Intelligent video monitoring system and method based on deep learning

Abstract

The invention provides an intelligent video monitoring system and method based on deep learning, and belongs to the technical field of video monitoring, and the system comprises a heterogeneous sensing array, a cognitive driving feature fusion module, a dynamic evolvable detection model, a causal reasoning tracking engine, a meta-knowledge enhancement behavior analysis module and a quantum-classical hybrid computing architecture. The heterogeneous sensing array comprises a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array and a millimeter wave radar, and is configured to generate multi-physical field sensing data; and the cognitive driving feature fusion module adopts a space-time-frequency spectrum joint coding technology, and integrates a three-dimensional convolutional network and a graph attention mechanism to realize cross-modal feature interaction. According to the invention, through multi-dimensional technical innovation, the intelligent video monitoring system with autonomous evolution capability is constructed, the perception capability, reasoning precision, resource efficiency and safety are obviously superior to those of a traditional scheme, and the high-standard requirements of smart cities, security and other scenes are met.

Description

Technical Field

[0001] The present invention relates to the technical field of video surveillance, and specifically to an intelligent video surveillance system and method based on deep learning. Background Art

[0002] The surveillance system is one of the most widely used systems in the security system. The currently more suitable construction site surveillance system on the market is a handheld video communication device, and video surveillance is now the mainstream. From the earliest analog surveillance to the booming digital surveillance in the previous years and then to the emerging network video surveillance now, there have been earth-shaking changes. Today, with the gradual unification of IP technology globally, it is necessary to re-understand the development history of the video surveillance system. From a technical perspective, the development of the video surveillance system is divided into the first-generation analog video surveillance system (CCTV), the second-generation digital video surveillance system (DVR) based on "PC + multimedia card", and the third-generation fully IP-based network video surveillance system (IPVS).

[0003] The existing surveillance systems still have the following technical defects:

[0004] Insufficient multimodal fusion: Traditional surveillance systems rely on a single sensor (such as a visible light camera), and their performance drops sharply in low-light and noise interference scenarios.

[0005] Limitations of static models: Fixed network structures cannot adapt to dynamic scene changes (such as fluctuations in the density of people flow), resulting in missed detections or false detections.

[0006] Behavior analysis depends on annotation: Anomaly detection requires a large amount of annotated data, and the generalization ability in small-sample scenarios is poor.

[0007] Low resource efficiency: Centralized processing in cloud computing leads to high latency, and the computing power at the edge is limited.

[0008] Therefore, an intelligent video surveillance system and method based on deep learning are proposed. Summary of the Invention

[0009] The present invention aims to solve the problems raised in the background art, and provides an intelligent video surveillance system and method based on deep learning.

[0010] The specific technical solutions are as follows:

[0011] An intelligent video surveillance system based on deep learning, comprising:

[0012] A heterogeneous sensing array, including a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array, and a millimeter-wave radar, configured to generate multi-physical field perception data;

[0013] The Cognitive-Driven Feature Fusion Module adopts spatio-temporal and spectral joint coding technology, integrating a 3D convolutional network and a graph attention mechanism to achieve cross-modal feature interaction;

[0014] The Dynamic Evolvable Detection Model includes a meta-learning framework based on neural architecture search, which can automatically adjust the network depth and width according to the scene complexity;

[0015] The Causal Inference Tracking Engine constructs a spatio-temporal causal graph model, integrating the target kinematic equation and the social force field model for trajectory prediction;

[0016] The Meta-Knowledge Enhanced Behavior Analysis Module integrates a pre-trained large language model and a domain knowledge graph to achieve zero-shot abnormal behavior reasoning;

[0017] The Quantum-Classical Hybrid Computing Architecture performs real-time detection in the classical computing layer and optimizes complex behavior patterns in the quantum simulation layer.

[0018] The above intelligent video surveillance system based on deep learning, wherein the cognitive-driven feature fusion module includes:

[0019] The Spectrum Sensing Sub-module extracts time-frequency domain features using a tunable Gabor wavelet group;

[0020] The Spatio-Temporal Graph Construction Unit models the multi-target motion trajectories as a dynamic heterogeneous graph;

[0021] The Field Coupling Attention Mechanism realizes electromagnetic-acoustic feature fusion through a feature propagation algorithm inspired by Maxwell's equations. Its cross-modal feature propagation process satisfies:

[0022] Formula 1:

[0023] Formula 2:

[0024] Where:

[0025] H represents the acoustic feature tensor;

[0026] Jv is the visual feature flux density;

[0027] D is the fused feature tensor;

[0028] E is the electromagnetic feature tensor;

[0029] ∈0 is the vacuum permittivity adjustment factor;

[0030] Pa is the learnable cross-modal projection matrix;

[0031] represents the tensor product operation;

[0032] Fv: Visual feature tensor;

[0033] The wavenumber vector calculation of the field coupling attention mechanism satisfies:

[0034] Equation 3:

[0035] where ω is the normalized frequency parameter of the feature channel;

[0036] c is the acousto-optic propagation speed ratio adjustment factor;

[0037] θ and φ are learnable azimuth parameters.

[0038] In the above intelligent video surveillance system based on deep learning, the dynamic evolvable detection model includes:

[0039] A super network controller that dynamically generates a detection network structure adapted to the current scene based on reinforcement learning;

[0040] A multi-physical field anchor box generator that generates three-dimensional detection anchor points by combining thermal radiation features and acoustic wave propagation characteristics;

[0041] An uncertainty-aware output layer configured with a Monte Carlo Dropout mechanism to quantify the detection confidence;

[0042] In the above intelligent video surveillance system based on deep learning, the causal inference tracking engine includes:

[0043] A counterfactual trajectory prediction unit that constructs a virtual intervention scenario for causal effect calculation;

[0044] A social relationship modeler that uses a graph neural network to learn implicit interaction rules between targets;

[0045] An energy function optimizer that solves the optimal trajectory hypothesis based on the Hamiltonian Monte Carlo method;

[0046] Among them, the trajectory prediction of the causal inference tracking engine (140) satisfies the improved Hamiltonian equation:

[0047]

[0048] Where:

[0049] q is the target position vector;

[0050] p is the momentum vector;

[0051] Vsocial is the social potential energy term;

[0052] Φscene is the scene constraint potential energy;

[0053] αj is the interaction strength coefficient.

[0054] The above-mentioned intelligent video surveillance system based on deep learning, wherein the meta-knowledge enhanced behavior analysis module includes:

[0055] A semantic distillation unit that transfers the common sense reasoning ability of a large language model to a lightweight classifier;

[0056] A causal discovery engine that identifies potential risk factors in a scene through invariance testing;

[0057] A virtual scene generator that synthesizes rare abnormal event training samples based on a generative adversarial network;

[0058] The semantic distillation unit of the meta-knowledge enhanced behavior analysis module executes a contrast loss function:

[0059]

[0060] Cosine similarity formula:

[0061] Where:

[0062] h LLM ∈Rd is the d-dimensional embedding vector output by the large language model;

[0063] hkg ∈ Rd is the feature vector of the knowledge graph entity encoded by the graph neural network;

[0064] τ ∈ (0, 1] is the temperature hyperparameter;

[0065] K is the batch size.

[0066] The present invention also provides an intelligent video surveillance method for an intelligent video surveillance system based on deep learning, including the following steps:

[0067] S1. Synchronously collect and spatio-temporally register multi-physical field data;

[0068] S2. Construct a dynamic feature hypergraph for cross-modal correlation analysis;

[0069] S3. Adaptive object detection based on online meta-learning;

[0070] S4. Apply causal inference to eliminate confounding biases in the tracking process;

[0071] S5. Combine physical laws and common sense knowledge for behavior semantic parsing;

[0072] S6. Use the quantum annealing algorithm to optimize the global resource allocation strategy.

[0073] The intelligent video surveillance method described above, wherein step S2 includes:

[0074] Establish an electromagnetic-acoustic joint propagation model for multi-sensor data correction;

[0075] Apply a hypergraph neural network to model cross-modal high-order associations;

[0076] Eliminate redundant feature dimensions through tensor decomposition.

[0077] The intelligent video surveillance method described above, wherein the online meta-learning in step S3 includes:

[0078] Construct a meta-feature vector containing scene complexity metrics;

[0079] Design a few-shot adaptation mechanism based on neural processes;

[0080] Adopt a curriculum learning strategy to gradually increase the detection difficulty.

[0081] The intelligent video surveillance method described above, wherein step S5 specifically includes:

[0082] Embed Newton's equations of motion into the neural network for physical compliance constraints;

[0083] Construct a behavior interpretation framework based on a causal mediation model;

[0084] Apply a contrastive language-image pre-training model to achieve natural language queries.

[0085] The intelligent video surveillance method described above, wherein it further includes:

[0086] Deploy a verifiable security module and use formal methods to ensure the interpretability of system decisions;

[0087] Establish a digital twin simulation environment to achieve system resilience testing under attack scenarios;

[0088] Design a blockchain-based model update verification mechanism to prevent adversarial attacks.

[0089] The intelligent video surveillance system based on deep learning provided by the present invention has the following advantages:

[0090] Multi-modal perception enhancement: Electromagnetic-acoustic-millimeter wave multi-physical field fusion, significantly improving environmental adaptability;

[0091] Dynamic adaptive ability: The network structure, detection model, and tracking strategy evolve in real time, significantly shortening the scene switching response time;

[0092] Complex behavior analysis: Combining zero-shot anomaly detection and causal reasoning, significantly reducing the false alarm rate and the missed detection rate;

[0093] Resource efficiency optimization: Quantum-classical collaborative computing and edge-cloud resource scheduling, significantly reducing the computing power requirements;

[0094] Security and Trust Assurance: Formal Verification and Blockchain Evidence Storage, significantly enhancing the system's anti-attack ability. Brief Description of the Drawings

[0095] Figure 1 It is a schematic diagram of the architecture of the intelligent video surveillance system based on deep learning provided by the present invention;

[0096] Figure 2 It is a schematic diagram of the architecture of the cognitive-driven feature fusion module in the intelligent video surveillance system based on deep learning provided by the present invention;

[0097] Figure 3 It is a schematic diagram of the architecture of the dynamically evolvable detection model in the intelligent video surveillance system based on deep learning provided by the present invention;

[0098] Figure 4 It is a schematic diagram of the architecture of the causal inference tracking engine in the intelligent video surveillance system based on deep learning provided by the present invention;

[0099] Figure 5 It is a schematic diagram of the architecture of the meta-knowledge enhanced behavior analysis module in the intelligent video surveillance system based on deep learning provided by the present invention.

[0100] In the drawings:

[0101] 110, heterogeneous sensing array;

[0102] 120, cognitive-driven feature fusion module; 121, spectrum sensing sub-module; 122, spatio-temporal graph construction unit; 123, field coupling attention mechanism;

[0103] 130, dynamically evolvable detection model; 131, hypernetwork controller; 132, multi-physical field anchor box generator; 133, uncertainty-aware output layer;

[0104] 140, causal inference tracking engine; 141, counterfactual trajectory prediction unit; 142, social relationship modeler; 143, energy function optimizer;

[0105] 150, meta-knowledge enhanced behavior analysis module; 151, semantic distillation unit; 152, causal discovery engine; 153, virtual scene generator;

[0106] 160, quantum-classical hybrid computing architecture. Detailed Embodiments

[0107] The technical solutions of the present invention will be further described below with reference to the drawings and through specific embodiments.

[0108] Among them, the attached drawings are only for illustrative purposes, showing only schematic diagrams rather than physical diagrams, and should not be construed as a limitation on this patent; in order to better illustrate the embodiments of the present invention, some components in the attached drawings will be omitted, enlarged or reduced, which does not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the attached drawings may be omitted.

[0109] In the attached drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if terms such as "upper", "lower", "left", "right", "inner", "outer", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the attached drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms used to describe the positional relationship in the attached drawings are only for illustrative purposes and should not be construed as a limitation on this patent. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0110] In the description of the present invention, unless otherwise clearly specified and defined, if terms such as "connection" are used to indicate the connection relationship between components, this term should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0111] The intelligent video surveillance system based on deep learning provided in this embodiment, as Figures 1 - 5 shown, includes: a heterogeneous sensing array 110, a cognitive-driven feature fusion module 120, a dynamically evolvable detection model 130, a causal inference tracking engine 140, a meta-knowledge enhanced behavior analysis module 150, and a quantum-classical hybrid computing architecture 160, where: the heterogeneous sensing array 110 is connected to the cognitive-driven feature fusion module 120, the cognitive-driven feature fusion module 120 is connected to the dynamically evolvable detection model 130, the dynamically evolvable detection model 130 is connected to the causal inference tracking engine 140, the causal inference tracking engine 140 is connected to the meta-knowledge enhanced behavior analysis module 150, and the meta-knowledge enhanced behavior analysis module 150 is connected to the quantum-classical hybrid computing architecture 160.

[0112] Among them, the heterogeneous sensing array 110 includes a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array, and a millimeter-wave radar, configured to generate multi-physical field perception data;

[0113] Among them, the cognitive-driven feature fusion module 120 adopts spatio-temporal and spectral joint coding technology, integrating a three-dimensional convolutional network and a graph attention mechanism to achieve cross-modal feature interaction;

[0114] Among them, the dynamic evolvable detection model 130 includes a meta-learning framework based on neural architecture search, which can automatically adjust the network depth and width according to the scene complexity;

[0115] Among them, the causal inference tracking engine 140 is used to construct a spatio-temporal causal graph model, and fuse the target kinematic equation and the social force field model for trajectory prediction;

[0116] Among them, the meta-knowledge enhanced behavior analysis module 150 integrates a pre-trained large language model and a domain knowledge graph to achieve zero-shot abnormal behavior reasoning;

[0117] Among them, the quantum-classical hybrid computing architecture 160 is used to perform real-time detection in the classical computing layer and optimize complex behavior patterns in the quantum simulation layer.

[0118] This deep learning-based intelligent video surveillance system integrates multi-physical field data (visible light / infrared, acoustic, millimeter wave) through a heterogeneous sensor array, significantly improving the perception robustness in complex environments (such as low light, bad weather).

[0119] The quantum-classical hybrid computing architecture realizes the division of labor between real-time detection and complex pattern optimization, taking into account both efficiency and accuracy.

[0120] Among them, the cognitive-driven feature fusion module 120 includes:

[0121] The spectral sensing sub-module 121 extracts time-frequency domain features using a tunable Gabor wavelet group;

[0122] The spatio-temporal graph construction unit 122 models the multi-target motion trajectory as a dynamic heterogeneous graph;

[0123] The field coupling attention mechanism 123 realizes electromagnetic-acoustic feature fusion through a feature propagation algorithm inspired by Maxwell's equations, and its cross-modal feature propagation process satisfies:

[0124] Formula 1:

[0125] Formula 2:

[0126] Where:

[0127] H represents the acoustic feature tensor, indicating the feature distribution of the audio signal in the time-frequency domain;

[0128] Jv is the visual feature flux density, which is the dynamic change rate of the visual features extracted by the visible light / infrared camera;

[0129] D is the fused feature tensor, containing the joint representation of electromagnetic and acoustic features;

[0130] E is the electromagnetic feature tensor;

[0131] ∈0 is the vacuum permittivity adjustment factor, used to balance the initial weight of electromagnetic features;

[0132] Pa is a learnable cross-modal projection matrix with dimension d×d, used to map acoustic features to the electromagnetic space;

[0133] represents the tensor product operation to achieve high-order interaction of cross-modal features;

[0134] Fv: Visual feature tensor, the spatio-temporal features extracted by 3D-CNN;

[0135] The wavenumber vector calculation of the field coupling attention mechanism satisfies:

[0136] Formula 3:

[0137] Among them, ω is the normalized frequency parameter of the feature channel, with a value range of [0,1], obtained by normalizing the spectral energy of the feature channel;

[0138] c is the sound-light propagation speed ratio adjustment factor, with an initial value of 3×10 8 m / s (speed of light), which can be trained and adjusted;

[0139] θ and φ are learnable azimuth parameters, used to define the spatial directivity of the wavenumber vector.

[0140] Workflow:

[0141] 1. Spectrum sensing: Extract the time-frequency features H of the acoustic signal through the Gabor wavelet group;

[0142] 2. Feature propagation: Based on the modified Maxwell equations (Formulas 1 and 2), dynamically fuse the visual feature: Fv and the acoustic feature H to generate the cross-modal tensor D;

[0143] 3. Wavenumber vector calculation: Generate a directionally adjustable wavenumber vector k according to Formula 3, used to weight the spatial contributions of different sensors;

[0144] 4. Cross-modal alignment: Through the field coupling attention mechanism, adaptively adjust the fusion weight of electromagnetic and acoustic features, and output the joint feature matrix.

[0145] The above field coupling attention mechanism models cross-modal feature interaction based on the Maxwell equation, solves the problem of electromagnetic-acoustic feature alignment deviation in traditional methods, and significantly improves the fusion accuracy;

[0146] The dynamic adjustment of the wavenumber vector adaptively optimizes the spatial perception weights of multiple sensors by learning the azimuth parameters (θ, φ), significantly reducing the redundant computational amount.

[0147] Among them, the dynamically evolvable detection model 130 includes:

[0148] A hypernetwork controller 131 that dynamically generates a detection network structure adapted to the current scene based on reinforcement learning;

[0149] A multi-physical-field anchor box generator 132 that generates three-dimensional detection anchor points by combining thermal radiation features and acoustic wave propagation characteristics;

[0150] An uncertainty-aware output layer 133 configured with a Monte Carlo Dropout mechanism to quantify the detection confidence;

[0151] The dynamically evolvable detection model adjusts the network structure in real time through neural architecture search to adapt to changes in scene complexity (such as sudden changes in pedestrian flow density), and the model inference speed is significantly improved;

[0152] The uncertainty-aware output layer quantifies the detection confidence, which can significantly reduce the false alarm rate (such as misdetecting pedestrians in foggy weather).

[0153] Among them, the causal inference tracking engine 140 includes:

[0154] A counterfactual trajectory prediction unit 141 that constructs a virtual intervention scenario for causal effect calculation;

[0155] A social relationship modeler 142 that uses a graph neural network to learn the implicit interaction rules between targets;

[0156] An energy function optimizer 143 that solves the optimal trajectory hypothesis based on the Hamiltonian Monte Carlo method;

[0157] Among them, the trajectory prediction of the causal inference tracking engine 140 satisfies the improved Hamiltonian equation:

[0158]

[0159] Among them:

[0160] q is the target position vector, with a dimension of 3×1 (three-dimensional space coordinates);

[0161] p is the momentum vector, p = mv, where m is the target mass (default unified as 1 kg) and v is the velocity vector;

[0162] Vsocial: The social potential energy term, calculated by the GNN, is expressed as:

[0163]

[0164] Among them, \(W_j\) is the interaction weight, and \(\sigma\) is the action range parameter;

[0165] \(\varPhi_{scene}\) is the scene constraint potential energy, which is generated based on the scene semantic segmentation map and is defined as:

[0166]

[0167] \(\alpha_j\) is the interaction intensity coefficient, which is calculated through the attention mechanism:

[0168] Workflow:

[0169] Trajectory initialization: Based on the position \(q_0\) output by the object detection module, the momentum \(p_0\) is initialized.

[0170] Potential energy calculation: Extract social relationships through GNN to generate \(V_{social}\); combine the scene segmentation results to generate \(\varPhi_{scene}\).

[0171] Equation solving: Numerically solve the Hamiltonian equation using the fourth-order Runge-Kutta method to predict \(q_{t + 1}\) and \(p_{t + 1}\) at the next moment.

[0172] Trajectory optimization: Screen the optimal trajectory hypothesis through the energy function optimizer (Hamiltonian Monte Carlo).

[0173] The causal inference tracking engine introduces the social potential energy \(V_{social}\) and the scene constraint potential energy \(\varPhi_{scene}\) to solve the problem of dense scene trajectory crossing, and the ID switching rate is significantly reduced;

[0174] Counterfactual trajectory prediction models the causal effect through virtual intervention scenarios, effectively improving the accuracy of trajectory prediction.

[0175] Among them, the meta-knowledge enhanced behavior analysis module 150 includes:

[0176] The semantic distillation unit 151 transfers the common sense reasoning ability of the large language model to the lightweight classifier;

[0177] The causal discovery engine 152 identifies potential risk factors in the scene through invariance testing;

[0178] The virtual scene generator 153 synthesizes training samples of rare abnormal events based on the adversarial generation network;

[0179] The semantic distillation unit of the meta-knowledge enhanced behavior analysis module 150 executes the contrast loss function:

[0180]

[0181] Cosine similarity formula:

[0182] in:

[0183] h LLM ∈Rd is the d-dimensional embedding vector output by a large language model (such as GPT-4), extracted by a mean pooling layer;

[0184] hkg∈Rd is the feature vector of the knowledge graph entity after being encoded by the graph neural network, which is generated by the graph convolutional network (GCN) encoding;

[0185] τ∈(0,1] is the temperature hyperparameter, which is used to adjust the steepness of the probability distribution;

[0186] K is the batch size, and the dynamic adjustment strategy is:

[0187] (For FP16 precision, the coefficient is 2; for FP32, it is 4).

[0188] Workflow:

[0189] Feature extraction: Extract h from the large language model and knowledge graph respectively. LLM and HKG;

[0190] Similarity calculation: Calculate the similarity matrix of positive sample pairs (diagonal) and negative sample pairs (off-diagonal) according to the cosine similarity formula;

[0191] Loss calculation: Use cross entropy loss to bring the similarity of positive sample pairs closer and push the similarity of negative sample pairs further away;

[0192] Back propagation: Gradient updates the parameters of the language model and knowledge graph encoder to achieve semantic alignment.

[0193] Semantic distillation contrast loss achieves semantic alignment between large language models and knowledge graphs, which can significantly improve the F1-score of zero-shot anomaly detection. The virtual scene generator synthesizes rare abnormal event training data, significantly reducing annotation costs.

[0194] This embodiment also provides an intelligent video monitoring method of an intelligent video monitoring system based on deep learning, comprising the following steps:

[0195] S1.Synchronous acquisition and spatiotemporal registration of multi-physics field data;

[0196] S2. Construct dynamic feature hypergraph for cross-modal correlation analysis;

[0197] S3. Adaptive object detection based on online meta-learning;

[0198] S4. Apply causal inference to eliminate confounding bias in the tracking process;

[0199] S5. Combine physical laws with common sense knowledge to analyze behavioral semantics;

[0200] S6. Optimize the global resource allocation strategy using the quantum annealing algorithm.

[0201] Among them, step S2 includes:

[0202] Establish an electromagnetic-acoustic joint propagation model for multi-sensor data correction;

[0203] Apply a hypergraph neural network to model cross-modal high-order associations;

[0204] Eliminate redundant feature dimensions through tensor decomposition.

[0205] Among them, the online meta-learning in step S3 includes:

[0206] Construct a meta-feature vector containing scene complexity metrics;

[0207] Design a few-shot adaptation mechanism based on neural processes;

[0208] Adopt a curriculum learning strategy to gradually increase the detection difficulty.

[0209] Among them, step S5 specifically includes:

[0210] Embed Newton's mechanical equations into the neural network for physical compliance constraints;

[0211] Construct a behavior interpretation framework based on the causal mediation model;

[0212] Apply the Contrastive Language-Image Pretraining (CLIP) model to achieve natural language queries.

[0213] This intelligent video surveillance method further includes:

[0214] Deploy a verifiable security module and use formal methods to ensure the interpretability of system decisions;

[0215] Establish a digital twin simulation environment to achieve system resilience testing under attack scenarios;

[0216] Design a blockchain-based model update verification mechanism to prevent adversarial attacks.

[0217] The dynamic feature hypergraph modeling (S2) in this intelligent video surveillance method improves the efficiency of cross-modal association analysis, significantly reduces feature redundancy, the quantum annealing algorithm (S6) optimizes the resource allocation strategy, significantly reduces the system energy consumption, and the blockchain model verification greatly improves the success rate of defending against adversarial attacks.

[0218] This embodiment provides the following specific experimental data of the quantum annealing algorithm in resource scheduling:

[0219] Experimental settings

[0220] Scenario: Large transportation hub monitoring system (8 edge nodes, 1 cloud quantum computing node).

[0221] Comparison method:

[0222] Traditional methods: Genetic Algorithm (GA), Simulated Annealing (SA)

[0223] This solution: Optimization algorithm based on D-Wave 2000Q quantum annealer

[0224] Optimization objectives: Task scheduling latency, energy consumption, and balanced resource utilization.

[0225] Experimental results

[0226] Index Genetic Algorithm (GA) Simulated Annealing (SA) Quantum Annealing (this solution) Average Task Latency (ms) 320 285 152 Total System Energy Consumption (kWh / day) 18.7 16.2 9.8 Standard Deviation of Resource Utilization 0.34 0.29 0.15 Complex Task Completion Rate (%) 72％ 81％ 95％

[0227] Conclusion: The quantum annealing algorithm reduces the latency by 52.5% compared to GA through parallel optimization of global resource allocation; the quantum annealer has a higher efficiency in solving the Ising model and reduces the energy consumption by 45.9%; the standard deviation of resource utilization is reduced to 0.15, significantly better than traditional methods.

[0228] The hardware implementation solution of the field-coupled attention mechanism in this embodiment is as follows:

[0229] Hardware architecture design

[0230] Platform: Xilinx Versal ACAP FPGA (AI Engine + programmable logic);

[0231] Core modules:

[0232] Field-coupled attention mechanism hardware architecture:

[0233] Sensor interface unit:

[0234] Supports multi-modal data input (HDMI 2.0 for cameras, I2S for microphones, SPI for millimeter-wave radars).

[0235] Time synchronization accuracy: ±1 μs.

[0236] Feature extraction engine:

[0237] Gabor wavelet bank: 16-channel parallel filtering, frequency resolution 0.1 Hz.

[0238] 3D-CNN acceleration core: Supports dilated convolution, peak computing power 12 TOPS.

[0239] Field-coupled computing unit:

[0240] Customized tensor product operation module Supports 4D tensor operations (16×16×16×16).

[0241] Wavenumber vector generator: Programmable azimuth angle (θ, φ) parameters with an accuracy of 0.01°.

[0242] Memory subsystem:

[0243] On-chip HBM2 memory: 8GB with a bandwidth of 460GB / s.

[0244] Feature cache: Dual-buffered design, supporting real-time data pipelining.

[0245] Resource consumption and performance

[0246] Resource Type Occupancy Ratio Description LUTs 63％ For Logic Operations and State Machine Control DSPSlices 78％ Accelerating Tensor Product and Wavenumber Vector Calculations BlockRAM 45％ Feature Caching and Parameter Storage Power Consumption 23W Peak Power Consumption (@1.2 GHz) Processing Latency 8 ms / frame Real - time Processing of 1080p Video Stream

[0247] 3. Implementation details

[0248] Optimization of tensor product operations:

[0249] Adopts the Winograd algorithm to reduce the computational complexity, reducing the multiplication operations to 1 / 4 of the traditional method.

[0250] Dynamic adjustment of wavenumber vector:

[0251] The θ and φ parameters are updated in real time through the on-chip microcontroller (ARM Cortex-R5), supporting online learning.

[0252] Test results of the actual scenario of zero-shot anomaly detection

[0253] Scene Abnormality Type F1 - score Precision Recall False Alarm Rate Subway Station (Peak Hours) Crowd Retrograde 0.83 0.85 0.81 0.09 Night Road (Low Light) Illegal Parking 0.78 0.80 0.76 0.12 Mall Entrance (Occluded Environment) Suspicious Item Left 0.81 0.83 0.79 0.07 Crossroads (Rain and Fog Weather) Pedestrians Running Red Lights 0.75 0.77 0.73 0.15

[0254] Comparative experiments

[0255] Method Average F1 - score Annotation Data Requirements Deployment Cost Supervised Learning (FasterR - CNN) 0.68 10,000+ Annotated Samples High CLIP Zero - Shot 0.71 0 Medium Method of this Solution 0.79 0 Low

[0256] Conclusion

[0257] Cross-scenario robustness: Under complex conditions such as low light and occlusion, the F1-score remains ≥0.75.

[0258] Cost advantage: Zero-shot learning reduces the annotation cost by 100%, and the deployment cost is reduced by 60% compared to supervised learning.

[0259] Real-time performance: The inference latency at the edge is ≤50ms, meeting the requirements of real-time monitoring.

[0260] Summary: The quantum annealing algorithm significantly improves the resource scheduling efficiency through global optimization, with the latency and energy consumption reduced by more than 45%; the FPGA implementation scheme of the field-coupled attention mechanism achieves real-time processing ability of 8 ms / frame with a power consumption of 23 W; the zero-shot anomaly detection has an average F1-score of 0.79 in four actual scenarios, verifying the practicability and robustness of the scheme.

[0261] In summary, the intelligent video surveillance system based on deep learning provided by this embodiment has the following advantages:

[0262] Enhanced multi-modal perception: The fusion of electromagnetic-acoustic-millimeter wave multi-physical fields significantly improves the environmental adaptability;

[0263] Dynamic adaptive ability: The network structure, detection model, and tracking strategy evolve in real time, significantly shortening the scene switching response time;

[0264] Complex behavior analysis: The combination of zero-shot anomaly detection and causal reasoning significantly reduces the false alarm rate and the missed alarm rate;

[0265] Optimized resource efficiency: Quantum-classical collaborative computing and edge-cloud resource scheduling significantly reduce the computing power requirements;

[0266] Secure and trustworthy guarantee: Formal verification and blockchain evidence storage significantly improve the system's anti-attack ability.

[0267] Working principle process

[0268] 1. Data acquisition and alignment:

[0269] Heterogeneous sensors (visible / infrared cameras, microphone arrays, millimeter wave radars) synchronously acquire multi-physical field data.

[0270] The spatio-temporal registration module aligns the timestamps and spatial coordinates of multi-source data.

[0271] 2. Feature fusion and detection:

[0272] The field-coupled attention mechanism fuses electromagnetic-acoustic features to generate cross-modal joint representations.

[0273] The dynamically evolvable detection model adaptively adjusts the network structure and outputs the object detection results and confidence levels.

[0274] 3. Object tracking and reasoning:

[0275] The causal reasoning engine predicts the trajectory based on the improved Hamiltonian equation and optimizes the path by combining social potential energy.

[0276] The meta-knowledge enhancement module aligns the language model and the knowledge graph through contrastive loss to identify abnormal behaviors.

[0277] 4. Resource Allocation and Optimization:

[0278] The quantum annealing algorithm optimizes the computing resource allocation, the edge side performs real-time detection, and the cloud updates the model.

[0279] Blockchain verification ensures the security of model updates, and the digital twin environment tests the system resilience.

[0280] The innovations of the present invention are as follows:

[0281] Interdisciplinary technology integration: introducing Maxwell's equations, quantum computing, and causal inference into video analysis to break through the limitations of traditional algorithms.

[0282] Dynamic adaptive architecture: realizing real-time optimization of network structure and detection strategy based on meta-learning and neural architecture search.

[0283] Knowledge-data dual drive: collaborative reasoning of large language models and knowledge graphs to reduce the dependence on labeled data.

[0284] Safe and efficient computing: quantum-classical hybrid architecture and blockchain verification to balance efficiency and security.

[0285] Summary:

[0286] Through multi-dimensional technological innovations, the present invention constructs an intelligent video surveillance system with the ability of autonomous evolution, which significantly surpasses traditional solutions in terms of perception ability, reasoning accuracy, resource efficiency, and security, meeting the high-standard requirements of scenarios such as smart cities and security.

[0287] The above are only the preferred embodiments of the present invention, and do not limit the implementation manners and protection scope of the present invention. For those skilled in the art, it should be able to realize that all the equivalent replacements and obvious changes made by using the description and illustration content of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent video surveillance system based on deep learning, characterized in that, Comprising: A heterogeneous sensing array (110), including a reconfigurable visible light / infrared dual-mode camera group, a distributed microphone array, and a millimeter-wave radar, configured to generate multi-physical field perception data; A cognition-driven feature fusion module (120), adopting a spatio-temporal - spectral joint coding technique, integrating a three-dimensional convolutional network and a graph attention mechanism to achieve cross-modal feature interaction; A dynamically evolvable detection model (130), including a meta-learning framework based on neural architecture search, capable of automatically adjusting the network depth and width according to the scene complexity; A causal inference tracking engine (140), constructing a spatio-temporal causal graph model, and fusing the target kinematic equation and the social force field model for trajectory prediction; A meta-knowledge enhanced behavior analysis module (150), integrating a pre-trained large language model and a domain knowledge graph to achieve zero-shot abnormal behavior inference; A quantum-classical hybrid computing architecture (160), performing real-time detection in the classical computing layer and optimizing complex behavior patterns in the quantum simulation layer.

2. The intelligent video surveillance system based on deep learning according to claim 1, wherein, The cognition-driven feature fusion module (120) includes: A spectral sensing sub-module (121), adopting a tunable Gabor wavelet group to extract time-frequency domain features; A spatio-temporal graph construction unit (122), modeling the multi-target motion trajectory as a dynamic heterogeneous graph; A field-coupled attention mechanism (123), realizing electromagnetic-acoustic feature fusion through a feature propagation algorithm inspired by Maxwell's equations, and its cross-modal feature propagation process satisfies: Formula 1: Formula 2: Where: H represents the acoustic feature tensor; Jv is the visual feature flux density; D is the fusion feature tensor; E is the electromagnetic feature tensor; ∈0 is the vacuum permittivity adjustment factor; Pa is a learnable cross-modal projection matrix; represents a tensor product operation; Fv: visual feature tensor; The wavenumber vector calculation of the field-coupled attention mechanism satisfies: Formula 3: Where ω is the normalized frequency parameter of the feature channel; c is the acoustic-optic propagation speed ratio adjustment factor; θ and φ are learnable azimuth parameters.

3. The intelligent video surveillance system based on deep learning according to claim 1, characterized in that, The dynamically evolvable detection model (130) includes: A super network controller (131), dynamically generating a detection network structure adapted to the current scene based on reinforcement learning; A multi-physical field anchor box generator (132), generating three-dimensional detection anchor points by combining thermal radiation characteristics and acoustic wave propagation characteristics; An uncertainty-aware output layer (133), configured with a Monte Carlo Dropout mechanism to quantify the detection confidence.

4. The intelligent video surveillance system based on deep learning according to claim 1, wherein The causal inference tracking engine (140) includes: A counterfactual trajectory prediction unit (141), constructing a virtual intervention scenario for causal effect calculation; A social relationship modeler (142), adopting a graph neural network to learn the implicit interaction rules between targets; An energy function optimizer (143), solving the optimal trajectory hypothesis based on the Hamiltonian Monte Carlo method; Wherein, the trajectory prediction of the causal inference tracking engine (140) satisfies the improved Hamiltonian equation: Where: q is the target position vector; p is the momentum vector; Vsocial is the social potential term; Φscene is the scene constraint potential; αj is the interaction strength coefficient.

5. The intelligent video surveillance system based on deep learning according to claim 1, wherein The meta-knowledge enhanced behavior analysis module (150) includes: A semantic distillation unit (151) that transfers the common sense reasoning ability of a large language model to a lightweight classifier; A causal discovery engine (152) that identifies potential risk factors in a scene through invariance testing; A virtual scene generator (153) that synthesizes training samples of rare abnormal events based on a generative adversarial network; The semantic distillation unit of the meta-knowledge enhanced behavior analysis module (150) executes a contrast loss function: Cosine similarity formula: Where: h LLM ∈ Rd is the d-dimensional embedding vector output by the large language model; hkg ∈ Rd is the feature vector of the knowledge graph entity encoded by the graph neural network; τ ∈ (0, 1] is the temperature hyperparameter; K is the batch size.

6. An intelligent video surveillance method for an intelligent video surveillance system based on deep learning according to any one of claims 1-5, characterized in that, It includes the following steps: S1. Synchronously collect multi-physical field data and perform spatio-temporal registration; S2. Construct a dynamic feature hypergraph for cross-modal correlation analysis; S3. Adaptive object detection based on online meta-learning; S4. Apply causal inference to eliminate confounding biases in the tracking process; S5. Combine physical laws and common sense knowledge for behavior semantic parsing; S6. Use the quantum annealing algorithm to optimize the global resource allocation strategy.

7. The intelligent video monitoring method according to claim 6, wherein, Step S2 includes: Establish an electromagnetic-acoustic joint propagation model for multi-sensor data correction; Apply a hypergraph neural network to model cross-modal high-order correlations; Eliminate redundant feature dimensions through tensor decomposition.

8. The intelligent video monitoring method according to claim 6, wherein, The online meta-learning in step S3 includes: Construct a meta-feature vector containing scene complexity metrics; Design a few-shot adaptation mechanism based on neural processes; Adopt a curriculum learning strategy to gradually increase the detection difficulty.

9. The intelligent video monitoring method according to claim 6, characterized in that, Step S5 specifically includes: Embed Newton's mechanical equations into a neural network for physical compliance constraints; Construct a behavior explanation framework based on a causal mediation model; Apply a contrastive language-image pre-training model to achieve natural language queries.

10. The intelligent video monitoring method according to claim 6, wherein It also includes: Deploy a verifiable security module and use formal methods to ensure the interpretability of system decisions; Establish a digital twin simulation environment to achieve system resilience testing under attack scenarios; Design a blockchain-based model update verification mechanism to prevent adversarial attacks.

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