A method and system for real-time monitoring and response of urban infrastructure damages

Abstract

The application provides a city infrastructure damage real-time monitoring and response method and system. The application arranges multiple image sensors at multiple key positions of city infrastructure, which are used to collect images of the infrastructure in real time, and construct an image sample library of infrastructure damage based on the collected image data; receives and processes the image data, extracts key features; processes and analyzes the real-time monitoring image data, identifies the damage condition of the infrastructure, and evaluates the damage degree. The application realizes real-time monitoring, accurate identification and rapid response to city infrastructure damage problems through infrastructure damage feature memory and generalization association technology based on diffusion generation. The application not only improves the efficiency and accuracy of city management and emergency rescue, but also provides strong data support for the intelligentization and refinement of city management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban infrastructure monitoring and response, and particularly relates to a real-time monitoring and response method and system for urban infrastructure damage. BACKGROUND

[0002] With the acceleration of urbanization, the scale and complexity of urban infrastructure, such as roads, bridges, tunnels, and pipelines, are increasing. Damage to these facilities not only affects the normal operation of the city, but also threatens the safety of citizens' lives and property. Therefore, real-time monitoring and rapid response to urban infrastructure damage are of great significance to urban management and emergency rescue.

[0003] Currently, the monitoring of urban infrastructure damage mainly relies on manual patrols and traditional sensor equipment. Although manual patrols can directly observe the damage to the facilities, they are inefficient and difficult to achieve real-time monitoring. While traditional sensor equipment can achieve real-time monitoring, it has high installation and maintenance costs and is difficult to cover all facilities comprehensively. In addition, existing monitoring systems often only provide basic information about damage and lack analysis of the causes and extent of damage, making it difficult to guide subsequent response measures. Low monitoring efficiency, difficult to achieve real-time monitoring. High installation and maintenance costs, difficult to cover all facilities comprehensively. Lack of damage cause analysis, difficult to guide subsequent response measures. SUMMARY

[0004] The purpose of the present application is to provide a real-time monitoring and response method and system for urban infrastructure damage, which is based on an infrastructure damage feature memory module and a generalized associative model generated based on diffusion to construct an infrastructure damage real-time monitoring network model. The system can monitor the operating state of the infrastructure in real time, timely detect and respond to damage, and improve the safety and reliability of urban infrastructure.

[0005] The technical solution of the present application is a real-time monitoring and response method for urban infrastructure damage, comprising:

[0006] Step 1: A plurality of image sensors are arranged at a plurality of key positions of urban infrastructure to collect images of the infrastructure in real time, and an image sample library of infrastructure damage is constructed based on the collected image data;

[0007] Step 2: Receive and process image data, extract key features;

[0008] Step 3: Process and analyze real-time monitoring image data to identify damage to the infrastructure and assess the extent of the damage;

[0009] As preferred, the image sensor in step 1 collects images of the image device network covering multiple aspects of the transportation system, energy system, and public buildings, and the system deploys high-resolution image sensors at key locations of the urban infrastructure. These sensors are installed at the following specific locations: image sensors are installed at major road intersections in the city, under specific bridges of the highway, at the entrance of the tunnel;

[0010] A real-time monitoring module is arranged for the image sensors at multiple key locations of the urban infrastructure to collect images of the infrastructure in real time and construct an image sample library of infrastructure damage through the collected image data;

[0011] The image sensor in step 1 collects images of the image device network covering multiple aspects of the transportation system, energy system, and public buildings, and the system deploys high-resolution image sensors at key locations of the urban infrastructure. These sensors are installed at the following specific locations:

[0012] Image sensors are installed at major road intersections in the city, under specific bridges of the highway, at the entrance of the tunnel;

[0013] As preferred, step 3 is as follows:

[0014] Step 3.1: Calculate the damage index of the urban infrastructure;

[0015] Step 3.2: Establish a correlation between normal urban infrastructure features and damaged features to assess the extent of damage;

[0016] The step 3.1 calculates the damage index of the urban infrastructure, which includes:

[0017] Crack identification, indentation identification, and fracture damage identification;

[0018] The damage index of the urban infrastructure is the sum of the damage degree identified by the crack identification sub-module, the indentation identification sub-module, and the fracture damage identification sub-module;

[0019] The damage index of the urban infrastructure is represented as:

[0020]

[0021] wherein, represents the damage degree of the crack, represents the damage degree of the indentation, represents the damage degree of the fracture;

[0022] The crack identification calculates the crack damage degree of each infrastructure according to the area of the crack, which is represented by the following formula:

[0023]

[0024] The crack area ratio refers to the ratio between the area of the crack and the target area, and the crack area ratio is approximately obtained by using the ratio of the pixels of the crack and the target pixels in the image obtained by the threshold-based segmentation method;

[0025] The concave recognition, the degree of concave represents:

[0026]

[0027] The degree of concave is described using the gradient of the function, Indicates the coordinates of the pixel, Indicates the pixel value.

[0028] The fracture damage recognition, the damage degree of the fracture is recognized by using the depth target detection algorithm YOLO, and the damage degree is divided into slight damage, moderate damage and serious damage;

[0029] As preferred, the normal city infrastructure features are associated with the damage features in step 3.2, and the damage degree is evaluated, which is specifically as follows:

[0030] Image collection is performed on all normal state infrastructures, and the data is stored in the normal feature library;

[0031] Each group of images extracts feature vectors by ResNet-50, and the model takes these normal features as the baseline;

[0032] The process performs a complete infrastructure health sampling once a day at night, and the collected data is denoised and geometrically corrected by the preprocessing module;

[0033] After the damage event occurs, the real-time image feedback of the automatic monitoring module is compared with the images in the normal feature library;

[0034] The diffusion generation module simulates noise on the damaged part, generates a new hypothetical damage scene by adding random noise to the original damage image, and the system will gradually denoise and imagine possible damage causes;

[0035] The specific calculation process is as follows:

[0036] Infrastructure damage feature recognition;

[0037] Generalization association based on diffusion generation;

[0038] For common damage phenomena in cities, the system collects images in real time through image sensors and marks the damage type; then using the collected damage samples, the system performs automatic model training through convolutional neural network; the training process is updated once a week to ensure that the model can recognize new damage features;

[0039] The model automatically compares historical damage data with the existing sample library, and gradually updates the recognition weight of the damage feature. Then in daily monitoring, the system compares the real-time collected images with the damage images in the sample library, and identifies the type and degree of damage through feature matching algorithm;

[0040] As preferred, the infrastructure damage feature recognition learns these representative damage features to deeply understand the complexity and irregularity of damage;

[0041] The infrastructure damage feature recognition model is represented as , wherein X represents the image sample of damaged infrastructure, E is the extraction network of infrastructure feature, which is composed of Resnet50; D represents the feature network of damaged infrastructure, which is composed of convolution;

[0042] C is the recognition network of damaged infrastructure, which is composed of linear layer. In order to ensure that D can express the feature representation of damaged infrastructure, the network parameter of E needs to be frozen when training the damaged infrastructure sample. C is to classify the type, and then the damage feature D can be embedded into the diffusion generation model to combine the normal city infrastructure with the damage feature;

[0043] As preferred, the diffusion generated generalization association constructs the city infrastructure damage sample;

[0044] The basic idea of embedding damage features into diffusion generation is to noise the damage feature;

[0045] In the noise adding process, the reverse damage denoising process is added, and in the denoising process, the damage noise adding process is added;

[0046] The damage feature is added to the diffusion model in the form of noise;

[0047] The noise adding process is expressed as:

[0048]

[0049] , wherein is used to control the weight of noise; represents noise; represents the image of damaged infrastructure X In different noise adding processes, the feature is , which is the last feature of the t+1 time step plus noise and removing damage feature ; wherein, t represents the current time step, which increases gradually with iteration;

[0050] The denoising process is expressed as:

[0051]

[0052] wherein, a weight for controlling noise; representing an image of a damaged facility X characteristics of different noise adding processes. is the feature of the t+1th time step from the last feature adding noise and removing damaged features wherein, t represents the current time step, which increases step by step with iteration;

[0053] generating a damaged facility sample image X for training in the formula E， the remaining parameters are frozen ；

[0054] By continuously collecting and analyzing real-time monitoring data and response results, the damage identification algorithm and the response measure library are optimized, the accuracy and efficiency of the system are improved, and the system is regularly upgraded and maintained to ensure stable operation of the system.

[0055] The application also provides a city infrastructure damage real-time monitoring and response system, comprising:

[0056] a real-time monitoring module, which is used for arranging a plurality of image sensors at a plurality of key positions of city infrastructure, is used for collecting images of infrastructure in real time, and is used for constructing an image sample library of infrastructure damage through collected image data;

[0057] a central processing system, which is used for receiving and processing image data and extracting key features;

[0058] a damage identification and analysis module, which is used for processing and analyzing real-time monitoring image data, identifying damage conditions of infrastructure, and evaluating damage degrees;

[0059] the damage identification and analysis module comprises an infrastructure damage feature identification module and a generalization association module based on diffusion generation.

[0060] The infrastructure damage feature memory module is used for calculating a damage index of city infrastructure, and comprises a crack identification sub-module, a depression identification sub-module and a fracture damage identification sub-module.

[0061] The damage index of city infrastructure is the sum of damage degrees identified by the crack identification sub-module, the depression identification sub-module and the fracture damage identification sub-module.

[0062] The crack identification sub-module is used for calculating a crack damage degree of each infrastructure according to an area of the crack.

[0063] The recess identification sub-module is used for identifying the degree of recess;

[0064] The fracture damage identification sub-module uses a deep target detection algorithm YOLO to identify the damage degree of fracture, and the damage degree is divided into slight damage, moderate damage and severe damage;

[0065] The generalization association module based on diffusion generation is used to associate normal urban infrastructure features with damaged features, and evaluate the damage degree;

[0066] The beneficial effects of the present application are that through real-time monitoring, automatic analysis, rapid response and continuous optimization, efficient management and processing of urban infrastructure damage are realized, the safety and reliability of urban infrastructure are improved, and economic losses and social impacts caused by damage are reduced.

[0067] The real-time monitoring and response system for urban infrastructure damage of the present application realizes real-time monitoring, accurate identification and rapid response to urban infrastructure damage problems through infrastructure damage feature memory and generalization association technology based on diffusion generation. The system not only improves the efficiency and accuracy of urban management and emergency rescue, but also provides strong data support for the intelligentization and refinement of urban management. According to the detection information, response decisions are made, and maintenance personnel and rescue resources are quickly dispatched, which can ensure that measures can be taken quickly when damage problems occur, and improve the efficiency and accuracy of emergency rescue. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 The method flowchart of the embodiment of the present application.

[0069] Figure 2 The urban flood infrastructure detection diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0071] In specific implementation, the method proposed by the technical solutions of the present application can be automatically run by computer software technology, and the system device of the method, such as computer readable storage medium storing the corresponding computer program of the technical solutions of the present application and computer equipment including running the corresponding computer program, should also be within the protection scope of the present application.

[0072] The specific embodiment of the method of the present application is a real-time monitoring and response method for urban infrastructure damage, specifically as follows: Figure 1 The specific embodiment of the method of the present application is a real-time monitoring and response method for urban infrastructure damage, specifically as follows:

[0073] The specific embodiment of the method of the present application is a real-time monitoring and response method for urban infrastructure damage, specifically as follows:

[0074] According to the type and distribution of urban infrastructure, the appropriate type and number of sensors are selected and deployed at key positions. For example, for roads and bridges, displacement sensors and stress sensors can be deployed to monitor their deformation and stress in real time; for tunnels and pipelines, temperature sensors and humidity sensors can be deployed to monitor their internal environment.

[0075] The operation data of the infrastructure is collected in real time through the sensor network, and data processing algorithms are used for data preprocessing and feature extraction. Preprocessing includes operations such as removing outliers and filling missing values to improve data quality. Feature extraction is to extract key features that can reflect infrastructure damage from raw data, such as displacement, stress, temperature, humidity, etc.

[0076] Step 1: multiple image sensors arranged at multiple key positions of urban infrastructure, for real-time acquisition of images of infrastructure, and construction of an image sample library of infrastructure damage through the acquired image data;

[0077] The image sensor network covers multiple aspects such as transportation systems, energy systems, and public buildings, and the system deploys high-resolution image sensors at key positions of urban infrastructure. These sensors are installed at the following specific locations: traffic at major road intersections in the city, under specific bridges on highways, at tunnel entrances;

[0078] The real-time monitoring module is used to arrange multiple image sensors at multiple key positions of urban infrastructure, for real-time acquisition of images of infrastructure, and construction of an image sample library of infrastructure damage through the acquired image data;

[0079] The image sensor network covers multiple aspects such as transportation systems, energy systems, and public buildings, and the system deploys high-resolution image sensors at key positions of urban infrastructure. These sensors are installed at the following specific locations:

[0080] Traffic at major road intersections in the city, under specific bridges on highways, at tunnel entrances;

[0081] Specifically, a camera is installed every 500 meters, which has waterproof and dustproof functions, uses all-weather visual and infrared imaging technology, and can capture road details and nighttime traffic conditions. Image data is transmitted in real time to the central monitoring system through the 5G network.

[0082] At key points of power grids and gas pipelines, 1-2 infrared imaging sensors are installed at each key point to detect temperature changes and surface cracks outside the pipeline. These sensors are regularly cleaned automatically to ensure long-term stable operation; infrared images and visible light images are collected every 15 minutes and automatically transmitted to the central control system through the Internet of Things platform.

[0083] In important public facilities such as fire stations and police stations in public buildings, fireproof monitoring cameras and smoke sensors are installed. These sensors are directly connected to the municipal emergency response system and can automatically trigger a fire alarm through the Internet of Things, while sending high-definition images to the cloud for artificial intelligence system to identify the fire.

[0084] Step 2: Receive and process image data, extract key features;

[0085] Step 3: Process and analyze real-time monitoring image data to identify damage to infrastructure and assess the extent of damage;

[0086] The step 3 is specifically as follows:

[0087] Step 3.1: Calculate the damage index of urban infrastructure;

[0088] Step 3.2: Establish a correlation between normal urban infrastructure features and damaged features to assess the extent of damage;

[0089] The step 3.1 calculates the damage index of urban infrastructure, including:

[0090] Crack identification, indentation identification, and fracture damage identification;

[0091] The damage index of urban infrastructure is the sum of the damage degree identified by the crack identification sub-module, the indentation identification sub-module, and the fracture damage identification sub-module;

[0092] The damage index of urban infrastructure is represented as:

[0093]

[0094] wherein, represents the damage degree of cracks, represents the damage degree of indentation, represents the damage degree of fracture;

[0095] The crack identification calculates the crack damage degree of each infrastructure according to the area of the crack, which is expressed by the following formula:

[0096]

[0097] The crack area ratio refers to the ratio between the area of the crack and the target area. The crack area ratio is obtained by using the threshold-based segmentation method to obtain the ratio of the pixels of the crack and the target pixels in the image.

[0098] The recess identification represents the degree of the recess as follows:

[0099]

[0100] The degree of the recess is described using the gradient of the function, represents the coordinates of the pixel, represents the pixel value.

[0101] The fracture damage identification uses the deep target detection algorithm YOLO to identify the damage degree of the fracture, which is divided into slight damage, moderate damage, and severe damage.

[0102] Step 3.2 establishes the association between the normal city infrastructure features and the damaged features, and evaluates the damage degree, as follows:

[0103] Image collection is performed on all infrastructures in normal state, and the data is stored in the normal feature library;

[0104] Each group of images extracts feature vectors through ResNet-50, and the model takes these normal features as the baseline;

[0105] This process performs a complete infrastructure health sampling every night, and the collected data is denoised and geometrically corrected by the preprocessing module;

[0106] After the damage event occurs, the real-time image feedback of the automatic monitoring module is used to compare the newly collected damaged images with the images in the normal feature library;

[0107] The diffusion generation module simulates noise for the damaged part, generates a new hypothetical damaged scene by adding random noise to the original damaged image, and the system will gradually denoise and guess the possible damage causes;

[0108] For example, after detecting cracks on a bridge, the system simulates the scenario of the cracks gradually expanding to predict possible further damage, and feeds back the results to the maintenance department.

[0109] The specific calculation process is as follows:

[0110] Infrastructure damage feature identification;

[0111] Generalized association based on diffusion generation;

[0112] For common damage phenomena in cities, the system collects images in real time through image sensors and marks the damage types; then using the collected damage samples, the system trains the model automatically through the convolutional neural network; the training process is updated once a week to ensure that the model can identify new damage features;

[0113] The model automatically compares historical damage data with the existing sample library and gradually updates the recognition weight of the damage feature. Then in daily monitoring, the system compares the real-time collected images with the damage images in the sample library, and identifies the type and degree of damage through feature matching algorithm;

[0114] For example, when detecting road cracks, the system will compare the crack size, depth and morphology in the sample library with the current image to determine whether immediate repair is needed.

[0115] The infrastructure damage feature recognition learns these representative damage features, and the model can deeply understand the complexity and irregularity of damage;

[0116] The infrastructure damage feature recognition model is represented as , wherein X represents the image sample of the damaged facility, E is the feature extraction network of the facility, which is composed of Resnet50; D represents the representation network of the facility damage, which is composed of convolution;

[0117] C is the recognition network of the damaged facility, which is composed of linear layers. In order to ensure that D can express the feature representation of facility damage, the E network parameters need to be frozen when training the damaged facility sample. C is to classify the type, and then the damage representation D can be embedded into the diffusion generation model to combine normal city facilities with damage features;

[0118] The generalized association based on diffusion generation constructs the city infrastructure damage sample;

[0119] The basic idea of embedding damage features into diffusion generation is to noise the damage features;

[0120] In the noise adding process, add the reverse damage denoising process, and in the denoising process, add the damage noise adding process;

[0121] The damage features are added in the form of noise to obtain the diffusion model;

[0122] The noise adding process is expressed as:

[0123]

[0124] , wherein, a weight for controlling the noise; representing the noise; representing the image of the damaged facility X characteristics of different noise adding processes, is the last feature of the t+1th time step adding noise and removing damaged features ; wherein, t represents the current time step, which increases step by step with iteration;

[0125] The denoising process is expressed as:

[0126]

[0127] wherein, a weight for controlling the noise; representing the image of the damaged facility X characteristics of different noise adding processes. is the last feature of the t+1th time step adding noise and removing damaged features . Wherein, t represents the current time step, which increases step by step with iteration;

[0128] generate damaged facility sample images X for training in the formula E， the remaining parameters are frozen ；

[0129] By continuously collecting and analyzing real-time monitoring data and response results, optimizing the damage identification algorithm and response measure library, the accuracy and efficiency of the system are improved; Regularly upgrade and maintain the system to ensure stable operation of the system.

[0130] The specific embodiment of the system of the application is a city infrastructure damage real-time monitoring and response system, comprising:

[0131] A real-time monitoring module is arranged in a plurality of key positions of the city infrastructure, and a plurality of image sensors are arranged for real-time acquisition of images of the infrastructure, and an image sample library of infrastructure damage is constructed through the acquired image data;

[0132] The model of the image sensor is TA3R-5M 500 million pixel high-definition camera;

[0133] A central processing system is used to receive and process image data and extract key features;

[0134] The model of the central processing system is an Ubuntu operating system platform equipped with a GTX3090 GPU processor;

[0135] The damage identification and analysis module is used for processing and analyzing real-time monitoring image data, identifying damage of the infrastructure, and evaluating the damage degree.

[0136] The damage identification and analysis module comprises an infrastructure damage feature identification module and a generalization association module based on diffusion generation.

[0137] The infrastructure damage feature memory module is used for calculating the damage index of the urban infrastructure, and comprises a crack identification submodule, a depression identification submodule, and a fracture damage identification submodule.

[0138] The damage index of the urban infrastructure is the sum of the damage degrees identified by the crack identification submodule, the depression identification submodule, and the fracture damage identification submodule.

[0139] The crack identification submodule is used for calculating the crack damage degree of each infrastructure according to the area of the crack.

[0140] The depression identification submodule is used for identifying the degree of depression.

[0141] The fracture damage identification submodule uses a deep target detection algorithm YOLO to identify the damage degree of fracture, which is divided into slight damage, moderate damage, and severe damage.

[0142] The generalization association module based on diffusion generation is used for associating normal urban infrastructure features with damage features and evaluating the damage degree.

[0143] The present application has the beneficial effect that through real-time monitoring, automatic analysis, rapid response, and continuous optimization, efficient management and processing of urban infrastructure damage are realized, the safety and reliability of urban infrastructure are improved, and economic losses and social impacts caused by damage are reduced.

[0144] As Figure 2 The city flood causes ground damage, road waterlogging, building collapse, tree toppling, and vehicle damage to be detected.

[0145] Once the infrastructure damage is identified, the system generates corresponding warning information according to the preset warning threshold and sends the warning information to the city management department and emergency rescue agencies. The warning information includes the location, type, and degree of damage, so that the relevant departments can respond in time. After receiving the warning information, the city management department and emergency rescue agencies will develop response decisions according to the actual situation, including dispatching maintenance personnel, allocating rescue resources, and implementing corresponding response measures. At the same time, the system also monitors and records the response process in real time for subsequent data analysis and optimization.

[0146] It should be understood that parts of the specification not specifically described in detail are part of the prior art.

[0147] It should be understood that the above description of the preferred embodiments is merely a detailed explanation and is not considered as a limitation to the scope of patent protection of the present application. Any modification or alternation made by those skilled in the art without departing from the scope of the present application as defined in the claims shall fall within the scope of the present application. The scope of patent protection of the present application shall be subject to the appended claims.

Claims

1. A method for real-time monitoring and response to damage to urban infrastructure, characterized in that, include: Step 1: Deploy multiple image sensors at key locations in urban infrastructure to acquire images of the infrastructure in real time, and build an image sample library of infrastructure damage using the acquired image data; Step 2: Receive and process image data, and extract key features; Step 3: Process and analyze the real-time monitoring image data to identify the damage to the infrastructure and assess the extent of the damage; Step 3 is as follows: Step 3.1: Calculate the damage index of urban infrastructure; Step 3.2: Establish a correlation between normal urban infrastructure features and damage features to assess the degree of damage; The specific calculation process for step 3.2 is as follows: Infrastructure damage feature identification; Generalized associations based on diffusion generation; To address common urban damage phenomena, the system uses image sensors to collect images in real time and label the damage types. Then, using the collected damage samples, the system uses a convolutional neural network to automatically train a model. The training process is updated weekly to ensure that the model can recognize new damage features. The model automatically compares historical damage data with the existing sample library and gradually updates the recognition weights of damage features. Then, in daily monitoring, the system compares the real-time collected images with the damage images in the sample library and identifies the type and extent of damage through feature matching algorithms. The infrastructure damage feature identification model, by learning these representative damage features, enables the model to gain a deeper understanding of the complexity and irregularity of the damage. Its infrastructure damage feature identification model is represented as ,in X The image samples represent damaged facilities, and E is the facility feature extraction network, which is composed of ResNet50. D The representation network indicating facility damage is composed of convolutions; C is the identification network for damaged facilities, consisting of linear layers. To ensure that D can express the characteristic representation of facility damage, the parameters of the E network need to be frozen when training damaged facility samples. C classifies the types, and the damage representation D can be embedded into the diffusion generation model to combine normal urban facilities with damage features. The generalized associations generated by the diffusion process are used to construct samples of urban infrastructure damage. The basic idea of ​​embedding damage features into diffusion generation is to noiseen the damage features; A reverse damage denoising process is added to the noise addition process, and a damage noise addition process is added to the denoising process. The damage characteristics are incorporated into the diffusion model as noise; The noise-addition process is expressed as follows: in, Weights used to control noise; Indicates noise; Images representing damaged facilities X Characteristics of different noise addition processes It is the feature from the previous time step t+1. Add noise and remove damaged features Where t represents the current time step, which increases gradually with each iteration; The denoising process is expressed as follows: in, Weights used to control noise; Images representing damaged facilities X Characteristics of different noise addition processes; It is the feature of the (t+1)th time step. Add noise and remove damaged features Where t represents the current time step, which increases gradually with each iteration; Generate sample images of damaged facilities X Used for training In the formula E， Freeze the remaining parameters; By continuously collecting and analyzing real-time monitoring data and response results, we optimize the damage identification algorithm and response measure library to improve the accuracy and efficiency of the system; and we regularly upgrade and maintain the system to ensure its stable operation.

2. The method for real-time monitoring and response to damage to urban infrastructure according to claim 1, characterized in that: The network of image acquisition devices from the image sensors covers multiple aspects, including transportation systems, energy systems, and public buildings.

3. The method for real-time monitoring and response to urban infrastructure damage according to claim 2, characterized in that: The image sensor is installed at locations including major road intersections in cities, under specific bridges on highways, and at tunnel entrances.

4. The method for real-time monitoring and response to urban infrastructure damage according to claim 3, characterized in that: Step 3.1, which calculates the damage index of urban infrastructure, includes: Crack identification, dent identification, fracture and damage identification; The damage index of urban infrastructure is the sum of the damage levels identified by the crack identification submodule, the dent identification submodule, and the fracture damage identification submodule; The damage index of the urban infrastructure is expressed as follows: in, Indicates the degree of damage caused by the crack. Indicates the degree of damage caused by the dent. Indicates the degree of damage caused by the breakage; The crack identification process calculates the extent of damage to each infrastructure component based on the crack area, using the following formula: The crack area ratio refers to the ratio between the area of ​​the crack and the area of ​​the target. The crack area ratio is approximately obtained by using a threshold-based segmentation method to obtain the ratio of crack pixels to target pixels in the image. The indentation identification, the degree of indentation is indicated as follows: The degree of concavity is described using the gradient of the function. Represents the coordinates of a pixel. Represents pixel value; The fracture and damage identification uses the YOLO deep target detection algorithm to identify the degree of fracture damage, which is divided into minor damage, moderate damage, and severe damage.

5. The method for real-time monitoring and response to damage to urban infrastructure according to claim 4, characterized in that: Step 3.2 involves establishing a correlation between normal urban infrastructure features and damage features to assess the degree of damage, as detailed below: Images of all infrastructure under normal conditions are captured and stored in a normal feature database; Each set of images has its feature vectors extracted using ResNet-50, and the model uses these normal features as a baseline. The process involves a complete infrastructure health sampling every night, and the collected data is denoised and geometrically corrected by a preprocessing module. After a damage event occurs, the newly acquired damaged image is compared with the image in the normal feature library through real-time image feedback from the automatic monitoring module. The diffusion generation module simulates noise in the damaged area by adding random noise to the original damaged image to generate a new hypothetical damage scenario. The system will gradually remove noise and infer the cause of the damage.

6. A real-time monitoring and response system for urban infrastructure damage, characterized in that, A method for real-time monitoring and response to urban infrastructure damage as described in any one of claims 1-5, comprising: The real-time monitoring module is used to deploy multiple image sensors at key locations in urban infrastructure to collect images of the infrastructure in real time and to build an image sample library of infrastructure damage based on the collected image data. The central processing system is used to receive and process image data and extract key features; The damage identification and analysis module is used to process and analyze real-time monitoring image data, identify the damage to infrastructure, and assess the extent of the damage. The damage identification and analysis module includes: an infrastructure damage feature identification module and a diffusion-based generalization association module; The infrastructure damage feature memory module is used to calculate the damage index of urban infrastructure, and consists of a crack identification submodule, a dent identification submodule, and a fracture damage identification submodule. The damage index of urban infrastructure is the sum of the damage levels identified by the crack identification submodule, the dent identification submodule, and the fracture damage identification submodule; The crack identification submodule is used to calculate the degree of crack damage to each infrastructure based on the area of ​​the crack. The dent recognition submodule is used to identify the degree of dent; The fracture and damage identification submodule uses the YOLO deep target detection algorithm to identify the degree of fracture damage, which is divided into minor damage, moderate damage, and severe damage. The diffusion-based generalization association module is used to establish associations between normal urban basic features and damaged features to assess the degree of damage.

Images