Intelligent monitoring system and method

Abstract

The present invention relates to an intelligent monitoring system and method for community security control. The system captures the visual and environmental data of the community through sensor devices, and uses these data to create and update a digital twin model. The sensor device includes a camera and a variety of environmental sensors. The data processing unit is responsible for receiving and analyzing the data transmitted from the sensor device, and using a machine learning algorithm to process the data in the digital twin model to identify potential security threats and predict their development trends. The event response unit initiates necessary security measures based on the processing results. In addition, the system also includes a user interaction interface that allows community managers and residents to view the digital twin model and related security information in real time, enhancing the user's sense of participation and the transparency of the system. The present invention provides a highly integrated, data-driven security control solution for the community, effectively improving the intelligence and response efficiency of community security management.

Description

Technical Field

[0001] The present invention relates to the field of monitoring technology, and in particular to an intelligent monitoring system and method. Background Art

[0002] In existing technologies, community security monitoring systems generally use cameras and basic sensor devices, such as door and window sensors, to monitor and record activities within the community. These systems are usually able to capture video data and rely on central monitoring systems to analyze the data in real time to detect abnormal behavior or security threats. With the advancement of technology, some systems have begun to adopt more advanced sensor technologies, such as infrared sensing and temperature change detection, to improve the speed and accuracy of response to potential security risks.

[0003] However, despite technological advances, existing community safety monitoring systems still have several important problems. First, these systems often lack the ability to comprehensively analyze environmental data and cannot fully utilize the collected environmental data such as temperature, humidity, and smoke to predict and prevent safety accidents caused by the environment in advance. In addition, the user interface usually does not provide sufficient information and control options, which limits the participation of residents and managers and their ability to control system operations.

[0004] Therefore, there is an urgent need to develop a new type of intelligent monitoring system and method. Summary of the invention

[0005] The present application provides an intelligent monitoring system and method to improve the intelligence and response efficiency of community security management.

[0006] This application provides an intelligent monitoring system for community security prevention and control, including:

[0007] Sensor devices, including cameras and environmental sensors, are distributed at key locations in the community to capture visual data and environmental data within the community; wherein the environmental sensors include temperature sensors, humidity sensors, smoke detectors, sound sensors, wind direction sensors, wind speed sensors, and rainfall sensors; and the environmental data include temperature data, humidity data, smoke level data, sound data, wind speed data, wind speed data, and rainfall;

[0008] A data processing unit connected to the sensor device, for receiving visual data and environmental data transmitted from the sensor device; creating and updating a digital twin model of the community based on the received visual data and environmental data; using a trained hybrid neural network model to process the received visual data and environmental data, identify potential security threats and predict their possible development trends; updating the digital twin model according to the potential security threats identified by the data processing unit and the prediction results of their development trends;

[0009] an event response unit, connected to the data processing unit, for initiating corresponding security measures according to the potential security threats identified by the data processing unit and the predicted results of their development trends; wherein the security measures include notifying security personnel or initiating emergency evacuation procedures;

[0010] A user interface allows community managers and residents to view the digital twin model and real-time safety information; and provides an input interface for submitting safety improvement suggestions and feedback information.

[0011] Furthermore, the hybrid neural network model includes a temporal convolutional network module, a recurrent generative adversarial network module and a graph convolutional network module; wherein the temporal convolutional network module is used to receive a video image sequence collected from a camera, and uses a temporal convolutional neural network to extract relevant features in the video image sequence to obtain a continuous visual feature representation in the time dimension; the recurrent generative adversarial network module is used to receive time series environmental data from environmental sensors, and uses a recurrent generative adversarial network to process the time series environmental data to generate prediction data for future environmental states; the graph convolutional network module is used to receive the continuous visual feature representation provided by the temporal convolutional network module and the prediction data provided by the recurrent generative adversarial network module, and uses a graph convolutional network to process the received data to obtain security threat assessment and development trend prediction.

[0012] Furthermore, the temporal convolutional network module includes three convolutional layers, each of which adopts batch normalization and ReLU activation function, and applies residual connection technology; wherein, the first convolutional layer is used to preliminarily extract basic visual features in the video image sequence; the second convolutional layer is used to identify edges and textures in the video image sequence; the third convolutional layer is used to capture the speed and direction of moving objects in the video image sequence; the output of the third convolutional layer is connected to a fully connected layer, which converts the convolutional features into continuous visual feature representations in the time dimension.

[0013] Furthermore, the recurrent generative adversarial network module includes a generator and a discriminator; wherein the generator is composed of three consecutive LSTM layers, each LSTM layer is used to gradually process and predict the future environmental state; each LSTM layer is followed by a batch normalization layer and a ReLU activation layer to improve the responsiveness and stability of the model; the discriminator is composed of two LSTM layers, which is used to evaluate the authenticity of the generator output and force the generator to produce a predicted output close to the real environmental data; the generator and the discriminator promote each other through adversarial training to improve the accuracy of the prediction of the future environmental state.

[0014] Furthermore, the graph convolutional network module includes a first graph convolutional layer, a second graph convolutional layer and a third graph convolutional layer connected in sequence, wherein the first graph convolutional layer receives the continuous visual feature representation provided by the temporal convolutional network module and the prediction data provided by the cyclic generative adversarial network module, and performs basic feature aggregation and update operations on each graph node, and enhances the preliminary recognition of the interaction between community key points by aggregating the features of the direct neighbor nodes of each node; the second graph convolutional layer is used to process the output of the first graph convolutional layer, and enhance the intermediate feature expression of the propagation path of potential security incidents; the third graph convolutional layer receives the processed features from the second layer, and performs final feature refinement and aggregation to provide a deeper level of recognition of the interaction between community key points;

[0015] Furthermore, the graph convolutional network module also includes an attention layer based on an attention mechanism, which is used to receive the output of the third graph convolutional layer and dynamically adjust the weights when aggregating neighbor node features.

[0016] Furthermore, the data processing unit includes a real-time data fusion module for synchronizing and fusing the visual data and environmental data received from the sensor device; the real-time data fusion module uses augmented reality technology to annotate and display objects identified in the visual data and events detected in the environmental data in the digital twin model.

[0017] Furthermore, the user interaction interface includes a simulation function, which allows the user to simulate event results through a digital twin model based on current environmental data and historical event data to evaluate the possible effects of different response strategies.

[0018] Furthermore, the user interface includes an emergency communication channel that is automatically activated when a high-level security threat is identified, allowing community managers and residents to communicate directly with the nearest emergency service provider.

[0019] This application provides an intelligent monitoring method for community security prevention and control, including:

[0020] Deploy sensor devices at key locations in the community to capture visual data and environmental data within the community, wherein the sensor devices include cameras and environmental sensors; the environmental sensors include temperature sensors, humidity sensors, smoke detectors, sound sensors, wind direction sensors, wind speed sensors, and rainfall sensors;

[0021] Creating and updating a digital twin model of the community based on the captured visual data and environmental data; the digital twin model is used to map and simulate the real-time status of the community;

[0022] A trained hybrid neural network model is used to analyze the collected visual data and environmental data to identify potential security threats and predict their possible development trends; wherein the hybrid neural network model includes a temporal convolutional network module, a recurrent generative adversarial network module, and a graph convolutional network module; wherein the temporal convolutional network module is used to process video image sequences; the recurrent generative adversarial network module is used to generate predictions of future environmental states; and the graph convolutional network module is used to integrate visual features and prediction data to conduct security threat assessment and development trend prediction;

[0023] Based on the identified potential security threats and the predicted results of their development trends, the digital twin model is dynamically updated and corresponding safety measures are initiated, including notifying security personnel or initiating emergency evacuation procedures.

[0024] The beneficial effects of the present application mainly include: (1) The system can comprehensively monitor the environmental status of the community by integrating multiple environmental sensors (such as temperature, humidity, smoke, sound, wind direction, wind speed and rainfall sensors). Such comprehensive data capture capabilities enable the system to respond faster and more accurately to environmental changes, and to warn of potential environmental safety issues in advance, rather than being limited to traditional visual monitoring. (2) By using machine learning algorithms to process data collected by sensor devices and cameras and analyzing them in digital twin models, the system can effectively identify and predict potential security threats and their possible development trends. This advanced data processing capability improves the accuracy of predictions and allows the system to perform real-time security management and response. (3) The event response unit can respond quickly based on the data analysis results received from the data processing unit, including notifying security personnel or initiating emergency evacuation procedures. This rapid response mechanism greatly improves the ability to handle emergencies and reduces possible personal injury and property losses. (4) Through the user interaction interface, community managers and residents can not only view the digital twin model and safety information in real time, but also submit safety improvement suggestions and feedback information through the input interface. This enhances residents' safety awareness and participation, and also makes community safety management more transparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of an intelligent monitoring system provided in the first embodiment of the present application.

[0026] Figure 2 This is a flow chart of an intelligent monitoring method provided in the second embodiment of the present application. DETAILED DESCRIPTION

[0027] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0028] The first embodiment of the present application provides an intelligent monitoring system. Figure 1 , which is a schematic diagram of the first embodiment of the present application. Figure 1 A first embodiment of the present application provides an intelligent monitoring system for detailed description.

[0029] The intelligent monitoring system is used for community security prevention and control, and includes a sensor device 101, a data processing unit 102, an event response unit 103 and a user interaction interface 104.

[0030] The sensor device 101, including cameras and environmental sensors, is distributed at key locations in the community and is used to capture visual data and environmental data within the community; wherein the environmental sensors include temperature sensors, humidity sensors, smoke detectors, sound sensors, wind direction sensors, wind speed sensors and rainfall sensors; and the environmental data includes temperature data, humidity data, smoke level data, sound data, wind speed data, wind speed data and rainfall.

[0031] Key locations in a community are those locations that are critical to the safety and effectiveness of the community. These locations are selected based on a number of factors, including traffic flow, residential concentration areas, entrances and exits, and the location of important facilities. The following are some common examples of key locations in a community:

[0032] 1. Entrances and exits: These are the main passages for people and vehicles in and out of the community. Monitoring these areas can help track all individuals and vehicles entering and leaving, increasing protection against unauthorized entry.

[0033] 2. Public areas: such as parks, playgrounds, sports facilities, community centers, etc. These areas usually have a large flow of people, and monitoring these areas helps prevent crime and ensure the safety of residents.

[0034] 3. Parking lots: Places where vehicles are concentrated are potential high-incidence areas of crime. Monitoring can help prevent theft, damage and timely detect any suspicious activities.

[0035] 4. Important facilities: such as water treatment facilities and other infrastructure, the safety of these facilities is directly related to the quality of life and safety of community residents.

[0036] 5. Boundary Areas: Monitoring the boundaries of your community can prevent illegal intrusions and other potential external threats.

[0037] 6. Traffic arteries: major streets and intersections. Monitoring these locations can help manage traffic flow and respond to traffic incidents in a timely manner.

[0038] 7. Building entrances and elevator entrances: Especially in apartment or office buildings, these points are key for security monitoring to protect the safety of residents and employees.

[0039] When implementing a security surveillance system, the specifics of each community may be different, so the selection of key locations should be customized based on the specific layout of the community, the needs of the residents, historical safety records, and potential risks. By installing cameras and environmental sensors at these key locations, the overall security of the community can be greatly enhanced, effectively preventing and responding to various emergencies in a timely manner.

[0040] In the intelligent monitoring system provided in this embodiment, the sensor device 101 includes a group of cameras and multiple environmental sensors, which are distributed in key locations in the community to capture visual and environmental data within the community.

[0041] First, cameras are used in this system to capture video data and provide real-time visual monitoring capabilities. These cameras should have high-resolution video recording capabilities and be able to operate in a variety of lighting conditions, including night vision. The cameras should also be able to perform remote control, such as angle adjustment and zoom functions, so that the coverage area can be adjusted from a central control system.

[0042] Environmental sensors include temperature sensors, humidity sensors, smoke detectors, sound sensors, wind direction sensors, wind speed sensors, and rain sensors. The specific functions of these sensors are as follows:

[0043] Temperature sensors are used to monitor ambient temperature, and the data can help identify fires or other temperature anomalies.

[0044] Humidity sensors provide ambient humidity information and are necessary for monitoring humid conditions or preventing problems caused by humidity.

[0045] Smoke detectors detect smoke particles in the air and are a key component in early fire warning.

[0046] Acoustic sensors capture sounds in the environment and can be used to identify unusual noises, such as breaking glass or other suspicious activity.

[0047] Wind direction and speed sensors are used to record the direction and speed of the wind, which is important for monitoring natural events and taking preventive measures.

[0048] Rain sensors measure precipitation and are essential for flood warning systems and stormwater drainage management.

[0049] The output data of these sensors include but are not limited to temperature data, humidity data, smoke level, sound level, wind direction, wind speed, rainfall, etc. Each sensor needs to have high sensitivity and reliability to accurately collect data under various climate conditions.

[0050] The sensor devices 101 are connected to the data processing unit 102 via a wired or wireless network, which is responsible for receiving and processing all the collected visual and environmental data. The selection and layout of sensors should be optimized according to the specific needs and geographical characteristics of the community to ensure that all key areas are covered while ensuring real-time transmission and processing of data. In addition, all sensors must be easy to install and maintain so that they can be easily replaced or upgraded when necessary.

[0051] The data processing unit 102 is connected to the sensor device and is used to receive the visual data and environmental data transmitted from the sensor device; based on the received visual data and environmental data, create and update the digital twin model of the community; use the trained neural network model to process the received visual data and environmental data, identify potential security threats and predict their possible development trends; update the digital twin model according to the potential security threats identified by the data processing unit and the prediction results of their development trends.

[0052] A digital twin is a highly detailed virtual model that uses data from the physical world to create a digital replica of a real system or object. This technology is mainly used to simulate, analyze and predict the performance of complex systems so that they can be tested and optimized without affecting the real system. Digital twin models usually contain a large amount of real-time data, which is collected from its physical counterpart through sensors and used to update the model to reflect real-time conditions.

[0053] The creation of a digital twin model is a complex process involving multiple technical steps, from data collection to real-time updating of the model. The following are the general steps for creating a digital twin model:

[0054] Before creating a digital twin model, you first need to define the purpose and scope of the model. This may include determining the physical objects or systems (such as buildings, equipment, infrastructure, entire ecosystems, etc.) that the model needs to simulate and monitor, as well as determining which functions and performance indicators the model needs to focus on.

[0055] The establishment of a digital twin model relies on a large amount of accurate data that reflects all aspects of the physical entity to be modeled. The data can come from sensors, field operation data, historical operation and maintenance records, physical test results, etc. For example, if the model is to monitor and maintain a factory production line, it is necessary to collect data such as the machine's operating speed, temperature, pressure, and failure rate.

[0056] Using the data collected, a virtual model is built using software tools. This step usually involves computer-aided design (CAD) software to build a detailed three-dimensional model. The model should be detailed enough to accurately reflect the physical properties and behavior of the entity. In some cases, engineering simulation software is also required to further simulate complex physical reactions and interactions.

[0057] Integrating collected real-time and historical data with virtual models enables the models to display current operating status and simulate future operations. This often requires data fusion techniques and advanced data analysis techniques, such as machine learning algorithms, to help predict and optimize system performance.

[0058] Before a model can be run, it must be calibrated and validated to ensure its accuracy and reliability. This may include comparing it to actual operational data to confirm that the model’s output is consistent with actual observations. This step is critical to ensuring that the model can reliably guide decision making.

[0059] Once the model is verified to be correct, it will be used in actual operations. Users can use the model to monitor the real-time operating status of the system, perform fault diagnosis, optimize operations and predict future maintenance needs. The model needs to be continuously updated and improved based on new data inputs to maintain its accuracy and relevance.

[0060] The digital twin model is a dynamic tool that needs to be constantly adjusted and optimized based on new technical information and operational experience. Continuous optimization helps improve the effectiveness of the model so that it can better serve its design purpose.

[0061] The key to digital twin technology is its ability to provide real-time, detailed feedback and deep insights to help people better understand and optimize their systems or products.

[0062] The data processing unit 102 is the core component of the intelligent monitoring system, responsible for processing the large amount of visual and environmental data collected from the sensor device 101 and performing data analysis and response tasks. The unit consists of both hardware and software to support efficient and real-time data processing and decision making.

[0063] In terms of hardware, the data processing unit 102 needs to be equipped with high-performance computing hardware, including but not limited to multi-core processors, high-speed memory and large-capacity storage systems. These hardware resources ensure that the unit can quickly process complex data from key locations in the community, including video streams and data collected from a variety of environmental sensors such as temperature, humidity, smoke, sound, wind direction, wind speed and rainfall sensors.

[0064] In terms of software, the data processing unit 102 runs advanced operating systems and software applications designed for data analysis, which can receive and integrate input data in different formats. In addition, the unit's built-in software also includes machine learning and data analysis algorithms, which can process and analyze input data to build and continuously update the digital twin model of the community. The digital twin model is a virtual model that fully reflects the state of the physical community and can display the current state and changes of data and events in real time.

[0065] In the intelligent monitoring system, creating and updating the digital twin model of the community is a core function that relies on the visual and environmental data received from sensor devices. The creation of the initial digital twin model begins with the comprehensive collection of visual and environmental data, including but not limited to video streams, temperature, humidity, smoke concentration, sound level, wind direction, wind speed, and rainfall at key locations in the community.

[0066] First, the system collects data through cameras and environmental sensors installed at key points in the community. Cameras provide real-time visual monitoring data, such as videos of people and vehicles, and daily activities, while environmental sensors provide key environmental indicators such as temperature and humidity. After the data is first collected, it will be sent to the data processing unit for preliminary screening and analysis.

[0067] In the data processing unit, machine learning algorithms process this integrated data for the first time to build a preliminary community model. This includes identifying the community's geographic layout, building locations, major transportation routes, and common resident activity patterns. The purpose of this phase is to create a basic, static community framework that will serve as the basis for the digital twin model.

[0068] First, the system needs to collect and integrate the community's visual and environmental data. Visual data mainly comes from cameras distributed in key locations in the community, capturing real-time images of roads, buildings, and public spaces. Environmental data includes information collected from devices such as temperature sensors, humidity sensors, and smoke detectors. These data provide the necessary raw information for building the model.

[0069] Next, the data is fed into a data processing unit, where machine learning algorithms begin their first analytical work. Generally, algorithms such as neural networks or decision trees can be used to process and interpret this data. The algorithm's task is to identify the geographical layout, the outline and location of buildings, the main traffic routes, and the flow of people and other relevant patterns perceived from the environmental data from the visual data.

[0070] For example, by analyzing video data collected from cameras, algorithms can identify the width of the road, the direction and frequency of traffic, the structure and relative position of buildings, etc. At the same time, combined with data provided by environmental sensors, such as temperature and humidity changes, it can help depict the environmental conditions in a specific area. This information is particularly important for understanding the activity patterns of residents.

[0071] The results of these identifications and analyses will be used to create a preliminary community model. This model is static at the initial stage, that is, it provides a snapshot of the community structure based on current data. This static model includes elements such as road networks, building layouts, and the location of key facilities, and stores this structured information in a centralized database or data framework.

[0072] Once the basic model is established, the system then enters the continuous update phase. During this phase, the system continuously receives new visual and environmental data, which are integrated into the existing digital twin model in real time through advanced data fusion technology. For example, if the camera detects a significant increase in the flow of people in a certain area, the model will update the flow status of the area. Similarly, if the temperature sensor or smoke detector detects an anomaly in a specific location, this information will also be marked at the corresponding location in the model.

[0073] In addition, the system uses machine learning algorithms to analyze continuously collected data to predict and simulate possible safety events, such as traffic congestion or fire development. In this way, the digital twin model is not just a static mirror of the community, but a living model that is dynamically updated and can provide real-time reactions and predictions.

[0074] This digital twin model based on real-time data updates provides a powerful tool for community safety monitoring, enabling community managers and residents to understand and respond to various situations more effectively, thereby improving the safety management and emergency response capabilities of the entire community. Through the implementation of this technology, the community monitoring system can provide detailed and accurate safety information, help prevent potential risks and optimize community resource allocation.

[0075] In addition, the data processing unit is also responsible for generating security alerts and communicating with the event response unit 103, which initiates corresponding security measures based on the information obtained from the data processing unit, such as notifying security personnel or initiating emergency evacuation procedures. The data processing unit also needs to be connected to the user interface 104 to provide real-time security information and digital twin model views to community managers and residents, while receiving user feedback and suggestions to further optimize system performance.

[0076] Furthermore, the hybrid neural network model includes a temporal convolutional network module, a recurrent generative adversarial network module and a graph convolutional network module; wherein the temporal convolutional network module is used to receive a video image sequence collected from a camera, and uses a temporal convolutional neural network to extract relevant features in the video image sequence to obtain a continuous visual feature representation in the time dimension; the recurrent generative adversarial network module is used to receive time series environmental data from environmental sensors, and uses a recurrent generative adversarial network to process the time series environmental data to generate prediction data for future environmental states; the graph convolutional network module is used to receive the continuous visual feature representation provided by the temporal convolutional network module and the prediction data provided by the recurrent generative adversarial network module, and uses a graph convolutional network to process the received data to obtain security threat assessment and development trend prediction.

[0077] In the intelligent monitoring system, the hybrid neural network model plays a vital role. By combining three different types of network modules, it effectively improves the accuracy and predictive ability of the system for community safety monitoring. This model includes a temporal convolutional network module, a recurrent generative adversarial network module, and a graph convolutional network module. Each module is designed for specific data types and processing requirements, ensuring that the most valuable information is extracted from different data sources.

[0078] First, the temporal convolutional network module is dedicated to processing video image sequences collected from cameras. This module uses a temporal convolutional neural network, a network designed specifically for video and sequence data that can identify and extract visual features that change over time, such as the movement of objects, the behavior patterns of people, etc. By analyzing the video sequence frame by frame, this module is able to capture visual features in the temporal dimension, which is crucial for real-time monitoring of activities and identifying abnormal behavior.

[0079] Next, the recurrent generative adversarial network module processes time series data from environmental sensors, such as temperature, humidity, and smoke indicators. This module consists of two parts: one is the generator, which is used to predict future environmental conditions based on historical data; the other is the discriminator, whose task is to evaluate the output of the generator to ensure that the predicted data is as close to the actual environmental data as possible. This adversarial training process makes the prediction more accurate and is very effective for predicting potential environmental threats such as fires.

[0080] Finally, the graph convolutional network module receives the data output from the first two modules. Using the graph convolutional network, this module is able to analyze and process the relationships and interactions between various monitoring points in the community, because the security of the community often depends on the interaction and connectivity between these points. By building a graph model, this module can not only evaluate current security threats, but also predict how these threats spread and develop within the community.

[0081] In summary, this hybrid neural network model achieves comprehensive control of community security by analyzing and processing various types of monitoring data in detail. From capturing instant events with video images, predicting potential risks through environmental data, to understanding the event propagation mechanism using graph networks, this model provides a powerful analysis and prediction tool for intelligent monitoring systems, greatly enhancing the system's ability to respond to potential security threats.

[0082] Furthermore, the temporal convolutional network module includes three convolutional layers, each of which adopts batch normalization and ReLU activation function, and applies residual connection technology; wherein, the first convolutional layer is used to preliminarily extract basic visual features in the video image sequence; the second convolutional layer is used to identify edges and textures in the video image sequence; the third convolutional layer is used to capture the speed and direction of moving objects in the video image sequence; the output of the third convolutional layer is connected to a fully connected layer, which converts the convolutional features into continuous visual feature representations in the time dimension.

[0083] The temporal convolutional network module is specially designed to process and analyze video image sequences collected from cameras. Through a series of carefully designed convolutional layers, the module gradually refines and extracts key visual information from video data, enabling the system to recognize and respond to dynamic events within the community.

[0084] The temporal convolutional network module contains three main convolutional layers, each with specific functions, and uses batch normalization and ReLU activation functions to optimize the network training process and maintain the nonlinearity of the network. In order to further enhance the learning ability of the model and avoid the gradient vanishing problem when training deep networks, residual connection technology is also applied to each layer.

[0085] In the first convolutional layer, the goal is to initially extract basic visual features, such as light and color distribution, from the video image sequence. This lays the foundation for more complex feature extraction later. The output of this layer is passed to the second convolutional layer, which focuses on identifying more specific visual elements, such as edges and textures. These features are crucial for understanding objects in the video and their environment.

[0086] The third convolutional layer is tasked with capturing and analyzing the speed and direction of moving objects in a video image sequence. This layer of high-level visual feature extraction is critical for surveillance systems to identify behavioral patterns, track moving objects, and respond to potential security threats in a timely manner.

[0087] Finally, the output of the third convolutional layer is not used directly for decision making, but is connected to a fully connected layer. The role of this fully connected layer is to convert the high-dimensional feature vectors obtained from each convolutional layer into a continuous visual feature representation that is more suitable for time series analysis and final security assessment. This continuous feature representation can provide a detailed view of the current dynamics of the community, thereby providing accurate input to other parts of the system for further analysis and decision making.

[0088] Through this hierarchical design, the temporal convolutional network module can not only effectively process complex video data, but also ensure that each step from the original image to the final feature representation is accurate and meaningful, ultimately achieving effective monitoring of community safety.

[0089] The following is the code implementation of the temporal convolutional network module, including the configuration of the convolutional layer, the specific implementation of activation, batch normalization, and residual connections.

[0090] import torch

[0091] import torch.nn as nn

[0092] class TimeConvolutionalNetwork(nn.Module):

[0093] def __init__(self):

[0094] super(TimeConvolutionalNetwork, self).__init__()

[0095] # The first convolutional layer

[0096] self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)

[0097] self.bn1 = nn.BatchNorm2d(num_features=16)

[0098] self.relu1 = nn.ReLU()

[0099] # The second convolutional layer

[0100] self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)

[0101] self.bn2 = nn.BatchNorm2d(num_features=32)

[0102] self.relu2 = nn.ReLU()

[0103] # The third convolutional layer

[0104] self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)

[0105] self.bn3 = nn.BatchNorm2d(num_features=64)

[0106] self.relu3 = nn.ReLU()

[0107] # Residual connection

[0108] self.residual1 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=1, stride=1, padding=0)

[0109] self.residual2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=1, stride=1, padding=0)

[0110] # Fully connected layer

[0111] self.fc = nn.Linear(in_features=64, out_features=10)

[0112] def forward(self, x):

[0113] # Process the first layer

[0114] x1 = self.conv1(x)

[0115] x1 = self.bn1(x1)

[0116] x1 = self.relu1(x1)

[0117] # Processing the second layer

[0118] x2 = self.conv2(x1)

[0119] x2 = self.bn2(x2)

[0120] x2 = self.relu2(x2)

[0121] x2 += self.residual1(x1) # Apply residual connection

[0122] # Processing the third layer

[0123] x3 = self.conv3(x2)

[0124] x3 = self.bn3(x3)

[0125] x3 = self.relu3(x3)

[0126] x3 += self.residual2(x2) # Apply residual connection

[0127] # Flattening and fully connected layers

[0128] x3 = torch.flatten(x3, 1)

[0129] output = self.fc(x3)

[0130] return output

[0131] # Instantiate and test the model

[0132] model = TimeConvolutionalNetwork()

[0133] input_tensor = torch.rand(1, 3, 64, 64) # Assume the input is a 64x64 RGB image

[0134] output = model(input_tensor)

[0135] print(output)

[0136] This implementation uses the PyTorch framework because it is widely used in machine learning and deep learning applications. The temporal convolutional neural network model defines three convolutional layers, each followed by batch normalization and ReLU activation functions to ensure the network's nonlinear processing capabilities and training stability. Residual connections are introduced between each convolutional layer to help solve the gradient vanishing problem that may occur in deep networks. The final output is passed through a fully connected layer to generate a feature vector for classification or further processing.

[0137] Furthermore, the recurrent generative adversarial network module includes a generator and a discriminator; wherein the generator is composed of three consecutive LSTM layers, each LSTM layer is used to gradually process and predict the future environmental state; each LSTM layer is followed by a batch normalization layer and a ReLU activation layer to improve the responsiveness and stability of the model; the discriminator is composed of two LSTM layers, which is used to evaluate the authenticity of the generator output and force the generator to produce a predicted output close to the real environmental data; the generator and the discriminator promote each other through adversarial training to improve the accuracy of the prediction of the future environmental state.

[0138] The RGAN module is introduced to take advantage of its powerful sequence data processing and generation capabilities to predict and simulate future changes in environmental states. This module consists of two main parts: a generator and a discriminator, which interact with each other through adversarial training to improve the accuracy and reliability of predictions.

[0139] The generator part includes three consecutive long short-term memory (LSTM) network layers. LSTM is a special type of recurrent neural network (RNN) that is well suited for processing and predicting long-term dependencies in time series data. In this system, the task of each LSTM layer is to gradually process the input environmental data (such as temperature, humidity, smoke, etc.) and predict the future state. In order to ensure the stability of the training process and improve the responsiveness of the network, each LSTM layer is followed by a batch normalization layer and a ReLU activation layer. These layers help standardize the intermediate output and introduce nonlinearity so that the model can better learn complex data patterns.

[0140] The discriminator part consists of two LSTM layers, and its main function is to evaluate the authenticity of the predicted data produced by the generator. By constantly comparing the output of the generator with the actual environment data, the discriminator can push the generator to improve its predictions so that the generated data is closer and closer to the real data. This adversarial training process is achieved by optimizing a loss function that aims to reduce the difference between the generated data and the real data.

[0141] The adversarial training of the generator and the discriminator is the core of this module. During the training process, the generator tries to generate increasingly accurate predictions of future environmental states, while the discriminator strives to identify the differences between generated data and real data. In this way, the two promote each other and continuously improve the overall performance of the system. This training mechanism not only enhances the model's ability to predict future environmental changes, but also ensures the credibility and practicality of the prediction results.

[0142] Below is the code implementation of the Recurrent Generative Adversarial Network (RGAN) module. This code illustrates how to build an RGAN model consisting of a generator and a discriminator for time series prediction of environmental data.

[0143] import torch

[0144] import torch.nn as nn

[0145] # Define the generator module

[0146] class Generator(nn.Module):

[0147] def __init__(self):

[0148] super(Generator, self).__init__()

[0149] # Define three consecutive LSTM layers

[0150] self.lstm1 = nn.LSTM(input_size=10, hidden_size=50, num_layers=1,batch_first=True)

[0151] self.bn1 = nn.BatchNorm1d(50)

[0152] self.lstm2 = nn.LSTM(50, 100, 1, batch_first=True)

[0153] self.bn2 = nn.BatchNorm1d(100)

[0154] self.lstm3 = nn.LSTM(100, 50, 1, batch_first=True)

[0155] self.bn3 = nn.BatchNorm1d(50)

[0156] # Define the fully connected layer conversion output

[0157] self.fc = nn.Linear(50, 10)

[0158] def forward(self, x):

[0159] # Process data step by step through the LSTM layer

[0160] x, _ = self.lstm1(x)

[0161] x = self.bn1(x)

[0162] x = torch.relu(x)

[0163] x, _ = self.lstm2(x)

[0164] x = self.bn2(x)

[0165] x = torch.relu(x)

[0166] x, _ = self.lstm3(x)

[0167] x = self.bn3(x)

[0168] x = torch.relu(x)

[0169] # Apply the fully connected layer to generate prediction results

[0170] x = x[:, -1, :]# Get the output of the last step of the sequence

[0171] output = self.fc(x)

[0172] return output

[0173] # Define the discriminator module

[0174] class Discriminator(nn.Module):

[0175] def __init__(self):

[0176] super(Discriminator, self).__init__()

[0177] # Define two LSTM layers

[0178] self.lstm1 = nn.LSTM(input_size=10, hidden_size=50, num_layers=1,batch_first=True)

[0179] self.lstm2 = nn.LSTM(50, 25, 1, batch_first=True)

[0180] # Define an output layer

[0181] self.fc = nn.Linear(25, 1)

[0182] def forward(self, x):

[0183] # LSTM layer processes data

[0184] x, _ = self.lstm1(x)

[0185] x, _ = self.lstm2(x)

[0186] # Apply the fully connected layer to evaluate authenticity

[0187] x = x[:, -1, :]# Get the output of the last step of the sequence

[0188] output = torch.sigmoid(self.fc(x))# Use sigmoid function to output the predicted authenticity

[0189] return output

[0190] # Create instances of the generator and discriminator

[0191] generator = Generator()

[0192] discriminator = Discriminator()

[0193] # Example input

[0194] input_data = torch.rand(1, 10, 10) # Assume there are 10 time steps, 10 features per step

[0195] # Generate prediction data

[0196] generated_data = generator(input_data)

[0197] # The discriminator evaluates the authenticity of the generated data

[0198] realism_score = discriminator(generated_data)

[0199] print("Generated Data:", generated_data)

[0200] print("Realism Score:", realism_score)

[0201] This implementation uses the PyTorch framework to define a recurrent generative adversarial network, which includes a generator for predicting future environmental states and a discriminator for evaluating these predictions. The generator consists of three LSTM layers, each followed by batch normalization and ReLU activation functions to stabilize and accelerate the training process. The discriminator consists of two LSTM layers and a fully connected layer to evaluate the authenticity of the generated data. Finally, the generated data and the discriminator's score can be used for further analysis or model optimization during training.

[0202] Furthermore, the graph convolutional network module includes a first graph convolutional layer, a second graph convolutional layer and a third graph convolutional layer connected in sequence, wherein the first graph convolutional layer receives the continuous visual feature representation provided by the temporal convolutional network module and the prediction data provided by the cyclic generative adversarial network module, and performs basic feature aggregation and update operations on each graph node, and enhances the preliminary recognition of the interaction between community key points by aggregating the features of the direct neighbor nodes of each node; the second graph convolutional layer is used to process the output of the first graph convolutional layer, and enhance the intermediate feature expression of the propagation path of potential security incidents; the third graph convolutional layer receives the processed features from the second layer, and performs final feature refinement and aggregation to provide a deeper level of recognition of the interaction between community key points;

[0203] Furthermore, the graph convolutional network module also includes an attention layer based on an attention mechanism, and the attention layer is used to receive the output of the third graph convolutional layer and dynamically adjust the weight when aggregating neighbor node features.

[0204] The graph convolutional network module plays a core role, especially in processing and analyzing complex community interaction data input from different data sources. Through multi-layer graph convolution processing, this module can effectively capture and understand the interactions between key points within the community and the potential development paths of security threats.

[0205] The graph convolutional network module first receives data from the temporal convolutional network module and the recurrent generative adversarial network module, which contains continuous visual feature representations and predictions of future environmental states. This data is input to the first graph convolutional layer of the graph convolutional network, whose main responsibility is to perform basic feature aggregation and updates on each graph node. In this process, each node will aggregate the feature information of its direct neighbors. In this way, the module can preliminarily identify the interactions between key points within the community.

[0206] Subsequently, the output of the first layer is passed to the second layer of graph convolutional layers, which further processes the data from the first layer and enhances the feature expression of the propagation path of potential security incidents in the community. The work of this layer ensures a smooth transition from preliminary feature aggregation to more refined feature expression, providing richer information for in-depth analysis of potential security incidents.

[0207] The output of the second layer is then passed to the third layer of graph convolution, where the final feature refinement and aggregation are performed. The processing result of the third layer is a deeper identification of the dynamics and potential risks within the community, providing the system with a detailed and accurate picture of the community's safety status.

[0208] In addition, the graph convolutional network module also includes an attention-based layer, which is located after the third graph convolutional layer. The role of the attention layer is to dynamically adjust the weights when aggregating neighbor node features. By learning which nodes or connections between nodes are most critical for predicting security threats within the community, this attention-based layer can optimize the output of the entire graph convolutional network, ensuring that the output feature representation not only reflects the current state of the community, but also predicts possible development trends.

[0209] Below is the implementation code of the graph convolutional network module:

[0210] import torch

[0211] import torch.nn as nn

[0212] import torch.nn.functional as F

[0213] class GraphConvolutionLayer(nn.Module):

[0214] def __init__(self, in_features, out_features):

[0215] super(GraphConvolutionLayer, self).__init__()

[0216] self.in_features = in_features

[0217] self.out_features = out_features

[0218] # Initialize weight parameters

[0219] self.weight = nn.Parameter(torch.FloatTensor(in_features, out_features))

[0220] nn.init.xavier_uniform_(self.weight)# Use Xavier to initialize weights and enhance the initial stability of the model

[0221] def forward(self, input, adj):

[0222] # Perform graph convolution operation

[0223] support = torch.mm(input, self.weight)# Linear transformation of input features

[0224] output = torch.spmm(adj, support)# Perform graph convolution using the adjacency matrix and transformed features

[0225] return output

[0226] class AttentionLayer(nn.Module):

[0227] def __init__(self, feature_dim):

[0228] super(AttentionLayer, self).__init__()

[0229] self.feature_dim = feature_dim

[0230] # Initialize attention weights

[0231] self.attention_weights = nn.Parameter(torch.FloatTensor(feature_dim,1))

[0232] nn.init.xavier_uniform_(self.attention_weights)# Initialize attention weights using Xavier

[0233] def forward(self, features):

[0234] # Calculate attention score

[0235] attention_scores = torch.mm(features, self.attention_weights)# Calculate the attention score of each node

[0236] attention_scores = F.softmax(attention_scores, dim=0)# Perform softmax on the scores to obtain normalized attention distribution

[0237] output = attention_scores * features# Adjust features based on attention distribution

[0238] return output

[0239] class GraphConvolutionalNetwork(nn.Module):

[0240] def __init__(self, input_dim):

[0241] super(GraphConvolutionalNetwork, self).__init__()

[0242] # The input dimension needs to match the output dimension of the previous module

[0243] self.gcn1 = GraphConvolutionLayer(input_dim, 512)# The first graph convolution layer

[0244] self.gcn2 = GraphConvolutionLayer(512, 256)# The second graph convolution layer

[0245] self.gcn3 = GraphConvolutionLayer(256, 128)# The third graph convolution layer

[0246] self.attention = AttentionLayer(128)# Attention layer

[0247] self.fc = nn.Linear(128, 2)# Final output layer, outputs two values: security threat assessment and development trend prediction

[0248] def forward(self, x, adj):

[0249] # Process graph data layer by layer

[0250] x = F.relu(self.gcn1(x, adj))# Pass the first layer of graph convolution and apply ReLU activation function

[0251] x = F.relu(self.gcn2(x, adj))# Pass the second layer of graph convolution and apply ReLU activation function

[0252] x = F.relu(self.gcn3(x, adj))# Pass the third layer of graph convolution and apply ReLU activation function

[0253] x = self.attention(x)# Apply attention layer to optimize feature representation

[0254] x = self.fc(x)# Get the final output through the fully connected layer

[0255] return x

[0256] # Example: Integrating visual features with environmental prediction data

[0257] visual_features = torch.rand(10, 512) # Visual features from temporal convolutional networks

[0258] environmental_predictions = torch.rand(10, 512) # Environmental predictions from a recurrent generative adversarial network

[0259] features = torch.cat((visual_features, environmental_predictions), dim=1)# Concatenate the two features

[0260] adjacency_matrix = torch.eye(10) # Use the identity matrix as an example adjacency matrix

[0261] # Initialize the model and test

[0262] model = GraphConvolutionalNetwork(input_dim=1024)# Initialize the model and specify the input dimension

[0263] output = model(features, adjacency_matrix)# Pass features and adjacency matrix to the model

[0264] print("Output (Threat Assessment and Trend Prediction):", output)# Print output results

[0265] The following is an example of the quantitative output of security threat assessment and development trend forecast:

[0266] Example 1: Illegal Intrusion

[0267] Security threat assessment: The system detects individuals illegally crossing the border through video analysis algorithms and outputs a probability value: "Probability of illegal intrusion detection: 85%".

[0268] Development trend prediction: Based on the intruder's current location and historical movement data, the model predicts the intruder's possible movement path and the time it will arrive at key areas. "It is expected that the intruder will reach the main residential area within 10 minutes, and the probability of invasion increases to 95%."

[0269] Example 2: Fire warning

[0270] Security threat assessment: Sensor data shows that the temperature and smoke concentration in a certain area are abnormal, and "current fire risk detection probability: 80%."

[0271] Development trend prediction: Combined with environmental factors and wind direction models, the speed and direction of fire spread are predicted, "Fire spread rate: 5 square meters per minute, expected to affect adjacent areas within 15 minutes."

[0272] Example 3: Abnormal community gathering

[0273] Security threat assessment: Through crowd density monitoring technology, abnormal crowd gathering in a public area was detected, "abnormal gathering detection probability: 70%".

[0274] Development trend forecast: Based on the current growth trend and time pattern, the scale and potential impact of the gathering are predicted. "The number of people gathered is expected to increase by 50% within 30 minutes, and the probability of escalation of the gathering event is 60%."

[0275] These quantitative outputs can not only directly reflect the current security situation, but also predict future development trends through specific numerical values, so that the security management system can dynamically adjust and respond based on these data. For example, the security system can adjust patrol routes or dispatch security personnel based on the probability of intrusion and the estimated arrival time, and the fire protection system can prepare corresponding fire-fighting resources based on the fire spread rate and the expected impact area.

[0276] In the smart community monitoring system, training a hybrid neural network model consisting of a temporal convolutional network module, a recurrent generative adversarial network module, and a graph convolutional network module is a critical multi-stage process.

[0277] Step 1: Data collection and preprocessing

[0278] 1. Video data collection: Continuous video image sequences are collected from surveillance cameras within the community. These data will be used to train a temporal convolutional network module to identify and extract key visual features.

[0279] 2. Environmental data collection: Collect time series data from environmental sensors (such as temperature, humidity, smoke, etc.). These data will be used to train the cyclic generative adversarial network module to predict future environmental states.

[0280] 3. Data preprocessing: Clean, denoise, and normalize the collected video and environmental data to ensure data quality and consistency and provide reliable input for model training.

[0281] Step 2: Model training

[0282] 1. Temporal convolutional network module training:

[0283] A temporal convolutional network is trained using video image sequences collected from a camera to extract continuous visual feature representations in the temporal dimension.

[0284] Focus on the extraction of key visual features such as dynamic events and character behavior recognition.

[0285] 2. Cyclic Generative Adversarial Network Module Training:

[0286] The environmental data is used to train a cyclic generative adversarial network, where the generator is responsible for simulating the prediction of future environmental states and the discriminator ensures the authenticity of the prediction.

[0287] The focus is on the model's ability to accurately predict future environmental changes, such as rising temperatures or increased smog concentrations.

[0288] 3. Graph convolutional network module training:

[0289] Combining the outputs of the temporal convolutional network and the recurrent generative adversarial network, a graph convolutional network is trained to process this data for security threat assessment and development trend prediction.

[0290] This module will analyze the interactions between different nodes within the community and predict the spread or evolution of potential threats.

[0291] Step 3: Model evaluation and testing

[0292] Perform a comprehensive evaluation of the trained model, using performance indicators such as cross-validation, precision, recall, etc. to test the performance of the model in actual scenarios.

[0293] Verify the model's response speed and accuracy to real-time data to ensure it can meet the needs of actual applications.

[0294] Step 4: Model deployment and implementation

[0295] Deploy the tested and verified models into the community monitoring system, process the monitoring data in real time, and adjust the monitoring and response strategies based on the security threat assessment and development trend prediction provided by the model.

[0296] Step 5: Continuous monitoring and iterative updates

[0297] Continuously monitor the performance of the model and iteratively update and optimize it based on new data and feedback.

[0298] Regularly review and adjust the model to address new challenges and improve the overall performance of the system.

[0299] Through the above steps, the intelligent community monitoring system can effectively utilize the powerful functions of the hybrid neural network model to continuously improve the community's safety level and response capabilities. This system can ensure the safety of community residents and optimize response measures, making community management more intelligent and efficient.

[0300] The data processing unit can also update the digital twin model based on the identified potential security threats and the predicted results of their development trends. The digital twin model is a high-precision virtual mapping of the community, which reflects the physical and environmental status of the community in real time. When the data processing unit identifies potential security threats and predicts their development trends, this information is used to update the digital twin model. The update process includes:

[0301] Real-time data integration: New observations and predictions are incorporated into the digital twin model in real time. This may include changing certain parameters in the model, such as adjusting the display status of areas with reduced visibility due to smoke detection, or updating the congestion status of areas with increased crowds.

[0302] Visualization of events and status: The updated model is able to display the current security status and predicted threat development on the user interface, such as showing the predicted path of fire spread, or showing the source and possible affected area of ​​abnormal noise identified by sound sensors.

[0303] Response strategy recommendations: The model can also provide response strategy recommendations based on the latest data and threat assessments to help community managers formulate response measures, such as optimizing evacuation routes and adjusting security forces.

[0304] Furthermore, the data processing unit includes a real-time data fusion module for synchronizing and fusing the visual data and environmental data received from the sensor device; the real-time data fusion module uses augmented reality technology to annotate and display objects identified in the visual data and events detected in the environmental data in the digital twin model.

[0305] A key component of the data processing unit is the real-time data fusion module. The main responsibility of this module is to synchronize and fuse data from various sensor devices, including but not limited to visual data and environmental data. Visual data usually comes from surveillance cameras that capture real-time images and videos within the community; while environmental data may include information collected from various environmental sensors such as temperature, humidity, and smoke. Through the integration and fusion of these data, the system can provide a comprehensive, multi-dimensional security monitoring solution.

[0306] The core function of the real-time data fusion module is to use augmented reality (AR) technology to annotate and display data in real time in the digital twin model. The digital twin model is a high-precision virtual model that mirrors the physical environment and operational status of the community. In this model, the real-time data fusion module not only displays visual data from cameras, such as the movement of monitored people or vehicles, but also synchronously displays related events detected by environmental sensors, such as fire alarms or gas leaks.

[0307] During the implementation process, the real-time data fusion module first needs to ensure the synchronization of the data received from the sensor devices. This involves calibrating the data timestamps to ensure the consistency of all data in time, so as to ensure that the information presented in the digital twin model is synchronized. Then, the module fuses these synchronized data and uses augmented reality technology to visualize them on the digital twin model.

[0308] For example, if a surveillance camera captures abnormal gathering behavior in a certain area, and the environmental sensor detects elevated CO2 levels in the same area, the real-time data fusion module will mark this information with an icon or color at the corresponding location in the digital twin model, and may clearly indicate "abnormal gathering + elevated CO2" with text or symbols, while providing specific numerical values ​​and time information. This display of information can enhance the responders' understanding of the situation and the speed of their response.

[0309] Through the above detailed implementation steps, the real-time data fusion module can be implemented and used to effectively improve the practicality and efficiency of the intelligent monitoring system. This method based on real-time data fusion and augmented reality technology can significantly improve the dynamic monitoring and emergency response capabilities of community safety management.

[0310] In general, the design and implementation of the data processing unit 102 ensures that the intelligent monitoring system can efficiently and accurately perform security monitoring and response tasks, and improve the community's security prevention and control capabilities by comprehensively processing multi-source data. The detailed description of this unit enables people with relevant technical backgrounds to understand, build and maintain the system.

[0311] The event response unit 103 is connected to the data processing unit and is used to initiate corresponding security measures based on the potential security threats identified by the data processing unit and the predicted results of their development trends; wherein the security measures include notifying security personnel or initiating emergency evacuation procedures.

[0312] The event response unit 103 plays a vital role in the intelligent monitoring system, and its main function is to implement specific countermeasures to deal with potential security threats and predicted risks identified by the data processing unit 102. The unit is designed to respond quickly and automatically to various security situations to ensure community safety and stability.

[0313] The event response unit 103 is tightly integrated with the data processing unit 102, receiving and analyzing data from the latter. When the data processing unit detects a potential security threat (such as an unusual temperature rise that may indicate a fire, or abnormal sound data that may indicate an unauthorized intrusion) through machine learning algorithms, the event response unit will be activated. The unit has a variety of built-in response protocols and procedures, and can select corresponding action plans based on different threat types and urgency levels.

[0314] In terms of specific operations, the incident response unit has multiple output channels and control mechanisms, including but not limited to alarm systems, announcement systems, and emergency contact systems. For example, in the face of a fire warning, the unit can automatically activate the alarm system in the community and notify security personnel and residents via text messages or the community's communication network. For intrusion incidents, it can activate additional security measures such as lighting systems and automatic access control systems to isolate or trap threats.

[0315] In addition, the unit is equipped with direct contact with external emergency services (such as the fire department or medical emergency), and can send out a distress signal automatically or after manual confirmation when necessary. The realization of all these functions depends on the advanced software and hardware configuration inside the unit. In terms of software, the incident response unit has built-in event management software, which can parse the information of the data processing unit and perform operations according to preset logic and rules. In terms of hardware, it may include network interfaces, control modules and various types of communication equipment to ensure efficient operation in various situations.

[0316] To ensure the effective operation of the incident response unit 103, the design and configuration of the unit must meet high security standards and reliability requirements. The system design should allow for flexible configuration and updating of response strategies to adapt to changes in community safety needs. At the same time, the implementation of the unit needs to consider compatibility with existing community infrastructure to ensure seamless collaboration with various security systems such as radio communications, networks, and physical security measures in an emergency.

[0317] In summary, the incident response unit 103 is a key component in the intelligent monitoring system. It not only needs to have the ability to quickly and accurately execute security measures, but also needs to be able to work efficiently with other systems and external services in the community to achieve comprehensive protection for community residents.

[0318] The user interaction interface 104 allows community managers and residents to view the digital twin model and real-time safety information; and provides an input interface for submitting safety improvement suggestions and feedback information.

[0319] The user interface 104 is a key component in the intelligent monitoring system, which provides an intuitive platform for community managers and residents to access, monitor and interact with community security information. The interface design is required to be intuitive and easy to use, and can adapt to the technical proficiency of various users, so as to ensure that all users can effectively utilize the system functions.

[0320] The user interface consists of several main parts. First, it provides a real-time view showing visual and environmental data from sensor devices 101, such as video streams, temperature readings, humidity levels, smoke detection results, etc. These data are updated in real time in graphical or numerical form, allowing users to instantly understand the current status of the community. In addition, the interface embeds a dynamic display of the digital twin model, which is a virtual replica of the community that shows the detailed structure of the community and various ongoing activities, enhancing the user's spatial perception and event understanding capabilities.

[0321] Secondly, the user interface includes an early warning and notification system, which automatically pushes security alerts and event updates to users based on information analyzed by the data processing unit 102. Alerts may include automatic notifications of fire, unauthorized intrusion, or other emergencies, which are presented in the form of pop-ups, sound signals, or other prominent markers to ensure that users can respond quickly.

[0322] In addition, the interface also provides a feedback and suggestion submission section. This section includes a variety of input interfaces, such as text boxes, voice recognition functions, or online forms, through which users can submit their security improvement suggestions, functional feedback, or other relevant information. These inputs will be directly transmitted to the data processing unit 102 and can be used for future system upgrades and improvements.

[0323] To achieve these functions, the user interface needs to be equipped with efficient front-end technology and back-end support. The front-end design should focus on the user experience and adopt modern UI / UX design principles, including responsive layout to adapt to various devices (such as smartphones, tablets and desktops). The back-end needs powerful server support to handle a large amount of data input and user requests, ensuring the smooth operation of the interface and the instant update of data.

[0324] Furthermore, the user interaction interface includes a simulation function, which allows the user to simulate event results through a digital twin model based on current environmental data and historical event data to evaluate the possible effects of different response strategies.

[0325] A key function, simulation, has been introduced in the user interface of the intelligent monitoring system. This function is designed to help users simulate detailed event outcomes through digital twin models based on current environmental data and historical event data. This simulation is mainly used to predict and evaluate the potential effects of different security response strategies in specific situations.

[0326] The simulation feature allows users to leverage a digital twin, a highly accurate virtual model that mirrors the physical environment and dynamics of a community in real time. Through this model, users can input current environmental data collected from sensors (such as cameras, temperature sensors, smoke detectors, etc.) as well as historical event data collected from similar situations in the past.

[0327] The implementation steps are as follows:

[0328] 1. Data integration: First, the system needs to synchronously collect data from various sensors, including but not limited to real-time video, temperature readings, smoke levels, etc. At the same time, the system will also access the stored historical event data, which includes past safety accident records and environmental conditions at the time.

[0329] 2. Simulation start: The user accesses the simulation function through the user interface. On the interface, the user can select a specific event type to simulate, such as "fire escape route efficiency" or "intrusion response time".

[0330] 3. Scenario setting: Users can set or adjust the specific parameters of the simulation, such as the specific location, time, environmental conditions, etc. These parameters can be automatically filled based on the current environmental data or manually entered.

[0331] 4. Strategy Selection: The user selects the response strategy they want to evaluate. For example, in a fire simulation, the strategy could be different evacuation routes or evacuation speeds.

[0332] 5. Simulation execution: The system uses the digital twin model to perform simulations based on the input data and the selected strategy. During the simulation, the system will calculate and display the expected results under different strategies, such as evacuation time, potential obstacles and safety hazards.

[0333] 6. Result analysis: After the simulation is completed, the system will provide a detailed result analysis, which may include graphical result display, such as heat map, dynamic path display, etc., as well as quantitative effect evaluation, such as total evacuation time, safety index score, etc.

[0334] 7. Strategy Adjustment: Based on the simulation results, users can adjust and optimize the strategy to improve the response effect. The system can store these adjustment records for future reference or automatically apply to similar situations.

[0335] Furthermore, the user interface includes an emergency communication channel that is automatically activated when a high-level security threat is identified, allowing community managers and residents to communicate directly with the nearest emergency service provider.

[0336] An important feature of the user interface in the smart surveillance system is the emergency communication channel. This channel is designed to automatically activate and provide a direct communication bridge when a high-level security threat is detected, allowing community managers and residents to quickly communicate with the nearest emergency service provider. The purpose of this feature is to provide rapid response capabilities at critical moments to ensure community safety.

[0337] Steps to implement an emergency communication channel:

[0338] First, an intelligent recognition mechanism needs to be integrated into the system, which can analyze the data collected from various sensors and cameras to determine whether there are high-level security threats. This includes using algorithms to analyze abnormal behaviors in visual data, potential danger signals in environmental sensor data, etc. The recognition mechanism should be able to evaluate and trigger alarms based on preset security level standards.

[0339] Once the system detects a high-level security threat, the emergency communication channel will be automatically activated. The channel is presented on the user interface as a prominent alarm signal and one or more communication options, such as a communication link to directly dial the nearest police station, fire station or medical emergency center. The user interface should be designed to be easy to operate even in high-pressure situations to ensure that users can quickly and effectively utilize this channel.

[0340] To achieve this function, the system backend needs to be efficiently integrated with the local emergency service system. This may include setting up automated communication protocols, such as automatically sending notifications containing precise location, incident type, and necessary safety information to relevant emergency service providers when the emergency communication channel is activated. In addition, the channel should also support two-way communication so that emergency service providers can send back instructions or ask more about the on-site situation.

[0341] In addition, the system should provide an administrative backend that allows community managers to configure and manage the settings of the emergency communication channel, such as updating the details of contacting emergency service providers, adjusting the security threat level that is automatically triggered, etc. The administrative interface should provide intuitive configuration tools and real-time status updates to ensure that all settings are up to date and effective.

[0342] In general, the user interface 104 is intended to provide a comprehensive and easy-to-operate platform for community residents and managers to enhance the transparency and interactivity of community security monitoring. Through such a design, the system not only improves the sense of security of residents, but also promotes collaboration and communication among community members.

[0343] In the above-mentioned embodiment, an intelligent monitoring system is provided. Correspondingly, the present application also provides an intelligent monitoring method. Figure 2, which is a flow chart of an embodiment of an intelligent monitoring method of the present application. Since this embodiment, i.e., the second embodiment, is basically similar to the first embodiment, the description is relatively simple, and the relevant parts can refer to the system part description of the first embodiment. The method embodiment described below is only illustrative.

[0344] The second embodiment of the present application provides an intelligent monitoring method, including:

[0345] Step S201: deploying sensor devices at key locations in the community to capture visual data and environmental data within the community, wherein the sensor devices include cameras and environmental sensors; the environmental sensors include temperature sensors, humidity sensors, smoke detectors, sound sensors, wind direction sensors, wind speed sensors, and rainfall sensors;

[0346] Step S202: creating and updating a digital twin model of the community based on the captured visual data and environmental data; the digital twin model is used to map and simulate the real-time status of the community;

[0347] Step S203: using a trained hybrid neural network model to analyze the collected visual data and environmental data to identify potential security threats and predict their possible development trends; wherein the hybrid neural network model includes a temporal convolutional network module, a recurrent generative adversarial network module and a graph convolutional network module; wherein the temporal convolutional network module is used to process video image sequences; the recurrent generative adversarial network module is used to generate predictions of future environmental states; and the graph convolutional network module is used to integrate visual features and prediction data to perform security threat assessment and development trend prediction;

[0348] Step S204: According to the identified potential security threats and the prediction results of their development trends, the digital twin model is dynamically updated, and corresponding safety measures are initiated, including notifying security personnel or initiating emergency evacuation procedures.

[0349] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. An intelligent monitoring system for community safety control, characterized in that: include: Sensor devices, including cameras and environmental sensors, are distributed at key locations in the community to capture visual data and environmental data within the community; wherein the environmental sensors include temperature sensors, humidity sensors, smoke detectors, sound sensors, wind direction sensors, wind speed sensors, and rainfall sensors; and the environmental data include temperature data, humidity data, smoke level data, sound data, wind speed data, wind speed data, and rainfall; A data processing unit connected to the sensor device, for receiving visual data and environmental data transmitted from the sensor device; creating and updating a digital twin model of the community based on the received visual data and environmental data; using a trained hybrid neural network model to process the received visual data and environmental data, identify potential security threats and predict their possible development trends; updating the digital twin model according to the potential security threats identified by the data processing unit and the prediction results of their development trends; an event response unit, connected to the data processing unit, for initiating corresponding security measures according to the potential security threats identified by the data processing unit and the predicted results of their development trends; wherein the security measures include notifying security personnel or initiating emergency evacuation procedures; A user interface that allows community managers and residents to view the digital twin model and real-time safety information; and provides an input interface for submitting safety improvement suggestions and feedback information; The hybrid neural network model includes a temporal convolutional network module, a recurrent generative adversarial network module and a graph convolutional network module; the temporal convolutional network module is used to receive a video image sequence collected from a camera, and extract relevant features in the video image sequence using a temporal convolutional neural network to obtain a continuous visual feature representation in the time dimension; the recurrent generative adversarial network module is used to receive time series environmental data from an environmental sensor, and process the time series environmental data using a recurrent generative adversarial network to generate prediction data for future environmental states; the graph convolutional network module is used to receive the continuous visual feature representation provided by the temporal convolutional network module and the prediction data provided by the recurrent generative adversarial network module, and process the received data using a graph convolutional network to obtain a security threat assessment and a development trend prediction; The temporal convolutional network module includes three convolutional layers, each of which uses batch normalization and ReLU activation function, and applies residual connection technology; the first convolutional layer is used to preliminarily extract basic visual features in video image sequences; the second convolutional layer is used to identify edges and textures in video image sequences; the third convolutional layer is used to capture the speed and direction of moving objects in video image sequences; the output of the third convolutional layer is connected to a fully connected layer, and the fully connected layer converts the convolutional features into continuous visual feature representations in the time dimension; the first convolutional layer has 3 input channels and 16 output channels; the second convolutional layer has 16 input channels and 32 output channels; the third convolutional layer has 32 input channels and 64 output channels, and the convolution kernel sizes of the first convolutional layer, the second convolutional layer and the third convolutional layer are all 3×3, with a step size of 1 and an edge padding of 1; The recurrent generative adversarial network module includes a generator and a discriminator; the generator is composed of three consecutive LSTM layers, each LSTM layer is used to gradually process and predict the future environmental state; each LSTM layer is followed by a batch normalization layer and a ReLU activation layer to improve the responsiveness and stability of the model; the discriminator is composed of two LSTM layers, which are used to evaluate the authenticity of the generator output and force the generator to produce a predicted output close to the real environmental data; the generator and the discriminator promote each other through adversarial training to improve the accuracy of the prediction of the future environmental state; The graph convolutional network module includes a first graph convolutional layer, a second graph convolutional layer and a third graph convolutional layer connected in sequence, wherein the first graph convolutional layer receives the continuous visual feature representation provided by the temporal convolutional network module and the prediction data provided by the cyclic generative adversarial network module, and performs basic feature aggregation and update operations on each graph node, and enhances the preliminary recognition of the interaction between community key points by aggregating the direct neighbor node features of each node; the second graph convolutional layer is used to process the output of the first graph convolutional layer to enhance the intermediate feature expression of the propagation path of potential security incidents; the third graph convolutional layer receives the processed features from the second layer, and performs final feature refinement and aggregation to provide a deeper level of recognition of the interaction between community key points; Among them, the graph convolutional network module also includes an attention layer based on the attention mechanism, and the attention layer is used to receive the output of the third graph convolutional layer and dynamically adjust the weight when aggregating neighbor node features.

2. The intelligent monitoring system according to claim 1, characterized in that: The data processing unit includes a real-time data fusion module for synchronizing and fusing visual data and environmental data received from the sensor device; the real-time data fusion module uses augmented reality technology to annotate and display objects identified in the visual data and events detected in the environmental data in the digital twin model.

3. The intelligent monitoring system according to claim 1, characterized in that: The user interaction interface includes a simulation function, which allows the user to simulate event results through a digital twin model based on current environmental data and historical event data to evaluate the possible effects of different response strategies.

4. The intelligent monitoring system according to claim 1, characterized in that: The user interface includes an emergency communication channel that is automatically activated when a high-level security threat is identified, allowing community managers and residents to communicate directly with the nearest emergency service provider.

5. An intelligent monitoring method for community safety control, characterized in that: include: Deploy sensor devices at key locations in the community to capture visual data and environmental data within the community, wherein the sensor devices include cameras and environmental sensors; the environmental sensors include temperature sensors, humidity sensors, smoke detectors, sound sensors, wind direction sensors, wind speed sensors, and rainfall sensors; Creating and updating a digital twin model of the community based on the captured visual data and environmental data; the digital twin model is used to map and simulate the real-time status of the community; A trained hybrid neural network model is used to analyze the collected visual data and environmental data to identify potential security threats and predict their possible development trends; wherein the hybrid neural network model includes a temporal convolutional network module, a recurrent generative adversarial network module, and a graph convolutional network module; wherein the temporal convolutional network module is used to process video image sequences; the recurrent generative adversarial network module is used to generate predictions of future environmental states; and the graph convolutional network module is used to integrate visual features and prediction data to conduct security threat assessment and development trend prediction; Dynamically updating the digital twin model based on the identified potential security threats and the predicted results of their development trends, and initiating corresponding security measures, including notifying security personnel or initiating emergency evacuation procedures; The temporal convolutional network module includes three convolutional layers, each of which uses batch normalization and ReLU activation function, and applies residual connection technology; the first convolutional layer is used to preliminarily extract basic visual features in video image sequences; the second convolutional layer is used to identify edges and textures in video image sequences; the third convolutional layer is used to capture the speed and direction of moving objects in video image sequences; the output of the third convolutional layer is connected to a fully connected layer, and the fully connected layer converts the convolutional features into continuous visual feature representations in the time dimension; the first convolutional layer has 3 input channels and 16 output channels; the second convolutional layer has 16 input channels and 32 output channels; the third convolutional layer has 32 input channels and 64 output channels, and the convolution kernel sizes of the first convolutional layer, the second convolutional layer and the third convolutional layer are all 3×3, with a step size of 1 and an edge padding of 1; The recurrent generative adversarial network module includes a generator and a discriminator; the generator is composed of three consecutive LSTM layers, each LSTM layer is used to gradually process and predict the future environmental state; each LSTM layer is followed by a batch normalization layer and a ReLU activation layer to improve the responsiveness and stability of the model; the discriminator is composed of two LSTM layers, which are used to evaluate the authenticity of the generator output and force the generator to produce a predicted output close to the real environmental data; the generator and the discriminator promote each other through adversarial training to improve the accuracy of the prediction of the future environmental state; The graph convolutional network module includes a first graph convolutional layer, a second graph convolutional layer and a third graph convolutional layer connected in sequence, wherein the first graph convolutional layer receives the continuous visual feature representation provided by the temporal convolutional network module and the prediction data provided by the cyclic generative adversarial network module, and performs basic feature aggregation and update operations on each graph node, and enhances the preliminary recognition of the interaction between community key points by aggregating the direct neighbor node features of each node; the second graph convolutional layer is used to process the output of the first graph convolutional layer to enhance the intermediate feature expression of the propagation path of potential security incidents; the third graph convolutional layer receives the processed features from the second layer, and performs final feature refinement and aggregation to provide a deeper level of recognition of the interaction between community key points; Among them, the graph convolutional network module also includes an attention layer based on the attention mechanism, and the attention layer is used to receive the output of the third graph convolutional layer and dynamically adjust the weight when aggregating neighbor node features.

Images