Unattended video remote monitoring system

Abstract

The invention provides an unattended video remote monitoring system. The unattended video remote monitoring system comprises a multi-mode monitoring module, a scene cognition module, a prediction and early warning module, an immune defense module, an autonomous control module and a knowledge evolution module. According to the system, intelligent perception of a scene is realized through collaborative acquisition of multi-source heterogeneous data and deep feature fusion, active early warning of risks is realized by using dynamic causal reasoning and a multi-agent game framework, and stable operation of the system is guaranteed based on an adaptive immune network and a three-level protection mechanism. And adopting a differential neural knowledge graph to support continuous optimization of the system. The technical bottlenecks of fixed threshold, passive response, scene understanding shallow layer and the like of a traditional monitoring system are broken through, autonomous perception, autonomous decision, autonomous operation and maintenance and autonomous optimization of the system are realized, and the system has important application value in the aspects of improving the monitoring quality, reducing the labor cost, enhancing the system reliability and the like.

Description

Technical Field

[0001] The invention belongs to the technical field of video monitoring, and in particular relates to an unmanned video remote monitoring system. Background Art

[0002] Video surveillance systems are widely used in security, production monitoring, environmental monitoring and other fields. However, traditional systems rely on manual supervision and are difficult to cope with the needs of expanding monitoring areas and complex scenarios. The main problems include:

[0003] Poor monitoring quality: Fixed recognition thresholds are difficult to adapt to environmental changes and lack automatic compensation and scene prediction capabilities.

[0004] Weak system reliability: Insufficient fault prevention and self-healing capabilities, and exception handling relies on manual intervention.

[0005] Limited scene understanding: feature extraction is superficial, semantic understanding is insufficient, and it cannot cope with complex decision-making needs.

[0006] Inefficient resource scheduling: lack of accurate profiling and efficient collaboration mechanisms, and poor scalability.

[0007] Insufficient optimization capabilities: Lack of knowledge accumulation and transfer makes it difficult to continuously improve performance.

[0008] To solve the above problems, there is an urgent need for an unattended remote monitoring system that can operate autonomously, self-optimize, and have deep intelligent analysis capabilities. Summary of the invention

[0009] The present invention provides an unattended video remote monitoring system for realizing autonomous perception, autonomous decision-making, autonomous operation and maintenance, and autonomous optimization in a video monitoring process. The system comprises:

[0010] The multimodal monitoring module is used to realize the collection and intelligent preprocessing of video surveillance data; construct a high-definition video acquisition array and a distributed environmental sensor network to realize the collaborative collection of multi-source heterogeneous data; build a scene feature mapping model based on a deep feature transformation network, and realize multi-scale feature extraction through adaptive weight fusion; use an improved wavelet transform combined with a recursive neural network to build a time series analysis model to extract scene fluctuation eigenvalues, change eigenvalues, and evolution eigenvalues; design a data quality assessment model based on information entropy and confidence, and calculate data integrity value, reliability value, and timeliness value in real time; dynamically optimize and adjust acquisition parameters, processing parameters, and transmission parameters; encapsulate the processed video data packet, environmental data packet, and quality assessment data packet, and pass them to the scene recognition module and knowledge evolution module.

[0011] The scene recognition module is used to achieve in-depth analysis and understanding of the monitoring scene; receive and parse the data packets transmitted by the multimodal monitoring module; build a scene feature extraction model, realize multi-scale feature fusion through a deep neural network, and output the scene physical feature vector, target behavior feature vector, and scene semantic feature vector; establish a scene relationship analysis model, and calculate the spatial relationship value, motion relationship value, and interaction relationship value between targets based on the spatiotemporal attention mechanism; generate a scene state dynamic model, and use a graph structure network to realize the quantitative representation of the scene state; build an anomaly recognition model based on contrastive learning, and output the scene state anomaly value and the target behavior anomaly value; encapsulate the scene state data packet and transmit it to the prediction and warning module and the knowledge evolution module, and encapsulate the abnormal feature data packet and transmit it to the prediction and warning module;

[0012] The prediction and warning module is used to realize intelligent warning of monitoring scenarios; receive and parse the scene status data packets and abnormal feature data packets transmitted by the scene recognition module; build a dynamic causal reasoning network, and extract the scene evolution law through Granger causality test and graph neural network; design a multi-agent game framework to establish a risk assessment model, calculate the risk level value, impact range value, and trend development value; build a warning decision network based on deep reinforcement learning, and generate warning information in combination with an explainable mechanism; encapsulate the warning data packet containing prediction data, risk data and warning data, and pass it to the autonomous control module and the knowledge evolution module.

[0013] The immune defense module is used to realize the autonomous operation and maintenance of the system; build a performance monitoring model to collect real-time statistics of the performance indicators of various dimensions of the system; adopt an adaptive immune network architecture to realize the fusion analysis of heterogeneous data; design a pattern matching prediction mechanism based on the memory library to dynamically generate defense strategies; build a three-level active protection mechanism to implement fault isolation, service migration and resource reallocation; establish an execution effect evaluation system to output the degree of performance improvement, risk reduction and abnormal change; encapsulate the system status data packet and fault handling data packet, and pass them to the autonomous control module and knowledge evolution module respectively.

[0014] The autonomous control module is used to realize the intelligent control decision of the system; receive the warning data packets from the prediction and warning module and the status data packets from the immune defense module; build a resource monitoring model, statistically calculate the resource occupancy value, storage resource usage value, and network resource utilization value; generate a control strategy model, output resource scheduling instructions and task processing instructions; execute control instructions and adjust system working parameters; encapsulate the execution result data packet and pass it to the knowledge evolution module.

[0015] The knowledge evolution module is used to achieve continuous optimization of the system; receive and parse the operating data packets transmitted by each module; build a differential neural knowledge graph architecture to achieve unified representation of multi-source heterogeneous knowledge; design a knowledge accumulation mechanism based on dynamic memory update; establish a retrieval and association analysis framework to explore potential associations between knowledge; generate targeted optimization strategies to continuously improve system performance; achieve module parameter optimization, processing strategy optimization and control method optimization; encapsulate optimized configuration data packets and distribute them to each functional module.

[0016] Beneficial effects of the present invention:

[0017] 1. Multimodal adaptive perception: Through collaborative acquisition of high-definition cameras and distributed sensor networks, adaptive weight fusion is used to achieve multi-scale feature extraction; data quality is evaluated based on information entropy and confidence, and parameters are dynamically adjusted to solve the problem of unstable monitoring caused by fixed thresholds.

[0018] 2. Deep scene understanding: Integrate physical, behavioral, and semantic three-dimensional features, quantify scene states based on spatiotemporal attention analysis and graph networks, and use comparative learning to improve anomaly recognition accuracy.

[0019] 3. Active warning decision-making: Based on causal reasoning to analyze scenario evolution, multi-agent game to assess risks, combined with deep reinforcement learning, to achieve the transition from passive response to active warning.

[0020] 4. Intelligent immune defense: Build a dynamically evolving adaptive immune network, combine memory library pattern matching to predict faults, and use a three-level active protection mechanism to ensure stable system operation and evaluate defense effectiveness.

[0021] 5. Closed-loop knowledge optimization: Differential neural knowledge graph unifies heterogeneous knowledge representation and accumulation, dynamic memory update maintains real-time knowledge, knowledge retrieval and analysis realize experience transfer, and continuously optimize system strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the architecture of the video remote monitoring system of the present invention is shown;

[0023] Figure 2 The flowchart of the video remote monitoring system of the present invention is shown. DETAILED DESCRIPTION

[0024] Exemplary embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Combination Figure 1 The present invention provides an unattended video remote monitoring system, comprising:

[0026] 1. Multimodal monitoring module

[0027] The multimodal monitoring module realizes all-round perception and analysis of monitoring scenes through intelligent data collection and preprocessing framework. The module integrates high-definition camera arrays and distributed environmental sensor networks, and provides high-quality input data through multimodal data collaborative collection and processing. Specifically, it includes:

[0028] A) Data Collection

[0029] Adopt high-definition camera array Cii=1 N (The scale N depends on the size of the monitoring area and is 16-64 cameras) and a distributed environmental sensor network Sjj = 1 M (M is the number of sensors, 20-50 nodes are deployed depending on the area size) to collect data collaboratively.

[0030] The camera array adopts a matrix layout to achieve full coverage of the monitoring area. Each camera node is equipped with an intelligent parameter control unit, and its parameter control vector P(t) is defined as:

[0031] P(t)=[τ exp (t),g(t),f(t),θ(t),r(t)] T

[0032] Where: τ exp (t) is the exposure time parameter, in milliseconds, with a range of [1ms, 100ms]; g(t) is the image gain parameter, dimensionless, with a range of [1.0, 5.0]; f(t) is the lens focal length parameter, in millimeters, with a range of [2.8mm, 12mm]; θ(t) = [θ h (t),θ v (t)] T is the field of view angle vector, θ h (t) is the horizontal field of view angle [60°, 110°], θ v (t) is the vertical viewing angle [40°, 90°]; r(t) = [r w (t),r h (t)] T is the resolution parameter vector, r w (t) and r h (t) represents the number of horizontal and vertical pixels respectively, and the maximum supported value is [3840, 2160].

[0033] The environmental sensor network builds a mesh topology through a self-organizing protocol, and the collected environmental parameter vector E(t) is defined as:

[0034]

[0035] Where: K is the number of environmental parameter types, D is the sampling dimension of each parameter, ekd (t) represents the d-th dimension sampling value of the k-th environmental parameter, including but not limited to light intensity (lux), temperature (°C), and humidity (%).

[0036] B). Time synchronization mechanism

[0037] The time synchronization mechanism of the video sequence V(t) and the environmental data E(t) is achieved by minimizing the time deviation and defining the synchronization time offset:

[0038]

[0039] in: represents the video feature transformation, where φ v (·) is the feature extraction function, W v is the transformation matrix; represents the environmental feature transformation, where φ e (·) is the feature extraction function, W e is the transformation matrix; |·|2 represents the L2 norm, which is used to calculate the Euclidean distance of the vector.

[0040] C) Feature Mapping and Compensation

[0041] The scene feature mapping model uses a deep feature transformation network to extract

[0042] Where: L is the number of feature pyramid layers (set to 3-5 layers), CNN l (·) is the operation of the lth layer of convolutional neural network. The weight coefficients of each layer are dynamically calculated:

[0043]

[0044] Where: h l (t) is the feature vector of the lth layer, and its dimension decreases with the number of layers (the basic dimension is 256, which is halved at each layer); exp is the natural exponential function.

[0045] The model introduces environmental feature compensation to improve the robustness of scene features:

[0046] F'(t)=F(t)+λ(t)·MLP(E(t))

[0047] The compensation coefficient λ(t) is adaptively adjusted according to the degree of environmental change:

[0048]

[0049] in: is the first-order difference; is the second-order difference; MLP(·) is a 3-layer perceptron network with hidden layer dimensions of [64, 32, 16]; σ(·) is the sigmoid activation function.

[0050] D). Timing analysis

[0051] The time series analysis model combines the maximum overlap discrete wavelet transform (MODWT) and recursive neural network to construct a triple feature (fluctuation eigenvalue, change eigenvalue, evolution eigenvalue) extraction framework and perform wavelet decomposition on the video sequence V(t):

[0052] D j (t),A J (t) = MODWT (V (t - τ: t))

[0053] Where: D j (t) is the detail coefficient of the jth layer, A J (t) is the J-th layer approximation coefficient, and τ is the time window length.

[0054] Fluctuation eigenvalue calculation:

[0055]

[0056] Where: J is the number of wavelet decomposition layers (value ranges from 3 to 5); γ j is the detail coefficient weight, satisfying Pool(·) is a global average pooling operation; LSTM1 is a long short-term memory network with a hidden layer dimension of 128.

[0057] Calculation of changing eigenvalues:

[0058] ξ change (t) = LSTM2([Pool(A J (t)),E(t)])

[0059] Evolution eigenvalue calculation:

[0060]

[0061] Where: N t is the time sequence length (24-48 frames), and Δt is the sampling interval (40ms).

[0062] A multi-head attention mechanism is used to fuse temporal features, and the fused temporal features are expressed as ξ(t):

[0063] ξ(t)=MultiHead([ξ wave (t),ξ change (t),ξ evolve (t)])

[0064] The multi-head attention calculation is defined as:

[0065] MultiHead(X) = [head1, head i , …, head h ]W O

[0066]

[0067] Among them: head i represents the i-th attention representation unit; h is the number of attention heads (value is 8); is the transformation matrix, R is a set of real numbers; is the output transformation matrix; d model is the model dimension (256), d k =d v =d model / h.

[0068] E) Data quality assessment

[0069] The data quality assessment model constructs three-dimensional assessment indicators, including integrity value, reliability value, and timeliness value. The definitions of each indicator are as follows:

[0070] a). Integrity value q c (t) is defined as:

[0071]

[0072] Where: H(V(t)) = -∑ i p i logp i is the information entropy of the video data; H(E(t)) = -∑ j q j logq j is the information entropy of environmental data; H max is the theoretical maximum entropy value (8 for 8-bit data); p i ,q j is the data distribution probability.

[0073] b). Reliability value q r (t) is calculated based on the feature prediction deviation and is defined as:

[0074]

[0075] in: is the feature predicted by the LSTM network based on historical data; σ is the variance parameter (value is 0.5);

[0076] c). Timeliness value q t(t) takes into account the transmission delay and is defined as:

[0077] q t (t) = exp(-μ·τ delay (t))

[0078] Where: τ delay (t) is the end-to-end delay in milliseconds; μ is the delay attenuation coefficient (value range [0.001, 0.01]);

[0079] The quality assessment vector is defined as:

[0080] Q(t)=[q c (t),q r (t),q t (t)] T

[0081] Based on the quality assessment results, gradient descent is used to optimize the parameters:

[0082]

[0083] Where: η is the learning rate (value is 0.01); Optimize the gradient vector for the parameters of the loss function, where the loss function is defined as:

[0084]

[0085] Among them, ω i is the weight coefficient, satisfying ∑ i∈c,r,t ω i =1, dynamically adjusted according to scenario requirements.

[0086] F). Data packet output

[0087] Finally, the module outputs three types of standard data packets:

[0088]

[0089] Among them: v (t) is a video data packet, including the video data V'(t) after wavelet decomposition, the scene feature vector F(t), and the parameter control vector P(t); e (t) is the environmental data packet, including the environmental parameter vector E(t) and the fused time series feature vector ξ(t); q (t) is the quality assessment data package, which contains the three-dimensional quality assessment vector Q(t), the parameter optimization gradient vector

[0090] 2. Scene recognition module

[0091] The scene recognition module uses a deep neural network architecture to achieve intelligent analysis and understanding of monitoring scenes. This module focuses on multimodal data fusion, feature extraction, relationship analysis, and anomaly detection, including:

[0092] A). Data input and preprocessing

[0093] The module first receives three types of standard data packets transmitted by the multimodal monitoring module as input for intelligent analysis:

[0094] Video Packet O v (t), including the video data V′(t), scene feature vector F(t) and parameter control vector P(t) after wavelet decomposition; environment data packet O e (t), including the environmental parameter vector E(t) and the fused time series feature vector ξ(t); the quality assessment data packet Oq(t), including the quality assessment vector Q(t) and the parameter optimization gradient vector

[0095] The above data is then parsed through multi-task learning to extract key features:

[0096]

[0097] in: E * (t),Q * (t) represents video features, environmental features, and data quality features respectively; α k is the task weight coefficient, satisfying And α k ≥0; is the loss function of the kth task, using cross entropy loss; W k and b k are the weight matrix and bias vector respectively.

[0098] B). Multi-scale feature extraction

[0099] Next, a multi-scale feature fusion architecture is constructed through the pyramid feature network to extract the physical feature vector F p (t):

[0100]

[0101] Where: L is the number of pyramid layers, ranging from [3,5]; CNN l is the lth layer of convolutional neural network; β l (t) is the adaptive weight coefficient:

[0102]

[0103] Among them, h l(t) is the hidden layer feature vector, W l is the weight matrix, w is the weight vector used for attention calculation, and b is the bias vector.

[0104] Use a two-stream spatiotemporal attention network to extract the behavior feature vector F b (t):

[0105] F b (t) = γ s (t)Ψ s (X s (t))+γ t (t)Ψ t (X t (t))

[0106] The two-stream network extracts spatiotemporal features respectively:

[0107]

[0108] Among them: s (X s (t)) is the spatial feature extracted by the spatial attention network (SA-NET); Ψ t (X t (t)) is the temporal feature extracted by the temporal attention network (TA-NET); τ is the time window length, ranging from [24, 48] frames; is the video feature vector within the time window [t-τ,t].

[0109] The weight coefficients are calculated through three layers of MLP:

[0110] [γ s (t),γ t (t)] = softmax(MLP([Ψ s (X s (t)); t (X t (t))]))

[0111] The scene semantic feature vector Fs(t) is generated by a multimodal Transformer:

[0112] Fs(t)=Transformer([Fp(t);F b (t); E * (t)])

[0113] Using 8-head attention mechanism:

[0114] MultiHead(Q,K,V)=Concat(head1,...,head8)W O

[0115]

[0116] C). Scene relationship analysis

[0117] Calculate the spatial relationship matrix R through the graph attention network (GAT) s (t):

[0118]

[0119] Where: p i (t) is the position feature vector of the i-th target entity; e ij (t) is the spatial association feature between the i-th and j-th target entities; N is the number of target entities, that is, the number of nodes in the graph.

[0120] The attention weight α between nodes ij The calculation formula of (t) is as follows:

[0121]

[0122] Where: a is a learnable weight vector, representing the parameters of the attention mechanism; represents the set of neighbor nodes of node i; W is a weight matrix used to perform linear transformation on the original feature vector of the node to generate high-order features that are more suitable for attention calculation; Wp i (t), Wp j (t), Wp k (t) represents the result of projecting the position feature vector of the i, j, and k nodes in the graph into the hidden feature space; || represents the feature concatenation operation, which concatenates two feature vectors into a new vector according to their dimensions; LeakyReLU is the activation function.

[0123] Motion relationship value R m (t) Using LSTM network to analyze time series association:

[0124]

[0125] Among them: LSTM m represents the LSTM network; τ is the time window length, ranging from [24,48] frames; v i (t-τ:t) represents the motion characteristics of the target entity i within the time window [t-τ,t]. The interaction relationship value R i (t) Calculated by Dynamic Causal Network (DCN):

[0126]

[0127] in: is the behavioral feature vector of the i-th target entity at time t; is the semantic feature vector of the i-th target entity at time t; N is the total number of target entities in the scene.

[0128] Use graph convolutional network (GCN) to quantify scene state:

[0129]

[0130] Where: S(t) is the scene state vector, which represents the overall characteristics of the scene; X is the node feature matrix, which contains the initial features of each node; W is the weight matrix, which is the learnable parameter of GCN and is used to perform linear transformation on the input features; is the adjacency matrix of the graph, adding the self-connection item I to include the node’s own information; is the degree matrix, corresponding to the adjacency matrix The node degree (number of connections per node); To normalize the adjacency matrix, the degree matrix is ​​used to standardize the adjacency matrix to avoid the eigenvalue being too large or too small due to different node degrees; σ is the ReLU activation function.

[0131] D). Anomaly Detection

[0132] Build a normal scenario prototype library M and analyze abnormalities through comparative learning:

[0133]

[0134] Where: M is the normal scene prototype library, which contains K scene prototype vectors; S k is the k-th normal scene state set; f θ (s) is the feature extraction function, which extracts the features of the scene state through the model. It is a multi-layer perceptron structure. θ (s)=MLP θ ([F p (t),F b (t),F s (t)]), θ is a learnable parameter, MLP θ A [512, 256, 128] network structure is adopted, the input is the concatenation of three types of feature vectors, and the output is a 128-dimensional scene state feature representation; K is the total number of categories of normal scenes.

[0135] The state abnormal value A s (t) is calculated as:

[0136]

[0137] Where: S(t) is the scene state vector at the current time t; c +is the normal scene prototype vector closest to the current state vector S(t); c k is the kth normal scene prototype vector; d(·,·) is the cosine distance; τ is the temperature parameter.

[0138] Behavioral outlier A b (t) is calculated based on the forecast deviation:

[0139]

[0140] Among them: F b is the actual behavior feature vector at the current time t; is the behavior feature vector predicted by the autoregressive model, and the behavior feature prediction value at time t+1: Where p is the autoregressive order, which is 3; φ i is the autoregressive coefficient matrix, estimated using the least squares method; ∈(t) is a white noise vector, used to describe the model prediction error;

[0141] E). Data output

[0142] Finally, this module encapsulates two types of data packets and passes them to the prediction and warning module:

[0143] Scene state data packet P s (t):

[0144] P s (t)={Fp(t),F b (t),F s (t),R s (t),R m (t),R i (t)}

[0145] Abnormal characteristic data packet P a (t):

[0146] P a (t) = {A s (t),A b (t),Q * (t)}

[0147] 3. Prediction and warning module

[0148] The prediction and warning module is responsible for in-depth analysis of the state information and abnormal data in the monitoring scene, and realizes dynamic monitoring, risk assessment and intelligent warning through causal reasoning and evolutionary modeling. It abstracts complex monitoring scenes into analyzable state variables and their causal relationships, predicts potential risk events using time series rules, and generates optimal warning decisions through game analysis and reinforcement learning. Specifically, it includes:

[0149] A). Data input

[0150] The module receives two types of data packets transmitted by the scene recognition module, including the scene status data packet P s (t) and abnormal characteristic data packet P a (t).

[0151] B) Dynamic causal reasoning

[0152] The module first builds a dynamic causal reasoning network based on the input data to generate a scene evolution graph.

[0153]

[0154] Where: Node set Contains the following key state variables: scene state vector S(t); abnormal state vector A(t) = [A s (t),A b (t)] T ; Relation state vector R(t) = [R s (t),R m (t),R i (t)] T ;

[0155] The edge set ε(t) is dynamically constructed through a two-layer causal discovery architecture:

[0156] The underlying causal discovery uses the Granger causality test to calculate the initial causal strength:

[0157]

[0158] Where: v i (τ),v j (τ) represents the values ​​of variable i and variable j at time τ respectively; T is the time window of causal analysis (the value is 3600 seconds);

[0159] Granger causality was assessed by the following F-test:

[0160]

[0161] Where: RSS0 and RSS1 are the residual sums of squares of the constrained model and the unconstrained model respectively; p is the lag order (the value is 3).

[0162] The top layer uses a graph neural network (GNN) to optimize the causal structure:

[0163]

[0164] Where: e ij(t) is the variable v after graph neural network optimization at time t i For variable v j The causal strength of GNN is a graph neural network with a two-layer graph convolution structure:

[0165]

[0166] Where: H (l+1) represents the output feature matrix of the graph neural network at the l+1 layer; σ is the activation function;

[0167] is the normalized Laplace matrix of the graph, where A is the adjacency matrix of the graph, representing the connection relationship between nodes, D is the degree matrix, and its diagonal elements are the degrees of the corresponding nodes; H (l) is the output feature matrix of the graph neural network at layer l; W (l) is the weight matrix of the lth layer.

[0168] C). Status prediction

[0169] Based on the causal graph structure, local and global information are integrated to predict future states:

[0170] in:

[0171] is the causal neighborhood of node i, through e ij (t) Threshold screening and determination;

[0172] w ij (t) is the dynamic causal strength, calculated by the attention mechanism:

[0173]

[0174] φ(·) is a nonlinear state transfer function, using a two-layer MLP:

[0175] φ(x)=MLP [128,256] (x)

[0176] h global (t) is the global state representation,

[0177] D) Risk Assessment

[0178] Quantifying risk using a multi-agent game framework:

[0179] M={P,S,A,R,γ}

[0180] Among them: P is the set of monitored objects, including N objects; S is the scene state space, dimension 256; A is the m-dimensional continuous action space; R is the reward function, which is defined as:

[0181]

[0182] In the formula, R i (s,a i ,a -i ) indicates that agent i is in state s and takes action a i And the other agents take action a -i The instant reward obtained when α, β, η are weight coefficients; v i Represent the predicted value and actual value of agent i respectively; A i is the abnormal penalty term, A i =A s (i)+A b (i) A s (i) A b (i) represents the state anomaly value and behavior anomaly value of node i respectively; γ is the discount factor, which is set to 0.99; a -i It is the action strategy set of other nodes.

[0183] The Nash equilibrium is used to solve the risk value of each agent:

[0184] in:

[0185] r i (t) represents the risk value of agent i at time t;

[0186] E(·) represents the expected value;

[0187] a i : Behavior strategy of agent i;

[0188] A i is the set of all possible actions or strategies that agent i can take in the current state;

[0189] a -i : Strategy combination of other agents;

[0190] R i : Instant reward function, which represents the immediate benefit or cost of agent i in a specific state and behavior;

[0191] γ discount factor, the value range is [0,1];

[0192] s t+k is the scene state after k steps, reflecting the scene features or the state of the monitored object at time t+k;

[0193] Based on the individual risk values, calculate the comprehensive risk index:

[0194] r(t)=[r level (t),r range (t),r trend (t)] T

[0195] Where: r level (t) represents the risk level corresponding to the highest basic risk value of all monitored objects in the system, which is divided into three levels of risk, including mild, moderate, and severe, and the corresponding risk level thresholds are 0.4, 0.6, and 0.8 respectively; r range (t) represents the risk range. By calculating the proportion of high-risk monitoring objects to the total number of objects, the coverage of high-risk states in the system is intuitively quantified; r trend (t) represents the overall risk trend, r trend (t) = 1 / N × d (∑ i∈P r i (t)) / dt, N is the total number of monitored objects in the system.

[0196] E). Early warning decision

[0197] Using deep reinforcement learning to build an early warning strategy network:

[0198]

[0199] Where: π θ is the early warning strategy network, parameterized as θ, which outputs the best early warning action under a given state; s t is the warning state vector; r(t) is the comprehensive risk index vector; is the predicted future scene state; a t It is the warning action at the current moment, for example, triggering an alarm, adjusting the monitoring strategy, etc. Encoder is a state encoder, which is used to map the input features to the latent space; Actor is a strategy network, which generates a warning strategy based on the encoded state.

[0200] F). Interpretability Analysis

[0201] Generate warning information W(t) through interpretability mechanism:

[0202]

[0203] Where: W(t) is the warning information, including the key risk areas and causal relationships in the scene; CAM is the class activation map, which locates the key risk areas and is used to identify the monitoring objects that have the greatest impact on the warning strategy under specific conditions; Attention is the attention weight, which explains the association strength between nodes in the causal graph; is the set of nodes in the causal graph; ε is the set of edges in the causal graph.

[0204] G). Output data packet

[0205] Finally, the module encapsulates the warning data packet P w (t):

[0206]

[0207] 4.Immune defense module

[0208] The immune defense module realizes the autonomous operation and maintenance of the system through the adaptive immune network (AIN), which includes four core processes: performance monitoring, fault prediction, immune response and strategy execution, to ensure the stability and robustness of the system operation. The details are as follows:

[0209] A). Performance monitoring

[0210] The module first establishes a performance monitoring model to quantify the system status in real time and obtain the performance state vector:

[0211] in:

[0212] H proc (t) = [h cpu (t),h mem (t),h gpu (t)] T ∈R 3

[0213] H stor (t) = [h io (t),h cache (t),h buffer (t)] T ∈R 3

[0214] H trans (t) = [h bw (t),h delay (t),h loss (t)] T ∈R 3

[0215] Where:

[0216] H proc (t) is the computing resource performance, including indicators: CPU usage h cpu (t), memory usage h mem (t), GPU usage rate h gpu (t);

[0217] H stor(t) is the storage resource performance, including indicators: disk I / O performance h io (t), cache hit rate h cache (t), buffer occupancy h buffer (t);

[0218] H trans (t) is the network resource performance, including indicators: bandwidth utilization h bw (t), network delay h delay (t), packet loss rate h loss (t).

[0219] Each performance indicator is quantized adaptively:

[0220]

[0221] v i (t) is the original performance measurement value.

[0222] [v i,min ,v i,max ] is the valid value range of historical statistics.

[0223] w i (t) is the dynamic weight coefficient, calculated as:

[0224]

[0225] In the formula, λ i (t), λ j (t) are the load intensity indicators of agents i and j respectively.

[0226] B). Fault prediction

[0227] Build immune adapters and predict failures through performance monitoring data.

[0228] Immune Adapters:

[0229]

[0230] in:

[0231] τ=300s: observation window length;

[0232] The first-order rate of change of the performance vector, Δt = 1s;

[0233] The second-order rate of change of the performance vector;

[0234] CNN encode : Three-layer convolutional network with the number of channels being [64, 128, 256].

[0235] Memory library construction:

[0236]

[0237] in:

[0238] K = 64, memory capacity, indicating the number of historical performance patterns stored;

[0239] p k (t) is the performance mode characteristic,

[0240] s k (t) is the system status description, s k (t) = Encoder state ([H(t),Context(t)]), Context(t) is the external environment and task load;

[0241] r k (t) is the response strategy vector, r k (t)=Generator([p k (t),s k (t)]), : Generator is a strategy generator that uses historical performance patterns and state descriptions to generate the optimal response strategy.

[0242] Fault prediction uses a hybrid architecture that combines pattern matching and deep learning methods:

[0243] R fault (t) = α(t)R pattern (t)+(1-α(t))R deep (t)

[0244]

[0245] w k (t) is the matching weight, which reflects the similarity between the current state and the pattern in the memory bank, and is calculated as:

[0246]

[0247] Adaptive Scaling Parameters:

[0248] b). Deep learning prediction:

[0249] R deep (t) = LSTM predict (I adapt (t-τ:t))

[0250] A two-layer LSTM is used with a hidden layer dimension of [128, 64], a dropout rate of 0.2, and an activation function of tanh.

[0251] Fusion coefficient:

[0252]

[0253] C). Troubleshooting

[0254] Fault handling is achieved through distributed immune response, including antibody generation, affinity calculation, clonal mutation and optimal strategy selection.

[0255] a). Antibody production:

[0256] A immune (t)=Generator([I adapt (t),R fault (t)])∈R 128

[0257] b). Affinity calculation:

[0258]

[0259] c). Clonal variation:

[0260]

[0261] in:

[0262] is a collection of cloned antibodies, representing the initial antibody A immune (t) multiple cloned antibodies are mutated to form candidate treatment solutions;

[0263] N c is the number of clones, N c =β·A(t) and round up, β=10;

[0264] σ mut is the variation intensity, σ mut =γ(1-A(t)),γ=0.1;

[0265] Clone(·) is an operation function used to generate a set of candidate antibodies.

[0266] d). Optimal strategy selection:

[0267]

[0268] Among them: Score(A) is the scoring function, In the formula, ω1 = 0.7, ω2 = 0.3, The expected cost of executing antibody strategy A, including resource consumption and time cost.

[0269] D). Strategy execution

[0270] The defense strategy execution module inherits the analysis results of the scenario recognition module and the prediction and warning module to build a three-level active protection mechanism:

[0271] a). Fault isolation strategy (first level protection):

[0272] According to the abnormal status value and risk level value, filter out the high-risk nodes that need to be isolated and perform active isolation:

[0273] Cisolate(t)={i|A s (i,t)>θ1and r level (i,t)>θ2}

[0274] Among them: A s (i,t) is the abnormal value of the state of node i at time t; r level (i, t) is the risk level value of node i; θ1=0.8, θ2=0.7 are double thresholds, the dividing line between the state abnormality value and the risk level value. Only nodes that exceed these two thresholds at the same time will be isolated.

[0275] Isolation priority calculation:

[0276]

[0277] The higher the outlier and risk value of a node, the higher its priority.

[0278] b). Service migration strategy (secondary protection):

[0279] Target node selection:

[0280]

[0281] in: is the set of available nodes; H j (t) is the performance indicator vector of node j, including CPU, memory, and network bandwidth; H target is the target performance vector, the ideal performance state set by the system, which is used to measure the gap between the migration target node and the expected performance; w1=0.4, w2=0.3, w3=0.3 are the balancing weights.

[0282] c). Resource reallocation strategy (three-level protection):

[0283] Resource allocation optimization goals:

[0284]

[0285] Optimize resource allocation under constraints to reduce performance deviation, risk and abnormal conditions. The constraints are:

[0286]

[0287] Where: N is the total number of camera nodes in the system; P(t) is the parameter control vector, which is defined according to the multimodal monitoring module; g i (t),f i (t) are the exposure time, gain and focal length parameters of the ith camera respectively; α1=0.4, α2=0.3, α3=0.3 are the optimization weights; G total 、F total They are respectively the upper limit of total exposure time, total gain, and total focal length allowed by the system.

[0288] Evaluation of the execution effect based on the three-level protection strategy:

[0289]

[0290] Where: ΔH(t) = H(t+Δt)-H(t): performance improvement vector; Δr(t) = r(t+Δt)-r(t): risk change vector; ΔA(t) = A s (t+Δt)-A s (t): abnormality change.

[0291] E). Data output

[0292] Finally, the defense execution results are encapsulated into two types of data packets:

[0293] System status data packet:

[0294] P s (t)={H(t),r(t),P(t)}

[0295] Fault handling data package:

[0296] P e (t)=Cisolate(t),P(t),E(t)

[0297] 5. Autonomous control module

[0298] The autonomous control module uses a deep reinforcement learning architecture to achieve intelligent dynamic control of system resources and tasks. Specifically, it includes:

[0299] A). Data reception

[0300] This module receives the following two types of key data packets: data packets P of the prediction and warning modulew (t), data packet P of the immune defense module s (t).

[0301] B). Resource monitoring

[0302] The module quantifies the usage status of system resources in real time by constructing a multi-dimensional resource status matrix M(t). The matrix contains three resource categories: processing resources, storage resources, and network resources. The matrix is ​​defined as follows:

[0303] a). Processing resource status:

[0304] CPU Utilization: In the formula, is the CPU usage time of node i at time t (ms), N is the number of nodes monitored in the system, The total time capacity of the system CPU resources; memory utilization: Where m i (t) is the memory resource used by node i at time t (MB), M total is the total memory resource capacity of the system (MB);

[0305] GPU Utilization: In the formula, g i (t) is the GPU resource (in units of GPU cores) used by node i at time t, G total The total GPU resource capacity of the system.

[0306] b). Storage resource status:

[0307] I / O status: In the formula, io i (t) is the I / O read and write operation volume of node i at time t (unit: MB / s), IO total is the maximum I / O bandwidth capacity of the system (MB / s); cache utilization: In the formula, cache i (t) is the amount of cache resources used by node i at time t (unit: MB), total is the total cache resource capacity of the system (unit: MB); buffer utilization: Where, buffer i (t) is the amount of buffer resources used by node i at time t (unit: MB), total The total buffer resource capacity of the system (unit: MB).

[0308] c). Network resource status:

[0309] Bandwidth usage: The average delay value is: i (t) is the bandwidth resource used by node i at time t (unit: Mbps), BW total is the total network bandwidth capacity of the system (unit: Mbps); The average packet loss rate is: i (t) is the network delay of node i at time t (unit: ms); packet loss rate: In the formula, loss i (t) is the packet loss rate (percentage) of node i at time t, Loss max The maximum packet loss rate (percentage) allowed by the system.

[0310] C). Control strategy model

[0311] The module optimizes the control strategy through the Actor-Critic architecture of deep reinforcement learning.

[0312] a). State space definition:

[0313] s t =[M(t); r(t); H(t); R fault (t)]

[0314] Includes resource status, risk status, performance indicators and failure risks.

[0315] b). Action space definition:

[0316] a t =[a resource (t); a task (t)]

[0317] Among them: a resource (t) is the resource allocation instruction, a resource (t) = [Δτ exp (t),Δg(t),Δf(t)], where, Δτ exp (t) is the exposure time adjustment, ranging from [-10ms, 10ms]; Δg(t): gain adjustment, ranging from [-0.5, 0.5]; Δf(t): focal length adjustment, ranging from [-1mm, 1mm]; a task (t) is the task processing instruction, a task (t) = [priority(t), schedule(t)], where priority(t) is the task priority vector, the sum of which is 1; schedule(t) is the scheduling strategy vector, a discrete value.

[0318] c). Strategic network design

[0319] π θ (a t |s t )=Actor(Encoder(s t ))

[0320] V φ (s t )=Critic(Encoder(s t ))

[0321] in:

[0322] The Encoder is a three-layer convolutional network [64, 128, 256]; the Actor is a two-layer fully connected network [256, 128]; and the Critic is a two-layer fully connected network [256, 1].

[0323] D) Reward function design

[0324] The reward function takes into account resource status, risk status and performance indicators:

[0325] R(s t ,a t )=ω1R resource (M(t))+ω2R risk (r(t))+ω3R perf (H(t))

[0326] in:

[0327] R resource (M(t)) is the resource reward, The state of incentive resources is close to the target state; R risk (r(t)) is the risk reward, R risk (r(t))=-[r level (t)+αr range (t)+βr trend (t)], the risk of punishment system increases; R perf (H(t)) is the performance reward, Encourage performance to approach the target state; ω1, ω2, ω3 are weight coefficients.

[0328] E). Data output

[0329] The module execution result is encapsulated as the execution result data packet P c (t), passed to the knowledge evolution module:

[0330] P c (t)={M(t),a t ,π θ (t),R(t)}

[0331] 6. Knowledge Evolution Module

[0332] The knowledge evolution module adopts the Differential Neural Knowledge Graph (DNEM) framework to integrate the data input of each functional module to achieve dynamic accumulation, adaptive update and continuous optimization of system knowledge. The details are as follows:

[0333] A). Data reception

[0334] The module receives the following packets:

[0335]

[0336] B). Construction of differential memory unit

[0337] Through the feature extraction model, multi-module data is fused into differential memory units:

[0338] E(t)=DNEM encode ([V′(t),S(t),r(t),H(t)])

[0339] The composition of the memory unit:

[0340]

[0341] in:

[0342] V′(t) is the video feature matrix; S(t) is the scene state matrix; r(t) is the risk assessment vector;

[0343] H(t) is the performance state vector; f k ,f v ,f s are three sets of fully connected neural networks used to map input features to key, value, and intensity vectors; the network structure is [512, 256, 128]; k(t), v(t)∈R 128 Represent the key vector and value vector respectively; s(t)∈R 64 Represents a strength vector, used to control the weight of memory updates.

[0344] C). Differential memory update

[0345] Memory updates follow an adaptive incremental update mechanism:

[0346] M(t)=M(t-1)+η(t)ΔM(t)

[0347] Incremental update items:

[0348]

[0349] in:

[0350] M(t): memory matrix, dynamically records the knowledge state of the system,

[0351] K: memory key matrix, storing all historical key vectors,

[0352] η(t): Adaptive learning rate, controlling the memory update amplitude, η(t)∈[0.01,0.1];

[0353] Softmax(·): Attention allocation mechanism that adjusts weights based on the matching degree of key vectors.

[0354] D) Memory retrieval and associative computing

[0355] The module retrieves relevant information from memory via a query mechanism:

[0356] R(t)=DNEM retrieve (E(t),M(t))

[0357] Retrieval steps:

[0358] a). Query vector:

[0359] q(t)=f q (E(t))

[0360] By the fully connected network f q Map input features.

[0361] b). Attention weight:

[0362]

[0363] Dynamically adjust the weights of memory units.

[0364] c). Search results:

[0365] R(t)=w(t)M(t)

[0366] Get the information from the memory matrix that best matches the current state.

[0367] E). Optimize strategy generation

[0368] According to the retrieval result R(t), the module generates the following optimization strategy:

[0369] a). Parameter optimization:

[0370] ΔP mod (t) = DNEM opt (R(t))

[0371] Used to adjust the parameters of the multi-modal monitoring module.

[0372] b). Rule optimization:

[0373] ΔP rule (t) = DNEM opt (R(t))

[0374] Used to update the scene recognition module rules.

[0375] c). Strategy Optimization:

[0376] ΔP strat (t) = DNEM opt (R(t))

[0377] Used to improve the prediction and warning module strategy.

[0378] F). Optimize the distribution of results

[0379] The module distributes the optimization results to the corresponding functional modules:

[0380]

[0381] Learning rate adjustment mechanism:

[0382]

[0383] Among them, λ0=0.01: basic learning rate, Δt=1s: update interval.

[0384] Combination Figure 2 ,This system builds an unattended video remote monitoring closed loop through the coordinated operation of six functional modules, covering six stages: data collection, scene analysis, risk warning, fault prevention, intelligent control and knowledge optimization. The specific work flow is as follows:

[0385] S1. Multi-source data collection and preprocessing

[0386] The system completes data collection through high-definition camera arrays and distributed environmental sensor networks, and combines multi-modal monitoring modules to generate standardized multi-modal data packets. Specifically, it includes:

[0387] S1.1 Video Data Collection

[0388] An array of high-definition cameras (16-64) provides video sequences;

[0389] Parameter configuration: exposure time [1ms, 100ms], gain [1.0, 5.0], focal length [2.8mm, 12mm];

[0390] Resolution support: highest, field of view: horizontal [60°, 110°], vertical [40°, 90°].

[0391] S1.2 Environmental data collection

[0392] A distributed environmental sensor network (20-50 nodes) collects environmental parameters, including light intensity (lux), temperature (℃), and humidity (%);

[0393] Adopt self-organizing protocol to build mesh topology and improve network stability and scalability.

[0394] S1.3 Data alignment and feature mapping

[0395] Minimize the time deviation between video sequence and environment data based on time synchronization mechanism;

[0396] The robustness of scene features is improved through the pyramid multi-scale feature extraction architecture combined with the environmental feature compensation mechanism.

[0397] S1.4 Timing Analysis and Feature Fusion

[0398] The improved wavelet transform (MODWT) is used to extract the fluctuation, change and evolution characteristics;

[0399] The 8-head attention mechanism and recurrent neural network (RNN) are used to realize the fusion of multi-dimensional time series features.

[0400] S1.5 Three-dimensional data quality assessment

[0401] Completeness: Evaluate the degree of missing data based on information entropy;

[0402] Reliability: Analyze data credibility through prediction bias;

[0403] Timeliness: Consider the impact of transmission delay on real-time performance.

[0404] S1.6. Output

[0405] Output standardized data packets v (t),O e (t),O q (t)} was used for subsequent analysis.

[0406] S2. Scene understanding and anomaly recognition

[0407] After receiving the data packet, the scene recognition module uses multi-task learning and hierarchical relationship analysis framework to complete the scene semantic modeling and anomaly detection. Specifically, it includes:

[0408] S2.1 Multi-scale feature extraction

[0409] Physical features: Extract scene physical information through residual CNN (256 dimensions);

[0410] Behavioral features: Use a two-stream spatiotemporal attention network to capture behavioral patterns (256 dimensions);

[0411] Semantic features: Multi-dimensional semantic features (64 dimensions) are fused through multimodal Transformer.

[0412] S2.2 Hierarchical relationship analysis

[0413] Spatial relations: quantifying spatial associations between objects using graph attention networks;

[0414] Movement relationship: Analyze the temporal movement pattern through LSTM;

[0415] Interaction relationship: Combine dynamic causal network to infer the interaction logic between multiple targets.

[0416] S2.3 Scenario State Modeling

[0417] Build a scene state dynamic model based on graph convolutional network (GCN);

[0418] Dynamically update the adjacency matrix and degree matrix to achieve quantitative representation of the scene state.

[0419] S2.4 Anomaly Detection

[0420] Build a normal scene prototype library and use contrastive learning strategy to calculate state anomalies and behavior anomalies.

[0421] S2.5. Output

[0422] Output scene status data packet P s (t) and abnormal characteristic data packet P a (t).

[0423] S3. Risk assessment and early warning decision-making

[0424] The prediction and warning module integrates scenario analysis and causal reasoning to achieve quantitative assessment of system risks and dynamic warning decisions. Specifically, it includes:

[0425] S3.1. Scenario evolution analysis

[0426] The scenario evolution graph is constructed based on the dynamic causal reasoning network, and the evolution rules between variables are analyzed through causal association.

[0427] S3.2. Risk assessment

[0428] Construct a multi-agent game framework to quantify system risks and comprehensively evaluate risk levels, impact scope and development trends.

[0429] S3.3. Warning decision generation

[0430] Combine deep reinforcement learning and explainability mechanisms to generate early warning decision information.

[0431] S3.4. Output

[0432] Output warning data packet P w (t).

[0433] S4. System defense and troubleshooting

[0434] The immune defense module monitors system performance in real time and implements multi-level protection strategies for different risk scenarios. Specifically, it includes:

[0435] S4.1 Performance Monitoring

[0436] Real-time quantitative system performance indicators covering processing resources, storage resources, and network resources.

[0437] S4.2 Fault prediction

[0438] Construct a fault prediction model based on adaptive immune network to assess potential risks.

[0439] S4.3 Three-level protection mechanism

[0440] Fault isolation: Identify and isolate high-risk nodes;

[0441] Service migration: dynamically dispatch services to low-risk nodes;

[0442] Resource reallocation: Optimize global resource allocation and improve system stability.

[0443] S4.4 Output

[0444] Output status data packet P s (t) and fault handling data packet P e (t).

[0445] S5. Resource Scheduling and Control Execution

[0446] The autonomous control module combines warning and defense information to achieve resource scheduling and task optimization through a reinforcement learning architecture. Specifically, it includes:

[0447] S5.1 Resource Monitoring Matrix

[0448] Real-time evaluation of resource status, including but not limited to multi-dimensional resource information such as CPU, memory, and network.

[0449] S5.2 Control strategy generation

[0450] Generate optimal control strategies based on the Actor-Critic architecture and dynamically adjust control parameters (exposure time, gain, focal length) and task priorities.

[0451] S5.3 Task Processing Execution

[0452] Efficiently schedule tasks under constraints and optimize system performance.

[0453] S5.4 Output

[0454] Output control result data packet P c (t).

[0455] S6. Knowledge accumulation and optimization and updating

[0456] The knowledge evolution module realizes the dynamic management and optimization of multi-source knowledge through differential neural knowledge graph.

[0457] Specifically include:

[0458] S6.1 Knowledge Representation

[0459] Construct differential memory units to achieve unified representation of multi-module data.

[0460] S6.2 Knowledge retrieval and association

[0461] Retrieve relevant knowledge from the memory matrix using a differentiable addressing mechanism.

[0462] S6.3 Optimization strategy generation

[0463] Provide parameter optimization, rule optimization and strategy optimization solutions to ensure continuous improvement of the system.

[0464] S6.4 Optimization results distribution

[0465] Distribute optimized configuration data packets to each module opt (t).

[0466] Through the above-mentioned accompanying drawings, the present invention proposes an unattended video remote monitoring system, which realizes the system's autonomous perception, autonomous decision-making, autonomous operation and maintenance, and autonomous optimization through the collaborative work of six functional modules: multimodal monitoring, scene recognition, prediction and warning, immune defense, autonomous control, and knowledge evolution. It innovatively integrates deep learning, causal reasoning, immune mechanism, and differential memory, breaking through the technical bottlenecks of fixed thresholds, passive response, and shallow scene understanding of traditional monitoring systems, and realizing an intelligent closed loop from scene perception to warning decision-making, and an operation and maintenance closed loop from fault prevention to knowledge accumulation. The system has important application value and promotion significance in improving monitoring quality, reducing labor costs, and enhancing system reliability.

[0467] It is worth noting that the protection scope of the present invention is not limited to the specific implementations described in the above embodiments. Equivalent variations or modifications made by those skilled in the art based on the enlightenment of the present invention should be included in the protection scope of the present invention as long as they do not deviate from the spirit of the technical solution of the present invention.

Claims

1. An unattended video remote monitoring system, characterized in that: include: Multimodal monitoring module, used to collect and intelligently preprocess monitoring data, build high-definition camera arrays and distributed environmental sensor networks, realize multi-scale feature extraction and time series analysis through deep feature transformation networks, adaptive weight fusion, improved wavelet transforms and recursive neural networks, generate video data packets, environmental data packets and quality assessment data packets, and transmit them to the scene recognition module and knowledge evolution module; The scene recognition module is used to receive and parse the data packets transmitted by the multimodal monitoring module, build a scene feature extraction model, a relationship analysis model and a scene state dynamic model based on a graph structure network through a deep neural network architecture, realize the detection of scene state anomalies and target behavior anomalies, encapsulate the scene state data packet and transmit it to the prediction and warning module and the knowledge evolution module, and encapsulate the abnormal feature data packet and transmit it to the prediction and warning module; The prediction and warning module is used to receive the scene status data packets and abnormal feature data packets transmitted by the scene recognition module, extract the scene evolution law through the dynamic causal reasoning network, combine the multi-agent game framework and deep reinforcement learning, encapsulate the warning data packets containing prediction data, risk data and warning data, and transmit them to the autonomous control module and knowledge evolution module; The immune defense module is used to monitor system performance in real time, predict faults based on the adaptive immune network and memory library pattern matching mechanism, implement fault isolation, service migration and resource reallocation through a three-level active protection mechanism, encapsulate system status data packets and fault processing data packets, and transmit them to the autonomous control module and knowledge evolution module respectively; The autonomous control module is used to receive the warning data packets from the prediction and warning module and the status data packets from the immune defense module, generate resource scheduling instructions and task processing instructions through the resource monitoring model and the control strategy model, execute the control instructions to adjust the system working parameters, encapsulate the execution result data packets, and pass them to the knowledge evolution module; The knowledge evolution module is used to receive data packets from each module, build a differential neural knowledge graph architecture, combine the dynamic memory update mechanism to achieve knowledge accumulation and optimization, and generate solutions for module parameter optimization, processing strategy optimization, and control method optimization through retrieval and association analysis.

2. The unattended video remote monitoring system according to claim 1, characterized in that: The multimodal monitoring module dynamically adjusts acquisition parameters, processing parameters and transmission parameters based on a data quality assessment model of information entropy and confidence, thereby improving data integrity, reliability and timeliness.

3. The unattended video remote monitoring system according to claim 1, characterized in that: The scene recognition module realizes the unified representation of scene physical features, target behavior features and scene semantic features through multi-scale feature fusion, and improves the accuracy of anomaly detection based on contrastive learning strategy.

4. The unattended video remote monitoring system according to claim 1, characterized in that: The prediction and warning module constructs a dynamic causal reasoning network through Granger causality test and graph neural network, and calculates the risk level value, impact range value and trend development value in combination with the multi-agent game framework.

5. The unattended video remote monitoring system according to claim 1, characterized in that: The autonomous control module adopts the Actor-Critic architecture of deep reinforcement learning to optimize the control strategy, and improves the operating efficiency and stability of the system through resource scheduling and task allocation.

6. The unattended video remote monitoring system according to claim 1, characterized in that: The immune defense module dynamically generates defense strategies based on the pattern matching prediction mechanism of the memory bank, and executes protective measures through a distributed immune response mechanism to evaluate the degree of performance improvement, risk reduction and abnormal change.

7. The unattended video remote monitoring system according to claim 1, characterized in that: The knowledge evolution module realizes the real-time and effectiveness of knowledge through incremental updates of differential memory units, and optimizes the multi-module collaboration strategy by dynamically adjusting the learning rate.

8. The unattended video remote monitoring system according to any one of claims 1 to 7, characterized in that: The modules exchange information through data packets, which include: video data packets, environmental data packets, quality assessment data packets, scene status data packets, abnormal feature data packets, warning data packets, system status data packets, fault handling data packets, execution result data packets and optimization configuration data packets.

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