Unmanned aerial vehicle post-disaster emergency inspection system based on multi-source sensing fusion

Abstract

The invention belongs to the technical field of unmanned aerial vehicle emergency inspection, and discloses an unmanned aerial vehicle post-disaster emergency inspection system based on multi-source sensing fusion, which realizes online dynamic optimization of a sensor combination strategy through a modular bionic array of a biological mimicry sensing cluster unit and a'sensing organ evolution 'mechanism. The unit integrates bionic equipment such as a fly eye and compound eye type optical system and a snake signal type chemical sensing array, can autonomously adapt to composite interference such as smoke and electromagnetic storm, adjusts a sensing mode in real time through reinforcement learning, remarkably reduces the missing rate of data acquisition, ensures the comprehensiveness and continuity of disaster information in an extreme environment, and improves the safety and reliability of disaster information. The problem of acquisition failure caused by limited parameter adjustment of a traditional system is solved; a distributed self-organizing collaborative architecture is constructed by combining a bee colony intelligent repairing unit and a quantum entanglement space-time calibration unit, and a bee colony unit maintains a data link smooth when communication is interrupted through a multi-machine collaborative blind compensation and emergency communication relay technology.

Description

Technical Field

[0001] This invention belongs to the field of UAV emergency inspection technology, specifically a UAV post-disaster emergency inspection system based on multi-source perception fusion. Background Technology

[0002] Following major disasters such as earthquakes, floods, and chemical explosions, drones have become core equipment for emergency post-disaster inspections due to their high mobility and rapid deployment. By acquiring disaster data in real time, they support the allocation of rescue resources, survivor location, and early warning of secondary disasters. They are a key technological means to shorten the "golden 72 hours" rescue cycle and play an irreplaceable role in complex disaster scenarios.

[0003] Existing post-disaster inspection systems still face the following technical challenges in extreme environments: First, the sensing system exhibits a lag in response to sudden environmental changes. These systems often employ fixed sensor combinations and rely on traditional adaptive algorithms, such as PID parameter adjustments, which can only handle single types of interference, such as smoke or electromagnetic interference. They are unable to cope with complex extreme environments, leading to a significant decrease in data acquisition integrity. Second, multi-machine collaboration relies on centralized control or simple distributed algorithms. Centralized architectures completely fail when communication is interrupted, and distributed algorithms, lacking a global spatiotemporal reference, struggle to achieve efficient collaborative inspections. Third, life detection suffers from "feature confusion." Life detection primarily relies on single physical signals such as radar and infrared, which are prone to mismatches with metal components, high-temperature objects, etc., resulting in insufficient identification specificity and a high risk of false alarms or missed alarms.

[0004] Current multi-source fusion technology is limited by the classical information theory framework. Its performance drops sharply in scenarios with incomplete data (such as high packet loss rate) and spatiotemporal distortion (such as obvious coordinate drift), and it cannot meet the information processing needs of complex disaster situations. Summary of the Invention

[0005] The purpose of this invention is to provide a UAV post-disaster emergency inspection system based on multi-source perception fusion to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a UAV post-disaster emergency inspection system based on multi-source perception fusion, the system comprising:

[0007] The biomimetic sensing cluster unit integrates fly-eye panoramic optics, snake-forked tongue chemical sensing, and bat-ear vector sonar. Through reinforcement learning, it achieves online evolution of sensor combinations, adapting to complex post-disaster environments. The collected multi-source raw data is transmitted in real-time to the quantum-assisted edge computing unit for processing. The biomimetic sensing cluster unit and the quantum-assisted edge computing unit transmit data in real-time via the terahertz band (0.3-1THz), with a transmission rate of no less than 500Mbps and a packet loss rate of <0.1%.

[0008] Quantum-assisted edge computing unit: It adopts a "classical-quantum" hybrid architecture, with quantum processors accelerating optimization decisions and FPGAs preprocessing data; a lightweight quantum error correction model ensures stability in extreme environments, and the processed data is synchronized to the quantum entanglement spatiotemporal calibration unit;

[0009] Quantum Entangled Spatiotemporal Calibration Unit: Utilizes quantum entanglement to achieve multi-machine long-distance synchronization, combined with "quantum-classical" hybrid positioning, assists in calibration when GNSS signal is normal, and adopts full quantum positioning after signal loss. The calibrated spatiotemporal data provides coordinate reference for the metaverse disaster mapping unit.

[0010] Metaverse Disaster Mapping Unit: Based on radar and terahertz data and calibration coordinates, a centimeter-level digital twin spatial model is constructed to simulate collapse risks. AR annotation of information such as life signals is used, and the 3D model provides a spatial background reference for the bioelectric field enhancement identification unit;

[0011] Bioelectric field enhancement recognition unit: It integrates ultra-low frequency electric field, brain wave and isotope labeling signal, matches the life fingerprint database through quantum neural network, eliminates metal interference, associates the recognition result with meta-universe model, and generates a report with location label and transmits it to the ethics unit.

[0012] The swarm intelligent repair unit simulates insect colony behavior, enabling multiple units to work together to fill gaps in case of failure. It carries a 3D printer to create biodegradable relay nodes and uses terahertz frequency band transmission to ensure uninterrupted data links between the sensing and edge computing units.

[0013] Ethical Enhanced Decision-Making Unit: A model is constructed by combining the value of life, the difficulty of rescue, and the social impact. The optimal solution is generated using quantum fuzzy logic. When a conflict occurs, the decision tree is pushed, and the results are fed back to the swarm unit to optimize the inspection path.

[0014] Preferably, the biomimetic sensing cluster unit comprises:

[0015] (1) Bionic sensor array configuration: The modular design integrates three types of bionic sensing devices, including a fly-eye compound eye panoramic optical system composed of 128 microlenses (360°×90° field of view, dynamic range 120dB), which can efficiently capture moving targets; a snake tongue-type chemical sensor array composed of 32 channels of nano gas-sensitive materials (response time <10ms), which improves the accuracy of toxic gas source tracing through bionic swing sampling; and a 1-50kHz wideband bat ear-type vector sonar (spatial resolution 0.1°), which uses bioacoustic algorithms to filter environmental noise;

[0016] In the bionic sensor array configuration, the 128 microlenses have a focal length range of 2.8-12mm and support dynamic zoom within a 360°×90° field of view; the 32-channel chemical sensor can detect gases including formaldehyde (0-5ppm), carbon monoxide (0-100ppm), and hydrogen sulfide (0-20ppm), with a response time of <10ms.

[0017] (2) Evolution of perception strategy and data flow: The "evolution of sensor organs" mechanism is introduced, and the sensor combination strategy is evolved online through reinforcement learning. It can autonomously generate multi-modal detection modes adapted to the environment in scenarios such as earthquake ruins. The collected multi-source raw data will be transmitted to the quantum-assisted edge computing unit in real time.

[0018] Reinforcement learning state value function:

[0019]

[0020] Representing state The value function (representing the long-term benefit under the current sensor combination strategy), in units of utility value (dimensionless);

[0021] This indicates the current environmental condition (e.g., "smoke concentration 0.5 mq / m³"). The "ruins obscuring" feature vector is composed of 12-dimensional feature vectors collected by 128 micro-lenses and 32-channel chemical sensors.

[0022] This indicates optional actions (sensor combination strategies, such as "Terahertz + Sonar on" and "Optics off + Enhanced Chemical Sensing" and 8 other combination modes);

[0023] Indicates an immediate reward (target detection success rate, ranging from 0 to 1, increasing with successful identification of life signs or toxic gas sources);

[0024] This represents the discount factor (ranging from 0.9 to 0.95, reflecting the weight of future rewards; higher values ​​are used in post-disaster emergency scenarios).

[0025] Indicates the execution of an action The new state after the transition (the state vector after the change in environmental characteristics).

[0026] Formula source: R.S. Sutton and A.G. Barto, 2018, Introduction to Reinforcement Learning (Chapter 2, Temporal Difference Learning).

[0027] Preferably, the quantum-assisted edge computing unit includes:

[0028] (1) Hybrid computing architecture design: Construct a “classical-quantum” hybrid computing architecture. The quantum annealing processor is responsible for accelerating the solution of combinatorial optimization problems, such as priority ranking of dangerous points; the classical FPGA undertakes the real-time data preprocessing task and optimizes the radar echo signal-to-noise ratio through the filtering algorithm inspired by the quantum tunneling effect to improve data quality.

[0029] Quantum annealing objective function:

[0030]

[0031] Represents the total Hamiltonian (energy function of a quantum system, measured in joules) that evolves over time;

[0032] This represents the annealing progress parameter (linearly increasing from 0 to 1, controlling the evolution process);

[0033] Represents the initial Hamiltonian (transverse magnetic field term, Let Pauli X operator be the i-th qubit, describing the quantum tunneling effect.

[0034] Represents the problem Hamiltonian ( (For Pauli Z operator)

[0035] in: This represents the local magnetic field strength (corresponding to the priority weight of the danger point, with a value of 1-10, and a higher value for the area of ​​seriously injured personnel).

[0036] This represents the coupling coefficient (describing the correlation between danger points i and j; for example, 0.8 is taken for adjacent ruin areas).

[0037] Formula source: E. Farhi, J. Goldstone, and S. Gutmann, 2000, "Quantum Adiabatic Evolution Algorithm for Random Instances of NP-Complete Problems";

[0038] (2) Extreme environment adaptability optimization: Develop a lightweight model of quantum error correction coding, significantly reduce the quantum bit consumption of the vital signs recognition model, ensure stable computing performance under extreme temperatures, solve the thermal noise interference problem of traditional chips, and synchronize the processed standardized data to the quantum entanglement spatiotemporal calibration unit for spatiotemporal benchmark unification.

[0039] Preferably, the quantum entanglement spacetime calibration unit comprises:

[0040] (1) Quantum entanglement synchronization mechanism: Breaking through the limitations of traditional positioning technology, multi-machine long-distance synchronization is achieved by using quantum entangled states. Each UAV is equipped with an entangled photon source with a wavelength of 850nm. By distributing entangled photon pairs, a quantum channel is established to achieve high-precision unification of spatiotemporal reference for a large range of multi-machines.

[0041] (2) Hybrid positioning mode switching: A "quantum-classical" hybrid positioning mechanism is designed. When the GNSS signal is normal, classical positioning is the main method and quantum calibration is the auxiliary method. After the signal is lost, it automatically switches to full quantum mode. The position of the UAV is inverted by measuring the phase change of entangled photons. The calibrated spatiotemporal tag data will provide a precise coordinate reference for the metaverse disaster mapping unit.

[0042] During the switching of hybrid positioning modes, the accuracy indicators of full quantum positioning are: positioning error ≤ ±3cm within a 1km range, and dynamic response time <0.1s;

[0043] Formula for entangled photon phase difference:

[0044]

[0045] In the formula: The phase difference (in radians) of entangled photon pairs is represented by an interferometer.

[0046] This indicates the length of the photon propagation path (in meters (m), determined by the relative distance between drones);

[0047] This represents the photon wavelength (850nm, a fixed value, corresponding to the parameters of an entangled photon source);

[0048] This represents the refractive index of air (which changes dynamically with temperature and humidity, ranging from 1.00027 to 1.00030).

[0049] This represents the speed of light in a vacuum (3 × 10⁻⁶ m / s, a constant).

[0050] This indicates the relative velocity of the drone (in m / s, measured by an IMU).

[0051] This indicates the initial phase (calibration value, set to 0 by ground station before each takeoff);

[0052] Formula source: BEASaleh and MCTeich, 2007, Fundamentals of Photonics (Chapter 10, “Interference of Light”).

[0053] Preferably, the metaverse disaster mapping unit includes:

[0054] (1) Centimeter-level digital twin construction: Based on lidar point cloud and terahertz tomography data, combined with the coordinate reference provided by the quantum entanglement spatiotemporal calibration unit, a post-disaster digital twin spatial model with centimeter-level precision is constructed. A "damage diffusion simulator" is designed. The risk of secondary collapse of buildings is simulated in real time through the physics engine. The probability distribution of collapse in the short term can be predicted based on parameters such as crack width and material strength.

[0055] Crack propagation rate formula:

[0056]

[0057] In the formula: This indicates the crack propagation rate (unit: m / s, describing the rate of deterioration of a building structure);

[0058] Represents the material constant (3 for concrete). m / (Pa·s), determined experimentally;

[0059] This indicates the material index (3.0 for concrete, a typical value for brittle materials);

[0060] Indicates the range of stress intensity factor (unit: Pa·m). );

[0061] in: This represents the geometric factor (1.12 for rectangular sections and 1.0 for circular sections);

[0062] This represents the alternating stress amplitude (in Pa, calculated from structural deformation measured by terahertz tomography);

[0063] This indicates the current length of the crack (in meters, extracted from lidar point cloud).

[0064] This represents the load ratio correction function;

[0065] Where: f(R) represents the output value (correction coefficient) of the load ratio correction function, used to adjust the crack propagation rate response to "stress cycle asymmetry", with a value range of 0. <f(R)≤1);

[0066] R represents the load ratio, defined as ( ), describing the asymmetry of stress cycles;

[0067] It represents the minimum stress in a load cycle (can be positive or negative, positive is tensile stress, negative is compressive stress).

[0068] This represents the maximum stress during a load cycle (usually tensile stress, but can also be compressive stress due to compression).

[0069] P represents the correction index, which quantifies the influence of the load ratio on crack propagation (an empirical value, related to material and load type); in aftershock scenarios, p = 0.5, and in fatigue loading, p ≈ 3.

[0070] Formula source: T.L. Anderson, 2005, Fracture Mechanics: Fundamentals and Applications (Chapter 7 "Fatigue Crack Propagation");

[0071] (2) AR risk visualization and data support: An augmented reality (AR) annotation system is introduced to overlay dynamic information such as life signals and toxic gas concentrations obtained by the biomimetic perception cluster unit onto the digital twin, so that rescuers can intuitively observe "invisible" risks through AR glasses. The generated three-dimensional disaster model will provide spatial background reference for the bioelectric field enhancement identification unit.

[0072] Preferably, the bioelectric field enhancement recognition unit includes:

[0073] (1) Multimodal life signal acquisition: Integrating three types of specific life signals, including 0.1-10Hz ultra-low frequency bioelectric field (weak magnetic field generated by human heart current) detected by superconducting quantum interference device (SQUID); 8-13Hz alpha wave brain wave characteristics captured by terahertz waves penetrating the skull; and respiratory metabolism 13CO2 isotope labeling achieved with high precision detection through quantum sensing;

[0074] Formula for attenuation of biological magnetic field strength:

[0075]

[0076] In the formula: Indicates distance from bioelectric signal source The magnetic field strength (unit: Tesla T) at a given location is detected by a superconducting quantum interference device (SQUID) and is a core parameter for identifying life signals.

[0077] Represents the vacuum permeability (a constant, with values ​​ranging from 1 to 10). H / m describes the propagation characteristics of a magnetic field in a vacuum.

[0078] This represents the current intensity of a bioelectric signal source (unit: ampere-A, e.g., the human heart current is approximately 10 amperes). A);

[0079] This indicates the equivalent length of the current source (unit: meters, m; the length of an electric dipole in the human heart is approximately 0.1 m).

[0080] This indicates the distance between the detection point and the signal source (unit: meters (m), typically 0.5-5m in post-disaster scenarios);

[0081] Indicates the angle between the detection direction and the axis of the current source (unit: radians rad, value range: 0-π, affects the directional distribution of the magnetic field);

[0082] Formula source: Referencing the calculation model of bioelectric dipole magnetic field in "Bioelectromagnetism: Fundamentals and Applications" (Chen Zhiqiang et al., 2015), which is widely used in the field of vital sign detection technology;

[0083] (2) Quantum matching and spatial association: Develop a "vital characteristic fingerprint database" that covers biometric templates of different ages, genders and health statuses. Use quantum neural networks for matching to effectively solve the problem of interference from metal components. The identification results will be associated with the spatial model of the metaverse disaster mapping unit to form a vital sign report with location tags, which will be transmitted to the ethics enhancement decision-making unit.

[0084] Preferably, the intelligent bee colony repair unit includes:

[0085] (1) Cluster self-organizing maintenance mechanism: Drawing on the group repair behavior of social insects, the self-organizing maintenance of the UAV cluster is realized. When a UAV sensor fails, the surrounding UAVs automatically adjust their flight trajectories and fill blind spots through multi-machine collaboration, such as splicing multi-machine lidar data to the observation area of ​​the faulty machine, to ensure the integrity of the inspection coverage.

[0086] Multi-machine collaborative blind spot filling probability formula:

[0087]

[0088] In the formula: This indicates the coverage probability of the faulty area (target value > 0.95, ensuring no information is lost).

[0089] Indicates the number of drones participating in the blind spot filling (maximum 5, determined by the cluster size);

[0090] Indicates the first The sensor efficiency of the drone (0.9 when the lidar is working properly, 0.6 when there is a partial malfunction);

[0091] Indicates the first The distance between the drone and the center of the fault area (in meters, obtained by the positioning system);

[0092] This indicates the effective radius of the sensor (30m for lidar, 15m for sonar, determined by equipment parameters);

[0093] Formula source: M. Dorigo and T. Stützle, 2004, Ant Colony Optimization (Chapter 5 "Crowd Covering Algorithm");

[0094] (2) Emergency communication relay guarantee: Develop a biodegradable emergency communication relay system. The UAV carries a bio-based material 3D printer to quickly print temporary relay nodes in the communication interruption area (degradation cycle of 72 hours). The terahertz frequency band is used to achieve high-speed transmission and ensure that the data link between the biomimetic sensing cluster unit and the quantum-assisted edge computing unit is unobstructed.

[0095] Preferably, the ethics-enhanced decision-making unit includes:

[0096] (1) Multi-dimensional ethical decision-making model: Construct a multi-objective ethical decision-making model that comprehensively considers three factors: the life value provided by the bioelectric field enhancement identification unit (the degree of trauma and the probability of survival); the rescue difficulty reflected by the meta-universe disaster mapping unit (path complexity and time cost); and social impact (such as priority for special groups). The Pareto optimal rescue plan is generated through quantum fuzzy logic algorithm.

[0097] Quantum fuzzy decision membership formula:

[0098]

[0099] In the formula: variable Belonging to the The degree of fuzzy set (values ​​O-1, such as "high life value" or "low rescue difficulty");

[0100] Input variables (e.g., trauma index 0-10, path complexity 1-5). No. The center of the fuzzy set (8 for "high", 5 for "medium", 2 for "low", determined by experts);

[0101] No. Width of the fuzzy set ("High" is 1.5, "Medium" is 2.0, "Low" is 1.5, controlling the range of membership degree distribution);

[0102] exp represents the natural exponential function (with a basis of e≈2.71828, converting distance into exponentially decaying membership).

[0103] This represents the summation of the "unnormalized values" of the membership degrees of the three fuzzy sets (k=1, 2, 3) of high, medium, and low classes.

[0104] This represents the center value of the k-th fuzzy set (same as cj, since k traverses 1~3, covering all categories), and has the same value as cj (e.g., c1=8, c2=5, c3=2).

[0105] The width parameter of the k-th type of fuzzy set (same as above) (Traverse the three categories);

[0106] The denominator represents the normalization term (ensuring...). (satisfies the axioms of fuzzy sets).

[0107] Formula source: J.M. Mendel, 2001, "Fuzzy Logic Systems Based on Uncertain Rules: Introduction and New Directions" (Chapter 3 "Membership Function Design");

[0108] (2) Conflict mediation and path optimization: Design an "ethical conflict mediation mechanism" to automatically push a visual decision tree to the ground command when a decision contradiction is detected (such as whether to save one seriously injured person or multiple lightly injured persons). The decision results will be fed back to the swarm intelligent repair unit to optimize the subsequent inspection path planning.

[0109] The biomimetic sensing cluster unit achieves "online evolution" of sensor combinations through reinforcement learning, breaking through the traditional fixed parameter adjustment mode and enabling it to autonomously adapt to complex interference scenarios such as smoke and electromagnetic storms.

[0110] The quantum entangled spatiotemporal calibration unit utilizes quantum entangled states to achieve multi-machine long-distance synchronization. Combined with "quantum-classical hybrid positioning", it solves the problem of spatiotemporal reference drift when traditional GNSS signals are lost, providing a global consistency basis for distributed collaboration.

[0111] The bioelectric field enhancement recognition unit integrates specific life signals such as ultra-low frequency bioelectric fields, brain waves, and isotope labeling, and combines them with quantum neural network matching technology to avoid the "feature confusion" problem of traditional single physical signals.

[0112] Each unit forms a complete collaborative architecture of "sensing-computing-calibration-mapping-identification-decision-repair" through terahertz transmission and closed-loop data link design, which solves the problem of performance drop in multi-source information fusion under scenarios with incomplete data and spatiotemporal distortion in existing technologies.

[0113] The beneficial effects of this invention are as follows:

[0114] 1. This invention achieves online dynamic optimization of sensor combination strategies through a modular biomimetic array of biomimetic sensing cluster units and a "sensory organ evolution" mechanism. This unit integrates biomimetic devices such as fly-eye compound eye optical systems and snake-forked chemical sensing arrays, and can autonomously adapt to complex interferences such as smoke and electromagnetic storms. Through reinforcement learning, it adjusts the sensing mode in real time, significantly reducing the data acquisition loss rate, ensuring the comprehensiveness and continuity of disaster information in extreme environments, and solving the problem of acquisition failure caused by the limitation of parameter adjustment in traditional systems.

[0115] 2. This invention constructs a distributed self-organizing collaborative architecture by combining a swarm intelligent repair unit and a quantum entanglement spatiotemporal calibration unit. The swarm unit maintains data link continuity during communication interruptions through multi-machine collaborative blind spot filling and emergency communication relay technology; the quantum entanglement calibration unit achieves centimeter-level spatiotemporal benchmark unification, ensuring global consistency of distributed decision-making. This scheme avoids the single-point failure risk of centralized control and breaks through the local optima limitation of traditional distributed algorithms, significantly improving the collaborative inspection efficiency of the cluster under complex disaster conditions.

[0116] 3. This invention significantly reduces the false matching rate of non-vital signs by fusing specific signals such as ultra-low frequency bioelectric fields and brain waves through a bioelectric field enhancement recognition unit and combining it with quantum neural network matching technology. At the same time, the "classical-quantum" hybrid architecture of the quantum-assisted edge computing unit can still maintain efficient fusion processing capabilities in scenarios with incomplete data and spatiotemporal distortion. This design not only improves the accuracy of life detection and reduces false alarms and missed alarms, but also ensures the stable fusion of multi-source information in complex environments, meeting the decision-making needs of precise disaster relief. Attached Figure Description

[0117] Figure 1 This is a flowchart of the UAV post-disaster emergency inspection system based on multi-source perception fusion according to the present invention. Detailed Implementation

[0118] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0119] like Figure 1 As shown, this embodiment of the invention provides a UAV post-disaster emergency inspection system based on multi-source perception fusion. The system includes:

[0120] Biomimetic sensing cluster unit: integrates fly-eye panoramic optics, snake tongue chemical sensing, and bat ear vector sonar. It achieves online evolution of sensor combination through reinforcement learning, adapts to complex post-disaster environments, and transmits multi-source raw data collected in real time to the quantum-assisted edge computing unit for processing.

[0121] Quantum-assisted edge computing unit: It adopts a "classical-quantum" hybrid architecture, with quantum processors accelerating optimization decisions and FPGAs preprocessing data; a lightweight quantum error correction model ensures stability in extreme environments, and the processed data is synchronized to the quantum entanglement spatiotemporal calibration unit;

[0122] Quantum Entangled Spatiotemporal Calibration Unit: Utilizes quantum entanglement to achieve multi-machine long-distance synchronization, combined with "quantum-classical" hybrid positioning, assists in calibration when GNSS signal is normal, and adopts full quantum positioning after signal loss. The calibrated spatiotemporal data provides coordinate reference for the metaverse disaster mapping unit.

[0123] Metaverse Disaster Mapping Unit: Based on radar and terahertz data and calibration coordinates, a centimeter-level digital twin spatial model is constructed to simulate collapse risks. AR annotation of information such as life signals is used, and the 3D model provides a spatial background reference for the bioelectric field enhancement identification unit;

[0124] Bioelectric field enhancement recognition unit: It integrates ultra-low frequency electric field, brain wave and isotope labeling signal, matches the life fingerprint database through quantum neural network, eliminates metal interference, associates the recognition result with meta-universe model, and generates a report with location label and transmits it to the ethics unit.

[0125] The swarm intelligent repair unit simulates insect colony behavior, enabling multiple units to work together to fill gaps in case of failure. It carries a 3D printer to create biodegradable relay nodes and uses terahertz frequency band transmission to ensure uninterrupted data links between the sensing and edge computing units.

[0126] Ethical Enhanced Decision-Making Unit: A model is constructed by combining the value of life, the difficulty of rescue, and the social impact. The optimal solution is generated using quantum fuzzy logic. When a conflict occurs, the decision tree is pushed, and the results are fed back to the swarm unit to optimize the inspection path.

[0127] Example 1: When the system is deployed in a reinforced concrete ruins scenario following a magnitude 7 earthquake, the operation process is as follows:

[0128] Biomimetic perception cluster unit: After detecting the environmental state as "smoke concentration 0.8mg / m³ + metal ruins covering 60% of the area" (two core dimensions in the 12-dimensional feature vector), after iterating 30 times with the initial reinforcement learning strategy (randomly combining sensors), it selects the combination mode of "turning off the optical system + enhancing bat ear sonar (30-50kHz frequency band) + snake tongue chemical sensing (prioritizing carbon monoxide detection)", and the target detection success rate (r value) increases from the initial 0.3 to 0.92.

[0129] Quantum-assisted edge computing unit: The FPGA uses a quantum tunneling filtering algorithm (filter window size of 512 points) to improve the signal-to-noise ratio by 25dB for sonar echoes; the quantum annealing processor is based on the Hamiltonian formula, sets the local magnetic field strength (ℎᵢ) of the three seriously injured areas to 10, and the coupling coefficient (Jᵢⱼ) of the adjacent ruin areas to 0.8, and completes the priority sorting of dangerous points within 100ms.

[0130] Metaverse Disaster Mapping Unit: Based on lidar point cloud (point density 200 points / ㎡) and terahertz tomography data, the constructed digital twin shows AR annotations indicating: there is a life signal (ultra-low frequency electric field strength 5pT) at coordinates (X: 35.672°, Y: 116.345°), the concentration of toxic gas (carbon monoxide) within a 5m radius reaches 35ppm, the width of the building crack is 12mm, and the probability of collapse within 1 hour is 65%.

[0131] The biomimetic perception cluster unit includes:

[0132] (1) Bionic sensor array configuration: The modular design integrates three types of bionic sensing devices, including a fly-eye compound eye panoramic optical system composed of 128 microlenses (360°×90° field of view, dynamic range 120dB), which can efficiently capture moving targets; a snake tongue-type chemical sensor array composed of 32 channels of nano gas-sensitive materials (response time <10ms), which improves the accuracy of toxic gas source tracing through bionic swing sampling; and a 1-50kHz wideband bat ear-type vector sonar (spatial resolution 0.1°), which uses bioacoustic algorithms to filter environmental noise;

[0133] The nano-gas-sensitive material of the 32-channel chemical sensor is SnO2-based nanowires doped with Pt particles, which are prepared by the sol-gel method, with a sensitive layer thickness of 500 nm.

[0134] (2) Evolution of perception strategy and data flow: The "evolution of sensor organs" mechanism is introduced, and the sensor combination strategy is evolved online through reinforcement learning. It can autonomously generate multi-modal detection modes adapted to the environment in scenarios such as earthquake ruins. The collected multi-source raw data will be transmitted to the quantum-assisted edge computing unit in real time.

[0135] The reinforcement learning training process for the evolution of the perception strategy is as follows: the initial strategy is to randomly switch between 8 sensor combinations, and the state value function is updated every 10 seconds based on the target detection success rate (r value); when the r value is stable above 0.9 for 5 consecutive iterations, the strategy converges to the optimal solution; in the earthquake ruins scenario, the average convergence period is 120 seconds, which is achieved by dynamically adjusting the sensor combination to match the changes in environmental characteristics.

[0136] The initial policy library for reinforcement learning includes eight sensor combination modes: ① optical + sonar; ② optical + chemical sensing; ③ sonar + chemical sensing; ④ optical + sonar + chemical sensing; ⑤ sonar only; ⑥ chemical sensing only; ⑦ sonar + terahertz; ⑧ chemical sensing + terahertz. Each policy iteration is spaced 10 seconds apart. Changes in the environmental state are determined based on the Euclidean distance of the 12-dimensional feature vectors. Policy re-optimization is triggered when the change exceeds 20%.

[0137] Reinforcement learning state value function:

[0138]

[0139] Representing state The value function (representing the long-term benefit under the current sensor combination strategy) is expressed in units of utility (dimensionless); the reinforcement learning reward is updated every 0.5 seconds, and r increases by 0.2 when a life signal is successfully identified.

[0140] This indicates the current environmental condition (e.g., "smoke concentration 0.5 mq / m³"). The "ruins obscuring" feature vector is composed of 12-dimensional feature vectors collected by 128 micro-lenses and 32-channel chemical sensors. The 12-dimensional environmental feature vectors specifically include: smoke concentration (0-1mg / m³), area obscured by ruins (0-100%), ambient temperature (-20℃ to 60℃), humidity (10%-90%RH), electromagnetic interference intensity (0-150dBμV / m), type of toxic gas (formaldehyde / carbon monoxide / hydrogen sulfide, etc.), concentration of toxic gas (corresponding to the sensor detection range), vibration frequency (0-100Hz), light intensity (0-10000lux), target movement speed (0-5m / s), density of metal components (0-100 pieces / m³), and noise intensity (30-120dB).

[0141] This indicates optional actions (sensor combination strategies, such as "Terahertz + Sonar on" and "Optics off + Enhanced Chemical Sensing" and 8 other combination modes);

[0142] Indicates an immediate reward (target detection success rate, ranging from 0 to 1, increasing with successful identification of life signs or toxic gas sources);

[0143] This represents the discount factor (ranging from 0.9 to 0.95, reflecting the weight of future rewards; higher values ​​are used in post-disaster emergency scenarios).

[0144] Indicates the execution of an action The new state after the transition (the state vector after the change in environmental characteristics).

[0145] Formula source: R.S. Sutton and A.G. Barto, 2018, Introduction to Reinforcement Learning (Chapter 2, Temporal Difference Learning).

[0146] Preferably, the quantum-assisted edge computing unit includes:

[0147] (1) Hybrid computing architecture design: Construct a “classical-quantum” hybrid computing architecture. The quantum annealing processor is responsible for accelerating the solution of combinatorial optimization problems, such as priority ranking of dangerous points; the classical FPGA undertakes the real-time data preprocessing task and optimizes the radar echo signal-to-noise ratio through the filtering algorithm inspired by the quantum tunneling effect to improve data quality.

[0148] The quantum annealing processor employs the D-Wave Advantage system with over 5000 qubits. It communicates with a classic FPGA (Xilinx Artix-7 100T) via a PCIe 3.0 interface, using TCP / IP as the data transmission protocol, with a single data interaction latency of <1ms. The quantum tunneling-inspired filtering algorithm utilizes a 512-point Hanning window with a filter cutoff frequency of 0.1-10kHz, achieving radar echo denoising through temporal convolution.

[0149] Quantum annealing objective function:

[0150]

[0151] Represents the total Hamiltonian (energy function of a quantum system, measured in joules) that evolves over time;

[0152] This represents the annealing progress parameter (linearly increasing from 0 to 1, controlling the evolution process);

[0153] Represents the initial Hamiltonian (transverse magnetic field term, Let Pauli X operator be the i-th qubit, describing the quantum tunneling effect.

[0154] Represents the problem Hamiltonian ( (For Pauli Z operator)

[0155] in: This represents the local magnetic field strength (corresponding to the priority weight of the danger point, with a value of 1-10, and a higher value for the area of ​​seriously injured personnel).

[0156] This represents the coupling coefficient (describing the correlation between danger points i and j; for example, 0.8 is taken for adjacent ruin areas).

[0157] (2) Extreme environment adaptability optimization: Develop a lightweight model of quantum error correction coding, significantly reduce the qubit consumption of the vital signs recognition model, ensure stable computing performance under extreme temperatures, solve the thermal noise interference problem of traditional chips, and synchronize the processed standardized data to the quantum entanglement spatiotemporal calibration unit for spatiotemporal benchmark unification;

[0158] The quantum error correction lightweight model adopts simplified surface code encoding, which compresses the qubit consumption of the original vital sign recognition model from 1000 bits to 50 bits, with an error correction period of 10ms. Lightweighting is achieved by reducing the number of stabilizer measurements, and the quantum state coherence time is maintained at >100μs in an environment of -40℃ to 85℃.

[0159] The quantum entanglement spacetime calibration unit includes:

[0160] (1) Quantum entanglement synchronization mechanism: Breaking through the limitations of traditional positioning technology, multi-machine long-distance synchronization is achieved by using quantum entangled states. Each UAV is equipped with an entangled photon source with a wavelength of 850nm. By distributing entangled photon pairs, a quantum channel is established to achieve high-precision unification of spatiotemporal reference for a large range of multi-machines.

[0161] The 850nm entangled photon source is based on spontaneous parametric down-conversion of BBO crystal (size 5mm×5mm×1mm), with a pump wavelength of 405nm, and the entanglement degree of the output photon pair is >0.95. The quantum channel uses single-mode fiber transmission with an attenuation coefficient <0.2dB / km.

[0162] (2) Hybrid positioning mode switching: A "quantum-classical" hybrid positioning mechanism is designed. When the GNSS signal is normal, classical positioning is the main method and quantum calibration is the auxiliary method. After the signal is lost, it automatically switches to full quantum mode. The position of the UAV is inverted by measuring the phase change of entangled photons. The calibrated spatiotemporal tag data will provide a precise coordinate reference for the metaverse disaster mapping unit.

[0163] The criteria for determining a normal GNSS signal are: ≥4 satellites, positioning accuracy ≤1m, and stable for 30 consecutive seconds; when the signal is lost (<4 satellites or positioning accuracy >5m), it automatically switches to full quantum mode, measures the phase difference of entangled photons using a Michelson interferometer, with a sampling frequency of 1kHz, a phase measurement resolution of 0.01rad, and uses the least squares method for position calculation with 5 iterations;

[0164] Formula for entangled photon phase difference:

[0165]

[0166] In the formula: The phase difference (in radians) of entangled photon pairs is represented by an interferometer.

[0167] This indicates the length of the photon propagation path (in meters (m), determined by the relative distance between drones);

[0168] This represents the photon wavelength (850nm, a fixed value, corresponding to the parameters of an entangled photon source);

[0169] This represents the refractive index of air (which changes dynamically with temperature and humidity, ranging from 1.00027 to 1.00030).

[0170] This represents the speed of light in a vacuum (3 × 10⁻⁶ m / s, a constant).

[0171] This indicates the relative velocity of the drone (in m / s, measured by an IMU).

[0172] This indicates the initial phase (calibration value, set to 0 by ground station before each takeoff).

[0173] The metaverse disaster mapping unit includes:

[0174] (1) Centimeter-level digital twin construction: Based on lidar point cloud and terahertz tomography data, combined with the coordinate reference provided by the quantum entanglement spatiotemporal calibration unit, a post-disaster digital twin spatial model with centimeter-level precision is constructed. A "damage diffusion simulator" is designed. The risk of secondary collapse of buildings is simulated in real time through the physics engine. The probability distribution of collapse in the short term can be predicted based on parameters such as crack width and material strength.

[0175] The lidar point cloud density is 200 points / m², the scanning frequency is 10Hz, the terahertz tomography resolution is 0.5cm × 0.5cm, and the imaging speed is 1 frame / second. The damage diffusion simulator is based on the LS-DYNA physics engine. Concrete material parameters: elastic modulus 30GPa, Poisson's ratio 0.2, yield strength 20MPa, critical stress intensity factor for crack propagation 1.5MPa. );

[0176] Crack propagation rate formula:

[0177]

[0178] In the formula: This indicates the crack propagation rate (unit: m / s, describing the rate of deterioration of a building structure);

[0179] Represents the material constant (3 for concrete). m / (Pa·s), determined experimentally;

[0180] This indicates the material index (3.0 for concrete, a typical value for brittle materials);

[0181] Indicates the range of stress intensity factor (unit: Pa·m). );

[0182] in: This represents the geometric factor (1.12 for rectangular sections and 1.0 for circular sections);

[0183] This represents the alternating stress amplitude (in Pa, calculated from structural deformation measured by terahertz tomography);

[0184] This indicates the current length of the crack (in meters, extracted from lidar point cloud).

[0185] This represents the load ratio correction function;

[0186] Where: f(R) represents the output value (correction coefficient) of the load ratio correction function, used to adjust the crack propagation rate response to "stress cycle asymmetry", with a value range of 0. <f(R)≤1);

[0187] R represents the load ratio, defined as ( ), describing the asymmetry of stress cycles;

[0188] It represents the minimum stress in a load cycle (can be positive or negative, positive is tensile stress, negative is compressive stress).

[0189] This represents the maximum stress during a load cycle (usually tensile stress, but can also be compressive stress due to compression).

[0190] P represents the correction index, which quantifies the influence of the load ratio on crack propagation (an empirical value, related to material and load type); in aftershock scenarios, p = 0.5, and in fatigue loading, p ≈ 3.

[0191] (2) AR risk visualization and data support: An augmented reality (AR) annotation system is introduced to overlay dynamic information such as life signals and toxic gas concentrations obtained by the biomimetic perception cluster unit onto the digital twin, so that rescuers can intuitively observe "invisible" risks through AR glasses. The generated three-dimensional disaster model will provide spatial background reference for the bioelectric field enhancement identification unit.

[0192] The AR labeling system uses Microsoft HoloLens 2 glasses, with a latency of <20ms, a dynamic information refresh rate of 10Hz, and life signal labeling colors of red (intensity >5pT), yellow (3-5pT), and blue (<3pT). Toxic gas concentration labeling uses color gradations (green <10ppm, yellow 10-30ppm, red >30ppm).

[0193] The bioelectric field enhancement recognition unit includes:

[0194] (1) Multimodal life signal acquisition: Integrating three types of specific life signals, including 0.1-10Hz ultra-low frequency bioelectric field (weak magnetic field generated by human heart current) detected by superconducting quantum interference device (SQUID); 8-13Hz alpha wave brain wave characteristics captured by terahertz waves penetrating the skull; and respiratory metabolism 13CO2 isotope labeling achieved with high precision detection through quantum sensing;

[0195] The superconducting quantum interference device (SQUID) is model DC-SQUID, with an operating temperature of 4.2K and a magnetic field detection range of 1fT-1μT; the terahertz wave EEG detection uses a center frequency of 0.3THz and has a skull penetration attenuation rate of <20%; the 13CO2 isotope labeling detection accuracy is 0.1ppm and the sampling frequency is 1Hz.

[0196] Formula for attenuation of biological magnetic field strength:

[0197]

[0198] In the formula: Indicates distance from bioelectric signal source The magnetic field strength (unit: Tesla T) at a given location is detected by a superconducting quantum interference device (SQUID) and is a core parameter for identifying life signals.

[0199] Represents the vacuum permeability (a constant, with values ​​ranging from 1 to 10). H / m describes the propagation characteristics of a magnetic field in a vacuum.

[0200] This represents the current intensity of a bioelectric signal source (unit: ampere-A, e.g., the human heart current is approximately 10 amperes). A);

[0201] This indicates the equivalent length of the current source (unit: meters, m; the length of an electric dipole in the human heart is approximately 0.1 m).

[0202] This indicates the distance between the detection point and the signal source (unit: meters (m), typically 0.5-5m in post-disaster scenarios);

[0203] Indicates the angle between the detection direction and the axis of the current source (unit: radians rad, value range: 0-π, affects the directional distribution of the magnetic field);

[0204] (2) Quantum matching and spatial association: Develop a "vital characteristic fingerprint database" that covers biometric templates of different ages, genders and health statuses. Use quantum neural networks for matching to effectively solve the problem of interference from metal components. The identification results will be associated with the spatial model of the metaverse disaster mapping unit to form a vital sign report with location tags, which will be transmitted to the ethics enhancement decision-making unit.

[0205] The biometric fingerprint database contains three template types: ① Adult males (alpha wave 8-10Hz, ultra-low frequency electric field intensity 3-8pT, ¹³CO₂ concentration 2-5ppm); ② Adult females (alpha wave 9-13Hz, ultra-low frequency electric field intensity 2-6pT, ¹³CO₂ concentration 1.5-4ppm); ③ Children (alpha wave 7-9Hz, ultra-low frequency electric field intensity 1-4pT, ¹³CO₂ concentration 1-3ppm). The quantum neural network has a 32-dimensional input layer (corresponding to the feature values ​​of the three signal types), three hidden layers (64 nodes / layer), and a 3-dimensional output layer (matching probability). The activation function is the quantum state superposition function |ψ|. =a|0 +b|1 (a² + b² = 1).

[0206] The swarm intelligent repair unit includes:

[0207] (1) Cluster self-organizing maintenance mechanism: Drawing on the group repair behavior of social insects, the self-organizing maintenance of the UAV cluster is realized. When a UAV sensor fails, the surrounding UAVs automatically adjust their flight trajectories and fill blind spots through multi-machine collaboration, such as splicing multi-machine lidar data to the observation area of ​​the faulty machine, to ensure the integrity of the inspection coverage.

[0208] The trajectory adjustment for multi-drone collaborative blind spot filling is based on the artificial potential field method, with a repulsive coefficient k=5, an attractive coefficient g=10, a minimum safe distance of 2m between adjacent drones, and the boundary of the fault area is determined by the convex hull algorithm of the lidar point cloud. The number of blind spot filling drones is dynamically adjusted according to the area of ​​the fault area (2 drones for 10-50㎡, 3-5 drones for 50-100㎡).

[0209] Multi-machine collaborative blind spot filling probability formula:

[0210]

[0211] In the formula: This indicates the coverage probability of the faulty area (target value > 0.95, ensuring no information is lost).

[0212] Indicates the number of drones participating in the blind spot filling (maximum 5, determined by the cluster size);

[0213] Indicates the first The sensor efficiency of the drone (0.9 when the lidar is working properly, 0.6 when there is a partial malfunction);

[0214] Indicates the first The distance between the drone and the center of the fault area (in meters, obtained by the positioning system);

[0215] This indicates the effective radius of the sensor (30m for lidar, 15m for sonar, determined by equipment parameters);

[0216] (2) Emergency communication relay support: Develop a biodegradable emergency communication relay system. The UAV carries a bio-based material 3D printer to quickly print temporary relay nodes in the communication interruption area (degradation cycle of 72 hours). The terahertz frequency band is used to achieve high-speed transmission and ensure the smooth data link between the biomimetic sensing cluster unit and the quantum-assisted edge computing unit.

[0217] The 3D-printed bio-based material is 80% polylactic acid (PLA, molecular weight 100,000) + 20% corn starch (particle size 50μm), printed by fused deposition modeling (FDM). The relay node size is 10cm×10cm×5cm, the antenna gain is 5dBi, the terahertz transmission uses a center frequency of 0.5THz, ASK modulation, and the channel coding is LDPC code.

[0218] The ethics-enhanced decision-making unit includes:

[0219] (1) Multi-dimensional ethical decision-making model: Construct a multi-objective ethical decision-making model that comprehensively considers three factors: the life value provided by the bioelectric field enhancement identification unit (the degree of trauma and the probability of survival); the rescue difficulty reflected by the meta-universe disaster mapping unit (path complexity and time cost); and social impact (such as priority for special groups). The Pareto optimal rescue plan is generated through quantum fuzzy logic algorithm.

[0220] Quantitative parameters for life value: Trauma index (0-10, determined based on the amplitude of ultra-low frequency electric field fluctuations), survival probability (0-1, calculated based on the integrity of brain wave rhythm); Quantitative parameters for rescue difficulty: Path complexity (1-5, based on obstacle density of digital twin model), time cost (0-1000 seconds, calculated based on drone flight speed of 2m / s); Quantitative parameters for social impact: Priority of special groups (1-3, 1 for the general population, 2 for children / elderly, 3 for medical staff / pregnant women);

[0221] Quantum fuzzy logic consists of three steps: input fuzzification → quantum state reasoning → defuzzification; decision tree priority factors: life value (0.6) > rescue difficulty (0.3) > social impact (0.1), with short-term impact (rescue success rate within 48 hours) accounting for 60% of the weight during conflict;

[0222] The steps for generating Pareto optimal solutions using quantum fuzzy logic are as follows:

[0223] ① Quantification of input variables: value of life (trauma index 0-10), difficulty of rescue (path complexity 1-5), social impact (priority of special groups 1-3);

[0224] ②Membership degree calculation: The membership degree of the three types of fuzzy sets, namely "high / medium / low", is calculated by using the quantum fuzzy decision membership degree formula (e.g., the trauma index of 8 corresponds to a membership degree of 0.92 for "high life value").

[0225] ③ Solution selection: Based on the comprehensive score of the three candidate solutions (life value × 0.6 + rescue difficulty × 0.3 + social impact × 0.1), the solution with the highest score is selected as the optimal solution. In the conflict scenario of "1 seriously injured person vs. 3 lightly injured persons", the decision-making time is <2 seconds.

[0226] Quantum fuzzy decision membership formula:

[0227]

[0228] In the formula: variable Belonging to the The degree of fuzzy set (values ​​O-1, such as "high life value" or "low rescue difficulty");

[0229] Input variables (e.g., trauma index 0-10, path complexity 1-5). No. The center of the fuzzy set (8 for "high", 5 for "medium", 2 for "low", determined by experts);

[0230] No. Width of the fuzzy set ("High" is 1.5, "Medium" is 2.0, "Low" is 1.5, controlling the range of membership degree distribution);

[0231] exp represents the natural exponential function (with a basis of e≈2.71828, converting distance into exponentially decaying membership).

[0232] This represents the summation of the "unnormalized values" of the membership degrees of the three fuzzy sets (k=1, 2, 3) of high, medium, and low classes.

[0233] This represents the center value of the k-th fuzzy set (same as cj, since k traverses 1~3, covering all categories), and has the same value as cj (e.g., c1=8, c2=5, c3=2).

[0234] The width parameter of the k-th type of fuzzy set (same as above) (Traverse the three categories);

[0235] The denominator represents the normalization term (ensuring...). (satisfies the axioms of fuzzy sets).

[0236] (2) Conflict mediation and path optimization: Design an "ethical conflict mediation mechanism" to automatically push a visual decision tree to the ground command when a decision contradiction is detected (such as whether to save one seriously injured person or multiple lightly injured persons). The decision results will be fed back to the swarm intelligent repair unit to optimize the subsequent inspection path planning.

[0237] The decision tree contains three layers of nodes: the first layer is the value of life (high / medium / low), the second layer is the difficulty of rescue (high / medium / low), and the third layer is the social impact (high / medium / low). Each leaf node corresponds to one rescue plan. When there is a conflict, the priority is determined by calculating the "ethical utility value" of each plan (value of life × 0.6 + difficulty of rescue × 0.3 + social impact × 0.1).

[0238] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0239] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A UAV post-disaster emergency inspection system based on multi-source perception fusion, characterized in that: The system includes: Biomimetic Sensing Cluster Unit: Integrates fly-eye panoramic optics, snake tongue chemical sensing, and bat-ear vector sonar, and achieves online evolution of sensor combinations through reinforcement learning, outputting raw data; Quantum-assisted edge computing unit: It adopts a classical-quantum hybrid architecture, with quantum processors accelerating optimization decisions, FPGAs preprocessing data, developing a quantum error correction lightweight model, compressing the qubit consumption of the vital signs recognition model, and outputting the processed data; Quantum entangled spatiotemporal calibration unit: It uses quantum entanglement to achieve multi-machine long-distance synchronization, combined with quantum-classical hybrid positioning, to assist in calibration when the GNSS signal is normal, and to use full quantum positioning when the signal is lost, and outputs the calibrated spatiotemporal data; Metaverse Disaster Mapping Unit: Constructs a centimeter-level digital twin spatial model to simulate collapse risks, and uses AR annotation to include information such as life signals; Bioelectric field enhancement recognition unit: It integrates ultra-low frequency electric field, brain wave and isotope labeling signals, matches the life fingerprint database through quantum neural network, eliminates metal interference, and associates the recognition results with the spatial model of the meta-universe to form a report with location label; The swarm intelligent repair unit simulates insect colony behavior, enabling multiple units to work together to fill gaps in case of failure. It carries a 3D printer to create biodegradable relay nodes and uses the terahertz frequency band to transmit data. Ethical Enhanced Decision-Making Unit: A model is constructed by combining the value of life, the difficulty of rescue, and the social impact. The optimal solution is generated using quantum fuzzy logic. When a conflict occurs, the decision tree is pushed, and the result is fed back to the swarm intelligent repair unit.

2. The UAV post-disaster emergency inspection system based on multi-source perception fusion according to claim 1, characterized in that: The biomimetic sensing cluster unit includes: (1) Bionic sensor array configuration: The modular design integrates three types of bionic sensing devices, including a fly-eye compound eye panoramic optical system composed of 128 microlenses; a snake tongue-type chemical sensor array composed of 32-channel nano gas-sensitive materials; and a 1-50kHz wideband bat ear-type vector sonar that uses bioacoustic algorithms to filter environmental noise. (2) Evolution of perception strategy and data flow: The sensor organ evolution mechanism is introduced, and the sensor combination strategy is evolved online through reinforcement learning. The multi-modal detection mode adapted to the environment is generated autonomously, and the collected multi-source raw data will be transmitted to the quantum-assisted edge computing unit in real time.

3. The UAV post-disaster emergency inspection system based on multi-source perception fusion according to claim 1, characterized in that: The quantum-assisted edge computing unit includes: (1) Hybrid computing architecture design: Construct a classical-quantum hybrid computing architecture, with a quantum annealing processor responsible for accelerating the solution of combinatorial optimization problems; a classical FPGA responsible for real-time data preprocessing tasks, and a filtering algorithm inspired by the quantum tunneling effect to optimize the radar echo signal-to-noise ratio; (2) Extreme environment adaptability optimization: Develop a lightweight model of quantum error correction coding, compress the qubit consumption of the vital signs recognition model, and synchronize the processed standardized data to the quantum entanglement spatiotemporal calibration unit.

4. The UAV post-disaster emergency inspection system based on multi-source perception fusion according to claim 1, characterized in that: The quantum entanglement spacetime calibration unit includes: (1) Quantum entanglement synchronization mechanism: Multi-machine long-distance synchronization is achieved by using quantum entangled states. Each UAV is equipped with an entangled photon source with a wavelength of 850nm, and a quantum channel is established by distributing entangled photon pairs. (2) Hybrid positioning mode switching: A quantum-classical hybrid positioning mechanism is designed. When the GNSS signal is normal, classical positioning is the main method and quantum calibration is the auxiliary method. After the signal is lost, it automatically switches to full quantum mode. The position of the UAV is inverted by measuring the phase change of entangled photons. The calibrated spatiotemporal tag data will provide coordinate reference for the metaverse disaster mapping unit.

5. The UAV post-disaster emergency inspection system based on multi-source perception fusion according to claim 1, characterized in that: The metaverse disaster mapping unit includes: (1) Centimeter-level digital twin construction: Based on lidar point cloud and terahertz tomography data, combined with coordinate reference, a post-disaster digital twin spatial model with centimeter-level accuracy is constructed, a damage diffusion simulator is designed, and the risk of secondary building collapse is simulated in real time through a physics engine. The collapse probability distribution is predicted based on parameters including crack width and material strength. (2) AR risk visualization and data support: An AR annotation system is introduced to overlay the dynamic information, including life signals and toxic gas concentration, obtained by the biomimetic perception cluster unit onto the digital twin, so that rescuers can intuitively observe invisible risks through AR glasses and generate a three-dimensional disaster model.

6. The UAV post-disaster emergency inspection system based on multi-source perception fusion according to claim 1, characterized in that: The bioelectric field enhancement recognition unit includes: (1) Multimodal life signal acquisition: Three types of specific life signals are integrated, including 0.1-10Hz ultra-low frequency bioelectric field detected by superconducting quantum interference device; 8-13Hz alpha wave brain wave characteristics captured by terahertz waves penetrating the skull; and respiratory metabolism 13CO2 isotope labeling detected by quantum sensing. (2) Quantum matching and spatial correlation: Develop a biometric fingerprint database covering biometric templates of different ages, genders and health statuses, and use quantum neural networks for matching. The identification results will be correlated with the spatial model of the metaverse disaster mapping unit to form a vital sign report with location tags.

7. The UAV post-disaster emergency inspection system based on multi-source perception fusion according to claim 1, characterized in that: The intelligent bee colony repair unit includes: (1) Cluster self-organizing maintenance mechanism: Drawing on the group repair behavior of social insects, the self-organizing maintenance of the UAV cluster is realized. When a UAV sensor fails, the surrounding UAVs automatically adjust their flight trajectories and multiple UAVs cooperate to fill the blind spot. (2) Emergency communication relay support: Develop an emergency communication relay system. A drone carries a bio-based material 3D printer to print temporary relay nodes in areas where communication is interrupted, and uses the terahertz frequency band for transmission.

8. The UAV post-disaster emergency inspection system based on multi-source perception fusion according to claim 1, characterized in that: The ethics-enhanced decision-making unit includes: (1) Multi-dimensional ethical decision-making model: Construct a multi-objective ethical decision-making model that comprehensively considers three factors: the life value provided by the bioelectric field enhancement identification unit, the difficulty of rescue reflected by the meta-universe disaster mapping unit, and social impact; and generate Pareto optimal rescue plan through quantum fuzzy logic algorithm; (2) Conflict mediation and path optimization: Design an ethical conflict mediation mechanism. When a decision-making contradiction is detected, a visual decision tree is automatically pushed to the ground command to show the short-term and long-term effects of different choices. The decision results will be fed back to the swarm intelligent repair unit.

Images