Reservoir safety intelligent inspection method and system based on unmanned aerial vehicle

Abstract

The invention discloses an unmanned aerial vehicle-based reservoir safety intelligent inspection method and system, and the method comprises the steps: transmitting entangled photons to an unmanned aerial vehicle cluster, generating a shared key and channel quality parameters through quantum state chromatography, and collecting laser point cloud data and visible light image data; encoding the laser point cloud data into a quantum state by using a shared key and a channel quality parameter, converting the visible light image data into a quantum image state, and performing time synchronization through quantum entanglement broadcast to generate an encrypted quantum data stream; calculating an environment interference parameter tensor according to a channel quality parameter, carrying out vortex waveguide error correction coding on the encrypted quantum data stream to generate a redundant coding data packet, extracting a quantum state fidelity parameter, and mapping the quantum state fidelity parameter to a Riemann space to generate a routing decision matrix; according to the method, entangled photons are prepared and transmitted through quantum key distribution, so that secure key sharing among unmanned aerial vehicle clusters is realized, and an encryption basis is provided for quantum communication.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) application technology, and in particular to a method and system for intelligent inspection of reservoir safety based on UAVs. Background Technology

[0002] With the continuous expansion of modern water conservancy projects, reservoirs, as crucial infrastructure for water resource regulation and flood control, face an increasingly urgent need for real-time monitoring of their operational status and identification of potential hazards. In recent years, the application of unmanned aerial vehicle (UAV) technology in the field of inspection has deepened. Leveraging its advantages of high mobility, flexible deployment, and wide field of view, it has been widely used in various engineering scenarios such as power line inspection, bridge inspection, and disaster assessment. Particularly in reservoir safety monitoring, remote sensing imaging and image recognition technologies based on UAV platforms have improved the efficiency of identifying defects such as cracks and seepage in reservoir dams.

[0003] However, existing UAV-based reservoir inspection equipment still faces numerous technical bottlenecks. First, in terms of data transmission, mainstream equipment uses traditional wireless communication protocols, lacking effective encryption mechanisms and anti-interference capabilities. This makes it susceptible to interference in complex electromagnetic environments or long-distance flight missions, affecting data integrity and security. Second, at the intelligent analysis level, most equipment relies on ground stations to centrally process collected image data, failing to achieve edge computing and distributed collaborative reasoning. This results in high decision-making latency and poor path planning adaptability, making it difficult to respond promptly to emergencies, especially in dynamically changing environments. Summary of the Invention

[0004] The purpose of this invention is to provide a method for intelligent inspection of reservoir safety based on unmanned aerial vehicles (UAVs) to solve the problems of insufficient data transmission security and delayed intelligent analysis response of existing equipment in complex environments.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a method for intelligent reservoir safety inspection based on unmanned aerial vehicles (UAVs), comprising: transmitting entangled photons to a UAV swarm; generating a shared key and channel quality parameters through quantum state tomography; and collecting laser point cloud data and visible light image data; using the shared key and channel quality parameters, encoding the laser point cloud data into quantum states and transforming the visible light image data into quantum image states; performing time synchronization through quantum entanglement broadcasting to generate an encrypted quantum data stream; calculating an environmental interference parameter tensor based on the channel quality parameters; performing vortex waveguide error correction encoding on the encrypted quantum data stream to generate redundant encoded data packets; simultaneously extracting quantum state fidelity parameters and mapping them to Riemannian space to generate a routing decision matrix; selecting the optimal computing node based on the routing decision matrix; performing quantum measurement reconstruction on the redundant encoded data packets; calculating the identification confidence level; and generating a decision instruction when the identification confidence level exceeds a dynamic security threshold; the decision instructions include crack repair decision instructions, leakage sealing decision instructions, and re-inspection decision instructions; and synchronizing the decision instructions to the execution terminal through a quantum teleportation protocol and outputting a reservoir dam state tensor matrix to generate a reservoir safety inspection report.

[0007] As a preferred embodiment of the UAV-based intelligent reservoir safety inspection method of the present invention, the specific steps for generating the shared key and channel quality parameters are as follows:

[0008] The control center prepares entangled photon pairs and transmits them to the drone swarm. The drone swarm performs quantum measurements on the received entangled photon pairs and transmits the measurement results back to the control center.

[0009] The control center performs quantum state tomography on the measurement results to calculate the purity of quantum entanglement;

[0010] When the purity of quantum entanglement is greater than the security purity threshold, a shared key and channel quality parameters are generated.

[0011] Scan the surface of the reservoir dam to generate laser point cloud data and images of the reservoir dam surface.

[0012] As a preferred embodiment of the UAV-based intelligent reservoir safety inspection method of the present invention, the specific steps for generating the encrypted quantum data stream are as follows:

[0013] A spherical harmonic basis projection is performed on the laser point cloud data using a shared key to generate a quantum state;

[0014] Quantum wavelet transform is performed on the surface image of the reservoir dam based on channel quality parameters to generate quantum image states;

[0015] By fusing quantum states and quantum image states through gradient correlation enhancement operators, spatiotemporally correlated quantum states are generated;

[0016] By using quantum entanglement broadcasting to send synchronization pulses to a drone swarm, timestamp alignment of spatiotemporally correlated quantum states is performed, and quantum error correction encoding is executed on the spatiotemporally correlated quantum states to generate encrypted quantum data streams.

[0017] As a preferred embodiment of the UAV-based intelligent reservoir safety inspection method of the present invention, the specific steps for generating redundant coded data packets are as follows:

[0018] Real-time acquisition of atmospheric optical and geomagnetic parameters, combined with channel quality parameters, to calculate the environmental interference parameter tensor;

[0019] The optimal orbital angular momentum mode is dynamically generated based on the environmental disturbance parameter tensor, and vortex waveguide error correction coding is performed on the encrypted quantum data stream to generate redundant coded data packets.

[0020] As a preferred embodiment of the UAV-based intelligent reservoir safety inspection method of the present invention, the specific steps for generating the routing decision matrix are as follows:

[0021] Quantum state fidelity parameters are extracted from redundant coded data packets and mapped to Riemannian space. Path solutions are generated through curvature-driven geodesic optimization.

[0022] Based on the path solution, a routing decision matrix is ​​generated through topological entanglement number phase filtering.

[0023] As a preferred embodiment of the UAV-based intelligent reservoir safety inspection method of the present invention, the specific steps for generating decision instructions are as follows:

[0024] A quantum graph Hamiltonian is constructed based on the routing decision matrix, and the optimal computing node is selected through the quantum random walk algorithm.

[0025] Perform quantum measurements on redundant coded data packets at the optimal computing node to compute quantum state topological invariants;

[0026] Based on the topological invariants of quantum states and combined with the quantum state fidelity parameters, the identification confidence level is calculated.

[0027] When the identification confidence level exceeds the upper limit of the dynamic safety threshold, a crack repair decision instruction is generated;

[0028] When the identification confidence level is between the lower limit and the upper limit of the dynamic safety threshold, a leakage sealing decision instruction is generated.

[0029] When the identification confidence level is lower than the lower limit of the dynamic security threshold, a re-inspection decision instruction is generated.

[0030] As a preferred embodiment of the UAV-based intelligent reservoir safety inspection method of the present invention, the specific steps for generating the reservoir safety inspection report are as follows:

[0031] The decision-making instructions are encoded into quantum states and synchronized to the drone swarm terminal via a quantum teleportation protocol to generate modulated quantum states.

[0032] Based on the dynamic configuration of quantum imaging parameters using modulated quantum states, and scanning of the generator state density matrix of the reservoir dam body according to the quantum imaging parameters;

[0033] The density of states matrix is ​​mapped to a Ricci manifold to generate the reservoir dam state tensor matrix, and eigenvalue decomposition is performed to generate a reservoir safety inspection report.

[0034] Secondly, this invention provides a UAV-based intelligent reservoir safety inspection system, comprising a key distribution module, a data encapsulation module, an anti-interference module, a decision generation module, and an execution reporting module. The key distribution module transmits entangled photons to a UAV cluster, generates a shared key and channel quality parameters through quantum state tomography, and collects laser point cloud data and visible light image data. The data encapsulation module uses the shared key and channel quality parameters to encode the laser point cloud data into quantum states and transform the visible light image data into quantum image states, achieving time synchronization through quantum entanglement broadcasting to generate an encrypted quantum data stream. The anti-interference module calculates environmental interference based on the channel quality parameters. The parameter tensor performs vortex waveguide error correction encoding on the encrypted quantum data stream to generate redundant encoded data packets. Simultaneously, it extracts quantum state fidelity parameters and maps them to Riemann space to generate a routing decision matrix. The decision generation module selects the optimal computing node based on the routing decision matrix, performs quantum measurement reconstruction on the redundant encoded data packets, and calculates the identification confidence level. When the identification confidence level exceeds a dynamic security threshold, it generates decision instructions. These decision instructions include crack repair decision instructions, leakage sealing decision instructions, and re-inspection decision instructions. The execution report module synchronizes the decision instructions to the execution terminal via a quantum teleportation protocol and outputs the reservoir dam state tensor matrix to generate a reservoir safety inspection report.

[0035] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the unmanned aerial vehicle-based intelligent reservoir safety inspection method as described in the first aspect of the present invention.

[0036] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the unmanned aerial vehicle-based intelligent reservoir safety inspection method as described in the first aspect of the present invention.

[0037] The beneficial effects of this invention are as follows: By preparing and transmitting entangled photons through quantum key distribution, secure key sharing among UAV clusters is realized, providing an encryption basis for quantum communication and achieving a security effect against quantum computing attacks; furthermore, by encoding laser point clouds and visible light images into quantum states through quantum data encapsulation, quantum fusion of multi-source heterogeneous data is realized, improving data transmission efficiency, and ultimately achieving the effect of maintaining high-fidelity transmission even in complex electromagnetic environments. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of a drone-based intelligent safety inspection method for reservoirs.

[0040] Figure 2 A flowchart for generating shared keys and channel quality parameters.

[0041] Figure 3 A flowchart for generating encrypted quantum data streams.

[0042] Figure 4 A flowchart for generating decision instructions. Detailed Implementation

[0043] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0046] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for intelligent inspection of reservoir safety based on unmanned aerial vehicles (UAVs), comprising the following steps:

[0047] S1: Emit entangled photons to the drone swarm, generate a shared key and channel quality parameters through quantum state tomography, and collect laser point cloud data and visible light image data.

[0048] S1.1: The control center prepares entangled photon pairs and transmits them to the UAV swarm. The UAV swarm performs quantum measurements on the received entangled photon pairs and transmits the measurement results back to the control center.

[0049] It should be noted that the control center prepares entangled photon pairs through a spontaneous parametric down-conversion process and transmits these pairs via a free-space optical path to the quantum receiving unit mounted on the UAV swarm. The UAV swarm uses a polarization beam splitter and a single-photon detector to perform Bell basis measurements on the received entangled photons. The Bell basis measurements include both horizontal and vertical polarization bases and diagonal polarization bases. After the Bell basis measurements are completed, the UAV swarm transmits the measurement results back to the control center via a classical communication channel.

[0050] S1.2: The control center performs quantum state tomography on the measurement results and calculates the quantum entanglement purity, expressed as:

[0051]

[0052] Where F represents the purity of quantum entanglement (0≤F≤1), A represents the photon retained by the control center, B represents the photon received by the UAV, x represents the x-axis direction of the Pauli operator, y represents the y-axis direction of the Pauli operator, and z represents the z-axis direction of the Pauli operator. This represents the Pauli operator measurement of photon A retained by the control center in the x-axis direction. This represents the Pauli operator measurement of photon B received by the UAV along the x-axis. This represents the Pauli operator measurement of photon A retained by the control center in the y-axis direction. This represents the Pauli operator measurement of photon B received by the UAV along the y-axis. This represents the Pauli operator measurement of photon A retained by the control center in the z-axis direction. This represents the Pauli operator measurement of photon B received by the UAV in the z-axis direction.

[0053] The specific process involves the control center reconstructing quantum states using quantum measurement data returned by a swarm of drones. By analyzing the quantum state projection results under different measurement bases, the quantum state characteristics of entangled photon pairs are fully reconstructed. Quantum state tomography is employed to comprehensively characterize the statistical properties of the quantum states. Based on the reconstructed quantum state characteristics, the quantum entanglement purity is further calculated to quantitatively assess the degree of entanglement of the photon pairs. The calculation of quantum entanglement purity is entirely based on actual measurement data, and the results directly reflect the transmission quality of the quantum channel.

[0054] S1.3: When the quantum entanglement purity is greater than the security purity threshold, generate a shared key and channel quality parameters.

[0055] The specific process includes: when the calculated purity of quantum entanglement exceeds a pre-set security purity threshold, the control center confirms that the quantum channel meets the secure communication requirements. The control center extracts a shared key based on the correlation of the measurement results; the generation process of the shared key utilizes the quantum no-cloning property to ensure security. Simultaneously, the control center analyzes parameters such as the bit error rate and count rate during the measurement process to comprehensively derive the channel quality parameters.

[0056] The security purity threshold is preset based on the security requirements of the quantum key distribution protocol and through theoretical analysis of the maximum tolerance error of the quantum channel under typical attacks.

[0057] S1.4: Scan the surface of the reservoir dam to generate laser point cloud data and images of the reservoir dam surface.

[0058] The specific process includes: a swarm of drones equipped with lidar performs a 3D scan of the reservoir dam surface, acquiring high-precision distance information by measuring the flight time of laser pulses, and forming laser point cloud data describing the geometric features of the dam. Simultaneously, the drone swarm acquires optical images of the dam surface using visible light cameras, recording the dam's appearance. The laser point cloud data contains spatial coordinate information, while the dam surface images provide texture details. The laser point cloud data and dam surface images are acquired synchronously and a spatial correspondence is established. During the acquisition process, the relative positions of the drones and the dam are kept stable to ensure spatial consistency of the data.

[0059] S2: Using a shared key and channel quality parameters, laser point cloud data is encoded into quantum states, and visible light image data is transformed into quantum image states. Time synchronization is achieved through quantum entanglement broadcasting to generate encrypted quantum data streams.

[0060] S2.1: Use the shared key to perform spherical harmonic basis projection on the laser point cloud data to generate quantum states.

[0061] The specific process includes: after denoising, filtering, coordinate normalization, and outlier removal, the laser point cloud data is decomposed into basis vectors using the spherical harmonic function expansion method. A shared key is used as a random phase modulation parameter to perform phase rotation operations during the spherical harmonic basis vector projection process. The spherical harmonic basis vector projection result is mapped to the quantum state Hilbert space, encoding the three-dimensional spatial coordinate information into quantum state amplitudes. A quantum state wavefunction is constructed through the superposition of orthogonal basis vectors, realizing the quantized representation of the laser point cloud data. The quantum state preparation process preserves the geometric characteristics of the point cloud data while endowing it with the quantum no-cloning property; the generated quantum state is used for quantum entanglement broadcast transmission.

[0062] S2.2: Perform quantum wavelet transform on the surface image of the reservoir dam based on the channel quality parameters to generate a quantum image state.

[0063] The specific process includes: after grayscale correction, noise suppression, geometric distortion correction, and contrast enhancement, the surface image of the reservoir dam is processed. The optimal wavelet decomposition level and quantization step size are then selected based on channel quality parameters. Discrete wavelet transform is used to perform multi-scale decomposition of the reservoir dam surface image, converting the image information to the frequency domain. A shared key controls the random phase modulation during wavelet coefficient quantization to ensure the security of the transformation process. The quantized wavelet coefficients are then converted into quantum superposition states using a quantum state mapping algorithm, forming a quantum image state containing multi-scale image features.

[0064] S2.3: Generate a spatiotemporally correlated quantum state by fusing quantum state and quantum image state through gradient correlation enhancement operator.

[0065] The specific process includes: fusing features between the quantum state and the quantum image state using a gradient correlation enhancement operator; the gradient correlation enhancement operator then derives the gradient similarity between the quantum state and the quantum image state in Hilbert space. Based on this gradient similarity, a spatiotemporal correlation matrix is ​​established, and the geometric features of the quantum state are coherently superimposed with the texture features of the quantum image state. The resulting spatiotemporally correlated quantum state simultaneously contains three-dimensional spatial information and two-dimensional texture features. The superposition process preserves the superposition properties of the quantum state while enhancing the correlation between features.

[0066] S2.4: Use quantum entanglement broadcasting to send synchronization pulses to the drone swarm, perform time stamp alignment of spatiotemporally correlated quantum states, and perform quantum error correction encoding on the spatiotemporally correlated quantum states to generate encrypted quantum data streams.

[0067] The specific process includes: the control center sending a synchronization pulse signal via quantum entanglement broadcast; the drone swarm receiving the pulse adjusts its local clock to achieve nanosecond-level timestamp alignment; and the timestamp-aligned time base is used to mark the transmission sequence of the spatiotemporally correlated quantum states. Subsequently, quantum error-correcting encoding is used to process the spatiotemporally correlated quantum states, adding redundant qubits to construct logical quantum states during the encoding process. A shared key controls the unitary transformation operation during the quantum error-correcting encoding process, achieving encryption protection of the quantum states. The spatiotemporally correlated quantum states, after quantum error-correcting encoding and encryption, form an encrypted quantum data stream.

[0068] S3: Calculate the environmental interference parameter tensor based on the channel quality parameters, perform vortex waveguide error correction coding on the encrypted quantum data stream, generate redundant coded data packets, extract the quantum state fidelity parameters, map them to the Riemann space, and generate the routing decision matrix.

[0069] S3.1: Real-time acquisition of atmospheric optical and geomagnetic parameters, combined with channel quality parameters, to calculate the environmental interference parameter tensor, expressed as:

[0070]

[0071] Where δ represents the environmental disturbance parameter tensor, Δt represents the integration time window, t0 represents the integration start time, t represents the time variable, S(t) represents the instantaneous signal-to-noise ratio at time t, α represents the weighting coefficient of atmospheric turbulence disturbance (0 < α ≤ 1), m represents the discrete frequency index of the atmospheric refractive index fluctuation spectrum, M represents the total number of spectrum sampling points, and P m n(P) represents the frequency value at the m-th discrete sampling frequency point. m ) indicates that at frequency value P m The amplitude of atmospheric refractive index fluctuations measured at the location, where β represents the weighting coefficient of geomagnetic gradient disturbance (0 < β ≤ 1). Let T denote the triaxial geomagnetic gradient tensor, and T denote the Frobenius norm.

[0072] The specific process involves sensors mounted on a drone swarm measuring the atmospheric refractive index pulsation spectrum and geomagnetic gradient tensor in real time, while simultaneously monitoring the instantaneous signal-to-noise ratio (SNR) changes of the quantum channel. Co-optimization is achieved by weighting and fusing quantum state topological invariants, quantum fidelity parameters, and channel SNR into a high-dimensional feature space using a nonlinear kernel function. Atmospheric turbulence interference weighting coefficients and geomagnetic gradient interference weighting coefficients adjust the contribution of different interference sources. The calculation process integrates the instantaneous SNR derivative within a sliding time window, combines discrete spectrum sampling results with Frobenius norm operations, and finally outputs an environmental interference parameter tensor.

[0073] S3.2: Dynamically generate the optimal orbital angular momentum mode based on the environmental disturbance parameter tensor, and perform vortex waveguide error correction coding on the encrypted quantum data stream to generate redundant coded data packets.

[0074] The specific process includes: inputting environmental interference parameter tensors into an orbital angular momentum mode selection algorithm to dynamically determine the optimal topological charge value based on the current interference characteristics; encoding the quantum information onto the vortex wavefront of the selected orbital angular momentum mode through helical phase plate modulation; adding auxiliary orbital angular momentum modes to construct quantum redundancy during the vortex waveguide error correction coding process, forming quantum states in a high-dimensional Hilbert space; the encoded quantum states in the high-dimensional Hilbert space possess a helical wavefront structure, capable of resisting phase distortion caused by atmospheric turbulence; and the generated redundant coded data packets contain both the main quantum state and auxiliary quantum states.

[0075] S3.3: Extract quantum state fidelity parameters from redundant coded data packets and map them to Riemann space. Generate path solutions through curvature-driven geodesic optimization.

[0076] The specific process includes obtaining quantum state fidelity parameters after redundant encoded data packets undergo quantum measurement. These parameters are then embedded into a high-dimensional manifold using differential geometry. In Riemannian space, the quantum state fidelity parameters are represented as a set of points on the high-dimensional manifold, and their spatial distribution reflects the transmission characteristics of the quantum channel. A curvature-driven algorithm is used to obtain the local geometric properties of the high-dimensional manifold and generate geodesic distances and curvature tensors. Geodesic paths are optimized based on these curvature characteristics to find the optimal transmission trajectory connecting the quantum nodes. The optimization process maintains the coherence constraints of the quantum states, and the final path solution satisfies the minimum distortion requirement for quantum information transmission.

[0077] S3.4: Based on the path solution, a routing decision matrix is ​​generated through topological entanglement number phase filtering.

[0078] The specific process includes inputting the path solution into the topology analysis module. By analyzing the cumulative phase change of quantum states on each candidate path, the entanglement number phase characteristics are analyzed using the complex plane encirclement integral method. The phase continuity during quantum state transmission is evaluated by comparing the phase entanglement numbers of different paths. The selection criterion requires that the phase difference between the entanglement number and the quantum decoherence tolerance threshold be less than the quantum information integrity threshold. Path solutions that meet the criteria are sorted according to phase stability, and a multi-dimensional routing evaluation index is constructed. The final generated routing decision matrix contains the quantum node connection relationships of the optimal transmission path, and the element values ​​of the routing decision matrix reflect the path quality score.

[0079] The quantum decoherence tolerance threshold is preset based on the quantum channel characteristics and quantum state coherence time, by experimentally measuring the decoherence rate of qubits under different environments.

[0080] S4: Select the optimal computing node based on the routing decision matrix, perform quantum measurement to restore the redundant coded data packets, and calculate the identification confidence. When the identification confidence exceeds the dynamic security threshold, generate a decision instruction. The decision instructions include crack repair decision instructions, leakage sealing decision instructions, and re-inspection decision instructions.

[0081] S4.1: Construct a quantum graph Hamiltonian based on the routing decision matrix, and select the optimal computing node through the quantum random walk algorithm.

[0082] The specific process includes converting the routing decision matrix into a quantum graph Hamiltonian, which corresponds to the quantum transition probabilities between computing nodes. The quantum random walk algorithm is initialized in a uniform superposition state and undergoes unitary evolution driven by the quantum graph Hamiltonian. During unitary evolution, quantum amplitudes are concentrated on the nodes corresponding to the optimal path, and the optimal computing node with the largest probability amplitude is extracted through quantum measurement. The selection criteria for the optimal computing node comprehensively consider quantum channel quality and computational resource load to ensure quantum state processing efficiency. The finally selected optimal computing node possesses the highest quantum state fidelity and the lowest transmission latency.

[0083] S4.2: Perform quantum measurements on redundant coded data packets at the optimal computing node to calculate the topological invariants of the quantum states, expressed as:

[0084]

[0085] Where C represents the topological invariant of the quantum state, k represents the row index variable of the grid on the surface of the reservoir dam, K represents the total number of rows of the grid on the surface of the reservoir dam, l represents the column index variable of the grid on the surface of the reservoir dam, L represents the total number of columns of the grid on the surface of the reservoir dam, i represents the operator for taking the imaginary part of a complex number, and ψ k,l ψ represents the quantum state vector at the k-th row and l-th column of the grid on the surface of the reservoir dam. k,l+1 ψ represents the quantum state vector at the k-th row and l+1-th column of the grid on the surface of the reservoir dam. k+1,l+1 ψ represents the quantum state vector at the (k+1)th row and (l+1)th column of the grid on the surface of the reservoir dam. l+1,l This represents the quantum state vector at the (k+1)th row and lth column of the grid on the surface of the reservoir dam.

[0086] The specific process includes: after receiving redundant coded data packets, the optimal computing node performs projection measurements using quantum measurement basis sets and inputs the data into the topological invariant calculation process to analyze the phase relationship of quantum states at each vertex of the grid on the reservoir dam surface. The quantum state topological invariant calculation uses the discrete logarithmic method, comparing the inner product phase difference of quantum states at adjacent grid points. The calculation results reflect the global topological characteristics of the quantum states on the reservoir dam surface and quantify the degree of phase distortion caused by surface defects.

[0087] S4.3: Based on the topological invariants of quantum states and combined with the quantum state fidelity parameters, calculate the identification confidence level. The expression is as follows:

[0088]

[0089] Where f represents the recognition confidence level (0 ≤ f ≤ 1), G represents the quantum fidelity (0 ≤ G ≤ 1), π represents pi, and exp represents the natural exponential function. Let represent the spatial gradient of quantum fidelity, γ represent the dynamic noise threshold of quantum fidelity, erfc represent the complementary error function, R represent the signal-to-noise ratio of the current channel, and μ represent the long-term statistical mean of the channel noise. The standard deviation of channel noise.

[0090] The specific process involves processing quantum state topological invariants and quantum state fidelity parameters using a feature fusion algorithm. The quantum state topological invariants reflect the global phase characteristics of the quantum states on the reservoir dam surface, while the quantum state fidelity parameters characterize the accuracy of quantum state transmission. The calculation process involves multiplying the absolute value of the quantum state topological invariants by the quantum state fidelity parameters and then normalizing by dividing by pi. The influence of the spatial gradient of the quantum state fidelity parameters is also considered, and noise interference is suppressed using a natural exponential function. Finally, a complementary error function correction term for the channel signal-to-noise ratio is introduced to comprehensively output the recognition confidence level. The recognition confidence level ranges from zero to one; a higher value indicates a more reliable reservoir dam state recognition result.

[0091] S4.4: When the identification confidence level exceeds the upper limit of the dynamic safety threshold, generate a crack repair decision instruction.

[0092] The specific process includes: First, the crack repair decision command generation process locates the anomalous region of quantum state topological invariants and then analyzes the crack's geometric features using quantum image state analysis to generate crack repair decision commands. These commands contain crack location coordinates, dimensional parameters, and repair level information, and their format conforms to engineering maintenance specifications. The generated crack repair decision commands are transmitted to the execution terminal via a quantum teleportation protocol, ensuring secure and reliable command transmission. The crack repair decision commands directly guide maintenance equipment in carrying out crack repair work on the reservoir dam body.

[0093] The dynamic safety threshold is a critical value pre-set based on the statistical analysis results of historical quantum measurement data and the requirements of the safety specifications for reservoir dam structures.

[0094] S4.5: When the identification confidence level is between the lower limit and the upper limit of the dynamic safety threshold, a leakage sealing decision instruction is generated.

[0095] The specific process includes: when the identification confidence level is between the lower and upper limits of the dynamic safety threshold, quantum state topological invariant analysis shows that the reservoir dam body has leakage risk characteristics. The leakage sealing decision command generation process first determines the seepage path distribution in the quantum image state and obtains the porosity parameter. The leakage sealing decision command includes the boundary coordinates of the seepage area, the amount of sealing material, and construction process requirements, which comply with the seepage prevention standards of water conservancy projects. The leakage sealing decision command is transmitted to the execution terminal after being encrypted with a quantum key to ensure the safe implementation of the seepage prevention plan. The leakage sealing decision command directly corresponds to the seepage characteristics identified by quantum measurement, guiding the precise implementation of sealing operations.

[0096] S4.6: When the identification confidence level is lower than the lower limit of the dynamic security threshold, generate a re-inspection decision instruction.

[0097] The specific process includes identifying areas where the confidence level is below the lower limit of the dynamic safety threshold, indicating that the quantum state topological invariant analysis has not met the deterministic judgment criteria. The re-inspection decision instruction generation process first marks the detection areas with insufficient confidence and records abnormal values ​​of quantum measurement parameters. The re-inspection decision instruction includes the coordinates of the re-inspection area, suggestions for adjusting detection parameters, and requirements for the frequency of re-measurement. The re-inspection decision instruction is transmitted to the control center via the quantum channel, triggering a new round of quantum state acquisition and analysis. The execution process of the re-inspection decision instruction prioritizes the use of higher-precision quantum measurement basis sets to improve the detection resolution of specific areas. The re-inspection decision instruction ensures that potentially risky areas receive secondary verification, avoiding the risk of misjudgment based on a single measurement result.

[0098] S5: Based on the decision command, it is synchronized to the execution terminal through the quantum teleportation protocol and outputs the reservoir dam state tensor matrix to generate a reservoir safety inspection report.

[0099] S5.1: Encode the decision command into a quantum state and synchronize it to the drone cluster terminal through the quantum teleportation protocol to generate a modulated quantum state.

[0100] The specific process includes: the decision command is converted into a sequence of qubits by a quantum state encoder, with the encoding process utilizing Bell states as quantum channel resources. During the execution of the quantum teleportation protocol, the control center and the UAV swarm terminal share entangled particle pairs and transmit Bell basis measurement results through a classical channel. The UAV swarm terminal performs a corresponding unitary transformation on its local quantum state based on the measurement results, reconstructing a quantum state identical to the original decision command. The reconstructed quantum state is then processed by a quantum modulator to generate a modulated quantum state suitable for transmission.

[0101] S5.2: Based on the dynamic configuration of quantum imaging parameters using modulated quantum states, the generator state density matrix of the reservoir dam body is scanned according to the quantum imaging parameters.

[0102] The specific process includes: modulating the quantum state input to the quantum imaging control unit to resolve the optimal scanning parameter configuration scheme. The optimal scanning parameter configuration scheme includes the optimal combination of laser power, pulse frequency, scanning resolution, and imaging focal length. The criterion for determining the optimal scanning parameter configuration scheme is to maximize scanning efficiency while ensuring quantum state fidelity and signal-to-noise ratio. A drone swarm adjusts the lidar power and visible light camera focal length to perform a coordinated scan of the reservoir dam. The scan data is processed by a quantum state reconstruction algorithm, fusing point cloud coordinates and pixel information into a composite quantum state. The composite quantum state is then subjected to quantum projection measurement to extract feature vectors and construct a state density matrix. The state density matrix fully characterizes the quantum state distribution on the surface of the reservoir dam, including quantum information about geometric deformation and material defects.

[0103] S5.3: Map the density of states matrix to the Ricci manifold to generate the reservoir dam state tensor matrix, perform eigenvalue decomposition, and generate a reservoir safety inspection report.

[0104] The specific process includes embedding the density of states matrix into a Ricci manifold through differential geometric transformation. The curvature of the Ricci manifold reflects the stress distribution characteristics of the reservoir dam body. The curvature tensor on the Ricci manifold is normalized and converted into the reservoir dam body state tensor matrix. The reservoir dam body state tensor matrix undergoes eigenvalue decomposition using a spectral decomposition algorithm to extract principal curvature eigenvalues ​​and eigenvectors, which are then input into a safety assessment algorithm to derive structural stability indicators and risk levels. The final generated reservoir safety inspection report includes a crack distribution map, seepage risk assessment, and maintenance priority ranking.

[0105] This embodiment also provides a UAV-based intelligent reservoir safety inspection system, including: a key distribution module, a data encapsulation module, an anti-interference module, a decision generation module, and an execution reporting module. The key distribution module transmits entangled photons to a UAV cluster, generates a shared key and channel quality parameters through quantum state tomography, and collects laser point cloud data and visible light image data. The data encapsulation module uses the shared key and channel quality parameters to encode the laser point cloud data into quantum states and transform the visible light image data into quantum image states, achieving time synchronization through quantum entanglement broadcasting to generate an encrypted quantum data stream. The anti-interference module calculates environmental interference parameters based on the channel quality parameters. The tensor module performs vortex waveguide error correction encoding on the encrypted quantum data stream to generate redundant encoded data packets. Simultaneously, it extracts quantum state fidelity parameters and maps them to Riemann space to generate a routing decision matrix. The decision generation module selects the optimal computing node based on the routing decision matrix, performs quantum measurement reconstruction on the redundant encoded data packets, and calculates the identification confidence level. When the identification confidence level exceeds a dynamic security threshold, it generates decision instructions. These decision instructions include crack repair decision instructions, leakage sealing decision instructions, and re-inspection decision instructions. The execution report module synchronizes the decision instructions to the execution terminal via a quantum teleportation protocol and outputs the reservoir dam state tensor matrix to generate a reservoir safety inspection report.

[0106] This embodiment also provides a computer device applicable to the intelligent reservoir safety inspection method based on unmanned aerial vehicles (UAVs), including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the intelligent reservoir safety inspection method based on UAVs proposed in the above embodiment.

[0107] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0108] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the intelligent reservoir safety inspection method based on unmanned aerial vehicles (UAVs) proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0109] In summary, this invention achieves secure key sharing among UAV clusters by preparing and transmitting entangled photons through quantum key distribution, providing an encryption foundation for quantum communication and achieving a security effect against quantum computing attacks. Furthermore, by encoding laser point clouds and visible light images into quantum states through quantum data encapsulation, it realizes the quantum fusion of multi-source heterogeneous data, improves data transmission efficiency, and ultimately achieves the effect of maintaining high-fidelity transmission even in complex electromagnetic environments.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent safety inspection of reservoirs based on unmanned aerial vehicles (UAVs), characterized in that: include, Entangled photons are emitted to a swarm of drones, and a shared key and channel quality parameters are generated through quantum state tomography. Laser point cloud data and visible light image data are also collected. Using a shared key and channel quality parameters, laser point cloud data is encoded into quantum states, and visible light image data is transformed into quantum image states. Time synchronization is achieved through quantum entanglement broadcasting to generate encrypted quantum data streams. The environmental interference parameter tensor is calculated based on the channel quality parameters. The encrypted quantum data stream is then subjected to vortex waveguide error correction coding to generate redundant coded data packets. At the same time, the quantum state fidelity parameters are extracted and mapped to the Riemann space to generate a routing decision matrix. The optimal computing node is selected based on the routing decision matrix, the redundant encoded data packets are restored by quantum measurement, and the identification confidence is calculated. When the identification confidence exceeds the dynamic security threshold, a decision instruction is generated. The decision instructions include crack repair decision instructions, leakage sealing decision instructions, and re-inspection decision instructions. Based on the decision command, the data is synchronized to the execution terminal via the quantum teleportation protocol, and the state tensor matrix of the reservoir dam is output to generate a reservoir safety inspection report.

2. The intelligent reservoir safety inspection method based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that: The specific steps for generating the shared key and channel quality parameters are as follows: The control center prepares entangled photon pairs and transmits them to the drone swarm. The drone swarm performs quantum measurements on the received entangled photon pairs and transmits the measurement results back to the control center. The control center performs quantum state tomography on the measurement results and calculates the purity of quantum entanglement; When the purity of quantum entanglement is greater than the security purity threshold, a shared key and channel quality parameters are generated. Scan the surface of the reservoir dam to generate laser point cloud data and images of the reservoir dam surface.

3. The intelligent reservoir safety inspection method based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that: The specific steps for generating the encrypted quantum data stream are as follows: A spherical harmonic basis projection is performed on the laser point cloud data using a shared key to generate a quantum state; Quantum wavelet transform is performed on the surface image of the reservoir dam based on channel quality parameters to generate quantum image states; By fusing quantum states and quantum image states through gradient correlation enhancement operators, spatiotemporally correlated quantum states are generated; By using quantum entanglement broadcasting to send synchronization pulses to a drone swarm, timestamp alignment of spatiotemporally correlated quantum states is performed, and quantum error correction encoding is executed on the spatiotemporally correlated quantum states to generate encrypted quantum data streams.

4. The intelligent reservoir safety inspection method based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that: The specific steps for generating redundant coded data packets are as follows. Real-time acquisition of atmospheric optical and geomagnetic parameters, combined with channel quality parameters, to calculate the environmental interference parameter tensor; The optimal orbital angular momentum mode is dynamically generated based on the environmental disturbance parameter tensor, and vortex waveguide error correction coding is performed on the encrypted quantum data stream to generate redundant coded data packets.

5. The intelligent reservoir safety inspection method based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that: The specific steps for generating the routing decision matrix are as follows: Quantum state fidelity parameters are extracted from redundant coded data packets and mapped to Riemannian space. Path solutions are generated through curvature-driven geodesic optimization. Based on the path solution, a routing decision matrix is ​​generated through topological entanglement number phase filtering.

6. The intelligent reservoir safety inspection method based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that: The specific steps for generating the decision instruction are as follows: A quantum graph Hamiltonian is constructed based on the routing decision matrix, and the optimal computing node is selected through the quantum random walk algorithm. Perform quantum measurements on redundant coded data packets at the optimal computing node to compute quantum state topological invariants; Based on the topological invariants of quantum states and combined with the quantum state fidelity parameters, the identification confidence level is calculated. When the identification confidence level exceeds the upper limit of the dynamic safety threshold, a crack repair decision instruction is generated; When the identification confidence level is between the lower limit and the upper limit of the dynamic safety threshold, a leakage sealing decision instruction is generated. When the identification confidence level is lower than the lower limit of the dynamic security threshold, a re-inspection decision instruction is generated.

7. The intelligent reservoir safety inspection method based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that: The specific steps for generating the reservoir safety inspection report are as follows: The decision-making instructions are encoded into quantum states and synchronized to the drone swarm terminal via a quantum teleportation protocol to generate modulated quantum states. Based on the dynamic configuration of quantum imaging parameters using modulated quantum states, and scanning of the generator state density matrix of the reservoir dam body according to the quantum imaging parameters; The density of states matrix is ​​mapped to a Ricci manifold to generate the reservoir dam state tensor matrix, and eigenvalue decomposition is performed to generate a reservoir safety inspection report.

8. A UAV-based intelligent reservoir safety inspection system, based on the UAV-based intelligent reservoir safety inspection method according to any one of claims 1 to 7, characterized in that: It includes a key distribution module, a data encapsulation module, an anti-interference module, a decision generation module, and an execution report module. The key distribution module is used to transmit entangled photons to the drone swarm, generate a shared key and channel quality parameters through quantum state tomography, and collect laser point cloud data and visible light image data; The data encapsulation module is used to encode laser point cloud data into quantum states using shared keys and channel quality parameters, transform visible light image data into quantum image states, synchronize time through quantum entanglement broadcasting, and generate encrypted quantum data streams. The anti-interference module is used to calculate the environmental interference parameter tensor based on the channel quality parameters, perform vortex waveguide error correction coding on the encrypted quantum data stream, generate redundant coded data packets, extract the quantum state fidelity parameters, map them to the Riemann space, and generate a routing decision matrix. The decision generation module is used to select the optimal computing node based on the routing decision matrix, perform quantum measurement to restore redundant coded data packets, and calculate the identification confidence. When the identification confidence exceeds the dynamic security threshold, a decision instruction is generated. The decision instructions include crack repair decision instructions, leakage sealing decision instructions, and re-inspection decision instructions. The execution report module is used to synchronize the decision command to the execution terminal via the quantum teleportation protocol, and output the reservoir dam state tensor matrix to generate a reservoir safety inspection report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent reservoir safety inspection method based on unmanned aerial vehicles as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent reservoir safety inspection method based on unmanned aerial vehicles as described in any one of claims 1 to 7.

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