Urban infrastructure damage real-time monitoring and response method and system

Abstract

The invention provides an urban infrastructure damage real-time monitoring and response method and system. A plurality of image sensors arranged at a plurality of key parts of urban infrastructure are used for acquiring images of the infrastructure in real time and constructing an image sample library of infrastructure damage through acquired image data; receiving and processing the image data, and extracting key features; and processing and analyzing the real-time monitoring image data, identifying the damage condition of the infrastructure, and evaluating the damage degree. Through infrastructure damage feature memory and a generalization association technology based on diffusion generation, real-time monitoring, accurate identification and rapid response of urban infrastructure damage problems are realized. According to the invention, the efficiency and accuracy of city management and emergency rescue are improved, and powerful data support is provided for intelligence and refinement of city management.

Description

Technical Field

[0001] The present invention relates to the technical field of urban infrastructure monitoring and response, and in particular to a method and system for real-time monitoring and response of urban infrastructure damage. Background Art

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

[0003] At present, the monitoring of urban infrastructure damage mainly relies on manual inspections and traditional sensor equipment. Although manual inspections can intuitively discover the damage of facilities, they are inefficient and difficult to achieve real-time monitoring. Although traditional sensor equipment can achieve real-time monitoring, the deployment and maintenance costs are high, and it is difficult to fully cover all facilities. In addition, the existing monitoring system can only provide basic information on the damage, and the analysis of the cause and extent of the damage is insufficient, making it difficult to guide subsequent response measures. The monitoring efficiency is low and it is difficult to achieve real-time monitoring. The deployment and maintenance costs are high, and it is difficult to fully cover all facilities. The analysis of the cause of the damage is insufficient, making it difficult to guide subsequent response measures. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for real-time monitoring and response of urban infrastructure damage. The core algorithm of the system is to build a real-time monitoring network model for infrastructure damage based on an infrastructure damage feature memory module and a generalized associative model based on diffusion generation. The system can monitor the operating status of the infrastructure in real time, discover and respond to damage in a timely manner, and improve the safety and reliability of urban infrastructure.

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

[0006] Step 1: Multiple image sensors are deployed at multiple key locations of urban infrastructure to collect images of the infrastructure in real time, and a sample library of damaged infrastructure images is constructed based on the collected image data;

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

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

[0009] Preferably, the image device network collected by the image sensor in step 1 covers multiple aspects such as transportation systems, energy systems, and public buildings, and the system deploys high-resolution image sensors at key locations of urban infrastructure. These sensors are installed in the following specific locations: image sensors are installed at major road intersections in the city, under specific bridges on highways, and at tunnel entrances;

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

[0011] The image sensor captures images from a network of devices covering multiple aspects such as transportation systems, energy systems, and public buildings. The system deploys high-resolution image sensors at key locations of urban infrastructure. These sensors are installed in the following specific locations:

[0012] In terms of traffic, image sensors are installed at major road intersections in cities, under specific bridges on highways, and at tunnel entrances;

[0013] Preferably, the step 3 is as follows:

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

[0015] Step 3.2: Associate normal urban infrastructure characteristics with damage characteristics to assess the extent of damage;

[0016] The step 3.1 of calculating the damage index of urban infrastructure includes:

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

[0018] The damage index of urban infrastructure is the sum of the damage degrees identified by the crack identification submodule, the dent identification submodule, and the fracture damage identification submodule;

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

[0020] D total =D crack +D deformation +D fracture

[0021] Among them, D crack Indicates the degree of damage of the crack, D deformation Indicates the degree of damage of the dent, D fracture Indicates the degree of damage of the fracture;

[0022] The crack identification calculates the degree of damage of each infrastructure crack according to the area of ​​the crack. The formula is as follows:

[0023]

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

[0025] The depression identification and the degree of depression are expressed as follows:

[0026]

[0027] The degree of concavity is described using the gradient of the function, where (i, j) represents the coordinates of the pixel and f(x) represents the pixel value.

[0028] The fracture damage identification uses the deep object detection algorithm YOLO to identify the damage degree of the fracture, and the damage degree is divided into slight damage, moderate damage, and severe damage;

[0029] Preferably, in step 3.2, the normal urban infrastructure characteristics are associated with the damage characteristics to assess the degree of damage, as follows:

[0030] Collect images of all infrastructure in normal state and store the data in the normal feature library;

[0031] Each set of images is passed through ResNet-50 to extract feature vectors, and the model uses these normal features as a baseline;

[0032] This process performs a complete infrastructure health sampling every night, and the collected data is denoised and geometrically corrected through a pre-processing module;

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

[0034] The diffusion generation module simulates the noise of the damaged part and generates a new hypothetical damage scene by adding random noise to the original damaged image. The system will gradually remove the noise and associate the possible causes of the damage.

[0035] The specific calculation process is as follows:

[0036] Identification of infrastructure damage features;

[0037] Generalized associations based on diffusion generation;

[0038] In response to common damage phenomena in cities, the system uses image sensors to collect images in real time and mark the damage types. Using the collected damage samples, the system uses convolutional neural networks to perform automatic model training. 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 weights of damage features. Then, in daily monitoring, the system compares the real-time collected images with the damaged images in the sample library and identifies the type and degree of damage through feature matching algorithms;

[0040] Preferably, the infrastructure damage feature identification, by learning these representative damage features, the model can deeply understand the complexity and irregularity of the damage;

[0041] Its infrastructure damage feature recognition model is expressed as Y = C (D (E (X))) where X represents the image sample of the damaged facility, E is the extraction network of the facility feature, which is composed of Resnet50; D represents the representation network of the facility damage, which is composed of convolution;

[0042] C is the identification network of damaged facilities, which consists of linear layers. In order to ensure that D can express the characteristic representation of facility damage, the E network parameters need to be frozen when training damaged facility samples. C classifies the types, and then the damage representation D can be embedded in the diffusion generation model to combine normal urban facilities with damage characteristics;

[0043] Preferably, the generalized association generated by the diffusion constructs a sample of damaged urban infrastructure;

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

[0045] Add the reverse damage denoising process to the denoising process, and add the damage denoising process to the denoising process;

[0046] The damage characteristics are added in the form of noise to obtain the diffusion model;

[0047] The noise addition process is expressed as:

[0048]

[0049] Among them, β is used to control the weight of noise; ∈ represents noise; x represents the characteristics of the image X of the damaged facility in different noise addition processes, x t+1 is the feature x of the previous time step t+1 t Add noise and remove corrupted features x td ; Where t represents the current time step, which increases gradually with iterations;

[0050] The denoising process is expressed as:

[0051]

[0052] Among them, β is used to control the weight of noise; x represents the characteristics of the image X of the damaged facility in different noise addition processes. t+1 It is the feature x of the previous time step t+1 t Add noise and remove corrupted features x td . Where t represents the current time step, which increases gradually with iterations;

[0053] Generate a damaged facility sample image X for training E in the formula Y=C(D(E(X))), and freeze the other parameters;

[0054] By continuously collecting and analyzing real-time monitoring data and response results, optimizing damage identification algorithms and response measures libraries, the accuracy and efficiency of the system can be improved; the system can be upgraded and maintained regularly to ensure stable operation of the system.

[0055] The present invention also proposes a real-time monitoring and response system for urban infrastructure damage, comprising:

[0056] A real-time monitoring module is used to deploy multiple image sensors at multiple key locations of urban infrastructure to collect images of the infrastructure in real time and to construct an image sample library of damaged infrastructure through the collected image data;

[0057] A central processing system for receiving and processing image data and extracting key features;

[0058] The damage identification and analysis module is used to process and analyze real-time monitoring image data, identify damage to infrastructure, and assess the extent of damage;

[0059] The damage identification and analysis module includes: an infrastructure damage feature identification module and a generalized association module based on diffusion generation;

[0060] The infrastructure damage feature memory module is used to calculate the damage index of urban infrastructure, and is composed of a crack identification submodule, a sag identification submodule, and a fracture damage identification submodule;

[0061] The damage index of urban infrastructure is the sum of the damage degrees identified by the crack identification submodule, the dent identification submodule, and the fracture damage identification submodule;

[0062] The crack identification submodule is used to calculate the degree of damage of each infrastructure crack according to the area of ​​the crack.

[0063] The depression identification submodule is used to identify the degree of depression;

[0064] The fracture damage identification submodule uses the deep object detection algorithm YOLO to identify the damage degree of the fracture, and the damage degree is divided into slight damage, moderate damage, and severe damage;

[0065] The generalized association module based on diffusion generation is used to associate normal urban basic features with damage features to assess the degree of damage;

[0066] The beneficial effects of the present invention are: through real-time monitoring, automatic analysis, rapid response and continuous optimization, efficient management and treatment of damage to urban infrastructure can be achieved, the safety and reliability of urban infrastructure can be improved, and the economic losses and social impacts caused by damage can be reduced.

[0067] The real-time monitoring and response system for urban infrastructure damage of the present invention realizes real-time monitoring, accurate identification and rapid response to urban infrastructure damage problems through infrastructure damage feature memory and generalized 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 intelligent and refined urban management. Response decisions are made based on detection information, and maintenance personnel and rescue resources are quickly dispatched, which can ensure that measures can be taken quickly when damage problems occur, thereby improving the efficiency and accuracy of emergency rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 : Flow chart of the method of an embodiment of the present invention.

[0069] Figure 2 : Urban flood infrastructure detection diagram according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] In specific implementation, the method proposed in the technical solution of the present invention can be implemented by technical personnel in this field using computer software technology to realize automatic operation process. System devices for implementing the method, such as computer-readable storage media storing the corresponding computer program of the technical solution of the present invention and computer equipment running the corresponding computer program, should also be within the scope of protection of the present invention.

[0072] The following is combined with Figure 1The specific implementation of the present invention is a method and system for real-time monitoring and response of urban infrastructure damage, as follows:

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

[0074] According to the type and distribution of urban infrastructure, select the appropriate type and quantity of sensors and deploy them in key locations. 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 infrastructure is collected in real time through the sensor network, and the data is preprocessed and feature extracted using data processing algorithms. Preprocessing includes operations such as removing outliers and filling missing values ​​to improve the quality of data. Feature extraction is to extract key features that can reflect infrastructure damage from the original data, such as displacement, stress, temperature, humidity, etc.

[0076] Step 1: Multiple image sensors are deployed at multiple key locations of urban infrastructure to collect images of the infrastructure in real time, and a sample library of damaged infrastructure images is constructed based on the collected image data;

[0077] The image sensor in step 1 collects images from a network of devices covering multiple aspects such as transportation systems, energy systems, and public buildings. The system deploys high-resolution image sensors at key locations of 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 on highways, and at tunnel entrances;

[0078] A real-time monitoring module is used to deploy multiple image sensors at multiple key locations of urban infrastructure to collect images of the infrastructure in real time and to construct an image sample library of damaged infrastructure through the collected image data;

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

[0080] In terms of traffic, image sensors are installed at major road intersections in cities, under specific bridges on highways, and at tunnel entrances;

[0081] Specifically, a camera is set up every 500 meters. The camera is waterproof and dustproof, and uses all-weather visual and infrared imaging technology to capture road details and traffic conditions at night. Image data is transmitted to the central monitoring system in real time via the 5G network;

[0082] In terms of energy, 1-2 infrared imaging sensors are installed at each key point of the power grid substation and gas pipeline, mainly used to detect temperature changes and surface cracks on the outside of the pipeline. These sensors are automatically cleaned regularly 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] Fire monitoring cameras and smoke sensors are installed in public buildings in important public facilities such as fire stations and police stations. These sensors are directly connected to the municipal emergency response system and can automatically trigger fire alarms through the Internet of Things, while sending high-definition images to the cloud for artificial intelligence systems to identify fire conditions.

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

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

[0086] The step 3 is as follows:

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

[0088] Step 3.2: Associate normal urban infrastructure characteristics with damage characteristics to assess the extent of damage;

[0089] The step 3.1 of calculating the damage index of urban infrastructure includes:

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

[0091] The damage index of urban infrastructure is the sum of the damage degrees identified by the crack identification submodule, the dent identification submodule, and the fracture damage identification submodule;

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

[0093] D total =D crack +D deformation +D fracture

[0094] Among them, D crack Indicates the degree of damage of the crack, D deformation Indicates the degree of damage of the dent, Dfracture Indicates the degree of damage of the fracture;

[0095] The crack identification calculates the degree of damage of each infrastructure crack according to the area of ​​the crack. The formula is as follows:

[0096]

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

[0098] The depression identification and the degree of depression are expressed as follows:

[0099]

[0100] The degree of concavity is described using the gradient of the function, where (i, j) represents the coordinates of the pixel and f(x) represents the pixel value.

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

[0102] In step 3.2, the normal urban infrastructure characteristics are associated with the damage characteristics to assess the extent of damage, as follows:

[0103] Collect images of all infrastructure in normal state and store the data in the normal feature library;

[0104] Each set of images is passed through ResNet-50 to extract feature vectors, and the model uses these normal features as a baseline;

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

[0106] After the damage event occurs, the newly collected damage image is compared with the image in the "= normal feature library through the real-time image feedback of the automatic monitoring module;

[0107] The diffusion generation module simulates the noise of the damaged part and generates a new hypothetical damage scene by adding random noise to the original damaged image. The system will gradually remove the noise and associate the possible causes of the damage.

[0108] For example, after detecting a crack in a bridge, the system simulates a scenario in which the crack gradually expands to predict possible further damage and feeds the results back to the maintenance department.

[0109] The specific calculation process is as follows:

[0110] Identification of infrastructure damage features;

[0111] Generalized associations based on diffusion generation;

[0112] In response to common damage phenomena in cities, the system uses image sensors to collect images in real time and mark the damage types. Using the collected damage samples, the system uses convolutional neural networks to perform automatic model training. The training process is updated once a week to ensure that the model can recognize new damage features.

[0113] 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 damaged images in the sample library and identifies the type and degree of damage through feature matching algorithms;

[0114] For example, when a road crack is detected, the system compares the size, depth, and shape of the crack in the sample library with the current image to determine whether immediate repair is needed.

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

[0116] Its infrastructure damage feature recognition model is expressed as Y = C (D (E (X))), where X represents the image sample of the damaged facility, E is the extraction network of the facility feature, which is composed of Resnet50; D represents the representation network of the facility damage, which is composed of convolution;

[0117] C is the identification network of damaged facilities, which consists of linear layers. In order to ensure that D can express the characteristic representation of facility damage, the E network parameters need to be frozen when training damaged facility samples. C classifies the types, and then the damage representation D can be embedded in the diffusion generation model to combine normal urban facilities with damage characteristics;

[0118] The generalized associations generated by the diffusion construct samples of urban infrastructure damage;

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

[0120] Add the reverse damage denoising process to the denoising process, and add the damage denoising process to the denoising process;

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

[0122] The noise addition process is expressed as:

[0123]

[0124] Among them, β is used to control the weight of noise; ∈ represents noise; x represents the characteristics of the image x of the damaged facility in different noise addition processes, x t+1 is the feature x of the previous time step t+1 t Add noise and remove corrupted features x td ; Where t represents the current time step, which increases gradually with iterations;

[0125] The denoising process is expressed as:

[0126]

[0127] Among them, β is used to control the weight of noise; x represents the characteristics of the image X of the damaged facility in different noise addition processes. t+1 It is the feature x of the previous time step t+1 t Add noise and remove corrupted features x td . Where t represents the current time step, which increases gradually with iterations;

[0128] Generate a damaged facility sample image X for training E in the formula Y=C(D(E(X))), and freeze the other parameters;

[0129] By continuously collecting and analyzing real-time monitoring data and response results, optimizing damage identification algorithms and response measures libraries, the accuracy and efficiency of the system can be improved; the system can be upgraded and maintained regularly to ensure stable operation of the system.

[0130] The specific implementation of the system of the present invention is a real-time monitoring and response system for urban infrastructure damage, including:

[0131] A real-time monitoring module is used to deploy multiple image sensors at multiple key locations of urban infrastructure to collect images of the infrastructure in real time and to construct an image sample library of damaged infrastructure through the collected image data;

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

[0133] A central processing system for receiving and processing image data and extracting key features;

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

[0135] The damage identification and analysis module is used to process and analyze real-time monitoring image data, identify damage to infrastructure, and assess the extent of damage;

[0136] The damage identification and analysis module includes: an infrastructure damage feature identification module and a generalized association module based on diffusion generation;

[0137] The infrastructure damage feature memory module is used to calculate the damage index of urban infrastructure, and is composed of a crack identification submodule, a sag identification submodule, and a fracture damage identification submodule;

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

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

[0140] The depression identification submodule is used to identify the degree of depression;

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

[0142] The generalized association module based on diffusion generation is used to associate normal urban basic features with damage features to assess the degree of damage;

[0143] The beneficial effects of the present invention are: through real-time monitoring, automatic analysis, rapid response and continuous optimization, efficient management and treatment of damage to urban infrastructure can be achieved, the safety and reliability of urban infrastructure can be improved, and the economic losses and social impacts caused by damage can be reduced.

[0144] like Figure 2 Urban flooding causes ground damage, road waterlogging, building collapse, tree toppling, and vehicle damage to detect.

[0145] Once damage to infrastructure is identified, the system will generate corresponding warning information based on the preset warning threshold and send the warning information to the city management department and emergency rescue agencies. The warning information includes the location, type, degree and other information of the damage, so that relevant departments can respond in time. After receiving the warning information, the city management department and emergency rescue agency will make response decisions based on the actual situation, including dispatching maintenance personnel, deploying rescue resources, etc., and implement corresponding response measures. At the same time, the system will also monitor and record the response process in real time for subsequent data analysis and optimization.

[0146] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0147] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of patent protection of the present invention. Under the enlightenment of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested for the present invention shall be based on the attached claims.

Claims

1. A method for real-time monitoring and response of urban infrastructure damage, characterized in that: include: Step 1: Multiple image sensors are deployed at multiple key locations of urban infrastructure to collect images of the infrastructure in real time, and a sample library of damaged infrastructure images is constructed based on the collected image data; Step 2: Receive and process image data and extract key features; Step 3: Process and analyze real-time monitoring image data to identify infrastructure damage and assess the extent of damage.

2. The method for real-time monitoring and response to urban infrastructure damage according to claim 1 is characterized by: The image device network captured by the image sensor described in step 1 covers multiple aspects such as transportation systems, energy systems, and public buildings. The system deploys high-resolution image sensors at key locations of urban infrastructure; sensors are installed in the following specific locations: image sensors are installed at major road intersections in the city, under specific bridges on highways, and at tunnel entrances.

3. The method for real-time monitoring and response to urban infrastructure damage according to claim 2 is characterized by: The image sensor captures images from a network of devices covering multiple aspects such as transportation systems, energy systems, and public buildings. The system deploys high-resolution image sensors at key locations of urban infrastructure; these sensors are installed at the following specific locations: In terms of traffic, image sensors are installed at 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 is characterized by: The step 3 is as follows: Step 3.1: Calculate the damage index of urban infrastructure; Step 3.2: Associate normal urban infrastructure characteristics with damage characteristics to assess the extent of damage.

5. The method for real-time monitoring and response to urban infrastructure damage according to claim 4 is characterized by: The step 3.1 of calculating 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 degrees 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: D total =D crack +D deformation +D fracture Among them, D crack Indicates the degree of damage of the crack, D deformation Indicates the degree of damage of the dent, D fracture Indicates the degree of damage of the fracture; The crack identification calculates the degree of damage of each infrastructure crack according to the area of ​​the crack. The formula is as follows: The crack area ratio refers to the ratio between the crack area and the target area. The crack area ratio is approximately obtained by using the ratio between the crack pixels and the target pixels in the image using a threshold-based segmentation method. The depression identification and the degree of depression are expressed as follows: The degree of concavity is described using the gradient of the function, where (i, j) represents the coordinates of the pixel and f(x) represents the pixel value; The fracture damage identification uses the deep object detection algorithm YOLO to identify the damage degree of the fracture, and the damage degree is divided into slight damage, moderate damage, and severe damage.

6. The method for real-time monitoring and response to urban infrastructure damage according to claim 5 is characterized by: In step 3.2, the normal urban infrastructure characteristics are associated with the damage characteristics to assess the extent of damage, as follows: Collect images of all infrastructure in normal state and store the data in the normal feature library; Each set of images is passed through ResNet-50 to extract feature vectors, and the model uses these normal features as a baseline; This process performs a complete infrastructure health sampling every night, and the collected data is denoised and geometrically corrected through a pre-processing module; After the damage event occurs, the newly collected damage image is compared with the image in the "= normal feature library through the real-time image feedback of the automatic monitoring module; The diffusion generation module simulates the noise of the damaged part and generates a new hypothetical damage scene by adding random noise to the original damaged image. The system will gradually remove the noise and associate the possible causes of the damage. The specific calculation process is as follows: Identification of infrastructure damage features; Generalized associations based on diffusion generation; In response to common damage phenomena in cities, the system uses image sensors to collect images in real time and mark the damage types. Using the collected damage samples, the system uses convolutional neural networks to perform automatic model training. The training process is updated once a week to ensure that the model can recognize new damage features. The model will automatically compare historical damage data with the existing sample library and gradually update the recognition weights of damage features. Then, in daily monitoring, the system will compare the real-time collected images with the damaged images in the sample library and identify the type and extent of damage through feature matching algorithms.

7. The method for real-time monitoring and response to urban infrastructure damage according to claim 6 is characterized by: The infrastructure damage feature recognition, by learning these representative damage features, the model can deeply understand the complexity and irregularity of the damage; Its infrastructure damage feature recognition model is expressed as Y = C (D (E (X))), where X represents the image sample of the damaged facility, E is the extraction network of the facility feature, which is composed of Resnet50; D represents the representation network of the facility damage, which is composed of convolution; C is the identification network of damaged facilities, which is composed of linear layers. In order to ensure that D can express the characteristic representation of facility damage, the E network parameters need to be frozen when training damaged facility samples. C classifies the types, and then the damage representation D can be embedded in the diffusion generation model to combine normal urban facilities with damage characteristics.

8. The method for real-time monitoring and response to urban infrastructure damage according to claim 7 is characterized by: The generalized associations generated by the diffusion construct samples of urban infrastructure damage; The basic idea of ​​embedding the damage features into diffusion generation is to noise the damage features; Add the reverse damage denoising process to the denoising process, and add the damage denoising process to the denoising process; The damage characteristics are added into the diffusion model in the form of noise.

9. The method for real-time monitoring and response to urban infrastructure damage according to claim 8, characterized in that: The noise addition process is expressed as: Among them, β is used to control the weight of noise; ∈ represents noise; x represents the characteristics of the image X of the damaged facility in different noise addition processes, x t+1 is the feature x of the previous time step t+1 t Add noise and remove corrupted features x td ; Where t represents the current time step, which increases gradually with iterations; The denoising process is expressed as: Among them, β is used to control the weight of noise; x represents the characteristics of the image X of the damaged facility in different noise addition processes; x t+1 It is the feature x of the previous time step t+1 t Add noise and remove corrupted features x td ; Where t represents the current time step, which increases gradually with iterations; Generate a damaged facility sample image X for training E in the formula Y=C(D(E(X))), and freeze the other parameters; By continuously collecting and analyzing real-time monitoring data and response results, optimizing damage identification algorithms and response measures libraries, the accuracy and efficiency of the system can be improved; the system can be upgraded and maintained regularly to ensure stable operation of the system.

10. A real-time monitoring and response system for urban infrastructure damage, characterized in that: include: A real-time monitoring module is used to deploy multiple image sensors at multiple key locations of urban infrastructure to collect images of the infrastructure in real time and to construct an image sample library of damaged infrastructure through the collected image data; A central processing system for receiving and processing image data and extracting key features; The damage identification and analysis module is used to process and analyze real-time monitoring image data, identify damage to infrastructure, and assess the extent of damage; The damage identification and analysis module includes: an infrastructure damage feature identification module and a generalized association module based on diffusion generation; The infrastructure damage feature memory module is used to calculate the damage index of urban infrastructure, and is composed of a crack identification submodule, a sag identification submodule, and a fracture damage identification submodule; The damage index of urban infrastructure is the sum of the damage degrees 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 damage of each infrastructure crack according to the area of ​​the crack. The depression identification submodule is used to identify the degree of depression; The fracture damage identification submodule uses the deep object detection algorithm YOLO to identify the damage degree of the fracture, and the damage degree is divided into slight damage, moderate damage, and severe damage; The generalized association module based on diffusion generation is used to associate normal urban basic features with damage features to assess the degree of damage.

Images