverage area of the hand contact skin on the heart rate detection mouse;

[0014] Analyze the coincidence coefficient between the coverage area and the transparent window corresponding to the PPG sensor;

[0015] When the coincidence coefficient meets a preset coincidence threshold, activate the LED corresponding to the PPG sensor to obtain an activated LED;

[0016] Emit specific wavelength light to the hand contact skin through the activated LED.

[0017] Optionally, the step of analyzing the dirt index of the hand contact skin includes:

[0018] Analyze the reflectivity coefficient and dirt type of the hand contact skin, where the dirt type includes grease, protein, and carbohydrate;

[0019] Determine the dirt concentration of the dirt type, where the dirt concentration includes grease concentration, protein concentration, and carbohydrate concentration;

[0020] Based on the reflectivity coefficient, the grease concentration, the protein concentration, and the carbohydrate concentration, use the following formula to calculate the dirt index of the hand contact skin:

[0021] (DI)=α*(RC)+β*(GC)+μ*(PC)+ρ*(CC)

[0022] Among them, (DI) represents the dirt index of the hand in contact with the skin, (RC) represents the reflectivity coefficient, α represents the weight of the reflectivity coefficient, (GC) represents the grease concentration, β represents the weight of the grease concentration, (PC) represents the protein concentration, μ represents the weight of the protein concentration, (CC) represents the carbohydrate concentration, and ρ represents the weight of the carbohydrate concentration.

[0023] Optionally, the converting the reflected light into a light electrical signal includes:

[0024] Collecting the photon energy generated by the reflected light reflected into the corresponding photodiode;

[0025] Constructing electron-hole pairs of the semiconductor material in the photodiode according to the photon energy;

[0026] Separating the electron-hole pairs by using the built-in electric field in the photodiode to obtain a separated current;

[0027] Converting the separated current into an initial light electrical signal by using the external circuit of the photodiode;

[0028] Amplifying the initial light electrical signal to obtain the light electrical signal.

[0029] Optionally, the constructing the multi-scale network of the printed 3D model according to the high-stress region and the boundary region includes:

[0030] Defining the regional network nodes of the high-stress region and the boundary region;

[0031] Constructing a rough network of the regional network nodes;

[0032] Constructing a mesoscopic network of the high-stress region and the boundary region according to the rough network;

[0033] Determining the key regions of the high-stress region and the boundary region;

[0034] Constructing a microscopic network of the key regions;

[0035] Establishing network bridges for the rough network, the mesoscopic network and the microscopic network;

[0036] Constructing the multi-scale network of the printed 3D model based on the network bridges, the rough network, the mesoscopic network and the microscopic network.

[0037] Optionally, the analyzing the interference coefficient of the ambient light frequency component on the frequency-domain light electrical signal includes:

[0038] Analyzing the frequency and frequency band width of the ambient light frequency component;

[0039] Based on the frequency and frequency band width, calculate the interference coefficient of the ambient light frequency component on the frequency domain optical signal by using the following formula:

[0040]

[0041] wherein, I represents the interference coefficient of the ambient light frequency component f on the frequency domain optical signal, X(f) represents the power of the frequency domain optical signal at the ambient light frequency component f, and f env represents the frequency of the ambient light frequency component f, and B env represents the frequency band width of the ambient light frequency component f, and f max represents the maximum frequency value of the frequency domain optical signal, and f mim represents the minimum frequency value of the frequency domain optical signal.

[0042] Optionally, calculating the signal peak value of the denoised optical signal includes:

[0043] Filter the denoised optical signal to obtain a filtered optical signal;

[0044] Derive the filtered optical signal to obtain a derived optical signal;

[0045] Mark the zero-crossing points of the derived optical signal;

[0046] Determine the local maximum values of the zero-crossing points;

[0047] Determine the signal peak value of the denoised optical signal according to the local maximum values and a preset amplitude threshold.

[0048] Optionally, analyzing the application scenario of the target user according to the computer screen operation data includes

[0049] defining the potential scenarios of the target user;

[0050] extracting the screen operation elements of the computer screen operation data;

[0051] analyzing the element relevance and scenario relevance of the screen operation elements;

[0052] Determine the application scenarios in the potential scenarios through the element relevance and scenario relevance.

[0053] Optionally, analyzing the scenario state of the target user based on the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario by using a preset state intelligent analysis algorithm includes:

[0054] Extract the heart rate value features, operation frequency features, operation type features, and application scenario features of the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario respectively;

[0055] Define the scenario state group of the target user;

[0056] Analyze the scenario state score of the scenario state group through the heart rate value features, operation frequency features, operation type features, and application scenario features by using the state intelligent analysis algorithm;

[0057] Determine the scenario state of the target user through the scenario state score.

[0058] To solve the above problems, the present invention also provides an intelligent mouse management system integrating a heart rate detection function, and the system includes:

[0059] A reflected light acquisition module, configured to determine the hand contact skin between the heart rate detection mouse and the target user, emit a specific wavelength light to the hand contact skin by using the PPG sensor corresponding to the heart rate detection mouse, and receive the reflected light of the specific wavelength light by using a preset photodiode;

[0060] A light signal denoising module, configured to convert the reflected light into a light signal, analyze the ambient light interference noise and motion artifact noise in the light signal, and denoise the light signal based on the ambient light interference noise and motion artifact noise to obtain a denoised light signal;

[0061] A heart rate value analysis module, configured to calculate the signal peak value of the denoised light signal, mark the sequence heart rate value of the target user according to the signal peak value, and collect the user behavior data of the target user corresponding to the sequence heart rate value period, wherein the user behavior data includes mouse behavior data and computer screen operation data;

[0062] An application scenario determination module, configured to analyze the mouse operation frequency and mouse operation type of the target user based on the user behavior data, and analyze the application scenario of the target user according to the computer screen operation data;

[0063] A scenario state analysis module, configured to analyze the scenario state of the target user based on the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario by using a preset state intelligent analysis algorithm, and construct a mouse management report of the heart rate detection mouse according to the scenario state.

[0064] Compared with the problems described in the background art, first, by ensuring a stable connection between the heart rate detection mouse and the skin of the user's hand, and combining with the PPG sensor to emit light of a specific wavelength, this solution can accurately capture the user's heart rate data, not only improving the convenience of measurement, but also ensuring the user's comfort. Second, by denoising the reflected light electrical signal, the influence of ambient light interference noise and motion artifact noise is effectively reduced, improving the accuracy and reliability of the heart rate data. This step is crucial for maintaining data quality in a changing usage environment. Furthermore, by calculating the signal peak of the denoised light electrical signal to mark the sequence heart rate value, and combining with the user behavior data, this solution can real-time monitor the physiological state of the user when operating the mouse, providing personalized feedback for the user. In addition, analyzing the mouse operation frequency and type, and judging the application scenario according to the computer screen operation data, helps to deeply understand the user's working mode and behavior habits, so as to provide more considerate services for the user. Finally, using the preset state intelligent analysis algorithm, this solution can comprehensively analyze multi-dimensional data such as heart rate, mouse operation frequency, operation type and application scenario, accurately analyze the user's scenario state, and construct a mouse management report accordingly. This report can not only help users manage and improve work efficiency, but also provide data support for health management, promoting the improvement of the user's quality of life. Therefore, the present invention can improve the user experience of users using intelligent mice. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 FIG. is a schematic flow chart of an intelligent mouse management method integrating heart rate detection function provided by an embodiment of the present invention;

[0066] Figure 2 FIG. is a schematic block diagram of a module for implementing the intelligent mouse management method integrating heart rate detection function provided by an embodiment of the present invention.

[0067] The implementation, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] 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.

[0069] An embodiment of the present application provides an intelligent mouse management method integrating heart rate detection function. The execution subject of the intelligent mouse management method integrating heart rate detection function 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 by the embodiment of the present application. In other words, the intelligent mouse management method integrating heart rate detection function 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.

[0070] Example 1:

[0071] Referring to Figure 1 As shown, it is a schematic flowchart of an intelligent mouse management method integrating a heart rate detection function provided by an embodiment of the present invention. In this embodiment, the intelligent mouse management method integrating the heart rate detection function includes:

[0072] S1. Determine the hand contact skin of the heart rate detection mouse and the target user, use the PPG sensor corresponding to the heart rate detection mouse to emit specific wavelength light to the hand contact skin, and use a preset photodiode to receive the reflected light of the specific wavelength light.

[0073] It should be explained that the heart rate detection mouse refers to a computer mouse integrated with heart rate monitoring technology, and the target user refers to the main consumer group of the heart rate detection mouse, such as programmers, designers, and game players. The hand contact skin refers to the skin surface where the user's finger (usually the index finger) contacts a specific heart rate detection area on the mouse when using the heart rate detection mouse.

[0074] The present invention can obtain the heart rate by using the PPG sensor corresponding to the heart rate detection mouse to emit specific wavelength light to the hand contact skin, ensuring the accuracy of heart rate measurement.

[0075] Specifically, the use of the PPG sensor corresponding to the heart rate detection mouse to emit specific wavelength light to the hand contact skin includes:

[0076] Analyze the dirt index of the hand contact skin;

[0077] According to the dirt index, determine the coverage area of the hand contact skin on the heart rate detection mouse;

[0078] Analyze the coincidence coefficient between the coverage area and the transparent window corresponding to the PPG sensor;

[0079] When the coincidence coefficient meets the preset coincidence threshold, activate the LED corresponding to the PPG sensor to obtain the activated LED;

[0080] Emit specific wavelength light to the hand contact skin through the activated LED.

[0081] Wherein, the dirt index is a quantitative index used to describe the degree of contamination of the skin surface contacted by the hand, the coverage area refers to the skin surface area in contact with the heart rate detection mouse when the user's hand touches the skin, the PPG sensor is a sensor used to monitor the heart rate by emitting light of a specific wavelength and detecting the changes in the light after penetrating the skin, the transparent window is a transparent area on the heart rate detection mouse for placing the PPG sensor, the coincidence coefficient is a value describing the degree of coincidence between the coverage area and the transparent window of the PPG sensor, the coincidence threshold is a criterion used to determine whether the coincidence coefficient is high enough, activating the LED means starting or turning on the LED of a specific wavelength in the PPG sensor, and the specific wavelength light refers to the light emitted by the PPG sensor for heart rate measurement, usually red light (about 660 nanometers).

[0082] Further, analyzing the dirt index of the skin contacted by the hand includes:

[0083] Analyzing the reflectivity coefficient and dirt type of the skin contacted by the hand, wherein the dirt type includes grease, protein, and carbohydrate;

[0084] Determining the dirt concentration of the dirt type, wherein the dirt concentration includes grease concentration, protein concentration, and carbohydrate concentration;

[0085] Based on the reflectivity coefficient, the grease concentration, the protein concentration, and the carbohydrate concentration, calculate the dirt index of the skin contacted by the hand using the following formula:

[0086] (DI) = α*(RC) + β*(GC) + μ*(PC) + ρ*(CC)

[0087] Wherein, (DI) represents the dirt index of the skin contacted by the hand, (RC) represents the reflectivity coefficient, α represents the weight of the reflectivity coefficient, (GC) represents the grease concentration, β represents the weight of the grease concentration, (PC) represents the protein concentration, μ represents the weight of the protein concentration, (CC) represents the carbohydrate concentration, and ρ represents the weight of the carbohydrate concentration.

[0088] Wherein, the reflectivity coefficient is the proportion of light reflected from the surface of the hand skin, the grease refers to the grease substances on the surface of the hand skin, the protein refers to the protein substances on the surface of the hand skin, the carbohydrate refers to the carbohydrate substances on the surface of the hand skin, the grease concentration refers to the concentration of grease on the skin surface, the protein concentration refers to the concentration of protein on the skin surface, the carbohydrate concentration refers to the concentration of carbohydrate on the skin surface, and the weight refers to the relative importance of the dirt concentration in calculating the dirt index.

[0089] It should be noted that in the present application, the fouling index calculated by the above formula can analyze whether the heart rate signal can be effectively monitored through hand contact with the skin, thereby improving the accuracy of subsequent heart rate analysis. By multiplying the fouling concentration by its corresponding weight and adding the results, a linear combination can be obtained, which reflects the comprehensive influence of each fouling concentration.

[0090] The present invention can effectively receive the reflected light of a specific wavelength by using a preset photodiode to receive the reflected light of the specific wavelength and convert it into useful physiological information. Among them, the photodiode refers to a photoelectric sensor integrated in the heart rate detection mouse and is used to detect light of a specific wavelength, and the reflected light refers to the light reflected from the hand skin of the target user.

[0091] S2. Convert the reflected light into a light electrical signal, analyze the ambient light interference noise and motion artifact noise in the light electrical signal, and denoise the light electrical signal based on the ambient light interference noise and motion artifact noise to obtain a denoised light electrical signal.

[0092] The present invention converts the reflected light into a light electrical signal as the data basis for heart rate analysis.

[0093] Specifically, the conversion of the reflected light into a light electrical signal includes:

[0094] Collect the photon energy generated when the reflected light is reflected into the photodiode corresponding to the reflected light;

[0095] According to the photon energy, construct electron-hole pairs in the semiconductor material of the photodiode;

[0096] Use the built-in electric field in the photodiode to separate the electron-hole pairs to obtain a separated current;

[0097] Use the external circuit of the photodiode to convert the separated current into an initial light electrical signal;

[0098] Amplify the initial light electrical signal to obtain the light electrical signal.

[0099] Among them, the photon energy refers to the energy transferred to electrons when photons of the reflected light strike a semiconductor material. The semiconductor material refers to a key component in a photodiode, usually silicon (Si), gallium arsenide (GaAs), etc. The electron-hole pair refers to when the photon energy is absorbed by the semiconductor material, an electron jumps from the valence band to the conduction band, leaving a positive charge hole of equal amount. The built-in electric field refers to the electric field generated in the semiconductor material due to doping of different types of impurities (n-type and p-type). The separated current refers to when the built-in electric field separates the electron-hole pair, the movement of electrons and holes forms a current. The external circuit refers to the circuit connected to the photodiode, which includes resistors, capacitors, and other electronic components for converting the separated current into an available electrical signal. The initial light electrical signal refers to the electrical signal initially converted by the external circuit of the photodiode. The light electrical signal refers to the electrical signal achieved through an amplifier in the external circuit to increase the intensity of the signal.

[0100] The present invention analyzes the ambient light interference noise and motion artifact noise in the light electrical signal, which can improve the signal quality and the accuracy of subsequent heart rate analysis.

[0101] Specifically, the analysis of the ambient light interference noise and motion artifact noise in the light electrical signal includes:

[0102] Remove the baseline drift in the light electrical signal to obtain a drift-removed light electrical signal;

[0103] Perform a fast Fourier transform on the drift-removed light electrical signal to obtain a frequency-domain light electrical signal;

[0104] Identify the ambient light frequency components of the frequency-domain light electrical signal;

[0105] Analyze the interference coefficient of the ambient light frequency components on the frequency-domain light electrical signal;

[0106] Determine the ambient light interference noise in the ambient light frequency components according to the interference coefficient;

[0107] Analyze the signal change and time-domain characteristics of the light electrical signal;

[0108] Analyze the motion artifact noise of the light electrical signal through the signal change and time-domain characteristics.

[0109] Among them, the detrended optical electrical signal refers to the PPG signal from which baseline drift has been removed through a certain signal processing method (such as high-pass filtering, wavelet transform, or polynomial fitting, etc.). The frequency-domain optical electrical signal refers to the signal obtained by converting the detrended time-domain PPG signal to the frequency domain through fast Fourier transform (FFT). The ambient light frequency component refers to the specific frequency related to ambient light interference identified in the frequency-domain optical electrical signal. The interference coefficient refers to the parameter quantifying the interference degree of the ambient light frequency component on the PPG signal. The ambient light interference noise refers to the noise part in the PPG signal caused by the ambient light frequency component. The signal variation refers to the fluctuations of the PPG signal in the time domain, including sudden changes in amplitude, spikes, mutations, etc. The time-domain feature refers to the features extracted from the time-domain waveform of the PPG signal, such as features like the mean, standard deviation, variance, heart rate variability (HRV) index, etc. The motion artifact noise refers to the noise in the PPG signal caused by the movement of the target user.

[0110] Further, analyzing the interference coefficient of the ambient light frequency component on the frequency-domain optical electrical signal includes:

[0111] Analyzing the frequency and frequency band width of the ambient light frequency component;

[0112] Based on the frequency and frequency band width, use the following formula to calculate the interference coefficient of the ambient light frequency component on the frequency-domain optical electrical signal:

[0113]

[0114] Among them, I represents the interference coefficient of the ambient light frequency component f on the frequency-domain optical electrical signal, X(f) represents the power of the frequency-domain optical electrical signal at the ambient light frequency component f, f env represents the frequency of the ambient light frequency component f, B env represents the frequency band width of the ambient light frequency component f, f max represents the maximum frequency of the frequency-domain optical electrical signal, f min represents the minimum frequency of the frequency-domain optical electrical signal.

[0115] Among them, the frequency refers to the central frequency of the ambient light interference in the frequency domain, the frequency band width refers to the frequency band range occupied by the ambient light interference in the frequency domain, the maximum frequency refers to the highest frequency considered in the analysis of the frequency-domain optical electrical signal, and the minimum frequency refers to the lowest frequency considered in the analysis of the frequency-domain optical electrical signal.

[0116] It should be noted that in this application, the interference coefficient calculated by the above formula can analyze the ambient light interference on the optical electrical signal, thereby further improving the data reliability of the optical electrical signal. The influence of the ambient light on the signal is measured by calculating the proportion of the power of the ambient light frequency component in a specific frequency band to the total power.

[0117] Based on the ambient light interference noise and motion artifact noise, the present invention denoises the optical electrical signal to obtain a denoised optical electrical signal, improving the signal quality. Among them, the denoised optical electrical signal refers to the signal obtained by removing or reducing the ambient light interference noise and motion artifact noise from the optical electrical signal.

[0118] S3. Calculate the signal peak value of the denoised optical electrical signal. According to the signal peak value, mark the sequential heart rate value of the target user, and collect the user behavior data of the target user corresponding to the time period of the sequential heart rate value, where the user behavior data includes mouse behavior data and computer screen operation data.

[0119] The present invention calculates the signal peak value of the denoised optical electrical signal as the data basis for later analysis of the heart rate value.

[0120] Specifically, the calculation of the signal peak value of the denoised optical electrical signal includes:

[0121] Filter the denoised optical electrical signal to obtain a filtered optical electrical signal;

[0122] Take the derivative of the filtered optical electrical signal to obtain a derivative optical electrical signal;

[0123] Mark the zero-crossing points of the derivative optical electrical signal;

[0124] Determine the local maximum values of the zero-crossing points;

[0125] According to the local maximum values and a preset amplitude threshold, determine the signal peak value of the denoised optical electrical signal.

[0126] Among them, the filtered optical electrical signal refers to the optical electrical signal after filtering processing, the derivative optical electrical signal refers to the signal obtained by taking the derivative operation on the filtered optical electrical signal, the zero-crossing point refers to the point in the derivative optical electrical signal where the derivative changes from positive to negative or from negative to positive, the local maximum value refers to the maximum value in the denoised optical electrical signal near the zero-crossing point, the amplitude threshold refers to a preset amplitude limit for determining which local maximum values are considered valid signal peak values, and the signal peak value refers to the local maximum value in the denoised optical electrical signal that exceeds the preset amplitude threshold.

[0127] Optionally, taking the derivative of the filtered optical signal to obtain a derivative optical signal can be achieved through central difference processing.

[0128] It should be explained that the sequence of heart rate values refers to a sequence of signal peaks continuously measured within a certain time period, the mouse behavior data refers to the record of all interactive behaviors of the user when using a computer mouse, and the computer screen operation data refers to the record of various operations performed by the user on the computer screen.

[0129] S4. Based on the user behavior data, analyze the mouse operation frequency and mouse operation type of the target user, and based on the computer screen operation data, analyze the application scenario of the target user.

[0130] It should be explained that the mouse operation frequency refers to the number of mouse operations performed by the user per unit time, which can be used to measure the activity of the user's interaction with the computer. The mouse operation type refers to different types of operations that the user can perform when using the mouse, such as click operation, double-click operation, drag operation, move operation, etc.

[0131] The present invention analyzes the application scenario of the target user based on the computer screen operation data, which can more deeply understand the user's behavior.

[0132] Specifically, analyzing the application scenario of the target user based on the computer screen operation data includes

[0133] defining the potential scenarios of the target user;

[0134] extracting the screen operation elements of the computer screen operation data;

[0135] analyzing the element relevance and scenario relevance of the screen operation elements;

[0136] determining the application scenarios in the potential scenarios through the element relevance and scenario relevance.

[0137] Among them, the potential scenarios refer to various environments and activity backgrounds where the target user may be, including work scenarios, learning scenarios, entertainment scenarios, etc. The screen operation elements refer to the specific operations and visual elements extracted from the computer screen operation data, such as opened applications and documents, mouse and keyboard operations (such as clicks, drags, typing). The element correlation refers to the mutual relationship between different screen operation elements. The scenario correlation refers to the connection between the screen operation elements and the potential scenarios, such as the frequency of a specific operation element appearing in a certain scenario, the matching degree of the combination of operation elements and a specific scenario, etc. The application scenario refers to the specific scenario where the user actually is finally determined by analyzing the element correlation and scenario correlation of the screen operation elements. For example, the user is currently editing a report (work scenario), or the user is playing a casual game during the break (entertainment scenario).

[0138] S5. Based on the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario, use a preset state intelligent analysis algorithm to analyze the scenario state of the target user, and construct a mouse management report for the heart rate detection mouse according to the scenario state.

[0139] Based on the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario, the present invention uses a preset state intelligent analysis algorithm to analyze the scenario state of the target user and provides a data basis for subsequent mouse management.

[0140] Specifically, the analyzing the scenario state of the target user by using a preset state intelligent analysis algorithm based on the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario includes:

[0141] Extract the heart rate value feature, operation frequency feature, operation type feature, and application scenario feature of the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario respectively;

[0142] Define the scenario state group of the target user;

[0143] Analyze the scenario state score of the scenario state group by using the state intelligent analysis algorithm through the heart rate value feature, operation frequency feature, operation type feature, and application scenario feature;

[0144] Determine the scenario state of the target user through the scenario state score.

[0145] Among them, the heart rate value feature refers to a quantitative index extracted from a sequence of heart rate values, which is used to describe the dynamic changes and patterns of the heart rate, such as patterns like average heart rate, heart rate variability, etc. The operation frequency feature refers to an index that describes the mouse operation frequency, such as the number of clicks per unit time. The operation type feature refers to an index that describes the mouse operation type, such as the proportion of various types of operations (clicking, moving, scrolling, long - pressing). The application scenario feature is extracted from the computer screen operation data and is used to describe the characteristic attributes of the application environment where the user is located. The scenario state group refers to a set of predefined user states. For example, the focused state: high heart rate variability, high - frequency mouse operation, office scenario; the relaxed state: low heart rate variability, low - frequency mouse operation, web - browsing scenario; the tense state: high heart rate mean, high - frequency click operation, game scenario, etc. The scenario state score refers to a parameter used to represent the possibility that the user is in a specific scenario state. The scenario state refers to the current mental or physiological state of the user determined according to the analysis results, such as focused, fatigued, etc. The state intelligent analysis algorithm refers to an algorithm used to analyze the possibility of the scenario state of the target user. Specifically, the state intelligent analysis algorithm can be constructed through a decision tree.

[0146] Finally, according to the scenario state, the present invention constructs a mouse management report of the heart rate detection mouse, which can better understand one's own behavior habits and physiological state, thereby improving work efficiency and health level. Among them, the mouse management report refers to a report that contains various data and statistical analysis results when the user uses the heart rate detection mouse. The mouse management report includes content such as heart rate data analysis, mouse operation statistics, scenario state analysis, work efficiency evaluation, and health suggestions.

[0147] Compared with the problems described in the background art, first, by ensuring a stable connection between the heart rate detection mouse and the skin of the user's hand in contact, and combining the PPG sensor to emit light of a specific wavelength, this solution can accurately capture the user's heart rate data, which not only improves the convenience of measurement but also ensures the user's comfort. Second, by denoising the reflected light electrical signal, the influence of ambient light interference noise and motion artifact noise is effectively reduced, improving the accuracy and reliability of the heart rate data. This step is crucial for maintaining data quality in a variable usage environment. Furthermore, by calculating the signal peak of the denoised light electrical signal to mark the sequential heart rate values and combining the user behavior data, this solution can real-time monitor the user's physiological state when operating the mouse and provide personalized feedback to the user. In addition, analyzing the mouse operation frequency and type, and judging the application scenario based on the computer screen operation data helps to deeply understand the user's working mode and behavior habits, thus providing more considerate services to the user. Finally, using the preset state intelligent analysis algorithm, this solution can comprehensively analyze multi-dimensional data such as heart rate, mouse operation frequency, operation type, and application scenario, accurately analyze the user's scenario state, and construct a mouse management report based on this. This report can not only help the user manage,

[0148] improve work efficiency, but also provide data support for health management and promote the improvement of the user's quality of life. Therefore, the present invention can improve the user experience of users using intelligent mice.

[0149] Embodiment 2:

[0150] As Figure 2 shown, it is a functional module diagram of an intelligent mouse management system integrating a heart rate detection function according to the present invention.

[0151] The intelligent mouse management system 200 integrating a heart rate detection function according to the present invention can be installed in an electronic device. According to the implemented functions, the intelligent mouse management system integrating a heart rate detection function can include a reflected light acquisition module 201, a light electrical signal denoising module 202, a heart rate value analysis module 203, an application scenario determination module 204, and a scenario state analysis module 205. The modules of 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.

[0152] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0153] The reflected light acquisition module 201 is used to determine the contact between the heart rate detection mouse and the skin of the target user's hand, use the PPG sensor corresponding to the heart rate detection mouse to emit light of a specific wavelength to the skin of the hand in contact, and use a preset photodiode to receive the reflected light of the specific wavelength light;

[0154] The optical signal denoising module 202 is configured to convert the reflected light into an optical signal, analyze the ambient light interference noise and motion artifact noise in the optical signal, and denoise the optical signal based on the ambient light interference noise and motion artifact noise to obtain a denoised optical signal;

[0155] The heart rate value analysis module 203 is configured to calculate the signal peak of the denoised optical signal, mark the sequence heart rate value of the target user according to the signal peak, and collect the user behavior data of the target user in the time period corresponding to the sequence heart rate value, where the user behavior data includes mouse behavior data and computer screen operation data;

[0156] The application scenario determination module 204 is configured to analyze the mouse operation frequency and mouse operation type of the target user based on the user behavior data, and analyze the application scenario of the target user according to the computer screen operation data;

[0157] The scenario state analysis module 205 is configured to analyze the scenario state of the target user by using a preset state intelligent analysis algorithm based on the sequence heart rate value, the mouse operation frequency, the mouse operation type, and the application scenario, and construct a mouse management report of the heart rate detection mouse according to the scenario state.

[0158] Specifically, each module in the intelligent mouse management system 200 with integrated heart rate detection function in the embodiment of the present invention adopts the same technical means as those in the Figure 1 intelligent mouse management method with integrated heart rate detection function described above, and can produce the same technical effects, which will not be elaborated here.

[0159] 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 can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0160] 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. An intelligent mouse management method integrating a heart rate detection function, characterized in that, The method includes: Determine that the heart rate detection mouse is in contact with the skin of the target user's hand, use the corresponding PPG sensor of the heart rate detection mouse to emit specific wavelength light to the skin in contact with the hand, and use a preset photodiode to receive the reflected light of the specific wavelength light; Convert the reflected light into a light electrical signal, analyze the ambient light interference noise and motion artifact noise in the light electrical signal, and denoise the light electrical signal based on the ambient light interference noise and motion artifact noise to obtain a denoised light electrical signal; Calculate the signal peak value of the denoised light electrical signal, mark the sequential heart rate values of the target user according to the signal peak value, and collect the user behavior data of the target user corresponding to the time period of the sequential heart rate values, where the user behavior data includes mouse behavior data and computer screen operation data; Based on the user behavior data, analyze the mouse operation frequency and mouse operation type of the target user, and analyze the application scenario of the target user according to the computer screen operation data; Based on the sequential heart rate values, the mouse operation frequency, the mouse operation type, and the application scenario, use a preset state intelligent analysis algorithm to analyze the scenario state of the target user, and construct a mouse management report for the heart rate detection mouse according to the scenario state.

2. The intelligent mouse management method integrating a heart rate detection function according to claim 1, characterized in that, The step of using the corresponding PPG sensor of the heart rate detection mouse to emit specific wavelength light to the skin in contact with the hand includes: Analyze the dirt index of the skin in contact with the hand; Determine the coverage area of the skin in contact with the hand on the heart rate detection mouse according to the dirt index; Analyze the coincidence coefficient between the coverage area and the transparent window corresponding to the PPG sensor; When the coincidence coefficient meets a preset coincidence threshold, activate the LED corresponding to the PPG sensor to obtain an activated LED; Emit specific wavelength light to the skin in contact with the hand through the activated LED.

3. The intelligent mouse management method integrating a heart rate detection function according to claim 2, wherein The step of analyzing the dirt index of the skin in contact with the hand includes: Analyze the reflectivity coefficient and dirt type of the skin in contact with the hand, where the dirt type includes grease, protein, and carbohydrate; Determine the dirt concentration of the dirt type, where the dirt concentration includes grease concentration, protein concentration, and carbohydrate concentration; Based on the reflectivity coefficient, the grease concentration, the protein concentration, and the carbohydrate concentration, use the following formula to calculate the dirt index of the skin in contact with the hand: (DI) = α*(RC) + β*(GC) + μ*(PC) + ρ*(CC) where (DI) represents the dirt index of the skin in contact with the hand, (RC) represents the reflectivity coefficient, α represents the weight of the reflectivity coefficient, (GC) represents the grease concentration, β represents the weight of the grease concentration, (PC) represents the protein concentration, μ represents the weight of the protein concentration, (CC) represents the carbohydrate concentration, and ρ represents the weight of the carbohydrate concentration.

4. The intelligent mouse management method integrating a heart rate detection function according to claim 3, characterized in that, The step of converting the reflected light into a light electrical signal includes: Collect the photon energy generated when the reflected light is reflected into the corresponding photodiode of the reflected light; Construct electron-hole pairs in the semiconductor material of the photodiode according to the photon energy; Separate the electron-hole pairs by using the built-in electric field in the photodiode to obtain a separated current; Convert the separated current into an initial optical signal by using the external circuit of the photodiode; Amplify the initial optical signal to obtain the optical signal.

5. The intelligent mouse management method integrating a heart rate detection function according to claim 4, characterized in that, Construct the multi-scale network for printing the 3D model according to the high-stress region and the boundary region, including: Define the regional network nodes of the high-stress region and the boundary region; Construct a rough network of the regional network nodes; Construct a mesoscopic network of the high-stress region and the boundary region according to the rough network; Determine the key regions of the high-stress region and the boundary region; Construct a microscopic network of the key regions; Establish network bridges for the rough network, the mesoscopic network, and the microscopic network; Construct the multi-scale network for printing the 3D model based on the network bridges, the rough network, the mesoscopic network, and the microscopic network.

6. The intelligent mouse management method integrating a heart rate detection function according to claim 5, characterized in that, Analyze the interference coefficient of the ambient light frequency component on the frequency-domain optical signal, including: Analyze the frequency and frequency bandwidth of the ambient light frequency component; Based on the frequency and frequency bandwidth, calculate the interference coefficient of the ambient light frequency component on the frequency-domain optical signal by using the following formula: Where, I represents the interference coefficient of the environmental light frequency component f on the frequency-domain optical signal, X(f) represents the power of the frequency-domain optical signal at the environmental light frequency component f, and f env represents the frequency of the environmental light frequency component f, and B env represents the bandwidth of the environmental light frequency component f, and f mxa represents the maximum frequency of the frequency-domain optical signal, and f min represents the minimum frequency of the frequency-domain optical signal.

7. The intelligent mouse management method integrating a heart rate detection function according to claim 6, characterized in that Calculate the signal peak value of the denoised optical signal, including: Filter the denoised optical signal to obtain a filtered optical signal; Take the derivative of the filtered optical signal to obtain a derivative optical signal; Mark the zero-crossing points of the derivative optical signal; Determine the local maximum values of the zero-crossing points; Determine the signal peak value of the denoised optical signal according to the local maximum values and a preset amplitude threshold.

8. The intelligent mouse management method integrating a heart rate detection function according to claim 7, characterized in that, Analyze the application scenario of the target user according to the computer screen operation data, including Define the potential scenarios of the target user; Extract the screen operation elements of the computer screen operation data; Analyze the element correlation and scenario correlation of the screen operation elements; Determine the application scenarios in the potential scenarios through the element correlation and scenario correlation.

9. The intelligent mouse management method integrating a heart rate detection function according to claim 8, characterized in that, Analyze the scenario state of the target user by using a preset state intelligent analysis algorithm based on the sequence heart rate values, the mouse operation frequency, the mouse operation type, and the application scenario, including: Extract the heart rate value features, operation frequency features, operation type features, and application scenario features of the sequence heart rate values, the mouse operation frequency, the mouse operation type, and the application scenario respectively; Define the scenario state group of the target user; Analyze the scenario state scores of the scenario state group by using the state intelligent analysis algorithm through the heart rate value features, operation frequency features, operation type features, and application scenario features; Determine the scenario state of the target user through the scenario state scores.

10. An intelligent mouse management system integrating a heart rate detection function, characterized in that, The system includes: A reflected light acquisition module, which is used to determine the contact between the heart rate detection mouse and the hand skin of the target user, use the PPG sensor corresponding to the heart rate detection mouse to emit specific wavelength light to the hand contact skin, and use a preset photodiode to receive the reflected light of the specific wavelength light; A light electrical signal denoising module, which is used to convert the reflected light into a light electrical signal, analyze the ambient light interference noise and motion artifact noise in the light electrical signal, and denoise the light electrical signal based on the ambient light interference noise and motion artifact noise to obtain a denoised light electrical signal; A heart rate value analysis module, which is used to calculate the signal peak value of the denoised light electrical signal, mark the sequence heart rate values of the target user according to the signal peak value, and collect the user behavior data of the target user in the time period corresponding to the sequence heart rate values, wherein the user behavior data includes mouse behavior data and computer screen operation data; An application scenario determination module, which is used to analyze the mouse operation frequency and mouse operation type of the target user based on the user behavior data, and analyze the application scenario of the target user according to the computer screen operation data; A scenario state analysis module, which is used to analyze the scenario state of the target user by using a preset state intelligent analysis algorithm based on the sequence heart rate values, the mouse operation frequency, the mouse operation type and the application scenario, and construct a mouse management report of the heart rate detection mouse according to the scenario state.

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