Dynamic configuration system of game controller

Abstract

The invention discloses a game controller dynamic configuration system, and relates to the technical field of computers. The system comprises a controller end and a host end. The controller end adopts a heterogeneous equipment hybrid communication protocol stack to realize bidirectional data synchronization between the host end and the controller end; the controller end comprises a trigger, a selector, an interceptor, a switch assembly, a dynamic resource configuration unit and a firmware upgrading interface; the host end comprises an equipment dynamic identification and registration module, a game semantic analysis module, a dynamic mapping strategy generation model, a user adaptive learning unit, a multi-modal feedback fusion unit and a security verification and exception handling unit. According to the method, the mapping relation library of the controller hardware features and the game scene parameters is established, the physical state data of the controller is collected in real time through the sensor group, and the dynamic matching rule is generated in combination with the role action threshold in the game scene, so that the controller configuration difficulty is reduced, and the controller configuration efficiency is improved.

Description

Technical Field

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

[0002] Traditional game controllers mostly use a static layout of physical buttons or joysticks, and cannot dynamically adjust function mapping according to game types or user habits. Specific defects include:

[0003] (1) Complex configuration process: Existing controller configuration needs to be implemented through code modification or special tools, and lacks visual interface support, resulting in a high threshold for ordinary users to operate;

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

[0005] As the demand for dynamic interaction grows, the diversification of game types (such as VR / AR, somatosensory games) requires controllers to respond to dynamic commands in real time, but existing technologies still rely on preset firmware updates and lack instant configuration capabilities. At the same time, players' demand for personalized operating experience has increased, but existing patents (such as Huali Technology CN108553884B) focus on innovation in physical control methods and do not solve the problem of dynamic customization at the software layer. For example, although existing patents such as (CN113144619B) propose a visual configuration method, it focuses on the game management background and does not deeply integrate the 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 the present invention is to provide a game controller dynamic configuration system, which solves the problems of difficult configuration of existing controllers, complicated configuration process and high professional level 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 group, and generating dynamic matching rules in combination with the character action thresholds in the game scene.

[0008] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

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

[0010] The controller side adopts a heterogeneous device hybrid communication protocol stack to achieve bidirectional data synchronization between the host side and the controller side; both the controller side and the host side are deployed with a multi-modal device collaboration framework;

[0011] The controller end includes a trigger, a selector, an interceptor, a switch component, a dynamic resource configuration unit and a firmware upgrade interface; the trigger supports physical pressing operation and is used to achieve multi-level tactile feedback in conjunction with a pressure sensor; the selector is used to switch the operation mode through a mechanical or electronic switching device; the interceptor is linked with the selector to change the effective stroke of the trigger through physical limit; the switch component is used to trigger signal capture and contactless operation detection; the dynamic resource configuration unit is used to identify the connection status of the device 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 end includes a device dynamic identification and registration module, a game semantic analysis 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 is used to scan and connect the device through a hybrid communication protocol, and allocate communication resources using a dynamic priority queue; the game semantic analysis module is used to extract key operation instruction sets and their semantic labels by reverse parsing the game process memory or API call chain; the dynamic mapping strategy generation model is used to generate a multi-level mapping scheme according to 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 is used to build a personalized operation portrait according to user operation data, and use an LSTM neural network to predict user intentions; the dynamic weight adjustment module is used to set the operation mode attenuation 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 exception handling unit is used to use two-factor authentication to ensure that the mapping scheme is reasonable.

[0013] As a preferred technical solution, the controller has two operation modes of a built-in adaptive mechanism:

[0014] One operation mode is: after the interception component intervenes, the trigger needs to be pressed in stages; that is, the middle position switch is triggered first, and then the end feedback motor is activated, which is used for delicate operation scenarios;

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

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

[0017] Step D1, initialization phase: load the embedded chip, start the kernel patch implementation, and configure the communication protocol stack;

[0018] Step D2, device identification and registration: perform protocol handshake, 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 change of the wireless device through RSSI;

[0020] Step D4, dynamic resource allocation: Generate a bandwidth allocation table according to the WRR algorithm, write it directly into 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 task ready queue of RTOS, the task is scheduled to the corresponding CPU core 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 packages (intermediate level) and log reports (lowest level), and set a benchmark weight coefficient for each type of data, where the real-time operation instruction weight is 1, the audio and video stream weight is 0.7, the status synchronization package weight is 0.4 and the log report weight is 0.1;

[0025] Step S2: Dynamically adjust the correction weight based on the network delay fluctuation rate. The specific formula is:

[0026]

[0027] In the formula, η is the correction coefficient, σ is the network delay fluctuation rate; when the delay standard deviation σ≥50ms is detected, 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; 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 a transmission path, and update the path score 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, automatically enable the backup channel and retransmit the shadow queue data.

[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 is the proportionality coefficient, e(t) is the deviation between the actual bandwidth utilization and the target value, K i is the integration coefficient, K d is the differential coefficient;

[0034] The closed-loop feedback control model is also equipped with an abnormal fuse mechanism. When it detects that the CPU utilization is greater than 90% for ten consecutive seconds, the memory usage exceeds the safety threshold of 85%, or the network jitter rate increases by 200% year-on-year, the downgrade protection is triggered and the low-priority queue is 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 hybrid communication protocols and dynamic resource scheduling of heterogeneous devices; the multimodal device collaboration framework on the controller side is used to implement the execution functions of signal acquisition and tactile feedback.

[0036] As a preferred technical solution, the process of the behavior feature modeling module building a personalized operation portrait based on user operation data and using the LSTM neural network to predict user intentions is as follows:

[0037] Step G1, multi-source data collection: collect static data and dynamic data; static data includes user basic data (device type, registration information) and operation preference configuration (default sensitivity setting); dynamic data includes operation timing data (click / move trajectory), state switching record (combat / exploration mode switching frequency) and abnormal operation events (number of false touches);

[0038] Step G2, data preprocessing: remove outliers and synchronize timestamps using the NTP protocol;

[0039] Step G3, building a project framework: calculating the key triggering times per unit time, analyzing the similarity of the operation sequence and the key pressure distribution histogram;

[0040] Step G4, LSTM prediction model: use LSTM to train the prediction model.

[0041] As a preferred technical solution, the specific process of the LSTM training 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, and associate tables through primary key fields;

[0043] Step G42: Using Kalman filtering to eliminate the jitter of the operation trajectory, and dividing the data into a training set, a validation set, and a test set according to the proportion;

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

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

[0046] Step G45: Use Bayesian optimization to find the best learning decay strategy and evaluation indicators;

[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 in combination with the character action threshold in the game scene. The specific process is as follows:

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

[0050] Step T2: Use a sliding window to count time domain features and frequency domain features to generate a standardized feature vector;

[0051] Step T3: Automatically explore action response boundaries in game scenarios 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 100 Hz, and zero-copy transmission is achieved through the ring buffer;

[0054] Step T6: Execute the rule engine.

[0055] As a preferred technical solution, the dual factors adopted by the security verification and exception handling unit include a knowledge factor and a physical factor; the knowledge factor is a preset configuration password or key that the user needs to enter as the basic condition for identity authentication; the physical factor generates a real-time biometric code through the hardware characteristics of the controller, which is matched with the benchmark data at the time of registration; when the dual factor verification is triggered, the system requires simultaneous verification of the knowledge factor and the physical factor, and uses a bidirectional LSTM network to analyze the controller operation timing data to dynamically verify the legitimacy of the operator's identity.

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

[0057] (1) The present invention 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 in combination with the character action threshold in the game scene, thereby reducing the difficulty of controller configuration and improving the configuration efficiency of the controller;

[0058] (2) The multi-modal device collaboration framework of the present invention realizes cross-device resource optimization through centralized scheduling on the host side, while using the cloud to expand service capabilities and avoid computing bottlenecks on the controller side;

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

[0060] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0062] Figure 1 The present invention is a schematic diagram of the structure of a game controller dynamic configuration system. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] In addition, 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] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] Embodiment 1

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

[0068] The controller uses a heterogeneous device hybrid communication protocol stack to achieve two-way data synchronization between the host and controller. Both the controller and host are deployed with a multi-modal device collaboration framework.

[0069] The controller end includes a trigger, a selector, an interceptor, a switch component, a dynamic resource configuration unit, and a firmware upgrade interface; the trigger supports physical pressing operations and is used in conjunction with a pressure sensor to achieve multi-level tactile feedback; the selector is used to switch the operating mode through a mechanical or electronic switching device; the interceptor and the selector are linked to change the effective stroke of the trigger through physical limits; the switch component is used to trigger signal capture and contactless operation detection; the dynamic resource configuration unit is used to identify the connection status of the device 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 includes a device dynamic identification and registration module, a game semantic analysis 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 is used to scan and connect to the device through a hybrid communication protocol, and allocate communication resources using a dynamic priority queue; the game semantic analysis module is used to extract key operation instruction sets and their semantic labels by reverse parsing the game process memory or API call chain; the dynamic mapping strategy generation model is used to generate a multi-level mapping scheme 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 is used to build a personalized operation portrait based on user operation data, and use an LSTM neural network to predict user intentions; the dynamic weight adjustment module is used to set the operation mode attenuation 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 exception handling unit is used to use two-factor authentication to ensure that the mapping scheme is reasonable.

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

[0072] As shown in the second embodiment, the controller has two operation modes of the built-in adaptive mechanism:

[0073] One operation mode is: after the interception component intervenes, the trigger needs to be pressed in stages; that is, the middle position switch is triggered first, and then the end feedback motor is activated, which is used for delicate operation scenarios;

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

[0075] The dynamic resource configuration unit identifies the connection status of the device through the 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, start the kernel patch implementation, and configure the communication protocol stack;

[0077] Step D2, device identification and registration: perform protocol handshake, 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 change of the wireless device through RSSI;

[0079] Step D4, dynamic resource allocation: Generate a bandwidth allocation table according to the WRR algorithm, write it directly into 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 task ready queue of RTOS, the task is scheduled to the corresponding CPU core 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 benchmark weight coefficient for each type of data;

[0084] Step S2: Dynamically adjust the correction weight based on the network delay fluctuation rate. The specific formula is:

[0085]

[0086] In the formula, η is the correction coefficient, σ is the network delay fluctuation rate;

[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 a transmission path, and update the path score 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, automatically enable the backup channel and retransmit the shadow queue data.

[0090] After the dynamic priority queue allocates communication resources, a closed-loop feedback control model is constructed; the adjustment amount Δ of the model is formulated as follows:

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

[0092] In the formula, K p is the proportionality coefficient, e(t) is the deviation between the actual bandwidth utilization and the target value, Ki is the integration coefficient, K d is the differential coefficient;

[0093] The closed-loop feedback control model is also equipped with an abnormal fuse mechanism. When it detects that the CPU utilization is greater than 90% for ten consecutive seconds, the memory usage exceeds the safety threshold of 85%, or the network jitter rate increases by 200% year-on-year, the downgrade protection is triggered and the low-priority queue is automatically closed until the system recovers.

[0094] The multimodal device collaboration framework on the host side is used to undertake hybrid communication protocols and dynamic resource scheduling of heterogeneous devices; the multimodal device collaboration framework on the controller side is used to implement the execution functions of signal acquisition and tactile feedback.

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

[0096] The process of the behavior feature modeling module building a personalized operation profile based on user operation data and using the LSTM neural network to predict user intentions is as follows:

[0097] Step G1, multi-source data collection: collect static data and dynamic data; static data includes user basic data (device type, registration information) and operation preference configuration (default sensitivity setting); dynamic data includes operation timing data (click / move trajectory), state switching record (combat / exploration mode switching frequency) and abnormal operation events (number of false touches);

[0098] Step G2, data preprocessing: remove outliers and synchronize timestamps using the NTP protocol;

[0099] Step G3, building a project framework: calculating the key triggering times per unit time, analyzing the similarity of the operation sequence and the key pressure distribution histogram;

[0100] Step G4, LSTM prediction model: use LSTM to train the prediction model.

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

[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:

[0106] Loss function: Focal Loss solves category 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 of LSTM training 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, and associate tables through primary key fields;

[0111] Step G42: Using Kalman filtering to eliminate the jitter of the operation trajectory, and dividing the data into a training set, a validation set, and a test set according to the proportion;

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

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

[0114] Step G45: Use Bayesian optimization to find the best learning decay strategy and evaluation indicators;

[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 the controller hardware features and the game scene parameters, collects the controller physical state data in real time through the sensor group, and generates dynamic matching rules based on the character action threshold in the game scene. The specific process is as follows:

[0117] Step T1: Collect raw data (such as pressure value, three-axis acceleration, displacement) through hardware such as force sensors and accelerometers to construct a multidimensional time series data set;

[0118] Step T2: Use a sliding window (such as a 50ms window) to count the time domain features (mean, variance) and frequency domain features (FFT energy spectrum) to generate a standardized feature vector; discretize continuous physical quantities (such as pressure values ​​0-100N) into 5 levels of intensity (light, medium, strong, etc.) to reduce the complexity of calculation.

[0119] Step T3: Automatically explore action response boundaries in game scenarios 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 100 Hz. Zero-copy transmission is achieved through a ring buffer. The feature extraction thread and the rule matching thread run asynchronously, and the delay is controlled within 5 ms.

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

[0123] When matching in real time, use:

[0124] Rete algorithm optimized version: reduces repeated calculations by sharing conditional nodes, and the matching delay is less than 3ms;

[0125] Event monitoring mechanism: register hardware input events (such as changes in the vibration intensity of the handle) to trigger rule reloading.

[0126] Verification and optimization of rule engine:

[0127] (1) Test methods include:

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

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

[0130] (2) Performance Optimization

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

[0132] Cache mechanism: LRU cache is performed on the successfully matched rule paths.

[0133] Embodiment 2

[0134] The controller of this embodiment has two operation modes of the built-in adaptive mechanism:

[0135] One operation mode is: after the interception component intervenes, the trigger needs to be pressed in stages; that is, the middle position switch is triggered first, and then the end feedback motor is activated, which is used for delicate operation scenarios;

[0136] Another operating mode is to fully release the trigger travel, allowing full-stroke 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) The first operation mode (staged pressing, fine operation scene)

[0139] Specific implementation case: Sniper aiming scene in shooting games

[0140] Middle position switch trigger: When the player presses the trigger lightly to 50% of the stroke, the interception component triggers the middle position switch, and the system automatically activates the scope zoom function (such as switching from 4x scope to 8x scope). At this time, the tactile feedback motor provides a clear sense of paragraph prompts.

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

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

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

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

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

[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 in a single scanning cycle (cycle <1ms); through the logic control override mechanism, it is ensured that the last pressing parameter takes priority (for example, only the end state is responded to when pressing continuously and quickly).

[0148] The dynamic resource configuration unit identifies the connection status of the device through the 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, start the kernel patch implementation, and configure the communication protocol stack;

[0150] Step D2, device identification and registration: perform protocol handshake, 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 change of the wireless device through RSSI;

[0152] Step D4, dynamic resource allocation: Generate a bandwidth allocation table according to the WRR algorithm, write it directly into 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 task ready queue of RTOS, the task is scheduled to the corresponding CPU core 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 benchmark weight coefficient for each type of data;

[0157] Step S2: Dynamically adjust the correction weight based on the network delay fluctuation rate. The specific formula is:

[0158]

[0159] In the formula, η is the correction coefficient, σ is the network delay fluctuation rate;

[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 a transmission path, and update the path score 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, automatically enable the backup channel and retransmit the shadow queue data.

[0163] It is worth noting that in the above system embodiment, the various units included 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 the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0164] In addition, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiments can be completed by instructing related hardware through a program, and the corresponding program can be stored in a computer-readable storage medium.

[0165] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A game controller dynamic configuration system, comprising a controller end and a host end, characterized in that: The controller side adopts a heterogeneous device hybrid communication protocol stack to achieve bidirectional data synchronization between the host side and the controller side; both the controller side and the host side are deployed with a multi-modal device collaboration framework; The controller end includes a trigger, a selector, an interceptor, a switch component, a dynamic resource configuration unit and a firmware upgrade interface; the trigger supports physical pressing operation and is used to achieve multi-level tactile feedback in conjunction with a pressure sensor; the selector is used to switch the operation mode through a mechanical or electronic switching device; the interceptor is linked with the selector to change the effective stroke of the trigger through physical limit; the switch component is used to trigger signal capture and contactless operation detection; the dynamic resource configuration unit is used to identify the connection status of the device 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 end includes a device dynamic identification and registration module, a game semantic analysis 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 is used to scan and connect to the device through a hybrid communication protocol, and allocate communication resources using a dynamic priority queue; the game semantic analysis module is used to extract key operation instruction sets and their semantic labels by reverse parsing the game process memory or API call chain; the dynamic mapping strategy generation model is used to generate a multi-level mapping scheme according to 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 is used to build a personalized operation portrait according to user operation data, and use an LSTM neural network to predict user intentions; The dynamic weight adjustment module is used to set the operation mode attenuation 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 exception handling unit is used to adopt two-factor authentication to ensure that the mapping scheme is reasonable.

2. A game controller dynamic configuration system according to claim 1, characterized in that: The controller has two operation modes with built-in adaptive mechanism: One operation mode is: after the interception component intervenes, the trigger needs to be pressed in stages; that is, the middle position switch is triggered first, and then the end feedback motor is activated, which is used for delicate operation scenarios; Another operating mode is to fully release the trigger travel, allowing full-stroke response for high-speed continuous operation.

3. A game controller dynamic configuration system according to claim 1, characterized in that: The dynamic resource configuration unit identifies the connection status of the device through the embedded chip and automatically allocates communication bandwidth and processing priority. The specific process is as follows: Step D1, initialization phase: load the embedded chip, start the kernel patch implementation, and configure the communication protocol stack; Step D2, device identification and registration: perform protocol handshake, complete device identity authentication and QoS level labeling; Step D3, real-time status monitoring: poll the connection status every 1ms and monitor the location change of the wireless device through RSSI; Step D4, dynamic resource allocation: Generate a bandwidth allocation table according to the WRR algorithm, write it directly into 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 task ready queue of RTOS, the task is scheduled to the corresponding CPU core 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 benchmark weight coefficient for each type of data; Step S2: Dynamically adjust the correction weight based on the network delay fluctuation rate. The specific formula is: In the formula, η is the correction coefficient, σ is the network delay fluctuation rate; 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 a transmission path, and update the path score 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, automatically enable the backup channel and retransmit the shadow queue data.

5. A game controller dynamic configuration system according to claim 4, characterized in that: After the dynamic priority queue allocates communication resources, a closed-loop feedback control model is constructed; the adjustment amount Δ formula of the model is: Δ=K p ×e(t)+K i ×∫e(t)dt+K d ×de(t) / dt; In the formula, K p is the proportionality coefficient, e(t) is the deviation between the actual bandwidth utilization and the target value, K i is the integration coefficient, K d is the differential coefficient; The closed-loop feedback control model is also equipped with an abnormal fuse mechanism. When it detects that the CPU utilization is greater than 90% for ten consecutive seconds, the memory usage exceeds the safety threshold of 85%, or the network jitter rate increases by 200% year-on-year, the downgrade protection is triggered and the low-priority queue is automatically closed until the system recovers.

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

7. A game controller dynamic configuration system according to claim 1, characterized in that: The process of the behavior feature modeling module building a personalized operation profile based on user operation data and using the LSTM neural network to predict user intentions is as follows: Step G1, multi-source data collection: collect static data and dynamic data; static data includes user basic data and operation preference configuration; dynamic data includes operation timing data, state switching records and abnormal operation events; Step G2, data preprocessing: remove outliers and synchronize timestamps using the NTP protocol; Step G3, building a project framework: calculating the key triggering times per unit time, analyzing the similarity of the operation sequence and the key pressure distribution histogram; Step G4, LSTM prediction model: use LSTM to train the prediction model.

8. A game controller dynamic configuration system according to claim 7, characterized in that: The specific process of the LSTM training prediction model is as follows: Step G41: collect time series data, including timestamps and feature dimensions, and associate static features with dynamic poem data, and associate tables through primary key fields; Step G42: Using Kalman filtering to eliminate the jitter of the operation trajectory, and dividing the data into a training set, a validation set, and a test set according to the proportion; Step G43: Design the network architecture including input layer, hidden layer and output layer; Step G44: Optimize strategy configuration, perform dynamic training control, and calculate loss function; Step G45: Use Bayesian optimization to find the best learning decay strategy and evaluation indicators; 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 the controller hardware features and the game scene parameters, collects the controller physical state data in real time through the sensor group, and generates dynamic matching rules in combination with the character action threshold in the game scene. The specific process is as follows: Step T1: Collect raw data through hardware such as force sensors and accelerometers to construct a multidimensional time series data set; Step T2: Use a sliding window to count time domain features and frequency domain features to generate a standardized feature vector; Step T3: Automatically explore action response boundaries in game scenarios through reinforcement learning; Step T4: Customize the rule base; Step T5: Process the data stream, the sensor group reports data at a frequency of 100 Hz, and zero-copy transmission is achieved through the ring buffer; Step T6: Execute the rule engine.

10. A game controller dynamic configuration system according to claim 1, characterized in that: The dual factors adopted by the security verification and exception handling unit include knowledge factors and physical factors; the knowledge factor is a preset configuration password or key that the user needs to enter as the basic condition for identity authentication; the physical factor generates a real-time biometric code through the hardware characteristics of the controller and matches it with the benchmark data during registration; when the dual factor verification is triggered, the system requires simultaneous verification of the knowledge factor and the physical factor, and uses a bidirectional LSTM network to analyze the controller operation timing data to dynamically verify the legitimacy of the operator's identity.

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