Intelligent security multi-scene monitoring and abnormal early warning processing method and system

Abstract

The application discloses a smart security multi-scene monitoring and abnormal early warning processing method and system, comprising the following steps: collecting multi-scene monitoring data through a cross-region security collaborative intelligent analysis platform, extracting target hierarchical features through a dynamic target multi-scale perception model, realizing model cross-scene rapid adaptation by using a security scene transfer learning algorithm, establishing continuous target motion trajectories by using a multi-feature fusion target tracking algorithm, integrating multi-node data to complete cross-region trajectory connection and verification, generating abnormal early warning based on target motion parameters and preset threshold comparison, and performing dynamic target perception, scene transfer learning, and feature fusion tracking modeling calculation process, which expands the step-by-step operation of scene adaptation, feature extraction, and cross-region collaboration. The application realizes multi-scene accurate monitoring and efficient abnormal early warning, is suitable for various cross-region security prevention and control scenes, and improves the scene adaptability and global collaboration capability of the security system.

Description

Technical Field

[0001] This invention relates to the field of intelligent security technology, and in particular to a method and system for intelligent security multi-scenario monitoring and abnormal early warning processing. Background Technology

[0002] With the continuous improvement of social security demands, security monitoring systems are evolving from traditional digitalization and networking to deep intelligence. Multi-scenario coverage and cross-regional collaboration have become the core development direction of the security field. Currently, various monitoring scenarios are characterized by complex and ever-changing environments, highly dynamic targets, and massive heterogeneous data. Traditional monitoring models are unable to meet the actual needs of real-time perception, accurate identification, and rapid response. The widespread adoption of cross-regional security applications further highlights the importance of data integration, scenario adaptation, and global control. There is an urgent need to build an integrated solution that can adapt to different environments, integrate multi-source data, and achieve accurate early warning of anomalies to address the security and prevention challenges in diverse security scenarios and improve the overall intelligence level and practical effectiveness of the security system.

[0003] Existing technologies suffer from two core drawbacks: First, they lack scene adaptability, making it difficult to maintain stable performance under different environmental conditions. When faced with complex situations such as changes in lighting and occlusion interference, they are prone to problems such as insufficient feature extraction and target recognition deviations. Furthermore, when deploying in new scenes, they often require a lot of adaptation work and cannot quickly achieve model-scene matching. Second, they have weak cross-regional collaboration and data integration capabilities. Monitoring systems in various regions are often deployed in a fragmented manner, lacking effective data association and trajectory connection mechanisms. This leads to trajectory breaks and information gaps when targets move across regions. At the same time, there is insufficient comprehensive utilization of multi-source monitoring data, making it difficult to form a global perspective for anomaly analysis, which affects the accuracy of anomaly event judgment and the timeliness of early warning. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method and system for intelligent security multi-scenario monitoring and abnormal early warning processing.

[0005] The technical solution adopted in this invention is an intelligent security multi-scenario monitoring and anomaly early warning processing method, comprising the following steps: S1, collecting monitoring data in multiple scenarios through a cross-regional security collaborative intelligent analysis platform, wherein the monitoring data includes spatial distribution information of dynamic targets, motion state parameters, and scene environment features; S2, calling a dynamic target multi-scale perception model to extract target-level features from the collected monitoring data, and constructing a multi-dimensional perception channel based on the target's scale change range in different scenarios; S3, using a security scenario transfer learning algorithm to transfer the model parameters obtained from training in the source scenario to the current target scenario, enabling rapid adaptation of the model in different scenarios; S4, using a multi-feature fusion target tracking algorithm to perform spatiotemporal correlation matching on the extracted target features, establishing a continuous representation of the target's motion trajectory; S5, integrating multi-node monitoring data through a cross-regional security collaborative intelligent analysis platform, and performing cross-regional connection and consistency verification on the target trajectory; S6, generating anomaly event judgment results and triggering corresponding early warning mechanisms based on the comparison results of target motion parameters and preset thresholds.

[0006] Furthermore, the calculation of the dynamic target multi-scale perception model in S2 includes the following steps: T1, selecting the scale range, motion rate, and contour complexity of the target in the security scenario as the basic parameters for modeling, constructing a multi-scale feature sampling grid, and obtaining target local features at different levels by traversing the monitoring data through a sliding window, wherein the grid division density is positively correlated with the target scale range; T2, performing spiking neuron conversion processing on the local features at each level, converting continuous feature signals into discrete pulse tensors through the LIF mechanism, and establishing a mapping relationship between features and pulse firing frequency, expressed as: ,in For the pulse characteristic tensor, The convolution weight matrix is... For local feature matrices, For bias terms, For impulse activation functions, T3 is the pulse attenuation coefficient; T4, a pulse pyramid fusion module is constructed, which performs upsampling and downsampling operations on pulse feature tensors of different scales, and performs complementary enhancement of multi-scale features through cross-layer connections. Simultaneously, a local sliding window self-attention mechanism is introduced to strengthen the features of the target calibration region; T5, a target detection head is constructed based on the fused multi-scale features. The feature dimensions are mapped through a multilayer perceptron, outputting the target's scale category, location coordinates, and confidence parameters, expressed as follows: ,in For the set of detection results, To fuse feature tensors, For multilayer perceptron operators, Attention is the self-attention computation function. For feature splicing operators.

[0007] Furthermore, the modeling process of the security scene transfer learning algorithm in S3 includes the following steps: Q1, extracting common feature dimensions of the source and target scenes, including spatial topology, illumination variation range, target motion mode, and security calibration parameters, establishing a scene feature vector library, and storing the convolutional and fully connected layer parameters of the source scene model hierarchically; Q2, calculating the feature similarity matrix between the source and target scenes, aligning the feature distribution under different scenes using a dynamic time warping algorithm, determining the weight coefficients for parameter transfer, and retaining the unique feature mapping layer of the target scene to adapt to scene differences; Q3, constructing a transfer parameter optimization objective function, fine-tuning the transferred model parameters, the expression being... ,in For the total parameter set of the model, The number of samples in the target scene. For loss function, For sample labels, For model prediction function, For the input sample, For migration parameters, To fine-tune the parameters, The regularization coefficient is . It is an L2 norm.

[0008] Furthermore, the calculation process of the multi-feature fusion target tracking algorithm in S4 includes the following steps: R1, extracting the visual and spatial features of the target from the monitoring data. Visual features include color histograms and texture feature vectors; spatial features include target bounding box coordinates, motion velocity, and acceleration. Dimensional standardization is performed on all features. R2, constructing an adaptive feature weight allocation mechanism to dynamically adjust the contribution ratio of visual and spatial features based on scene complexity. The expression is: ,in To fuse feature weights, For adaptive coefficients, For visual feature weights, R3 represents the spatial feature weights; R4 uses Kalman filtering to predict the target's trajectory, generating the target's position confidence interval for the next frame, and combines the Hungarian algorithm to match the detection results with the predicted trajectory; R5 establishes a trajectory continuity verification mechanism, calculating the cosine similarity and IoU overlap rate of target features in adjacent frames, and completing and correcting broken trajectories, expressed as follows: ,in To match scores, For balance coefficient, The cosine similarity function is used. and These are the features of the current frame and the previous frame, respectively. Let be the intersection-union ratio function. and R1 represents the target bounding boxes of the current frame and the previous frame, respectively; R2 integrates the correlation results of multiple frames and outputs the complete motion trajectory of the target, including the position coordinates, motion parameters and feature vector sets at each time point.

[0009] Further, step S6 includes extracting the target's motion rate, trajectory offset, and dwell time calibration indicators based on the target trajectory parameters output by S5, comparing them with a preset anomaly judgment threshold, and calculating the anomaly confidence level through weighted summation, expressed as follows: ,in For abnormal confidence levels, The number of anomaly detection indicators. The weight coefficient for the k-th indicator is... This represents the actual measured value of the k-th indicator. This represents the normal threshold for the k-th indicator.

[0010] Furthermore, this method adopts a distributed heterogeneous computing architecture, including an edge-end intelligent monitoring terminal, regional edge computing nodes, and a cloud-based collaborative server. The edge terminal integrates an infrared thermal imaging sensor and a visible light high-definition camera, supporting synchronous acquisition of multispectral data. The regional edge computing nodes are equipped with FPGA chips and GPU clusters to perform parallel computing of the dynamic target multi-scale perception model. A single node supports real-time processing of no less than 32 channels of monitoring video. The cloud server adopts a distributed file storage system to store historical monitoring data and model parameters. The number of convolutional layers in the dynamic target multi-scale perception model is set to 12, and the number of feature map channels increases from 64 to 1024. The fine-tuning learning rate of the security scene transfer learning algorithm ranges from 0.001 to 0.01. The feature dimension of the multi-feature fusion target tracking algorithm is unified to 512 dimensions. The node communication latency of cross-regional collaborative analysis is controlled within a preset range, and the abnormal warning response time does not exceed a preset value.

[0011] Further, S3 includes the following sub-steps: S31, decompose the inter-layer parameters of the source scene model, separate the feature extraction layer and the classification prediction layer, retain the convolution kernel parameters of the feature extraction layer as the basis for transfer, and randomly initialize the parameters of the classification prediction layer; S32, collect a small number of labeled samples of the target scene to construct a scene adaptation dataset, including target samples under different lighting and occlusion conditions, and the samples cover various motion states and scale changes of the target; S33, based on the scene feature similarity matrix, selectively transfer the parameters of the source scene feature extraction layer, directly reuse the layer parameters with similarity higher than a preset threshold, and partially replace the layer parameters with similarity lower than the threshold; S34, iteratively train the transferred model on the target scene adaptation dataset, update the parameters using a mini-batch gradient descent algorithm, fix the first 80% of the parameters of the feature extraction layer during training, and only fine-tune the last 20% of the parameters and the parameters of the classification prediction layer.

[0012] Further, S4 includes the following sub-steps: S41, using a multi-scale feature pyramid structure to extract shallow texture features and deep semantic features of the target. Shallow features are obtained through 1 to 3 layers of convolution, and deep features are obtained through 6 to 8 layers of convolution and pooling operations. Features at each layer interact through lateral connections; S42, constructing a feature attention mechanism module to assign channel weights to the extracted multi-scale features, enhancing the response strength of the target calibration features and suppressing the interference of background noise features; S43, introducing spatiotemporal context information, fusing the target features of the current frame with the historical features of the previous 5 frames, and modeling the temporal correlation of target motion through a recurrent neural network to improve the robustness of feature representation.

[0013] Further, S5 includes the following sub-steps: S51, establishing a time synchronization mechanism for cross-regional monitoring nodes, and calibrating the timestamps of the monitoring data of each node through the network time protocol to ensure the time consistency of cross-regional data; S52, extracting the target trajectory data output by each regional node, including trajectory ID, timestamp, and location coordinate calibration information, and constructing a cross-regional trajectory index library; S53, based on the target's feature vector and motion parameters, using the nearest neighbor algorithm to perform association matching of the same target's trajectories in different regions, and calculating the feature similarity and motion continuity of cross-regional trajectories; S54, smoothing the associated cross-regional trajectories to eliminate trajectory jitter during node switching, and completing trajectory breakpoints through interpolation algorithms; S55, generating a globally unified target trajectory report, including the target's motion path, dwell time, and cross-regional transfer time in each region.

[0014] This intelligent security multi-scenario monitoring and anomaly early warning system comprises: a multimodal monitoring data acquisition and preprocessing unit, a dynamic target multi-scale feature perception and extraction unit, a cross-scenario model parameter transfer and adaptation unit, a multi-feature fusion target trajectory tracking unit, a cross-regional collaborative analysis and trajectory verification unit, and an abnormal event intelligent judgment and early warning unit. The multimodal monitoring data acquisition and preprocessing unit collects multi-scenario monitoring data through a distributed sensor network and outputs it to the dynamic target multi-scale feature perception and extraction unit. The dynamic target multi-scale feature perception and extraction unit extracts features from the input data and transmits the results to the cross-scenario model parameter transfer and adaptation unit. The model parameter migration and adaptation unit performs cross-scene migration and optimization of model parameters, and outputs the adapted feature data to the multi-feature fusion target trajectory tracking unit. The multi-feature fusion target trajectory tracking unit continuously tracks the target trajectory and sends the trajectory data to the cross-regional collaborative analysis and trajectory verification unit. The cross-regional collaborative analysis and trajectory verification unit integrates multi-regional trajectory data and performs consistency verification, and outputs global trajectory data to the abnormal event intelligent judgment and early warning unit. The abnormal event intelligent judgment and early warning unit performs abnormal judgment based on preset thresholds and global trajectory data, triggers corresponding early warning operations, and each unit interacts and collaborates through high-speed Ethernet and a distributed bus.

[0015] Beneficial Effects: This invention proposes an intelligent security multi-scenario monitoring and anomaly early warning processing method and system. It integrates multi-scenario monitoring data through a cross-regional collaborative analysis platform, and accurately extracts target features under different scenarios using a dynamic target multi-scale perception mechanism. This solves the problem of insufficient scenario adaptability in traditional technologies, allowing for rapid adaptation to different scenario environments without extensive retraining. Even in the face of complex environmental interference, it can stably capture key target information. Through security scenario transfer learning, it achieves efficient parameter transfer between source and target scenarios, and combined with local fine-tuning to adapt to scenario differences, significantly reducing the adaptation cost and time cost of deploying new scenarios. Utilizing multi-feature fusion target tracking technology to construct a spatiotemporal correlation and trajectory verification mechanism ensures the continuous integrity of target trajectories. Combined with cross-regional trajectory connection and consistency verification, it breaks through the trajectory breakage and information disconnection dilemma caused by fragmented deployment in traditional systems, forming a global monitoring perspective. The hardware adopts a distributed heterogeneous computing architecture, and the software optimizes parameter configuration, enabling real-time processing of monitoring data and rapid anomaly judgment, significantly improving early warning response efficiency. Meanwhile, each functional unit processes data through efficient data interaction, coordinating the entire process from data collection, feature extraction, scene adaptation, trajectory tracking to anomaly warning. This ensures the comprehensiveness and accuracy of multi-scene monitoring, strengthens cross-regional security collaboration capabilities, and solves the shortcomings of traditional technologies in scene adaptation and cross-regional collaboration. Attached Figure Description

[0016] Figure 1This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 This is a flowchart of method step S3 of the present invention; Figure 3 This is a flowchart of method step S4 of the present invention; Figure 4 This is a flowchart of step S5 of the method of the present invention; Figure 5 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, the intelligent security multi-scenario monitoring and anomaly early warning processing method includes the following steps: S1, collecting monitoring data from multiple scenarios through a cross-regional security collaborative intelligent analysis platform, the monitoring data including the spatial distribution information of dynamic targets, motion state parameters, and scene environment characteristics; S2, calling a dynamic target multi-scale perception model to extract target-level features from the collected monitoring data, and constructing a multi-dimensional perception channel based on the target's scale change range in different scenarios; S3, using a security scenario transfer learning algorithm to transfer the model parameters obtained from training in the source scenario to the current target scenario, enabling rapid adaptation of the model in different scenarios; S4, using a multi-feature fusion target tracking algorithm to perform spatiotemporal correlation matching on the extracted target features, establishing a continuous representation of the target's motion trajectory; S5, integrating multi-node monitoring data through the cross-regional security collaborative intelligent analysis platform, and performing cross-regional connection and consistency verification on the target trajectory; S6, generating anomaly event judgment results and triggering corresponding early warning mechanisms based on the comparison results of target motion parameters and preset thresholds.

[0019] Step S1 involves collecting multi-scenario monitoring data through a cross-regional security collaborative intelligent analysis platform. The platform connects to distributed edge-deployed intelligent monitoring terminals, each integrating an infrared thermal imaging sensor and a visible light high-definition camera. This supports simultaneous multispectral data acquisition, covering various security scenarios such as indoor and outdoor environments, alternating light and dark zones, open squares, and narrow passages. The collected monitoring data specifically includes the spatial distribution information of dynamic targets, motion state parameters, and scene environmental characteristics. Spatial distribution information includes the target's two-dimensional coordinates, relative distance, and distribution density within the monitoring image, with the acquisition frequency consistent with the monitoring terminal's frame rate. Motion state parameters include the target's movement speed, acceleration, direction of motion, and trajectory curvature. Speed ​​is calculated using the difference in target position between adjacent frames, and acceleration is derived from three consecutive frames of speed data. Scene environmental characteristics include light intensity, background complexity, occlusion degree, and environmental noise level. Light intensity is collected in real-time by the terminal's light sensor, background complexity is determined by the variance of pixel grayscale values, and occlusion degree is determined based on the pixel occlusion ratio of the target area. During data acquisition, each monitoring node transmits data via high-speed Ethernet. The bandwidth of a single node is configured according to a preset standard to ensure the simultaneous acquisition of 32 or more monitoring video feeds. The acquired raw data is encapsulated and stored in a cloud-based distributed file storage system, providing a complete and continuous data source for subsequent feature extraction. By comprehensively collecting multi-dimensional data, the shortcomings of traditional single-dimensional monitoring data are overcome, laying a solid data foundation for the entire security monitoring and early warning process. This ensures that subsequent algorithm models can conduct accurate analysis based on rich information, enhancing the overall data support capabilities of the security system.

[0020] Step S2 invokes a dynamic target multi-scale perception model to extract target-level features from the collected monitoring data. At the software level, the model has 12 convolutional layers, and the number of feature map channels increases from 64 to 1024. First, based on historical monitoring data statistics of target scale variations in different security scenarios, multi-dimensional perception channels are constructed. The number of channels is set to 8 to 16 depending on the scenario complexity, with each channel corresponding to a specific target scale range. During implementation, a multi-scale feature sampling grid is first constructed. The grid density is positively correlated with the target scale range; smaller targets correspond to denser grids. A fixed-size sliding window traverses the monitoring data to obtain local target features at different levels. The window's sliding step size is dynamically adjusted according to the scale range. Subsequently, spiking neuron conversion processing is performed on the local features at each level. A specific mechanism converts continuous feature signals into discrete pulse tensors, establishing a mapping relationship between features and pulse firing frequencies. Then, a pulse pyramid fusion module is constructed to perform upsampling and downsampling operations on pulse feature tensors of different scales. Multi-scale feature complementarity enhancement is achieved through cross-layer connections, while a local sliding window self-attention mechanism is introduced to strengthen the features of key target regions. Finally, a target detection head is constructed based on the fused multi-scale features. A multilayer perceptron maps the feature dimensions, outputting the target's scale category, location coordinates, and confidence parameters. Through multi-dimensional perceptual channels and feature fusion mechanisms, key features of targets at different scales are accurately captured, solving the problem of poor scale adaptability in traditional feature extraction. This provides high-quality feature support for subsequent target tracking and anomaly detection, improving the comprehensiveness and accuracy of target recognition.

[0021] Step S3 employs a security scene transfer learning algorithm to transfer the model parameters trained in the source scene to the current target scene, achieving rapid model adaptation in different scenes. The algorithm fine-tunes the learning rate, ranging from 0.001 to 0.01. During implementation, the source scene model is first decomposed into inter-layer parameters, separating the feature extraction layer and the classification prediction layer. The convolutional kernel parameters of the feature extraction layer are retained as the basis for transfer, while the parameters of the classification prediction layer are randomly initialized. Simultaneously, common feature dimensions between the source and target scenes are extracted, including key security parameters such as spatial topology, illumination variation range, and target motion patterns, establishing a scene feature vector library. The parameters of the convolutional and fully connected layers of the source scene model are stored hierarchically. Next, the feature similarity matrix between the source and target scenes is calculated. A dynamic time warping algorithm is used to align the feature distribution under different scenes, determining the weight coefficients for parameter transfer. The unique feature mapping layer of the target scene is retained to adapt to scene differences. Based on the feature similarity matrix, the parameters of the source scene feature extraction layer are selectively transferred. Layer parameters with similarity higher than a preset threshold are directly reused, while those lower than the threshold are partially replaced. Subsequently, a small number of labeled samples from the target scene were collected to construct a scene adaptation dataset, including target samples under different lighting and occlusion conditions. The number of samples was controlled between 500 and 1000 sets, covering various motion states and scale changes of the target. The transferred model was iteratively trained on this dataset, using a mini-batch gradient descent algorithm to update parameters. The number of training iterations was set to 200 to 500. During training, the first 80% of the parameters in the feature extraction layer were fixed, and only the last 20% of the parameters and the classification prediction layer parameters were fine-tuned. By utilizing parameter selective transfer and local fine-tuning, the sample requirements and time cost of training the model in new scenes are significantly reduced, solving the problems of cumbersome retraining and low adaptation efficiency of traditional models in different scenes, and improving the flexibility and timeliness of model deployment.

[0022] Step S4 employs a multi-feature fusion target tracking algorithm to perform spatiotemporal correlation matching on the extracted target features, establishing a continuous representation of the target's motion trajectory. The algorithm's feature dimension is unified to 512 dimensions. During implementation, visual and spatial features of the target are first extracted from the monitoring data. Visual features include color histograms and texture feature vectors, while spatial features include target bounding box coordinates, motion velocity, and acceleration. Dimensional standardization is performed on all types of features to ensure uniformity. Subsequently, an adaptive feature weight allocation mechanism is constructed, dynamically adjusting the contribution ratio of visual and spatial features based on scene complexity. Scene complexity is determined comprehensively by considering background noise level and target occlusion degree. Next, a specific filtering algorithm is used to predict the target's motion trajectory, generating a confidence interval for the target's position in the next frame. The confidence interval range is dynamically adjusted based on motion velocity and acceleration. The Hungarian algorithm is combined to achieve correlation matching between the detection results and the predicted trajectory. When calculating the matching score, feature similarity and positional overlap are comprehensively considered. A trajectory continuity verification mechanism was then established, calculating the similarity and overlap rate of target features in adjacent frames. Similarity and overlap rate thresholds were set, and trajectory segments below these thresholds were identified as broken. Interpolation algorithms were used to complete and correct these segments. Simultaneously, spatiotemporal context information was introduced, fusing the target features of the current frame with historical features from the previous five frames. A recurrent neural network was used to model the temporal correlation of target motion, improving the robustness of feature representation. Finally, the results from multiple frames were integrated to output the complete target trajectory, including position coordinates, motion parameters, and feature vector sets at each moment. Through multi-feature fusion and trajectory verification mechanisms, the continuity and accuracy of the target trajectory are ensured, solving the problem of trajectory breakage caused by occlusion and environmental interference in traditional tracking. This provides reliable trajectory data support for cross-regional trajectory connection and anomaly warning.

[0023] Step S5 integrates multi-node monitoring data through a cross-regional security collaborative intelligent analysis platform, performing cross-regional connection and consistency verification of target trajectories, with platform node communication latency controlled within a preset range. During implementation, a time synchronization mechanism for cross-regional monitoring nodes is first established. The monitoring data of each node is timestamped using a network time protocol, with calibration accuracy controlled at the millisecond level to ensure time consistency of cross-regional data. Subsequently, target trajectory data output by each regional node is extracted, including key information such as trajectory ID, timestamp, location coordinates, motion parameters, and feature vectors, to construct a cross-regional trajectory index library. The index library is stored hierarchically according to region ID and trajectory ID, supporting rapid retrieval. Next, based on the target's feature vector and motion parameters, the nearest neighbor algorithm is used to match the trajectories of the same target in different regions, calculating the feature similarity and motion continuity of cross-regional trajectories. Similarity thresholds and continuity thresholds are set to filter out matching trajectory pairs that meet the conditions. The associated cross-regional trajectories are then smoothed. A moving average algorithm is used to eliminate trajectory jitter during node switching, and a linear interpolation algorithm is used to fill in trajectory breakpoints, ensuring a smooth transition. Simultaneously, the trajectory data undergoes consistency verification, checking the rationality of motion parameters and removing abnormal fluctuations. Finally, a globally unified target trajectory report is generated, including the target's movement path, dwell time, cross-regional transfer time, and changes in movement status in each region. The report is stored in a structured manner in chronological order and synchronized to a cloud server for subsequent anomaly analysis. By leveraging cross-regional data integration and trajectory verification, the information barriers caused by the fragmented deployment of traditional monitoring systems are broken down, enabling global tracking of target trajectories and providing global data support for comprehensive analysis of abnormal events.

[0024] Step S6 generates an anomaly judgment result and triggers the corresponding early warning mechanism based on the comparison result of the target motion parameters and preset thresholds. The anomaly early warning response time does not exceed the preset value. During implementation, key anomaly judgment indicators of the target are first extracted from the global trajectory data output in step S5, including movement speed, trajectory offset, dwell time, frequency of sudden changes in movement direction, number of cross-regional transfers, and intrusion into specific areas. The number of indicators is set to 6 to 8, and each indicator is assigned a corresponding weight coefficient according to the importance of the security scenario, with a total weight coefficient of 1. Then, the system's preset normal threshold ranges for each indicator are retrieved. The threshold ranges are determined based on statistical analysis of a large amount of historical normal trajectory data and support dynamic adjustment according to different security scenarios. For example, the dwell time threshold in densely populated areas is lower than that in open areas, and the intrusion judgment threshold in key control areas is more stringent. Next, the actual measured values ​​of each extracted indicator are compared with the corresponding preset thresholds to calculate the degree of abnormality for each indicator. The overall anomaly confidence level is calculated through weighted summation, and an anomaly confidence threshold is set. When the calculated result exceeds this threshold, it is determined as an abnormal event. Simultaneously, the anomaly event level is determined based on the degree of abnormality of each indicator, categorized into three levels: general anomaly, important anomaly, and emergency anomaly. An anomaly event determination report is then generated, including information such as the time and location of the anomaly, the targets involved, the anomaly indicators, the anomaly level, and trajectory fragments. The report is pushed to the security monitoring center in real time. Finally, the corresponding early warning mechanism is triggered according to the anomaly event level. General anomalies only trigger a system pop-up notification; important anomalies trigger audible and visual alarms and SMS notifications; and emergency anomalies, in addition to the above warnings, simultaneously activate on-site monitoring equipment to focus and capture images, and send real-time location and trajectory information to relevant security personnel to ensure rapid response and handling. Through multi-indicator weighted determination and graded early warning, accurate identification and rapid response to anomalies are achieved, solving the problems of high false alarm rates and untimely responses in traditional early warning systems, and improving the practical effectiveness of the security system.

[0025] Preferably, the calculation of the dynamic target multi-scale perception model in S2 includes the following steps: T1, selecting the scale range, motion rate, and contour complexity of the target in the security scenario as the basic parameters for modeling, constructing a multi-scale feature sampling grid, and obtaining target local features at different levels by traversing the monitoring data through a sliding window, wherein the grid density is positively correlated with the target scale range; T2, performing spiking neuron conversion processing on the local features at each level, converting continuous feature signals into discrete pulse tensors through the LIF mechanism, and establishing a mapping relationship between features and pulse firing frequency, expressed as: ,in For the pulse characteristic tensor, The convolution weight matrix is... For local feature matrices, For bias terms, For impulse activation functions, T3 is the pulse attenuation coefficient; T4, a pulse pyramid fusion module is constructed, which performs upsampling and downsampling operations on pulse feature tensors of different scales, and performs complementary enhancement of multi-scale features through cross-layer connections. Simultaneously, a local sliding window self-attention mechanism is introduced to strengthen the features of the target calibration region; T5, a target detection head is constructed based on the fused multi-scale features. The feature dimensions are mapped through a multilayer perceptron, outputting the target's scale category, location coordinates, and confidence parameters, expressed as follows: ,in For the set of detection results, To fuse feature tensors, For multilayer perceptron operators, Attention is the self-attention computation function. For feature splicing operators.

[0026] Specifically, in step S2, the calculation of the dynamic target multi-scale perception model achieves accurate extraction of multi-scale target features through four sub-steps, T1 to T4. In stage T1, the scale range, motion rate, and contour complexity of the target in the security scenario are selected as core modeling parameters. These parameters directly relate to the target's feature performance in different scenarios. The constructed multi-scale feature sampling grid needs to form a positively correlated partition density with the target scale range to ensure that small-scale targets can be captured by a denser grid. When traversing the monitoring data through a sliding window, the window step size is dynamically adjusted according to the scale range, thereby obtaining local features of the target at different levels, laying the foundation for subsequent feature processing. In stage T2, spiking neuron transformation is used to process local features at each level. Based on the information encoding characteristics of spiking neurons, the LIF mechanism can convert continuous feature signals into discrete pulse tensors. This transformation method can effectively preserve the temporal correlation and key information of the features. The constructed feature-pulse firing frequency mapping formula is based on the weighted integration of local features through convolution operations, combined with bias term adjustment and activation function transformation, and then signal attenuation is controlled by the pulse attenuation coefficient. The parameter values ​​have been optimized through extensive scenario testing to ensure the effectiveness and robustness of the feature transformation. The pulse pyramid fusion module constructed in stage T3 achieves scale unification of pulse feature tensors at different scales through upsampling and downsampling operations. Cross-layer connectivity promotes information complementarity among multi-scale features, while a local sliding window self-attention mechanism focuses on key target regions, suppressing background interference and further enhancing feature discriminability. Stage T4 constructs a target detection head based on the fused multi-scale features. A multilayer perceptron is responsible for mapping and transforming feature dimensions, converting high-dimensional fused features into directly outputtable detection results. A self-attention computation function strengthens the correlation between features, and feature concatenation integrates information from different dimensions, ultimately outputting the target's scale category, location coordinates, and confidence parameters. This entire process, through step-by-step progressive processing, fully leverages the advantages of multi-scale perception, adapts to the variable target scale characteristics in security scenarios, and ensures the comprehensiveness and accuracy of feature extraction.

[0027] Preferably, the modeling process of the security scene transfer learning algorithm in S3 includes the following steps: Q1, extracting common feature dimensions between the source scene and the target scene, including spatial topology, illumination variation range, target motion mode, and security calibration parameters, establishing a scene feature vector library, and storing the convolutional and fully connected layer parameters of the source scene model in layers; Q2, calculating the feature similarity matrix between the source scene and the target scene, aligning the feature distribution under different scenes using a dynamic time warping algorithm, determining the weight coefficients for parameter transfer, and retaining the unique feature mapping layer of the target scene to adapt to scene differences; Q3, constructing a transfer parameter optimization objective function, fine-tuning the transferred model parameters, the expression being... ,in For the total parameter set of the model, The number of samples in the target scene. For loss function, For sample labels, For model prediction function, For the input sample, For migration parameters, To fine-tune the parameters, The regularization coefficient is . It is an L2 norm.

[0028] Specifically, in step S3, the security scene transfer learning algorithm achieves rapid cross-scene adaptation of the model through three sub-steps, Q1 to Q3. In Q1, common feature dimensions between the source and target scenes are extracted, including core security parameters such as spatial topology, illumination variation range, and target motion patterns. These parameters are key factors determining model adaptability. A scene feature vector library is established to systematically store key scene information. Simultaneously, the parameters of the convolutional and fully connected layers of the source scene model are stored hierarchically, providing a foundation for subsequent selective transfer. Convolutional layer parameters are responsible for feature extraction, while fully connected layer parameters are responsible for classification prediction. Hierarchical storage facilitates targeted processing. In Q2, the feature similarity matrix between the source and target scenes is calculated. A dynamic time warping algorithm aligns the feature distribution across different scenes. This algorithm effectively handles the time distortion problem of feature sequences, ensuring the comparability of features across different scenes. Based on the similarity matrix, the weight coefficients for parameter transfer are determined; parameters with higher similarity have greater transfer weights. At the same time, the unique feature mapping layer of the target scene is preserved, specifically adapting to the personalized features of the target scene and avoiding the loss of scene-specific information during the transfer process. The transfer parameter optimization objective function constructed in Phase Q3 focuses on minimizing prediction loss. It calculates the prediction error of target scene samples and uses regularization to constrain the fluctuation range of fine-tuning parameters, preventing overfitting. The values ​​of each parameter in the formula are rigorously calibrated, the sample size is set within a reasonable range based on the complexity of the target scene, the loss function is selected to suit the type of classification and regression task, the regularization coefficient balances the model's fitting effect and generalization ability, and the fine-tuning focuses on optimizing the model's adaptation in the target scene. The entire implementation process, through three stages—common feature extraction, similarity matching, and parameter optimization—achieves efficient reuse of source scene model parameters. Model fine-tuning requires only a small number of target scene samples, significantly reducing the time and resource costs of training models for new scenes. This addresses the drawback of traditional models requiring retraining in different scenes, improving the deployment efficiency and adaptability of security systems in diverse scenarios.

[0029] Preferably, the calculation process of the multi-feature fusion target tracking algorithm in S4 includes the following steps: R1, extracting visual and spatial features of the target from the monitoring data. Visual features include color histograms and texture feature vectors, while spatial features include target bounding box coordinates, motion speed, and acceleration. Dimensional standardization is performed on all features. R2, constructing an adaptive feature weight allocation mechanism to dynamically adjust the contribution ratio of visual and spatial features based on scene complexity. The expression is: ,in To fuse feature weights, For adaptive coefficients, For visual feature weights, R3 represents the spatial feature weights; R4 uses Kalman filtering to predict the target's trajectory, generating the target's position confidence interval for the next frame, and combines the Hungarian algorithm to match the detection results with the predicted trajectory; R5 establishes a trajectory continuity verification mechanism, calculating the cosine similarity and IoU overlap rate of target features in adjacent frames, and completing and correcting broken trajectories, expressed as follows: ,in To match scores, For balance coefficient, The cosine similarity function is used. and These are the features of the current frame and the previous frame, respectively. Intersection over union function and R1 represents the target bounding boxes of the current frame and the previous frame, respectively; R2 integrates the correlation results of multiple frames and outputs the complete motion trajectory of the target, including the position coordinates, motion parameters and feature vector sets at each time point.

[0030] Specifically, in step S4, the multi-feature fusion target tracking algorithm establishes a continuous representation of the target's motion trajectory through five sub-steps, R1 to R5. In R1, visual and spatial features of the target are extracted from the monitoring data. The color histogram and texture feature vector in the visual features reflect the target's appearance, while the target bounding box coordinates, motion velocity, and acceleration in the spatial features characterize the target's motion state. Dimensional standardization is performed on various features to ensure balanced weights for different feature types in the subsequent fusion process, avoiding fusion bias caused by differences in feature dimensions. Feature dimensions are uniformly set to a reasonable range to ensure computational efficiency and feature representation capabilities. In R2, an adaptive feature weight allocation mechanism is constructed, dynamically adjusting the contribution ratio of visual and spatial features based on scene complexity. Scene complexity is comprehensively determined by parameters such as background noise level and target occlusion degree. The adaptive coefficient is dynamically updated according to the real-time scene state, enabling feature fusion to adapt to different scene conditions. The formula design fully considers the complementarity of the two types of features, ensuring that more stable feature types are prioritized in complex scenes. In stage R3, Kalman filtering is used to predict the target's trajectory. This filter can accurately predict the target's position confidence interval in the next frame based on historical motion parameters. The confidence interval range is dynamically adjusted with motion speed and acceleration. Combined with the Hungarian algorithm, optimal matching between the detection results and the predicted trajectory is achieved, solving the data association problem in multi-target tracking. Stage R4 establishes a trajectory continuity verification mechanism. By calculating the cosine similarity and intersection-union ratio of target features in adjacent frames, the continuity of the trajectory is comprehensively evaluated. A reasonable threshold standard is set, and interpolation algorithms are used to complete and correct broken trajectories. The balance coefficient in the formula adjusts the weight of the two similarity indicators to ensure the accuracy of trajectory matching. The introduction of spatiotemporal context information utilizes historical features to enhance the robustness of current frame features, improving trajectory stability under occlusion and interference scenarios. Stage R5 integrates multi-frame association results, outputting a complete motion trajectory including position coordinates, motion parameters, and feature vector sets at each time point. The entire process, through multi-feature fusion, trajectory prediction, and continuity verification, solves the trajectory breakage problem caused by occlusion and environmental interference in traditional tracking, providing reliable trajectory data support for cross-regional trajectory connection and anomaly warning.

[0031] Preferably, step S6 includes extracting the target's motion rate, trajectory offset, and dwell time calibration indicators based on the target trajectory parameters output by S5, comparing them with a preset anomaly judgment threshold, and calculating the anomaly confidence level through weighted summation, expressed as follows: ,in For abnormal confidence levels, The number of anomaly detection indicators. The weight coefficient for the k-th indicator is... This represents the actual measured value of the k-th indicator. This represents the normal threshold for the k-th indicator.

[0032] Specifically, the abnormal event judgment calculation process in step S6, based on the global trajectory data output in step S5, extracts key abnormal judgment indicators such as the target's movement speed, trajectory offset, and dwell time. These indicators comprehensively reflect the rationality of the target's movement. The number of indicators is set to 6 to 8, including multiple dimensions such as the target's speed, trajectory, and time, ensuring the comprehensiveness of the abnormal judgment. Each indicator is assigned a corresponding weight coefficient according to its importance in the security scenario, with the total weight coefficient being 1. The higher the importance of the indicator, the greater the weight. For example, the weight of the intrusion indicator in key prevention and control areas is higher than that of ordinary movement speed indicators. The weight allocation is determined through extensive security scenario case analysis and expert evaluation to ensure the scientific nature of the judgment. The system's preset normal threshold ranges for each indicator are derived from the statistical analysis of massive historical normal trajectory data. By statistically analyzing normal movement data in different security scenarios (such as densely populated areas, open spaces, and key prevention and control areas), reasonable threshold ranges for each scenario are determined. The thresholds support dynamic adjustment and can be updated according to changes in scenarios and prevention and control needs, improving the adaptability of the judgment. Anomaly confidence is calculated using a weighted summation method. The ratio of the actual measured value of each indicator to its corresponding normal threshold is taken as the degree of anomaly for that indicator. This ratio is then combined with weighting coefficients to obtain the overall anomaly confidence. This calculation method comprehensively considers the anomalies of various indicators, avoiding misjudgments caused by a single indicator anomaly. A clearly defined anomaly confidence threshold is set; when the calculated result exceeds this threshold, it is judged as an anomaly event. Furthermore, based on the degree of anomaly of each indicator, three levels are classified: general anomaly, important anomaly, and emergency anomaly. Different levels correspond to different early warning mechanisms. The entire calculation process is logically rigorous, and the parameter values ​​are scientifically and reasonably selected. It can accurately identify anomaly events in security scenarios, solving the problems of high false alarm rates and one-sided judgments in traditional early warning systems, and providing a reliable basis for rapid response and handling.

[0033] Preferably, the method adopts a distributed heterogeneous computing architecture, including an edge-end intelligent monitoring terminal, a regional edge computing node, and a cloud collaborative server. The edge terminal integrates an infrared thermal imaging sensor and a visible light high-definition camera, supporting synchronous acquisition of multispectral data. The regional edge computing node is equipped with an FPGA chip and a GPU cluster to perform parallel computing of the dynamic target multi-scale perception model. A single node supports real-time processing of no less than 32 channels of monitoring video. The cloud server adopts a distributed file storage system to store historical monitoring data and model parameters. The number of convolutional layers in the dynamic target multi-scale perception model is set to 12, the number of feature map channels increases from 64 to 1024, the fine-tuning learning rate of the security scene transfer learning algorithm ranges from 0.001 to 0.01, the feature dimension of the multi-feature fusion target tracking algorithm is unified to 512 dimensions, the node communication latency of cross-regional collaborative analysis is controlled within a preset range, and the abnormal warning response time does not exceed a preset value.

[0034] Preferred, such as Figure 2As shown, step S3 includes the following sub-steps: S31, decompose the inter-layer parameters of the source scene model, separate the feature extraction layer and the classification prediction layer, retain the convolution kernel parameters of the feature extraction layer as the basis for transfer, and randomly initialize the parameters of the classification prediction layer; S32, collect a small number of labeled samples of the target scene to construct a scene adaptation dataset, including target samples under different lighting and occlusion conditions, and the samples cover various motion states and scale changes of the target; S33, based on the scene feature similarity matrix, selectively transfer the parameters of the source scene feature extraction layer, directly reuse the layer parameters with similarity higher than a preset threshold, and partially replace the layer parameters with similarity lower than the threshold; S34, iteratively train the transferred model on the target scene adaptation dataset, update the parameters using a mini-batch gradient descent algorithm, fix the first 80% of the parameters of the feature extraction layer during training, and only fine-tune the last 20% of the parameters and the parameters of the classification prediction layer.

[0035] Specifically, step S3 includes S31, which involves decomposing the inter-layer parameters of the model trained on the source scene, clearly separating the feature extraction layer responsible for basic general feature extraction from the classification prediction layer responsible for scene-specific classification output. The convolutional kernel parameters of the feature extraction layer, containing common features of different scene targets, are fully preserved as the basis for transfer. The parameters of the classification prediction layer are reset using random initialization to avoid interference from the source scene classification logic with the target scene adaptation. S32, 500 to 1000 sets of labeled samples from the target scene are collected to construct an adaptation dataset. The samples cover different lighting intensities, 0 to 80% occlusion ratios, and various motion states of the target, including slow movement, fast running, and stillness. They also include small, medium, and large scale variations to ensure the dataset fully represents the characteristics of the target scene. S33, a selective transfer strategy is implemented based on the scene feature similarity matrix. A similarity threshold of 0.75 is set. Parameters of the feature extraction layer above this threshold are directly reused, while those below the threshold are partially replaced and integrated with unique features of the target scene at a ratio of 30% to 50%, achieving the fusion of general and specific features. S34 trains the transferred model on the adaptation dataset for 200 to 500 iterations, using mini-batch gradient descent to update parameters. During training, the first 80% of the parameters in the feature extraction layer are fixed to retain general extraction capabilities, while only the last 20% of parameters and the classification prediction layer parameters are fine-tuned. This balances model stability and scene adaptability. The entire step-by-step process significantly reduces the target scene sample requirements and training costs, while improving adaptation efficiency and accuracy.

[0036] Preferred, such as Figure 3As shown, S4 includes the following sub-steps: S41, using a multi-scale feature pyramid structure to extract shallow texture features and deep semantic features of the target. Shallow features are obtained through 1 to 3 layers of convolution, and deep features are obtained through 6 to 8 layers of convolution and pooling operations. Features of each layer interact through lateral connections; S42, constructing a feature attention mechanism module to allocate channel weights to the extracted multi-scale features, enhance the response strength of the target calibration features, and suppress the interference of background noise features; S43, introducing spatiotemporal context information, fusing the target features of the current frame with the historical features of the previous 5 frames, and modeling the temporal correlation of the target motion through a recurrent neural network to improve the robustness of feature representation.

[0037] Specifically, step S4 optimizes the implementation process of the multi-feature fusion target tracking algorithm through three sub-steps, S41 to S43, enhancing the comprehensiveness and robustness of target feature representation. S41 employs a multi-scale feature pyramid structure to extract features, clearly defining the extraction paths for shallow and deep features. Shallow texture features are obtained through 1 to 3 layers of convolution operations, accurately capturing basic appearance details such as target edges and contours. Deep semantic features are extracted through a combination of 6 to 8 layers of convolution and pooling operations, focusing on the target's high-level semantic and categorical essential features. Features at each layer achieve real-time information interaction through a lateral connection mechanism, allowing shallow details and deep semantics to complement each other. The number of feature map channels increases from 64 to 512, improving feature dimensionality and representational capabilities. S42 constructs a feature attention mechanism module, assigning channel-level weights to multi-scale features. By analyzing the response intensity of each feature channel in security scenarios, the module assigns high weights (0.6 to 0.9) to channels containing key target information, strengthening effective feature responses, suppressing background noise interference, and improving feature discriminative power. S43 introduces spatiotemporal context information, fusing the target features of the current frame with the historical features of the previous 5 frames. It models the temporal correlation of the target motion through a recurrent neural network, making full use of historical motion states to assist in the feature expression of the current frame. This effectively alleviates the feature distortion problem in complex scenarios such as occlusion and lighting changes, and improves the robustness of feature expression. The three steps, from feature extraction and weight optimization to spatiotemporal fusion, are progressively advanced to ensure the comprehensiveness, relevance, and stability of the target features, providing high-quality feature support for subsequent trajectory association matching.

[0038] Preferred, such as Figure 4As shown, step S5 includes the following sub-steps: S51, establishing a time synchronization mechanism for cross-regional monitoring nodes, and calibrating the timestamps of the monitoring data of each node through the network time protocol to ensure the time consistency of cross-regional data; S52, extracting the target trajectory data output by each regional node, including trajectory ID, timestamp, and location coordinate calibration information, and constructing a cross-regional trajectory index library; S53, based on the target's feature vector and motion parameters, using the nearest neighbor algorithm to perform association matching of the same target's trajectories in different regions, and calculating the feature similarity and motion continuity of cross-regional trajectories; S54, smoothing the associated cross-regional trajectories to eliminate trajectory jitter during node switching, and completing trajectory breakpoints through interpolation algorithms; S55, generating a globally unified target trajectory report, including the target's motion path, dwell time, and cross-regional transfer time in each region.

[0039] Specifically, step S5 utilizes a step-by-step cross-regional trajectory connection and verification process to achieve global and precise tracking of the target trajectory. S51 establishes a cross-regional monitoring node time synchronization mechanism, using a network time protocol to timestamp the monitoring data of each node, with calibration accuracy controlled within 10 milliseconds, ensuring cross-regional data time consistency and laying the time foundation for trajectory connection. S52 extracts trajectory data output from each regional node, including key information such as trajectory ID, timestamp, location coordinates, and movement speed. A cross-regional trajectory index is constructed hierarchically by regional ID and trajectory ID, with the index retrieval response time controlled within 50 milliseconds, improving data processing efficiency. S53, based on the target's 512-dimensional feature vector and motion parameters, uses the nearest neighbor algorithm to achieve cross-regional trajectory association matching for the same target. It calculates feature cosine similarity and motion continuity scores, setting a similarity threshold of 0.8 and a continuity threshold of 0.6 to double-screen effective associated trajectory pairs, ensuring matching accuracy. S54 smooths the associated trajectory, employing a moving average algorithm to eliminate trajectory jitter caused by node switching, and using a linear interpolation algorithm to complete trajectory breakpoints, with the interpolation interval controlled within 2 frames to achieve smooth transitions across regions. Simultaneously, consistency checks are performed, removing abnormal data with motion parameter fluctuations exceeding 30% to ensure trajectory rationality. S55 generates a globally unified trajectory report, including information such as the target's movement path, dwell time, and cross-regional transfer time in each region. This information is structured and stored in chronological order and synchronized to the cloud server, providing complete global trajectory data for subsequent anomaly analysis. This step-by-step process breaks down regional information barriers, enabling continuous and accurate target tracking across regions.

[0040] like Figure 5As shown, the intelligent security multi-scenario monitoring and anomaly early warning system is applied to intelligent security multi-scenario monitoring and anomaly early warning methods. It includes: a multimodal monitoring data acquisition and preprocessing unit, a dynamic target multi-scale feature perception and extraction unit, a cross-scenario model parameter transfer and adaptation unit, a multi-feature fusion target trajectory tracking unit, a cross-regional collaborative analysis and trajectory verification unit, and an abnormal event intelligent judgment and early warning unit. The multimodal monitoring data acquisition and preprocessing unit collects multi-scenario monitoring data through a distributed sensor network and outputs it to the dynamic target multi-scale feature perception and extraction unit. The dynamic target multi-scale feature perception and extraction unit extracts features from the input data and transmits the results to the cross-scenario model parameters. The migration and adaptation unit performs cross-scene model parameter migration and optimization, and outputs the adapted feature data to the multi-feature fusion target trajectory tracking unit. The multi-feature fusion target trajectory tracking unit continuously tracks the target trajectory and sends the trajectory data to the cross-regional collaborative analysis and trajectory verification unit. The cross-regional collaborative analysis and trajectory verification unit integrates multi-regional trajectory data and performs consistency verification, and outputs global trajectory data to the abnormal event intelligent judgment and early warning unit. The abnormal event intelligent judgment and early warning unit performs abnormal judgment based on preset thresholds and global trajectory data, triggers corresponding early warning operations, and each unit interacts and collaborates through high-speed Ethernet and a distributed bus.

[0041] The intelligent security multi-scenario monitoring and anomaly early warning processing method and system accurately captures multi-level features of targets in different scenarios through a dynamic target multi-scale perception mechanism. Even in the face of complex environmental interference, it can stably extract key information, completely solving the problem of insufficient scenario adaptability of traditional technologies. Through security scenario transfer learning technology, it efficiently reuses source scenario model parameters, requiring only a small number of target scenario samples to complete adaptation, significantly reducing the time and resource costs of deploying new scenarios and avoiding the cumbersome process of retraining required by traditional technologies. At the same time, the multi-feature fusion target tracking technology ensures the continuity and integrity of target trajectories through multi-dimensional feature association and trajectory verification. Combined with a cross-regional collaborative analysis platform, it realizes multi-node data integration and trajectory connection, breaking the information disconnect and trajectory breakage dilemma caused by the fragmented deployment of traditional systems, forming a global monitoring perspective.

[0042] This invention employs a distributed heterogeneous computing architecture, combined with software-level parameter optimization configuration, to achieve real-time processing of monitoring data and rapid response to abnormal events, significantly improving the operational efficiency and practical effectiveness of security systems. Each functional unit forms a full-process collaborative mechanism through efficient data interaction, from multimodal data acquisition, multi-scale feature extraction, and cross-scenario parameter adaptation to trajectory tracking, cross-regional verification, and anomaly early warning; each link precisely addresses actual security needs. Through refined designs such as layered parameter migration, feature attention enhancement, and spatiotemporal context fusion, model adaptability and trajectory tracking accuracy are further improved, ensuring comprehensive coverage of multi-scenario monitoring and strengthening cross-regional collaborative prevention and control capabilities. This addresses the shortcomings of traditional technologies in scenario adaptation and collaborative analysis, providing a more reliable and efficient solution for intelligent security.

[0043] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent security multi-scenario monitoring and anomaly early warning processing, characterized in that, Includes the following steps: S1, collects monitoring data from multiple scenarios through a cross-regional security collaborative intelligent analysis platform. The monitoring data includes spatial distribution information of dynamic targets, motion state parameters, and scene environment characteristics. S2, invoke the dynamic target multi-scale perception model to extract target-level features from the collected monitoring data, and construct a multi-dimensional perception channel based on the target scale change range under different scenarios; S3 uses a security scene transfer learning algorithm to transfer the model parameters obtained from training in the source scene to the current target scene, enabling the model to quickly adapt to different scenes. S4. The multi-feature fusion target tracking algorithm is used to perform spatiotemporal correlation matching on the extracted target features to establish a continuous representation of the target's motion trajectory. S5 integrates multi-node monitoring data through a cross-regional security collaborative intelligent analysis platform to perform cross-regional connection and consistency verification of target trajectories. S6. Based on the comparison results between the target motion parameters and the preset threshold, generate the abnormal event judgment result and trigger the corresponding early warning mechanism.

2. The intelligent security multi-scenario monitoring and anomaly early warning processing method according to claim 1, characterized in that, The calculation of the dynamic target multi-scale perception model in S2 includes the following steps: T1, selecting the scale range, motion rate, and contour complexity of the target in the security scene as the basic parameters for modeling, constructing a multi-scale feature sampling grid, and obtaining local features of the target at different levels by traversing the monitoring data through a sliding window, wherein the grid density is positively correlated with the target scale range; T2, performing spiking neuron conversion processing on the local features at each level, converting continuous feature signals into discrete pulse tensors through the LIF mechanism, and establishing a mapping relationship between features and pulse firing frequency, expressed as: ,in For the pulse characteristic tensor, The convolution weight matrix is... For local feature matrices, For bias terms, For impulse activation functions, This is the pulse attenuation coefficient; T3 constructs a pulse pyramid fusion module, performing upsampling and downsampling operations on pulse feature tensors of different scales. Multi-scale feature complementarity is enhanced through cross-layer connections, and a local sliding window self-attention mechanism is introduced to strengthen the target calibration region features. T4 constructs a target detection head based on the fused multi-scale features. The feature dimensions are mapped using a multilayer perceptron, outputting the target's scale category, location coordinates, and confidence parameters, expressed as follows: ,in For the set of detection results, To fuse feature tensors, MLP stands for Multilayer Perceptron Operator, and Attention is the self-attention computation function. For feature splicing operators.

3. The intelligent security multi-scenario monitoring and anomaly early warning processing method according to claim 1, characterized in that, The modeling process of the security scene transfer learning algorithm in S3 includes the following steps: Q1, extract the common feature dimensions of the source scene and the target scene, including spatial topology, illumination variation range, target motion mode security calibration parameters, establish a scene feature vector library, and store the convolutional layer and fully connected layer parameters of the source scene model in layers; Q2, calculate the feature similarity matrix of the source scene and the target scene, align the feature distribution under different scenes through dynamic time warping algorithm, determine the weight coefficient of parameter transfer, and at the same time retain the unique feature mapping layer of the target scene to adapt to scene differences; Q3, Construct the objective function for optimizing the transfer parameters, and fine-tune the transferred model parameters during training. The expression is: ,in For the total parameter set of the model, The number of samples in the target scene. For loss function, For sample labels, For model prediction function, For the input sample, For migration parameters, To fine-tune the parameters, The regularization coefficient is . It is an L2 norm.

4. The intelligent security multi-scenario monitoring and anomaly early warning processing method according to claim 1, characterized in that, The calculation process of the multi-feature fusion target tracking algorithm in S4 includes the following steps: R1, extracting visual and spatial features of the target from the monitoring data. Visual features include color histograms and texture feature vectors, while spatial features include target bounding box coordinates, motion velocity, and acceleration. Dimensional standardization is performed on all features. R2, constructing an adaptive feature weight allocation mechanism to dynamically adjust the contribution ratio of visual and spatial features based on scene complexity. The expression is: ,in To fuse feature weights, For adaptive coefficients, For visual feature weights, R3 represents the spatial feature weights; R4 uses Kalman filtering to predict the target's trajectory, generating the target's position confidence interval for the next frame, and combines the Hungarian algorithm to match the detection results with the predicted trajectory; R5 establishes a trajectory continuity verification mechanism, calculating the cosine similarity and IoU overlap rate of target features in adjacent frames, and completing and correcting broken trajectories, expressed as follows: ,in To match scores, For balance coefficient, The cosine similarity function is used. and These are the features of the current frame and the previous frame, respectively. Let be the intersection-union ratio function. and These are the bounding boxes for the current frame and the previous frame, respectively. R5 integrates the correlation results of multiple frames and outputs the complete motion trajectory of the target, including the position coordinates, motion parameters and feature vector set at each time.

5. The intelligent security multi-scenario monitoring and anomaly early warning processing method according to claim 1, characterized in that, S6 includes extracting the target's motion rate, trajectory offset, and dwell time calibration indicators based on the target trajectory parameters output by S5, comparing them with a preset anomaly detection threshold, and calculating the anomaly confidence level through weighted summation, expressed as follows: ,in For abnormal confidence levels, The number of anomaly detection indicators. The weight coefficient for the k-th indicator is... This represents the actual measured value of the k-th indicator. This represents the normal threshold for the k-th indicator.

6. The intelligent security multi-scenario monitoring and anomaly early warning processing method according to claim 1, characterized in that, This method adopts a distributed heterogeneous computing architecture, including an edge-end intelligent monitoring terminal, a regional edge computing node, and a cloud collaborative server. The edge terminal integrates an infrared thermal imaging sensor and a visible light high-definition camera, supporting synchronous acquisition of multispectral data. The regional edge computing node is equipped with an FPGA chip and a GPU cluster to perform parallel computing of dynamic target multi-scale perception models. A single node supports real-time processing of no less than 32 channels of monitoring video. The cloud server uses a distributed file storage system to store historical monitoring data and model parameters. The number of convolutional layers in the dynamic target multi-scale perception model is set to 12, and the number of feature map channels increases from 64 to 1024. The fine-tuning learning rate of the security scene transfer learning algorithm ranges from 0.001 to 0.

01. The feature dimension of the multi-feature fusion target tracking algorithm is unified to 512 dimensions. The node communication latency of cross-regional collaborative analysis is controlled within a preset range, and the abnormal warning response time does not exceed a preset value.

7. The intelligent security multi-scenario monitoring and anomaly early warning processing method according to claim 1, characterized in that, S3 includes the following sub-steps: S31, decompose the inter-layer parameters of the source scene model, separate the feature extraction layer and the classification prediction layer, retain the convolution kernel parameters of the feature extraction layer as the basis for transfer, and randomly initialize the parameters of the classification prediction layer; S32, collect a small number of labeled samples of the target scene to construct a scene adaptation dataset, including target samples under different lighting and occlusion conditions, and the samples cover various motion states and scale changes of the target; S33, based on the scene feature similarity matrix, selectively transfer the parameters of the source scene feature extraction layer, directly reuse the layer parameters with similarity higher than a preset threshold, and partially replace the layer parameters with similarity lower than the threshold; S34, iteratively train the transferred model on the target scene adaptation dataset, update the parameters using a mini-batch gradient descent algorithm, fix the first 80% of the parameters of the feature extraction layer during training, and only fine-tune the last 20% of the parameters and the parameters of the classification prediction layer.

8. The intelligent security multi-scenario monitoring and anomaly early warning processing method according to claim 1, characterized in that, S4 includes the following sub-steps: S41, using a multi-scale feature pyramid structure to extract shallow texture features and deep semantic features of the target. Shallow features are obtained through 1 to 3 layers of convolution, and deep features are obtained through 6 to 8 layers of convolution and pooling operations. Features of each layer interact with each other through lateral connections; S42, constructing a feature attention mechanism module to allocate channel weights to the extracted multi-scale features, enhance the response strength of the target labeling features, and suppress the interference of background noise features; S43 introduces spatiotemporal context information, fuses the target features of the current frame with the historical features of the previous 5 frames, and improves the robustness of feature representation by modeling the temporal correlation of target motion through a recurrent neural network.

9. The intelligent security multi-scenario monitoring and anomaly early warning processing method according to claim 1, characterized in that, S5 includes the following steps: S51, establishing a time synchronization mechanism for cross-regional monitoring nodes, and calibrating the timestamps of the monitoring data of each node through the network time protocol to ensure the time consistency of cross-regional data; S52, Extract target trajectory data output from nodes in each region, including trajectory ID, timestamp, and location coordinate calibration information, and construct a cross-regional trajectory index library; S53, Based on the target's feature vector and motion parameters, use the nearest neighbor algorithm to match the trajectory of the same target in different regions, and calculate the feature similarity and motion continuity of the cross-regional trajectories; S54, Smooth the associated cross-regional trajectories to eliminate trajectory jitter during node switching, and fill in trajectory breakpoints using an interpolation algorithm; S55, Generate a globally unified target trajectory report, including the target's motion path, dwell time, and cross-regional transfer time in each region.

10. An intelligent security multi-scenario monitoring and anomaly early warning system, characterized in that, The system is applied to the intelligent security multi-scenario monitoring and anomaly early warning processing method described in claim 1, comprising: a multimodal monitoring data acquisition and preprocessing unit, a dynamic target multi-scale feature perception and extraction unit, a cross-scenario model parameter transfer and adaptation unit, a multi-feature fusion target trajectory tracking unit, a cross-regional collaborative analysis and trajectory verification unit, and an abnormal event intelligent judgment and early warning unit; the multimodal monitoring data acquisition and preprocessing unit acquires multi-scenario monitoring data through a distributed sensor network and outputs it to the dynamic target multi-scale feature perception and extraction unit; the dynamic target multi-scale feature perception and extraction unit extracts features from the input data and transmits the results to the cross-scenario model parameter transfer and adaptation unit; The cross-scene model parameter migration and adaptation unit completes the cross-scene migration and optimization of model parameters, and outputs the adapted feature data to the multi-feature fusion target trajectory tracking unit; the multi-feature fusion target trajectory tracking unit continuously tracks the target trajectory and sends the trajectory data to the cross-regional collaborative analysis and trajectory verification unit; the cross-regional collaborative analysis and trajectory verification unit integrates multi-regional trajectory data and performs consistency verification, and outputs global trajectory data to the abnormal event intelligent judgment and early warning unit; the abnormal event intelligent judgment and early warning unit performs abnormal judgment based on preset thresholds and global trajectory data, triggers corresponding early warning operations, and each unit interacts and collaborates through high-speed Ethernet and a distributed bus.

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