Handle device calibration method, apparatus, device, and storage medium

Abstract

This invention relates to the field of gamepad calibration, and more particularly to a method, apparatus, device, and storage medium for calibrating gamepad devices. The method includes the following steps: performing a process-level deep scan of the game program and conducting a comprehensive assessment of control difficulty to generate a scene control difficulty coefficient; calculating adaptive sensitivity based on the scene control difficulty coefficient to obtain an adaptive sensitivity coefficient; extracting gamepad operation logs, analyzing player control styles, and constructing a personalized control profile; performing dynamic configuration calibration based on the personalized control profile and the adaptive sensitivity coefficient, and monitoring and optimizing the calibration effect to construct a dynamic device calibration mechanism. This invention, based on analysis of different game scenarios, dynamically and accurately calibrates gamepad control parameters, improving user experience and adaptability to various games.

Description

Technical Field

[0001] This invention relates to the field of handle calibration, and more particularly to a method, apparatus, device, and storage medium for calibrating handle devices. Background Technology

[0002] In practical applications, different types of games (such as first-person shooters, racing games, action-adventure games, and simulation games) have significant differences in the sensitivity and control logic of gamepad operation. Even within the same game, different mission scenarios (such as sniper mode, melee mode, and driving mode) require different control parameters to ensure the accuracy and comfort of operation. However, existing gamepad control parameter settings are mostly static presets or manual adjustments, which cannot dynamically adapt to real-time changes in the game scenario, greatly limiting the player's immersion and operational flexibility.

[0003] Individual player differences are also a significant factor affecting the performance of a gamepad. Different players have significant variations in their control methods, hand shapes, and button frequencies, resulting in different perceptions of sensitivity and control feedback. Traditional standardized parameter configurations struggle to meet personalized and diverse operational needs, often leading to insufficient control precision or oversensitivity, thus impacting the gaming experience. Current mainstream gamepads typically lack intelligent awareness of game context, failing to effectively identify the current game scene and player control status, and unable to automatically adjust parameters or optimize performance during gameplay. Even some high-end devices with customization features usually rely on manual settings, preset modes, or limited scene recognition capabilities, lacking real-time performance and a high degree of intelligence. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method, apparatus, device, and storage medium for calibrating a handle device, thereby resolving at least one of the aforementioned technical problems.

[0005] To achieve the above objectives, the present invention provides a method for calibrating a handle device, comprising the following steps:

[0006] Step S1: Perform a process-level deep scan of the game program and conduct a comprehensive assessment of the control difficulty to generate a scene control difficulty coefficient;

[0007] Step S2: Calculate the adaptive sensitivity based on the scene control difficulty coefficient to obtain the adaptive sensitivity coefficient;

[0008] Step S3: Extract the controller operation log, analyze the player's control style, and build a personalized control profile;

[0009] Step S4: Perform dynamic configuration calibration based on personalized control profile and adaptive sensitivity coefficient, monitor and optimize calibration effect, and build a dynamic calibration mechanism for the device.

[0010] This specification provides a handheld device calibration apparatus for performing the handheld device calibration method described above, comprising:

[0011] The scene evaluation module is used to perform a process-level deep scan of the game program and conduct a comprehensive evaluation of the control difficulty to generate a scene control difficulty coefficient.

[0012] The sensitivity calculation module is used to perform adaptive sensitivity calculation based on the scene control difficulty coefficient to obtain the adaptive sensitivity coefficient.

[0013] The control style module is used to extract gamepad operation logs, analyze player control styles, and build personalized control profiles.

[0014] The dynamic calibration module is used to perform dynamic configuration calibration based on personalized control profiles and adaptive sensitivity coefficients, and to monitor and optimize the calibration effect, thus building a dynamic calibration mechanism for the equipment.

[0015] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the handheld device calibration method described in any of the preceding claims.

[0016] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the handheld device calibration method described in any of the preceding claims.

[0017] The specific benefits of this invention are as follows: It enables real-time identification and hierarchical processing of different game content (or different stages within the game), providing accurate context for subsequent sensitivity adjustments. It precisely quantifies the complexity of game controls, avoiding the "one-size-fits-all" approach of traditional gamepad settings that leads to poor operational experience in different games or levels. It improves the interaction and perception capabilities between the device and game layers, laying the foundation for intelligent adjustment. It avoids overly sensitive or sluggish operation, automatically reducing sensitivity in high-precision scenarios (such as sniping) and automatically increasing sensitivity in fast-paced scenarios (such as racing). It improves the consistency and controllability of operational response, reducing the adaptation cost for players in different games or scenarios. It reduces the burden of players frequently manually adjusting device settings, achieving a "plug-and-play, automatic adaptation" experience. It implements a "personally tailored" adjustment logic for device settings, better aligning with players' long-term operating habits. It provides differentiated experiences for different players (such as those who prefer precise controls vs. those who prefer fast-paced sprints), improving the accuracy of personalized adaptation. It supports cross-game adaptation migration (even when changing games, it can quickly restore the familiar control style). A dynamic, closed-loop optimization mechanism is implemented throughout the entire process, automatically identifying and correcting operational incompatibilities. This enhances the system's adaptability and self-evolution capabilities, allowing for appropriate adjustments as player skills change. It avoids the problem of "long-term failure after a single calibration," ensuring the device is always in the most suitable state for the player and the scenario. Ultimately, this builds an intelligent, highly adaptable, and low-intervention device control ecosystem, significantly improving user satisfaction and usage time. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the steps of a handheld device calibration method according to the present invention;

[0019] Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1.

[0020] Figure 3 This is a detailed flowchart illustrating the implementation steps of step S2;

[0021] Figure 4 This is a flowchart illustrating the detailed implementation steps of step S3. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0023] This application provides a method, apparatus, device, and storage medium for calibrating a gamepad device. The execution entities of the gamepad device calibration method, apparatus, device, and storage medium include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered as general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio / image management system, an information management system, and a cloud data management system.

[0024] Please see Figures 1 to 4 This invention provides a method for calibrating a handheld device, comprising the following steps:

[0025] Step S1: Perform a process-level deep scan of the game program and conduct a comprehensive assessment of the control difficulty to generate a scene control difficulty coefficient;

[0026] Step S2: Calculate the adaptive sensitivity based on the scene control difficulty coefficient to obtain the adaptive sensitivity coefficient;

[0027] Step S3: Extract the controller operation log, analyze the player's control style, and build a personalized control profile;

[0028] Step S4: Perform dynamic configuration calibration based on personalized control profile and adaptive sensitivity coefficient, monitor and optimize calibration effect, and build a dynamic calibration mechanism for the device.

[0029] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of a handheld device calibration method according to the present invention. In this example, the steps of the handheld device calibration method include:

[0030] Step S1: Perform a process-level deep scan of the game program and conduct a comprehensive assessment of the control difficulty to generate a scene control difficulty coefficient;

[0031] In this embodiment, all running processes are scanned using the Windows API functions EnumProcesses() and GetModuleFileNameEx() to identify currently active game processes. The game type is initially categorized based on process name, executable file path, and digital signature information. Next, DirectX Hook technology is used for deep monitoring of the game's graphics rendering pipeline. Real-time game frame data is obtained by intercepting the Present() function call, with a sampling frequency set to 60 FPS to ensure timely capture of scene changes. Computer vision analysis is then performed on the acquired game footage, using the YOLO v5 object detection algorithm to identify key objects such as UI elements, hostile targets, and interactive objects. The detection accuracy threshold is set to 0.85 to balance accuracy and processing efficiency.

[0032] Subsequently, the identified scene elements were subjected to complex quantitative evaluation, establishing an evaluation model encompassing four dimensions: target density, movement speed variance, visual focus dispersion, and interaction frequency. Target density was calculated by the number of detected objects per unit area, with a threshold range of 0-20 objects per screen. Movement speed variance was quantified by the standard deviation of target displacement changes between consecutive frames, ranging from 0-500 pixels per frame. Visual focus dispersion was calculated using the entropy method to determine the dispersion of attention distribution, with a value range of 0-5. Interaction frequency was determined by analyzing the average number of operations per unit time in historical operation logs, ranging from 0-15 operations per second. Finally, the four dimensions were fused using a weighted average method, with weights set to 0.3, 0.25, 0.2, and 0.25 respectively, generating a comprehensive scene control difficulty coefficient. The value range was standardized to 0-1, where above 0.8 was defined as high-difficulty scenes, 0.4-0.8 as medium-difficulty scenes, and below 0.4 as low-difficulty scenes.

[0033] Step S2: Calculate the adaptive sensitivity based on the scene control difficulty coefficient to obtain the adaptive sensitivity coefficient;

[0034] In this embodiment, a sensitivity adaptive calculation model is established based on the obtained scene control difficulty coefficient. This model adopts a piecewise function design to adapt to the differences in control requirements in different difficulty ranges. For low-difficulty scenes (coefficient ≤ 0.4), the sensitivity adjustment adopts a linear gain function S = 1.2 + 0.5 × D, where S is the sensitivity multiple and D is the difficulty coefficient. This setting aims to improve the control response speed in low-difficulty scenes. For medium-difficulty scenes (0.4 < coefficient ≤ 0.8), the square root function S = 1.0 + 0.3 × √D is used for adjustment to maintain a smooth transition in sensitivity. For high-difficulty scenes (coefficient > 0.8), the logarithmic function S = 0.8 + 0.2 × log(1 + D) is used to achieve fine control of sensitivity and prevent control errors caused by oversensitivity.

[0035] In the specific calculation process, the differentiated needs of different game types also need to be considered, and a game type correction coefficient library needs to be established. The correction coefficient for FPS games is set to 1.15, emphasizing the importance of quick aiming; the correction coefficient for RTS games is 0.85, focusing on precise selection; the correction coefficient for RPG games is 1.0, maintaining standard response; and the correction coefficient for racing games is 1.3, highlighting the need for rapid reaction. The final adaptive sensitivity coefficient is calculated using the formula F_final = S × K × A, where K is the game type correction coefficient and A is the personal adaptive adjustment factor (initial value set to 1.0).

[0036] To verify the accuracy of the calculations, a sliding window technique was used to evaluate the effect of sensitivity adjustments over the past 30 seconds. The effectiveness of the calculation results was verified by analyzing the degree of improvement in player control accuracy (improved target hit rate, smoothness of control trajectory, etc.). When the deviation between the calculation results and actual needs exceeded 15%, a parameter correction mechanism was automatically triggered to optimize the parameter configuration of the calculation model through feedback learning.

[0037] Step S3: Extract the controller operation log, analyze the player's control style, and build a personalized control profile;

[0038] In this embodiment, the player's controller operation data is continuously collected through the underlying driver interface to establish a detailed operation log database containing fields such as timestamp, operation type, input value, and duration. The data collection frequency is set to 1000Hz to ensure accurate recording of micro-operations. A rolling storage mechanism is also implemented to retain approximately 604,800 operation records from the most recent 7 days, ensuring the timeliness and representativeness of the data. The collected raw data is preprocessed to remove obvious outliers (such as data where a single input value exceeds three times the normal range) and accidental touches. The data cleaning rate is controlled within 5% to maintain the integrity of the sample.

[0039] In the control style analysis phase, player behavior is quantitatively evaluated from multiple dimensions. Joystick force analysis involves statistically analyzing the distribution of input amplitude across different actions, calculating the average force value, force variance, and force change gradient. The average force value ranges from 0-100%, the variance from 0-25, and the change gradient is expressed as the force change rate per millisecond, ranging from 0-5% / ms. Operation frequency analysis involves statistically analyzing the distribution of the number of actions performed by the player per unit of time, calculating the operation density index, with a normal range set at 2-12 actions / second. Control accuracy analysis involves comparing the deviation between the player's intended action and the actual execution result, calculating the control accuracy index. An excellent level is defined as a deviation of less than 5%, a good level as 5-15%, and a level requiring improvement as greater than 15%.

[0040] Based on the above analysis, the K-means clustering algorithm was used to categorize player control styles into five typical types: Precise (high precision + low frequency, approximately 25% of all players), Aggressive (high frequency + medium precision, approximately 30%), Steady (medium frequency + high precision, approximately 20%), Random (low precision + low frequency, approximately 15%), and Skillful (high frequency + high precision, approximately 10%). Each category was configured with corresponding parameter optimization strategies. For example, precise players are suited to lower sensitivity settings to leverage their precise control advantages, while aggressive players require higher response speed settings. This ultimately resulted in personalized control profiles encompassing control type, skill level, preferred parameters, and adaptability, providing precise individual characteristic data support for subsequent dynamic calibration.

[0041] Step S4: Perform dynamic configuration calibration based on personalized control profile and adaptive sensitivity coefficient, monitor and optimize calibration effect, and build a dynamic calibration mechanism for the device.

[0042] In this embodiment, a calibration objective function F(x) = α×P + β×R + γ×C is constructed, where P represents the improvement in control precision, R represents the optimization of response speed, and C represents the user comfort score. The weight parameters α, β, and γ are set to 0.4, 0.3, and 0.3, respectively. Historical data verification shows that this weight configuration can achieve the best comprehensive calibration effect. During the specific calibration process, core parameters such as joystick dead zone, sensitivity curve, button trigger threshold, and vibration feedback intensity are adjusted in real time.

[0043] The joystick deadband parameters are customized based on the player's control style. Precision players set the deadband to 5-8% to avoid accidental touches, aggressive players reduce the deadband to 2-4% to improve responsiveness, and conservative players maintain the standard 6-7% setting. The sensitivity curve uses a three-segment design: a linear response in the low input range (0-30%) to ensure precise control, a slightly exponential function in the medium input range (30-70%) to enhance response speed, and a restrictive function in the high input range (70-100%) to prevent overreaction. The button trigger threshold is adjusted according to the player's preferred pressing force: 30-50% for players with light pressure, 50-70% for players with moderate pressure, and 70-90% for players with heavy pressure.

[0044] The calibration effect monitoring employs a multi-level evaluation system, including objective performance indicators and subjective experience evaluation. Objective indicators are quantified by analyzing changes in player control performance before and after calibration, primarily monitoring improvements in target hit rate (expected improvement of 10-25%), smoothness of control trajectory (deviation reduction of 15-30%), and reduction in operation latency (expected reduction of 5-15ms). Subjective experience evaluation is obtained through implicit feedback collection techniques, including analyzing indirect indicators such as changes in player hesitation time, frequency of repeated operations, and game smoothness.

[0045] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0046] Perform a process-level deep scan of the game program to extract basic game information; based on the basic game information, hook the underlying API functions to obtain the game engine type and rendering pipeline architecture;

[0047] Game type classification and identification are performed based on game engine type to obtain game type; rendering call distribution analysis is performed based on rendering pipeline architecture to generate rendering screen call parameters;

[0048] Based on basic game information, real-time game pages are extracted, game scenes are analyzed, and scene elements are marked;

[0049] Interactively identify scene elements, calculate element density and spatial distribution, and generate scene element characteristics;

[0050] The scene complexity is calculated based on the parameters called in the rendered image and the characteristics of scene elements.

[0051] Based on the game type, a comprehensive assessment of the difficulty of controlling the scene complexity is conducted to generate a scene control difficulty coefficient.

[0052] In this embodiment, process-level deep scanning is the initial and fundamental operation in the entire process. Its main purpose is to accurately obtain the memory structure, module loading information, and API call interfaces exposed by the game during runtime. Specifically, this is typically achieved by hooking the target process using an injection debugger (such as the Debug API on the Windows platform or the ptrace mechanism under Linux) and reading its process space's dynamic library loading table, thread stack information, and graphics card driver interface call details. To avoid impacting game operation, the scanning process employs a phased strategy: first, the main loop entry point is captured during the initial game loading phase, and then memory segments are periodically sampled during the stable game operation phase. Setting sampling intervals (e.g., 50ms) and memory segment size limits (e.g., no more than 64MB per scan) ensures that performance is not significantly slowed down. The extracted basic information includes, but is not limited to: the running platform, CPU / GPU usage, API call table, memory resource distribution, and resource loading paths such as textures / audio. This data forms the basis for subsequent identification of the game engine, rendering architecture, and scene analysis. Key low-level functions are hooked to identify the game's engine and rendering pipeline mode. Hooking typically employs dynamic function substitution techniques (such as Detours, Frida, or LD_PRELOAD) to intercept rendering-related APIs (e.g., DirectX's Present, OpenGL's SwapBuffers, Vulkan's vkQueueSubmit) and record their call parameters. By analyzing these parameters, the engine type can be inferred, such as the common resource management patterns of the Unity engine, the rendering frame synchronization logic unique to the Unreal Engine, and the deferred rendering architecture of CryEngine. To reduce false positives, a "feature fingerprint library" is constructed in the experiment, containing API call features of over 200 common game engines; matching the collected function call trajectories achieves an accuracy rate of over 95%. Simultaneously, by monitoring the phased call distribution of the rendering pipeline (e.g., the call ratio of vertex shading, pixel shading, and geometry processing), the game's rendering mode (forward rendering, deferred rendering, Vulkan rendering based on pipeline state objects, etc.) can be further inferred. This process provides a core basis for subsequent rendering distribution analysis and complexity assessment.

[0053] Given the engine and rendering characteristics, a "game type discrimination model" can be constructed to determine the gameplay category of a game. Different engines tend to serve certain game types; for example, Unity is more suited to casual, mobile, or indie games, while Unreal Engine is commonly used for FPS, RPG, or high-fidelity 3D scenes. Specifically, engine features, API call patterns, and game runtime data (such as physics engine call frequency, network request structure, and input event distribution) are input into a trained classification model. In the experiment, a classifier combining random forests and deep neural networks was used. The training set included over 500 labeled games of different types (MOBA, FPS, RPG, ACT, simulation, etc.), and type discrimination was achieved through multi-feature weighting. The classification accuracy was between 80% and 90%, with errors mainly arising from hybrid games that merged different types. The obtained game type not only exists as a label but also affects the weight calculation method of the subsequent difficulty assessment model. For example, the same scene complexity may correspond to a much higher control difficulty in an FPS game than in a simulation game.

[0054] The system continuously intercepts rendering API calls, recording the number of calls, call order, video memory transfer size, and GPU time consumption for each frame. The sampling frequency is typically set to 60Hz (60 frames per second), and metrics such as vertex count, texture binding count, and shader switching count are recorded within each frame. For example, a deferred rendering FPS game might average 1500 DrawCalls and over 300 texture bindings per frame, while a 2D casual game might only have a few dozen calls. These parameters are compiled into a rendering call distribution table to reflect the game scene complexity and rendering bottlenecks. By establishing a distribution histogram and weighted averaging of parameters, a "rendering screen call parameter set" is formed, which is the core input for subsequent calculations of scene complexity. Real-time acquisition of the rendered screen is combined with screenshot interfaces (such as the DXGI Desktop Duplication API or GPU driver layer interception), and scene elements are then parsed and labeled using image segmentation and object detection algorithms. The experiment uses a hybrid detection framework of YOLOv7 and Mask-RCNN to identify characters, UI elements, and scene objects in the game. To enhance accuracy, secondary verification is performed using resource paths (such as texture filenames) from the basic information. The parsed results are output as a labeled graph, with each element accompanied by its category, location bounding box, and confidence score. For example, in an FPS scene, detected elements include enemy models, scene obstacles, and interactive props; in a simulation game, these might be UI controls, buildings, resource icons, etc. The key to this step is to correlate the underlying rendering data with high-level visual information, providing scene context for subsequent interaction recognition and complexity calculations.

[0055] Based on the correlation between input events and rendering events: when an element changes state after player input (such as a UI button being triggered, an enemy losing health, or an object shifting), it can be determined as an interactive element. In the experiment, the input signal and rendering frame changes are time-aligned, with the error range controlled within 16ms. Simultaneously, by calculating the spatial distribution density (number of elements / unit screen area) and relative distance distribution (average nearest neighbor distance) of scene elements, spatial structural features of the elements can be generated. For example, in a MOBA game, there are dozens of enemy and friendly units on the map, resulting in a high element density, while in an FPS scene, interactive targets are fewer but scattered. The resulting "scene element characteristics" include the number of elements, interaction frequency, and spatial clustering, which directly determine the operational complexity and the adjustment benchmark for controller parameters. Complexity = α × rendering load coefficient + β × interactive element coefficient, where α and β are weighting parameters, taken as 0.6 and 0.4 respectively in the experiment to favor the contribution of rendering performance to complexity. The rendering load coefficient is derived from the call parameters generated in step four, such as the number of DrawCalls, the number of texture bindings, and GPU time consumption; the interaction element coefficient is composed of the element density and interaction frequency from step six. To ensure numerical comparability, all indicators need to be normalized before calculation. Experiments show that in high-intensity FPS battle scenarios, the complexity index can be as high as 0.85 (in the range of 0 to 1), while in static interfaces of simulation management games, the complexity can be as low as 0.2. The scene complexity value not only measures the computational pressure on the screen but also intuitively reflects the cognitive and reaction burden required for player operation.

[0056] Scene complexity is converted into a "control difficulty coefficient" that can be used for controller calibration. Different types of games have different sensitivities to complexity, so a weighted adjustment is needed based on the game type. For example, in FPS games, complexity requires extremely high control precision, so the complexity index needs to be magnified to 1.2 times; while in simulation games, the control intensity is lower, and the complexity index can be reduced to 0.8 times. A mapping function was designed in the experiment to map the complexity value (0, 1) to the difficulty coefficient range (0.3, 1.5), ensuring that even in low-complexity scenes, a certain control difficulty benchmark can still be provided. The comprehensive evaluation results show that in a MOBA team battle scene, the control difficulty coefficient reaches 1.35, while in a single-player story RPG cutscene, the coefficient is only 0.4. The generated difficulty coefficient will be directly used to adjust the controller's sensitivity, vibration feedback, and force feedback parameters in real time to achieve scene adaptive control.

[0057] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0058] Based on game genre, perform genre-specific sensitivity analysis to obtain specific sensitivity data;

[0059] Based on dedicated sensitivity data, the difficulty coefficient of scene operation is analyzed to determine the joystick sensitivity requirements and obtain the sensitivity range for the current scene.

[0060] Identify the joystick response parameters of the controller;

[0061] Based on the joystick response parameters, an adaptive sensitivity matching calculation is performed on the sensitivity range, and dynamic sensitivity fine-tuning is carried out to obtain the adaptive sensitivity coefficient.

[0062] In this embodiment, after identifying the game type, different game categories exhibit significant differences in their sensitivity requirements. For example, FPS games demand extremely high precision in small-amplitude aiming movements, thus favoring a low sensitivity range to improve accuracy; while racing games tend to require a wide range and rapid joystick input response to ensure smooth steering. Therefore, the key first step is to construct a "type-specific sensitivity curve library." The specific implementation method is as follows: through large-scale player experiments and historical game data collection, the joystick input distribution and error tolerance of various games in actual operation are statistically analyzed. Fifty players of varying skill levels are recruited to perform 30 minutes of gameplay in each of the four major game categories: FPS, MOBA, racing, and RPG. Joystick displacement, input duration, and corresponding operational results (such as hit rate and steering angle deviation) are collected. By comparing the actual effects with the input amplitude, an "optimal sensitivity range" for each type is constructed. For example, in FPS games, the optimal sensitivity range is 0.2-0.5 (0-1 normalized interval), while in racing games, a range of 0.6-0.9 better suits the control requirements. The output dedicated sensitivity data is not only a range but also includes the shape characteristics of the response curve (linear, accelerated, buffered). This data directly provides a reference for subsequent sensitivity matching. The dedicated sensitivity curve obtained in the previous step is multiplied or mapped and corrected by the control difficulty coefficient. For example, when the control difficulty coefficient of an FPS scene is 1.2, the originally recommended sensitivity range of 0.2-0.5 will be linearly amplified to 0.2-4-0.6 to match high-intensity operational demands. Specifically, the "scene dynamic sensitivity extrapolation method" is used: different complexity scenes are selected in the same type of game (such as sniping scenes, close-quarters combat scenes, and open exploration scenes in FPS), and the player's performance at different sensitivities is tested, recording operational deviations and reaction speeds. The results show that there is an approximately linear relationship between the difficulty coefficient and sensitivity requirements, with a deviation of no more than ±0.05. Therefore, by adding the control difficulty coefficient as an adjustment factor to the type sensitivity curve during calculation, the "current scene sensitivity range" can be obtained. This range reflects the range of joystick sensitivity required by players in specific scenarios. For example, the range is higher (0.5-0.8) during MOBA team battles and lower (0.3-0.5) during story exploration.

[0063] After determining the theoretical sensitivity requirements, it's necessary to consider the inherent response characteristics of the controller hardware. Different brands, models, and even different batches of the same model may exhibit variations in joystick response curves and dead zone sizes. Identifying joystick response parameters involves obtaining the actual mapping relationship between hardware input and output through calibration experiments. Fixed-amplitude joystick displacements (e.g., 0.1, 0.2, 0.3 up to 1.0) are gradually applied, while simultaneously recording the values ​​returned by the controller's drive layer. By comparing the theoretical input with the actual output, a response curve can be plotted, and linearity deviations can be detected. Experiments revealed that some low-end controllers exhibit a large dead zone in the 0-0.2 range, while high-end controllers show non-linear enhancement, meaning an excessively fast response at medium to high displacements. Experimental parameters included: a sampling frequency of 1000Hz, a displacement step of 0.05, and repetitions of 10 times per increment to smooth for errors. The resulting joystick response parameters included: dead zone size, sensitivity amplification factor, and response curve function type. These parameters will serve as hardware correction factors for subsequent sensitivity matching calculations, ensuring that the theoretical sensitivity range matches the hardware characteristics, thereby avoiding the problem of "theoretical requirements being out of sync with actual operation".

[0064] By comparing the theoretical sensitivity curve with the hardware response curve, the minimum mean square error (MSE) method is used for fitting to find the optimal matching point. For example, if the theoretically recommended sensitivity is 0.3~0.6, but the hardware has a dead zone of 0.05 in the lower range, the curve needs to be shifted to the right to offset the dead zone effect. Secondly, a dynamic fine-tuning mechanism is introduced: during actual gameplay, player operation deviations (such as aiming deviation, over-input, and reaction delay) are continuously monitored. If the deviation exceeds a threshold consecutively (e.g., 10 operations with a deviation >15%), the sensitivity coefficient is automatically adjusted within ±0.05. Experiments show that dynamic fine-tuning can improve the player's hit rate by 8%~12% and reduce steering overshoot in racing games by 15%. The resulting adaptive sensitivity coefficient is a real-time updated value that considers scene complexity, game type requirements, and corrects for the controller's hardware characteristics, making small dynamic adjustments during operation to ensure the control experience is always optimal.

[0065] In this embodiment, reference Figure 4 The diagram below illustrates the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0066] Collect all historical controller operation data from players and extract controller operation logs;

[0067] Based on the controller operation log, manual device control parameters are identified to obtain player settings parameters in different scenarios;

[0068] Player control behavior is analyzed based on the controller operation log to generate multi-dimensional behavioral data, including joystick displacement trajectory, button timing, force changes and operation frequency.

[0069] Based on multi-dimensional behavioral data, the player's joystick force distribution characteristics, directional preferences, and operation rhythm patterns are identified to generate a player control mode.

[0070] We analyze the evolution of control styles based on player control patterns to construct personalized control profiles.

[0071] In this embodiment, during game runtime, raw signals from the controller input are captured via the driver layer interface or SDK API, including the X / Y displacement values ​​of the joystick (sampling frequency 100Hz~200Hz), button trigger and release timestamps, trigger pressure values, etc. To ensure the effectiveness of long-term analysis, the data collection is not limited to a single game but covers multiple time periods and scenarios to extract common patterns across scenarios. In the experimental design, operation data was collected from 30 players for two consecutive weeks, with an average game time of 1 hour per day, generating approximately 100MB of raw data per player. After collection, the data needs to be converted into a unified format "controller operation log," which includes fields such as timestamp, input type, input amplitude, and duration. To avoid noise interference, preprocessing is also required, such as zeroing out joystick jitter signals less than 0.02 and filtering out empty data from non-operation periods. The implicit control parameters of players in different scenarios are identified from the log. Different scenarios (such as FPS battles, MOBA team battles, racing, and RPG exploration) have significantly different requirements for sensitivity, response latency, and key combinations, and players often develop their own optimal settings through continuous experimentation. The method involves combining operation logs with scenario annotation information, using clustering and statistical analysis to identify parameter characteristics. For example, if a player in an FPS sniping scenario generally uses a joystick displacement of less than 0.3 and a reaction time within 150ms, it can be inferred that they prefer lower sensitivity settings; while in a racing scenario, the joystick is often pulled to its maximum of 0.9 or higher, indicating a preference for high sensitivity and fast response. The operation logs are categorized by scenario type using the K-means clustering algorithm to obtain the average sensitivity, key response latency, and trigger force range for each scenario. Taking 10 FPS players as an example, the identified average aiming sensitivity range is between 0.25 and 0.45, which highly matches their actual configuration in the manual settings menu (error less than ±0.05).

[0072] Through in-depth analysis of operation logs, behavioral data covering multiple dimensions was generated. Core dimensions include: ① Joystick displacement trajectory: recording the joystick's path curve on a two-dimensional plane, analyzing its smoothness, number of direction switches, and average amplitude; ② Button timing: extracting the time interval between button triggering and release, identifying common key combination sequences; ③ Force variation: statistically analyzing the distribution of pressing force and average response force using data from trigger buttons and pressure sensor buttons; ④ Operation frequency: counting the number of operations per unit time, reflecting the player's pace. Experimental parameters were set as follows: sampling frequency 120Hz, trajectory curve generation 120 points per second, button timing resolution 1ms, and force normalized to the 0-1 range. Data analysis of 20 FPS players revealed that expert players have shorter joystick trajectories with smaller fluctuations, while beginners exhibit larger swings; expert players' button timing is more regular, with an average combo interval controlled between 80-120ms, while beginners' fluctuations range from 50-300ms. The generated multi-dimensional behavioral data provides more granular feature information for identifying player habits and patterns.

[0073] Identification is achieved through force distribution characteristics: By statistically analyzing the probability density distribution of joystick displacement amplitude, the commonly used force range of players is obtained. For example, some players are accustomed to lightly pushing the joystick (0.1~0.3 range accounts for 70%), while others are accustomed to large movements (above 0.7 accounts for 50%). Secondly, directional preference analysis is performed: The trajectory data is unfolded in a polar coordinate system, and the frequency of use in each direction is calculated to identify whether players tend to operate diagonally or favor horizontal / vertical axis operations. Finally, the operation rhythm pattern is analyzed: Through Fourier transform or time series analysis, the dominant frequency value of operation frequency is extracted. For example, a player's operation rhythm is 2Hz in a MOBA scenario (one operation every 0.5 seconds), which increases to 5Hz in an FPS scenario. Experimental results show that different players have significantly different control patterns. For example, conservative players are characterized by small displacements and low rhythms, while aggressive players exhibit large displacements and high-frequency inputs.

[0074] Historical data is compared and analyzed over time to identify trends in player style. Specifically, this involves comparing control mode parameters of the same player at different times, such as whether the force distribution gradually shifts from 0.3 to 0.5, whether the tempo increases from 2Hz to 4Hz, and whether the directional preference changes from horizontal movement to diagonal movement. Tracking the operation data of 10 players for three consecutive months revealed that beginners often exhibit large movements and irregular rhythms, while as proficiency increases, these movements gradually converge to smaller movements and a more stable rhythm, reflecting the evolution of learning and skill. Based on these trends, a personalized control profile can be constructed, including tags such as "operational stability," "aggressiveness," "rhythm preference," and "directional selection." This profile not only assists in real-time sensitivity calibration but also plays a role in recommending game modes and optimizing controller hardware, achieving a more adaptive and personalized experience.

[0075] In this embodiment, step S4 includes the following steps:

[0076] Based on personalized control profiles and adaptive sensitivity coefficients, multi-objective balance analysis of the scene is performed to obtain the optimal scene control configuration;

[0077] Identify the current controller's configuration parameters;

[0078] The configuration parameters are identified based on the optimal scenario control configuration to obtain the real-time configuration deviation.

[0079] Dynamic configuration calibration is performed based on real-time configuration deviations, and the calibration effect is monitored and optimized to obtain a dynamic calibration mechanism for the equipment.

[0080] The game scene changes are detected based on the real-time game page, and the difference in control parameters for scene changes is calculated to obtain the difference in control parameters for the latest scene.

[0081] Based on the difference in control parameters, the dynamic calibration mechanism of the device is optimized for handle control response delay, and seamless calibration is performed.

[0082] In this embodiment, both aspects are combined to perform a multi-objective balance analysis of the scene to obtain an optimal scene control configuration. Multi-objective balance refers to finding a trade-off between different indicators: on the one hand, it must meet the operational requirements of the scene (e.g., FPS games require high precision, racing games require a wide range of responses); on the other hand, it must conform to the personalized control habits formed by players over time (e.g., primarily using light joystick movements or high-frequency rhythms). The implementation method typically employs a multi-objective optimization model, such as the Pareto optimal solution with weights. Four balance dimensions are set: ① Sensitivity matching degree, i.e., the degree of consistency between scene requirements and adaptive sensitivity coefficients; ② Personalization fit, i.e., whether the configuration is highly consistent with the player's control profile; ③ Stability index, reflecting whether the operation is smooth during long-term gameplay; ④ Load tolerance, preventing excessive fatigue caused by high-intensity operation. Experiments using 20 players in three types of game scenarios showed that the optimal configuration obtained through multi-objective optimization can maintain a balance between operational accuracy and comfort, with the error controlled within ±0.03. The generated "optimal scene control configuration" is a set of specific parameters, including joystick sensitivity curves, button response latency, and haptic feedback intensity, used to drive subsequent deviation identification and calibration. This is achieved by reading parameter tables stored in the driver layer and game interface, combined with actual input response testing. Data collected includes joystick dead zone size, displacement response curves, button trigger latency, trigger button pressure threshold, and vibration feedback intensity. During the experiment, a standardized testing method was used: the joystick was pushed at fixed amplitudes (0.1, 0.2, 0.3…1.0) on the experimental platform, and the returned values ​​were recorded and compared with theoretical inputs to infer curve characteristics. In button testing, high-speed sampling (1ms resolution) was used to measure the latency between button triggering and recognition. Experiments showed that some gamepads, nominally having a dead zone of 0.05 at the factory, actually measured 0.08, while some gamepads used for more than six months exhibited a drift value of 0.03. Through this identification process, a "current gamepad configuration parameter table" can be obtained, providing an objective benchmark for subsequent deviation analysis.

[0083] The two sets of parameters were compared item by item to calculate the deviation. For example, the optimal configuration requires a low-range joystick sensitivity of 0.25, but the current controller's actual response is 0.30, resulting in a deviation of +0.05. For greater accuracy, deviation identification involves more than just numerical comparison; dynamic factors such as non-linear errors and time delays during operation are also considered. A two-layer comparison method was used in the experiment: ① Static deviation identification, which directly compares the numerical differences recorded in the parameter table; ② Dynamic deviation identification, which measures the difference between the player's input and the expected output by simulating operational scenarios. For example, in an FPS scenario, the aiming error is required to be no more than ±2°, but the test found an average deviation of ±3.5°, indicating that the current sensitivity is too high. The experimental parameters were set as follows: each dynamic test operation was repeated 50 times, and the average deviation was used as the baseline. The results show that the dynamic deviation is basically consistent with the static parameter deviation, but the dynamic deviation better reflects the real problem under high-load scenarios.

[0084] The calibration mechanism adjusts parameters based on real-time deviations and continuously monitors and optimizes the process. Two calibration methods are employed: ① Parameter-level calibration: directly modifying sensitivity parameters in the controller driver layer or within the game to align with the optimal scenario configuration; ② Algorithm-level compensation: adding a mapping function to the driver layer to perform a secondary transformation on the input signal to compensate for hardware deviations. The calibration mechanism runs in the background with a sampling frequency of 60Hz, updating deviation detection every second. If a deviation exceeds a threshold (e.g., sensitivity difference > 0.05, latency > 10ms), automatic fine-tuning is triggered and takes effect in the next frame. A "calibration effect monitoring" system is also introduced: by recording player performance (e.g., hit rate, turning accuracy, consistency of operation rhythm), the differences before and after calibration are compared. If the performance improvement is lower than expected (e.g., less than 5%), the calibration strategy is dynamically adjusted. Experimental results show that the mechanism converges to the optimal configuration within 30 seconds and maintains a deviation of less than ±0.02 during long-term operation.

[0085] In this embodiment, the specific steps for detecting game scene changes based on the real-time game page and calculating the difference in control parameters for scene changes to obtain the control parameter difference of the latest scene are as follows:

[0086] Detect changes in the game scene based on the real-time game page and identify the changed game scene;

[0087] Based on the transformed game scene, the differences between the upper and lower scenes are analyzed, and the change in control complexity is calculated to obtain the control complexity change index.

[0088] Based on the control complexity change index, the change in controller configuration parameters is predicted to obtain the configuration change parameters.

[0089] The control parameter difference is calculated based on the configuration change parameters to obtain the control parameter difference for the latest scenario.

[0090] In this embodiment, scene changes on the game page are detected and identified. Rendered frames are captured in real-time (sampling frequency 3060fps), and features are extracted from consecutive frames. Features include color histograms, texture distribution, object identification labels, and UI element layout. For example, when a player switches from indoor combat to an open outdoor area, lighting features, the number of objects, and the sense of scene space all change significantly. In the experiment, a deep learning scene classification model (based on a combination of ResNet50 and YOLOv7) is used to quickly classify each frame, achieving an accuracy rate of over 92%. To avoid misjudgments caused by short transition frames, a time window (0.5 seconds to 1 second) is set, confirming a scene switch only when multiple consecutive frames are identified as a new scene. This method efficiently detects scene changes and identifies the "transformed scene," such as switching from "sniper mode" to "melee combat mode," or from "urban scene" to "outdoor scene." This step provides the basic input for subsequent complexity change analysis. The differences between the preceding and following scenes are compared, and the magnitude of the change in control complexity is calculated. Scene differences are mainly reflected in three aspects: ① Element density differences, i.e., changes in the number of interactive objects, such as increasing from 5 targets to 15; ② Rendering load differences, such as increasing the number of DrawCalls from 800 times / frame to 1500 times / frame; ③ Operation rhythm differences, such as increasing the key trigger frequency per unit time from 2Hz to 5Hz. A weighted difference model is adopted: Difference = w1 × Element Change Rate + w2 × Render Call Change Rate + w3 × Operation Frequency Difference, where w1:w2:w3 is set to 0.4:0.4:0.2 to ensure that rendering and element density have high weights. The result calculated by this model is the "Control Complexity Change Index". For example, when the player switches from story exploration to a large-scale battle scene, the index can quickly rise from 0.2 to 0.75.

[0091] As complexity increases, sensitivity needs to be reduced and dead zone narrowed to improve precision control; conversely, as complexity decreases, sensitivity can be increased or the response range widened to ensure smooth operation. This is achieved by constructing a "complexity-parameter prediction model." In experiments, the performance of 50 players under different complexity scenarios was collected, and regression analysis was used to establish a mapping relationship between complexity changes and configuration parameter adjustments. For example, when the complexity index increases by 0.3, the joystick sensitivity needs to decrease by an average of 0.05, the button trigger latency needs to be shortened by 5ms, and the vibration feedback intensity needs to be increased by 15%. The prediction model takes the complexity change index as input and outputs a set of configuration change parameters, including joystick sensitivity correction values, dead zone adjustment amounts, button latency correction values, and vibration feedback gain. To avoid over-adjustment, threshold limits are set (e.g., no more than ±0.1 per adjustment), and exponential smoothing is used to reduce jitter. The obtained configuration change parameters are the "theoretical correction amounts," providing a target reference for subsequent differential calculations. After obtaining the predicted configuration change parameters, they need to be compared with the actual configuration of the current device, and the "control parameter difference" is obtained through differential calculation. The core of differential calculation is to calculate the difference between "ideal parameters and actual parameters," which is divided into static and dynamic parts: the static difference reflects the numerical differences in the configuration table, while the dynamic difference is corrected through real-time operational performance (such as aiming error and button response latency). In the experimental design, static differential calculation is achieved by directly comparing two parameter sets, while dynamic differential calculation needs to be verified in simulated operations, such as continuously executing 50 precise aiming tasks and statistically analyzing whether the operational deviation is consistent with the predicted correction amount. The experiment found that when the complexity increases rapidly, relying solely on static differences will underestimate the adjustment requirements; therefore, dynamic differential calculation is a necessary supplement. The output of differential calculation is a "control parameter difference matrix for the latest scenario," which includes the adjustment direction and magnitude for each control dimension. For example: joystick sensitivity -0.07, dead zone -0.02, button latency -8ms, vibration feedback +12%. These differences will be directly input into the calibration module for rapid correction of device configuration, achieving a seamless transition in the operating experience.

[0092] In this embodiment, the specific steps for optimizing the handle control response delay based on the control parameter difference and performing seamless calibration are as follows:

[0093] Based on the difference in control parameters, the impact on player control experience is quantified to obtain the quantified value of control impact.

[0094] Based on the quantitative value of the influence of manipulation, parameter adjustment transition decisions are made, and parameter adjustment transition data is generated.

[0095] Based on parameter adjustment transition data, smoothly adjust equipment parameters and generate a smooth adjustment strategy;

[0096] The smooth adjustment strategy optimizes the handle control response latency and performs seamless calibration.

[0097] In this embodiment, the control parameter differences obtained for the latest scene cannot be directly applied to the device because different adjustments have varying degrees of impact on the player's control experience. These differences are converted into quantitative indicators to reflect their magnitude of influence on the control experience. A "parameter difference-perceived impact" model is established, and experimental data is used to fit the relationship between different parameter differences and the player's subjective experience. For example, when the joystick sensitivity difference is ±0.05, the player's aiming deviation increases by an average of 8%, while when the difference is ±0.1, the deviation increases by 20%, indicating that the perceived impact is not linear. Twenty players were invited to experience scene operations under different parameter differences, and regression modeling was performed combining operational performance (hit rate, overshoot rate) and subjective ratings (comfort, burden). The model output is the "control impact quantification value," typically represented in the range of 0 to 1, with larger values ​​indicating a more significant impact of the adjustment on the control experience. For example, in an FPS scene, a sensitivity difference of -0.07 corresponds to an impact quantification value of 0.65, indicating that the player can clearly perceive the difference; while in an RPG exploration scene, the same difference results in an impact of only 0.3. This step ensures that parameter adjustments are not only a technical difference but also consider the player's subjective experience. A tiered strategy is set: if the quantization value is low (<0.3), the adjustment has little impact on the player's experience and can be applied all at once; if the quantization value is medium (0.3-0.7), a segmented transition is used, breaking the difference down into 2-3 small adjustments; if the quantization value is high (>0.7), dynamic interpolation is required, adjusting slowly over multiple frames. An exponential smoothing method is used to fit the transition curve, ensuring a natural transition during the adjustment process. For example, when the sensitivity difference is -0.07 and the impact quantization value is 0.65, the adjustment is completed in 3 steps within 5 seconds, with each correction being approximately -0.023. Experimental data shows that in 30 test scenarios, segmented transitions significantly reduce the player's "abruptness rating," by an average of 40%. The generated "parameter adjustment transition data" includes the number of adjustments, the magnitude of each adjustment, and the time interval, providing clear guidance for device execution. During each frame rendering cycle (16.7ms), the controller parameters are gradually updated based on the transition data, ensuring that the adjustment values ​​are continuously distributed within the preset range. For example, when the sensitivity needs to decrease from 0.35 to 0.28 within 5 seconds, the sensitivity will be decreased by 0.00023 per frame until the target is achieved. To avoid player input lag due to excessive input, "predictive compensation" is added to the underlying algorithm: by detecting the direction and amplitude of the player's input, if the current operation intensity is high, the adjustment is temporarily suspended until the operation stabilizes before continuing. In experiments, the smooth adjustment strategy can control the rate of change of aiming error within ±2% in FPS games, making the parameter changes almost imperceptible to the player. The generated "smooth adjustment strategy" includes adjustment curve functions, frame-level update rates, and compensation trigger conditions, laying the foundation for imperceptible calibration.

[0098] In this embodiment, a handheld device calibration apparatus is provided for performing the handheld device calibration method described above, including:

[0099] The scene evaluation module is used to perform a process-level deep scan of the game program and conduct a comprehensive evaluation of the control difficulty to generate a scene control difficulty coefficient.

[0100] The sensitivity calculation module is used to perform adaptive sensitivity calculation based on the scene control difficulty coefficient to obtain the adaptive sensitivity coefficient.

[0101] The control style module is used to extract gamepad operation logs, analyze player control styles, and build personalized control profiles.

[0102] The dynamic calibration module is used to perform dynamic configuration calibration based on personalized control profiles and adaptive sensitivity coefficients, and to monitor and optimize the calibration effect, thus building a dynamic calibration mechanism for the equipment.

[0103] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the handheld device calibration method described in any of the preceding claims.

[0104] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the handheld device calibration method described in any of the preceding claims.

[0105] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0106] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A handle device calibration method, characterized by, Includes the following steps: Step S1: Perform a process-level deep scan of the game program and conduct a comprehensive assessment of the control difficulty to generate a scene control difficulty coefficient; Step S2: Calculate the adaptive sensitivity based on the scene control difficulty coefficient to obtain the adaptive sensitivity coefficient; Step S3: Extract the controller operation log, analyze the player's control style, and build a personalized control profile; Step S4: Perform dynamic configuration calibration based on personalized control profile and adaptive sensitivity coefficient, monitor and optimize calibration effect, and build a dynamic calibration mechanism for the device; The specific steps of step S2 are as follows: Based on game genre, perform genre-specific sensitivity analysis to obtain specific sensitivity data; Based on dedicated sensitivity data, the difficulty coefficient of scene operation is analyzed to determine the joystick sensitivity requirements and obtain the sensitivity range for the current scene. Identify the joystick response parameters of the controller; Based on the joystick response parameters, an adaptive sensitivity matching calculation is performed on the sensitivity range, and dynamic sensitivity fine-tuning is carried out to obtain the adaptive sensitivity coefficient. The specific steps of step S4 are as follows: Based on personalized control profiles and adaptive sensitivity coefficients, multi-objective balance analysis of the scene is performed to obtain the optimal scene control configuration; Identify the current controller's configuration parameters; The configuration parameters are identified based on the optimal scenario control configuration to obtain the real-time configuration deviation. Dynamic configuration calibration is performed based on real-time configuration deviations, and the calibration effect is monitored and optimized to obtain a dynamic calibration mechanism for the equipment. The game scene changes are detected based on the real-time game page, and the difference in control parameters for scene changes is calculated to obtain the difference in control parameters for the latest scene. Based on the difference in control parameters, the dynamic calibration mechanism of the device is optimized for handle control response delay, and seamless calibration is performed. The specific steps for detecting game scene changes based on the real-time game page and calculating the difference in control parameters for scene changes to obtain the control parameter difference for the latest scene are as follows: Detect changes in the game scene based on the real-time game page and identify the changed game scene; Based on the transformed game scene, the differences between the upper and lower scenes are analyzed, and the change in control complexity is calculated to obtain the control complexity change index. Based on the control complexity change index, the change in controller configuration parameters is predicted to obtain the configuration change parameters. The control parameter difference is calculated based on the configuration change parameters to obtain the control parameter difference for the latest scenario; The specific steps for optimizing the handle control response delay based on the control parameter difference and performing seamless calibration are as follows: Based on the difference in control parameters, the impact on player control experience is quantified to obtain the quantified value of control impact. Based on the quantitative value of the influence of manipulation, parameter adjustment transition decisions are made, and parameter adjustment transition data is generated. Based on parameter adjustment transition data, smoothly adjust equipment parameters and generate a smooth adjustment strategy; The smooth adjustment strategy optimizes the handle control response latency and performs seamless calibration.

2. The handle device calibration method of claim 1, wherein, The specific steps of step S1 are as follows: Perform a process-level deep scan of the game program to extract basic game information; based on the basic game information, hook the underlying API functions to obtain the game engine type and rendering pipeline architecture; Game type is determined by classifying and identifying the game type based on the game engine type. Rendering call distribution analysis is performed based on the rendering pipeline architecture to generate rendering screen call parameters; Based on basic game information, real-time game pages are extracted, game scenes are analyzed, and scene elements are marked; Interactively identify scene elements, calculate element density and spatial distribution, and generate scene element characteristics; The scene complexity is calculated based on the parameters called in the rendered image and the characteristics of scene elements. Based on the game type, a comprehensive assessment of the difficulty of controlling the scene complexity is conducted to generate a scene control difficulty coefficient.

3. The handheld device calibration method according to claim 1, characterized in that, Step S3 is as follows: Collect all historical controller operation data from players and extract controller operation logs; Based on the controller operation log, manual device control parameters are identified to obtain player settings parameters in different scenarios; Player control behavior is analyzed based on the controller operation log to generate multi-dimensional behavioral data, including joystick displacement trajectory, button timing, force changes and operation frequency. Based on multi-dimensional behavioral data, the player's joystick force distribution characteristics, directional preferences, and operation rhythm patterns are identified to generate a player control mode. We analyze the evolution of control styles based on player control patterns to construct personalized control profiles.

4. A handle device calibration device, characterized in that, For performing the handheld device calibration method as described in claim 1, comprising: The scene evaluation module is used to perform a process-level deep scan of the game program and conduct a comprehensive evaluation of the control difficulty to generate a scene control difficulty coefficient. The sensitivity calculation module is used to perform adaptive sensitivity calculation based on the scene control difficulty coefficient to obtain the adaptive sensitivity coefficient. The control style module is used to extract gamepad operation logs, analyze player control styles, and build personalized control profiles. The dynamic calibration module is used to perform dynamic configuration calibration based on personalized control profiles and adaptive sensitivity coefficients, and to monitor and optimize the calibration effect, thus building a dynamic calibration mechanism for the equipment.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the handheld device calibration method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the handheld device calibration method according to any one of claims 1 to 3.

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