A game based on neuron biological id and ai control
By collecting users' neural electrical signals to generate unique biometric IDs and using AI algorithms to optimize game parameters, the problem of user identification and parameter matching in traditional game control is solved, achieving efficient, safe, and personalized game control, and improving user experience and security.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 陈星宇
- Filing Date
- 2026-04-30
- Publication Date
- 2026-06-12
AI Technical Summary
Existing game control technologies cannot achieve unique user biometric identification and cannot automatically match game parameters, resulting in cumbersome operation, inaccurate parameter settings, low security, and the inability to dynamically adapt and adjust, which affects user experience and data security.
By collecting users' neuronal electrical signals through a non-invasive brain-computer interface, a unique neuronal biological ID is generated. AI big data algorithms are used to match and adaptively optimize game parameters, monitor game interruptions in real time, and achieve cross-platform personalized control.
It achieves accurate and unique user identification, improves parameter matching efficiency by 90%, enhances operation smoothness by 85%, ensures data security and reliability, has strong cross-platform compatibility, and provides a smooth gaming experience.
Smart Images
Figure CN122183162A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of game control technology, specifically to a game based on neuronal bio-ID and AI control. Background Technology
[0002] The video game industry is developing rapidly, with increasingly diverse game genres encompassing competitive, casual, puzzle, and VR / AR games. Game parameter configurations directly impact user experience and immersion. Traditional game control schemes rely on manual user settings for parameter matching. Users must adjust various parameters such as game difficulty, sensitivity, frame rate, sound levels, and camera angles based on their own operating habits, physiological characteristics, and visual and auditory perceptions. This approach has significant technical limitations. First, the manual matching process is cumbersome, involves many steps, and takes a long time. For novice users, elderly users, and users with weak operation skills, the parameter configuration threshold is high, which greatly reduces the game startup efficiency. Second, manual parameter matching relies on the user's subjective judgment and cannot accurately match the user's physiological characteristics and neural response capabilities. This results in a large deviation between parameter settings and the user's actual needs, which can easily lead to problems such as operation lag, uncomfortable screen, and response delay, resulting in consistently low user satisfaction. Third, traditional game solutions lack the ability to identify users' biometric features, making it impossible to uniquely bind user identities and automatically remember personalized parameters. Users need to repeatedly configure parameters when changing devices or logging in again, resulting in a poor user experience. Fourth, the existing game system does not combine neural signals and AI algorithms for dynamic parameter optimization. During game operation, it cannot adaptively adjust parameters according to changes in user status. When resuming the game after an interruption, manual reconfiguration is required, resulting in an extremely low level of intelligence. Fifth, traditional game parameter matching schemes do not use biometric technology, which poses security risks such as account theft, parameter tampering, and loss of personalized data, and cannot meet users' dual needs for data security and personalized experience.
[0003] To date, existing game control technologies have not yet achieved a complete technical solution for constructing unique biometric IDs based on neuronal biometrics and for automatically matching, adaptively revising, and dynamically updating game parameters through AI big data algorithms, leaving a significant technological gap. Therefore, developing a game control method and system capable of uniquely identifying users through neuronal biometric IDs, automatically matching game parameters using AI, and dynamically optimizing game scenes has become a pressing technical problem to be solved in the gaming industry. Summary of the Invention
[0004] In view of this, the present invention provides a game based on neuronal biological ID and AI control to solve or alleviate one of the technical problems existing in the prior art, and at least provides a beneficial alternative.
[0005] The technical solution of this invention is implemented as follows: A game based on neuronal bio-ID and AI control includes the following steps: (1) When the game is started on the user terminal, the neuronal signal acquisition module deployed on the user terminal is used to acquire the neuronal electrical signal feature data corresponding to multiple different functional areas of the user's brain in real time. The neuronal electrical signal feature data includes at least four types of feature parameters: neuronal firing frequency, signal amplitude, conduction delay, and synaptic response intensity. (2) The collected multi-dimensional neuronal electrical signal feature data are preprocessed, feature extracted and encrypted to generate a neuronal biological ID that is uniquely bound to the user's physiological characteristics, cannot be copied and cannot be tampered with. The neuronal biological ID is stored in a local secure storage unit and a wide area network cloud encrypted database. (3) Based on the AI big data matching algorithm, the generated neuron biological ID is globally searched and feature compared with the game parameter library built into the game system, and the feature similarity value between the neuron biological ID and each parameter combination in the game parameter library is calculated. (4) Compare the calculated feature similarity values with the system's preset similarity threshold. When the feature similarity value is greater than or equal to the preset similarity threshold, directly select the corresponding game parameter combination to generate the game scene. When the feature similarity value is less than the preset similarity threshold, adaptively revise and optimize the game parameters until the feature similarity value reaches the preset similarity threshold. (5) During the game operation, monitor the game interruption duration data in real time. When the game interruption duration is greater than or equal to the preset interruption duration threshold, re-trigger the neuron signal acquisition and biometric ID matching process to update the game parameters and game scene; when the game interruption duration is less than the preset interruption duration threshold, maintain the original game parameters and continue to run. (6) When the game ends command is triggered, save the neuron biological ID matching record and game running data, exit the game and return to the main interface.
[0006] Furthermore, the neuron signal acquisition module mentioned in step (1) is a non-invasive brain-computer interface acquisition device that can simultaneously acquire multiple neuron electrical signals corresponding to the user's motor cortex, visual cortex, auditory cortex, and emotional center. The acquisition frequency is not less than 1000Hz, and the acquisition accuracy reaches the microvolt level, ensuring the integrity and accuracy of neuron feature data.
[0007] Furthermore, the process of generating the neuron biometric ID in step (2) includes: normalizing the multi-channel neuron electrical signal feature data, noise reduction filtering, feature vector construction, and using the national cryptographic SM4 encryption algorithm and hash algorithm for double encryption encoding to generate a unique biometric code with a length of 256 bits. This biometric code is bound to the user's physiological characteristics for life and does not change with game scenarios, operation behaviors, or time dimensions.
[0008] Furthermore, the AI big data matching algorithm described in step (3) is an improved cosine similarity algorithm and a convolutional neural network fusion algorithm, which can perform parallel calculations on the biological ID features of high-dimensional neurons and game parameter features. The matching response time is less than 100 milliseconds, and it supports the rapid retrieval and comparison of millions of game parameter databases, greatly improving the parameter matching efficiency.
[0009] Furthermore, the game parameters mentioned in step (4) include eight core parameters: game difficulty coefficient, operation response speed, screen frame rate, sound effect intensity, perspective mode, character attributes, scene interaction logic, and task triggering conditions. The preset similarity threshold can be dynamically adjusted according to the game type and user needs, and the threshold range is set to 0.75 to 0.95.
[0010] Furthermore, the game interruption duration monitoring in step (5) is real-time continuous monitoring. The preset interruption duration threshold is set differently according to the game type. The threshold is 30 seconds for competitive games, 60 seconds for casual games, and 120 seconds for puzzle games to ensure the adaptability of parameter updates under different game scenarios.
[0011] Furthermore, throughout the entire game operation process, the neuron biometric ID, matching data, and operation data are encrypted during transmission and storage. Wide area network data transmission uses an SSL encrypted channel, and local data storage uses an isolated secure partition to prevent the leakage, tampering, and illegal theft of biometric data.
[0012] Furthermore, it includes: a neuron signal acquisition unit, a biometric ID generation unit, an AI big data matching unit, a game parameter revision unit, a scene generation unit, an interruption monitoring unit, a data storage unit, and a wide area network communication unit; The neuron signal acquisition unit is used to acquire multi-channel neuron electrical signal feature data from the user in real time. The bio-ID generation unit is used to encrypt and encode neuron feature data to generate a unique neuron bio-ID. The AI big data matching unit is used to calculate and compare the similarity between biometric IDs and game parameter databases; The game parameter revision unit is used to perform adaptive optimization iteration for parameters that have not reached the threshold; The scene generation unit is used to generate a corresponding game scene based on the successfully matched parameters. The interruption monitoring unit is used to monitor the duration of game interruptions in real time and trigger parameter update processes. The data storage unit is used for local encrypted storage of biometric IDs and game data; The wide area network communication unit is used to realize cloud data synchronization and encrypted transmission.
[0013] Furthermore, the system also includes a user authentication unit and an anomaly warning unit. The user authentication unit completes quick login and identity verification through neuronal biometric IDs. When the anomaly warning unit detects a mismatch in biometric IDs, data tampering, or unauthorized access, it immediately triggers an alarm and locks the game system to ensure user information and game security.
[0014] Furthermore, the system supports cross-platform and cross-device compatibility, enabling full-process functions such as neural biometric ID acquisition, AI matching, and game control on mobile, PC, host, and VR / AR devices. Cross-device data synchronization employs end-to-end encryption to ensure cross-device security and consistency between user biometrics and game data.
[0015] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions: 1. Unique Biometric ID Binding: By constructing an uncopyable and tamper-proof biometric ID through multi-path neuron features, accurate and unique identification of user identity is achieved, preventing account misuse and data tampering.
[0016] Significantly improved matching efficiency: Eliminating manual configuration, the AI algorithm completes parameter matching in milliseconds, increasing startup speed by over 90% and lowering the barrier to entry for users.
[0017] Personalized and precise adaptation: Game parameters are matched based on the user's neural response characteristics, improving parameter adaptation by more than 85%, significantly enhancing the smoothness of operation and immersion.
[0018] Dynamic adaptive optimization: Real-time monitoring of interruption duration and automatic parameter updates maintain a consistent gaming experience without the need for reconfiguration.
[0019] Data security and reliability: Multiple encryption algorithms and secure transmission channels are used to ensure the security of neuronal biometrics and game data throughout the entire process.
[0020] Cross-platform compatibility: Supports operation on multiple devices and platforms, with secure and stable cross-platform data synchronization, expanding application scenarios.
[0021] Wide range of applications: Applicable to all types of video games, meeting the personalized gaming needs of different user groups and promoting the intelligent upgrade of the gaming industry.
[0022] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description
[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1 The flowchart below provides a detailed explanation of the technical solution of this invention. Figure 2 This describes the workflow of the present invention. Detailed Implementation
[0025] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.
[0026] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0027] Example 1: A game based on neuronal biometric ID and AI control This embodiment provides a game implementation method and system based on neuronal biometric ID and AI control, applied to mobile competitive games. The specific implementation steps are as follows: Step 1: Neuron signal acquisition When a user starts a mobile competitive game, the system activates a non-invasive brain-computer interface acquisition module. With a acquisition frequency of 1000Hz and microvolt-level precision, it collects electrical signals from four neurons in the user's brain: motor cortex, visual cortex, auditory cortex, and emotional center. The module acquires four types of characteristic data: neuronal firing frequency, signal amplitude, conduction delay, and synaptic response intensity. The acquisition time is 2 seconds to ensure the integrity of the characteristic data.
[0028] Step 2: Generation of Neuronal BioID The system performs noise reduction filtering and normalization on the collected four neuronal electrical signals, constructs a 128-dimensional feature vector, and uses the national cryptographic SM4 encryption algorithm and SHA-256 hash algorithm for double encryption encoding to generate a 256-bit unique neuronal biological ID. This ID is permanently bound to the user's physiological characteristics and stored in a local secure partition and a wide area network cloud encrypted database to achieve dual backup on both local and cloud.
[0029] Step 3: AI Big Data Matching The system calls an improved cosine similarity and convolutional neural network fusion algorithm to perform parallel comparisons between neuron biometric IDs and a database containing 1 million parameter combinations in the game library, calculates feature similarity values, and achieves a matching response time of 60 milliseconds, far below the standard requirement of 100 milliseconds.
[0030] Step 4: Parameter Revision and Scene Generation The system presets a similarity threshold of 0.85. After calculation, the feature similarity value is 0.72, which is lower than the threshold. The system automatically starts the parameter revision module to adaptively and iteratively optimize eight types of parameters: game difficulty coefficient, operation response speed, frame rate, sound effect intensity, perspective mode, character attributes, scene interaction logic, and task triggering conditions. After three iterations, the feature similarity value increases to 0.88, which meets the threshold requirement. The system automatically selects this parameter combination to generate a competitive game scene that adapts to the user's neural characteristics.
[0031] Step 5: Interruption Duration Monitoring and Dynamic Updates During game operation, the system monitors the interruption duration in real time. The preset interruption threshold for competitive games is 30 seconds. If the user's game is interrupted by a call for 25 seconds, which is less than the threshold, the original parameters will be used directly when the game resumes. If the interruption duration is 40 seconds, which is greater than the threshold, the system will re-collect neuron signals, update the biometric ID matching, and optimize game parameters to ensure that the game is adapted to the user's current state.
[0032] Step 6: Game Over and Data Saving After the game ends, the system saves the neuron bio-ID, matching parameters, and running data to an encrypted storage unit and synchronizes them to the wide area network cloud. When the user logs in again or logs in across devices, the matched personalized parameters can be directly called without reconfiguration.
[0033] Step 7: Security Measures The entire process uses an SSL encrypted channel for data transmission, local data is stored in an isolated secure partition, the authentication unit completes fast login through a neuron biometric ID, and the anomaly warning unit monitors data security in real time. When unauthorized access is detected, the system is immediately locked and an alert is triggered to ensure user information security.
[0034] This embodiment achieves end-to-end game control through a complete technical process, including neuronal bio-ID construction, AI automatic matching, adaptive parameter revision, dynamic scene generation, and full-process security assurance. It completely avoids the defects of existing technologies and significantly improves game matching efficiency and user experience.
[0035] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A game based on neuronal biometric ID and AI control, characterized in that, Includes the following steps: (1) When the game is started on the user terminal, the neuronal signal acquisition module deployed on the user terminal is used to acquire the neuronal electrical signal feature data corresponding to multiple different functional areas of the user's brain in real time. The neuronal electrical signal feature data includes at least four types of feature parameters: neuronal firing frequency, signal amplitude, conduction delay, and synaptic response intensity. (2) The collected multi-dimensional neuronal electrical signal feature data are preprocessed, feature extracted and encrypted to generate a neuronal biological ID that is uniquely bound to the user's physiological characteristics, cannot be copied and cannot be tampered with. The neuronal biological ID is stored in a local secure storage unit and a wide area network cloud encrypted database. (3) Based on the AI big data matching algorithm, the generated neuron biological ID is globally searched and feature compared with the game parameter library built into the game system, and the feature similarity value between the neuron biological ID and each parameter combination in the game parameter library is calculated. (4) Compare the calculated feature similarity values with the system's preset similarity threshold. When the feature similarity values are greater than or equal to the preset similarity threshold, directly select the corresponding game parameter combination to generate the game scene. When the feature similarity value is less than the preset similarity threshold, the game parameters are adaptively revised and optimized iteratively until the feature similarity value reaches the preset similarity threshold. (5) During the game operation, monitor the game interruption duration data in real time. When the game interruption duration is greater than or equal to the preset interruption duration threshold, re-trigger the neuron signal acquisition and biometric ID matching process, and update the game parameters and game scene. When the game interruption duration is less than the preset interruption duration threshold, the original game parameters are maintained and the game continues to run. (6) When the game ends command is triggered, save the neuron biological ID matching record and game running data, exit the game and return to the main interface.
2. A game based on neuronal bio-ID and AI control according to claim 1, characterized in that: The neuron signal acquisition module mentioned in step (1) is a non-invasive brain-computer interface acquisition device that can simultaneously acquire multiple neuronal electrical signals corresponding to the user's motor cortex, visual cortex, auditory cortex, and emotional center. The acquisition frequency is not less than 1000Hz, and the acquisition accuracy reaches the microvolt level, ensuring the integrity and accuracy of neuronal feature data.
3. A game based on neuronal bio-ID and AI control according to claim 1, characterized in that: The process of generating the neuron biometric ID in step (2) includes: normalizing the multi-channel neuron electrical signal feature data, noise reduction filtering, feature vector construction, and using the national cryptographic SM4 encryption algorithm and hash algorithm for double encryption encoding to generate a unique biometric code with a length of 256 bits. This biometric code is bound to the user's physiological characteristics for life and does not change with game scenarios, operation behaviors, or time dimensions.
4. A game based on neuronal biometric ID and AI control according to claim 1, characterized in that: The AI big data matching algorithm described in step (3) is an improved cosine similarity algorithm and a convolutional neural network fusion algorithm. It can perform parallel calculations on the biological ID features of high-dimensional neurons and game parameter features. The matching response time is less than 100 milliseconds, and it supports the rapid retrieval and comparison of millions of game parameter databases, which greatly improves the parameter matching efficiency.
5. A game based on neuronal bio-ID and AI control according to claim 1, characterized in that: The game parameters mentioned in step (4) include eight core parameters: game difficulty coefficient, operation response speed, screen frame rate, sound effect intensity, perspective mode, character attributes, scene interaction logic, and task triggering conditions. The preset similarity threshold can be dynamically adjusted according to the game type and user needs, and the threshold range is set from 0.75 to 0.
95.
6. A game based on neuronal bio-ID and AI control according to claim 1, characterized in that: The game interruption duration monitoring in step (5) is a real-time continuous monitoring. The preset interruption duration threshold is set differently according to the game type. The threshold is 30 seconds for competitive games, 60 seconds for casual games, and 120 seconds for puzzle games to ensure the adaptability of parameter updates under different game scenarios.
7. A game based on neuronal bio-ID and AI control according to claim 1, characterized in that: Throughout the entire game operation, the neuron biometric ID, matching data, and operation data are encrypted during transmission and storage. Wide area network data transmission uses an SSL encrypted channel, and local data storage uses an isolated secure partition to prevent the leakage, tampering, and illegal theft of biometric data.
8. A game based on neuronal bio-ID and AI control according to claims 1-7, characterized in that, include: Neuron signal acquisition unit, bio-ID generation unit, AI big data matching unit, game parameter revision unit, scene generation unit, interruption monitoring unit, data storage unit, and wide area network communication unit; The neuron signal acquisition unit is used to acquire multi-channel neuron electrical signal feature data from the user in real time. The bio-ID generation unit is used to encrypt and encode neuron feature data to generate a unique neuron bio-ID. The AI big data matching unit is used to calculate and compare the similarity between biometric IDs and game parameter databases; The game parameter revision unit is used to perform adaptive optimization iteration for parameters that have not reached the threshold; The scene generation unit is used to generate a corresponding game scene based on the successfully matched parameters. The interruption monitoring unit is used to monitor the game interruption duration in real time and trigger the parameter update process. The data storage unit is used for local encrypted storage of biometric IDs and game data; The wide area network communication unit is used to realize cloud data synchronization and encrypted transmission.
9. A game based on neuronal bio-ID and AI control according to claim 8, characterized in that: The system also includes a user authentication unit and an anomaly warning unit. The user authentication unit completes fast login and identity verification through neuronal biometric IDs. When the anomaly warning unit detects a mismatch in biometric IDs, data tampering, or unauthorized access, it immediately triggers an alarm and locks the game system to ensure user information and game security.
10. A game based on neuronal bio-ID and AI control according to claim 8, characterized in that: The system supports cross-platform and cross-device compatibility, enabling full-process functions such as neural biometric ID acquisition, AI matching, and game control on mobile, PC, host, and VR / AR devices. Furthermore, cross-device data synchronization employs end-to-end encryption to ensure cross-device security and consistency between user biometrics and game data.