Intelligent control method and system for vibration effect of gamepad

Abstract

The invention discloses an intelligent control method for the vibration effect of a gamepad, and the method comprises the steps: building a vibration mode mapping relation for different game scenes through employing a preset vibration effect parameter library, and obtaining a vibration mode parameter corresponding to a current game scene; according to the vibration mode parameter, generating a motor driving signal through a PWM modulation technology; response delay caused by motor inertia is processed, the motor state is predicted in real time, and a driving signal is adjusted in advance; monitoring the working temperature of the motor in real time; according to the motor operation time accumulated value, the driving signal is dynamically adjusted through a preset abrasion compensation model, and performance attenuation caused by abrasion is compensated; a motor driving signal is generated through a PWM modulation technology, a sectional type voltage regulation algorithm is combined, the frequency can be kept stable when the amplitude is reduced, so that the slight vibration effect is achieved, meanwhile, when strong vibration is needed, a frequency compensation algorithm is called, it is ensured that the frequency is kept stable when the voltage is increased, and excessive violent vibration is avoided.

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 to enhance players' immersion and interactive experience. However, existing vibration technology faces many technical challenges in practical applications. First of all, the amplitude and frequency of the vibration motor are the key factors that determine the vibration effect, but there is a mutually restrictive relationship between the two. For example, although lowering the voltage can reduce the amplitude to achieve slight vibration, it will also cause the vibration frequency to decrease, making the vibration effect appear less delicate. Conversely, although increasing the voltage can increase the amplitude to achieve strong vibration, the increase in frequency may make the vibration effect too intense, even beyond the player's comfort range.

[0003] In addition, the response speed and stability of the vibration motor are also urgent issues to be solved. When the game scene switches quickly, the vibration motor needs to quickly adjust the amplitude and frequency, but the inertia of the motor will cause a response delay, making the vibration effect out of sync with the game screen. This asynchrony not only affects the immersion of the game, but may also cause players to misjudge the game operation. At the same time, after working for a long time, the performance of the vibration motor will gradually decline due to heat and wear, further affecting the stability and accuracy of the vibration effect.

[0004] Another technical difficulty is how to achieve more precise vibration control without increasing hardware costs. Most existing vibration motors use a simple voltage regulation method, which makes it difficult to achieve complex vibration effects. Although the vibration effect can be improved by increasing the number of motors or improving the motor structure, this will increase the weight and cost of the handle and affect the player's experience. Therefore, how to achieve more precise vibration control by optimizing the control algorithm and circuit design based on the existing hardware has become a technical problem that needs to be solved urgently. 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 in this way: A method for intelligently controlling vibration effects of a game controller comprises the following steps: Using the preset vibration effect parameter library, 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 a preset wear compensation model to compensate for the performance degradation caused by wear.

[0007] Furthermore, the process of obtaining the vibration mode parameters corresponding to the current game scene includes: Extract vibration effect data through the vibration effect parameter library, combine it with the scene information in the game scene library, and 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, obtain the vibration mode parameter value; If the matching fails, the default vibration mode parameter value is used.

[0008] Furthermore, the process of generating the motor drive signal includes: Obtain the initial data required for generating 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 lower than a preset threshold. If so, call the segmented voltage regulation algorithm to generate a driving 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.

[0009] Furthermore, 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 get 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.

[0010] Furthermore, the process of real-time monitoring of the motor operating temperature includes: Use a temperature sensor to periodically obtain the motor temperature value, compare the temperature value with a preset threshold, and if the temperature value exceeds the threshold, obtain the current motor power value; 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 a 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.

[0011] Furthermore, 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; According to the accumulated value of the running time, the wear degree is determined by using a preset wear compensation model; Calculate the performance attenuation value based on the degree of wear; Determine the compensation value according to the performance attenuation value; According to the compensation value, an adjustment value is generated by using an adjustment algorithm; According to the adjustment value, the driving signal is updated.

[0012] Furthermore, it 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.

[0013] Furthermore, the process of predicting the optimal vibration parameter combination includes: Get the current operating status data of the hardware device, including vibration frequency, amplitude and duration; Adopt 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; For the training data set, the back propagation algorithm is used to optimize the network weights and improve the prediction accuracy; According to the optimized model, the current operating status data is input to predict the best vibration parameter combination; Use the prediction results to generate new control instructions to adjust the vibration output of the hardware device.

[0014] Furthermore, the process of real-time monitoring the deviation between the vibration parameters and the expected effect and triggering the adaptive adjustment mechanism includes: Obtain the raw data of the vibration sensor, perform data preprocessing 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 according to the deviation direction and magnitude; Use machine learning algorithms to analyze historical adjustment data, combine 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.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention generates a motor drive signal through PWM modulation technology, and combines it with a segmented voltage regulation algorithm to keep the frequency stable when reducing the amplitude, thereby achieving a slight vibration effect. At the same time, when strong vibration is required, the frequency compensation algorithm is called to ensure that the frequency remains stable when the voltage increases to avoid excessive vibration. By predicting the optimal vibration parameter combination, the amplitude and frequency are dynamically adjusted to achieve more precise vibration control; 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 ensured to be synchronized with the game screen to avoid the loss of immersion caused by delay; 3. Use temperature sensors to monitor the motor's operating temperature in real time, and adjust the motor's power output through a dynamic power limiting algorithm to reduce the motor's operating temperature. According to the accumulated value of the motor's operating time, dynamically adjust the drive signal to compensate for performance degradation caused by wear and ensure the stability and accuracy of the vibration effect. 4. By optimizing the vibration control algorithm and using FPGA to achieve multi-channel parallel processing, more sophisticated vibration control can be achieved without increasing the number of motors or complex structures. Based on the vibration effect evaluation index, the deviation between the vibration parameters and the expected effect is monitored in real time, and the adjustment parameters are optimized through machine learning algorithms to further improve the vibration effect. 5. The present invention also establishes a vibration mode mapping relationship through a preset vibration effect parameter library and a game scene library, which can quickly match corresponding vibration mode parameters according to different game scenes, and combine with an adaptive adjustment mechanism to adjust the vibration effect in real time to adapt to rapidly changing game scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flowchart of the steps of a method for intelligently controlling vibration effects of a game controller in Example 1; 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

[0017] In order to make the purpose, 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 creative work are within the scope of protection of the present invention.

[0018] Example 1 like Figure 1 As shown, this embodiment provides a method for intelligently controlling vibration effects of a game controller, comprising the following steps: Using the preset vibration effect parameter library, 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 a preset wear compensation model to compensate for the performance degradation caused by wear.

[0019] Furthermore, the process of obtaining the vibration mode parameters corresponding to the current game scene includes: Extract vibration effect data through the vibration effect parameter library, combine it with the scene information in the game scene library, and 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, obtain the vibration mode parameter value; If the matching fails, the default vibration mode parameter value is used; Input the obtained vibration mode parameter value into the vibration effect generation module to generate vibration effect data, drive the vibration device through the vibration effect data, realize the vibration effect feedback of the game scene, optimize the parameter value in the vibration effect parameter library according to the vibration effect feedback data, and update the mapping relationship; In one embodiment, a preset vibration effect parameter library is first 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; Combine the scene information in the game scene library, such as battle scenes, exploration scenes and plot scenes, to establish a vibration mode mapping relationship, map the battle scene to a parameter combination of high frequency and high amplitude, map the exploration scene to a parameter combination of medium frequency and medium amplitude, and map the plot scene to a parameter combination of low frequency and low amplitude; 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 frequency is 150Hz and the amplitude is 0.4mm. If the matching fails, for example, the scene information is missing or abnormal, the default vibration mode parameter values ​​are used, with a frequency of 100Hz and an amplitude of 0.3mm. Input the obtained vibration mode parameter value into the vibration effect generation module to generate vibration effect data, for example, generate vibration waveform data with a frequency of 150 Hz and an amplitude of 0.4 mm; Drive the vibration device through the 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 the vibration effect; According to the vibration effect feedback data, such as the actual frequency and amplitude data collected by the vibration sensor, the parameter values ​​in the vibration effect parameter library are optimized. If the actual frequency is 145 Hz, the frequency value of the battle scene in the parameter library is adjusted to 145 Hz. Update the mapping relationship, for example, update the frequency value of the battle scene to 145Hz to ensure the accuracy of subsequent matching.

[0020] Furthermore, the process of generating the motor drive signal includes: Obtain the initial data required for generating 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 lower than a preset threshold. If so, call the segmented voltage regulation algorithm to generate a driving 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; While increasing the voltage, the vibration frequency is maintained within a comfortable range through the reverse frequency compensation mechanism; In one embodiment, according to preset vibration mode parameters, such as a frequency of 50 Hz and an amplitude of 0.5 mm, initial data required for generating a motor drive signal is obtained; The initial data is processed using PWM modulation technology, and the duty cycle is set to 30% to obtain the motor drive signal; 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 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 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.

[0021] Furthermore, 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 get 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; 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 there is a delay, the delay time is calculated. The delay time is predicted by the Kalman filter algorithm to obtain the predicted value of the motor state. According to the predicted value of the motor state, the generation parameters of the drive signal are adjusted to optimize the output of the drive signal; Use PWM modulation technology to process the optimized drive signal and generate a preliminary drive signal. Determine whether the vibration intensity is lower than the preset threshold. If it is lower than the preset threshold, call the segmented voltage regulation algorithm to generate a drive signal with stable frequency and reduced amplitude. If the vibration intensity is higher than the preset threshold, call the frequency compensation algorithm to generate a drive signal with increased voltage and stable frequency. 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; In one embodiment, a Kalman filter algorithm is used to process the motor state data, and an estimated value of the current state of the motor is obtained by combining the current motor speed and acceleration data through the state equation and the observation equation, for example, the motor speed is estimated to be 1200 rpm and the acceleration is 5 m / s²; Based on the estimated value of the motor state, determine whether the 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. According to 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; The optimized drive signal is processed by PWM modulation technology to generate a preliminary drive signal with a duty cycle of 30%. It is determined whether the vibration intensity is lower than the preset threshold. If the vibration intensity is lower than 0.5 g, the segmented voltage regulation algorithm is called to reduce the voltage from 5 V to 3 V to generate a drive signal with stable frequency and reduced amplitude. If the vibration intensity is higher than 0.5 g, the frequency compensation algorithm is called to increase the voltage from 5 V to 7 V to generate a drive signal with increased voltage and stable frequency. A vibration effect is generated according to the adjusted driving signal, and it is determined whether the vibration effect is synchronized with the game screen. If not, the driving signal is readjusted and the above steps are repeated.

[0022] Furthermore, the process of real-time monitoring of the motor operating temperature includes: Use a temperature sensor to periodically obtain the motor temperature value, compare the temperature value with a preset threshold, and if the temperature value exceeds the threshold, obtain the current motor power value; 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 a new power output value; Update the motor power output value by adjusting the value, reduce the motor operating temperature value, and determine whether the temperature value is reduced below the threshold; Specifically, a temperature sensor is used to collect the working temperature data of the motor to obtain a real-time temperature value. According to a preset temperature threshold, it is determined whether the real-time temperature value exceeds the threshold. If it exceeds the threshold, a dynamic power limiting algorithm is triggered to obtain the vibration parameters of the current vibration device, including frequency, amplitude and timing data. The vibration effect evaluation model is used to analyze the current vibration parameters to determine whether the vibration effect meets the expected range. If the vibration effect meets the expected range, the preliminary power adjustment parameters are generated according to the dynamic power limitation algorithm. The historical adjustment data is analyzed and the preliminary power adjustment parameters are optimized in combination with the current vibration parameters to obtain the 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. 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. In one embodiment, a temperature sensor is used to collect the working temperature data of the motor, and the real-time temperature value is obtained to be 75°C. According to the preset temperature threshold of 80°C, it is determined whether the real-time temperature value exceeds the threshold. If it exceeds the threshold, the dynamic power limiting algorithm is triggered; Obtain the vibration parameters of the current vibration equipment, including frequency 50Hz, amplitude 2mm and time series data, and use the vibration effect evaluation model to analyze the current vibration parameters to determine whether the vibration effect meets the expected range. If the model output deviation value is 5%, it is within the allowable range. If the vibration effect meets the expected range, generate preliminary power adjustment parameters according to the dynamic power limiting algorithm to reduce the power to 80%; The machine learning algorithm is used to analyze the historical adjustment data, combined with the current vibration parameters, to optimize the preliminary power adjustment parameters, and the dynamic power adjustment parameters are obtained to reduce the power to 75%. 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 with a frequency of 50Hz and an amplitude of 1.8mm; Monitor the adjusted vibration parameters in real time to determine whether they meet the expected effect range. If the deviation value is 8%, which exceeds the allowable range, restart the dynamic power limit algorithm. Update the vibration effect evaluation model based on multiple adjustment results, continuously optimize the parameter generation logic of the dynamic power limit algorithm, and adjust the deviation threshold to 6%.

[0023] Furthermore, 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 value of the running time, the wear degree is determined by using a preset wear compensation model; Calculate the performance attenuation value based on the degree of wear; Determine the compensation value according to the performance attenuation value; According to the compensation value, an adjustment value is generated by using an adjustment algorithm; According to the adjustment value, the driving signal is updated.

[0024] Specifically, a time sensor is used to periodically obtain the running time of the motor to obtain a cumulative running time value, a preset wear compensation model is used to determine the degree of wear according to the cumulative running time value, a performance attenuation value is calculated according to the degree of wear, a compensation value is determined according to the performance attenuation value, an adjustment algorithm is used to generate an adjustment value according to the compensation value, and a drive signal is updated according to the adjustment value; 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. According to 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. According to the wear degree of 0.75, combined with the motor performance attenuation curve formula, the performance attenuation value is calculated to be 15%. According to the performance attenuation value of 15%, a proportional compensation formula is used to determine that the compensation value is 1.2 times. According to the compensation value of 1.2 times, a PID adjustment algorithm is used to generate an adjustment value of 1200 rpm. According to the adjustment value of 1200 rpm, the drive signal is updated to a pulse width modulation signal of 1200 rpm.

[0025] Furthermore, it 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.

[0026] Furthermore, the process of predicting the optimal vibration parameter combination includes: Get the current operating status data of the hardware device, including vibration frequency, amplitude and duration; Adopt 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; For the training data set, the back propagation algorithm is used to optimize the network weights and improve the prediction accuracy; According to the optimized model, the current operating status data is input to predict the best vibration parameter combination; Use the prediction results to generate new control instructions to adjust the vibration output of the hardware device; Specifically, the current operating status data of the hardware equipment is obtained, including vibration frequency, amplitude and duration, and the vibration parameter combination and corresponding effects are extracted from the historical data to construct a training data set; a deep learning network architecture is used to build an optimization model for the vibration control algorithm; 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; According to the optimized model, the current operating status data is input to predict the best vibration parameter combination, and the prediction results are used to generate new control instructions to adjust the vibration output of the hardware equipment.

[0027] 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}; A deep learning network architecture was constructed, using a multi-layer perceptron model. The number of nodes in the input layer was 3 (corresponding to frequency, amplitude, and duration), the hidden layer used the ReLU activation function, and the output layer had 1 node (predicted effect score). The network weights were optimized using the stochastic gradient descent method and the back propagation algorithm. The learning rate was set to 0.01, the number of iterations was 1000, and the mean square error of the final model on the validation set was reduced to below 0.05. 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]. The control instructions are generated according to the prediction results to adjust the output parameters of the vibration equipment. The training data set is updated every 24 hours, and the newly added vibration parameters and effect scores are added to the database. The deep learning model is retrained, and vibration effect evaluation indicators such as stability index are set. The deviation between the current vibration parameters and the expected effect is calculated in real time. If the deviation exceeds the preset threshold of 0.2, the adaptive adjustment mechanism is triggered. The adaptive adjustment mechanism generates new control parameters based on the current deviation direction and amplitude using a genetic algorithm, 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.

[0028] 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}; A deep learning network architecture was constructed, using a multi-layer perceptron model. The number of nodes in the input layer was 3 (corresponding to frequency, amplitude, and duration), the hidden layer used the ReLU activation function, and the output layer had 1 node (predicted effect score). The network weights were optimized using the stochastic gradient descent method and the back propagation algorithm. The learning rate was set to 0.01, the number of iterations was 1000, and the mean square error of the final model on the validation set was reduced to below 0.05. 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]. The control instructions are generated according to the prediction results to adjust the output parameters of the vibration equipment. The training data set is updated every 24 hours, and the newly added vibration parameters and effect scores are added to the database. The deep learning model is retrained, and vibration effect evaluation indicators such as stability index are set. The deviation between the current vibration parameters and the expected effect is calculated in real time. If the deviation exceeds the preset threshold of 0.2, the adaptive adjustment mechanism is triggered. The adaptive adjustment mechanism generates new control parameters based on the current deviation direction and amplitude using a genetic algorithm, 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.

[0029] Example 2 like Figure 2 As shown, this embodiment provides a game controller vibration effect intelligent control system, 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; The vibration effect generation module generates a corresponding vibration signal according to the needs of the game scene; The vibration effect generation module generates a motor drive signal 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.

[0030] The motor driving and control module converts the signal generated by the vibration effect generating module into an actual driving signal of the motor; 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 a slight vibration is required, the module will call the segmented voltage regulation algorithm to generate a drive signal with a stable frequency and reduced amplitude; when a strong vibration is required, the frequency compensation algorithm will be called to ensure that the vibration effect is both strong and delicate.

[0031] 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 intelligent optimization and adaptive adjustment module trains the network model through historical data, predicts the best 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.

[0032] The motor state monitoring and compensation module monitors the working state of the motor in real time, including temperature, operating time and wear. Based on the accumulated value of the operating time, the drive signal is dynamically adjusted through a preset wear compensation model to compensate for the performance degradation caused by wear. The motor state monitoring and compensation module obtains the motor temperature through the temperature sensor, and reduces the motor power through the dynamic power limitation algorithm when the temperature exceeds the threshold. At the same time, the motor state monitoring and compensation module dynamically adjusts the drive signal based on the accumulated running time through the preset wear compensation model to compensate for the performance degradation caused by wear.

[0033] The vibration effect generation module calls the preset vibration effect parameter library according to the game scene requirements, and generates the motor drive signal in combination with the PWM modulation technology to achieve a variety of vibration effects from mild to strong, and optimizes the clarity and fineness of the vibration signal through intelligent algorithms. The motor drive and control module receives the generated signal, and accurately controls the motor through PWM modulation technology, supporting segmented voltage regulation and frequency compensation algorithms to ensure the stability and accuracy of the vibration effect; 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.

[0034] The specific embodiments of the invention are described in detail above, but they are only examples. 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 only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached 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: Using the preset vibration effect parameter library, 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 a preset wear compensation model to compensate for the performance degradation caused by wear.

2. The method for intelligently controlling vibration effects of a game controller according to claim 1, characterized in that: The process of obtaining the vibration mode parameters corresponding to the current game scene includes: Extract vibration effect data through the vibration effect parameter library, combine it with the scene information in the game scene library, and 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, obtain 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, characterized in that: The process of generating the motor drive signal includes: Obtain the initial data required for generating 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 lower than a preset threshold. If so, call the segmented voltage regulation algorithm to generate a driving 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.

4. The method for intelligently controlling vibration effects of a game controller according to claim 1, characterized in that: 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 get 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.

5. The method for intelligently controlling vibration effects of a game controller according to claim 1, characterized in that: The process of real-time monitoring of the motor operating temperature includes: Use a temperature sensor to periodically obtain the motor temperature value, compare the temperature value with a preset threshold, and if the temperature value exceeds the threshold, obtain the current motor power value; According to the motor power value and the preset limit value, the adjustment value is calculated to obtain a 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.

6. The method for intelligently controlling vibration effects of a game controller according to claim 1, characterized in that: 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; According to the accumulated value of the running time, the wear degree is determined by using a preset wear compensation model; Calculate the performance attenuation value based on the degree of wear; 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.

7. The method for intelligently controlling vibration effects of a game controller according to claim 1, characterized in that: 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.

8. The method for intelligently controlling vibration effects of a game controller according to claim 1, characterized in that: The process of predicting the best vibration parameter combination includes: Get the current operating status data of the hardware device, including vibration frequency, amplitude and duration; Adopt 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, the current operating status data is input to predict the best vibration parameter combination; Use the prediction results to generate new control instructions to adjust the vibration output of the hardware device.

9. The method for intelligently controlling vibration effects of a game controller according to claim 1, characterized in that: 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 of the vibration sensor, perform data preprocessing 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 according to 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.

10. An intelligent control system for vibration effects of a game controller, 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 of 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 value of the operating time, the drive signal is dynamically adjusted through a preset wear compensation model to compensate for the performance degradation caused by wear.

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