Intelligent control system and method for wireless mouse

Abstract

The invention discloses an intelligent control system and method for a wireless mouse, relates to the technical field of intelligent control of the wireless mouse, and solves the problems that in intelligent control of the wireless mouse, the polling rate of the wireless mouse is difficult to dynamically control according to the use habit of a user, and the polling rate of the wireless mouse is difficult to adjust when the polling rates of different time periods are adjusted. And smooth connection of the polling rate is difficult. According to the method, the click time point and the click frequency are analyzed to obtain the use time period of the current wireless mouse; inputting the use duration and the charging and discharging time point into a battery analysis model to obtain a battery attenuation curve, and obtaining an electric quantity available value based on the residual electric quantity, the battery attenuation curve and the environment temperature; a control instruction is sent based on the current time and the use time period, and the polling rate of the wireless mouse is dynamically regulated and controlled based on the control instruction and the electric quantity available value; according to the wireless mouse, the polling rate of the wireless mouse can be adjusted according to needs, unnecessary energy consumption is reduced, and the service life of a battery is prolonged.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control of mice, relates to wireless mouse intelligent control technology, and specifically is an intelligent control system and method for wireless mice. Background Art

[0002] Traditional wired mice are connected to a computer through a cable. Although stable, they have problems such as inconvenient use and limited movement. With the maturity of wireless communication technology, wireless mice have gradually become the mainstream products in the market, providing users with greater freedom and convenience. However, as a mouse powered by a battery, the performance and usage duration of a wireless mouse highly depend on battery technology. Currently, mainstream products generally use lithium batteries or alkaline batteries for power supply. In actual use, due to the increased power consumption of the high polling rate and optical engine of high-end gaming mice, the battery life may be shortened to dozens of hours, forcing heavy users to charge every week. In severe cases, there will be an embarrassing situation of sudden power off during the game. In addition, frequent charging not only affects the user experience but also reduces the service life of the battery.

[0003] Currently, most intelligent control systems for wireless mice have difficulty dynamically controlling the polling rate of a wireless mouse according to the user's usage habits during the intelligent control of the wireless mouse, resulting in the polling rate remaining at a relatively high level for a long time, increasing the battery power consumption while also increasing unnecessary data transmission. At the same time, most intelligent control systems for wireless mice have difficulty smoothly connecting the polling rates when adjusting the polling rates at different time periods. They only set the polling rates according to the polling rates of two different time periods, which may cause a sudden feeling of adjustment and affect the user experience.

[0004] Therefore, the present invention discloses an intelligent control system and method for wireless mice to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. For this purpose, the present invention proposes an intelligent control system and method for wireless mice to solve the technical problems that it is difficult to dynamically control the polling rate of a wireless mouse according to the user's usage habits during the intelligent control of the wireless mouse, and it is difficult to smoothly connect the polling rates when adjusting the polling rates at different time periods. The present invention analyzes the click time points and click frequencies to obtain the current usage period of the wireless mouse; inputs the usage duration and charge and discharge time points into a battery analysis model to obtain a battery attenuation curve, and obtains an available power value based on the remaining power, the battery attenuation curve, and the ambient temperature; issues a control instruction based on the current time and the usage period, and dynamically adjusts the polling rate of the wireless mouse based on the control instruction and the available power value to solve the above problems.

[0006] To achieve the above object, a first aspect of the present invention provides an intelligent control system for a wireless mouse, comprising: an intelligent analysis module, and a data collection module, a polling control module and a database connected thereto;

[0007] The data collection module: is used to obtain the basic data of the wireless mouse and the ambient temperature; wherein, the basic data includes the usage duration, charge and discharge time points, click time points, click frequency and remaining power;

[0008] The intelligent analysis module: is used to analyze the click time points and click frequency to obtain the current usage period of the wireless mouse; input the usage duration and charge and discharge time points into the battery analysis model to obtain the battery attenuation curve, and obtain the available power value based on the remaining power, battery attenuation curve and ambient temperature; wherein, the battery analysis model is obtained through artificial intelligence model training; the usage period includes a frequently used period, an infrequently used period and a non - used period;

[0009] The polling control module: is used to issue a control instruction based on the current time and usage period, and dynamically adjust the polling rate of the wireless mouse based on the control instruction and the available power value.

[0010] Preferably, the obtaining of the basic data of the wireless mouse and the ambient temperature includes:

[0011] Obtaining the usage duration of the wireless mouse through an embedded timer, obtaining the charging time point and the stop - charging time point of the wireless mouse through an embedded bidirectional current monitoring module, and integrating each of the charging time points with the subsequent nearest stop - charging time point as the charging time point; obtaining the time point of each click of the wireless mouse using a high - speed opto - coupled isolation acquisition circuit and a microcontroller, and marking the time point as the click time point; obtaining the click frequency of the wireless mouse per unit time through a frequency counter, obtaining the remaining power of the wireless mouse in real - time through a power monitoring device; obtaining the ambient temperature at the location of the wireless mouse through a temperature sensor installed on the wireless mouse; wherein, the unit time is set manually.

[0012] Preferably, the analyzing of the click time points and click frequency to obtain the current usage period of the wireless mouse includes:

[0013] Extracting the non - click time points among the time points of a day, integrating the continuous non - click time points to obtain several time periods, and marking the time periods with a duration greater than a first duration threshold as non - used periods; wherein, the first duration threshold and the second duration threshold are set by experience;

[0014] Re - label each non - click time point in the time period not marked as the non - usage period as a click time point, obtain the click frequency of the wireless mouse within the unit time where each click time point is located every day, and integrate the click frequencies of the wireless mouse within the unit time of each historical day into several frequency processing groups based on the click time points;

[0015] Extract eigenvalue for each frequency processing group to obtain the standard click frequency of each click time point, mark the click time points with the standard click frequency greater than the frequency threshold as target time points, mark the unit time with target time points as target unit time, integrate consecutive target unit times to obtain several time periods, and mark the time periods with a duration greater than the second duration threshold as common usage periods; Integrate the time points in a day that do not belong to the common usage period and the non - usage period into several non - common usage periods; Among them, the frequency threshold is obtained through experience.

[0016] Preferably, the extracting eigenvalue for each frequency processing group to obtain the standard click frequency of each click time point includes:

[0017] Obtain the variance of the click frequencies in each frequency processing group, and determine whether the variance of this frequency processing group is greater than the defined value; If yes, remove the click frequency with the largest difference from the mode of the click frequencies in this frequency processing group, and re - perform the variance judgment until the variance of this frequency processing group is not greater than the defined value, then retain the remaining click frequencies of this frequency processing group; If no, retain the click frequencies of this frequency processing group; Among them, the defined value is obtained through manual setting;

[0018] Extract each frequency processing group in turn, and obtain the maximum value ZD, mode ZA, and percentile ZB of the retained click frequencies in the extracted frequency processing group; Based on the formula BP = α1×ZA + α2×ZB + α3×ZD, obtain the standard click frequency BP of the click time point corresponding to the frequency processing group; Among them, α1, α2, and α3 are all proportional adjustment coefficients greater than 0, and α1 + α2 + α3 = 1, α1>α2>α3.

[0019] Preferably, the battery analysis model is obtained through training of an artificial intelligence model, including:

[0020] Extract the usage duration, charge - discharge time points, and battery attenuation curve from the historical reference data; Among them, the historical reference data includes several usage durations and charge - discharge time points, and the battery attenuation curve set by experts according to the usage duration and charge - discharge time points;

[0021] Integrate the usage duration, charge and discharge time points, and battery decay curve into several groups of training data and test data. Use the training data to train the artificial intelligence model, use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a battery analysis model with the usage duration and charge and discharge time points as inputs and the battery decay curve as the output; wherein, the artificial intelligence model includes a BP neural network model and an RBF neural network model, the abscissa of the battery decay curve is time, and the ordinate is the battery capacity.

[0022] Preferably, obtaining the available power value based on the remaining power, battery decay curve, and environmental temperature includes:

[0023] A1: Extract the battery capacity DL of the wireless mouse from the battery decay curve based on the current time, and determine whether the battery capacity DL is less than the capacity threshold; if so, issue a warning of too low battery capacity; if not, jump to A2; wherein, both the capacity threshold and the power threshold are obtained through manual setting;

[0024] A2: Extract the remaining power YD of the wireless mouse, and determine whether the remaining power YD is less than the power threshold; if so, issue a warning of too low power; if not, jump to A3;

[0025] A3: Extract the environmental temperature HW, and determine whether the environmental temperature HW is less than the middle value YDZ of the suitable temperature range; if so, obtain the available power value DYZ of the wireless mouse based on the formula DYZ = exp(-β1×|HW - YDZ| / YDZ)×YD×DL; if not, obtain the available power value DYZ of the wireless mouse based on the formula DYZ = exp(-β2×|HW - YDZ| / YDZ)×YD×DL; wherein, both β1 and β2 are amplitude adjustment coefficients of the exp() function set manually, the value ranges of β1 and β2 are (0, 2], and β1 > β2; the suitable temperature range is obtained through manual setting.

[0026] Preferably, issuing a control instruction based on the current time and usage period includes:

[0027] B1: Extract the current time, and determine whether the current time is a common period; if so, issue control instruction one; if not, jump to B2;

[0028] B2: Determine whether the current time is an uncommon period; if so, issue control instruction two; if not, issue control instruction three; wherein, the control instructions include control instruction one, control instruction two, and control instruction three.

[0029] Preferably, dynamically regulating the polling rate of the wireless mouse based on the control instruction and the available power value includes:

[0030] When receiving control instruction one, extract the available power value DYZ, and obtain the dynamic polling rate DTL of the wireless mouse based on the formula DTL = δ1×(exp(DYZ / BYZ)-1)×BL; where BYZ is the standard available power value set manually, BL is the standard polling rate set manually; δ1 is the amplitude adjustment coefficient set manually, and the value range of δ1 is (0,2];

[0031] When receiving control instruction two, extract the available power value DYZ, and determine whether the available power value DYZ is greater than the standard available power value BYZ; if yes, obtain the dynamic polling rate DTL of the wireless mouse based on the formula DTL = δ2×(exp(DYZ / BYZ)-1)×BL; if no, obtain the dynamic polling rate DTL of the wireless mouse based on the formula DTL = δ3×ln(DYZ / BYZ+1)×BL; both δ2 and δ3 are the amplitude adjustment coefficients set manually, and the value ranges of δ2 and δ3 are (0,2];

[0032] When receiving control instruction three, use the minimum polling rate as the dynamic polling rate DTL; set a transition period and a temporary polling rate between two different usage periods; where the minimum polling rate is obtained through manual setting.

[0033] Preferably, setting a transition period and a temporary polling rate between two different usage periods includes:

[0034] C1: Determine whether the type of the usage period where the current time is located is the same as that of the next usage period; if yes, do not set a transition period and a temporary polling rate; if no, jump to C2;

[0035] C2: Mark the time point connecting the two different usage periods as the defining time point, and mark the time period formed by the m time points before and after the defining time point as the transition period; where m is obtained through manual setting;

[0036] C3: Mark the usage period before the transition period as the previous usage period, and mark the usage period after the transition period as the next usage period; obtain the dynamic polling rate DTL of the wireless mouse in the usage period where the current time is located and mark it as the dynamic polling rate of the previous usage period; mark the average value of the dynamic polling rates of the most recent i usage periods of the same type as the next usage period as the dynamic polling rate of the next usage period; where i is obtained through manual setting;

[0037] C4: Use each time point in the transition period as the abscissa, the dynamic polling rate in the previous usage period as the ordinate value of the first time point in the transition period, and the dynamic polling rate in the subsequent usage period as the ordinate value of the last time point in the transition period. Connect the ordinate values of the first time point and the last time point to obtain a transition curve, and obtain the temporary polling rate of each time point in the transition period through the transition curve.

[0038] The first aspect of the present invention provides an intelligent control method for a wireless mouse, including the following steps:

[0039] S1: Obtain the basic data and environmental temperature of the wireless mouse;

[0040] S2: Analyze the click time points and click frequencies to obtain the current usage period of the wireless mouse; input the usage duration and charge / discharge time points into the battery analysis model to obtain the battery attenuation curve, and obtain the available power value based on the remaining power, battery attenuation curve, and environmental temperature;

[0041] S3: Issue a control command based on the current time and usage period, and dynamically adjust the polling rate of the wireless mouse based on the control command and the available power value.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. The present invention analyzes the click time points and click frequencies to obtain the current usage period of the wireless mouse; inputs the usage duration and charge / discharge time points into the battery analysis model to obtain the battery attenuation curve, and obtains the available power value based on the remaining power, battery attenuation curve, and environmental temperature; issues a control command based on the current time and usage period, and dynamically adjusts the polling rate of the wireless mouse based on the control command and the available power value, solving the technical problems in the intelligent control of wireless mice that it is difficult to dynamically control the polling rate of wireless mice according to users' usage habits, and it is difficult to smoothly connect the polling rates when adjusting the polling rates in different periods; the present invention can adjust the polling rate of the wireless mouse as needed, reduce unnecessary energy consumption, and extend the service life of the battery.

[0044] 2. When the battery is in different ambient temperatures, the available time of the displayed battery power is not consistent with the actual available time. For example, when the temperature is relatively high, the internal chemical reaction of the battery accelerates, resulting in an increase in the discharge efficiency and a shortening of the usage duration; or when the temperature is relatively low, the internal resistance of the battery increases, leading to a decrease in the migration speed of lithium ions and a reduction in the available capacity and the endurance time. When the battery of the wireless mouse is at a relatively appropriate temperature, the usage time of the battery will increase. The present invention analyzes different ambient temperatures to obtain the available value of the battery power, and adjusts the trend change speed of the left and right sides of the function graph through the amplitude adjustment coefficient. This can make the prediction of the available value of the battery power of the wireless mouse a dynamic value that follows the environmental changes, increasing the environmental adaptability of the prediction.

[0045] 3. The present invention adjusts the polling rate of the wireless mouse according to different usage periods, maintains a high response during common usage periods, and maintains an appropriate low response during non-common usage periods. By maintaining a relatively low response during different periods, the wireless mouse can set different data transmission frequencies according to the usage habits of different users, which can reduce the power consumption of the wireless mouse and thus extend the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 is a schematic diagram of the operation steps of the present invention;

[0048] Figure 2 is a schematic diagram of the system module of the present invention;

[0049] Figure 3 is a schematic diagram of the operation steps for dynamically regulating the polling rate of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0051] Please refer to Figure 1 - Figure 2 , the first aspect embodiment of the present invention provides an intelligent control system for a wireless mouse, including: an intelligent analysis module, and a data collection module, a polling control module, and a database connected thereto;

[0052] Data collection module: used to obtain the basic data and ambient temperature of the wireless mouse; among them, the basic data includes usage duration, charge and discharge time points, click time points, click frequency, and remaining battery power;

[0053] Intelligent analysis module: used to analyze the click time points and click frequency to obtain the usage period of the current wireless mouse; input the usage duration and charge and discharge time points into the battery analysis model to obtain the battery attenuation curve, and obtain the available battery value based on the remaining battery power, battery attenuation curve, and ambient temperature; among them, the battery analysis model is obtained through artificial intelligence model training; the usage period includes common usage period, infrequent usage period, and non-usage period;

[0054] Polling control module: used to issue control instructions based on the current time and usage period, and dynamically adjust the polling rate of the wireless mouse based on the control instructions and available battery value.

[0055] In this application, obtaining the basic data and ambient temperature of the wireless mouse includes:

[0056] Obtain the usage duration of the wireless mouse through an embedded timer, obtain the charging time point and stop charging time point of the wireless mouse through an embedded bidirectional current monitoring module, and integrate each charging time point with the subsequent nearest stop charging time point as the charging time point; use a high-speed optocoupler isolation acquisition circuit and a microcontroller to obtain the time point of each click of the wireless mouse, and mark the time point as the click time point; obtain the click frequency of the wireless mouse within a unit time through a frequency counter, obtain the remaining battery power of the wireless mouse in real time through a battery monitoring device; obtain the ambient temperature at the location of the wireless mouse through a temperature sensor installed on the wireless mouse; among them, the unit time is obtained through manual setting.

[0057] It should be noted that in obtaining the charging time point and stop charging time point of the wireless mouse through the embedded bidirectional current monitoring module, the MAX40200 can be used for the current monitoring module;

[0058] In using a high-speed optocoupler isolation acquisition circuit and a microcontroller to obtain the time point of each click of the wireless mouse, the Arduino can be used for the microcontroller;

[0059] In obtaining the click frequency of the wireless mouse within a unit time through a frequency counter, the HEF4060BT can be used for the frequency counter.

[0060] In this application, analyzing the click time points and click frequency to obtain the usage period of the current wireless mouse includes:

[0061] Extract the non-click time points at each time point of a day, integrate the continuous non-click time points to obtain several time periods, and mark the time periods with a duration greater than a first duration threshold as unused time periods; wherein, the first duration threshold and the second duration threshold are set through experience.

[0062] Relabel each non-click time point in the time periods not marked as unused time periods as a click time point, obtain the click frequency of the wireless mouse within the unit time where each click time point of each day is located, and integrate the click frequencies of the wireless mouse within the unit time of each historical day based on the click time points into several frequency processing groups.

[0063] Extract eigenvalue for each frequency processing group to obtain the standard click frequency of each click time point, mark the click time points with a standard click frequency greater than a frequency threshold as target time points, mark the unit time with target time points as target unit time, integrate the continuous target unit times to obtain several time periods, and mark the time periods with a duration greater than a second duration threshold as frequently used time periods; integrate the time points in a day that do not belong to the frequently used time periods and the unused time periods into several infrequently used time periods; wherein, the frequency threshold is obtained through experience.

[0064] It should be noted that in the present invention, the target unit time is used as a point to divide the frequently used time periods. This is done to avoid frequent adjustment of the wireless mouse caused by small frequently used time periods when dividing the frequently used time periods using time points, thereby increasing the practicality of the system.

[0065] It should be noted that relabeling each non-click time point in the time periods not marked as unused time periods as a click time point is to remove the short continuous non-click time points between two clicks, avoid the instability of the mouse caused by the system frequently adjusting the polling rate in a short time, and thus the user experience is not smooth.

[0066] It should be noted that to obtain the click frequency of the wireless mouse within the unit time where each click time point of each day is located, an example is as follows: if the time point is 10:00:01 in a day, and the unit time is set to one unit time every 5 seconds;

[0067] The unit time where 10:00:01 is located is: 10:00:00 - 10:00:05;

[0068] Obtain the number of clicks of the wireless mouse within 10:00:00 - 10:00:05 of each day, and mark the obtained number as the click frequency of the wireless mouse within the unit time of 10:00:00 - 10:00:05; wherein, the click frequency is the number of clicks of the wireless mouse within the unit time divided by 1.

[0069] It should be noted that the click frequency of the wireless mouse in the present invention is not the number of mouse clicks per second, but the number of mouse clicks in a unit of time.

[0070] It should be noted that the click frequency is the number of clicks of the wireless mouse in a unit of time divided by 1, where 1 represents a unit of time.

[0071] It should be noted that based on the click time points, the click frequencies of the wireless mouse in a unit of time for each historical day are integrated into several frequency processing groups. For example:

[0072] If the click time points are: 10:00:01, 10:20:01, 10:30:01; and the corresponding unit of time is: 10:00:00 - 10:00:05, 10:20:00 - 10:20:05, 10:30:00 - 10:30:05;

[0073] Then the click frequencies in the unit of time of 10:00:00 - 10:00:05 for each historical day are integrated into one frequency processing group;

[0074] The click frequencies in the unit of time of 10:20:00 - 10:20:05 for each historical day are integrated into one frequency processing group;

[0075] The click frequencies in the unit of time of 10:30:00 - 10:30:05 for each historical day are integrated into one frequency processing group.

[0076] In this application, eigenvalue extraction is performed on each frequency processing group to obtain the standard click frequency of each click time point, including:

[0077] Obtain the variance of the click frequencies in each frequency processing group, and determine whether the variance of this frequency processing group is greater than the defined value; if so, remove the click frequency that differs the most from the mode of the click frequencies in this frequency processing group, and re - perform the variance judgment until the variance of this frequency processing group is not greater than the defined value, then retain the remaining click frequencies of this frequency processing group; if not, retain the click frequencies of this frequency processing group; where the defined value is obtained through manual setting;

[0078] Extract each frequency processing group in turn, and obtain the maximum value ZD, mode ZA, and percentile ZB of the retained click frequencies in the extracted frequency processing group; based on the formula BP = α1×ZA + α2×ZB + α3×ZD, obtain the standard click frequency BP of the click time point corresponding to the frequency processing group; where α1, α2, and α3 are all proportional adjustment coefficients greater than 0, and α1 + α2 + α3 = 1, α1 > α2 > α3.

[0079] It should be noted that the present invention uses percentiles when calculating eigenvalues. The reason for using percentiles is that there may be outliers or extreme points in the data set, and these values may have a greater impact on statistics such as standard values. Using percentiles can reduce the interference of these outliers or extreme points and improve the accuracy of system analysis.

[0080] It should be noted that each frequency processing group is extracted in chronological order.

[0081] It should be noted that in the present invention, the percentile ZB is the value at a specific percentile in each group. For example, if the data in each group is sorted in ascending order, and the artificially set percentile is 90%, then the data at the 90% position in each group sorted in order is the percentile ZB of the frequency processing group. If there is no data at the 90% position in the frequency processing group, the data closest to the 90% position is used as the percentile of the frequency processing group.

[0082] It should be noted that α1, α2, and α3 are all proportion adjustment coefficients greater than 0 set manually. The reason for α1>α2>α3 is that α1 is multiplied by the mode ZA of the click frequency, α2 is multiplied by the percentile ZB of the click frequency, and α3 is multiplied by the maximum value ZD of the click frequency. For the frequency processing group, the mode represents the value that appears most frequently in the data of the frequency processing group, and can be indirectly understood as the most stable value of the frequency processing group. Therefore, the present invention sets the largest proportional coefficient for the mode. Also, because the percentile is set manually to remove the interference of outliers or extreme points, the proportional coefficient set by the present invention for the percentile is between the proportional coefficients of the mode and the maximum value. At the same time, the maximum value is an extreme value and cannot absolutely represent the situation of the frequency processing group compared with the mode and the percentile, and can only be used as a benchmark value. Therefore, the present invention sets the smallest proportional coefficient for the maximum value.

[0083] The battery analysis model in this application is obtained by training an artificial intelligence model, including:

[0084] Extract the usage duration, charge and discharge time points, and battery decay curve from the historical reference data. Among them, the historical reference data includes several usage durations and charge and discharge time points, as well as the battery decay curve set by experts according to the usage duration and charge and discharge time points;

[0085] Integrate the usage duration, charge and discharge time points, and battery attenuation curve into several groups of training data and test data. Use the training data to train the artificial intelligence model, use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a battery analysis model with the usage duration and charge and discharge time points as inputs and the battery attenuation curve as the output; wherein, the artificial intelligence model includes a BP neural network model and an RBF neural network model, the abscissa of the battery attenuation curve is time, and the ordinate is the battery capacity.

[0086] Specifically, the specific steps of using the test data to test the trained artificial intelligence model and adjusting the artificial intelligence model according to the test results are as follows:

[0087] Input the usage duration and charge and discharge time points in the test data into the trained artificial intelligence model to obtain the corresponding battery attenuation curve. Compare the corresponding battery attenuation curve with the corresponding battery attenuation curve in the test data. When the coincidence degree of the two is within the threshold, and the threshold is obtained according to experience, no parameter adjustment is required, and the next group of test data is tested; if it is not within the threshold, the corresponding parameters are adjusted until the coincidence degree of the two is within the threshold, and then the next group of test data is tested. When the number of test data with the coincidence degree of the battery attenuation curve obtained from all test data and the corresponding battery attenuation curve within the threshold accounts for 95% or more of the total number of test data, a battery analysis model with the usage duration and charge and discharge time points as inputs and the battery attenuation curve as the output is obtained.

[0088] It should be noted that in the two-dimensional coordinate where the battery attenuation curve is located, the abscissa is time, and the ordinate is the battery capacity after attenuation.

[0089] In this application, the available power value is obtained based on the remaining power, battery attenuation curve, and environmental temperature, including:

[0090] A1: Extract the battery capacity DL of the wireless mouse from the battery attenuation curve based on the current time, and determine whether the battery capacity DL is less than the capacity threshold; if so, issue a low battery capacity warning; if not, jump to A2; wherein, both the capacity threshold and the power threshold are obtained by manual setting;

[0091] A2: Extract the remaining power YD of the wireless mouse, and determine whether the remaining power YD is less than the power threshold; if so, issue a low power warning; if not, jump to A3;

[0092] A3: Extract the ambient temperature HW, and determine whether the ambient temperature HW is less than the median value YDZ of the suitable temperature range; if so, obtain the available battery power value DYZ of the wireless mouse based on the formula DYZ = exp(-β1×|HW - YDZ| / YDZ)×YD×DL; if not, obtain the available battery power value DYZ of the wireless mouse based on the formula DYZ = exp(-β2×|HW - YDZ| / YDZ)×YD×DL; where β1 and β2 are both amplitude adjustment coefficients of the exp() function set manually, the value ranges of β1 and β2 are (0, 2], and β1 > β2; the suitable temperature range is obtained by manual setting.

[0093] It should be noted that when the battery is in different ambient temperatures, the available time of the displayed battery power is not consistent with the actual available time. For example, when the temperature is high, the internal chemical reaction of the battery accelerates, resulting in an increase in the discharge efficiency and a shortening of the usage duration; or when the temperature is low, the internal resistance of the battery increases, resulting in a decrease in the migration speed of lithium ions and a reduction in the available capacity and the battery life; when the battery of the wireless mouse is at a relatively appropriate temperature, the usage time of the battery will increase. In the present invention, the "V"-shaped function formed by exp(-|x|) is used to analyze different ambient temperatures to obtain the available battery power value, and the amplitude adjustment coefficient is used to adjust the change rate of the trends on both sides of the "V" shape. This can make the prediction of the available battery power value of the wireless mouse a dynamic value that follows the environmental changes, increasing the environmental adaptability of the prediction.

[0094] It should be noted that the remaining battery power YD is a value representing the percentage of the remaining battery power of the wireless mouse.

[0095] It should be noted that both β1 and β2 are amplitude adjustment coefficients of the exp() function set manually, and β1 > β2 because: the amplitude adjustment coefficients β1 and β2 are used to change the change rate of the curve trend. The greater the value of the adjustment coefficient, the greater the influence on exp(-|x|). Since the low temperature has a more significant impact on the available battery power value for the battery, the value of the amplitude adjustment coefficient β1 is greater than that of the amplitude adjustment coefficient β2.

[0096] In this embodiment, if the suitable temperature range is set to 15 - 35°C, then the median value YDZ = 25°C;

[0097] The ambient temperature HW = 32°C is extracted. Since the ambient temperature HW = 32°C is not less than the median value YDZ = 25°C of the suitable temperature range, based on the formula DYZ = exp(-β2×|HW - YDZ| / YDZ)×YD×DL = exp(-0.2×|32 - 25| / 25)×80%×2000 = 1512.86 mAh, the available battery value DYZ of the wireless mouse is obtained; where, in this embodiment, the remaining battery power YD of the wireless mouse is 80%, and the battery capacity DL of the wireless mouse is 2000 mAh.

[0098] In this application, control instructions are issued based on the current time and usage period, including:

[0099] B1: Extract the current time and determine whether the current time is a common usage period; if yes, issue control instruction one; if not, jump to B2;

[0100] B2: Determine whether the current time is an uncommon usage period; if yes, issue control instruction two; if not, issue control instruction three; where, the control instructions include control instruction one, control instruction two, and control instruction three.

[0101] Please refer to Figure 3 , in this application, the polling rate of the wireless mouse is dynamically regulated based on the control instructions and the available battery value, including:

[0102] When control instruction one is received, extract the available battery value DYZ, and based on the formula DTL = δ1×(exp(DYZ / BYZ)-1)×BL, obtain the dynamic polling rate DTL of the wireless mouse; where, BYZ is the standard available battery value set manually, BL is the standard polling rate set manually; δ1 is the amplitude adjustment coefficient set manually, and the value range of δ1 is (0, 2];

[0103] When control instruction two is received, extract the available battery value DYZ, and determine whether the available battery value DYZ is greater than the standard available battery value BYZ; if yes, based on the formula DTL = δ2×(exp(DYZ / BYZ)-1)×BL, obtain the dynamic polling rate DTL of the wireless mouse; if not, based on the formula DTL = δ3×ln(DYZ / BYZ + 1)×BL, obtain the dynamic polling rate DTL of the wireless mouse; both δ2 and δ3 are the amplitude adjustment coefficients set manually, and the value range of δ2 and δ3 is (0, 2];

[0104] When control instruction three is received, use the minimum polling rate as the dynamic polling rate DTL; set a transition period and a temporary polling rate between two different usage periods; where, the minimum polling rate is obtained through manual setting.

[0105] It should be noted that the present invention adjusts the polling rate of the wireless mouse by using different time periods, maintains high responsiveness during common time periods, maintains an appropriate low responsiveness during non-common time periods, and maintains a relatively low responsiveness at different times. This can enable the wireless mouse to set different data transmission frequencies according to the usage habits of different users, reduce the power consumption of the wireless mouse, and thus extend the battery life.

[0106] In this application, a transition period and a temporary polling rate are set between two different usage time periods, including:

[0107] C1: Determine whether the type of the usage time period where the current time is located is the same as that of the next usage time period; if yes, do not set a transition period and a temporary polling rate; if not, jump to C2;

[0108] C2: Mark the time point connecting the two different usage time periods as the defining time point, and mark the time period formed by the m time points before and m time points after the defining time point as the transition period; where m is obtained through manual setting;

[0109] C3: Mark the usage time period before the transition period as the previous usage time period, and mark the usage time period after the transition period as the next usage time period; obtain the dynamic polling rate DTL of the wireless mouse in the usage time period where the current time is located and mark it as the dynamic polling rate of the previous usage time period; mark the average value of the dynamic polling rates of the most recent i usage time periods of the same type as the next usage time period as the dynamic polling rate of the next usage time period; where i is obtained through manual setting;

[0110] C4: Use the time points in the transition period as the abscissa, the dynamic polling rate of the previous usage time period as the ordinate value of the first time point in the transition period, the dynamic polling rate of the next usage time period as the ordinate value of the last time point in the transition period, connect the ordinate values of the first time point and the last time point to obtain a transition curve, and obtain the temporary polling rate of each time point in the transition period through the transition curve.

[0111] It should be noted that the transition period and the temporary polling rate set by the present invention are not preset in advance, but are dynamically set according to the usage time period where the current time is located. The specific explanation is as follows:

[0112] When the usage time period where the current time is located is different from the next usage time period, trigger the operation of setting the transition period and the temporary polling rate;

[0113] Since the dynamic polling rate for the usage period in which the current time is located has been calculated, there is no need to use the historical dynamic polling rate at this time. However, the dynamic polling rate for the next usage period has not been calculated yet, and an anchor value is required for analysis at this time. The present invention uses the average value of the dynamic polling rates of the most recent i usage periods of the same type as the next usage period as this anchor value; then, the temporary polling rate is analyzed through the anchor value. Among them, the anchor value does not use the dynamic polling rate of the most recent usage period of the same type as the next usage period in order to avoid excessive errors caused by sudden calculation deviations.

[0114] It should be noted that the average value of the dynamic polling rates of the most recent i usage periods of the same type as the next usage period can be illustrated by the following examples:

[0115] If the usage period type corresponding to the next usage period is a common usage period, the average value of the dynamic polling rates DTL of the current wireless mouse in the most recent i common usage periods is obtained;

[0116] If the usage period type corresponding to the next usage period is an uncommon usage period, the average value of the dynamic polling rates DTL of the current wireless mouse in the most recent i uncommon usage periods is obtained;

[0117] If the usage period type corresponding to the next usage period is a non - usage period, the average value of the dynamic polling rates DTL of the current wireless mouse in the most recent i non - usage periods is obtained.

[0118] It should be noted that obtaining the temporary polling rate of each time point in the transition period through the transition curve can be understood as: marking the ordinate value of each time point in the transition curve in the transition period as the temporary polling rate of each time point.

[0119] An embodiment of the second aspect of the present invention provides an intelligent control method for a wireless mouse, including the following steps:

[0120] S1: Obtain the basic data and ambient temperature of the wireless mouse;

[0121] S2: Analyze the click time points and click frequencies to obtain the usage period of the current wireless mouse; input the usage duration and charge - discharge time points into the battery analysis model to obtain the battery decay curve, and obtain the available power value based on the remaining power, battery decay curve, and ambient temperature;

[0122] S3: Issue a control instruction based on the current time and usage period, and dynamically adjust the polling rate of the wireless mouse based on the control instruction and the available power value.

[0123] Some of the data in the above formula is the numerical value obtained after removing the dimension. The formula is the one closest to the actual situation obtained through software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0124] The working principle of the present invention:

[0125] The present invention first obtains the basic data of the wireless mouse and the ambient temperature, which provides data support for subsequent analysis. Then, it analyzes the click time points and click frequencies to obtain the current usage period of the wireless mouse. This step divides the target unit time as a point to divide the common usage periods, avoiding frequent regulation of the wireless mouse caused by relatively small common usage periods. It inputs the usage duration and charge / discharge time points into the battery analysis model to obtain the battery attenuation curve, and obtains the available battery value based on the remaining battery power, battery attenuation curve, and ambient temperature. This step enables the prediction of the available battery value of the wireless mouse to be a dynamic value that follows environmental changes, increasing the environmental adaptability of the prediction. Finally, it issues a control instruction based on the current time and usage period, and dynamically regulates the polling rate of the wireless mouse based on the control instruction and the available battery value. Doing so can enable the wireless mouse to set different data transmission frequencies according to different users' usage habits, and can reduce the power consumption of the wireless mouse.

[0126] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. 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 method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent control system for a wireless mouse, characterized in that: include: Intelligent analysis module, and data collection module, polling control module and database connected thereto; The data collection module is used to obtain basic data and ambient temperature of the wireless mouse; wherein the basic data includes usage time, charging and discharging time points, click time points, click frequency and remaining power; The intelligent analysis module is used to analyze the click time point and click frequency to obtain the current usage period of the wireless mouse; input the usage time and the charge and discharge time point into the battery analysis model to obtain the battery attenuation curve, and obtain the available power value based on the remaining power, the battery attenuation curve and the ambient temperature; wherein the battery analysis model is obtained by training the artificial intelligence model; the usage period includes the common period, the uncommon period and the unused period; The polling control module is used to issue a control instruction based on the current time and the usage period, and dynamically adjust the polling rate of the wireless mouse based on the control instruction and the available power value.

2. The intelligent control system for a wireless mouse according to claim 1, characterized in that: The obtaining of basic data and ambient temperature of the wireless mouse includes: The use time of the wireless mouse is obtained through an embedded timer, the charging time point and the charging stop time point of the wireless mouse are obtained through an embedded bidirectional current monitoring module, and each charging time point and the subsequent most recent charging stop time point are integrated into a charging time point; a high-speed optocoupler isolation acquisition circuit and a microcontroller are used to obtain the time point of each click of the wireless mouse, and the time point is marked as a click time point; the click frequency of the wireless mouse per unit time is obtained through a frequency counter, and the remaining power of the wireless mouse is obtained in real time through a power monitoring device; the ambient temperature of the location of the wireless mouse is obtained through a temperature sensor installed on the wireless mouse.

3. The intelligent control system for a wireless mouse according to claim 1, characterized in that: The analysis of the click time point and the click frequency to obtain the current usage period of the wireless mouse includes: Extract non-click time points from each time point of the day, integrate consecutive non-click time points to obtain several time periods, and mark the time periods with a duration greater than a duration threshold of 1 as unused time periods; Re-mark each non-click time point in the time period not marked as an unused time period as a click time point, obtain the click frequency of the wireless mouse in the unit time of each click time point every day, and integrate the click frequency of the wireless mouse in the unit time of each historical day into several frequency processing groups based on the click time point; Feature value extraction is performed on each frequency processing group to obtain the standard click frequency of each click time point, the click time point with a standard click frequency greater than the frequency threshold is marked as the target time point, the unit time with a target time point is marked as the target unit time, and the continuous target unit time is integrated to obtain several time periods, and the time period with a duration greater than the duration threshold 2 in the time period is marked as a commonly used time period; the time points in a day that are not commonly used time periods and unused time periods are integrated into several uncommon time periods.

4. The intelligent control system for a wireless mouse according to claim 3, characterized in that: The feature value extraction of each frequency processing group to obtain the standard click frequency of each click time point includes: Obtain the variance of the click frequency in each frequency processing group, and determine whether the variance of the frequency processing group is greater than a defined value; if so, remove the click frequency in the frequency processing group that is most different from the mode of the click frequency, and re-determine the variance until the variance of the frequency processing group is no greater than the defined value, and retain the remaining click frequencies of the frequency processing group; if not, retain the click frequencies of the frequency processing group; Each frequency processing group is extracted in turn, the maximum value, mode and percentile of the retained click frequency in the extracted frequency processing group are obtained, and weighted summation is performed to obtain the standard click frequency of the click time point corresponding to the frequency processing group.

5. The intelligent control system for a wireless mouse according to claim 1, characterized in that: The battery analysis model is obtained through artificial intelligence model training, including: Extracting the usage time, charge and discharge time points and battery attenuation curve from the historical reference data; wherein the historical reference data includes a number of usage time and charge and discharge time points, and the battery attenuation curve set by experts based on the usage time and charge and discharge time points; The usage time, charge and discharge time points, and battery attenuation curve are integrated into several groups of training data and test data, the training data is used to train the artificial intelligence model, the test data is used to test the trained artificial intelligence model, and the artificial intelligence model is adjusted according to the test results; finally, a battery analysis model is obtained with the usage time and charge and discharge time points as input and the battery attenuation curve as output; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model, the horizontal axis of the battery attenuation curve is time, and the vertical axis is battery capacity.

6. The intelligent control system for a wireless mouse according to claim 5, characterized in that: The method of obtaining the available power value based on the remaining power, the battery attenuation curve and the ambient temperature includes: A1: extracting the battery capacity DL of the wireless mouse from the battery attenuation curve based on the current time, and determining whether the battery capacity DL is less than a capacity threshold; if yes, issuing a low battery capacity warning; if no, jumping to A2; A2: extract the remaining power YD of the wireless mouse, and determine whether the remaining power YD is less than the power threshold; if yes, issue a low power warning; if no, jump to A3; A3: Extract the ambient temperature HW, and determine whether the ambient temperature HW is less than the middle value YDZ of the suitable temperature range; if yes, obtain the available power value DYZ of the wireless mouse based on the formula DYZ=exp(-β1×|HW-YDZ| / YDZ)×YD×DL; if no, obtain the available power value DYZ of the wireless mouse based on the formula DYZ=exp(-β2×|HW-YDZ| / YDZ)×YD×DL; wherein β1 and β2 are both amplitude adjustment coefficients of the exp() function, and the value range of β1 and β2 is (0,2], and β1>β2.

7. The intelligent control system for a wireless mouse according to claim 1, characterized in that: The issuing of control instructions based on the current time and the usage period includes: B1: Extract the current time and determine whether the current time is a common time period; if yes, issue control instruction 1; if no, jump to B2; B2: Determine whether the current time is a non-usual time period; if yes, issue control instruction 2; if no, issue control instruction 3; wherein the control instructions include control instruction 1, control instruction 2 and control instruction 3.

8. The intelligent control system for a wireless mouse according to claim 7, characterized in that: The method of dynamically adjusting the polling rate of the wireless mouse based on the control instruction and the available power value includes: When the control instruction 1 is received, the available power value DYZ is extracted, and the dynamic polling rate DTL of the wireless mouse is obtained based on the formula DTL=δ1×(exp(DYZ / BYZ)-1)×BL; wherein BYZ is the standard available power value, BL is the standard polling rate; δ1 is the amplitude adjustment coefficient, and the value range of δ1 is (0,2]; When the control instruction 2 is received, the available power value DYZ is extracted to determine whether the available power value DYZ is greater than the standard available power value BYZ; if yes, the dynamic polling rate DTL of the wireless mouse is obtained based on the formula DTL=δ2×(exp(DYZ / BYZ)-1)×BL; if no, the dynamic polling rate DTL of the wireless mouse is obtained based on the formula DTL=δ3×ln(DYZ / BYZ+1)×BL; δ2 and δ3 are both amplitude adjustment coefficients, and the value range of δ2 and δ3 is (0,2]; When the control instruction three is received, the minimum polling rate is used as the dynamic polling rate DTL; and a transition period and a temporary polling rate are set between two different use periods.

9. The intelligent control system for a wireless mouse according to claim 8, characterized in that: The step of setting a transition period and a temporary polling rate between two different usage periods includes: C1: Determine whether the usage period of the current time is the same as the type of the next usage period; if yes, do not set the transition period and temporary polling rate; if no, jump to C2; C2: Mark the time point connecting two different usage periods as the defining time point, and mark the time period formed by the m time points before and after the defining time point as the transition period; C3: Mark the usage period before the transition period as the previous usage period, and mark the usage period after the transition period as the later usage period; obtain the dynamic polling rate DTL of the wireless mouse in the usage period of the current time and mark it as the dynamic polling rate of the previous usage period; mark the average value of the dynamic polling rates of the most recent i usage periods of the same type as the later usage period as the dynamic polling rate of the later usage period; C4: Take each time point in the transition period as the horizontal coordinate, the dynamic polling rate of the previous use period as the vertical coordinate value of the first time point in the transition period, and the dynamic polling rate of the subsequent use period as the vertical coordinate value of the last time point in the transition period. Connect the vertical coordinate value of the first time point and the vertical coordinate value of the last time point to obtain a transition curve, and obtain the temporary polling rate of each time point in the transition period through the transition curve.

10. An intelligent control method for a wireless mouse, based on the intelligent control system for a wireless mouse according to any one of claims 1 to 9, characterized in that: S1: Get basic data and ambient temperature of the wireless mouse; S2: Analyze the click time point and click frequency to obtain the current usage period of the wireless mouse; input the usage time and the charge and discharge time point into the battery analysis model to obtain the battery attenuation curve, and obtain the available power value based on the remaining power, the battery attenuation curve and the ambient temperature; S3: issuing a control instruction based on the current time and the usage period, and dynamically adjusting the polling rate of the wireless mouse based on the control instruction and the available power value.

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