Sensitivity setting optimization method and system for game joystick

Abstract

The invention discloses a sensitivity setting optimization method for a joystick. The method comprises the following steps: acquiring a current displacement coordinate and a current hardware sensitivity value of the joystick; whether the current hardware sensitivity value is in a normal state or not is judged, and if the current hardware sensitivity value is in an abnormal state, sensitivity hardware optimization setting is executed; presetting a time period T, and when the time meets the preset time period T, executing sensitivity software period optimization setting; according to the method, the hardware sensitivity calibration coefficient corresponding to the rocker of the player is determined by using the artificial intelligence algorithm according to the rocker shaking track curve, the physiological feature data and the activity feature data of the player, the same rocker can adapt to the sensitivity calibration coefficients of different players, the sensitivity identification mode is multi-modal, and the sensitivity is high. And real-time hardware sensitivity calibration and periodic software sensitivity calibration are combined, so that the satisfaction degree of a player in using the rocker is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of game joystick sensitivity optimization, and in particular to a method and system for optimizing the sensitivity setting of a game joystick. Background Art

[0002] The game joystick is an important part of the game controller. It is usually used to adjust the field of view and control the movement of virtual characters. Sensitivity is one of the criteria for judging the quality of the game joystick.

[0003] Chinese patent application number CN202210690085 discloses a method for setting the sensitivity of a game joystick, a game controller and a storage medium, comprising the steps of: obtaining the total stroke of the game joystick and determining the trajectory range of the game joystick; based on the trajectory range, from the center position of the game joystick to the maximum trajectory boundary of the game joystick, along the diameter direction, dividing the trajectory range into a first area, a second area and a third area according to a preset ratio; sensing and obtaining the area where the game joystick is located; sensing the operations on the three rollers in real time; obtaining a sensitivity adjustment value according to a one-to-one correspondence between the preset three rollers and the three areas and a rolling operation performed on the roller corresponding to the area where the game joystick is located; setting the three sensitivity adjustment values ​​respectively obtained according to the rolling operations performed on the corresponding rollers when the game joystick is in the three areas as the sensitivities of the first area, the second area and the third area respectively. The present application can improve the sensitivity of the game joystick.

[0004] The above scheme still has several limitations, specifically: it is impossible to adaptively adjust the hardware sensitivity of the joystick according to individual differences, that is, the hardware sensitivity of the joystick is not adjusted according to personal habits and preferences, it does not consider that the same joystick needs to be optimized for hardware sensitivity settings under different abnormal situations, and it does not calibrate the sensitivity of the software programs that the joystick depends on. In the process of joystick sensitivity calibration, it does not consider the method of combining real-time calibration of hardware sensitivity with periodic calibration of software program sensitivity. Summary of the invention

[0005] The purpose of the present invention is to provide a method for optimizing the sensitivity setting of a game joystick, so as to solve the above-mentioned problems existing in the prior art.

[0006] The specific application is as follows:

[0007] A method for optimizing the sensitivity setting of a game joystick comprises the following steps:

[0008] Get the current displacement coordinates and current hardware sensitivity value of the game joystick;

[0009] Determine whether the current hardware sensitivity value is in a normal state. If it is in an abnormal state, perform sensitivity hardware optimization settings;

[0010] Preset time period T, when the time meets the preset time period T, execute sensitivity software cycle optimization setting;

[0011] The specific steps of sensitivity hardware optimization setting are as follows:

[0012] Obtaining the relative displacement coordinates of the joystick after calibration according to the joystick sensitivity optimization formula, the preset optimization strategy and the displacement coordinates of the joystick;

[0013] The parameters of the joystick sensitivity optimization formula include a preset sensitivity calibration coefficient and a preset sensitivity calibration function;

[0014] The preset optimization strategy is: determining the preset sensitivity calibration coefficient of the first gamer according to the first joystick shaking trajectory curve, the first physiological characteristic data and the first activity characteristic data of the first gamer.

[0015] Further, the current hardware sensitivity value is calculated by the induced voltage corresponding to the position of the joystick, and the induced voltage deviation threshold is preset. If the deviation value of the corresponding induced voltage is greater than the preset induced voltage deviation threshold within a preset time period, the current hardware sensitivity value is set to an abnormal state, otherwise the current hardware sensitivity value is set to a normal state;

[0016] The joystick sensitivity optimization formula is as follows:

[0017] game_x=game_scale*Fun(game_x_pos),

[0018] game_x_pos=SensitivitGetPos(x_pos),

[0019] game_y=game_scale*Fun(game_y_pos),

[0020] game_y_pos=SensitivitGetPos(y_pos),

[0021] Among them, game_scale represents the preset sensitivity calibration coefficient, Fun() represents the preset displacement calibration function, game_x represents the relative displacement coordinate of the joystick in the x-axis direction, game_y represents the relative displacement coordinate of the joystick in the y-axis direction, game_x_pos represents the sensitivity calibration of the joystick in the x-axis direction, game_y_pos represents the sensitivity calibration of the joystick in the y-axis direction, x_pos represents the current displacement coordinate of the joystick in the x-axis direction, y_pos represents the current displacement coordinate of the joystick in the y-axis direction, and the preset displacement calibration function adopts a nonlinear quadratic function.

[0022] Furthermore, the method further comprises the steps of:

[0023] The absolute coordinates of the joystick on the screen are obtained according to the relative displacement coordinates, which are expressed as:

[0024] curr_game_x+=game_x,

[0025] curr_game_y+=game_y, where curr_game_x represents the absolute coordinate of the joystick in the x-axis direction, and curr_game_y represents the absolute coordinate of the joystick in the y-axis direction.

[0026] Furthermore, before determining the preset sensitivity calibration coefficient of the first gamer, the method further includes:

[0027] Acquire first physiological characteristic data of a first gamer controlling a first joystick, wherein the first physiological characteristic data at least includes historical first physiological characteristic data;

[0028] Acquiring first activity characteristic data of the first game player when manipulating the first joystick;

[0029] Obtaining a first joystick shaking trajectory curve when the first game player controls the first joystick;

[0030] The step of obtaining the first physiological characteristic data of the first gamer who controls the first joystick comprises:

[0031] A first sensor group for collecting visual data, pressure data, and contact area data is configured on the surface of the first joystick;

[0032] Using the first sensor group to capture multi-view images of the first gamer, and obtain three-dimensional hand data including palms, fingers and skin;

[0033] Detecting pressure distribution data of the first gamer's fingers and palms on the joystick by the first sensor group;

[0034] Using the first sensor group to detect changes in the contact area of ​​the palm and fingers of the first operation on the joystick;

[0035] Using the first sensor group to detect the change in the deflection angle of the palm and the finger when performing the first operation on the joystick;

[0036] Using computer vision technology, semantically annotating the three-dimensional hand data, the pressure distribution data, the contact area change, and the deflection angle change, and extracting physiological feature data representing physiological features;

[0037] Based on the acquired physiological characteristic data, a hand physiological characteristic parameter model including the first physiological characteristic data is constructed using a neural network algorithm;

[0038] The step of obtaining first activity characteristic data of the first game player when manipulating the first joystick includes:

[0039] Using an inertial sensor, obtaining movement change data of a hand-controlled joystick of the first player in motion, acceleration, and posture in three-dimensional space;

[0040] An electromagnetic position tracker is arranged on the first player's upper surface to calculate the three-dimensional coordinates of the palm through the magnetic field distribution in space;

[0041] The first player wears a multi-channel intelligent monitoring bracelet to monitor the hand physiological data during the operation in real time, and the hand physiological data at least includes palm skin conductance and temperature changes;

[0042] Natural language technology is used to semantically annotate the motion change data, the three-dimensional coordinates of the palm, and the hand physiological data, and to extract activity state feature data representing the activity state.

[0043] Furthermore, the obtaining of the first joystick shaking trajectory curve when the first player manipulates the first joystick includes:

[0044] A laser tracker is configured on the first joystick to obtain the joystick coordinates in a two-dimensional plane of the active surface of the first joystick in real time;

[0045] Detecting joystick motion data of the first joystick in a preset three-dimensional space by a high-speed motion tracking sensor built into the first joystick;

[0046] Combining the joystick coordinates with the joystick motion data, and calculating a position coordinate sequence of the first joystick in the three-dimensional space through a data fusion algorithm;

[0047] The position coordinate sequence is imported into the three-dimensional model software, and the first joystick shaking trajectory curve is generated from all the discrete coordinate points through a three-dimensional curve fitting tool.

[0048] Furthermore, the step of determining the preset sensitivity calibration coefficient of the first gamer according to the first joystick shaking trajectory curve, the first physiological characteristic data and the first activity characteristic data of the first gamer comprises:

[0049] extracting hand physiological characteristic features and activity features from the first physiological characteristic data and the first activity feature data respectively;

[0050] According to the hand physiological characteristics and the activity characteristics, a clustering algorithm is used to identify the joystick control mode used by the first game player;

[0051] Analyzing the user's speed preference and joystick amplitude preference according to the first joystick shaking trajectory curve, and fitting the joystick control parameters of the first game player;

[0052] According to the joystick control mode and the joystick control parameter, a preset sensitivity calibration coefficient matching the joystick control mode and the joystick control parameter is selected in a knowledge base through a BP neural network algorithm.

[0053] Furthermore, the method for obtaining the current hardware sensitivity value also includes:

[0054] Presetting a first deep learning model, the first deep learning model is used to identify that a first gamer switches to a second gamer, and if a gamer switching operation occurs, setting a current hardware sensitivity value to an abnormal state;

[0055] The input of the first deep learning model is the first physiological characteristic data and first activity characteristic data of the player.

[0056] Furthermore, the preset time period T, when the time meets the preset time period T, the specific implementation process of executing the sensitivity software cycle optimization setting is:

[0057] Extract the joystick temperature and shaking resistance corresponding to the preset time period T of the joystick, identify the inertial joystick temperature and inertial shaking resistance corresponding to the preset time period T of the first game player, and then extract the inertial joystick temperature T corresponding to each software program in the preset time period T c and inertial rocker resistance R c ;

[0058] Extract the rocker detection temperature T within the preset time period T c1 and shaking detection resistance R c1 , the compensation function of analyzing the sensitivity control of each software program over time is:

[0059]

[0060] Then, when each software program meets the preset time period T, each sensitivity is adjusted to:

[0061]

[0062] Where W represents the compensation weight of the inertial stick temperature, and Q represents the compensation weight of the inertial stick resistance.

[0063] Furthermore, the method for obtaining the current hardware sensitivity value also includes:

[0064] Obtaining a preset sensitivity detection signal sending frequency;

[0065] Control the host to continuously send a sensitivity detection signal to the first joystick at the sensitivity detection signal sending frequency, wherein each data packet of the sensitivity detection signal carries a first detection time when the host sends the sensitivity detection signal and a data packet number;

[0066] The control host receives a sensitivity detection response signal returned by the joystick, wherein the data packet of the sensitivity detection response signal carries the second detection time when the first joystick sends the sensitivity detection response signal and the data packet number of the sensitivity detection signal to which it responds;

[0067] When a sensitivity detection response signal in response to the sensitivity detection signal returned by the first joystick is not received within a preset time period after the first detection time, marking the sensitivity detection signal as unresponsive;

[0068] The number of sensitivity detection signals that did not respond was recorded;

[0069] Determine whether the number of sensitivity detection signals that do not respond within a unit time is greater than a preset response number threshold;

[0070] If the number of unresponsive sensitivity detection signals within a unit time is greater than a preset response number threshold, the current hardware sensitivity value of the first joystick is set to an abnormal state.

[0071] A sensitivity setting optimization system for a game joystick, comprising:

[0072] The sensitivity hardware calibration module is used to obtain the current displacement coordinates and current hardware sensitivity value of the joystick; determine whether the current hardware sensitivity value is in a normal state, and if it is in an abnormal state, perform sensitivity hardware optimization settings;

[0073] The sensitivity software calibration module is used to preset a time period T. When the time meets the preset time period T, the sensitivity software cycle optimization setting is executed.

[0074] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0075] The embodiment of the present invention provides a method for obtaining the current displacement coordinates and the current hardware sensitivity value of a game joystick; judging whether the current hardware sensitivity value is in a normal state, and if it is in an abnormal state, executing sensitivity hardware optimization setting; the specific steps of the sensitivity hardware optimization setting are: obtaining the relative displacement coordinates of the calibrated joystick according to the joystick sensitivity optimization formula, the preset optimization strategy and the displacement coordinates of the joystick; the parameters of the joystick sensitivity optimization formula include a preset sensitivity calibration coefficient and a preset sensitivity calibration function; the preset optimization strategy is: determining the preset sensitivity calibration coefficient of the first gamer according to the first joystick shaking trajectory curve, the first physiological characteristic data and the first activity characteristic data of the first gamer; the present invention determines the hardware sensitivity calibration coefficient corresponding to the joystick of the gamer by using an artificial intelligence algorithm for the joystick activity trajectory curve, the physiological characteristic data and the activity characteristic data of the gamer, so that the same joystick can adapt to the sensitivity calibration coefficients of different gamers, and a multi-modal sensitivity recognition method is combined with real-time hardware sensitivity calibration and periodic software sensitivity calibration to improve the satisfaction of the gamer in using the joystick. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a flow chart of a method for optimizing the sensitivity setting of a game joystick provided by an embodiment of the present invention;

[0077] Figure 2 It is a schematic diagram of a sensitivity hardware optimization setting process of a method for optimizing the sensitivity setting of a game joystick provided by an embodiment of the present invention;

[0078] Figure 3 It is a system diagram of a game joystick sensitivity setting optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0080] Example 1

[0081] The embodiment of the present invention provides a method for optimizing the sensitivity setting of a game joystick, such as Figure 1 , including the following steps:

[0082] S1: Get the current displacement coordinates and current hardware sensitivity value of the game joystick;

[0083] S2: Determine whether the current hardware sensitivity value is in a normal state. If it is in an abnormal state, perform sensitivity hardware optimization setting;

[0084] S3: preset time period T, when the time meets the preset time period T, execute the sensitivity software cycle optimization setting;

[0085] The specific steps of sensitivity hardware optimization setting are as follows: Figure 2 :

[0086] S21: Obtaining the relative displacement coordinates of the joystick after calibration according to the joystick sensitivity optimization formula, the preset optimization strategy and the current displacement coordinates;

[0087] S22: the parameters of the joystick sensitivity optimization formula include a preset sensitivity calibration coefficient and a preset sensitivity calibration function;

[0088] S23: The preset optimization strategy is: determining the preset sensitivity calibration coefficient of the first gamer according to the first joystick shaking trajectory curve, the first physiological characteristic data and the first activity characteristic data of the first gamer.

[0089] Further, the current hardware sensitivity value is calculated by the induced voltage corresponding to the position of the joystick, and the induced voltage deviation threshold is preset. If the deviation value of the corresponding induced voltage is greater than the preset induced voltage deviation threshold within a preset time period, the current hardware sensitivity value is set to an abnormal state, otherwise the current hardware sensitivity value is set to a normal state;

[0090] Specifically, the induced voltage corresponding to the current joystick is obtained through the voltage sensor, and the deviation value of the induced voltage is calculated and compared with the preset induced voltage deviation threshold. This method is a multi-modal hardware sensitivity identification method. This method is the sensitivity identification of the joystick hardware. Through this method, if the sensitivity of the joystick hardware itself is abnormal, it can be identified by calculating the induced voltage of the joystick.

[0091] The joystick sensitivity optimization formula is as follows:

[0092] game_x=game_scale*Fun(game_x_pos),

[0093] game_x_pos=SensitivitGetPos(x_pos),

[0094] game_y=game_scale*Fun(game_y_pos),

[0095] game_y_pos=SensitivitGetPos(y_pos), wherein game_scale represents the preset sensitivity calibration coefficient, Fun() represents the preset displacement calibration function, game_x represents the relative displacement coordinate of the joystick in the x-axis direction, game_y represents the relative displacement coordinate of the joystick in the y-axis direction, game_x_pos represents the sensitivity calibration of the joystick in the x-axis direction, game_y_pos represents the sensitivity calibration of the joystick in the y-axis direction, x_pos represents the current displacement coordinate of the joystick in the x-axis direction, y_pos represents the current displacement coordinate of the joystick in the y-axis direction, and the preset displacement calibration function adopts a nonlinear quadratic function.

[0096] Specifically, the displacement ratio of the joystick on different coordinate axes can be obtained through the displacement coordinate function and the displacement coordinates of the joystick on different coordinate axes. For example, game_x_pos is obtained through SensitivitGetPos(x_pos), which represents the displacement ratio of the joystick in the current x-axis direction. In this embodiment, its value range is defined as (-1,1). When its value is 0, it means that the joystick has not moved. When its value is -1, it means that the joystick has moved to the far left. When its value is 1, it means that the joystick has moved to the far right. When the joystick is up, its value is positive, and when the joystick is down, its value is negative. Similarly, SensitivitGetPos(game_pos) represents the displacement ratio of the current joystick on the Y-axis, and the value range of game_scale is [0,1].

[0097] Further, the method further comprises the steps of:

[0098] The absolute coordinates of the joystick on the screen are obtained according to the relative displacement coordinates, which are expressed as:

[0099] curr_game_x+=game_x,

[0100] curr_game_y+=game_y, where curr_game_x represents the absolute coordinate of the joystick in the x-axis direction, and curr_game_y represents the absolute coordinate of the joystick in the y-axis direction.

[0101] Furthermore, before determining the preset sensitivity calibration coefficient of the first gamer, the method further includes:

[0102] Acquire first physiological characteristic data of the first game player controlling the first joystick, wherein the first physiological characteristic data at least includes historical first physiological characteristic data;

[0103] Acquiring first activity characteristic data of the first game player during the process of manipulating the first joystick;

[0104] Acquire a first joystick shaking trajectory curve when the first player is manipulating the first joystick;

[0105] The step of obtaining the first physiological characteristic data of the first gamer who controls the first joystick comprises:

[0106] A first sensor group for collecting visual data, pressure data, and contact area data is configured on the surface of the first joystick;

[0107] Using the first sensor group to capture multi-view images of the first gamer, and obtain three-dimensional hand data including palms, fingers and skin;

[0108] Specifically, the first sensor group includes but is not limited to infrared reflective sensors and high frame rate cameras.

[0109] Detecting pressure distribution data of the first gamer's fingers and palms on the joystick by the first sensor group;

[0110] Using the first sensor group to detect changes in the contact area of ​​the palm and fingers of the first operation on the joystick;

[0111] Using the first sensor group to detect the change in the deflection angle of the palm and the finger when performing the first operation on the joystick;

[0112] Using computer vision technology, semantically annotating the three-dimensional hand data, the pressure distribution data, the contact area change, and the deflection angle change, and extracting physiological feature data representing physiological features;

[0113] Based on the acquired physiological characteristic data, a hand physiological characteristic parameter model including the first physiological characteristic data is constructed using a neural network algorithm;

[0114] The step of obtaining first activity characteristic data of the first game player when manipulating the first joystick includes:

[0115] Using an inertial sensor, obtaining movement change data of a hand-controlled joystick of the first player in motion, acceleration, and posture in three-dimensional space;

[0116] An electromagnetic position tracker is arranged on the first player's upper surface to calculate the three-dimensional coordinates of the palm through the magnetic field distribution in space;

[0117] The first player wears a multi-channel intelligent monitoring bracelet to monitor the hand physiological data during the operation in real time, and the hand physiological data at least includes palm skin conductance and temperature changes;

[0118] Natural language technology is used to semantically annotate the motion change data, the three-dimensional coordinates of the palm, and the hand physiological data, and to extract activity state feature data representing the activity state.

[0119] Specifically, in this step, image processing algorithms such as segmentation and edge detection are used to mark the palm, fingers and other areas in the hand image; in the three-dimensional coordinate model, a machine learning algorithm is used to judge different fingers and knuckles and label the image areas; a pressure heat map obtained by a pressure test is used; the three-dimensional structural parameters, pressure value distribution and corresponding contact area changes in each semantically labeled area are counted as features of the area; the above-mentioned multi-source features are input into a machine learning algorithm to train a parameter model for predicting the physiological structure of the hand; through this combination of image analysis and artificial intelligence technology, the physiological characteristics of the hand can be automatically and efficiently acquired.

[0120] Furthermore, the obtaining of the first joystick shaking trajectory curve when the first player manipulates the first joystick includes:

[0121] A laser tracker is configured on the first joystick to obtain the joystick coordinates in a two-dimensional plane of the active surface of the first joystick in real time;

[0122] Detecting joystick motion data of the first joystick in a preset three-dimensional space by a high-speed motion tracking sensor built into the first joystick;

[0123] Combining the joystick coordinates with the joystick motion data, and calculating a position coordinate sequence of the first joystick in the three-dimensional space through a data fusion algorithm;

[0124] The position coordinate sequence is imported into the three-dimensional model software, and the first joystick shaking trajectory curve is generated from all the discrete coordinate points through a three-dimensional curve fitting tool.

[0125] Furthermore, the step of determining the preset sensitivity calibration coefficient of the first gamer according to the first joystick shaking trajectory curve, the first physiological characteristic data and the first activity characteristic data of the first gamer comprises:

[0126] extracting hand physiological characteristic features and activity features from the first physiological characteristic data and the first activity feature data respectively;

[0127] Specifically, in this step, the extracted physiological feature data is first analyzed and converted to construct a feature vector with better distinguishing ability; the feature-engineered data set is used as the sample space, and the physiological parameter value corresponding to each sample is labeled; the fitting and generalization performance of different statistical learning algorithms on the sample set is compared, and appropriate algorithms such as decision tree models, SVM, and deep learning are selected; parameter training is performed using the selected machine learning algorithm and training data set to learn the mapping relationship between physiological features and parameter values; the model performance is evaluated on an independent test set, and the prediction performance is further improved by adjusting parameters and frameworks; the model is continued to be trained using the newly collected hand physiological feature data to adapt it to more people, and adversarial networks can also be used to generate diversity in feature data to improve the model's generalization recognition ability and robustness.

[0128] According to the hand physiological characteristics and the activity characteristics, a clustering algorithm is used to identify the joystick control mode used by the first game player;

[0129] Analyzing the user's speed preference and joystick amplitude preference according to the first joystick shaking trajectory curve, and fitting the joystick control parameters of the first game player;

[0130] According to the joystick control mode and the joystick control parameter, a preset sensitivity calibration coefficient matching the joystick control mode and the joystick control parameter is selected in a knowledge base through a BP neural network algorithm.

[0131] Furthermore, the method for obtaining the current hardware sensitivity value also includes:

[0132] Presetting a first deep learning model, the first deep learning model is used to identify that a first gamer switches to a second gamer, and if a gamer switching operation occurs, setting a current hardware sensitivity value to an abnormal state;

[0133] The input of the first deep learning model is the first physiological characteristic data and first activity characteristic data of the player.

[0134] Specifically, the preset first deep learning model of this embodiment adopts the fitting and generalization performance of different statistical machine learning algorithms in the sample set, and suitable algorithms such as decision trees, random forests, and deep learning can be selected; parameter training is performed using the selected machine learning model and training data set to learn the mapping relationship between physiological characteristics and activity characteristics to distinguish the characteristic behaviors of different players; diverse data can be generated through an adversarial network for the test set, thereby enhancing the generalization ability and robustness of the model, and further improving the prediction performance through parameter adjustment and framework adjustment.

[0135] It should be noted that by using a deep learning model to identify the switching actions of different gamers using game joysticks, the gamers' satisfaction with using the joysticks can be improved. Different gamers use different sensitivity parameters. The input of the deep learning model is the gamer's first physiological characteristic data and first activity characteristic data, which can speed up feature extraction and recognition. There is no need to use additional features for calculation, and the existing features that have been extracted can be used to speed up the recognition speed.

[0136] Furthermore, the preset time period T, when the time meets the preset time period T, the specific implementation process of executing the sensitivity software cycle optimization setting is:

[0137] Extract the joystick temperature and shaking resistance corresponding to the preset time period T of the joystick, identify the inertial joystick temperature and inertial shaking resistance corresponding to the preset time period T of the first game player, and then extract the inertial joystick temperature T corresponding to each software program in the preset time period T c and inertial rocker resistance R c ;

[0138] Extract the rocker detection temperature T within the preset time period T c1 and shaking detection resistance R c1 , the compensation function of analyzing the sensitivity control of each software program over time is:

[0139]

[0140] Then, when each software program meets the preset time period T, each sensitivity is adjusted to:

[0141]

[0142] Where W represents the compensation weight of the inertial stick temperature, and Q represents the compensation weight of the inertial stick resistance.

[0143] Specifically, the preset time period T can be set to one day or one week, and the time period can be set according to needs.

[0144] This embodiment adjusts the control sensitivity of each software program within a preset time period T by analyzing the compensation function of the sensitivity control of each software program that increases over time. As the user continues to use the joystick for a longer time, the sensitivity may need to be fine-tuned due to various factors (such as joystick temperature fatigue, shaking resistance fatigue, joystick aging, etc.). Then, through the compensation function of the software program that increases over time, the sensitivity of each software program can be automatically adjusted to ensure that the user can maintain the best operational satisfaction during the game.

[0145] Furthermore, the method for obtaining the current hardware sensitivity value also includes:

[0146] Obtaining a preset sensitivity detection signal sending frequency;

[0147] Control the host to continuously send a sensitivity detection signal to the first joystick at the sensitivity detection signal sending frequency, wherein each data packet of the sensitivity detection signal carries a first detection time when the host sends the sensitivity detection signal and a data packet number;

[0148] The control host receives a sensitivity detection response signal returned by the joystick, wherein the data packet of the sensitivity detection response signal carries the second detection time when the first joystick sends the sensitivity detection response signal and the data packet number of the sensitivity detection signal to which it responds;

[0149] When a sensitivity detection response signal in response to the sensitivity detection signal returned by the first joystick is not received within a preset time period after the first detection time, marking the sensitivity detection signal as unresponsive;

[0150] The number of sensitivity detection signals that did not respond was recorded;

[0151] Determine whether the number of sensitivity detection signals that do not respond within a unit time is greater than a preset response number threshold;

[0152] If the number of unresponsive sensitivity detection signals within a unit time is greater than a preset response number threshold, the current sensitivity value of the first joystick is set to an abnormal state.

[0153] Specifically, the control host continuously sends the network detection signal to the joystick at the sensitivity detection response signal sending frequency, which means that the control host continuously sends the network detection signal to the joystick at a time interval corresponding to the sensitivity detection response signal sending frequency, and the network detection signal sending frequency corresponds to a smaller time interval. For example, a value between 5 milliseconds and 50 milliseconds can be taken as the time interval. The control host includes but is not limited to a processor. The control host can be connected to the first joystick via a wired network or a wireless network, and the sensitivity detection signal sending frequency can be set to 2 minutes or 5 minutes.

[0154] It should be noted that in some cases, abnormal game joystick sensitivity is caused by the loss of network connection. At this time, the only way to determine whether the joystick sensitivity is normal is to obtain the network sensitivity status. Adding this method of obtaining the sensitivity status can improve the redundancy of the method and further improve the satisfaction of gamers.

[0155] It should be noted that during the joystick sensitivity calibration process, priority is given to the real-time joystick hardware sensitivity calibration, because the sensitivity of the joystick is mainly reflected in the sensitivity of the hardware. However, combining the software sensitivity calibration with the related software programs that the joystick depends on can further optimize the joystick's usage satisfaction, because the joystick also requires software programs to cooperate with the control operation.

[0156] Embodiment 2, as Figure 3 ,

[0157] A sensitivity setting optimization system for a game joystick, comprising:

[0158] The sensitivity hardware calibration module is used to obtain the current displacement coordinates and current hardware sensitivity value of the joystick; determine whether the current hardware sensitivity value is in a normal state, and if it is in an abnormal state, perform sensitivity hardware optimization settings;

[0159] The sensitivity software calibration module is used to preset a time period T. When the time meets the preset time period T, the sensitivity software cycle optimization setting is executed.

[0160] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0161] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0162] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0163] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0164] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0165] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the apparatus according to an embodiment of the present invention. The present invention may also be implemented as a device or device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may have the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

Claims

1. A method for optimizing the sensitivity setting of a game joystick, characterized in that: The following steps are involved: Get the current displacement coordinates and current hardware sensitivity value of the joystick; Determine whether the current hardware sensitivity value is in a normal state. If it is in an abnormal state, perform sensitivity hardware optimization settings; Preset time period T, when the time meets the preset time period T, execute sensitivity software cycle optimization setting; The specific steps of sensitivity hardware optimization setting are as follows: Obtaining the relative displacement coordinates of the joystick after calibration according to the joystick sensitivity optimization formula, the preset optimization strategy and the current displacement coordinates; The parameters of the joystick sensitivity optimization formula include a preset sensitivity calibration coefficient and a preset sensitivity calibration function; The preset optimization strategy is: determining a preset sensitivity calibration coefficient of the first gamer according to a first joystick shaking trajectory curve, first physiological characteristic data and first activity characteristic data of the first gamer.

2. The method for optimizing the sensitivity setting of a game joystick according to claim 1, characterized in that: The current hardware sensitivity value is calculated by the induced voltage corresponding to the position of the joystick, and the induced voltage deviation threshold is preset. If the deviation value of the corresponding induced voltage is greater than the preset induced voltage deviation threshold within a preset time period, the current hardware sensitivity value is set to an abnormal state, otherwise the current hardware sensitivity value is set to a normal state; The joystick sensitivity optimization formula is as follows: game_x=game_scale*Fun(game_x_pos), game_x_pos=SensitivitGetPos(x_pos), game_y=game_scale*Fun(game_y_pos), game_y_pos=SensitivitGetPos(y_pos), Among them, game_scale represents the preset sensitivity calibration coefficient, Fun() represents the preset displacement calibration function, game_x represents the relative displacement coordinates of the joystick in the x-axis direction, game_y represents the relative displacement coordinates of the joystick in the y-axis direction, game_x_pos represents the sensitivity calibration of the joystick in the x-axis direction, game_y_pos represents the sensitivity calibration of the joystick in the y-axis direction, x_pos represents the current displacement coordinates of the joystick in the x-axis direction, y_pos represents the current displacement coordinates of the joystick in the y-axis direction, and the preset displacement calibration function adopts a nonlinear quadratic function.

3. The method for optimizing the sensitivity setting of a game joystick according to claim 2, characterized in that: Also includes the steps: The absolute coordinates of the joystick on the screen are obtained according to the relative displacement coordinates, which are expressed as: curr_game_x+=game_x, curr_game_y+=game_y, Among them, curr_game_x represents the absolute coordinate of the joystick in the x-axis direction, and curr_game_y represents the absolute coordinate of the joystick in the y-axis direction.

4. The method for optimizing the sensitivity setting of a game joystick according to claim 1, characterized in that: Before determining the preset sensitivity calibration coefficient of the first gamer, the method further includes: Acquire first physiological characteristic data of a first gamer controlling a first joystick, wherein the first physiological characteristic data at least includes historical first physiological characteristic data; Acquiring first activity characteristic data of the first game player when manipulating the first joystick; Obtaining a first joystick shaking trajectory curve when the first game player controls the first joystick; The step of obtaining the first physiological characteristic data of the first gamer who controls the first joystick comprises: A first sensor group for collecting visual data, pressure data, and contact area data is configured on the surface of the first joystick; Using the first sensor group to capture multi-view images of the first gamer, and obtain three-dimensional hand data including palms, fingers and skin; Detecting pressure distribution data of the first gamer's fingers and palms on the joystick by the first sensor group; Using the first sensor group to detect changes in the contact area of ​​the palm and fingers of the first operation on the joystick; Using the first sensor group to detect the change in the deflection angle of the palm and the finger when performing the first operation on the joystick; Using computer vision technology, semantically annotating the three-dimensional hand data, the pressure distribution data, the contact area change, and the deflection angle change, and extracting physiological feature data representing physiological features; Based on the acquired physiological characteristic data, a hand physiological characteristic parameter model including the first physiological characteristic data is constructed using a neural network algorithm; The step of obtaining first activity characteristic data of the first game player when manipulating the first joystick includes: Using an inertial sensor, obtaining movement change data of a hand-controlled joystick of the first player in motion, acceleration, and posture in three-dimensional space; An electromagnetic position tracker is arranged on the first player's upper surface to calculate the three-dimensional coordinates of the palm through the magnetic field distribution in space; The first player wears a multi-channel intelligent monitoring bracelet to monitor the hand physiological data during the operation in real time, and the hand physiological data at least includes palm skin conductance and temperature changes; Natural language technology is used to semantically annotate the motion change data, the three-dimensional coordinates of the palm, and the hand physiological data, and to extract activity state feature data representing the activity state.

5. The method for optimizing the sensitivity setting of a game joystick according to claim 4, characterized in that: The step of obtaining the first joystick shaking trajectory curve when the first player manipulates the first joystick comprises: A laser tracker is configured on the first joystick to obtain the joystick coordinates in a two-dimensional plane of the active surface of the first joystick in real time; Detecting joystick motion data of the first joystick in a preset three-dimensional space by a high-speed motion tracking sensor built into the first joystick; Combining the joystick coordinates with the joystick motion data, and calculating a position coordinate sequence of the first joystick in the three-dimensional space through a data fusion algorithm; The position coordinate sequence is imported into the three-dimensional model software, and the first joystick shaking trajectory curve is generated from all the discrete coordinate points through a three-dimensional curve fitting tool.

6. The method for optimizing the sensitivity setting of a game joystick according to claim 5, characterized in that: The step of determining the preset sensitivity calibration coefficient of the first gamer according to the first joystick shaking trajectory curve, the first physiological characteristic data and the first activity characteristic data of the first gamer comprises: extracting hand physiological characteristic features and activity features from the first physiological characteristic data and the first activity feature data respectively; According to the hand physiological characteristics and the activity characteristics, a clustering algorithm is used to identify the joystick control mode used by the first game player; Analyzing the user's speed preference and joystick amplitude preference according to the first joystick shaking trajectory curve, and fitting the joystick control parameters of the first game player; According to the joystick control mode and the joystick control parameter, a preset sensitivity calibration coefficient matching the joystick control mode and the joystick control parameter is selected in a knowledge base through a BP neural network algorithm.

7. The method for optimizing the sensitivity setting of a game joystick according to claim 1, characterized in that: The method for obtaining the current hardware sensitivity value also includes: A first deep learning model is preset, wherein the first deep learning model is used to identify that a first gamer switches to a second gamer, and if a gamer switching operation occurs, a current hardware sensitivity value is set to an abnormal state; The input of the first deep learning model is the first physiological characteristic data and first activity characteristic data of the player.

8. The method for optimizing the sensitivity setting of a game joystick according to claim 1, characterized in that: The preset time period T, when the time meets the preset time period T, the specific implementation process of executing the sensitivity software cycle optimization setting is: Extract the joystick temperature and shaking resistance corresponding to the preset time period T of the joystick, identify the inertial joystick temperature and inertial shaking resistance corresponding to the preset time period T of the first game player, and then extract the inertial joystick temperature T corresponding to each software program in the preset time period T c and inertial shaking resistance R c ; Extract the rocker detection temperature T within the preset time period T c1 and shaking detection resistance R c1 , the compensation function of analyzing the sensitivity control of each software program over time is: Then, when each software program meets the preset time period T, each sensitivity is adjusted to: Where W represents the compensation weight of the inertial rocker temperature, and Q represents the compensation weight of the inertial rocking resistance.

9. The method for optimizing the sensitivity setting of a game joystick according to claim 1, characterized in that: The method for obtaining the current hardware sensitivity value also includes: Obtaining a preset sensitivity detection signal sending frequency; Control the host to continuously send a sensitivity detection signal to the first joystick at the sensitivity detection signal sending frequency, wherein each data packet of the sensitivity detection signal carries a first detection time when the host sends the sensitivity detection signal and a data packet number; The control host receives a sensitivity detection response signal returned by the joystick, wherein the data packet of the sensitivity detection response signal carries the second detection time when the first joystick sends the sensitivity detection response signal and the data packet number of the sensitivity detection signal to which it responds; When a sensitivity detection response signal in response to the sensitivity detection signal returned by the first joystick is not received within a preset time period after the first detection time, marking the sensitivity detection signal as unresponsive; The number of sensitivity detection signals that did not respond was recorded; Determine whether the number of sensitivity detection signals that do not respond within a unit time is greater than a preset response number threshold; If the number of unresponsive sensitivity detection signals within a unit time is greater than a preset response number threshold, the current hardware sensitivity value of the first joystick is set to an abnormal state.

10. A system for optimizing the sensitivity setting of a game joystick, used for executing the method for optimizing the sensitivity setting of a game joystick according to any one of claims 1 to 9, characterized in that: include: The sensitivity hardware calibration module is used to obtain the current displacement coordinates and current hardware sensitivity value of the joystick; Determine whether the current hardware sensitivity value is in a normal state. If it is in an abnormal state, perform sensitivity hardware optimization settings; The sensitivity software calibration module is used to preset a time period T. When the time meets the preset time period T, the sensitivity software cycle optimization setting is executed.

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