A game controller dynamic configuration system

Abstract

This invention discloses a dynamic configuration system for a game controller, relating to the field of computer technology. The invention includes a controller and a host. The controller employs a heterogeneous device hybrid communication protocol stack to achieve bidirectional data synchronization between the host and controller. The controller includes triggers, selectors, interceptors, switch components, a dynamic resource configuration unit, and a firmware upgrade interface. The host includes a device dynamic identification and registration module, a game semantic parsing module, a dynamic mapping strategy generation model, a user adaptive learning unit, a multimodal feedback fusion unit, and a security verification and exception handling unit. This invention reduces the difficulty of controller configuration and improves its efficiency by establishing a mapping relationship library between controller hardware features and game scene parameters, collecting controller physical state data in real time through a sensor array, and generating dynamic matching rules by combining this data with character action thresholds in the game scene.

Description

Technical Field

[0001] This invention belongs to the field of computer technology, and in particular relates to a dynamic configuration system for a game controller. Background Technology

[0002] Traditional game controllers mostly use a static layout of physical buttons or joysticks, which cannot dynamically adjust function mappings according to game type or user habits. Specific drawbacks include:

[0003] (1) Complex configuration process: Existing controller configuration requires code modification or special tools, lacking visual interface support, resulting in a high operating threshold for ordinary users;

[0004] (2) Insufficient configuration for multiple scenarios: With the popularization of cloud gaming and cross-platform devices, a single configuration scheme is difficult to meet the compatibility requirements of different terminals (such as PCs, consoles, and mobile devices).

[0005] With the increasing demand for dynamic interaction and the diversification of game types (such as VR / AR and motion-sensing games), controllers require real-time response to dynamic commands. However, existing technologies still rely on pre-installed firmware updates and lack real-time configuration capabilities. Meanwhile, players' demands for personalized operating experiences are rising, but existing patents (such as Huali Technology CN108553884B) focus on innovation in physical control methods and do not address the issue of dynamic customization at the software level. Furthermore, while existing patents such as CN113144619B propose a visual configuration method, they focus on the game management backend and do not deeply integrate controller hardware interaction.

[0006] Therefore, there is an urgent need for a system that combines dynamic parameter adjustment, cross-platform compatibility, and intelligent learning to achieve real-time reconstruction and scenario-based adaptation of controller functions. Summary of the Invention

[0007] The purpose of this invention is to provide a dynamic configuration system for game controllers. By establishing a mapping relationship library between controller hardware features and game scene parameters, and by collecting controller physical state data in real time through a sensor group, dynamic matching rules are generated by combining the character action thresholds in the game scene. This solves the problems of high difficulty in configuring existing controllers, cumbersome configuration process, and high level of expertise.

[0008] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0009] This invention is a dynamic configuration system for a game controller, comprising a controller end and a host end;

[0010] The controller uses a heterogeneous device hybrid communication protocol stack to achieve bidirectional data synchronization between the host and the controller; both the controller and the host are deployed with a multimodal device collaboration framework.

[0011] The controller includes a trigger, a selector, an interceptor, a switch assembly, a dynamic resource configuration unit, and a firmware upgrade interface. The trigger supports physical pressing and is used in conjunction with a pressure sensor to achieve multi-level tactile feedback. The selector is used to switch operating modes via a mechanical or electronic switching device. The interceptor is linked to the selector and changes the effective travel of the trigger through physical limits. The switch assembly is used for trigger signal capture and contactless operation detection. The dynamic resource configuration unit is used to identify the device's connection status through an embedded chip and automatically allocate communication bandwidth and processing priority. The firmware upgrade interface is a reserved USB-C and wireless OTA upgrade channel.

[0012] The host terminal includes a device dynamic identification and registration module, a game semantic parsing module, a dynamic mapping strategy generation model, a user adaptive learning unit, a multimodal feedback fusion unit, and a security verification and anomaly handling unit. The device dynamic identification and registration module scans for devices connected to the host via a hybrid communication protocol and allocates communication resources using a dynamic priority queue. The game semantic parsing module extracts key operation instruction sets and their semantic tags by reverse-analyzing game process memory or API call chains. The dynamic mapping strategy generation model generates multi-level mapping schemes based on the controller capability matrix and game operation requirements. The user adaptive learning unit includes a behavior feature modeling module and a dynamic weight adjustment module. The behavior feature modeling module constructs personalized operation profiles based on user operation data and uses an LSTM neural network to predict user intent. The dynamic weight adjustment module sets operation mode decay factors to ensure the configuration scheme dynamically evolves with user habits. The multimodal feedback fusion unit establishes a spatiotemporal synchronization model for tactile, auditory, and visual feedback to achieve cross-device feedback fusion. The security verification and anomaly handling unit uses two-factor authentication to ensure the mapping scheme is reasonable.

[0013] As a preferred technical solution, the controller has two operating modes with built-in adaptive mechanisms:

[0014] One operating mode is as follows: after the intercepting component intervenes, the trigger needs to be pressed in stages; that is, first trigger the intermediate position switch, and then activate the end feedback motor, which is used for scenarios of fine operation.

[0015] Another operating mode is to fully release the trigger travel, supporting full-travel response, for high-speed continuous operation.

[0016] As a preferred technical solution, the dynamic resource configuration unit identifies the device's connection status through an embedded chip and automatically allocates communication bandwidth and processing priority. The specific process is as follows:

[0017] Step D1, Initialization Phase: Load the embedded chip, initiate the kernel patch implementation, and configure the communication protocol stack;

[0018] Step D2, Device Identification and Registration: Perform a protocol handshake to complete device identity authentication and QoS level labeling;

[0019] Step D3, Real-time Status Monitoring: Poll the connection status every 1ms and monitor the location changes of wireless devices via RSSI;

[0020] Step D4, Dynamic Resource Allocation: Generate a bandwidth allocation table based on the WRR algorithm, write it directly to the network chip register through the DMA controller, and trigger the hardware compression module to encode the high-bandwidth stream in real time;

[0021] Step D5, Priority Scheduling Execution: Through the RTOS's task ready queue, tasks are scheduled to the corresponding CPU cores according to the EDF algorithm;

[0022] Step D6, Adaptive Adjustment: Train a lightweight LSTM model based on historical data to predict device traffic fluctuation trends.

[0023] As a preferred technical solution, the specific process of the device dynamic identification and registration module using a dynamic priority queue to allocate communication resources is as follows:

[0024] Step S1: Divide the communication data into four priority queues: real-time operation instructions (highest level), audio and video streams (second highest level), status synchronization packets (intermediate level), and log reports (lowest level). Set a baseline weight coefficient for each type of data. The weight of real-time operation instructions is 1, audio and video streams are 0.7, status synchronization packets are 0.4, and log reports are 0.1.

[0025] Step S2: Dynamically adjust the correction weights based on network latency volatility, using the following formula:

[0026]

[0027] In the formula, η is the correction coefficient and σ is the network latency fluctuation rate; when the latency standard deviation σ is detected to be ≥50ms, the real-time instruction weight increases by 63%;

[0028] Step S3: Allocate minimum guaranteed bandwidth according to priority. When a high-priority queue is idle, a low-priority queue is allowed to borrow bandwidth resources.

[0029] Step S4: Establish a multi-dimensional evaluation model to select the transmission path, and update the path scoring table every five seconds to automatically switch to the optimal path;

[0030] Step S5: Set up a failover mechanism. When three consecutive transmission failures are detected, the backup channel is automatically activated and the shadow queue data is retransmitted.

[0031] As a preferred technical solution, the dynamic priority queue allocates communication resources and then constructs a closed-loop feedback control model; the adjustment amount Δ of the model is formulated as follows:

[0032] Δ=K p ×e(t)+K i ×∫e(t)dt+K d ×de(t) / dt;

[0033] In the formula, K p K is the proportionality coefficient, e(t) is the deviation between the actual bandwidth utilization and the target value, and K is the proportionality coefficient. i K is the integral coefficient. d These are the differential coefficients;

[0034] The closed-loop feedback control model is also equipped with an abnormal circuit breaker mechanism. When any one of the following is detected for ten consecutive seconds, such as CPU utilization exceeding 90%, memory usage exceeding the safety threshold of 85%, or network jitter rate increasing by 200% year-on-year, a degradation protection is triggered, and low-priority queues are automatically closed until the system recovers.

[0035] As a preferred technical solution, the multimodal device collaboration framework on the host side is used to undertake the hybrid communication protocol and dynamic resource scheduling of heterogeneous devices; the multimodal device collaboration framework on the controller side is used to implement the signal acquisition and tactile feedback execution functions.

[0036] As a preferred technical solution, the process of the behavior feature modeling module constructing personalized operation profiles based on user operation data and predicting user intent using an LSTM neural network is as follows:

[0037] Step G1, Multi-source data acquisition: Collect static and dynamic data; static data includes basic user data (device type, registration information) and operation preference configuration (default sensitivity settings); dynamic data includes operation timing data (click / movement trajectory), state switching records (frequency of switching between combat / exploration modes) and abnormal operation events (number of accidental touches);

[0038] Step G2, Data Preprocessing: Remove outliers and synchronize timestamps using the NTP protocol;

[0039] Step G3: Build the project framework: Calculate the number of key presses per unit time, analyze the similarity of operation sequences and the distribution histogram of key pressure;

[0040] Step G4, LSTM prediction model: The prediction model is trained using LSTM.

[0041] As a preferred technical solution, the specific process for training the LSTM prediction model is as follows:

[0042] Step G41: Collect time series data, including timestamps and feature dimensions, and associate static features with dynamic poem data, using primary key fields to link tables;

[0043] Step G42: Use Kalman filtering on the data to eliminate operation trajectory jitter, and divide it into training set, validation set and test set according to the proportion;

[0044] Step G43: Design the network architecture, including the input layer, hidden layers, and output layer;

[0045] Step G44: Optimize strategy configuration, perform dynamic training control, and calculate the loss function;

[0046] Step G45: Use Bayesian optimization to find the optimal learning decay strategy and evaluate the metrics;

[0047] Step G46: Online mechanical energy rolling prediction and production deployment optimization.

[0048] As a preferred technical solution, the dynamic mapping strategy generation model establishes a mapping relationship library between controller hardware features and game scene parameters, collects controller physical state data in real time through a sensor group, and generates dynamic matching rules by combining character action thresholds in the game scene. The specific process is as follows:

[0049] Step T1: Collect raw data using hardware such as force sensors and accelerometers to construct a multidimensional time series dataset;

[0050] Step T2: Use a sliding window to statistically analyze time-domain and frequency-domain features to generate standardized feature vectors;

[0051] Step T3: Automatically explore action response boundaries in the game scene through reinforcement learning;

[0052] Step T4: Customize the rule base;

[0053] Step T5: Process the data stream. The sensor group reports data at a frequency of 100Hz, and zero-copy transmission is achieved through a circular buffer.

[0054] Step T6: Execute the rule engine.

[0055] As a preferred technical solution, the security verification and anomaly handling unit employs a dual-factor authentication method that includes a knowledge factor and a physical factor. The knowledge factor is based on a preset configuration password or key that the user must input, serving as the foundation for identity verification. The physical factor generates a real-time biometric code through the controller's hardware features and matches it with the baseline data during registration. When the dual-factor authentication is triggered, the system requires simultaneous verification of both the knowledge factor and the physical factor, and uses a bidirectional LSTM network to analyze the controller's operation timing data to dynamically verify the operator's legitimacy.

[0056] The present invention has the following beneficial effects:

[0057] (1) This invention establishes a mapping relationship library between controller hardware features and game scene parameters, collects controller physical state data in real time through sensor groups, and generates dynamic matching rules by combining character action thresholds in the game scene, thereby reducing the difficulty of controller configuration and improving controller configuration efficiency.

[0058] (2) The multimodal device collaboration framework of the present invention achieves cross-device resource optimization through centralized scheduling on the host side, while utilizing cloud-based service capabilities to avoid computing power bottlenecks on the controller side.

[0059] (3) This invention uses a multi-dimensional parameter analysis algorithm to extract features from user operation patterns, analyzes user operation habits through the standard deviation of continuous operation interval time, identifies the potential intentions of high-frequency operation action sequences based on LSTM network, outputs controller key mapping scheme and sensitivity dynamic adjustment coefficient, and realizes real-time calibration of physical operation and virtual action.

[0060] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0061] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a schematic diagram of a game controller dynamic configuration system according to the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0065] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1 The present application will be further described in detail below with reference to embodiments. 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 application.

[0066] Example 1

[0067] Please see Figure 1 As shown, the present invention is a dynamic configuration system for a game controller, including a controller end and a host end;

[0068] The controller uses a heterogeneous device hybrid communication protocol stack to achieve bidirectional data synchronization between the host and the controller; both the controller and the host are deployed with a multimodal device collaboration framework.

[0069] The controller includes a trigger, selector, interceptor, switch assembly, dynamic resource configuration unit, and firmware upgrade interface. The trigger supports physical pressing operation and is used in conjunction with a pressure sensor to achieve multi-level tactile feedback. The selector is used to switch operating modes via mechanical or electronic switching devices. The interceptor is linked with the selector and changes the effective travel of the trigger through physical limits. The switch assembly is used for trigger signal capture and contactless operation detection. The dynamic resource configuration unit is used to identify the device's connection status through an embedded chip and automatically allocate communication bandwidth and processing priority. The firmware upgrade interface is a reserved USB-C and wireless OTA upgrade channel.

[0070] The host-side architecture includes a device dynamic identification and registration module, a game semantic parsing module, a dynamic mapping strategy generation model, a user adaptive learning unit, a multimodal feedback fusion unit, and a security verification and anomaly handling unit. The device dynamic identification and registration module scans for devices connected to the host via a hybrid communication protocol and allocates communication resources using a dynamic priority queue. The game semantic parsing module extracts key operation instruction sets and their semantic tags by reverse-analyzing game process memory or API call chains. The dynamic mapping strategy generation model generates multi-level mapping schemes based on the controller capability matrix and game operation requirements. The user adaptive learning unit includes a behavior feature modeling module and a dynamic weight adjustment module. The behavior feature modeling module constructs personalized operation profiles based on user operation data and uses an LSTM neural network to predict user intent. The dynamic weight adjustment module sets operation mode decay factors to ensure the configuration scheme dynamically evolves with user habits. The multimodal feedback fusion unit establishes a spatiotemporal synchronization model for tactile, auditory, and visual feedback to achieve cross-device feedback fusion. The security verification and anomaly handling unit uses two-factor authentication to ensure the mapping scheme is reasonable.

[0071] The core of the multimodal device collaboration framework is deployed on the host side, which is used to handle the hybrid communication protocols of heterogeneous devices (such as Bluetooth / Wi-Fi dual-channel redundant transmission) and dynamic resource scheduling. The lightweight multimodal device collaboration framework is deployed on the controller side, which only needs to implement the functions of signal acquisition and haptic feedback. This multimodal device collaboration framework achieves cross-device resource optimization through centralized scheduling on the host side, while utilizing cloud-based extended service capabilities to avoid the computing power bottleneck on the controller side.

[0072] As shown in Example 2, the controller has two operating modes with a built-in adaptive mechanism:

[0073] One operating mode is as follows: after the intercepting component intervenes, the trigger needs to be pressed in stages; that is, first trigger the intermediate position switch, and then activate the end feedback motor, which is used for scenarios of fine operation.

[0074] Another operating mode is to fully release the trigger travel, supporting full-travel response, for high-speed continuous operation.

[0075] The dynamic resource allocation unit identifies the device's connection status through an embedded chip and automatically allocates communication bandwidth and processing priority. The specific process is as follows:

[0076] Step D1, Initialization Phase: Load the embedded chip, initiate the kernel patch implementation, and configure the communication protocol stack;

[0077] Step D2, Device Identification and Registration: Perform a protocol handshake to complete device identity authentication and QoS level labeling;

[0078] Step D3, Real-time Status Monitoring: Poll the connection status every 1ms and monitor the location changes of wireless devices via RSSI;

[0079] Step D4, Dynamic Resource Allocation: Generate a bandwidth allocation table based on the WRR algorithm, write it directly to the network chip register through the DMA controller, and trigger the hardware compression module to encode the high-bandwidth stream in real time;

[0080] Step D5, Priority Scheduling Execution: Through the RTOS's task ready queue, tasks are scheduled to the corresponding CPU cores according to the EDF algorithm;

[0081] Step D6, Adaptive Adjustment: Train a lightweight LSTM model based on historical data to predict device traffic fluctuation trends.

[0082] The specific process of the device dynamic identification and registration module using dynamic priority queues to allocate communication resources is as follows:

[0083] Step S1: Divide the communication data into four priority queues: real-time operation instructions, audio and video streams, status synchronization packets, and log reports, and set a baseline weight coefficient for each type of data;

[0084] Step S2: Dynamically adjust the correction weights based on network latency volatility, using the following formula:

[0085]

[0086] In the formula, η is the correction coefficient and σ is the network latency volatility;

[0087] Step S3: Allocate minimum guaranteed bandwidth according to priority. When a high-priority queue is idle, a low-priority queue is allowed to borrow bandwidth resources.

[0088] Step S4: Establish a multi-dimensional evaluation model to select the transmission path, and update the path scoring table every five seconds to automatically switch to the optimal path;

[0089] Step S5: Set up a failover mechanism. When three consecutive transmission failures are detected, the backup channel is automatically activated and the shadow queue data is retransmitted.

[0090] After allocating communication resources using a dynamic priority queue, a closed-loop feedback control model is constructed; the formula for the model's adjustment amount Δ is:

[0091] Δ=K p ×e(t)+K i ×∫e(t)dt+K d ×de(t) / dt;

[0092] In the formula, K p K is the proportionality coefficient, e(t) is the deviation between the actual bandwidth utilization and the target value, and K is the proportionality coefficient.i K is the integral coefficient. d These are the differential coefficients;

[0093] The closed-loop feedback control model is also equipped with an abnormal circuit breaker mechanism. When any one of the following is detected for ten consecutive seconds, such as CPU utilization exceeding 90%, memory usage exceeding the safety threshold of 85%, or network jitter rate increasing by 200% year-on-year, degradation protection is triggered, and low-priority queues are automatically closed until the system recovers.

[0094] The host-side multimodal device collaboration framework is used to handle heterogeneous device hybrid communication protocols and dynamic resource scheduling; the controller-side multimodal device collaboration framework is used to implement signal acquisition and haptic feedback execution functions.

[0095] This scheme forms a closed-loop optimization system through dynamic weight adjustment of multi-level queues, intelligent path selection, and elastic bandwidth allocation. Compared with the traditional static priority allocation scheme, it can improve the success rate of high-priority data packet transmission by 40% and reduce end-to-end latency by 35%.

[0096] The behavioral feature modeling module constructs personalized user profiles based on user operation data and uses LSTM neural networks to predict user intent, as follows:

[0097] Step G1, Multi-source data acquisition: Collect static and dynamic data; static data includes basic user data (device type, registration information) and operation preference configuration (default sensitivity settings); dynamic data includes operation timing data (click / movement trajectory), state switching records (frequency of switching between combat / exploration modes) and abnormal operation events (number of accidental touches);

[0098] Step G2, Data Preprocessing: Remove outliers and synchronize timestamps using the NTP protocol;

[0099] Step G3: Build the project framework: Calculate the number of key presses per unit time, analyze the similarity of operation sequences and the distribution histogram of key pressure;

[0100] Step G4, LSTM prediction model: The prediction model is trained using LSTM.

[0101] When designing the LSTM prediction model, the network architecture is as follows:

[0102] (1) Input layer: 50-step time window (including 12-dimensional features such as operation type, coordinates, and pressure value);

[0103] (2) Hidden layer: bidirectional LSTM unit (128 neurons) + Dropout (0.2);

[0104] (3) Output layer: Softmax classification (predicting 6 core intentions within the next 3 seconds);

[0105] The training optimization strategy is as follows:

[0106] Loss function: Focal Loss addresses class imbalance (α = 0.75, γ = 2);

[0107] Dynamic learning rate: cosine annealing strategy (initial lr = 0.001);

[0108] Data augmentation: Time warping of operation sequences.

[0109] The specific process for training the LSTM prediction model is as follows:

[0110] Step G41: Collect time series data, including timestamps and feature dimensions, and associate static features with dynamic poem data, using primary key fields to link tables;

[0111] Step G42: Use Kalman filtering on the data to eliminate operation trajectory jitter, and divide it into training set, validation set and test set according to the proportion;

[0112] Step G43: Design the network architecture, including the input layer, hidden layers, and output layer;

[0113] Step G44: Optimize strategy configuration, perform dynamic training control, and calculate the loss function;

[0114] Step G45: Use Bayesian optimization to find the optimal learning decay strategy and evaluate the metrics;

[0115] Step G46: Online mechanical energy rolling prediction and production deployment optimization.

[0116] The dynamic mapping strategy generation model establishes a mapping relationship library between controller hardware features and game scene parameters. It collects controller physical state data in real time through a sensor array and generates dynamic matching rules by combining them with character action thresholds in the game scene. The specific process is as follows:

[0117] Step T1: Collect raw data (such as pressure values, triaxial acceleration, and displacement) using hardware such as force sensors and accelerometers to construct a multidimensional time series dataset;

[0118] Step T2: Use a sliding window (e.g., a 50ms window) to statistically analyze time-domain features (mean, variance) and frequency-domain features (FFT energy spectrum) to generate a standardized feature vector; discretize the continuous physical quantity (e.g., pressure value 0-100N) into 5 intensity levels (light, medium, strong, etc.) to reduce computational complexity.

[0119] Step T3: Automatically explore action response boundaries in the game scene through reinforcement learning (Q-Learning);

[0120] Step T4: Customize the rule base;

[0121] Step T5: Process the data stream. The sensor group reports data at a frequency of 100Hz. Zero-copy transmission is achieved through a circular buffer. The feature extraction thread and the rule matching thread run asynchronously, with the delay controlled within 5ms.

[0122] Step T6: Execute the rule engine. The rule engine uses a graph database (Neo4j) to store the dependencies between rules and uses JSON format to define rule metadata.

[0123] For real-time matching, the following method is used:

[0124] Optimized version of Rete algorithm: Reduces redundant calculations by sharing condition nodes, matching latency <3ms;

[0125] Event listening mechanism: Register hardware input events (such as changes in controller vibration intensity) to trigger rule overload.

[0126] Validation and optimization of the rules engine:

[0127] (1) The testing methods include:

[0128] Unit testing: Verifies the accuracy of the condition-action mapping for a single rule;

[0129] Stress test: Simulate 1000+ concurrent rule triggering scenarios and test throughput (≥5000TPS);

[0130] (2) Performance optimization

[0131] Compilation optimization: Precompile high-frequency rules into native C++ code (improving execution speed by 5 times);

[0132] Caching mechanism: LRU cache is used for successfully matched rule paths.

[0133] Example 2

[0134] The controller in this embodiment has two operating modes with an adaptive mechanism:

[0135] One operating mode is as follows: after the intercepting component intervenes, the trigger needs to be pressed in stages; that is, first trigger the intermediate position switch, and then activate the end feedback motor, which is used for scenarios of fine operation.

[0136] Another operating mode is to fully release the trigger travel, supporting full-travel response, for high-speed continuous operation.

[0137] The specific application scenarios and implementation examples of the two types of operation modes are as follows:

[0138] (1) First operation mode (staged pressing, fine operation scenario)

[0139] Specific implementation example: Sniper rifle aiming scenario in shooting games.

[0140] Intermediate position switch trigger: When the player presses the trigger to 50% of its travel, the interceptor triggers the intermediate position switch, and the system automatically activates the scope zoom function (such as switching from a 4x scope to an 8x scope). At this time, the haptic feedback motor provides clear tactile feedback.

[0141] End feedback activation: When the press continues to the end of the stroke, the end haptic motor generates a high-intensity vibration (such as simulating the recoil force of a sniper rifle), and at the same time, the game completes the accurate shooting judgment (the error range is controlled within ±0.5 pixel level accuracy).

[0142] The position of the trigger is monitored in real time during the PLC input sampling stage (accuracy ±0.01mm); the conditional branching mechanism in logic control is used to execute different operations in stages (such as zooming triggered at the middle position and shooting triggered at the end).

[0143] (2) Second operating mode (full-process rapid response, high-speed operation scenario)

[0144] Specific implementation example: Continuous throttle control in racing games

[0145] Full travel release: The interceptor component is completely disengaged from the trigger, allowing the player to quickly press to the maximum travel (response time <10ms), achieving linear acceleration control of the racing car's throttle from 0-100%.

[0146] High-frequency feedback support: The haptic motor provides continuous vibration at a frequency of 1000Hz (simulating engine vibration frequency), while updating throttle parameters in real time through the output refresh stage (processing 1000 input signals per second).

[0147] The PLC centralized sampling mode is adopted to complete input detection and output response within a single scan cycle (cycle < 1ms); through the logic control overlay mechanism, it is ensured that the parameters of the last press take effect first (e.g., only the end state is responded to when pressing continuously and rapidly).

[0148] The dynamic resource allocation unit identifies the device's connection status through an embedded chip and automatically allocates communication bandwidth and processing priority. The specific process is as follows:

[0149] Step D1, Initialization Phase: Load the embedded chip, initiate the kernel patch implementation, and configure the communication protocol stack;

[0150] Step D2, Device Identification and Registration: Perform a protocol handshake to complete device identity authentication and QoS level labeling;

[0151] Step D3, Real-time Status Monitoring: Poll the connection status every 1ms and monitor the location changes of wireless devices via RSSI;

[0152] Step D4, Dynamic Resource Allocation: Generate a bandwidth allocation table based on the WRR algorithm, write it directly to the network chip register through the DMA controller, and trigger the hardware compression module to encode the high-bandwidth stream in real time;

[0153] Step D5, Priority Scheduling Execution: Through the RTOS's task ready queue, tasks are scheduled to the corresponding CPU cores according to the EDF algorithm;

[0154] Step D6, Adaptive Adjustment: Train a lightweight LSTM model based on historical data to predict device traffic fluctuation trends.

[0155] The specific process of the device dynamic identification and registration module using dynamic priority queues to allocate communication resources is as follows:

[0156] Step S1: Divide the communication data into four priority queues: real-time operation instructions, audio and video streams, status synchronization packets, and log reports, and set a baseline weight coefficient for each type of data;

[0157] Step S2: Dynamically adjust the correction weights based on network latency volatility, using the following formula:

[0158]

[0159] In the formula, η is the correction coefficient and σ is the network latency volatility;

[0160] Step S3: Allocate minimum guaranteed bandwidth according to priority. When a high-priority queue is idle, a low-priority queue is allowed to borrow bandwidth resources.

[0161] Step S4: Establish a multi-dimensional evaluation model to select the transmission path, and update the path scoring table every five seconds to automatically switch to the optimal path;

[0162] Step S5: Set up a failover mechanism. When three consecutive transmission failures are detected, the backup channel is automatically activated and the shadow queue data is retransmitted.

[0163] It is worth noting that the various units included in the above system embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0164] Furthermore, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the corresponding program can be stored in a computer-readable storage medium.

[0165] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic configuration system for a game controller, comprising a controller end and a host end, characterized in that: The controller uses a heterogeneous device hybrid communication protocol stack to achieve bidirectional data synchronization between the host and the controller; both the controller and the host are deployed with a multimodal device collaboration framework. The controller includes a trigger, a selector, an interceptor, a switch assembly, a dynamic resource configuration unit, and a firmware upgrade interface. The trigger supports physical pressing and is used in conjunction with a pressure sensor to achieve multi-level tactile feedback. The selector is used to switch operating modes via a mechanical or electronic switching device. The interceptor is linked to the selector and changes the effective travel of the trigger through physical limits. The switch assembly is used for trigger signal capture and contactless operation detection. The dynamic resource configuration unit is used to identify the device's connection status through an embedded chip and automatically allocate communication bandwidth and processing priority. The firmware upgrade interface is a reserved USB-C and wireless OTA upgrade channel. The host terminal includes a device dynamic identification and registration module, a game semantic parsing module, a dynamic mapping strategy generation model, a user adaptive learning unit, a multimodal feedback fusion unit, and a security verification and exception handling unit. The device dynamic identification and registration module scans for devices connected to the host via a hybrid communication protocol and allocates communication resources using a dynamic priority queue. The game semantic parsing module extracts key operation instruction sets and their semantic tags by reverse-analyzing game process memory or API call chains. The dynamic mapping strategy generation model generates multi-level mapping schemes based on the controller capability matrix and game operation requirements. The user adaptive learning unit includes a behavior feature modeling module and a dynamic weight adjustment module. The behavior feature modeling module constructs personalized operation profiles based on user operation data and uses an LSTM neural network to predict user intent. The dynamic weight adjustment module is used to set the operation mode decay factor to ensure that the configuration scheme evolves dynamically with user habits. The multimodal feedback fusion unit is used to establish a spatiotemporal synchronization model of tactile, auditory, and visual feedback to achieve cross-device feedback fusion; the security verification and anomaly handling unit is used to ensure the rationality of the mapping scheme by adopting two-factor authentication.

2. The game controller dynamic configuration system according to claim 1, characterized in that, The controller has two operating modes with built-in adaptive mechanisms: One operating mode is as follows: after the intercepting component intervenes, the trigger needs to be pressed in stages; that is, first trigger the intermediate position switch, and then activate the end feedback motor, which is used for scenarios of fine operation. Another operating mode is: fully release the trigger travel, supporting full-travel response, for high-speed continuous operation.

3. The game controller dynamic configuration system according to claim 1, characterized in that, The dynamic resource allocation unit identifies the device's connection status through an embedded chip and automatically allocates communication bandwidth and processing priority. The specific process is as follows: Step D1, Initialization Phase: Load the embedded chip, initiate the kernel patch implementation, and configure the communication protocol stack; Step D2, Device Identification and Registration: Perform a protocol handshake to complete device identity authentication and QoS level labeling; Step D3, Real-time Status Monitoring: Poll the connection status every 1ms and monitor the location changes of wireless devices via RSSI; Step D4, Dynamic Resource Allocation: Generate a bandwidth allocation table based on the WRR algorithm, write it directly to the network chip register through the DMA controller, and trigger the hardware compression module to encode the high-bandwidth stream in real time; Step D5, Priority Scheduling Execution: Through the RTOS's task ready queue, tasks are scheduled to the corresponding CPU cores according to the EDF algorithm; Step D6, Adaptive Adjustment: Train a lightweight LSTM model based on historical data to predict device traffic fluctuation trends.

4. A game controller dynamic configuration system according to claim 1, characterized in that, The specific process of the device dynamic identification and registration module using dynamic priority queues to allocate communication resources is as follows: Step S1: Divide the communication data into four priority queues: real-time operation instructions, audio and video streams, status synchronization packets, and log reports, and set a baseline weight coefficient for each type of data; Step S2: Dynamically adjust the correction weights based on network latency volatility, using the following formula: ； In the formula, For correction factor, Network latency volatility; Step S3: Allocate minimum guaranteed bandwidth according to priority. When a high-priority queue is idle, a low-priority queue is allowed to borrow bandwidth resources. Step S4: Establish a multi-dimensional evaluation model to select the transmission path, and update the path scoring table every five seconds to automatically switch to the optimal path; Step S5: Set up a failover mechanism. When three consecutive transmission failures are detected, the backup channel is automatically activated and the shadow queue data is retransmitted.

5. A game controller dynamic configuration system according to claim 4, characterized in that, The dynamic priority queue allocates communication resources and then constructs a closed-loop feedback control model; the adjustment amount of the model... The formula is: ； In the formula, This is the proportionality coefficient. This represents the deviation between the actual bandwidth utilization and the target value. The integral coefficient is... These are the differential coefficients; The closed-loop feedback control model is also equipped with an abnormal circuit breaker mechanism. When any one of the following is detected for ten consecutive seconds, such as CPU utilization exceeding 90%, memory usage exceeding the safety threshold of 85%, or network jitter rate increasing by 200% year-on-year, degradation protection is triggered, and low-priority queues are automatically closed until the system recovers.

6. A game controller dynamic configuration system according to claim 1, characterized in that, The host-side multimodal device collaboration framework is used to handle heterogeneous device hybrid communication protocols and dynamic resource scheduling; the controller-side multimodal device collaboration framework is used to implement signal acquisition and tactile feedback execution functions.

7. A game controller dynamic configuration system according to claim 1, characterized in that, The process of the behavior feature modeling module in constructing personalized operation profiles based on user operation data and predicting user intent using an LSTM neural network is as follows: Step G1, Multi-source data acquisition: Acquire static and dynamic data; static data includes user basic data and operation preference configuration; dynamic data includes operation time sequence data, state transition records, and abnormal operation events; Step G2, Data Preprocessing: Remove outliers and synchronize timestamps using the NTP protocol; Step G3: Build the project framework: Calculate the number of key presses per unit time, analyze the similarity of operation sequences and the distribution histogram of key pressure; Step G4, LSTM prediction model: The prediction model is trained using LSTM.

8. A game controller dynamic configuration system according to claim 7, characterized in that, The specific process for training the LSTM prediction model is as follows: Step G41: Collect time series data, including timestamps and feature dimensions, and associate static features with dynamic poem data, using primary key fields to link tables; Step G42: Use Kalman filtering on the data to eliminate operation trajectory jitter, and divide it into training set, validation set and test set according to the proportion; Step G43: Design the network architecture, including the input layer, hidden layers, and output layer; Step G44: Optimize strategy configuration, perform dynamic training control, and calculate the loss function; Step G45: Use Bayesian optimization to find the optimal learning decay strategy and evaluate the metrics; Step G46: Online mechanical energy rolling prediction and production deployment optimization.

9. A game controller dynamic configuration system according to claim 1, characterized in that, The dynamic mapping strategy generation model establishes a mapping relationship library between controller hardware features and game scene parameters, collects controller physical state data in real time through sensor arrays, and generates dynamic matching rules by combining character action thresholds in the game scene. The specific process is as follows: Step T1: Collect raw data using force sensors and accelerometers to construct a multidimensional time series dataset; Step T2: Use a sliding window to statistically analyze time-domain and frequency-domain features to generate standardized feature vectors; Step T3: Automatically explore action response boundaries in the game scene through reinforcement learning; Step T4: Customize the rule base; Step T5: Process the data stream. The sensor group reports data at a frequency of 100Hz, and zero-copy transmission is achieved through a circular buffer. Step T6: Execute the rule engine.

10. A game controller dynamic configuration system according to claim 1, characterized in that, The security verification and anomaly handling unit employs a dual-factor authentication method, which includes a knowledge factor and a physical factor. The knowledge factor is based on a preset configuration password or key that the user must input, serving as the foundation for identity verification. The physical factor generates a real-time biometric code using the controller's hardware features and matches it with the baseline data during registration. When the dual-factor authentication is triggered, the system requires simultaneous verification of both the knowledge factor and the physical factor, and uses a bidirectional LSTM network to analyze the controller's operation timing data to dynamically verify the operator's legitimacy.

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