A method and system for intelligently controlling vibration effects of a game controller

Abstract

The present invention discloses an intelligent control method for the vibration effect of a game controller, comprising: using a preset vibration effect parameter library, establishing a vibration mode mapping relationship for different game scenes, and obtaining the vibration mode parameters corresponding to the current game scene; generating a motor drive signal through PWM modulation technology according to the vibration mode parameters; processing the response delay caused by the motor inertia, predicting the motor state in real time, and adjusting the drive signal in advance; monitoring the motor operating temperature in real time; dynamically adjusting the drive signal through a preset wear compensation model according to the accumulated value of the motor running time, to compensate for the performance degradation caused by wear; generating a motor drive signal through PWM modulation technology, and combining it with a segmented voltage regulation algorithm, which can maintain frequency stability when reducing the amplitude, thereby achieving a slight vibration effect. At the same time, when strong vibration is required, calling the frequency compensation algorithm to ensure that the frequency remains stable when the voltage increases, and avoid excessively severe vibration.

Description

Technical Field

[0001] The present invention relates to the technical field of entertainment equipment, and in particular to a method and system for intelligently controlling vibration effects of a game controller. Background Art

[0002] In the field of game controllers, vibration technology is an important means of enhancing player immersion and interactive experience. However, existing vibration technology faces many technical challenges in practical application. First, the amplitude and frequency of the vibration motor are key factors in determining the vibration effect, but there is a mutually restrictive relationship between the two. For example, while lowering the voltage can reduce the amplitude for mild vibration, it will also cause the vibration frequency to decrease, making the vibration effect less subtle. Conversely, while increasing the voltage can increase the amplitude for stronger vibration, the increased frequency may make the vibration effect too intense, even beyond the player's comfort range.

[0003] Furthermore, the response speed and stability of the vibration motor are pressing issues. When game scenes change rapidly, the vibration motor needs to quickly adjust its amplitude and frequency. However, the motor's inertia can cause response delays, making the vibration effect out of sync with the game screen. This asynchrony not only affects the immersive gaming experience but can also cause players to misjudge game operations. Furthermore, after prolonged operation, the vibration motor's performance gradually degrades due to heat and wear, further affecting the stability and accuracy of the vibration effect.

[0004] Another technical difficulty lies in achieving more refined vibration control without increasing hardware costs. Existing vibration motors mostly use simple voltage regulation, making it difficult to achieve complex vibration effects. While the vibration effect can be improved by increasing the number of motors or improving the motor structure, this increases the weight and cost of the controller, affecting the player's experience. Therefore, how to achieve more precise vibration control by optimizing the control algorithm and circuit design based on existing hardware has become a pressing technical problem. Summary of the Invention

[0005] In order to solve the above-mentioned technical problems, the present invention provides a method for intelligently controlling vibration effects of a game controller.

[0006] The technical solution of the present invention is achieved as follows:

[0007] A method for intelligently controlling vibration effects of a game controller comprises the following steps:

[0008] Use the preset vibration effect parameter library to establish vibration mode mapping relationships for different game scenes and obtain the vibration mode parameters corresponding to the current game scene;

[0009] Generate motor drive signals through PWM modulation technology according to vibration pattern parameters;

[0010] Deal with the response delay caused by motor inertia, predict the motor status in real time, and adjust the drive signal in advance;

[0011] Real-time monitoring of motor operating temperature;

[0012] According to the accumulated running time of the motor, the drive signal is dynamically adjusted through the preset wear compensation model to compensate for the performance degradation caused by wear.

[0013] Furthermore, the process of obtaining the vibration mode parameters corresponding to the current game scene includes:

[0014] Extract vibration effect data from the vibration effect parameter library and combine it with scene information in the game scene library to establish a vibration mode mapping relationship;

[0015] According to the current game scene information, the corresponding vibration mode parameters are matched from the mapping relationship;

[0016] If the match is successful, obtain the vibration mode parameter value;

[0017] If the matching fails, the default vibration mode parameter value is used.

[0018] Furthermore, the process of generating the motor drive signal includes:

[0019] Obtain the initial data required to generate the motor drive signal through the preset vibration mode parameters;

[0020] The initial data is processed using PWM modulation technology to obtain the motor drive signal;

[0021] Determine whether the vibration intensity is below a preset threshold. If so, invoke a segmented voltage regulation algorithm to generate a drive signal with a stable frequency and reduced amplitude.

[0022] If the vibration intensity is higher than the preset threshold, the frequency compensation algorithm is called to generate a driving signal with increased voltage and stable frequency.

[0023] Furthermore, the process of processing the response delay caused by the motor inertia and adjusting the drive signal in advance includes:

[0024] Get the current state of the motor and determine whether the motor inertia causes response delay. If there is a delay, calculate the delay time to obtain the predicted value of the motor state.

[0025] Adjust the driving signal according to the predicted value, reduce the influence of inertia on the vibration effect through signal optimization, and obtain the optimized driving signal;

[0026] Generate a vibration effect based on the optimized driving signal, and determine whether the vibration effect is synchronized with the game screen. If not, readjust the driving signal.

[0027] Furthermore, the process of real-time monitoring of the motor operating temperature includes:

[0028] A temperature sensor is used to periodically obtain the motor temperature value, and the temperature value is compared with a preset threshold. If the temperature value exceeds the threshold, the current motor power value is obtained;

[0029] According to the motor power value and the preset limit value, the dynamic power limit algorithm is used to calculate the adjustment value to obtain the new power output value;

[0030] The motor power output value is updated by adjusting the value, the motor operating temperature value is reduced, and it is determined whether the temperature value is reduced below the threshold.

[0031] Furthermore, the process of dynamically adjusting the driving signal includes:

[0032] Use a time sensor to periodically obtain the motor running time and calculate the cumulative running time value;

[0033] According to the accumulated running time, the wear degree is determined using a preset wear compensation model;

[0034] Calculate the performance attenuation value based on the wear degree;

[0035] Determine the compensation value according to the performance attenuation value;

[0036] generating an adjustment value using an adjustment algorithm according to the compensation value;

[0037] According to the adjustment value, the driving signal is updated.

[0038] Furthermore, it also includes:

[0039] Optimize the vibration control algorithm, train the network model through historical data, and predict the best vibration parameter combination;

[0040] Optimize signal processing flow through integrated circuit design and use FPGA to achieve multi-channel parallel processing;

[0041] Establish vibration effect evaluation indicators, monitor the deviation between vibration parameters and expected effects in real time, and trigger the adaptive adjustment mechanism.

[0042] Furthermore, the process of predicting the optimal vibration parameter combination includes:

[0043] Obtain the current operating status data of the hardware device, including vibration frequency, amplitude and duration;

[0044] Adopting deep learning network architecture to build an optimization model for vibration control algorithm;

[0045] Extract vibration parameter combinations and corresponding effects from historical data as training data sets;

[0046] For the training data set, the back propagation algorithm is used to optimize the network weights and improve the prediction accuracy;

[0047] According to the optimized model, the current operating status data is input to predict the optimal vibration parameter combination;

[0048] Use the prediction results to generate new control instructions and adjust the vibration output of the hardware device.

[0049] Furthermore, the process of real-time monitoring of the deviation between the vibration parameters and the expected effect and triggering the adaptive adjustment mechanism includes:

[0050] Obtain the raw data from the vibration sensor, perform data preprocessing based on frequency, amplitude, and timing, and obtain standardized vibration parameters;

[0051] Input the standardized vibration parameters into the pre-established vibration effect evaluation model to determine the deviation between the vibration parameters and the expected effect;

[0052] If the deviation exceeds the preset threshold, the adaptive adjustment mechanism is activated to generate preliminary adjustment parameters based on the deviation direction and magnitude;

[0053] Use machine learning algorithms to analyze historical adjustment data, combine them with current vibration parameters, optimize preliminary adjustment parameters, and obtain dynamic adjustment parameters;

[0054] The dynamic adjustment parameters are input into the control module to adjust the frequency, amplitude and timing of the vibration equipment and generate new control instructions.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. This invention generates motor drive signals through PWM modulation technology and combines it with a segmented voltage regulation algorithm to maintain a stable frequency while reducing the amplitude, thereby achieving a mild vibration effect. At the same time, when strong vibration is required, a frequency compensation algorithm is called to ensure that the frequency remains stable when the voltage is increased, avoiding excessive vibration. By predicting the optimal vibration parameter combination, the amplitude and frequency are dynamically adjusted to achieve more precise vibration control.

[0057] 2. By predicting the motor status in real time, calculating the response delay time, and adjusting the drive signal in advance, the impact of motor inertia on the vibration effect is reduced. Through signal optimization, the vibration effect is synchronized with the game screen, avoiding the loss of immersion caused by delays.

[0058] 3. A temperature sensor is used to monitor the motor's operating temperature in real time, and the motor's power output is adjusted through a dynamic power limiting algorithm to reduce the motor's operating temperature. The drive signal is dynamically adjusted based on the accumulated motor operating time to compensate for performance degradation caused by wear and ensure the stability and accuracy of the vibration effect.

[0059] 4. By optimizing the vibration control algorithm and implementing multi-channel parallel processing using FPGA, more precise vibration control can be achieved without increasing the number of motors or complex structures. Based on vibration effect evaluation indicators, the deviation between vibration parameters and expected effects is monitored in real time, and the adjustment parameters are optimized through machine learning algorithms to further improve the vibration effect.

[0060] 5. The present invention also establishes a vibration pattern mapping relationship through a preset vibration effect parameter library and a game scene library, which can quickly match the corresponding vibration pattern parameters according to different game scenes. Combined with the adaptive adjustment mechanism, the vibration effect can be adjusted in real time to adapt to the rapidly changing game scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flowchart of the steps of a method for intelligently controlling vibration effects of a game controller according to Example 1;

[0062] Figure 2 This is a framework diagram of an intelligent control system for vibration effects of a game controller according to Example 2. DETAILED DESCRIPTION

[0063] In order to make the purposes, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0064] Example 1

[0065] like Figure 1 As shown, this embodiment provides a method for intelligently controlling vibration effects of a game controller, comprising the following steps:

[0066] Use the preset vibration effect parameter library to establish vibration mode mapping relationships for different game scenes and obtain the vibration mode parameters corresponding to the current game scene;

[0067] Generate motor drive signals through PWM modulation technology according to vibration pattern parameters;

[0068] Deal with the response delay caused by motor inertia, predict the motor status in real time, and adjust the drive signal in advance;

[0069] Real-time monitoring of motor operating temperature;

[0070] According to the accumulated running time of the motor, the drive signal is dynamically adjusted through the preset wear compensation model to compensate for the performance degradation caused by wear.

[0071] Furthermore, the process of obtaining the vibration mode parameters corresponding to the current game scene includes:

[0072] Extract vibration effect data from the vibration effect parameter library and combine it with scene information in the game scene library to establish a vibration mode mapping relationship;

[0073] According to the current game scene information, the corresponding vibration mode parameters are matched from the mapping relationship;

[0074] If the match is successful, get the vibration mode parameter value;

[0075] If the matching fails, the default vibration mode parameter value is used;

[0076] Input the obtained vibration pattern parameter values ​​into the vibration effect generation module to generate vibration effect data. The vibration effect data is used to drive the vibration device to achieve vibration effect feedback in the game scene. Based on the vibration effect feedback data, the parameter values ​​in the vibration effect parameter library are optimized and the mapping relationship is updated.

[0077] In one embodiment, first, a preset vibration effect parameter library is used to extract vibration effect data including a frequency range of 50 Hz to 200 Hz and an amplitude range of 0.1 mm to 0.5 mm;

[0078] Combine scene information in the game scene library, such as combat scenes, exploration scenes, and plot scenes, to establish a vibration pattern mapping relationship. Map combat scenes to a high-frequency and high-amplitude parameter combination, exploration scenes to a medium-frequency and medium-amplitude parameter combination, and plot scenes to a low-frequency and low-amplitude parameter combination.

[0079] According to the current game scene information, the corresponding vibration mode parameters are matched from the mapping relationship. If the current scene is a battle scene, the matching parameter values ​​are 150Hz frequency and 0.4mm amplitude. If the matching fails, for example, the scene information is missing or abnormal, the default vibration mode parameter values ​​are used, which are 100Hz frequency and 0.3mm amplitude.

[0080] Input the obtained vibration pattern parameter values ​​into the vibration effect generation module to generate vibration effect data, for example, vibration waveform data with a frequency of 150 Hz and an amplitude of 0.4 mm;

[0081] Drive the vibration device through vibration effect data to achieve vibration effect feedback in the game scene. For example, generate a motor drive signal through PWM modulation technology to drive the device to perform vibration effects;

[0082] Based on vibration effect feedback data, such as actual frequency and amplitude data collected by the vibration sensor, the parameter values ​​in the vibration effect parameter library are optimized. For example, if the actual frequency is 145Hz, the frequency value of the battle scene in the parameter library is adjusted to 145Hz.

[0083] Update the mapping relationship, for example, update the frequency value of the battle scene to 145Hz to ensure the accuracy of subsequent matching.

[0084] Furthermore, the process of generating the motor drive signal includes:

[0085] Obtain the initial data required to generate the motor drive signal through the preset vibration mode parameters;

[0086] The initial data is processed using PWM modulation technology to obtain the motor drive signal;

[0087] Determine whether the vibration intensity is below a preset threshold. If so, invoke a segmented voltage regulation algorithm to generate a drive signal with a stable frequency and reduced amplitude.

[0088] If the vibration intensity is higher than the preset threshold, the frequency compensation algorithm is called to generate a driving signal with increased voltage and stable frequency;

[0089] While increasing the voltage, the vibration frequency is maintained within a comfortable range through the reverse frequency compensation mechanism;

[0090] In one embodiment, initial data required for generating a motor drive signal is obtained according to preset vibration pattern parameters, such as a frequency of 50 Hz and an amplitude of 0.5 mm;

[0091] The initial data is processed using PWM modulation technology, and the duty cycle is set to 30% to obtain the motor drive signal;

[0092] Determine whether the vibration intensity requirement is lower than the preset threshold. If the vibration intensity requirement is 0.3N, which is lower than the preset threshold of 0.5N, the segmented voltage regulation algorithm is called to reduce the voltage from 5V to 3V in segments while keeping the frequency stable at 50Hz. If the vibration intensity requirement is 0.8N, which is higher than the preset threshold of 0.5N, the frequency compensation algorithm is called to increase the voltage from 5V to 7V, and the frequency is stabilized at 50Hz through the reverse frequency compensation mechanism.

[0093] Furthermore, the process of processing the response delay caused by the motor inertia and adjusting the drive signal in advance includes:

[0094] Get the current state of the motor and determine whether the motor inertia causes response delay. If there is a delay, calculate the delay time to obtain the predicted value of the motor state.

[0095] Adjust the driving signal according to the predicted value, reduce the influence of inertia on the vibration effect through signal optimization, and obtain the optimized driving signal;

[0096] Generate a vibration effect based on the optimized drive signal and determine whether the vibration effect is synchronized with the game screen. If not, readjust the drive signal.

[0097] Specifically, a Kalman filter algorithm is used to process the motor state data to obtain an estimated value of the motor's current state. Based on the estimated value of the motor state, it is determined whether the motor inertia causes a response delay. If a delay exists, the delay time is calculated.

[0098] The Kalman filter algorithm is used to predict the delay time and obtain the predicted value of the motor state. Based on the predicted value of the motor state, the generation parameters of the drive signal are adjusted to optimize the output of the drive signal.

[0099] PWM modulation technology is used to process the optimized drive signal to generate a preliminary drive signal. The vibration intensity is judged to be lower than the preset threshold. If it is lower than the preset threshold, the segmented voltage regulation algorithm is called to generate a drive signal with stable frequency and reduced amplitude. If the vibration intensity is higher than the preset threshold, the frequency compensation algorithm is called to generate a drive signal with increased voltage and stable frequency.

[0100] Generate a vibration effect based on the adjusted driving signal, and determine whether the vibration effect is synchronized with the game screen. If not, readjust the driving signal and repeat the above steps;

[0101] In one embodiment, a Kalman filter algorithm is used to process the motor state data. The state equation and observation equation are combined with the current motor speed and acceleration data to obtain an estimated value of the current motor state. For example, the motor speed is estimated to be 1200 rpm and the acceleration is 5 m / s².

[0102] Based on the estimated motor state, determine whether motor inertia causes response delay. If the delay time exceeds 10ms, calculate the delay time through the Kalman filter algorithm to obtain the predicted value of the motor state. For example, the predicted motor speed is 1250 rpm and the acceleration is 5.2 m / s².

[0103] Based on the predicted value of the motor state, the generation parameters of the drive signal are adjusted, and the frequency of the drive signal is adjusted from 50 Hz to 52 Hz to optimize the output of the drive signal;

[0104] PWM modulation technology is used to process the optimized drive signal, generating a preliminary drive signal with a duty cycle of 30%. The system then determines whether the vibration intensity is below a preset threshold. If the vibration intensity is below 0.5 g, the segmented voltage regulation algorithm is invoked to reduce the voltage from 5 V to 3 V, generating a drive signal with a stable frequency and reduced amplitude. If the vibration intensity is above 0.5 g, the frequency compensation algorithm is invoked to increase the voltage from 5 V to 7 V, generating a drive signal with increased voltage and a stable frequency.

[0105] Generate a vibration effect according to the adjusted driving signal, and determine whether the vibration effect is synchronized with the game screen. If not, readjust the driving signal and repeat the above steps.

[0106] Furthermore, the process of real-time monitoring of the motor operating temperature includes:

[0107] A temperature sensor is used to periodically obtain the motor temperature value, and the temperature value is compared with a preset threshold. If the temperature value exceeds the threshold, the current motor power value is obtained;

[0108] According to the motor power value and the preset limit value, the dynamic power limit algorithm is used to calculate the adjustment value to obtain the new power output value;

[0109] Update the motor power output value by adjusting the value, reduce the motor operating temperature value, and determine whether the temperature value drops below the threshold;

[0110] Specifically, a temperature sensor is used to collect the motor operating temperature data to obtain the real-time temperature value. Based on the preset temperature threshold, it is determined whether the real-time temperature value exceeds the threshold. If it exceeds the threshold, the dynamic power limiting algorithm is triggered to obtain the vibration parameters of the current vibration device, including frequency, amplitude and timing data;

[0111] A vibration effect evaluation model is used to analyze the current vibration parameters to determine whether the vibration effect is within the expected range. If the vibration effect is within the expected range, preliminary power adjustment parameters are generated according to the dynamic power limitation algorithm. The historical adjustment data is analyzed and combined with the current vibration parameters to optimize the preliminary power adjustment parameters to obtain dynamic power adjustment parameters. The dynamic power adjustment parameters are input into the control module to adjust the power output of the vibration equipment and generate new control instructions.

[0112] Monitor the adjusted vibration parameters in real time to determine whether they meet the expected effect range. If not, restart the dynamic power limiting algorithm, update the vibration effect evaluation model based on multiple adjustment results, and continuously optimize the parameter generation logic of the dynamic power limiting algorithm.

[0113] In one embodiment, a temperature sensor is used to collect motor operating temperature data, and a real-time temperature value of 75°C is obtained. Based on a preset temperature threshold of 80°C, it is determined whether the real-time temperature value exceeds the threshold. If the threshold is exceeded, a dynamic power limiting algorithm is triggered;

[0114] The vibration parameters of the current vibration device are obtained, including a frequency of 50 Hz, an amplitude of 2 mm, and time series data. A vibration effect evaluation model is used to analyze the current vibration parameters to determine whether the vibration effect meets the expected range. If the model output deviation value is 5%, which is within the allowable range, and if the vibration effect meets the expected range, preliminary power adjustment parameters are generated based on the dynamic power limiting algorithm to reduce the power to 80%.

[0115] A machine learning algorithm was used to analyze historical adjustment data and, combined with current vibration parameters, optimize the initial power adjustment parameters. The dynamic power adjustment parameters were obtained as follows: power reduction to 75%. The dynamic power adjustment parameters were input into the control module to adjust the power output of the vibration equipment, generating new control instructions with a frequency of 50 Hz and an amplitude of 1.8 mm.

[0116] The adjusted vibration parameters are monitored in real time to determine whether they meet the expected performance range. If the deviation exceeds the allowable range by 8%, the dynamic power limiting algorithm is restarted. Based on multiple adjustment results, the vibration effect evaluation model is updated, and the parameter generation logic of the dynamic power limiting algorithm is continuously optimized, with the deviation threshold adjusted to 6%.

[0117] Furthermore, the process of dynamically adjusting the driving signal includes:

[0118] Use a time sensor to periodically obtain the motor running time and calculate the cumulative running time value;

[0119] According to the accumulated value of running time, the wear degree is determined by using a preset wear compensation model;

[0120] Calculate the performance attenuation value based on the wear degree;

[0121] Determine the compensation value according to the performance attenuation value;

[0122] generating an adjustment value using an adjustment algorithm according to the compensation value;

[0123] According to the adjustment value, the driving signal is updated.

[0124] Specifically, a time sensor is used to periodically obtain the running time of the motor to obtain an accumulated running time value, a preset wear compensation model is used to determine the wear degree based on the accumulated running time value, a performance degradation value is calculated based on the wear degree, a compensation value is determined based on the performance degradation value, an adjustment algorithm is used to generate an adjustment value based on the compensation value, and a drive signal is updated based on the adjustment value;

[0125] In one embodiment, a time sensor is used to obtain the motor running time in a period of 100 milliseconds, and the total running time is accumulated to 500 hours. Based on the accumulated running time of 500 hours, a preset wear compensation model is used to determine the wear degree to be 0.75 through linear regression analysis. Based on the wear degree of 0.75, combined with the motor performance attenuation curve formula, the performance attenuation value is calculated to be 15%. Based on the performance attenuation value of 15%, a proportional compensation formula is used to determine a compensation value of 1.2 times. Based on the compensation value of 1.2 times, a PID adjustment algorithm is used to generate an adjustment value of 1200 rpm. Based on the adjustment value of 1200 rpm, the drive signal is updated to a pulse width modulation signal of 1200 rpm.

[0126] Furthermore, it also includes:

[0127] Optimize the vibration control algorithm, train the network model through historical data, and predict the best vibration parameter combination;

[0128] Optimize signal processing flow through integrated circuit design and use FPGA to achieve multi-channel parallel processing;

[0129] Establish vibration effect evaluation indicators, monitor the deviation between vibration parameters and expected effects in real time, and trigger the adaptive adjustment mechanism.

[0130] Furthermore, the process of predicting the optimal vibration parameter combination includes:

[0131] Obtain the current operating status data of the hardware device, including vibration frequency, amplitude and duration;

[0132] Adopting deep learning network architecture to build an optimization model for vibration control algorithm;

[0133] Extract vibration parameter combinations and corresponding effects from historical data as training data sets;

[0134] For the training data set, the back propagation algorithm is used to optimize the network weights and improve the prediction accuracy;

[0135] According to the optimized model, the current operating status data is input to predict the optimal vibration parameter combination;

[0136] Use the prediction results to generate new control instructions and adjust the vibration output of the hardware device;

[0137] Specifically, the current operating status data of the hardware device, including vibration frequency, amplitude, and duration, is obtained. Vibration parameter combinations and corresponding effects are extracted from historical data to construct a training dataset. A deep learning network architecture is used to build an optimization model for the vibration control algorithm.

[0138] For the training data set, the back-propagation algorithm can be used to optimize the network weights and train the model to improve the prediction accuracy;

[0139] According to the optimized model, the current operating status data is input to predict the optimal vibration parameter combination, and the prediction results are used to generate new control instructions to adjust the vibration output of the hardware equipment.

[0140] In one embodiment, the hardware device collects current operating status data through sensors, such as vibration frequency of 60 Hz, amplitude of 2 mm, and duration of 5 seconds, and extracts historical data from a database, including different vibration parameter combinations and their corresponding effect scores, such as {frequency: 55 Hz, amplitude: 1.8 mm, duration: 4.5 seconds, effect score: 8.5};

[0141] A deep learning network architecture was constructed using a multi-layer perceptron model. The input layer had three nodes (corresponding to frequency, amplitude, and duration), the hidden layer used the ReLU activation function, and the output layer had one node (predicting the effect score). Stochastic gradient descent and backpropagation were used to optimize network weights. The learning rate was set to 0.01, and the number of iterations was 1000. The final model achieved a mean square error of less than 0.05 on the validation set.

[0142] The current operating status data [60Hz, 2mm, 5s] is input into the optimized model, and the predicted optimal vibration parameter combination is [58Hz, 1.9mm, 4.8s]. Based on the prediction results, control instructions are generated to adjust the output parameters of the vibration equipment.

[0143] The training dataset is updated every 24 hours, with newly added vibration parameters and effect scores added to the database. The deep learning model is retrained, and vibration effect evaluation indicators, such as the stability index, are set. The deviation between the current vibration parameters and the expected effect is calculated in real time. If the deviation exceeds a preset threshold of 0.2, the adaptive adjustment mechanism is triggered.

[0144] The adaptive adjustment mechanism uses a genetic algorithm to generate new control parameters based on the current deviation direction and amplitude, such as adjusting the frequency to 59 Hz, the amplitude to 1.95 mm, and the duration to 4.9 seconds, and sends new control instructions to the hardware device.

[0145] In one embodiment, the hardware device collects current operating status data through sensors, such as vibration frequency of 60 Hz, amplitude of 2 mm, and duration of 5 seconds, and extracts historical data from a database, including different vibration parameter combinations and their corresponding effect scores, such as {frequency: 55 Hz, amplitude: 1.8 mm, duration: 4.5 seconds, effect score: 8.5};

[0146] A deep learning network architecture was constructed using a multi-layer perceptron model. The input layer had three nodes (corresponding to frequency, amplitude, and duration), the hidden layer used the ReLU activation function, and the output layer had one node (predicting the effect score). Stochastic gradient descent and backpropagation were used to optimize network weights. The learning rate was set to 0.01, and the number of iterations was 1000. The final model achieved a mean square error of less than 0.05 on the validation set.

[0147] The current operating status data [60Hz, 2mm, 5s] is input into the optimized model, and the predicted optimal vibration parameter combination is [58Hz, 1.9mm, 4.8s]. Based on the prediction results, control instructions are generated to adjust the output parameters of the vibration equipment.

[0148] The training dataset is updated every 24 hours, with newly added vibration parameters and effect scores added to the database. The deep learning model is retrained, and vibration effect evaluation indicators, such as the stability index, are set. The deviation between the current vibration parameters and the expected effect is calculated in real time. If the deviation exceeds a preset threshold of 0.2, the adaptive adjustment mechanism is triggered.

[0149] The adaptive adjustment mechanism uses a genetic algorithm to generate new control parameters based on the current deviation direction and amplitude, such as adjusting the frequency to 59 Hz, the amplitude to 1.95 mm, and the duration to 4.9 seconds, and sends new control instructions to the hardware device.

[0150] Example 2

[0151] like Figure 2 As shown, this embodiment provides an intelligent control system for vibration effects of a game controller, including a vibration effect generation module, a motor drive and control module, an intelligent optimization and adaptive adjustment module, and a motor state monitoring and compensation module;

[0152] The vibration effect generation module generates a corresponding vibration signal according to the needs of the game scene;

[0153] The vibration effect generation module generates motor drive signals by calling the preset vibration effect parameter library and combining it with PWM modulation technology. The vibration effect generation module can generate a variety of vibration effects from mild to strong according to the requirements of different game scenarios, and optimize the clarity and fineness of the vibration signal through intelligent algorithms.

[0154] The motor driving and control module converts the signal generated by the vibration effect generating module into an actual driving signal for the motor;

[0155] The motor drive and control module uses PWM modulation technology to precisely control the motor and supports segmented voltage regulation and frequency compensation algorithms. For example, when slight vibration is required, the module will call the segmented voltage regulation algorithm to generate a drive signal with stable frequency and reduced amplitude; when strong vibration is required, the frequency compensation algorithm will be called to ensure that the vibration effect is both strong and delicate.

[0156] The intelligent optimization and adaptive adjustment module uses deep learning and machine learning algorithms to perform real-time optimization and adaptive adjustment of vibration effects;

[0157] The intelligent optimization and adaptive adjustment module trains the network model through historical data, predicts the optimal vibration parameter combination, and dynamically adjusts the vibration effect according to the current motor operating status. The intelligent optimization and adaptive adjustment module also monitors the deviation between the vibration parameters and the expected effect in real time. When the deviation exceeds the preset threshold, the adaptive adjustment mechanism is activated to optimize the vibration effect.

[0158] The motor status monitoring and compensation module monitors the motor's operating status in real time, including temperature, operating time, and wear. Based on the accumulated operating time, it dynamically adjusts the drive signal through a preset wear compensation model to compensate for performance degradation caused by wear.

[0159] The motor status monitoring and compensation module obtains the motor temperature through a temperature sensor and reduces the motor power through a dynamic power limiting algorithm when the temperature exceeds a threshold. At the same time, the motor status monitoring and compensation module dynamically adjusts the drive signal based on the accumulated running time through a preset wear compensation model to compensate for the performance degradation caused by wear.

[0160] The vibration effect generation module calls the preset vibration effect parameter library according to the game scene requirements and combines PWM modulation technology to generate motor drive signals to achieve a variety of vibration effects from mild to strong. It also optimizes the clarity and fineness of the vibration signal through intelligent algorithms. The motor drive and control module receives the generated signal and precisely controls the motor through PWM modulation technology. It supports segmented voltage regulation and frequency compensation algorithms to ensure the stability and accuracy of the vibration effect.

[0161] At the same time, the intelligent optimization and adaptive adjustment module uses deep learning and machine learning algorithms to monitor the deviation between the vibration effect and the expected target in real time, predict the optimal vibration parameter combination based on historical data, and dynamically adjust the vibration effect. The motor status monitoring and compensation module monitors the motor's temperature, operating time and wear in real time, and dynamically adjusts the drive signal through a dynamic power limiting algorithm and wear compensation model to compensate for performance degradation caused by wear, thereby ensuring the stability and service life of the motor.

[0162] While the specific embodiments of the present invention have been described in detail above, they are intended only as examples, and the present invention is not limited to the specific embodiments described above. Those skilled in the art should understand that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and that various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligently controlling vibration effects of a game controller, characterized in that: The following steps are involved: Use the preset vibration effect parameter library to establish vibration mode mapping relationships for different game scenes and obtain the vibration mode parameters corresponding to the current game scene; Generate motor drive signals through PWM modulation technology according to vibration pattern parameters; Deal with the response delay caused by motor inertia, predict the motor status in real time, and adjust the drive signal in advance; Real-time monitoring of motor operating temperature; According to the accumulated running time of the motor, the drive signal is dynamically adjusted through the preset wear compensation model to compensate for the performance degradation caused by wear; The process of generating the motor drive signal includes: Obtain the initial data required to generate the motor drive signal through the preset vibration mode parameters; The initial data is processed using PWM modulation technology to obtain the motor drive signal; Determine whether the vibration intensity is below a preset threshold. If so, invoke a segmented voltage regulation algorithm to generate a drive signal with a stable frequency and reduced amplitude. If the vibration intensity is higher than the preset threshold, the frequency compensation algorithm is called to generate a driving signal with increased voltage and stable frequency.

2. The method for intelligently controlling vibration effects of a game controller according to claim 1, wherein: The process of obtaining the vibration mode parameters corresponding to the current game scene includes: Extract vibration effect data from the vibration effect parameter library and combine it with scene information in the game scene library to establish a vibration mode mapping relationship; According to the current game scene information, the corresponding vibration mode parameters are matched from the mapping relationship; If the match is successful, get the vibration mode parameter value; If the matching fails, the default vibration mode parameter value is used.

3. The method for intelligently controlling vibration effects of a game controller according to claim 1, wherein: The process of processing the response delay caused by the motor inertia and adjusting the drive signal in advance includes: Get the current state of the motor and determine whether the motor inertia causes response delay. If there is a delay, calculate the delay time to obtain the predicted value of the motor state. Adjust the driving signal according to the predicted value, reduce the influence of inertia on the vibration effect through signal optimization, and obtain the optimized driving signal; Generate a vibration effect based on the optimized driving signal, and determine whether the vibration effect is synchronized with the game screen. If not, readjust the driving signal.

4. The method for intelligently controlling vibration effects of a game controller according to claim 1, wherein: The process of real-time monitoring of the motor operating temperature includes: A temperature sensor is used to periodically obtain the motor temperature value, and the temperature value is compared with a preset threshold. If the temperature value exceeds the threshold, the current motor power value is obtained; According to the motor power value and the preset limit value, the adjustment value is calculated to obtain the new power output value; The motor power output value is updated by adjusting the value, the motor operating temperature value is reduced, and it is determined whether the temperature value is reduced below the threshold.

5. The method for intelligently controlling vibration effects of a game controller according to claim 1, wherein: The process of dynamically adjusting the driving signal includes: Use a time sensor to periodically obtain the motor running time and calculate the cumulative running time value; According to the accumulated running time, the wear degree is determined using a preset wear compensation model; Calculate the performance attenuation value based on the wear degree; Determine the compensation value according to the performance attenuation value; generating an adjustment value according to the compensation value; According to the adjustment value, the driving signal is updated.

6. The method for intelligently controlling vibration effects of a game controller according to claim 1, wherein: Also includes: Optimize the vibration control algorithm, train the network model through historical data, and predict the best vibration parameter combination; Optimize signal processing flow through integrated circuit design and use FPGA to achieve multi-channel parallel processing; Establish vibration effect evaluation indicators, monitor the deviation between vibration parameters and expected effects in real time, and trigger the adaptive adjustment mechanism.

7. The method for intelligently controlling vibration effects of a game controller according to claim 6, wherein: The process of predicting the optimal vibration parameter combination includes: Obtain the current operating status data of the hardware device, including vibration frequency, amplitude and duration; Adopting deep learning network architecture to build an optimization model for vibration control algorithm; Extract vibration parameter combinations and corresponding effects from historical data as training data sets; Optimize network weights for training data sets to improve prediction accuracy; According to the optimized model, input the current operating status data to predict the best vibration parameter combination; Use the prediction results to generate new control instructions and adjust the vibration output of the hardware device.

8. The method for intelligently controlling vibration effects of a game controller according to claim 6, wherein: The process of real-time monitoring of the deviation between the vibration parameters and the expected effect and triggering the adaptive adjustment mechanism includes: Obtain the raw data from the vibration sensor, perform data preprocessing based on frequency, amplitude, and timing, and obtain standardized vibration parameters; Input the standardized vibration parameters into the pre-established vibration effect evaluation model to determine the deviation between the vibration parameters and the expected effect; If the deviation exceeds the preset threshold, the adaptive adjustment mechanism is activated to generate preliminary adjustment parameters based on the deviation direction and magnitude; Analyze historical adjustment data, combine with current vibration parameters, optimize preliminary adjustment parameters, and obtain dynamic adjustment parameters; The dynamic adjustment parameters are input into the control module to adjust the frequency, amplitude and timing of the vibration equipment and generate new control instructions.

9. A game controller vibration effect intelligent control system, used to implement the game controller vibration effect intelligent control method according to any one of claims 1 to 8, characterized in that: It includes vibration effect generation module, motor drive and control module, intelligent optimization and adaptive adjustment module and motor status monitoring and compensation module; The vibration effect generation module generates a corresponding vibration signal according to the needs of the game scene; The motor driving and control module converts the signal generated by the vibration effect generating module into an actual driving signal for the motor; The intelligent optimization and adaptive adjustment module uses deep learning and machine learning algorithms to perform real-time optimization and adaptive adjustment of vibration effects; The motor status monitoring and compensation module monitors the working status of the motor in real time, including temperature, operating time and wear. Based on the accumulated operating time, the module dynamically adjusts the drive signal through a preset wear compensation model to compensate for performance degradation caused by wear.

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