A data processing method and system for a wireless mouse

Abstract

This invention relates to the field of mouse data processing, specifically to a data processing method and system for a wireless mouse. The method first acquires the displacement increment data of the wireless mouse at each moment within each time period of the day. It then filters out mouse displacement moments within each time period. Based on the number of mouse displacement moments in each time period, the distance between adjacent mouse displacement moments, and the displacement increment data at each moment, the movement intensity of the wireless mouse during the target time period is obtained. The time periods of the day are merged and periodically analyzed to obtain multiple periodic time periods for the wireless mouse each day. Based on the movement intensity of the wireless mouse in the same periodic time periods on different days, the load level of the wireless mouse in each periodic time period is obtained. Subsequently, the data return rate of the wireless mouse is adjusted to obtain an adjusted data return rate. This invention can effectively control the data return rate of a wireless mouse, reducing its energy consumption.

Description

Technical Field

[0001] This invention relates to the field of mouse data processing, and more specifically to a data processing method and system for a wireless mouse. Background Technology

[0002] As a primary input control device for computers, the wireless mouse breaks physical limitations and gets rid of the constraints of cables compared to the traditional wired mouse. It achieves data transmission through 2.4GHz wireless networks, Bluetooth, and other methods, increasing the user's freedom of use. However, wireless mice still have problems, such as signal interference and data transmission stability issues, power supply and power consumption issues, etc. There is an urgent need for a solution that can balance the data transmission stability and power consumption of wireless mice.

[0003] In related technologies, the load on a wireless mouse is usually determined based on the displacement rate of the mouse used by the user, and the polling rate of the wireless mouse is adjusted accordingly to control the frequency at which the wireless mouse reports its own position data. However, since the frequency of use of the wireless mouse varies in different time periods and scenarios, existing methods cannot effectively control the data polling rate of the wireless mouse, resulting in high energy consumption for the wireless mouse to upload position data. Summary of the Invention

[0004] To address the technical problem that existing methods cannot effectively control the data return rate of wireless mice, resulting in high energy consumption when uploading position data, the present invention aims to provide a data processing method and system for wireless mice. The specific technical solution adopted is as follows:

[0005] This invention proposes a data processing method for a wireless mouse, the method comprising:

[0006] The displacement increment data of the wireless mouse is acquired in real time for each time period of each day, and the displacement increment data is two-dimensional data;

[0007] Take any day as the target day, and any time period within the target day as the target time period. Based on the displacement increment data at each moment within the target time period, filter out mouse displacement moments from all moments within the target time period. Based on the number of mouse displacement moments in the target time period, the distance between adjacent mouse displacement moments, and the displacement increment data at each moment, obtain the motion intensity of the wireless mouse in the target time period. Based on the difference in motion intensity between adjacent time periods of the target day, merge the time periods of the target day to obtain multiple merged time periods of the target day, where each merged time period corresponds to a number.

[0008] Based on the time periods included in the merged time period with the same number on different days, the wireless mouse is obtained in multiple periodic time periods each day; based on the movement intensity of the wireless mouse in each time period within the same periodic time period on different days, the load levels of different periodic time periods are divided to obtain the load level of the wireless mouse in each periodic time period.

[0009] The data rate of the wireless mouse is adjusted according to the load level of the wireless mouse in each period to obtain the adjusted data rate of the wireless mouse.

[0010] Furthermore, the step of filtering out mouse movement moments from all moments in the target time period includes:

[0011] If both dimensions of the displacement increment data at each moment in the target time period are both 0, then each moment in the target time period is taken as the moment when the mouse is stationary.

[0012] All moments in the target time period other than the moments when the mouse is stationary are taken as the mouse displacement moments of the target time period.

[0013] Furthermore, obtaining the motion intensity of the wireless mouse during the target time period includes:

[0014] The number of all mouse displacement moments in the target time period is used as the numerator, the number of all moments in the target time period is used as the denominator, and the ratio is used as the first motion coefficient of the wireless mouse in the target time period.

[0015] By performing a negative correlation mapping on the average of the absolute values ​​of the differences between all two adjacent mouse displacement moments in the target time period, the second motion coefficient of the wireless mouse in the target time period is obtained.

[0016] Based on the calculation formula for the third motion coefficient, the third motion coefficient of the wireless mouse during the target time period is obtained. The calculation formula for the third motion coefficient is as follows:

[0017]

[0018] in, This represents the third motion coefficient of the wireless mouse during the target time period; and These represent the wireless mouse's [number]th [time period] within the target time period. Data from two dimensions of the displacement increment data at each time point; Indicates the number of moments within the target time period;

[0019] The motion intensity of the wireless mouse during the target time period is obtained based on the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse during the target time period.

[0020] Further, obtaining the motion intensity of the wireless mouse during the target time period based on the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse during the target time period includes:

[0021] The motion intensity of the wireless mouse during the target time period is obtained by combining the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse during the target time period and then normalizing them.

[0022] Furthermore, the multiple merged time periods for obtaining the target day include:

[0023] The absolute value of the difference in motion intensity between any two adjacent time periods of the target day is normalized to obtain the motion intensity difference degree between any two adjacent time periods of the target day.

[0024] Two adjacent time periods with a difference in exercise intensity less than a preset difference threshold are merged, and the time period corresponding to the merged consecutive time periods is taken as a merged time period of the target day.

[0025] Furthermore, the acquisition of the wireless mouse during multiple periodic time periods each day includes:

[0026] The time period corresponding to the intersection of the time periods included in the merged time period with the same number for all days is taken as the multiple periodic time periods of the wireless mouse each day.

[0027] Furthermore, obtaining the load level of the wireless mouse in each period includes:

[0028] The average of the motion intensity across all time periods within each cycle of the day is taken as the overall motion intensity level of the wireless mouse within each cycle of the day.

[0029] The average value of the overall motion intensity level of the wireless mouse over the same period of all days is normalized to obtain the load assessment value of the wireless mouse in each period of the cycle.

[0030] Based on the load assessment value of the wireless mouse in each period, the load levels of different period are divided to obtain the load level of the wireless mouse in each period.

[0031] Furthermore, the process of dividing the load into different periodic time periods based on the load assessment value of the wireless mouse in each periodic time period to obtain the load level of the wireless mouse in each periodic time period includes:

[0032] If the load assessment value of the wireless mouse in each period is greater than the preset load threshold, then the wireless mouse is at a high load level in each period.

[0033] If the load assessment value of the wireless mouse in each period is not greater than the preset load threshold, then the wireless mouse is at a low load level in each period.

[0034] Furthermore, the rate of return for obtaining adjustment data from the wireless mouse includes:

[0035] The current period of time in which the wireless mouse is currently located is taken as the current period of time. If the load level of the current period of time is high load level, the product of the preset first adjustment coefficient and the standard data return rate of the wireless mouse is taken as the adjusted data return rate of the wireless mouse in the current period of time.

[0036] If the load level of the current period is low, the product of the preset second adjustment coefficient and the standard data return rate of the wireless mouse will be used as the adjusted data return rate of the wireless mouse in the current period. The preset first adjustment coefficient is greater than 1, and the preset second adjustment coefficient is less than 1.

[0037] The present invention also proposes a data processing system for a wireless mouse, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the data processing methods for a wireless mouse.

[0038] The present invention has the following beneficial effects:

[0039] This invention addresses the issue that existing methods cannot effectively control the data reporting rate of wireless mice, resulting in high energy consumption for uploading location data. Therefore, it first acquires real-time displacement increment data of the wireless mouse at each moment of each day. Considering that the frequency of mouse movement varies depending on the user's usage scenario (i.e., the mouse exists in stationary and rapidly moving states), the invention first filters out mouse displacement moments from all moments within the target time period. These moments represent the times when the mouse moves due to user interaction. The intensity of this movement reflects the frequency of mouse movement during the target time period. Motion moments with similar intensity are merged into a larger merged time period to avoid frequent adjustments to the data reporting frequency. Then, overlapping periodic time periods are selected from the merged time periods with the same number on different days. Since the usage of the wireless mouse is similar across different days within the same periodic time period, the load on the wireless mouse can be accurately predicted. Different load levels are then set for different periodic time periods, and the data reporting rate is effectively adjusted based on the load level of the wireless mouse in each periodic time period, thereby significantly reducing the energy consumption for uploading location data. Attached Figure Description

[0040] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart of a data processing method for a wireless mouse provided in one embodiment of the present invention. Detailed Implementation

[0042] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a data processing method and system for a wireless mouse according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0044] The following description, in conjunction with the accompanying drawings, details the specific solution of the data processing method and system for a wireless mouse provided by this invention.

[0045] Please see Figure 1 The diagram illustrates a data processing method for a wireless mouse according to an embodiment of the present invention, the method comprising:

[0046] Step S1: Real-time acquisition of the displacement increment data of the wireless mouse at each moment of each day. The displacement increment data is two-dimensional data.

[0047] This invention first uses the built-in optical sensor and digital signal processor of the wireless mouse to collect real-time displacement increment data of the wireless mouse at each moment of each day. The displacement increment data is two-dimensional data, specifically in the form of… ,in This represents the horizontal displacement of the wireless mouse at the current moment relative to the previous moment. This represents the vertical displacement of the wireless mouse's current position relative to the previous position, and and It can be positive or negative.

[0048] The length of the time period is generally 1 to 3 seconds. In one embodiment of the present invention, the length of the time period is set to 1 second, that is, the displacement increment data of the wireless mouse at each moment within one second of each day is collected. The specific value of the time period length can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0049] Step S2: Take any day as the target day, and any time period within the target day as the target time period. Based on the displacement increment data at each moment within the target time period, filter out the mouse displacement moments from all moments within the target time period. Based on the number of mouse displacement moments in the target time period, the distance between adjacent mouse displacement moments, and the displacement increment data at each moment, obtain the motion intensity of the wireless mouse in the target time period. Based on the difference in motion intensity between adjacent time periods of the target day, merge the time periods of the target day to obtain multiple merged time periods of the target day, where each merged time period corresponds to a number.

[0050] Since users' wireless mouse usage varies across different days and different times of the same day, it is necessary to first analyze any time period within any given day, designating any given day as the target day and any time period within the target day as the target time period. Furthermore, users experience both high-load and low-load mouse usage scenarios. In high-load scenarios, the wireless mouse frequently shifts position, while in low-load scenarios, the shift frequency is lower or the mouse remains stationary. Therefore, this embodiment of the invention first filters out mouse shift moments from all moments within the target time period based on the shift increment data at each moment. These mouse shift moments are the times when the mouse moves due to user interaction. Subsequently, the intensity of the wireless mouse's movement during the target time period can be accurately analyzed based on the number of mouse shift moments and the distance between adjacent mouse shift moments.

[0051] Preferably, in one embodiment of the present invention, the method for obtaining the mouse displacement time specifically includes:

[0052] If both dimensions of the displacement increment data at each moment in the target time period are 0, then each moment in the target time period is taken as the moment when the mouse is stationary. The position of the wireless mouse does not change relative to the previous moment when the mouse is stationary. Therefore, all moments in the target time period other than the moment when the mouse is stationary can be taken as the mouse displacement moments of the target time period.

[0053] The higher the proportion of mouse displacement moments in the target time period, the shorter the time interval between adjacent mouse displacement moments, and the greater the distance the wireless mouse moves in the target time period, the more frequently the user uses the wireless mouse during the target time period. In other words, the greater the motion intensity of the wireless mouse in the target time period, the more frequently the user uses the wireless mouse. Therefore, we can analyze the number of mouse displacement moments, the distance between adjacent mouse displacement moments, and the displacement increment data at each moment in the target time period. The obtained motion intensity reflects the frequency of the wireless mouse's movement in the target time period. The greater the motion intensity of the wireless mouse in the target time period, the more frequently the wireless mouse moves in the target time period, which means that the load of the wireless mouse is higher in the target time period. Subsequently, we can accurately analyze the load of the wireless mouse in different time periods based on the motion intensity.

[0054] Preferably, in one embodiment of the present invention, the method for obtaining the motion intensity of the wireless mouse during a target time period specifically includes:

[0055] First, the number of all mouse displacement moments in the target time period is used as the numerator, and the number of all moments in the target time period is used as the denominator. The ratio is used as the first motion coefficient of the wireless mouse in the target time period. The larger the first motion coefficient, the larger the proportion of mouse displacement moments in the target time period, and thus the more frequently the wireless mouse moves in the target time period.

[0056] By performing a negative correlation mapping on the average of the absolute values ​​of the differences between all adjacent mouse displacement moments in the target time period, the second motion coefficient of the wireless mouse in the target time period is obtained. The larger the second motion coefficient, the shorter the time interval between adjacent mouse displacement moments in the target time period, and thus the more frequently the wireless mouse moves in the target time period.

[0057] As an example, in one embodiment of the present invention, the expression for the second motion coefficient of the wireless mouse during the target time period can be, for example, as follows:

[0058]

[0059] in, This represents the second motion coefficient of the wireless mouse during the target time period; Indicates the wireless mouse's position during the target time period. Each mouse movement moment; Indicates the wireless mouse's position during the target time period. At the moment of mouse movement, the first The mouse movement time and the first mouse displacement moment Each mouse displacement moment is defined as two adjacent mouse displacement moments. This represents the number of mouse movement moments within the target time period. This indicates the number of adjacent mouse movement moments within the target time period; Represented by natural constant An exponential function with base 0 is used for negative correlation mapping.

[0060] It should be noted that negative correlation mapping can also be achieved through other basic mathematical operations in other embodiments of the present invention, which will not be elaborated here.

[0061] Based on the calculation formula for the third motion coefficient, the third motion coefficient of the wireless mouse during the target time period is obtained. The calculation formula for the third motion coefficient is as follows:

[0062]

[0063] in, This represents the third motion coefficient of the wireless mouse during the target time period; and These represent the wireless mouse's [number]th [time period] within the target time period. Data from two dimensions of displacement increment data at each time point; This indicates the number of moments within the target time period.

[0064] The larger the third motion coefficient, the longer the displacement distance of the wireless mouse during the target time period, and thus the more obvious the movement of the wireless mouse during the target time period.

[0065] Then, based on the first, second, and third motion coefficients of the wireless mouse during the target time period, the motion intensity of the wireless mouse during the target time period can be obtained.

[0066] Preferably, in one embodiment of the present invention, the method for obtaining the motion intensity of the wireless mouse during a target time period further includes:

[0067] After synthesizing and normalizing the first, second, and third motion coefficients of the wireless mouse during the target time period, the calculation results are limited to... Within the range, the motion intensity of the wireless mouse during the target time period can be obtained.

[0068] In one embodiment of the present invention, the normalization process can be specifically, for example, maximum and minimum value normalization. Furthermore, the normalization in subsequent steps can all adopt maximum and minimum value normalization. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of values, or activation functions and hyperbolic tangent functions can be used to implement the normalization process. These will not be elaborated or limited further.

[0069] As an example, in one embodiment of the present invention, the expression for the motion intensity of the wireless mouse during the target time period can be, for example, as follows:

[0070]

[0071]

[0072] in, This indicates the intensity of the wireless mouse's movement during the target time period; This represents the first motion coefficient of the wireless mouse during the target time period; This represents the second motion coefficient of the wireless mouse during the target time period; This represents the third motion coefficient of the wireless mouse during the target time period; This indicates the number of mouse movement moments within the target time period; Indicates the number of moments within the target time period; This represents the normalization function, used for normalization processing.

[0073] The same method described above can be used to obtain the motion intensity of the wireless mouse in each time period of the target day. Since the embodiments of the present invention divide the length of each time period into short segments, with the unit of time period length being only seconds, the user's use of the wireless mouse in different time periods of the target day is similar. For example, for multiple consecutive time periods, the wireless mouse has similar motion intensity. Therefore, based on the difference in motion intensity between adjacent time periods of the target day, the time periods of the target day can be merged to obtain multiple merged time periods of the target day. The merged time period is obtained by merging multiple time periods. The length of a single merged time period is longer than the length of a single time period, which can avoid frequent adjustments to the data reporting frequency of the wireless mouse, thereby reducing the power consumption of the wireless mouse.

[0074] Preferably, in one embodiment of the present invention, the method for obtaining multiple merged time periods of the target day specifically includes:

[0075] The absolute value of the difference in motion intensity between any two adjacent time periods on the target day is normalized, and the calculation result is limited to... Within a certain range, the difference in motion intensity between any two adjacent time periods of the target day is obtained. The smaller the difference in motion intensity, the more similar the usage of the wireless mouse is between the two adjacent time periods. Therefore, two adjacent time periods with a motion intensity difference less than a preset difference threshold can be merged, and the time period corresponding to the merged consecutive time periods is taken as a merged time period of the target day. The preset difference threshold ranges from [value missing]. In one embodiment of the present invention, the preset difference threshold is set to 0.4. The preset difference threshold can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0076] It should be noted that after obtaining multiple merged time periods for the target day, each merged time period needs to be numbered according to the time sequence. In one embodiment of the present invention, non-zero natural numbers can be used to number the merged time periods for the target day. For example, the first merged time period is marked as value 1, the second merged time period is marked as value 2, and so on, for subsequent calculation and analysis.

[0077] It should also be noted that during the merging process, a certain time period and the adjacent time periods may not meet the merging conditions. Therefore, this time period can be treated as a separate merging time period.

[0078] Using the same method described above, multiple merged time periods for each day can be obtained, and the merged time periods for each day can be numbered.

[0079] Step S3: Based on the time periods included in the merged time periods with the same number on different days, obtain the multiple periodic time periods of the wireless mouse each day; based on the motion intensity of the wireless mouse in each time period of the same periodic time period on different days, divide the load levels of different periodic time periods to obtain the load level of the wireless mouse in each periodic time period.

[0080] Since users have similar usage habits for wireless mice on different days, meaning that the intensity of user usage of wireless mice has similar characteristics within the same time period on different days—for example, the wireless mouse is either in a high-load or low-load state within the same time period on different days—this embodiment of the invention first obtains multiple periodic time periods of the wireless mouse each day based on the time periods included in the merged time periods with the same number on different days. The usage of the wireless mouse is similar within the same periodic time period on different days. Subsequently, the load of the wireless mouse within the periodic time period can be accurately predicted, thereby effectively controlling the data reporting frequency of the wireless mouse and reducing energy consumption.

[0081] Preferably, in one embodiment of the present invention, the method for acquiring wireless mouse data during multiple periodic time periods each day specifically includes:

[0082] The time interval corresponding to the intersection of the merged time intervals with the same number for all days is taken as the multiple periodic time intervals of the wireless mouse each day. In other words, the time intervals that overlap between the merged time intervals with the same number for all days are taken as the multiple periodic time intervals of the wireless mouse each day. For example, if the time interval from 9:00 AM to 10:00 AM is the time interval that overlaps between the merged time intervals with the same number for all days, then the time interval from 9:00 AM to 10:00 AM each day is a periodic time interval for each day.

[0083] Users use wireless mice at varying intensities throughout the day, resulting in different load levels for the mouse in different time periods. Higher activity levels during each period indicate a higher load. Therefore, by analyzing the activity levels of the wireless mouse within the same time period on different days, load levels can be categorized for different time periods. This allows for adjustments to the data return rate of the wireless mouse based on its load level in each period, thereby reducing energy consumption.

[0084] Preferably, in one embodiment of the present invention, the method for obtaining the load level of the wireless mouse in each period specifically includes:

[0085] The average motion intensity across all time periods within each daily cycle is used as the overall motion intensity level of the wireless mouse within that cycle. The average motion intensity level of the wireless mouse across all days in the same cycle is then normalized, limiting the calculation results to... Within a certain range, the load assessment value of the wireless mouse in each period is obtained. The larger the load assessment value in a certain period, the greater the load of the wireless mouse in that period.

[0086] Therefore, based on the load assessment value of the wireless mouse in each period, the load level of different period can be divided, thereby obtaining the load level of the wireless mouse in each period.

[0087] Preferably, in one embodiment of the present invention, the method for obtaining the load level of the wireless mouse in each periodic time further includes:

[0088] If the load assessment value of the wireless mouse in each period is greater than the preset load threshold, the wireless mouse is at a high load level for that period. If the load assessment value of the wireless mouse in each period is not greater than the preset load threshold, the wireless mouse is at a low load level for that period. The preset load threshold has a range of values. In one embodiment of the present invention, the preset load threshold is set to 0.6. The preset load threshold can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0089] This gives us the load level of the wireless mouse in each cycle period. We can then adjust its data return rate based on the load level of the current cycle period to reduce its power consumption.

[0090] Step S4: Adjust the data rate of the wireless mouse according to the load level of the wireless mouse in each period to obtain the adjusted data rate of the wireless mouse.

[0091] Once the load level of the wireless mouse in each period is obtained, the data polling rate of the wireless mouse can be adjusted according to the load level in each period to obtain the adjusted data polling rate of the wireless mouse. This allows the wireless mouse to effectively control the data polling rate based on the intensity of user use, thereby effectively reducing the power consumption of the wireless mouse.

[0092] Preferably, in one embodiment of the present invention, the method for obtaining the adjustment data polling rate of the wireless mouse specifically includes:

[0093] The current period of time in which the wireless mouse is currently located is taken as the current period of time. If the load level of the current period of time is high, it is necessary to increase the frequency of data reporting by the wireless mouse to make the trajectory more consistent and provide a better user experience. Therefore, the product of the preset first adjustment coefficient and the standard data return rate of the wireless mouse can be used as the adjusted data return rate of the wireless mouse in the current period of time.

[0094] If the load level of the current period is low, the frequency of data reporting by the wireless mouse needs to be reduced to balance the mouse's power consumption. Therefore, the product of the preset second adjustment coefficient and the standard data reporting rate of the wireless mouse can be used as the adjusted data reporting rate of the wireless mouse in the current period. The preset first adjustment coefficient is greater than 1, and the preset second adjustment coefficient is less than 1. In one embodiment of the present invention, the preset first adjustment coefficient is set to 1.5 and the preset second adjustment coefficient is set to 0.5. The specific values ​​of the preset first adjustment coefficient and the preset second adjustment coefficient can also be set by the implementer according to the specific implementation scenario, and are not limited here.

[0095] For time periods outside the periodic period, the data rate of the wireless mouse does not need to be adjusted, so that the wireless mouse reports data at the standard data rate. The standard data rate is a built-in parameter of the wireless mouse and is a known value.

[0096] The system obtains the adjustment data return rate of the wireless mouse within the current period. The wireless mouse can then report data at the corresponding adjustment data return rate within the current period, ensuring a good user experience while avoiding excessive power consumption.

[0097] One embodiment of the present invention provides a data processing system for a wireless mouse. The system includes a memory, a processor, and a computer program. The memory is used to store the corresponding computer program, and the processor is used to run the corresponding computer program. When the computer program runs in the processor, it can implement the methods described in steps S1 to S4.

[0098] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A data processing method for a wireless mouse, characterized in that, The method includes: The displacement increment data of the wireless mouse is acquired in real time for each time period of each day, and the displacement increment data is two-dimensional data; Take any day as the target day, and any time period within the target day as the target time period. Based on the displacement increment data at each moment within the target time period, filter out mouse displacement moments from all moments within the target time period. Based on the number of mouse displacement moments in the target time period, the distance between adjacent mouse displacement moments, and the displacement increment data at each moment, obtain the motion intensity of the wireless mouse in the target time period. Based on the difference in motion intensity between adjacent time periods of the target day, merge the time periods of the target day to obtain multiple merged time periods of the target day, where each merged time period corresponds to a number. Based on the time periods included in the merged time period with the same number on different days, the wireless mouse is obtained in multiple periodic time periods each day; based on the movement intensity of the wireless mouse in each time period within the same periodic time period on different days, the load levels of different periodic time periods are divided to obtain the load level of the wireless mouse in each periodic time period. The data rate of the wireless mouse is adjusted according to the load level of the wireless mouse in each period to obtain the adjusted data rate of the wireless mouse.

2. The data processing method for a wireless mouse according to claim 1, characterized in that, The process of filtering mouse movement moments from all moments in the target time period includes: If both dimensions of the displacement increment data at each moment in the target time period are both 0, then each moment in the target time period is taken as the moment when the mouse is stationary. All moments in the target time period other than the moments when the mouse is stationary are taken as the mouse displacement moments of the target time period.

3. The data processing method for a wireless mouse according to claim 1, characterized in that, The method of obtaining the motion intensity of the wireless mouse during the target time period includes: The number of all mouse displacement moments in the target time period is used as the numerator, the number of all moments in the target time period is used as the denominator, and the ratio is used as the first motion coefficient of the wireless mouse in the target time period. By performing a negative correlation mapping on the average of the absolute values ​​of the differences between all two adjacent mouse displacement moments in the target time period, the second motion coefficient of the wireless mouse in the target time period is obtained. Based on the calculation formula for the third motion coefficient, the third motion coefficient of the wireless mouse during the target time period is obtained. The calculation formula for the third motion coefficient is as follows: in, This represents the third motion coefficient of the wireless mouse during the target time period; and These represent the wireless mouse's [number]th [time period] within the target time period. Data from two dimensions of the displacement increment data at each time point; Indicates the number of moments within the target time period; The motion intensity of the wireless mouse during the target time period is obtained based on the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse during the target time period.

4. The data processing method for a wireless mouse according to claim 3, characterized in that, The process of obtaining the motion intensity of the wireless mouse during the target time period based on the first motion coefficient, the second motion coefficient, and the third motion coefficient includes: The motion intensity of the wireless mouse during the target time period is obtained by combining the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse during the target time period and then normalizing them.

5. The data processing method for a wireless mouse according to claim 1, characterized in that, The multiple merged time periods for obtaining the target day include: The absolute value of the difference in motion intensity between any two adjacent time periods of the target day is normalized to obtain the motion intensity difference degree between any two adjacent time periods of the target day. Two adjacent time periods with a difference in exercise intensity less than a preset difference threshold are merged, and the time period corresponding to the merged consecutive time periods is taken as a merged time period of the target day.

6. The data processing method for a wireless mouse according to claim 1, characterized in that, The acquisition of the wireless mouse during multiple time periods each day includes: The time period corresponding to the intersection of the time periods included in the merged time period with the same number for all days is taken as the multiple periodic time periods of the wireless mouse each day.

7. The data processing method for a wireless mouse according to claim 1, characterized in that, The load levels of the wireless mouse in each period include: The average of the motion intensity across all time periods within each cycle of the day is taken as the overall motion intensity level of the wireless mouse within each cycle of the day. The average value of the overall motion intensity level of the wireless mouse over the same period of all days is normalized to obtain the load assessment value of the wireless mouse in each period of the cycle. Based on the load assessment value of the wireless mouse in each period, the load levels of different period are divided to obtain the load level of the wireless mouse in each period.

8. The data processing method for a wireless mouse according to claim 7, characterized in that, The process of classifying the load levels of the wireless mouse in each period based on the load assessment value of the wireless mouse in each period includes: If the load assessment value of the wireless mouse in each period is greater than the preset load threshold, then the wireless mouse is at a high load level in each period. If the load assessment value of the wireless mouse in each period is not greater than the preset load threshold, then the wireless mouse is at a low load level in each period.

9. The data processing method for a wireless mouse according to claim 1, characterized in that, The rate of return for obtaining adjustment data from the wireless mouse includes: The current period of time in which the wireless mouse is currently located is taken as the current period of time. If the load level of the current period of time is high load level, the product of the preset first adjustment coefficient and the standard data return rate of the wireless mouse is taken as the adjusted data return rate of the wireless mouse in the current period of time. If the load level of the current period is low, the product of the preset second adjustment coefficient and the standard data return rate of the wireless mouse will be used as the adjusted data return rate of the wireless mouse in the current period. The preset first adjustment coefficient is greater than 1, and the preset second adjustment coefficient is less than 1.

10. A data processing system for a wireless mouse, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 9.

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