Wireless mouse power consumption adjustment method and device based on Polar code and SLE

Abstract

The present invention relates to a method and device for adjusting power consumption of a wireless mouse based on Polar code and SLE, and the method comprises: obtaining historical operation information and channel information of a user using a target wireless mouse in multiple scenarios, and constructing and training a hidden Markov probability model according to the operation information and channel information; determining the selection mapping mode, punching parameters and decoding parameters of the Polar code corresponding to each scenario according to the trained hidden Markov probability model; obtaining the user's operation information in real time, and adaptively matching the selection mapping mode, punching parameters and decoding parameters of the Polar code of the target wireless mouse based on the operation information and the hidden Markov probability model; and adjusting the power consumption of the target wireless mouse according to the selection mapping mode, punching parameters and decoding parameters. The present invention realizes adaptive adjustment of the power consumption of a wireless mouse by combining the hidden Markov probability model with the Polar encoding and decoding parameters and the scenario.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless mice, and in particular relates to a method and a device for adjusting power consumption of a wireless mouse based on Polar code and SLE. Background Art

[0002] Existing wireless mouse products are designed to minimize energy consumption (for example, by using low-power sensors and optimized wireless signal processing algorithms), but these devices often lack the ability to dynamically adjust power consumption during actual user use.

[0003] Specific issues include: 1) Fixed power consumption settings: Most wireless mice do not provide users with the option to adjust power consumption levels, which means that users cannot optimize power consumption and extend battery life based on actual usage needs (such as gaming, graphic design or text editing); 2) The trade-off between response speed and power consumption: Users may consume more power when high response speed is required (such as gaming or professional applications), but cannot reduce power consumption accordingly during light use (such as browsing the web).

[0004] As a technology in SparkLink, SLE (SparkLink Low Energy) uses single carrier transmission, and the working frequency band is the unlicensed band of 2.4GHz. The bandwidth supports 1MHz, 2MHz and 4MHz, and the modulation mode supports GFSK, BPSK, QPSK and 8PSK. SLE supports one-to-many reliable multicast, 4KHz short-delay interaction, secure pairing, privacy protection and other features. While ensuring the transmission efficiency as much as possible, it fully considers the energy saving factor. Summary of the invention

[0005] In order to improve the adaptability of wireless mouse power consumption adjustment and extend the life of the wireless mouse, a first aspect of the present invention provides a wireless mouse power consumption adjustment method based on Polar code and SLE, comprising: obtaining historical operation information and channel information of a user using a target wireless mouse in multiple scenarios, and constructing and training a hidden Markov probability model according to the operation information and channel information; determining a selection mapping mode, punching parameters and decoding parameters of the Polar code corresponding to each scenario according to the trained hidden Markov probability model; obtaining the user's operation information in real time, and adaptively matching the selection mapping mode, punching parameters and decoding parameters of the Polar code of the target wireless mouse based on the operation information and the hidden Markov probability model; and adjusting the power consumption of the target wireless mouse according to the selection mapping mode, punching parameters and decoding parameters.

[0006] In some embodiments of the present invention, constructing and training a hidden Markov probability model based on the operation information and channel information includes: constructing a hidden Markov state space, observation space, initial probability distribution, state transition probability matrix and observation probability matrix based on the historical operation information and channel information; iterating the initial probability distribution, state transition probability matrix and observation probability matrix based on the Baum-Welch algorithm; and determining the optimal state sequence of the observation sequence through the Viterbi algorithm.

[0007] Furthermore, the construction of a hidden Markov state space, an observation space, an initial probability distribution, a state transition probability matrix and an observation probability matrix based on the historical operation information and the channel information includes: constructing a hidden Markov state space according to the operation frequency of the target wireless mouse used by the user and the channel quality; and determining the initial probability distribution, the state transition probability matrix and the observation probability matrix according to the time distribution of the user using the target wireless mouse in each scenario.

[0008] In some embodiments of the present invention, determining the selection mapping mode, puncturing parameters and decoding parameters of the Polar code corresponding to each scene according to the trained hidden Markov probability model includes: determining the selection mapping mode, μ-law compression parameters and decoding parameters of the Polar code corresponding to each scene according to the hidden Markov probability model.

[0009] In some embodiments of the present invention, adjusting the power consumption of the target wireless mouse according to the selected mapping mode, punching parameters and decoding parameters includes: adjusting the peak-to-average power ratio of the target wireless mouse according to the selected mapping mode; adjusting the encoding bit rate of the Polar code of the target wireless mouse according to the punching parameters; and adjusting the decoding power consumption of the Polar code of the target wireless mouse according to the decoding parameters.

[0010] Further, adjusting the encoding bit rate of the Polar code of the target wireless mouse according to the punching parameters includes: adjusting the encoding bit rate of the Polar code of the target wireless mouse by maximizing a minimum reliability score based on the punching parameters.

[0011] According to a second aspect of the present invention, a wireless mouse power consumption adjustment device based on Polar code and SLE is provided, comprising: a construction module, used to obtain historical operation information and channel information of a user using a target wireless mouse in multiple scenarios, and to construct and train a hidden Markov probability model according to the operation information and channel information; a determination module, used to determine the selection mapping mode, punching parameters and decoding parameters of the Polar code corresponding to each scenario according to the trained hidden Markov probability model; an adaptive module, used to obtain the user's operation information in real time, and adaptively match the selection mapping mode, punching parameters and decoding parameters of the Polar code of the target wireless mouse based on the operation information and the hidden Markov probability model; and an adjustment module, used to adjust the power consumption of the target wireless mouse according to the selection mapping mode, punching parameters and decoding parameters.

[0012] The third aspect of the present invention provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the Polar code and SLE wireless mouse power consumption adjustment method provided in the first aspect of the present invention.

[0013] A fourth aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for adjusting power consumption of a wireless mouse using Polar code and SLE provided in the first aspect of the present invention is implemented.

[0014] The beneficial effects of the present invention are:

[0015] The present invention dynamically adjusts the bit rate of the Polar code through SLE according to the activity state of the mouse (such as movement speed and frequency of use). When the user is active (such as gaming or design work), a high bit rate is used to ensure the sensitivity and accuracy of the mouse response. When the user is less active, such as reading or watching videos, the bit rate is reduced to reduce energy consumption. On the other hand, by utilizing the rate compatibility characteristics of the Polar code, a bit rate matching system is established, which can adjust the rate through redundancy or deletion while maintaining the polarization characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a basic flow chart of a method for adjusting power consumption of a wireless mouse using Polar code and SLE in some embodiments of the present invention;

[0017] Figure 2 is a schematic diagram of the principle of a hidden Markov model in some embodiments of the present invention;

[0018] Figure 3It is a schematic diagram of the principle of a method for adjusting power consumption of a wireless mouse using Polar code and SLE in some embodiments of the present invention;

[0019] Figure 4 It is a structural schematic diagram of a wireless mouse power consumption adjustment device based on Polar code and SLE in some embodiments of the present invention;

[0020] Figure 5 It is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. DETAILED DESCRIPTION

[0021] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0022] refer to Figure 1 and Figure 2 In a first aspect of the present invention, a method for adjusting power consumption of a wireless mouse based on Polar code and SLE is provided, comprising:

[0023] S100. Obtaining historical operation information and channel information of a user using a target wireless mouse in multiple scenarios, and constructing and training a hidden Markov probability model based on the operation information and channel information;

[0024] S200. Determine the selection mapping mode, puncturing parameters and decoding parameters of the Polar code corresponding to each scene according to the trained hidden Markov probability model;

[0025] S300 real-time acquisition of user operation information, based on the operation information and hidden Markov probability model, adaptive matching target wireless mouse Polar encoding selection mapping mode, punching parameters and decoding parameters;

[0026] S400. Adjust the power consumption of the target wireless mouse according to the selected mapping mode, punching parameters and decoding parameters.

[0027] It can be understood that user operation information may include: mouse movement trajectory, click frequency and position, scroll wheel usage, usage time period and duration, etc.; channel information includes: channel quality indicators (such as signal-to-noise ratio, SNR), environmental interference conditions, channel conditions in different scenarios (such as office environment, home environment), peak-to-average power ratio, etc.

[0028] In step S100 of some embodiments of the present invention, constructing and training a hidden Markov probability model according to the operation information and the channel information includes:

[0029] S101. Constructing a hidden Markov state space, an observation space, an initial probability distribution, a state transition probability matrix and an observation probability matrix based on the historical operation information and the channel information;

[0030] The hidden Markov probability model in this embodiment is expressed as HMM: = {S, O, π, A, B}, where:

[0031] State set S: represents all possible implicit states of the system, S = {S1, S2, ..., S M ,}; specifically includes the click, move, idle, gesture, etc. of the wireless mouse. The scene C set includes office, game, browsing, etc.

[0032] Observation space O: represents all observation values ​​that the system can directly observe, O = {o1, o2, ..., o M ,};

[0033] The initial state probability distribution π represents the probability that the system is in a certain state at the initial moment, π i =P(q1=s i ), 1≤i≤N; i represents the state ordinal number, p represents the probability, and q represents the step size or the number of moments.

[0034] State transition probability matrix A: represents the probability of the system transitioning from one state to another. Observation probability matrix, A = {a ij}, a ij =P(q t+1 =s j |q i =s i ), 1≤i, j≤N;

[0035] B: represents the probability of each state generating a certain observation,

[0036] B{b jk}, b jk =P(o t =v k |q t =s j ), 1≤j≤N, 1≤k≤M.

[0037] It can be understood that the above state transition probability represents the user's behavior frequency probability model, and specific parameters may include click frequency, movement frequency, idle frequency and gesture frequency. Similarly, it also includes state changes of multiple parameters related to the power consumption model.

[0038] S102. Iterate the initial probability distribution, state transition probability matrix and observation probability matrix based on the Baum-Welch algorithm; determine the optimal state sequence of the observation sequence through the Viterbi algorithm.

[0039] Specifically, the parameters of the HMM are trained by the Baum-Welch algorithm (EM algorithm), and the initial probability distribution, state transition matrix and observation probability matrix are iteratively optimized.

[0040] Step E: Calculate the expected value under the current parameters;

[0041] Forward probability α t (i): indicates that the state at time t is s i And generate the observation sequence O1, O2, ..., O t The probability of t (i) = P(O1, O2, ..., O t ,q t =s i |λ);

[0042] Recursive formula:

[0043] Backward probability β t (i): indicates that the state at time t is s i And generate the observation sequence O t+1 , O t+2 , ..., O T probability.

[0044] β t (i) = P(O t+1 , O t+2 , ..., O T |q t =s i ,λ),

[0045] Recursive formula:

[0046] It should be noted that λ in the formula is a set of parameters of the Hidden Markov Model (HMM). Specifically, λ represents a triple containing the following three elements:

[0047] λ=(π,A,B)

[0048] Where: π is the initial state probability distribution, which indicates the probability that the system is in a certain state at the initial moment; A is the state transition probability matrix, which indicates the probability that the system transfers from one state to another. B is the observation probability matrix, which indicates the probability that each state generates a certain observation.

[0049] Then proceed to step M: maximize the expected value and update the parameters.

[0050] Specifically, update the initial probability distribution π: i =γ1(i), where γ1(i) represents the state s at time ti probability.

[0051] Update the state transition probability matrix A:

[0052]

[0053] where ξ t (i, j) indicates that the state is s at time t i And transfer to state s at time t+1 j probability.

[0054] Update the state transition probability matrix B:

[0055]

[0056] Where I(·) is an indicator function, which takes the value of 1 when the condition is met and 0 otherwise.

[0057] Then the optimal state sequence of the observation sequence is determined by the Viterbi algorithm and initialized:

[0058] δ1(i)=π i b i (o1), 1≤i≤N;

[0059] Then recursively or iteratively deduce the parameters:

[0060]

[0061] Finally, backtrack the parameters:

[0062] δt(j) means reaching state s at time t j And generate partial observation sequence O1, O2, ..., O t The maximum probability of t (j) is the state s reached at time t j The most likely previous state of t * represents the most likely state at time t.

[0063] Further, in step S101, the constructing of a hidden Markov state space, an observation space, an initial probability distribution, a state transition probability matrix and an observation probability matrix based on the historical operation information and the channel information includes: constructing a hidden Markov state space according to the operation frequency of the target wireless mouse used by the user and the channel quality; and determining the initial probability distribution, the state transition probability matrix and the observation probability matrix according to the time distribution of the user using the target wireless mouse in each scenario.

[0064] In step S200 of some embodiments of the present invention, determining the selection mapping mode, puncturing parameters and decoding parameters of the Polar code corresponding to each scene according to the trained hidden Markov probability model includes: determining the selection mapping mode, μ-law compression parameters and decoding parameters of the Polar code corresponding to each scene according to the hidden Markov probability model.

[0065] Specifically, PAPR is reduced by selecting different mapping modes, and μ-law, as a compression technology, can reduce the dynamic range of the signal, thereby reducing PAPR. By combining SLM (selective mapping) and μ-law compression, PAPR can be further reduced and the transmission efficiency of the system can be improved. In the selective mapping mode, the minimum PAPR is used as the optimal solution. The μ-law compression model is expressed as:

[0066]

[0067] Where μ is the compression parameter, x is the input signal, and F(x) is the compressed signal.

[0068] In S400 of some embodiments of the present invention, adjusting the power consumption of the target wireless mouse according to the selected mapping mode, punching parameters and decoding parameters includes:

[0069] S401. Adjusting the peak-to-average power ratio of the target wireless mouse according to the selected mapping model;

[0070] Specifically, the selective mapping mode (SLM) reduces the PAPR by selecting different mapping modes.

[0071]

[0072] Where s(t) represents the representation of the signal in the time domain, and E[·] represents the expected value.

[0073] S402. Adjusting the encoding bit rate of the Polar code of the target wireless mouse according to the punching parameters;

[0074] Further, adjusting the encoding bit rate of the Polar code of the target wireless mouse according to the punching parameters includes: adjusting the encoding bit rate of the Polar code of the target wireless mouse by maximizing a minimum reliability score based on the punching parameters.

[0075] Specifically, the rate of the Polar code is expressed as:

[0076]

[0077] The reliability score after punching is: Where R represents the reliability score and LLR(.) represents the maximum likelihood probability.

[0078] S403. Adjust the decoding power consumption of the Polar code of the target wireless mouse according to the decoding parameters.

[0079] Set decoding parameters according to the selected decoder to optimize decoding performance. The decoding complexity is expressed as: C = f (bit rate, algorithm complexity);

[0080] The power consumption model is expressed as:

[0081]

[0082] Without loss of generality, power consumption can be expressed as a function of multiple factors, including the selected mapping mode, puncturing parameters, decoding complexity, and channel quality. That is:

[0083] P=f(mapping_mode, puncture_pattern, decoder, Q), where mapping_mode, puncture_pattern, decoder, and Q represent the selection of mapping mode, puncturing parameters, decoding complexity, and channel quality, respectively.

[0084] It can be understood that by combining rate-compatible punctured Polar codes, adaptive transmission construction methods, fast simplified continuous cancellation of Polar decoders, and PAPR reduction technology, an efficient adaptive adjustment system is implemented to meet the transmission requirements of wireless mice under different channel conditions. This solution can not only optimize transmission efficiency and reliability, but also effectively reduce power consumption and PAPR, providing more stable and efficient communication performance for wireless mice.

[0085] Example 2

[0086] refer to Figure 4 According to a second aspect of the present invention, a wireless mouse power consumption adjustment device 1 based on Polar code and SLE is provided, comprising: a construction module 11, used to obtain historical operation information and channel information of a user using a target wireless mouse in multiple scenarios, and to construct and train a hidden Markov probability model according to the operation information and channel information; a determination module 12, used to determine the selection mapping mode, punching parameters and decoding parameters of the Polar code corresponding to each scenario according to the trained hidden Markov probability model; an adaptive module 13, used to obtain the user's operation information in real time, and adaptively match the selection mapping mode, punching parameters and decoding parameters of the Polar code of the target wireless mouse based on the operation information and the hidden Markov probability model; an adjustment module 14, used to adjust the power consumption of the target wireless mouse according to the selection mapping mode, punching parameters and decoding parameters.

[0087] Furthermore, the construction module 11 includes: a construction unit, which is used to construct a hidden Markov state space, observation space, initial probability distribution, state transition probability matrix and observation probability matrix based on the historical operation information and channel information; an iteration unit, which is used to iterate the initial probability distribution, state transition probability matrix and observation probability matrix based on the Baum-Welch algorithm; and a determination unit, which is used to determine the optimal state sequence of the observation sequence through the Viterbi algorithm.

[0088] Example 3

[0089] refer to Figure 5 According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the wireless mouse power consumption adjustment method based on Polar code and SLE in the first aspect of the present invention.

[0090] The electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0091] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 5 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0092] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above functions defined in the method of the embodiment of the present disclosure are executed. It should be noted that the computer-readable medium described in the embodiment of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, an apparatus, or a device. In an embodiment of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, an apparatus, or a device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wire, optical cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0093] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more computer programs. When the one or more programs are executed by the electronic device, the electronic device:

[0094] Computer program code for performing the operations of embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, Python, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0095] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adjusting power consumption of a wireless mouse based on Polar code and SLE, characterized in that: include: Acquire historical operation information and channel information of a user using a target wireless mouse in multiple scenarios, and construct and train a hidden Markov probability model based on the operation information and channel information; Determine the selection mapping mode, puncturing parameters and decoding parameters of the Polar code corresponding to each scenario based on the trained hidden Markov probability model; Acquire the user's operation information in real time, and adaptively match the selected mapping mode, punching parameters, and decoding parameters of the Polar code of the target wireless mouse based on the operation information and the hidden Markov probability model; The power consumption of the target wireless mouse is adjusted according to the selected mapping mode, punching parameters and decoding parameters.

2. The method for adjusting power consumption of a wireless mouse based on Polar code and SLE according to claim 1, characterized in that: The constructing and training of a hidden Markov probability model according to the operation information and the channel information comprises: Constructing a hidden Markov state space, an observation space, an initial probability distribution, a state transition probability matrix, and an observation probability matrix based on the historical operation information and the channel information; Iterate the initial probability distribution, state transition probability matrix and observation probability matrix based on the Baum-Welch algorithm; The optimal state sequence of the observation sequence is determined by the Viterbi algorithm.

3. The method for adjusting power consumption of a wireless mouse based on Polar code and SLE according to claim 2, characterized in that: The constructing of the state space, observation space, initial probability distribution, state transition probability matrix and observation probability matrix of the hidden Markov based on the historical operation information and channel information comprises: Construct a hidden Markov state space according to the user's operation frequency of the target wireless mouse and the channel quality; The initial probability distribution, state transition probability matrix and observation probability matrix are determined according to the time distribution of the user using the target wireless mouse in each scenario.

4. The method for adjusting power consumption of a wireless mouse based on Polar code and SLE according to claim 1, characterized in that: The method of determining the selection mapping mode, puncturing parameters and decoding parameters of the Polar code corresponding to each scene according to the trained hidden Markov probability model includes: The selection mapping mode, μ-law compression parameters and decoding parameters of the Polar code corresponding to each scene are determined according to the hidden Markov probability model.

5. The method for adjusting power consumption of a wireless mouse based on Polar code and SLE according to claim 1, characterized in that: The step of adjusting the power consumption of the target wireless mouse according to the selected mapping mode, punching parameters and decoding parameters comprises: adjusting the peak-to-average power ratio of the target wireless mouse according to the selected mapping mode; Adjusting the encoding bit rate of the Polar code of the target wireless mouse according to the punching parameters; The decoding power consumption of the Polar code of the target wireless mouse is adjusted according to the decoding parameters.

6. The method for adjusting power consumption of a wireless mouse based on Polar code and SLE according to claim 5, characterized in that: The step of adjusting the encoding bit rate of the Polar code of the target wireless mouse according to the punching parameter includes: Based on the punching parameters, the encoding bit rate of the Polar code of the target wireless mouse is adjusted by maximizing the minimum reliability score.

7. A wireless mouse power consumption adjustment device based on Polar code and SLE, characterized in that: include: A construction module is used to obtain historical operation information and channel information of a user using a target wireless mouse in multiple scenarios, and to construct and train a hidden Markov probability model based on the operation information and channel information; A determination module is used to determine the selection mapping mode, puncturing parameters and decoding parameters of the Polar code corresponding to each scene according to the trained hidden Markov probability model; An adaptive module, used for acquiring the user's operation information in real time, and adaptively matching the selected mapping mode, punching parameters and decoding parameters of the Polar code of the target wireless mouse based on the operation information and the hidden Markov probability model; The adjustment module is used to adjust the power consumption of the target wireless mouse according to the selected mapping mode, punching parameters and decoding parameters.

8. The wireless mouse power consumption adjustment device based on Polar code and SLE according to claim 7, characterized in that: The building blocks include: A construction unit, configured to construct a hidden Markov state space, an observation space, an initial probability distribution, a state transition probability matrix, and an observation probability matrix based on the historical operation information and the channel information; An iteration unit, used for iterating the initial probability distribution, the state transition probability matrix and the observation probability matrix based on the Baum-Welch algorithm; The determination unit is used to determine the optimal state sequence of the observation sequence through the Viterbi algorithm.

9. An electronic device, comprising: one or more processors; A storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for adjusting power consumption of a wireless mouse based on Polar code and SLE as described in any one of claims 1 to 6.

10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method for adjusting power consumption of a wireless mouse based on Polar code and SLE as described in any one of claims 1 to 6 is implemented.

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