Data processing method and system of wireless mouse

Abstract

The invention relates to the field of mouse data processing, in particular to a data processing method and system of a wireless mouse. The method comprises the following steps: firstly, acquiring displacement increment data of the wireless mouse at each moment in each time period of each day, screening out mouse displacement moments in each time period, and calculating the displacement increment data of the wireless mouse according to the quantity of the mouse displacement moments in each time period, the distance between the adjacent mouse displacement moments and the displacement increment data of each moment; the method comprises the following steps: acquiring the motion intensity of a wireless mouse in a target time period, combining and periodically analyzing each time period of each day to obtain a plurality of periodic time periods of the wireless mouse in each day, and acquiring the load level of the wireless mouse in each periodic time period according to the motion intensity of the wireless mouse in each time period in the same periodic time periods of different days. The data return rate of the wireless mouse is adjusted, and the adjusted data return rate of the wireless mouse is obtained. According to the invention, the data return rate of the wireless mouse can be effectively controlled, and the energy consumption of the wireless mouse is reduced.

Description

Technical Field

[0001] The present invention relates to the field of mouse data processing, and in particular to a data processing method and system for a wireless mouse. Background Art

[0002] As the main input control device for computers, wireless mice break through physical limitations and the constraints of cables compared to traditional wired mice. They achieve data transmission through 2.4GHz wireless networks, Bluetooth, and other methods, increasing user freedom of use. However, wireless mice still have problems, such as signal interference and data transmission stability, 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 borne by a wireless mouse is usually determined based on the displacement rate of the mouse used by the user, and the reporting rate of the wireless mouse is adjusted to control the frequency with which the wireless mouse reports its own position data. However, since users use wireless mice at different times, the existing methods are unable to effectively control the data reporting rate of the wireless mouse, resulting in high energy consumption when uploading position data. Summary of the Invention

[0004] In order to solve the technical problem that the existing methods cannot effectively control the data reporting rate of wireless mice, resulting in high energy consumption of wireless mice uploading position data, the purpose of the present invention is to provide a data processing method and system for wireless mice. The technical solutions adopted are as follows: The present invention provides a data processing method for a wireless mouse, the method comprising: Acquire the displacement increment data of the wireless mouse at each time in each period of each day in real time, wherein the displacement increment data is two-dimensional data; Taking any day as a target day and any time period within the target day as a target time period, filtering out mouse displacement moments from all moments within the target time period based on the displacement increment data at each moment within the target time period; obtaining the motion intensity of the wireless mouse within the target time period based on the number of mouse displacement moments within the target time period, the distance between adjacent mouse displacement moments, and the displacement increment data at each moment; merging the time periods of the target day based on the difference in motion intensity between adjacent time periods of the target day to obtain multiple merged time periods of the target day, wherein each merged time period corresponds to a number; Obtain multiple periodic time periods of the wireless mouse on each day based on the time periods included in the merged time periods with the same number on different days; and divide the different periodic time periods into load levels based on the motion intensity of the wireless mouse in each period of the same periodic time periods on different days to obtain the load level of the wireless mouse in each periodic time period; The data reporting rate of the wireless mouse is adjusted according to the load level of the wireless mouse in each periodic time period to obtain an adjusted data reporting rate of the wireless mouse.

[0005] Furthermore, filtering out the mouse displacement moments from all moments in the target period includes: If the data of the two dimensions of the displacement increment data at each moment in the target period are both 0, then each moment in the target period is regarded as the mouse stationary moment; All other moments in the target period except the mouse static moment are used as the mouse displacement moments in the target period.

[0006] Furthermore, obtaining the motion intensity of the wireless mouse during the target period includes: The number of all mouse displacement moments in the target period is used as the numerator, the number of all moments in the target period is used as the denominator, and the ratio is used as the first motion coefficient of the wireless mouse in the target period; Performing negative correlation mapping on the average of the absolute values ​​of the differences between all two adjacent mouse displacement moments in the target period to obtain a second motion coefficient of the wireless mouse in the target period; Based on the calculation formula of the third motion coefficient, the third motion coefficient of the wireless mouse in the target period is obtained. The calculation formula of the third motion coefficient is: in, Indicates the third motion coefficient of the wireless mouse during the target period; and They represent the first Data of two dimensions in the displacement increment data at a moment; represents the number of moments in the target period; The motion intensity of the wireless mouse in the target period is obtained based on the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse in the target period.

[0007] Furthermore, obtaining the motion intensity of the wireless mouse in the target period based on the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse in the target period includes: The first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse in the target period are integrated and normalized to obtain the motion intensity of the wireless mouse in the target period.

[0008] Furthermore, the multiple merged time periods of the target day are obtained including: Normalizing the absolute value of the difference in exercise intensity between any two adjacent time periods on the target day to obtain a degree of difference in exercise intensity between any two adjacent time periods on the target day; Merge two adjacent time periods whose exercise intensity difference is less than a preset difference threshold, and use the time period corresponding to the merged multiple consecutive time periods as a merged time period of the target day.

[0009] Furthermore, the obtaining of the wireless mouse in multiple periodic time periods every day includes: The time period corresponding to the intersection of each time period included in the combined time period with the same number on all days is used as the multiple periodic time periods of the wireless mouse on each day.

[0010] Furthermore, obtaining the load level of the wireless mouse in each periodic time period includes: The average value of the exercise intensity of all time periods in each period of each cycle time period of each day is used as the overall exercise intensity level of the wireless mouse in each period of each day; Normalizing the average value of the overall exercise intensity level of the wireless mouse in the same periodic time period on all days to obtain a load evaluation value of the wireless mouse in each periodic time period; Based on the load evaluation value of the wireless mouse in each periodic time period, load levels are divided for different periodic time periods to obtain the load level of the wireless mouse in each periodic time period.

[0011] Furthermore, the load level classification of different periodic time periods based on the load evaluation 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: If the load evaluation value of the wireless mouse in each periodic time period is greater than a preset load threshold, the wireless mouse is at a high load level in each periodic time period; If the load evaluation value of the wireless mouse in each periodic time period is not greater than a preset load threshold, the wireless mouse is at a low load level in each periodic time period.

[0012] Furthermore, obtaining the adjusted data reporting rate of the wireless mouse includes: The current cycle time period of the wireless mouse is used as the current cycle time period. If the load level of the current cycle time period is a high load level, the product value of the preset first adjustment coefficient and the standard data reporting rate of the wireless mouse is used as the adjusted data reporting rate of the wireless mouse in the current cycle time period. If the load level in the current periodic time period is a low load level, a product value of a preset second adjustment coefficient and a standard data reporting rate of the wireless mouse is used as the adjusted data reporting rate of the wireless mouse in the current periodic time period, wherein the preset first adjustment coefficient is greater than 1, and the preset second adjustment coefficient is less than 1.

[0013] The present invention also proposes a data processing system for a wireless mouse, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements any one of the steps of a wireless mouse data processing method.

[0014] The present invention has the following beneficial effects: The present invention takes into account that existing methods cannot effectively control the data reporting rate of wireless mice, resulting in high energy consumption when uploading position data. Therefore, the present invention first obtains real-time incremental displacement data of the wireless mouse at each moment in each time period of each day. Considering that the movement frequency of the wireless mouse varies in different usage scenarios, namely, the wireless mouse may be in a stationary state or a rapidly moving state, the present invention first filters out mouse displacement moments from all moments in a target time period. The mouse displacement moments are moments when the mouse moves when the user uses the mouse. The obtained motion intensity can then be used to reflect the frequency of wireless mouse movement in the target time period. Time periods with similar motion intensities are merged into a larger merged time period to avoid frequent subsequent adjustments to the wireless mouse data reporting frequency. Then, overlapping periodic time periods are selected from the same numbered merged time periods on different days. Since wireless mouse usage in the same periodic time period on different days is similar, the load of the wireless mouse can be accurately predicted. Different load levels can be set for different periodic time periods. The data reporting rate of the wireless mouse is effectively adjusted based on the load level of the wireless mouse in each periodic time period, thereby significantly reducing the energy consumption of the wireless mouse when uploading position data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a flow chart of a data processing method for a wireless mouse provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, provides a detailed description of a wireless mouse data processing method and system according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless defined otherwise, 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 belongs.

[0019] The following describes in detail a data processing method and system for a wireless mouse provided by the present invention with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a data processing method for a wireless mouse provided by one embodiment of the present invention, the method comprising: Step S1: acquiring displacement increment data of the wireless mouse at each time in each period of each day in real time, where the displacement increment data is two-dimensional data.

[0021] The embodiment of the present invention first collects the displacement increment data of the wireless mouse at each time in each period of each day in real time through the built-in optical sensor and digital signal processor of the wireless mouse. The displacement increment data is a two-dimensional data, and its specific form is ,in Indicates the horizontal displacement of the wireless mouse position at the current moment relative to the previous moment. Indicates the vertical displacement of the wireless mouse at the current moment relative to the previous moment, and and Can be positive or negative.

[0022] Among them, 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 each 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.

[0023] Step S2: Take any day as the target day, and any time period in the target day as the target time period. Filter out mouse displacement moments from all moments in the target time period based on the displacement increment data at each moment in the target time period. Obtain the motion intensity of the wireless mouse in 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. Merge each time period of the target day based on the difference in motion intensity between adjacent time periods of the target day to obtain multiple merged time periods of the target day, wherein each merged time period corresponds to a number.

[0024] Since the user's usage of the wireless mouse may vary on different days and at different times of the same day, it is necessary to first analyze any time period in any day, take any day as the target day, and take any time period in the target day as the target time period. At the same time, when the user uses the wireless mouse, there are high-load mouse usage scenarios and low-load mouse usage scenarios. In the high-load usage scenario, the wireless mouse will frequently move, while in the low-load usage scenario, the wireless mouse will move less frequently or be in a stationary state. Therefore, an embodiment of the present invention first filters out the mouse displacement moments from all moments in the target time period based on the displacement increment data at each moment in the target time period. The mouse displacement moment is the moment when the mouse moves when the user uses the mouse. Subsequently, based on the number of mouse displacement moments and the distance between adjacent mouse displacement moments, the motion intensity of the wireless mouse in the target time period can be accurately analyzed.

[0025] Preferably, in one embodiment of the present invention, the method for obtaining the mouse displacement moment specifically includes: If the data of both dimensions of the displacement increment data at each moment in the target period are both 0, then each moment in the target period is regarded as the mouse static moment. The position of the wireless mouse at the mouse static moment does not change relative to the previous moment. Therefore, all other moments in the target period except the mouse static moment can be regarded as the mouse displacement moments of the target period.

[0026] The greater the proportion of mouse displacement moments in the target period, the shorter the time interval between adjacent mouse displacement moments, and the longer the length of movement of the wireless mouse in the target period, the more frequently the user uses the wireless mouse in the target period, that is, the greater the movement intensity of the wireless mouse in the target period. Therefore, the number of mouse displacement moments in the target period, the distance between adjacent mouse displacement moments, and the displacement increment data at each moment can be analyzed. The obtained movement intensity reflects the frequency of movement of the wireless mouse in the target period. The greater the movement intensity of the wireless mouse in the target period, the more frequently the wireless mouse moves in the target period, that is, the higher the load of the wireless mouse in the target period. Subsequently, the load of the wireless mouse in different time periods can be accurately analyzed based on the movement intensity.

[0027] Preferably, in one embodiment of the present invention, the method for obtaining the motion intensity of the wireless mouse during the target period specifically includes: First, the number of all mouse displacement moments in the target period is used as the numerator, the number of all moments in the target period is used as the denominator, and the ratio is used as the first motion coefficient of the wireless mouse in the target period. The larger the first motion coefficient, the greater the proportion of mouse displacement moments in the target period, and thus the more frequent the movement of the wireless mouse in the target period.

[0028] A negative correlation mapping is performed on the average of the absolute values ​​of the differences between all two adjacent mouse displacement moments in the target period to obtain the second motion coefficient of the wireless mouse in the target period. The larger the second motion coefficient, the shorter the time interval between adjacent mouse displacement moments in the target period, which further indicates that the wireless mouse moves more frequently in the target period.

[0029] As an example, in one embodiment of the present invention, the expression of the second motion coefficient of the wireless mouse in the target period can be specifically, for example: in, Represents the second motion coefficient of the wireless mouse during the target period; Indicates the wireless mouse’s Mouse displacement moment; Indicates the wireless mouse’s Mouse displacement moment, The mouse displacement moment and the The mouse displacement moments are two adjacent mouse displacement moments; represents the number of mouse displacement moments in the target period, then Indicates the number of two adjacent mouse displacement moments in the target period; Expressed as a natural constant An exponential function with base , used for negative correlation mapping.

[0030] It should be noted that in other embodiments of the present invention, negative correlation mapping may be achieved through other basic mathematical operations, which will not be described in detail here.

[0031] Based on the calculation formula of the third motion coefficient, the third motion coefficient of the wireless mouse in the target period is obtained. The calculation formula of the third motion coefficient is: in, Indicates the third motion coefficient of the wireless mouse during the target period; and They represent the first The two-dimensional data in the displacement increment data at each moment; Indicates the number of moments in the target period.

[0032] The larger the third motion coefficient is, the longer the displacement distance of the wireless mouse in the target period is, which further indicates that the movement of the wireless mouse in the target period is more obvious.

[0033] Then, the motion intensity of the wireless mouse in the target period can be obtained based on the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse in the target period.

[0034] Preferably, in one embodiment of the present invention, the method for obtaining the motion intensity of the wireless mouse during the target period further includes: The first motion coefficient, the second motion coefficient and the third motion coefficient of the wireless mouse in the target period are integrated and normalized, and the calculation results are limited to range, thereby obtaining the motion intensity of the wireless mouse during the target period.

[0035] In one embodiment of the present invention, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can all adopt maximum and minimum value normalization processing. 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 normalization processing, which will not be repeated or limited.

[0036] As an example, in one embodiment of the present invention, the expression of the motion intensity of the wireless mouse in the target period may be specifically, for example: in, Indicates the motion intensity of the wireless mouse during the target period; Indicates the first motion coefficient of the wireless mouse in the target period; Represents the second motion coefficient of the wireless mouse during the target period; Indicates the third motion coefficient of the wireless mouse during the target period; Indicates the number of mouse displacement moments in the target period; represents the number of moments in the target period; Represents the normalization function, used for normalization processing.

[0037] The same method as above can be used to obtain the motion intensity of the wireless mouse during each time period of the target day. Since the embodiment of the present invention divides each time period into shorter periods, with the unit of the time period length being only seconds, the user's usage of the wireless mouse during different time periods of the target day is similar. For example, the wireless mouse has similar motion intensity for multiple consecutive time periods. 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, and the length of a single merged time period is longer than the length of a single time period. This can avoid frequent adjustment of the data reporting frequency of the wireless mouse, thereby reducing the energy consumption of the wireless mouse.

[0038] Preferably, in one embodiment of the present invention, the method for obtaining multiple merged time periods of a target day specifically includes: Normalize the absolute value of the difference in exercise intensity between any two adjacent periods on the target day and limit the calculation result to The range of the exercise intensity difference between any two adjacent time periods of the target day is obtained. The smaller the exercise intensity difference, the more similar the usage of the wireless mouse in the two adjacent time periods. Therefore, the two adjacent time periods with an exercise intensity difference less than the preset difference threshold can be merged, and the time period corresponding to the merged multiple consecutive time periods is used as a merged time period of the target day. The value range of the preset difference threshold is 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.

[0039] It should be noted that after obtaining multiple merged time periods of the target day, each merged time period needs to be numbered in chronological order. In one embodiment of the present invention, non-zero natural numbers can be used to number the merged time periods of the target day. For example, the first merged time period is marked with a value of 1, the second merged time period is marked with a value of 2, and so on, for subsequent calculation and analysis.

[0040] It should also be noted that during the merging process, it may happen that a certain time period and the time periods on both sides do not meet the merging conditions, so this time period can be used as a merged time period alone.

[0041] By using the same method as above, multiple merged time periods of each day can be obtained, and the merged time periods of each day can be numbered.

[0042] Step S3: Based on the time periods included in the merged time periods with the same number on different days, multiple periodic time periods of the wireless mouse on each day are obtained; based on the motion intensity of the wireless mouse in each period in the same periodic time periods on different days, different periodic time periods are divided into load levels to obtain the load level of the wireless mouse in each periodic time period.

[0043] Since users have similar usage habits for wireless mice on different days, that is, within the same time period on different days, the user's usage intensity of the wireless mouse has similar characteristics. For example, within the time period on different days, the wireless mouse is in a high-load state or a low-load state. In other words, the user's usage of the wireless mouse has certain periodic characteristics. Therefore, the embodiment of the present invention first obtains multiple periodic time periods of the wireless mouse in each day based on the time periods included in the merged time periods with the same number on different days. Since the usage of the wireless mouse in the same periodic time period on different days is similar, the load of the wireless mouse in the periodic time period can be accurately predicted subsequently, thereby effectively controlling the data reporting frequency of the wireless mouse and reducing energy consumption.

[0044] Preferably, in one embodiment of the present invention, the method for obtaining the wireless mouse in multiple periodic time periods every day specifically includes: The time periods corresponding to the intersection of the time periods included in the merged time periods with the same number on all days are used as the multiple periodic time periods of the wireless mouse on each day. That is to say, the time periods overlapping by the merged time periods with the same number on all days are used as the multiple periodic time periods of the wireless mouse on each day. For example, if the time period from 9:00 a.m. to 10:00 a.m. is the time period overlapping by a certain merged time period with the same number on all days, then the time period from 9:00 a.m. to 10:00 a.m. every day is a periodic time period of each day.

[0045] The user's usage intensity of the wireless mouse varies in different periodic time periods of the day, so the load degree of the wireless mouse in different periodic time periods also varies. The greater the movement intensity of the wireless mouse in each period of the periodic time period, the higher the load of the wireless mouse in the periodic time period. Therefore, the load level of the wireless mouse in each periodic time period can be divided into different periodic time periods according to the movement intensity of the wireless mouse in each periodic time period on different days. The load level of the wireless mouse in each periodic time period can be obtained. Subsequently, the data return rate of the wireless mouse can be effectively adjusted based on the load level of the wireless mouse in each periodic time period, thereby reducing the energy consumption of the wireless mouse.

[0046] Preferably, in one embodiment of the present invention, the method for obtaining the load level of the wireless mouse in each periodic time period specifically includes: The average value of the exercise intensity of all periods in each cycle time period of each day is used as the overall exercise intensity level of the wireless mouse in each cycle time period of each day. The average value of the overall exercise intensity level of the wireless mouse in the same cycle time period of all days is normalized and the calculation result is limited to The load evaluation value of the wireless mouse in each periodic time is obtained within the range. The larger the load evaluation value of a periodic time is, the greater the load degree of the wireless mouse in the periodic time is.

[0047] Therefore, based on the load evaluation value of the wireless mouse in each periodic time period, load levels of different periodic time periods can be divided, thereby obtaining the load level of the wireless mouse in each periodic time period.

[0048] Preferably, in one embodiment of the present invention, the method for obtaining the load level of the wireless mouse in each periodic time period further comprises: If the load evaluation value of the wireless mouse in each periodic time period is greater than the preset load threshold, the wireless mouse is at a high load level in each periodic time period; if the load evaluation value of the wireless mouse in each periodic time period is not greater than the preset load threshold, the wireless mouse is at a low load level in each periodic time period. The preset load threshold value range is 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.

[0049] Thus, the load level of the wireless mouse in each cycle time period is obtained. Subsequently, based on the load level of the cycle time period in which the wireless mouse is currently located, its data reporting rate can be adjusted to reduce its energy consumption.

[0050] Step S4: adjusting the data reporting rate of the wireless mouse according to the load level of the wireless mouse in each periodic time period to obtain an adjusted data reporting rate of the wireless mouse.

[0051] After obtaining the load level of the wireless mouse in each periodic time period, the data reporting rate of the wireless mouse can be adjusted according to the load level of the wireless mouse in each periodic time period to obtain the adjusted data reporting rate of the wireless mouse. This enables the wireless mouse to effectively control the data reporting rate of the wireless mouse based on the intensity of user use, thereby effectively reducing the energy consumption of the wireless mouse.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining the adjusted data reporting rate of the wireless mouse specifically includes: The current cycle time period of the wireless mouse is taken as the current cycle time period. If the load level of the current cycle time period is a high load level, it is necessary to increase the frequency of the wireless mouse reporting data to make the trajectory more coherent and provide a better user experience for the user. Therefore, the product value of the preset first 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 cycle time period.

[0053] If the load level in the current cycle time period is a low load level, it is necessary to reduce the frequency of the wireless mouse reporting data to balance the energy consumption of the mouse. Therefore, the product value of the preset second 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 cycle time period, wherein the preset first adjustment coefficient is greater than the value 1, and the preset second adjustment coefficient is less than the value 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.

[0054] For other time periods outside the periodic time period, the data reporting rate of the wireless mouse may not be adjusted, so that the wireless mouse reports data at the standard data reporting rate, wherein the standard data reporting rate is a built-in parameter of the wireless mouse and is a known value.

[0055] The adjusted data reporting rate of the wireless mouse in the current cycle time period is obtained, and the wireless mouse can report data at the corresponding adjusted data reporting rate in the current cycle time period, thereby ensuring user experience while avoiding large energy consumption of the wireless mouse.

[0056] An embodiment of the present invention provides a data processing system for a wireless mouse, the system comprising a memory, a processor, and a computer program, wherein 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 method described in steps S1 to S4.

[0057] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A data processing method for a wireless mouse, characterized in that: The method comprises: Acquire the displacement increment data of the wireless mouse at each time in each period of each day in real time, wherein the displacement increment data is two-dimensional data; Taking any day as a target day and any time period within the target day as a target time period, filtering out mouse displacement moments from all moments within the target time period based on the displacement increment data at each moment within the target time period; obtaining the motion intensity of the wireless mouse within the target time period based on the number of mouse displacement moments within the target time period, the distance between adjacent mouse displacement moments, and the displacement increment data at each moment; merging the time periods of the target day based on the difference in motion intensity between adjacent time periods of the target day to obtain multiple merged time periods of the target day, wherein each merged time period corresponds to a number; Obtain multiple periodic time periods of the wireless mouse on each day based on the time periods included in the merged time periods with the same number on different days; and divide the different periodic time periods into load levels based on the motion intensity of the wireless mouse in each period of the same periodic time periods on different days to obtain the load level of the wireless mouse in each periodic time period; The data reporting rate of the wireless mouse is adjusted according to the load level of the wireless mouse in each periodic time period to obtain an adjusted data reporting rate of the wireless mouse.

2. The data processing method of a wireless mouse according to claim 1, characterized in that: The step of filtering out the mouse displacement moments from all moments in the target period includes: If the data of the two dimensions of the displacement increment data at each moment in the target period are both 0, then each moment in the target period is regarded as the mouse stationary moment; All other moments in the target period except the mouse static moment are used as the mouse displacement moments in the target period.

3. The data processing method of a wireless mouse according to claim 1, characterized in that: Obtaining the motion intensity of the wireless mouse during the target period includes: The number of all mouse displacement moments in the target period is used as the numerator, the number of all moments in the target period is used as the denominator, and the ratio is used as the first motion coefficient of the wireless mouse in the target period; Performing negative correlation mapping on the average of the absolute values ​​of the differences between all two adjacent mouse displacement moments in the target period to obtain a second motion coefficient of the wireless mouse in the target period; Based on the calculation formula of the third motion coefficient, the third motion coefficient of the wireless mouse in the target period is obtained. The calculation formula of the third motion coefficient is: in, Indicates the third motion coefficient of the wireless mouse during the target period; and They represent the first Data of two dimensions in the displacement increment data at a moment; represents the number of moments in the target period; The motion intensity of the wireless mouse in the target period is obtained based on the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse in the target period.

4. The data processing method of a wireless mouse according to claim 3, characterized in that: The obtaining the motion intensity of the wireless mouse in the target period based on the first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse in the target period includes: The first motion coefficient, the second motion coefficient, and the third motion coefficient of the wireless mouse in the target period are integrated and normalized to obtain the motion intensity of the wireless mouse in the target period.

5. The data processing method for a wireless mouse according to claim 1, wherein: The multiple combined time periods for obtaining the target day include: Normalizing the absolute value of the difference in exercise intensity between any two adjacent time periods on the target day to obtain a degree of difference in exercise intensity between any two adjacent time periods on the target day; Merge two adjacent time periods whose exercise intensity difference is less than a preset difference threshold, and use the time period corresponding to the merged multiple consecutive time periods 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 method of obtaining a wireless mouse in multiple periodic time periods every day includes: The time period corresponding to the intersection of each time period included in the combined time period with the same number on all days is used as the multiple periodic time periods of the wireless mouse on each day.

7. The data processing method for a wireless mouse according to claim 1, characterized in that: Obtaining the load level of the wireless mouse in each periodic time period includes: The average value of the exercise intensity of all time periods in each period of each cycle time period of each day is used as the overall exercise intensity level of the wireless mouse in each period of each day; Normalizing the average value of the overall exercise intensity level of the wireless mouse in the same periodic time period on all days to obtain a load evaluation value of the wireless mouse in each periodic time period; Based on the load evaluation value of the wireless mouse in each periodic time period, load levels are divided for different periodic time periods to obtain the load level of the wireless mouse in each periodic time period.

8. The data processing method for a wireless mouse according to claim 7, characterized in that: The dividing the load levels of different periodic time periods based on the load evaluation 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: If the load evaluation value of the wireless mouse in each periodic time period is greater than a preset load threshold, the wireless mouse is at a high load level in each periodic time period; If the load evaluation value of the wireless mouse in each periodic time period is not greater than a preset load threshold, the wireless mouse is at a low load level in each periodic time period.

9. The data processing method for a wireless mouse according to claim 1, characterized in that: The obtaining of the adjusted data return rate of the wireless mouse comprises: The current cycle time period of the wireless mouse is used as the current cycle time period. If the load level of the current cycle time period is a high load level, the product value of the preset first adjustment coefficient and the standard data reporting rate of the wireless mouse is used as the adjusted data reporting rate of the wireless mouse in the current cycle time period. If the load level in the current periodic time period is a low load level, a product value of a preset second adjustment coefficient and a standard data reporting rate of the wireless mouse is used as the adjusted data reporting rate of the wireless mouse in the current periodic time period, wherein 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, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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