Physical space behavior supervision and active immunization system and method for multi-modal ai

Abstract

This invention discloses a physical space behavior monitoring and proactive immune system and method for multimodal AI, belonging to the field of artificial intelligence technology. It aims to solve the technical problems of existing AI security protection measures being easily bypassed, lacking physical world prediction capabilities, and being difficult to identify disguised attacks. The system is deployed on an FPGA module, MCU module, or independent hardware chip with equivalent logic processing capabilities, independent of the AI ​​agent and physical device controller. It includes an instruction perception module, a pre-analysis engine module, a side-channel perception array, a hybrid decision module, and a hybrid blocking module. The hybrid blocking module sends blocking signals to the physical device through a dedicated GPIO interface and a unidirectional signal isolation link, physically cutting off the circuit. The blocking action is independent of the physical device's operating system and cannot be bypassed by software programs. This invention achieves hardware-level immune protection, physical world prediction, covert attack identification, and high reliability assurance, significantly improving the accuracy and reliability of threat detection.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence security technology, specifically, it relates to a physical space behavior monitoring and proactive immune system and method for multimodal AI. Background Technology

[0002] With the rapid development of artificial intelligence (AI) technology, multimodal AI is increasingly being applied in fields such as industrial control, smart homes, and autonomous driving. Multimodal AI systems, by integrating multiple sensory modalities such as vision, language, and hearing, can understand and execute complex environmental interaction tasks, playing a crucial role in improving production efficiency and convenience. However, AI systems may face risks of loss of control, malicious attacks, or unexpected behaviors during task execution. These risks could lead to serious consequences such as damage to physical equipment, personal injury, or leakage of sensitive data. The security of AI systems has become a key bottleneck restricting their further widespread application.

[0003] Currently, security protection technologies for AI systems mainly focus on software-level monitoring and defense. By deploying software firewalls or behavior monitoring modules between AI agents and physical devices, AI commands can be filtered and abnormal behaviors can be detected. This protection method can play a certain role in strengthening security in specific scenarios.

[0004] However, existing AI security technologies suffer from several fatal flaws: First, software protection mechanisms can be bypassed or disabled by AI processes with higher privileges. When an AI agent gains root access or equivalent privileges, it can terminate or penetrate the software protection layer, rendering security monitoring ineffective. Second, existing technologies lack the ability to predict the actual impact on the physical world. Software protection can only analyze the semantic legality of instructions, but cannot assess the potential physical conflicts or equipment damage that may result from the execution of instructions in the physical space, making it difficult to prevent physical harm before it occurs. Third, existing technologies struggle to identify malicious side-channel behavior behind disguised "benign" instructions. Attackers may use the normal functions of the AI ​​system as cover to steal sensitive information or trigger unauthorized physical operations through hidden data channels, and traditional protection methods cannot effectively identify such disguised attacks. These problems collectively result in AI systems lacking effective hardware-level protection against malicious attacks or unexpected loss of control, necessitating an unbypassable proactive immune system that simultaneously incorporates semantic analysis, physical prediction, and side-channel detection. Summary of the Invention

[0005] The purpose of this invention is to provide a physical space behavior monitoring and proactive immune system and method for multimodal AI, mainly to solve the technical problems that existing AI security protection measures can be bypassed, lack the ability to predict the physical world, and are difficult to identify disguised attacks.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A physical space behavior monitoring and proactive immune system for multimodal AI, deployed on an FPGA module, MCU module, or independent hardware chip with equivalent logic processing capabilities, independent of the AI ​​agent and physical device controller, includes:

[0008] The instruction perception module is used to capture high-level semantic instructions output by the AI ​​agent in real time through a non-intrusive bypass listening method, and to parse out the control target and execution parameters based on natural language processing technology.

[0009] The pre-simulation engine module is used to construct a digital twin model of a virtual environment consistent with the physical space, map the execution parameters to the virtual environment for simulation pre-simulation, and output physical conflict detection results;

[0010] A side-channel sensing array is used to synchronously acquire side-channel signals of physical devices, including chip load rate, memory bandwidth, and bus data stream.

[0011] The hybrid decision module is used to make high-risk determinations based on at least two of the side-channel fingerprint data, AI command behavior data, and system operation log data, and generate control decision signals.

[0012] A hybrid blocking module is used to connect a fail-safe hard interrupt unit in series in the power supply circuit or core control signal circuit of the physical device. It remains conductive under normal conditions and physically disconnects the circuit only when a high-risk control decision signal is received. The hybrid blocking module is directly connected to the actuator via a dedicated GPIO interface, and the connection link does not pass through the host bus, operating system, or network protocol stack. A unidirectional signal isolation component is connected in series in the connection link. This unidirectional signal isolation component is configured to block the data reception path, retaining only the data transmission path and the common ground, forming a unidirectional, non-returnable, and tamper-proof transmission loop for control commands. The FPGA module, MCU module, or independent hardware chip with equivalent logic processing capabilities is externally shielded with an integrated anti-tampering detection circuit.

[0013] Furthermore, in this invention, the high-risk determination specifically includes:

[0014] The side-channel fingerprint data, AI command behavior data, and system operation log data are time-aligned, and the time alignment is based on a unified timestamp window for synchronization processing.

[0015] A joint determination is made based on at least two of the side-channel fingerprint data, AI command behavior data, and system operation log data. When at least two of the data meet the corresponding high-risk determination conditions, a high-risk decision is triggered.

[0016] Furthermore, in this invention, the side-channel sensing array is configured with an abnormal behavior feature fingerprint database, the abnormal behavior feature fingerprint including a combination of data theft features: the GPU computing core load rate is lower than a preset first threshold, and the video memory read bandwidth is higher than a preset second threshold; wherein, the first threshold and the second threshold are dynamically calibrated according to the baseline performance of the hardware platform.

[0017] Furthermore, in this invention, the virtual environment model is configured to periodically update sensor data to calibrate the consistency between the virtual environment and the physical space, thereby ensuring the accuracy of the simulation.

[0018] Furthermore, in this invention, the fail-safe architecture is configured such that when the system itself experiences a power outage, crash, or communication loss, the hard interrupt unit automatically resets to the on state, ensuring that the service continuity of the physical device is not affected.

[0019] Furthermore, in this invention, the virtual environment model includes a geometric model, a kinematic model, and a dynamic model of the physical device, used to simulate the changes in the physical state of the device after executing instructions, including position, velocity, acceleration, and force conditions.

[0020] Furthermore, in this invention, the side-channel sensing array is deployed at the signal acquisition point of the physical device, and uses high-precision sensors to collect electromagnetic radiation, power consumption changes and timing characteristics generated during device operation in real time, forming a multi-dimensional side-channel signal feature vector.

[0021] Based on the above system, the present invention also provides a physical space behavior monitoring and active immunity method for multimodal AI, comprising the following steps:

[0022] S1, through FPGA module, MCU module or independent hardware chip with equivalent logic processing capability, bypasses and captures high-level semantic instructions of AI agent;

[0023] S2 performs physical simulation and pre-playing of high-level semantic instructions in a virtual environment to predict physical conflicts;

[0024] S3 collects side-channel signals from physical devices to identify covert abnormal behavior;

[0025] S4. Cross-validation and joint determination are performed based on at least two of the following: side-channel fingerprint data, AI command behavior data, and system operation log data.

[0026] S5. If the risk is deemed high, a one-way blocking signal is sent through the dedicated GPIO interface to physically disconnect the device circuit; otherwise, repeat steps S1 to S5.

[0027] Furthermore, in this invention, the cross-validation and joint determination rules in step S4 are as follows: the side channel signal is used to verify the actual execution of the AI ​​instruction at the physical layer. If the instruction semantics are benign but the side channel shows abnormalities, it is determined to be a spoofing attack.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] (1) This invention deploys the active immune monitoring system independently on an FPGA module, MCU module, or independent hardware chip with equivalent logic processing capabilities, outside of the AI ​​agent and physical device. It achieves blocking control through a dedicated GPIO interface and a unidirectional signal isolation link. The blocking action is completely independent of the operating system and software environment of the physical device, thus avoiding the defects of traditional software protection mechanisms that can be bypassed or shut down by high-privilege AI processes from the architectural level. At the same time, the hybrid blocking module adopts a fault-safe architecture design. When the monitoring system itself experiences power failure, crash, or other faults, it can automatically reset to the conduction state. While achieving high-reliability protection, it also ensures the business continuity of the physical device, and can fundamentally provide multimodal AI systems with underlying hardware-level security immunity capabilities.

[0030] (2) By constructing a digital twin virtual environment that is highly synchronized with the physical space, this invention can perform a full-process simulation and pre-play of the execution effect of AI commands before they are actually executed. Based on the geometric model, kinematic model and dynamic model of physical equipment, it can accurately predict physical conflicts that may occur during the execution of commands, such as joint over-limit, spatial collision and trajectory over-limit. It can complete risk identification and interception before physical damage or equipment damage occurs, breaking through the technical limitations of traditional software protection that can only perform semantic legality verification and cannot assess the actual impact of physical space, and realizing full-cycle safety control of the physical behavior of AI systems.

[0031] (3) This invention employs a heterogeneous information fusion and judgment mechanism based on side-channel fingerprint data, AI command behavior data, and system operation log data. By using a unified timestamp window to achieve temporal alignment of multi-source information and combining at least two data joint judgment rules, it can cross-verify command semantics, physical execution effects, and actual device operating status. It can identify both explicit high-risk commands at the semantic level and disguised attacks that appear "benign" but actually exhibit side-channel anomalies. This solves the technical problem that traditional protection methods cannot effectively identify malicious behaviors such as covert data theft and unauthorized physical operations, significantly improving the comprehensiveness and accuracy of threat detection. Attached Figure Description

[0032] Figure 1 This is a block diagram illustrating the overall structural principle of the present invention.

[0033] Figure 2 This is a flowchart of the execution of the hybrid decision module in this invention.

[0034] Figure 3 This is a flowchart of the system operation of the present invention. Detailed Implementation

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments. The embodiments of the present invention include, but are not limited to, the following embodiments.

[0036] Example 1

[0037] like Figure 1 As shown, this invention discloses a physical space behavior monitoring and proactive immune system for multimodal AI. This system is deployed on an FPGA module, MCU module, or an independent hardware chip with equivalent logic processing capabilities, independent of the AI ​​agent and physical device controller. This independent hardware chip can be implemented using an industrial-grade embedded computing platform. The core processor can be a TMS320C6678 multi-core digital signal processor, which has eight C66x floating-point cores and a maximum clock speed of 1.25GHz, possessing powerful parallel computing capabilities to meet the computing power requirements for real-time semantic instruction parsing, virtual environment simulation, and information fusion decision-making. The system hardware is equipped with an independent power supply system, using a 24V DC power supply, and is isolated from the power systems of the AI ​​agent and physical devices through an isolation transformer to reduce the impact of electromagnetic interference and ground potential differences. The independent hardware is externally shielded with an electromagnetic shielding shell, and internally integrates anti-tampering detection circuitry. The electromagnetic shielding shell can be reinforced using a potting process, with potting materials such as epoxy resin, polyurethane, or thermally conductive silicone. The anti-tamper detection circuit is used to detect physical deformation of the casing or unauthorized opening behavior, and executes data protection and security cut-off actions after being triggered.

[0038] The instruction perception module, as the front-end perception unit of the system, is responsible for capturing high-level semantic instructions output by the AI ​​agent in real time through a non-intrusive bypass monitoring method. In this embodiment, the hardware architecture of the instruction perception module includes three parts: a network traffic mirroring port, a PCIe bus sniffer, and a dedicated instruction parsing accelerator. The network traffic mirroring port is implemented using a 100Mbps Ethernet optical module, which copies the communication traffic between the AI ​​agent and the physical device controller to this module through the switch port mirroring function; the PCIe bus sniffer is implemented based on a Xilinx Artix-7 series FPGA chip, which can capture PCIe transaction layer data packets between the AI ​​agent and external devices at a sampling rate of 40MHz and parse out the control instructions in them; the dedicated instruction parsing accelerator is implemented using a field-programmable gate array (FPGA) in conjunction with an ARM Cortex-M4 soft-core processor, and has a built-in instruction semantic recognition algorithm model based on a long short-term memory network (LSTM), which can convert the raw instruction stream into structured control targets and execution parameters. The instruction sensing module communicates with the system main control unit via a high-speed serial RapidIO interface, with a transmission bandwidth of up to 10Gbps and an instruction capture delay controlled within 50 microseconds, ensuring real-time requirements.

[0039] The pre-simulation engine module is responsible for constructing a virtual environment model consistent with the physical space and mapping the execution parameters output by the AI ​​agent to the virtual environment for simulation pre-simulation, outputting physical conflict detection results. The hardware core of this module is implemented using the NVIDIA Jetson AGX Xavier edge computing platform, equipped with a 512-core Volta architecture GPU and an 8-core ARMv8.2 processor, capable of rendering the 3D geometric model of the robotic arm and performing kinematic simulations in real time within the virtual environment. The geometric modeling of the virtual environment model is established using CAD data import, including 3D mesh models of key components such as the robotic arm base, upper arm, lower arm, and end effector, with a model accuracy better than 0.1 mm. The kinematic modeling part establishes the kinematic equations of the robotic arm based on the Denavit-Hartenberg (DH) parametric method, capable of forward calculation of the end effector pose and inverse calculation of joint angles. The dynamic modeling part uses the Lagrange equations to establish the dynamic model of the robotic arm, capable of simulating and calculating joint torques, inertial coupling, and gravity compensation. The pre-simulation engine module and the side-channel sensing array synchronize data via gigabit Ethernet. The parameter calibration cycle of the virtual environment model is set to 100 milliseconds, that is, the actual sensor data of the physical device is received every 100 milliseconds to correct the model, ensuring that the consistency error between the virtual environment and the physical space is controlled within the allowable range.

[0040] The side-channel sensing array is configured to synchronously acquire side-channel signals from the physical device, including multi-dimensional information such as chip load rate, memory bandwidth, bus data flow, electromagnetic radiation, power consumption changes, and timing characteristics. In this embodiment, the hardware deployment of the side-channel sensing array includes the following sensor nodes: The first sensor node is deployed near the main control chip of the robotic arm controller, using a Roots coil high-frequency current probe to measure the current waveform on the chip's power supply pins, with a sampling rate of 100MS / s and a resolution of 12 bits, to capture instantaneous power consumption changes generated during chip operation; the second sensor node is deployed near the memory slots on the controller motherboard, using a logic analyzer to capture the access timing and bandwidth data of the DDR4 memory bus, with a data sampling rate of 200MHz, enabling real-time statistics of memory read / write throughput and access intervals; the third sensor node is deployed at the ventilation opening of the device casing, using a broadband EMI antenna to receive electromagnetic radiation generated during device operation, covering a frequency range of 100MHz to 6GHz, to extract characteristic spectrum information; the fourth sensor node is deployed near the network interface, using a network traffic probe to capture network communication data between the physical device and external devices, analyzing the protocol type, data volume, and timing characteristics of outgoing traffic. The raw data collected by the four types of sensor nodes mentioned above are preprocessed through an FPGA data acquisition card, including signal filtering, feature extraction, and data compression. The processed feature vectors are then transmitted to the system main control unit via the PCIe bus for subsequent analysis. The side-channel sensing array has a built-in abnormal behavior feature fingerprint database, storing three types of fingerprint templates: data theft feature combinations, malicious control feature combinations, and abnormal operation feature combinations. The specific criteria for determining data theft feature combinations are: the computing core load rate is lower than a preset first threshold, the video memory read bandwidth is higher than a preset second threshold, and the timing correlation between network outgoing traffic and video memory read bandwidth is higher than a preset third threshold. The side-channel anomaly determination thresholds are: the GPU computing core load rate is lower than a first preset threshold, and the video memory read bandwidth is higher than a second preset threshold; the first and second preset thresholds are dynamically calibrated based on the baseline performance of the hardware platform. Those skilled in the art will understand that the above thresholds are only preferred embodiments for specific hardware platforms; in practical applications, dynamic threshold ranges corresponding to different hardware platforms can be obtained through baseline calibration, and the determination logic falls within the scope of protection of this invention.

[0041] The hybrid decision module is configured to make high-risk determinations based on at least two of the following: side-channel fingerprint data, AI command behavior data, and system operation log data, and generate control decision signals. The hardware implementation of this module can be built using Trusted Execution Environment (TEE) technology, encapsulating the decision logic in an ARM TrustZone tamper-proof hardware module to ensure that the decision process is not interfered with by malicious software.

[0042] like Figure 2As shown, the decision algorithm of the hybrid decision module is implemented based on a joint decision mechanism of at least two heterogeneous data. The specific logic is as follows: The system maintains multiple independent decision channels. The first channel performs a one-dimensional decision based on the AI ​​instruction behavior data output by the instruction perception module. The decision conditions include whether the target object of the instruction is a dangerous area, whether the execution parameters of the instruction exceed the safety threshold, and whether the timing characteristics of the instruction are abnormal. The second channel performs a one-dimensional decision based on the physical conflict detection results and system operation log data output by the pre-simulation engine module. The decision conditions include whether the joint angle of the robotic arm exceeds the limit, whether the end effector collides with the safety fence, and whether the movement trajectory crosses the personnel activity area. The third channel performs a one-dimensional decision based on the side channel fingerprint data output by the side channel perception array. The decision conditions include whether the side channel signal collected in real time matches any feature combination in the abnormal behavior feature fingerprint database. When at least two data in the above decision channels meet the corresponding high-risk decision conditions, the hybrid decision module generates a high-risk control decision signal and sends it to the hybrid blocking module through a high-speed digital isolator. The hybrid decision module has a decision processing cycle of 10 milliseconds, meaning that a complete information fusion and decision calculation is completed every 10 milliseconds, ensuring that the system can respond quickly to abnormal behavior of AI agents.

[0043] The hybrid blocking module adopts a fail-safe architecture, including a hard interrupt unit connected in series in the power supply circuit or core control signal circuit of the physical device. The hard interrupt unit remains in a conducting state under normal conditions, and only physically disconnects the circuit when a high-risk control decision signal is received. In this embodiment, the hardware implementation of the hybrid blocking module includes the following key parts: The first part is a dual-redundant hard interrupt unit, consisting of two sets of mutually redundant electromagnetic relays. Each set of relays has a rated contact current of 10A and a withstand voltage of 250VAC, fully meeting the power supply circuit requirements of the industrial robotic arm. The second part is an independent hardware watchdog, used to monitor the operating status of the active immune system main control unit and the hybrid decision module. When a non-high-risk fault such as abnormal host signal, interruption, loss of connection, or watchdog timeout is detected, the hard interrupt unit remains or resets to a conducting state to avoid false disconnection of the physical device due to faults in the monitoring system itself. The hard interrupt unit performs a safety disconnection action only when the hybrid decision module outputs a high-risk control decision signal. The third part is a unidirectional signal isolation component, which can be selected from an optocoupler isolator, a magnetic coupler isolator, a relay switch, or a unidirectional fiber optic transmission module. The hybrid blocking module is directly connected to the actuator via a dedicated GPIO interface, bypassing the host bus, operating system, and network protocol stack. A unidirectional signal isolation component is connected in series within this link, configured to block the data reception path while retaining only the data transmission path and common ground, forming a unidirectional, non-returnable, and tamper-proof transmission loop for control commands. The connection between the hybrid blocking module and the physical device controller is hard-wired, directly connected in series in the enable signal circuit of the robotic arm driver. When the hard interrupt unit activates, the driver's enable signal is physically cut off, and the robotic arm immediately stops all movement, achieving reliable protection of the physical device.

[0044] like Figure 3 As shown, after the system architecture is built, assuming the multimodal AI quality inspection system is running normally in an industrial automated production line scenario, its workflow includes the following main stages:

[0045] The first stage is the instruction capture and parsing stage, with a time window from T0 to T0+50ms. At time T0, the multimodal AI system identifies a defective product on the conveyor belt using a visual sensor and decides to control the robotic arm to grab the product and place it in the waste bin. The AI ​​agent generates a high-level semantic instruction: "Grab the target object (coordinates X:120mm, Y:350mm, Z:80mm) and move it to the throwing position (coordinates X:800mm, Y:200mm, Z:150mm)", which is sent to the physical device controller via industrial Ethernet. Simultaneously, the network traffic mirror port of the instruction perception module and the PCIe bus sniffer synchronously capture the instruction data stream. After receiving the original instruction, the dedicated instruction parsing accelerator first performs protocol parsing on the instruction, extracting header information such as the protocol type, target address, and data length. Then, it uses the built-in LSTM algorithm model to perform semantic recognition on the instruction payload, converting the natural language instruction into a structured control target and execution parameters, including the target object coordinates, grabbing posture, movement path sequence, and end effector opening / closing state. The parsing results are transmitted to the timestamp marking module of the system main control unit through the high-speed serial RapidIO interface. This module adds precise timestamp information to each instruction, with a timestamp accuracy better than 1 microsecond, laying the foundation for subsequent timing alignment processing.

[0046] The second stage is the virtual environment pre-simulation stage, with a time window from T0+50ms to T0+150ms. The system main control unit sends the parsed execution parameters to the pre-simulation engine module, initiating the virtual environment simulation pre-simulation process. The pre-simulation engine module first constructs the motion path planning for the robotic arm in the virtual environment based on the target coordinates and throwing position in the execution parameters. The path planning algorithm uses a fifth-order polynomial interpolation method to ensure the continuity of joint angles, angular velocities, and angular accelerations. Then, based on the DH parameter method, it performs forward kinematics calculation to calculate the three-dimensional position and orientation of the robotic arm's end effector at each sampling moment. Simultaneously, it performs inverse kinematics calculation based on the dynamic model to calculate the torque values ​​that each joint needs to output during movement. During the pre-simulation, the following physical conflict conditions are detected in real time: whether the joint angle exceeds the mechanical limit, whether the end effector collides with a safety fence or workpiece, whether the motion trajectory crosses the personnel safety area, and whether the joint torque exceeds the rated value of the actuator. The pre-simulation engine module transmits the physical conflict detection results to the system main control unit via gigabit Ethernet. If any physical conflict is detected, a conflict alarm flag and conflict type description are generated.

[0047] The third stage is the side-channel signal acquisition stage, with a time window from T0 to T0+200ms. The four sensor nodes of the side-channel sensing array continuously acquire side-channel signals from the physical device during system operation. Within a 200ms time window after the AI ​​agent issues a capture command, the sensor nodes synchronously acquire data according to a set sampling rate: a high-frequency current probe acquires the chip power supply current waveform at a sampling rate of 100MS / s; the FPGA data acquisition card performs digital filtering on the raw current waveform to extract instantaneous power consumption characteristics; a logic analyzer captures the access timing data of the DDR4 memory bus at a sampling rate of 200MHz, statistically analyzing the memory read bandwidth and access interval; a broadband EMI antenna acquires the device's electromagnetic radiation spectrum at a resolution of 1MHz, extracting the power spectral density of characteristic frequency points; and a network traffic probe captures outgoing network data packets from the physical device, calculating the timing correlation between outgoing network traffic and memory access. The raw data collected by the four sensor nodes are preprocessed to form a multi-dimensional side channel feature vector, which is then transmitted to the feature matching module of the system main control unit via the PCIe bus. This module calculates the similarity between the real-time collected side channel feature vector and the template in the abnormal behavior feature fingerprint database, and outputs the matching score for each feature combination.

[0048] The fourth stage is the information fusion and joint judgment stage, with a time window from T0+150ms to T0+160ms. After receiving the judgment input from the above channels, the system main control unit starts the hybrid judgment module to perform heterogeneous information fusion and cross-validation judgment. First, the system's main control unit performs time-series alignment processing on the input data from each channel. Based on a unified timestamp window, it synchronizes and aligns AI command behavior data, system operation log data, and side-channel fingerprint data. The timestamp window width is set to 20ms to ensure that multi-source information within the same time window can be correlated and analyzed. Then, the hybrid decision module performs single-dimensional decisions: The first decision channel determines the risk of AI command behavior based on the command semantic recognition results. If the target position of the command is located in the safe area of ​​the robotic arm's workspace and the execution parameters are within the normal range, the channel is determined to be low-risk; otherwise, it is determined to be high-risk. The second decision channel determines physical feasibility based on the virtual pre-simulation results and system operation log data. If no physical conflict is detected during the pre-simulation and the torques of each joint are within the safety threshold, the channel is determined to be low-risk; otherwise, it is determined to be high-risk. The third decision channel determines physical authenticity based on the side-channel feature matching results. If the matching score between the side-channel feature vector and the abnormal behavior feature fingerprint database is lower than the threshold, the channel is determined to be low-risk; otherwise, it is determined to be high-risk. Finally, the hybrid decision module makes a joint judgment based on at least two data points. When at least two data points meet the corresponding high-risk judgment conditions, a high-risk control decision signal is generated. If only one data point meets the high-risk judgment condition while the other data points are judged to be low-risk, the system judges it as normal behavior and does not trigger a blocking action. If all channels are judged to be low-risk, the system judges it as completely normal and directly allows the instruction to proceed.

[0049] The fifth stage is the physical disconnection and blocking stage, with a time window from T0+160ms to T0+170ms. After the hybrid decision module generates a high-risk control decision signal, this signal is transmitted to the hybrid blocking module's drive circuit via a dedicated GPIO interface and a unidirectional signal isolation component. Upon receiving the high-risk control decision signal, the drive circuit immediately applies a drive current to the backup relay coil of the dual-redundant hard interrupt unit. The relay contacts switch from the on state to the off state, physically disconnecting the enable signal circuit of the robotic arm driver. This connection link does not pass through the host bus, operating system, or network protocol stack. After the robotic arm driver loses its enable signal, it immediately blocks all motor drive signals, and the robotic arm completely stops moving within 200 milliseconds under the influence of inertia and load gravity. Simultaneously, the hybrid blocking module feeds back the blocking action execution status to the system master control unit through an independent fault indication interface. The system master control unit records the timestamp, trigger cause, and execution result of the blocking event and sends alarm information to the remote monitoring center.

[0050] Example 2

[0051] This embodiment uses a multimodal AI voice interaction system in a smart home scenario as an application example. In this scenario, the multimodal AI system interacts with users through technologies such as voice recognition, natural language understanding, and visual perception to control home appliances such as smart door locks, smart curtains, and smart lighting. Since the home environment directly relates to the personal and property safety of users, if the AI ​​system is maliciously attacked or makes a misjudgment, it could lead to safety incidents such as unauthorized door opening or curtains trapping users. Therefore, implementing hardware-level behavioral monitoring and proactive immune protection for the system is of significant practical importance.

[0052] The system architecture in this embodiment is similar to that in Embodiment 1, but the hardware selection and module configuration have been optimized for consumer-grade application scenarios. The network traffic mirroring port of the instruction perception module has been replaced with a Wi-Fi wireless network monitoring adapter, implemented using the Atheros AR9382 chip, which can capture encrypted communication traffic between the AI ​​system and the cloud server in monitoring mode. The PCIe bus sniffer has been replaced with a USB protocol analyzer, implemented based on the Cypress EZ-USB FX3 chip, which can capture USB communication data between the AI ​​system and peripherals. The hardware platform of the pre-simulation engine module has been downgraded to an Intel NUC mini-PC, equipped with a quad-core i5 processor and integrated graphics. Although the computing power is not as good as an industrial-grade platform, it is sufficient to meet the needs of simple device control instruction simulation pre-simulation in home scenarios. The virtual environment model has been simplified to a two-dimensional planar model, containing only device position coordinates and simple motion trajectories, without establishing complex three-dimensional geometric and dynamic models. The sensor node configuration of the side-channel perception array has also been simplified accordingly, retaining only two sensor nodes: power consumption monitoring and communication traffic analysis. Power consumption monitoring is implemented using a USB power meter, and communication traffic analysis is implemented using a software-defined network probe. The hard interrupt unit of the hybrid blocking module has been replaced with a single solid-state relay with a contact rated current of 2A and a withstand voltage of 120VAC, which is directly connected in series in the power supply circuit of the smart door lock.

[0053] The workflow of this embodiment is basically the same as that of Embodiment 1, with the following differences: After capturing the voice control command of the AI ​​system, the command perception module first performs intent parsing through a locally deployed lightweight semantic recognition model to extract the control target device ID, action type, and parameter value; the pre-playing engine module simulates the device's motion trajectory in a two-dimensional virtual environment based on the extracted execution parameters to detect whether it exceeds the safety boundary; the side channel perception array monitors the device's power consumption curve and communication traffic in real time and matches them with the abnormal behavior feature fingerprint database; the hybrid decision module performs information fusion judgment based on at least two data points, and if it is determined to be high-risk, it cuts off the power circuit of the smart door lock through a solid-state relay to prevent unauthorized door opening events from occurring.

[0054] Example 3

[0055] This embodiment uses a multimodal AI perception and decision-making system in an autonomous driving scenario as an application example. In this scenario, the multimodal AI system integrates information from multiple sensors, such as cameras, LiDAR, and millimeter-wave radar, to perform environmental perception and path planning, controlling core driving functions such as steering, acceleration, and braking. Since vehicles travel on public roads, directly impacting the lives of drivers, passengers, and pedestrians, a malfunction or malicious attack on the AI ​​system could lead to serious traffic accidents. Therefore, implementing hardware-level behavioral monitoring and proactive immune protection for the system is of paramount practical importance.

[0056] The system architecture in this embodiment has been comprehensively upgraded in terms of safety and reliability to meet the requirements of the functional safety standard ISO 26262. The command perception module adopts a triple-redundant architecture, with three independent command parsing channels deployed on different hardware platforms. A voting mechanism ensures the correctness of the command parsing results; if any channel fails, the system automatically switches to the backup channel to ensure the continuous availability of the command capture function. The pre-simulation engine module adopts a dual-redundant configuration, with two independent virtual environment simulation engines running synchronously to independently pre-simulate the same command. If the pre-simulation results of the two engines are inconsistent, a safe stopping action is triggered. The side-channel perception array's sensor nodes are expanded to six, adding heartbeat signal monitoring for the vehicle's CAN bus and signal quality monitoring for the high-precision positioning antenna, collecting vehicle operating status information from multiple dimensions. The hybrid blocking module adopts a quad-redundant hard interrupt unit, consisting of four sets of relays connected in parallel. The failure of any one set of relays does not affect the normal operation of the other relays; simultaneously, a mechanical hard-wired switch is added as a final physical blocking method to ensure that the vehicle's power output can be manually cut off in the event of any software failure.

[0057] This embodiment incorporates a security redundancy mechanism in its decision-making logic: when the hybrid decision module performs information fusion based on at least two data points, if only one channel is determined to be high-risk while the other two are determined to be low-risk, the system will not immediately block it. Instead, it will enter an enhanced monitoring state, continuously tracking the execution of the instruction within a subsequent 50-millisecond time window. If abnormal changes occur in the side-channel signal or the virtual simulation results deteriorate during enhanced monitoring, a blocking action will be triggered immediately. If no abnormalities are observed during enhanced monitoring, the enhanced monitoring state will be deactivated, and normal monitoring will resume. This progressive decision-making mechanism effectively reduces the probability of false blocking, ensuring both security and system availability.

[0058] Through the detailed descriptions of the three embodiments above, it is clear that the physical space behavior monitoring and proactive immune system and method for multimodal AI described in this invention have broad applicability and flexible deployment capabilities. They can provide customized hardware-level behavior monitoring and proactive immune protection solutions for the security needs of different application scenarios such as industrial automation, smart homes, and autonomous driving. Through the comprehensive application of multiple technologies, the security protection level of multimodal AI systems in physical space behavior monitoring is fundamentally improved. This effectively solves the technical defects of existing technologies, such as the possibility of bypassing software protection, lack of physical world prediction capabilities, and difficulty in identifying disguised attacks, demonstrating significant technological advancements and broad application prospects.

[0059] The above embodiments are merely one of the preferred embodiments of the present invention and should not be used to limit the scope of protection of the present invention. Any modifications or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but solve the same technical problem as the present invention, should be included within the scope of protection of the present invention.

Claims

1. A physical space behavior monitoring and proactive immune system for multimodal AI, characterized in that, Deployed on FPGA modules, MCU modules, or independent hardware chips with equivalent logic processing capabilities, independent of AI agents and physical device controllers, including: The instruction perception module is used to capture high-level semantic instructions output by the AI ​​agent in real time through non-intrusive bypass listening, and to parse out the control target and execution parameters based on natural language processing technology. The pre-simulation engine module is used to construct a digital twin model of a virtual environment consistent with the physical space, map the execution parameters to the virtual environment for simulation pre-simulation, and output physical conflict detection results; A side-channel sensing array is used to synchronously acquire side-channel signals of physical devices, including chip load rate, memory bandwidth, and bus data stream. The hybrid decision module is used to make high-risk determinations based on at least two of the side-channel fingerprint data, AI command behavior data, and system operation log data, and generate control decision signals. A hybrid blocking module is used to connect a fail-safe hard interrupt unit in series in the power supply circuit or core control signal circuit of the physical device. It remains conductive under normal conditions and physically disconnects the circuit only when a high-risk control decision signal is received. The hybrid blocking module is directly connected to the actuator via a dedicated GPIO interface, and the connection link does not pass through the host bus, operating system, or network protocol stack. A unidirectional signal isolation component is connected in series in the connection link. This unidirectional signal isolation component is configured to block the data reception path, retaining only the data transmission path and the common ground, forming a unidirectional, non-returnable, and tamper-proof transmission loop for control commands. The FPGA module, MCU module, or independent hardware chip with equivalent logic processing capabilities is externally shielded with an integrated anti-tampering detection circuit.

2. The physical space behavior monitoring and proactive immune system for multimodal AI according to claim 1, characterized in that, The high-risk determination specifically includes: The side-channel fingerprint data, AI command behavior data, and system operation log data are time-aligned, and the time alignment is based on a unified timestamp window for synchronization processing. A joint determination is made based on at least two of the side-channel fingerprint data, AI command behavior data, and system operation log data. When at least two of the data meet the corresponding high-risk determination conditions, a high-risk decision is triggered.

3. The physical space behavior monitoring and proactive immune system for multimodal AI according to claim 2, characterized in that, The side-channel sensing array is configured with an abnormal behavior feature fingerprint database, which includes a combination of data theft features: the GPU computing core load rate is lower than a preset first threshold and the video memory read bandwidth is higher than a preset second threshold; wherein, the first threshold and the second threshold are dynamically calibrated according to the baseline performance of the hardware platform.

4. The physical space behavior monitoring and proactive immune system for multimodal AI according to claim 3, characterized in that, The virtual environment model is configured to periodically update sensor data to calibrate the consistency between the virtual environment and the physical space, thereby ensuring the accuracy of the simulation.

5. The physical space behavior monitoring and proactive immune system for multimodal AI according to claim 4, characterized in that, The fail-safe architecture is configured such that when the system itself experiences a power outage, crash, or communication loss, the hard interrupt unit automatically resets to the on state, ensuring that the service continuity of the physical device is not affected.

6. The physical space behavior monitoring and proactive immune system for multimodal AI according to claim 4, characterized in that, The virtual environment model includes the geometric model, kinematic model, and dynamic model of the physical device, which is used to simulate the changes in the physical state of the device after executing instructions, including position, velocity, acceleration, and force conditions.

7. The physical space behavior monitoring and proactive immune system for multimodal AI according to claim 6, characterized in that, The side-channel sensing array is deployed at the signal acquisition point of the physical device. It uses high-precision sensors to collect electromagnetic radiation, power consumption changes and timing characteristics generated during device operation in real time, forming a multi-dimensional side-channel signal feature vector.

8. A physical space behavior monitoring and proactive immunity method for multimodal AI, characterized in that, The system implementation based on any one of claims 1-7 includes the following steps: S1, through FPGA module, MCU module or independent hardware chip with equivalent logic processing capability, bypasses and captures high-level semantic instructions of AI agent; S2 performs physical simulation and pre-playing of high-level semantic instructions in a virtual environment to predict physical conflicts; S3 collects side-channel signals from physical devices to identify covert abnormal behavior; S4. Cross-validation and joint determination are performed based on at least two of the following: side-channel fingerprint data, AI command behavior data, and system operation log data. S5. If the risk is deemed high, a one-way blocking signal is sent through the dedicated GPIO interface to physically disconnect the device circuit; otherwise, repeat steps S1 to S5.

9. The physical space behavior monitoring and active immunization method for multimodal AI according to claim 8, characterized in that, The cross-validation and joint judgment rules in step S4 are as follows: use side channel signals to verify the actual execution of AI instructions at the physical layer. If the instruction semantics are benign but the side channel shows abnormalities, it is judged as a spoofing attack.

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