Application method and system of AI-based intelligent identification and early warning method in urban and rural management

Abstract

The invention relates to the technical field of urban management, in particular to an application method and system of an AI-based intelligent identification and early warning method in urban and rural management. Comprising the following steps that a high-low altitude heterogeneous sensing network is constructed, a space-time synchronous sensing matrix is formed through a multi-spectral imaging module carried by an unmanned aerial vehicle and a ground video monitoring array, and the ground video monitoring array is deployed at an iron tower high-position monitoring point and is integrated with an air quality sensor; establishing a multi-modal data alignment channel, applying spatial geocoding to the aerial image data of the unmanned aerial vehicle, and generating a time synchronization data stream having the same geographic coordinate system as the ground monitoring video frame; and performing three-level feature fusion analysis: performing registration superposition on visible light and infrared spectrum data in a pixel-level fusion layer, thereby fundamentally solving the problem of space-time alignment of multi-source heterogeneous data, and ensuring seamless fusion of high-altitude and low-altitude sensing data in a complex environment.

Description

Technical Field

[0001] The present invention relates to the field of urban management technology, and specifically to an application method and system for AI-based intelligent identification and early warning methods in urban and rural governance. Background Art

[0002] In the field of integrated urban and rural governance, the data fusion problem of high- and low-altitude collaborative monitoring systems has long constrained the improvement of regulatory effectiveness. Current technology has the following key flaws: Existing urban and rural governance monitoring systems generally use independently operated high- and low-altitude sensing devices, and the multimodal data collected by drones and ground monitoring arrays suffer from serious spatiotemporal mismatches. Field tests have shown that due to the lack of a unified spatiotemporal reference framework, monitoring data acquired by different sensors has a 200-500ms delay error in time synchronization, resulting in positioning deviations of more than 3 meters in spatial registration. This spatiotemporal asynchrony makes it impossible to effectively correlate multi-source data on abnormal events. For example, when a drone detects illegal construction, the ground camera cannot provide continuous process records due to time asynchrony, or the target is lost due to coordinate deviations.

[0003] Especially under severe weather conditions, the failure probability of single-modal sensors (such as visible light cameras) relied on by traditional systems is as high as 40%, while heterogeneous data such as multispectral and infrared data are difficult to complement each other due to the lack of an effective fusion mechanism.

[0004] Based on the above problems, there is an urgent need for a technical solution that can fundamentally solve the problem of spatiotemporal alignment of multi-source heterogeneous data, ensure the seamless integration of high- and low-altitude perception data in complex environments, and meet the stringent requirements of modern urban and rural governance for the reliability and real-time performance of monitoring systems. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the existing technology and propose an application method and system of AI-based intelligent identification and early warning methods in urban and rural governance, including: The application of AI-based intelligent identification and early warning methods in urban and rural governance includes the following processes: S1. Build a heterogeneous high- and low-altitude perception network. This network uses drones equipped with multispectral imaging modules to form a spatiotemporally synchronized perception matrix with a ground-based video surveillance array. The ground-based video surveillance array is deployed at high-altitude monitoring points on the tower and integrates air quality sensors. S2. Establish a multimodal data alignment pipeline to apply spatial geocoding to the drone aerial imagery data, generating a time-synchronized data stream with the same geographic coordinate system as the ground surveillance video frames. S3. Perform three-level feature fusion analysis: register and overlay visible light and infrared spectral data at the pixel-level fusion layer, extract building edge and thermal anomaly features at the feature-level fusion layer, and generate anomaly event probability distribution maps by combining GIS basemap data at the decision-level fusion layer. S4. Dynamically build an event response chain. When the risk assessment value of any monitored area exceeds a set threshold, a drone formation is automatically triggered to conduct multi-angle surround photography of the target area, while simultaneously activating the zoom tracking mode of the three nearest ground monitoring gimbals. S5. Implement online evolution of model parameters, construct adversarial training scenarios based on historical false positive sample sets, and dynamically adjust the convolution kernel weight parameters and classifier confidence threshold of the object detection network through gradient backpropagation.

[0006] Preferably, the construction of the spatiotemporal synchronization perception matrix in step S1 specifically includes: S11: Deploy Beidou differential positioning base stations on the drone take-off and landing platforms to ensure that the timestamp error of all data collection devices is less than 10ms; S12: Establish a geometric constraint relationship between the azimuth angle α of the ground monitoring gimbal and the flight altitude H of the UAV. When H>100 meters, α automatically increases the compensation angle Δα=arctan(H / D), where D is the horizontal distance between the devices. S13: Frequency division multiplexing is used to allocate wireless channel resources, with the 5.8 GHz band designated for drone video backhaul and the 2.4 GHz band for sensor data transmission.

[0007] Preferably, the implementation method of the three-level feature fusion analysis in step S3 is: pixel-level fusion uses bilinear interpolation to align multispectral data, and the calculation formula is: ; in is the credibility weight of the i-th sensor, is the spatial resolution scaling factor; Feature-level fusion extracts cross-modal features through a parallel CNN architecture, with the main path processing visible light images and the bypass branch processing infrared data. Decision-level fusion introduces spatial semantic constraint rules to automatically increase the sensitivity threshold of dust monitoring when a construction site is identified.

[0008] Preferably, the activation conditions of the event response link in step S4 include: two or more abnormal features appearing simultaneously in the same geographic grid; the movement speed of the target object in three consecutive frames of images exceeds a set threshold; there is a significant contradiction between the ground sensor data and the video analysis results; the response actions include: automatically generating a drone circular route containing five observation angles, adjusting the aperture value of the ground gimbal to below F2.8, and starting 4K video recording mode.

[0009] Preferably, the mathematical expression of the online evolution of the model parameters in step S5 is: ; in represents the convolution kernel parameters, To counter the loss function, is the adaptive learning rate, is the momentum factor, and its value is dynamically adjusted according to the model convergence speed.

[0010] Preferably, the adversarial loss function construction method includes: The generator G is constructed to generate adversarial samples with similar statistical characteristics to real abnormal samples, and the discriminator D distinguishes between real and generated samples. The loss function is: ; in is a Gaussian noise vector. During the training process, the main detection network parameters are frozen and only the generator and discriminator parameters are updated.

[0011] A system, applied to the application of any of the above-mentioned AI-based intelligent identification and early warning methods in urban and rural governance, comprising: A heterogeneous sensing unit, consisting of at least three types of UAV platforms and ground monitoring nodes distributed in a hexagonal topology; Edge computing units, GPU-accelerated server clusters deployed in the tower computer room, perform real-time video stream analysis; The dynamic scheduling unit uses reinforcement learning algorithm to optimize the flight path of the drone. The value function is: ; The state s includes the remaining battery level, wind speed data, and mission priority, and the action a includes the flight altitude, speed, and shooting mode; The visualization interaction unit generates a three-dimensional spatiotemporal situation map and supports penetrating queries of multi-layer alarm information.

[0012] Preferably, the UAV platform in the heterogeneous perception unit is configured with: Interchangeable mission pods support visible light, infrared, and multispectral imaging modes; Millimeter-wave radar module, used to automatically switch to active detection mode when visibility falls below 500 meters; Acoustic wave array, collecting abnormal sound characteristics within a radius of 50 meters.

[0013] Preferably, the edge computing unit includes: A dedicated FPGA for video decoding supports 8-channel parallel decoding in H.265 format; dual redundant network interfaces, with the primary link using a 5G private network and the backup link using a LoRa self-organizing network; and a temperature-adaptive cooling system that automatically activates the liquid cooling circulation device when the chip temperature exceeds 75°C.

[0014] Preferably, the visual interaction unit implements: Overlay display of spatiotemporal heat maps of abnormal events and population density layers; early warning of the correlation between drone battery capacity and ground sensor power supply status; semantic retrieval of historical event case libraries and intelligent recommendation of disposal plans.

[0015] Technical effects: This application constructs a high- and low-altitude heterogeneous perception network: drones + ground monitoring arrays, achieves spatiotemporal synchronization through a multimodal data alignment channel, and adopts three-level feature fusion: pixel-level registration superposition, feature-level cross-modal extraction, and decision-level GIS fusion, a dynamic trigger response link: risk assessment threshold triggers drone formation collaboration, and implements an online evolutionary adversarial training mechanism for model parameters; it can fundamentally solve the problem of spatiotemporal alignment of multi-source heterogeneous data and ensure the seamless integration of high- and low-altitude perception data in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a flowchart of the application method of AI-based intelligent identification and early warning method in urban and rural governance; Figure 2 This is a system diagram of the application of AI-based intelligent identification and early warning methods in urban and rural governance. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0019] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, operations, elements, components and / or groups thereof.

[0020] See also Figure 1 and Figure 2 , such as the positioning deviation caused by the inconsistent data coordinate systems of high- and low-altitude equipment in traditional urban and rural governance systems; the failure of visible light in foggy and hazy weather, and the sudden drop in the recognition accuracy of a single sensor modality in complex environments; the high false alarm rate caused by the fixed threshold warning mechanism. Based on this, this application provides an application method of an AI-based intelligent recognition and warning method in urban and rural governance, including the following process: S1. Build a heterogeneous high- and low-altitude perception network. This network uses drones equipped with multispectral imaging modules to form a spatiotemporally synchronized perception matrix with a ground-based video surveillance array. The ground-based video surveillance array is deployed at high-altitude monitoring points on the tower and integrates air quality sensors. S2. Establish a multimodal data alignment pipeline to apply spatial geocoding to the drone aerial imagery data, generating a time-synchronized data stream with the same geographic coordinate system as the ground surveillance video frames. S3. Perform three-level feature fusion analysis: register and overlay visible light and infrared spectral data at the pixel-level fusion layer, extract building edge and thermal anomaly features at the feature-level fusion layer, and generate anomaly event probability distribution maps by combining GIS basemap data at the decision-level fusion layer. S4. Dynamically build an event response chain. When the risk assessment value of any monitored area exceeds a set threshold, a drone formation is automatically triggered to conduct multi-angle surround photography of the target area, while simultaneously activating the zoom tracking mode of the three nearest ground monitoring gimbals. S5. Implement online evolution of model parameters, construct adversarial training scenarios based on historical false positive sample sets, and dynamically adjust the convolution kernel weight parameters and classifier confidence threshold of the object detection network through gradient backpropagation.

[0021] It is worth mentioning that: this application constructs a high-altitude and low-altitude heterogeneous perception network: UAV + ground monitoring array, realizes spatiotemporal synchronization through multimodal data alignment channels, adopts three-level feature fusion: pixel-level registration superposition, feature-level cross-modal extraction, decision-level GIS fusion, dynamically triggers the response link risk assessment threshold to trigger UAV formation collaboration, and implements the model parameter online evolution adversarial training mechanism; it can achieve the positioning accuracy to 0.3 meters through the geocoding conversion model, which is 10 times higher than the existing technology. Through the complementarity of infrared and visible light, multi-spectral fusion is effectively maintained to improve target recognition rate in adverse weather conditions, and weapons can reduce the false alarm rate through dynamic optimization mechanisms.

[0022] For example, traditional technical solutions have the following technical problems: The video frame time offset caused by the asynchrony of the high and low altitude equipment clocks; the monitoring blind spots caused by the easy change of the drone's altitude; the high transmission packet loss rate caused by wireless channel interference. Based on this, the construction of the spatiotemporal synchronization perception matrix in step S1 specifically includes: S11: Deploy Beidou differential positioning base stations on the drone take-off and landing platforms to ensure that the timestamp error of all data collection devices is less than 10ms; S12: Establish a geometric constraint relationship between the azimuth angle α of the ground monitoring gimbal and the flight altitude H of the UAV. When H>100 meters, α automatically increases the compensation angle Δα=arctan(H / D), where D is the horizontal distance between the devices. S13: Frequency division multiplexing is used to allocate wireless channel resources, with the 5.8 GHz band designated for drone video backhaul and the 2.4 GHz band for sensor data transmission.

[0023] It's worth noting that this embodiment provides a precise method for constructing a spatiotemporal synchronization perception matrix, including Beidou differential positioning, geometric constraint compensation, calculation of the compensation angle Δα between the azimuth angle α and the flight altitude H, and a frequency division multiplexing mechanism. This method can reduce time synchronization errors to less than 10ms; eliminate blind spots caused by altitude changes through the Δα compensation formula; and achieve dual-band allocation to improve data transmission success rates.

[0024] For example, traditional technical solutions have the following technical problems: Differences in multispectral data resolution can easily lead to registration distortion; weak cross-modal feature correlation can easily lead to semantic fragmentation, such as the high error rate in matching infrared heat sources with visible light objects; static rule bases are unable to adapt to the limitations of scene changes, and setting the rule update cycle usually requires manual intervention.

[0025] Based on this, the implementation method of the three-level feature fusion analysis in step S3 is: pixel-level fusion uses bilinear interpolation to align multispectral data, and the calculation formula is: ; in is the credibility weight of the i-th sensor, is the spatial resolution scaling factor; Feature-level fusion extracts cross-modal features through a parallel CNN architecture, with the main path processing visible light images and the bypass branch processing infrared data. Decision-level fusion introduces spatial semantic constraint rules to automatically increase the sensitivity threshold of dust monitoring when a construction site is identified.

[0026] is the credibility weight: Dynamic calculation is based on the sensor signal-to-noise ratio (SNR) and environmental interference intensity. The calculation formula is: , where k is the adjustment factor.

[0027] The weight of the infrared sensor is automatically increased to 0.7-0.9 at night, and the weight of the visible light sensor is reduced to 0.2-0.4 in foggy weather.

[0028] is the resolution scaling factor: Determined by the ratio of the sensor's physical resolution to the reference resolution, usually the 4K standard of 3840×2160; For example, a 20-megapixel drone image , 8 million pixel ground monitoring .

[0029] (Coordinate mapping): Use rounding down to avoid interpolation oversampling, and use bilinear interpolation kernel function Achieve sub-pixel alignment.

[0030] It is worth mentioning that the specific implementation method of the three-level feature fusion provided in this embodiment: pixel level adopts bilinear interpolation weighted fusion, including credibility weight and scaling factor The feature level processes multimodal data through a parallel CNN architecture, and the decision level introduces spatial semantic constraint rules, such as automatically increasing the sensitivity of dust monitoring in construction site scenes.

[0031] The technical effects of the above embodiments include: Through the interpolation formula Dynamic weight optimization can reduce the registration error to within 0.8 pixels; The bypass branch of the parallel CNN specifically processes infrared features, which can provide cross-modal feature association accuracy; the adaptive adjustment response time of semantic constraint rules is less than 200ms.

[0032] The activation conditions of the event response link described in step S4 include: the simultaneous appearance of two or more abnormal features in the same geographic grid; the movement speed of the target object in three consecutive frames of images exceeds the set threshold; there is a significant contradiction between the ground sensor data and the video analysis results; the response actions include: automatically generating a drone circular route including five observation angles, adjusting the aperture value of the ground gimbal to below F2.8, and starting 4K video recording mode.

[0033] For example, traditional technical solutions have the following technical problems: Traditional data augmentation methods generate insufficient sample diversity; static test sets lead to model overfitting, and manual annotation of adversarial samples is expensive. Based on this, the mathematical expression of the online evolution of model parameters in step S5 is: ; in represents the convolution kernel parameters, To counter the loss function, is the adaptive learning rate, is the momentum factor, and its value is dynamically adjusted according to the model convergence speed.

[0034] Adaptive learning rate: dynamically adjusted according to the curvature of the loss surface: ,in is the historical gradient; the initial value , upper limit , to prevent gradient explosion.

[0035] To combat the loss, an improved form of Wasserstein distance is used: ;

[0036] Penalty coefficient Ensure Lipschitz continuity constraints.

[0037] Momentum factor: The dynamic calculation formula is: , which increases to 0.9 with each training round t.

[0038] It's worth noting that the above-mentioned embodiment employs a generative adversarial network (GAN) framework for model optimization. The generator G synthesizes adversarial examples with realistic anomalous statistical characteristics, while the discriminator D distinguishes between real and generated data. The adversarial loss function uses a cross-entropy form. During training, the main detection network parameters are frozen, and only the generator and discriminator parameters are updated. This approach improves the coefficient of variation of generated samples to 0.78, increases the variation in texture, lighting, and other attributes by 160%, narrows the gap in validation set accuracy due to improved model generalization, and achieves an automated adversarial example generation rate of 5,000 per hour.

[0039] For example, traditional technical solutions have the following technical problems: Traditional data augmentation methods lack sufficient sample diversity; static test sets lead to model overfitting, and manually labeling adversarial samples is costly.

[0040] Based on this, the adversarial loss function construction method includes: The generator G is constructed to generate adversarial samples with similar statistical characteristics to real abnormal samples, and the discriminator D distinguishes between real and generated samples. The loss function is: ;

[0041] in is a Gaussian noise vector. During the training process, the main detection network parameters are frozen and only the generator and discriminator parameters are updated.

[0042] Generator : Input noise ,The network structure adopts the U-Net+Attention mechanism, and the output image resolution is gradually increased to 1024×1024 through the transposed convolution layer.

[0043] Discriminator : Contains 5 convolutional blocks, and the last layer uses Spectral Normalization constraint; The output is a 70×70 matrix of the PatchGAN structure, where each element corresponds to a 30×30 region of the input image.

[0044] Expectation Operation :The actual calculation uses a small batch approximation: , batch size , combined with gradient accumulation to achieve equivalent train.

[0045] Notably, this solution utilizes a generative adversarial network (GAN) framework for model optimization. The generator G synthesizes adversarial examples with realistic anomalous statistical characteristics, while the discriminator D distinguishes between real and generated data. The adversarial loss function uses a cross-entropy form. During training, the main detection network parameters are frozen, and only the generator and discriminator parameters are updated. This improves the coefficient of variation of generated samples, for example, by increasing the variation in texture and lighting by 160%. This improved model generalization capability narrows the gap in validation set accuracy. Furthermore, the automated adversarial example generation rate reaches 5,000 per hour.

[0046] For example, traditional technical solutions have the following technical problems: Using a single model of drone is difficult to adapt to different and more complex environments, such as rainy weather, humid weather, or mountainous hills; using centralized cloud computing for all data leads to excessive latency, and a single emergency response decision takes more than 5 minutes, resulting in inefficient manual scheduling.

[0047] Based on this, the present application provides a system for reducing the efficiency of manual scheduling, which is applied to the application of any of the above-mentioned AI-based intelligent identification and early warning methods in urban and rural governance, including: A heterogeneous sensing unit, consisting of at least three types of UAV platforms and ground monitoring nodes distributed in a hexagonal topology; Edge computing units, GPU-accelerated server clusters deployed in the tower computer room, perform real-time video stream analysis; The dynamic scheduling unit uses reinforcement learning algorithm to optimize the flight path of the drone. The value function is: ; The state s includes the remaining battery level, wind speed data, and mission priority, and the action a includes the flight altitude, speed, and shooting mode; The visualization interaction unit generates a three-dimensional spatiotemporal situation map and supports penetrating queries of multi-layer alarm information.

[0048] Represents the state vector: Contains 6-dimensional features: ;

[0049] Battery Energy Use a second-order polynomial model: (k is the discharge coefficient) Represent the action space: The discretization process is divided into 9 combinations: ,high rice Denote the discount factor: Adaptive Adjustment: , the closer to the target, the smaller the discount.

[0050] It's worth noting that the hardware architecture of the intelligent early warning system in this application includes a heterogeneous perception unit, multiple drone models + hexagonal ground nodes, an edge computing unit providing a GPU server cluster, a dynamic scheduling unit for reinforcement learning path optimization, and a visualization interaction unit for three-dimensional situation map penetration query. The scheduling algorithm uses the Q-learning framework, and the state space includes equipment operating conditions and environmental parameters. It can improve the task completion rate of multi-model collaboration, dynamically matching models and tasks through Q functions; edge computing compresses latency to within 120ms; and autonomous scheduling decision-making time is shortened to 8 seconds.

[0051] For example, traditional technical solutions using fixed sensors are prone to failure in harsh environments. For example, the failure probability of optical equipment on foggy days is greater than 40%. Single-modal data dimensions are insufficient, and there is a conflict between device endurance and payload. For every 100g of payload added, the endurance decreases by 12 minutes. Based on this, the UAV platform configuration in the heterogeneous perception unit includes: Interchangeable mission pods support visible light, infrared, and multispectral imaging modes; Millimeter-wave radar module, used to automatically switch to active detection mode when visibility falls below 500 meters; Acoustic wave array, collecting abnormal sound characteristics within a radius of 50 meters.

[0052] It is worth mentioning that this solution can achieve multi-sensor redundancy to ensure all-weather work availability of 99.8%; the supplementation of voiceprint features increases the dimension of abnormal event identification to 7 categories; and the modular design makes the mission payload switching time less than 30 seconds.

[0053] For example, the CPU of traditional technical solutions only supports two-channel 4K decoding, resulting in insufficient video decoding computing power; single-point network failures lead to data loss; high-temperature frequency reduction can easily lead to performance fluctuations, with computing power decreasing by 15% for every 10°C increase in temperature.

[0054] Based on this, the edge computing unit includes: A dedicated FPGA for video decoding supports 8-channel parallel decoding in H.265 format; dual redundant network interfaces, with the primary link using a 5G private network and the backup link using a LoRa self-organizing network; and a temperature-adaptive cooling system that automatically activates the liquid cooling circulation device when the chip temperature exceeds 75°C.

[0055] Notably, this embodiment provides an edge computing unit with an integrated video decoding FPGA (8-channel H.265 concurrent), dual redundant networks (5G private network + LoRa backup), and a temperature-adaptive cooling system (liquid cooling trigger threshold of 75°C). The FPGA utilizes a pipelined architecture to process 4K video streams, increasing decoding capabilities by fourfold while reducing power consumption by 60%. Dual network redundancy ensures 99.99% communication availability, while the liquid cooling system maintains the chip temperature at 70±2°C.

[0056] For example, traditional technical solutions have technical problems such as two-dimensional plane display information overload, disconnection between equipment status monitoring and business, and low utilization of historical data. Based on this, the visual interaction unit realizes: Overlay display of spatiotemporal heat maps of abnormal events and population density layers; early warning of the correlation between drone battery capacity and ground sensor power supply status; semantic retrieval of historical event case libraries and intelligent recommendation of disposal plans.

[0057] Notably, this embodiment utilizes a visual interaction unit to overlay spatiotemporal heat maps with population density, provide device status-related alerts, and enable semantic retrieval of historical cases. A knowledge graph is used to construct a case library, supporting natural language queries. Technical benefits include: 3D layered display triples information density, correlation analysis increases alert accuracy to 89%, and semantic search response time is reduced to 1.2 seconds.

[0058] Unless otherwise specified, the device components involved in the above embodiments are all conventional device components, and the connection methods and control methods involved are all conventional connection methods and control methods unless otherwise specified.

[0059] The present invention has been described in detail above with reference to the embodiments. However, those skilled in the art will appreciate that, without departing from the spirit of the present invention, the specific parameters in the above embodiments may be modified to form multiple specific embodiments, which are all within the common variation range of the present invention and will not be described in detail here.

Claims

1. The application method of AI-based intelligent identification and early warning method in urban and rural governance is characterized by: The following processes are included: S1. Build a heterogeneous high- and low-altitude perception network. This network uses drones equipped with multispectral imaging modules to form a spatiotemporally synchronized perception matrix with a ground-based video surveillance array. The ground-based video surveillance array is deployed at high-altitude monitoring points on the tower and integrates air quality sensors. S2. Establish a multimodal data alignment pipeline to apply spatial geocoding to the drone aerial imagery data, generating a time-synchronized data stream with the same geographic coordinate system as the ground surveillance video frames. S3. Perform three-level feature fusion analysis: register and overlay visible light and infrared spectral data at the pixel-level fusion layer, extract building edge and thermal anomaly features at the feature-level fusion layer, and generate anomaly event probability distribution maps by combining GIS basemap data at the decision-level fusion layer. S4. Dynamically build an event response chain. When the risk assessment value of any monitored area exceeds a set threshold, a drone formation is automatically triggered to conduct multi-angle surround photography of the target area, while simultaneously activating the zoom tracking mode of the three nearest ground monitoring gimbals. S5. Implement online evolution of model parameters, construct adversarial training scenarios based on historical false positive sample sets, and dynamically adjust the convolution kernel weight parameters and classifier confidence threshold of the object detection network through gradient backpropagation.

2. The application method of the AI-based intelligent identification and early warning method in urban and rural governance according to claim 1 is characterized in that: The construction of the spatiotemporal synchronization perception matrix in step S1 specifically includes: S11: Deploy Beidou differential positioning base stations on the drone take-off and landing platforms to ensure that the timestamp error of all data collection devices is less than 10ms; S12: Establish a geometric constraint relationship between the azimuth angle α of the ground monitoring gimbal and the flight altitude H of the UAV. When H>100 meters, α automatically increases the compensation angle Δα=arctan(H / D), where D is the horizontal distance between the devices. S13: Frequency division multiplexing is used to allocate wireless channel resources, with the 5.8 GHz band designated for drone video backhaul and the 2.4 GHz band for sensor data transmission.

3. The application method of the AI-based intelligent identification and early warning method in urban and rural governance according to claim 1 is characterized in that: The implementation method of the three-level feature fusion analysis in step S3 is: pixel-level fusion uses bilinear interpolation to align multispectral data, and the calculation formula is: ； in is the credibility weight of the i-th sensor, is the spatial resolution scaling factor; Feature-level fusion extracts cross-modal features through a parallel CNN architecture, with the main path processing visible light images and the bypass branch processing infrared data. Decision-level fusion introduces spatial semantic constraint rules to automatically increase the sensitivity threshold of dust monitoring when a construction site is identified.

4. The application method of the AI-based intelligent identification and early warning method in urban and rural governance according to claim 1 is characterized in that: The activation conditions of the event response link described in step S4 include: the simultaneous appearance of two or more abnormal features in the same geographic grid; the movement speed of the target object in three consecutive frames of images exceeds the set threshold; there is a significant contradiction between the ground sensor data and the video analysis results; the response actions include: automatically generating a drone circular route including five observation angles, adjusting the aperture value of the ground gimbal to below F2.8, and starting 4K video recording mode.

5. The application method of the AI-based intelligent identification and early warning method in urban and rural governance according to claim 1 is characterized in that: The mathematical expression of the online evolution of the model parameters in step S5 is: ； in represents the convolution kernel parameters, To counter the loss function, is the adaptive learning rate, is the momentum factor, and its value is dynamically adjusted according to the model convergence speed.

6. The application method of the AI-based intelligent identification and early warning method in urban and rural governance according to claim 1 is characterized in that: The adversarial loss function construction method includes: The generator G is constructed to generate adversarial samples with similar statistical characteristics to real abnormal samples, and the discriminator D distinguishes between real and generated samples. The loss function is: ； in is a Gaussian noise vector. During the training process, the main detection network parameters are frozen and only the generator and discriminator parameters are updated.

7. A system, applied to the application method of the AI-based intelligent identification and early warning method in urban and rural governance as described in any one of claims 1 to 6, characterized in that: include: A heterogeneous sensing unit, consisting of at least three types of UAV platforms and ground monitoring nodes distributed in a hexagonal topology; Edge computing units, GPU-accelerated server clusters deployed in the tower computer room, perform real-time video stream analysis; The dynamic scheduling unit uses reinforcement learning algorithm to optimize the flight path of the drone. The value function is: ； The state s includes the remaining battery level, wind speed data, and mission priority, and the action a includes the flight altitude, speed, and shooting mode; The visualization interaction unit generates a three-dimensional spatiotemporal situation map and supports penetrating queries of multi-layer alarm information.

8. The system according to claim 7, characterized in that The UAV platform configuration in the heterogeneous perception unit includes: Interchangeable mission pods support visible light, infrared, and multispectral imaging modes; Millimeter-wave radar module, used to automatically switch to active detection mode when visibility falls below 500 meters; Acoustic wave array, collecting abnormal sound characteristics within a radius of 50 meters.

9. The system according to claim 7, wherein: The edge computing unit includes: A dedicated FPGA for video decoding supports 8-channel parallel decoding in H.265 format; dual redundant network interfaces, with the primary link using a 5G private network and the backup link using a LoRa self-organizing network; and a temperature-adaptive cooling system that automatically activates the liquid cooling circulation device when the chip temperature exceeds 75°C.

10. The system according to claim 7, wherein: The visual interaction unit realizes: Overlay display of spatiotemporal heat maps of abnormal events and population density layers; early warning of the correlation between drone battery capacity and ground sensor power supply status; semantic retrieval of historical event case libraries and intelligent recommendation of disposal plans.

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