An intelligent monitoring and early warning system and method for urban infrastructure

Abstract

The present invention discloses an intelligent monitoring and early warning system and method for urban infrastructure, which mainly relates to the application technology field of the Internet of Things in urban monitoring. A sensor integration module, a data acquisition module, a communication module, a data fusion and processing module, an intelligent diagnosis module, an early warning decision module, an adaptive optimization module, a user interface module, a report generation module, and a cloud service management module are integrated into an intelligent monitoring and early warning system for urban infrastructure that integrates Internet of Things technology, big data analysis, intelligent early warning, and adaptive optimization functions. The beneficial effect of the present invention is that it can realize real-time monitoring, analysis, early warning, and optimization of the status of urban infrastructure through highly integrated and intelligent technical means.

Description

Technical Field

[0001] The present invention relates to the technical field of application of Internet of Things in urban monitoring, and in particular to an intelligent monitoring and early warning system and method for urban infrastructure. Background Art

[0002] With the acceleration of urbanization, the scale and complexity of urban infrastructure are increasing, which puts higher requirements on the monitoring and management of these infrastructures. Traditional monitoring systems mostly use manual inspections and simple automated equipment. These methods have problems such as low efficiency, slow response, and limited data processing capabilities. They are difficult to meet the needs of modern cities for real-time, accurate, and forward-looking monitoring.

[0003] Urban infrastructure, such as bridges, roads, and pipelines, is the cornerstone of urban operations. Their safe operation is directly related to the city's public safety and the quality of life of residents. However, due to the influence of the natural environment and usage load, these infrastructures may have various problems, such as structural damage and functional degradation, which require timely detection and maintenance. Existing monitoring systems often lack effective data integration capabilities and cannot achieve comprehensive analysis and processing of multi-source data, resulting in the inability to timely discover and warn of potential risks.

[0004] The rise of IoT technology has provided a new solution for the intelligent monitoring of urban infrastructure. By deploying a large number of sensors, the operation data of infrastructure can be collected in real time. However, how to efficiently process and analyze these massive amounts of data, extract valuable information from them, and make accurate warnings remains a technical challenge. In addition, most existing systems lack adaptive learning capabilities and cannot automatically optimize monitoring strategies based on real-time feedback to adapt to the ever-changing monitoring environment and needs.

[0005] Therefore, it is necessary to develop an intelligent monitoring and early warning system for urban infrastructure that integrates Internet of Things technology, big data analysis, intelligent early warning and adaptive optimization functions. Such a system can comprehensively improve the monitoring efficiency and early warning accuracy of urban infrastructure and provide strong technical support for the construction of smart cities. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent monitoring and early warning system and method for urban infrastructure, which can realize real-time monitoring, analysis, early warning and optimization of the status of urban infrastructure through highly integrated and intelligent technical means.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0008] On the one hand, an intelligent monitoring and early warning system for urban infrastructure is provided, comprising:

[0009] Sensor integration modules to: connect and coordinate multiple types of urban infrastructure sensors;

[0010] Data acquisition module, used to: collect sensor data in real time and perform preliminary processing;

[0011] The communication module is used to: safely and efficiently transmit the collected data to the central processing unit;

[0012] Data fusion and processing module, used to: integrate data from different sources and perform advanced data analysis;

[0013] Intelligent diagnostic module, which is used to: analyze the processed data and identify the operating status and potential problems of the infrastructure;

[0014] The early warning decision module is used to: generate early warning information based on the diagnosis results and classify them according to the risk level;

[0015] Adaptive optimization module, used to automatically adjust monitoring parameters based on early warning feedback and system performance;

[0016] A user interface module is used to: provide a user interaction interface, allowing the user to configure monitoring options and access reports;

[0017] Report generation module, used to: generate customized reports based on user requirements and monitoring data;

[0018] The cloud service management module is used to deploy, manage and maintain the entire monitoring system on the cloud platform.

[0019] Preferably, the data acquisition module has data synchronization and timing control functions.

[0020] Preferably, the sensor integration module includes but is not limited to bridge monitoring, traffic monitoring, environmental monitoring and pipe network monitoring sensors.

[0021] Preferably, the communication module uses secure encryption technology to ensure the security of data transmission.

[0022] Preferably, the blowing port is located between the feed port and the grinding roller, and an air guide plate is installed on the upper wall of the blowing port at a certain angle to allow wind to blow toward the grinding roller. One end of the blowing port is connected to an air guide pipe, and the air guide pipe is connected to the fan.

[0023] Preferably, the data fusion and processing module uses machine learning and data mining techniques.

[0024] Preferably, the intelligent diagnosis module is capable of monitoring the health status of urban infrastructure in real time.

[0025] Preferably, the early warning decision module includes a multi-level early warning mechanism to adapt to different risk situations.

[0026] Preferably, the adaptive optimization module utilizes a deep learning algorithm to continuously optimize the early warning model.

[0027] Preferably, the user interface module supports multi-terminal access, including desktop and mobile devices.

[0028] On the other hand, a monitoring method applied to the above-mentioned urban infrastructure intelligent monitoring and early warning system is provided, comprising the following steps:

[0029] S1: Configure the sensor integration module to connect with sensors of urban infrastructure;

[0030] S2: collect sensor data in real time through the data acquisition module and perform preliminary processing;

[0031] S3: using the communication module to transmit the data to the central processing unit;

[0032] S4: Integrate and analyze data in the data fusion and processing module;

[0033] S5: Smart diagnostic module evaluates infrastructure status and identifies problems;

[0034] S6: Based on the diagnosis results, the warning decision module generates and distributes warning information;

[0035] S7: The adaptive optimization module adjusts the monitoring strategy based on system performance and early warning feedback;

[0036] S8: The user configures monitoring options and views reports through the user interface module;

[0037] S9: The report generation module generates customized monitoring reports according to requirements;

[0038] S10: The cloud service management module is responsible for the deployment, management and maintenance of the system;

[0039] S11: The system continuously improves monitoring performance based on user feedback and warning effects.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. Comprehensive real-time monitoring and data integration: This system achieves comprehensive monitoring of key parameters of urban infrastructure through carefully designed sensor integration modules. These sensors can accurately capture key information such as stress changes in bridges, road usage conditions, and liquid level fluctuations in pipe networks. The data acquisition module further ensures the real-time collection and preliminary processing of this data, including synchronizing data streams and executing timing control. The communication module uses the most advanced encryption technologies, such as TLS or SSL, to ensure the security of data during transmission and prevent data leakage or unauthorized access;

[0042] 2. In-depth data analysis and intelligent early warning: The data fusion and processing module uses complex algorithms, such as neural networks and support vector machines, to conduct in-depth analysis of the collected data to identify the current status and potential risks of the infrastructure. The intelligent diagnosis module can evaluate and predict the health of the infrastructure, while the early warning decision module issues timely and accurate early warning information based on these diagnostic results through a multi-level early warning mechanism. The adaptive optimization module uses deep learning technology to automatically adjust the diagnosis and early warning algorithms based on real-time feedback and historical data, so that the system can evolve by itself and continuously improve the accuracy and response speed of early warnings;

[0043] 3. High degree of user customization and system reliability: The user interface module provides an intuitive and easy-to-use interactive interface, allowing users to customize monitoring parameters and report formats according to their needs. The report generation module generates reports containing detailed analysis, charts and suggestions based on the user's customized needs, supporting users to make more informed decisions. The cloud service management module uses cloud computing technologies such as distributed computing and load balancing to ensure high reliability and elastic expansion of the system. In addition, the system continuously optimizes itself based on user feedback and system performance through a closed-loop feedback mechanism, achieving continuous improvement of monitoring strategies and early warning models, and significantly improving the intelligence level and user satisfaction of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a schematic diagram of the modules of the intelligent monitoring and early warning system for urban infrastructure of the present invention;

[0045] Figure 2 This is a data processing flow chart of the system of the present invention;

[0046] Figure 3 This is a flow chart of the adaptive learning and optimization algorithm of the system of the present invention;

[0047] Figure 4 This is a flow chart of the initialization and feedback loop of the system of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the application equally.

[0049] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention and should not be understood as limitations on the present invention.

[0050] In the present invention, terms such as "fixed connection", "connected", "connection", etc. should be understood in a broad sense, indicating that it can be fixedly connected, integrally connected or detachably connected; it can be directly connected or indirectly connected through an intermediate medium. Relevant scientific research or technical personnel in this field can determine the specific meanings of the above terms in the present invention according to specific circumstances, and they should not be understood as limiting the present invention.

[0051] Example:

[0052] like Figure 1-4 As shown, this embodiment provides an intelligent monitoring and early warning system for urban infrastructure, including the following steps:

[0053] Accurate sensor deployment and initialization: The system deploys a series of high-precision sensors at key monitoring points of urban infrastructure. For example, bridge stress sensors are installed at key stress points of bridges with a resolution of 0.01 microstrain per microstrain and a sampling frequency of 100Hz to monitor tiny structural changes in real time. Traffic flow cameras are deployed on major traffic arteries with a resolution of 1920×1080 pixels and a frame rate of 25fps to ensure that every passing vehicle can be clearly captured. Environmental monitors monitor temperature (-40℃ to 85℃), humidity (0% to 100%RH) and PM2.5 (0 to 500μg / m 3) and other environmental parameters. The underground pipe network liquid level sensors are deployed at key nodes of the pipe network with a measurement accuracy of 0.1% and an update frequency of 1 second to monitor water level changes in real time. The data acquisition module sets the corresponding acquisition parameters according to the type of sensor and monitoring requirements. For example, the data acquisition frequency of the bridge stress sensor is set to 100Hz to ensure that the dynamic response of the bridge under traffic load can be captured. The collected raw data is pre-processed by a digital signal processor (DSP), including using a bandpass filter to remove high-frequency noise and low-frequency drift beyond the natural frequency range of the bridge, and performing analog-to-digital conversion (ADC) to convert the analog signal into a digital signal with a 12-bit resolution. The pre-processed data is transmitted through a secure communication module. The module uses the AES256-bit encryption algorithm to ensure the security of data during transmission. The data is stored in a local cache with a capacity of 1GB before transmission to prevent data loss caused by network interruption. The system is synchronized with the UTC time through the NTP service to ensure that the timestamp accuracy of all sensor data is within 1 millisecond, providing an accurate basis for the timing analysis of the data. After the data arrives at the central processing unit, it is further cleaned and formatted. Outlier detection algorithms, such as the Z-Score method, are used to identify and remove data points that are outside the normal range. Data formatters convert cleaned data into a unified format, such as JSON format, for easy storage in an SQL database and to facilitate subsequent data analysis and report generation. The system implements a set of intelligent monitoring programs to track the performance indicators of data acquisition modules in real time, such as acquisition delay, data loss rate, and system error rate. Regular maintenance plans include calibration of sensors to ensure measurement accuracy and functional testing of data acquisition hardware to prevent and identify potential failures.

[0054] Secure data transmission: Before data transmission, the communication module performs encryption operations on the data. Set the AES 256-bit encryption standard and define the key length as 256 bits to ensure the security of data encryption. This encryption process involves the generation of an initialization vector (IV), which is usually 128 bits to increase the randomness of the encryption. Select the appropriate communication protocol based on the data transmission medium and security requirements. For example, in a Wi-Fi network, select the WPA2-PSK (pre-shared key) mode and the encryption algorithm is AES-CCMP to provide security for data transmission. For cellular networks, you may choose LTE or 5G, which provide built-in security features such as IPsec and TLS. Configure the relevant parameters of data transmission, such as the frequency of the wireless signal (2.4GHz or 5GHz), the transmission power (dBm), and the transmission rate of the wired network (such as 1Gbps). At the same time, set the timeout retransmission mechanism for data transmission, including the number of retransmissions and the timeout period (for example, the maximum number of retransmissions is 5 times and the timeout period is 2 seconds). After receiving the data, the central processing unit first performs an integrity check, uses the SHA-256 algorithm to generate a message digest, and compares it with the message digest of the transmitted data. When decrypting, the same key and algorithm as the encryption end are used, such as AES256-bit decryption, to ensure the readability of the data. The system implements real-time monitoring and records all transmission activities, including the sending and receiving timestamps, source addresses, and destination addresses of data packets. Any abnormal behavior, such as a packet loss rate exceeding 5% or a transmission delay exceeding a set threshold (e.g., 500 milliseconds), will be recorded and trigger a security alarm. The system dynamically adjusts transmission parameters according to the current network conditions, such as adjusting the transmission power or changing the communication channel to optimize transmission efficiency. For errors that occur during transmission, the system will execute corresponding error handling strategies based on the error type (such as CRC check failure or timeout error), such as requesting retransmission or switching to an alternate communication link.

[0055] Data fusion and intelligent processing: Before data fusion, preprocessing is performed to ensure data quality. Data normalization converts all sensor readings to the [0,1] interval. For example, the stress readings are converted from actual microstrain units to dimensionless values ​​using the range normalization method. In data cleaning, the threshold is set to 3 times the standard deviation to identify and remove outliers. In the feature extraction step, key features are extracted from the normalized data, such as statistical parameters such as the peak value, mean, and standard deviation of the bridge stress data. PCA is used for feature dimension reduction, retaining the energy of 95% of the variance, reducing the feature dimension, and removing redundant information. In the data fusion stage, the weighted average fusion rule is used to assign different weights according to the reliability and accuracy of each sensor. For example, a high-precision stress sensor may be assigned a weight of 0.7, while a displacement sensor may be assigned a weight of 0.3. The Kalman filter is used to estimate the state of the dynamic system, and the initial value of the prediction error covariance is set to a diagonal matrix with 1000 diagonal elements. In the pattern recognition stage, the SVM classifier is used to classify the data, the kernel function is set to the radial basis function (RBF), and its parameters C and γ are determined using the cross-validation method. The goal of the classifier is to distinguish between normal and abnormal states to achieve fault detection. The intelligent processing module evaluates the current state of urban infrastructure based on the pattern recognition results. For example, if the bridge status assessment result exceeds the preset threshold (such as 80% health index), the system automatically triggers maintenance recommendations. Finally, the system compares the assessment results with historical maintenance data to verify the accuracy of the intelligent processing. Through the feedback mechanism, the system adjusts the parameters of the feature extraction and fusion algorithms according to the verification results, such as adjusting the variance threshold of PCA or the parameters C and γ of SVM to optimize the model performance.

[0056] Real-time intelligent diagnosis: The intelligent diagnosis module receives the output of the data fusion and processing module and first performs preprocessing. Data denoising uses wavelet transform, sets the wavelet coefficient threshold to 0.05, and removes high-frequency noise in the signal. The IQR (interquartile range) method is used for outlier processing, and 1.5 times the IQR is set as the judgment standard for outliers. The Z-score method is used for data standardization, with a target mean of 0 and a standard deviation of 1. Feature engineering is performed to extract diagnostic features. For example, statistical features such as mean (μ = 0), standard deviation (σ = 1), skewness (Skewness = -0.2), and kurtosis (Kurtosis = 3.0) extracted from bridge stress time series data. Feature selection uses model-based feature importance, such as Feature Importance of random forest, and selects features with an importance score of more than 0.8. The random forest classifier is selected for model training, and the number of trees is set to 100, the maximum depth is 20, and the learning rate is 0.01. The model performance is evaluated using 5-fold cross validation, and the accuracy, recall, and F1 score are set as performance indicators. Deploy the trained model to monitor and diagnose real-time data. For example, for real-time monitoring of bridge health status, the model update frequency is set to once every 10 seconds to adapt to possible rapid changes. The health index (HI) calculation formula is defined as HI = (μ + σ + Skewness + Kurtosis) / 4, where the weights of various parameters are assigned according to their impact on the health status. The range of the health index is set to 0-100, corresponding to the state from severe damage to intact. When the real-time monitored health index is lower than the preset threshold (such as HI <30), the system automatically marks it as a potential problem point and triggers the alarm mechanism. The alarm information includes timestamp (accurate to milliseconds), location information, health index and problem type. The system generates a diagnostic report containing a time series graph, health index change trend, model prediction results and problem point details. The report generation cycle is set according to actual needs, such as generating a comprehensive report once an hour. By collecting diagnostic results and actual maintenance data, the intelligent diagnosis module uses online learning or incremental learning strategies to continuously update model parameters. For example, the weights of the random forest model are updated using online gradient descent with a learning rate of 0.001 to adapt to the new data distribution.

[0057] Multi-level early warning decision: Define risk assessment criteria and map the health index (HI) to risk levels. For example, set thresholds: HI ≥ 80 for "low risk", 60 ≤ HI < 80 for "medium risk", 40 ≤ HI < 60 for "high risk", and HI < 40 for "urgent risk". The intelligent diagnosis module provides the latest calculated health index at fixed time intervals (such as every 5 minutes). These indexes are input into the early warning decision module in real time through the API interface. The early warning decision module has a built-in logical judgment algorithm, which uses if-else conditional statements to determine the risk level based on the real-time updated health index. For example, if HI < 40, the risk level is "urgent". For each risk level, the system generates an early warning message containing a risk description, recommended measures, and response guidelines. The information templates vary according to the risk level. For example, the "urgent risk" early warning message template contains a quick response guide and an emergency contact number. Set parameters for the multi-level warning information release mechanism, such as "low risk" through dashboard updates with periodic checks, "medium risk" through hourly email summaries, "high risk" through real-time SMS and application notifications, and "urgent risk" through automatic voice calls and integrated emergency response systems. Warning information is dynamically updated based on the continuous output of the intelligent diagnosis module. At the same time, establish a feedback mechanism, such as user confirmation of receipt of warnings and feedback on on-site responses, to adjust the accuracy of warnings and response processes. All warning events and their responses are recorded in the database, including timestamps, risk levels, warning types, response measures, and results. Use data analysis tools, such as SQL queries and data visualization techniques, to review and analyze warning records regularly. Based on warning records and feedback, review the effectiveness of risk assessment standards and warning release mechanisms regularly (e.g., quarterly). Use machine learning techniques, such as random forest classifiers, to optimize risk level judgment logic based on historical data and adjust model parameters to improve warning accuracy.

[0058] Adaptive learning and optimization: The system regularly collects performance monitoring data, including but not limited to warning accuracy (target value: ≥95%), average response time (target value: <2 minutes), false alarm rate (target value: <5%), and missed alarm rate (target value: <2%). These data are recorded in the performance monitoring database, and each record contains a timestamp and performance indicator value. User feedback is collected through a customized feedback management system, including user response time to warnings, accuracy evaluation, and improvement suggestions. The system conducts a comprehensive analysis of the collected feedback once a quarter. A deep neural network (DNN) model is trained using a deep learning framework such as TensorFlow or PyTorch. The model structure includes an input layer, three hidden layers (each hidden layer contains 128 neurons), and an output layer. ReLU is used as the activation function, cross entropy is used as the loss function, and the Adam optimizer is used for parameter updates with a learning rate of 0.001. Based on the prediction results of the DNN model, the system automatically adjusts the warning threshold. For example, if the model predicts that the false alarm rate is high at the current threshold of 80, the system may automatically adjust the threshold to 85 and test the performance at the new threshold. Validate the adjusted model using the reserved 20% test dataset. Validation metrics include accuracy, precision, recall, and F1 score. If performance improves, update the model and deploy it in production. Set up a continuous learning loop to update the DNN model every three months using the latest performance data and user feedback. The loop includes data collection, model training, parameter adjustment, and model validation. Apply the validated optimization results to the actual monitoring system. For example, if the model recommends reducing the response time threshold for a certain risk level from 2 minutes to 1.5 minutes, the system will update this parameter and reconfigure the early warning mechanism. Notify users about system updates and optimization results through email, system notifications, and online seminars. Provide detailed change logs and usage guides to help users understand system changes and make full use of new features.

[0059] User customization service: Conduct user demand surveys and collect data using online questionnaires and user interviews. The questionnaire includes questions such as "What monitoring parameters are you most concerned about?" and "How often do you want to receive system reports?" to quantify user needs. Design the user interface, using a responsive grid layout with 12 pixels as the base unit to ensure good display on different devices. The interface design follows the design principles of Figma or Sketch to ensure aesthetics and consistency. Use JavaScript frameworks such as React.js to develop front-end logic, and the back-end uses RESTful API to communicate with the front-end to ensure that the response time of user operations does not exceed 200 milliseconds. Realize real-time data display, use chart libraries such as ECharts or D3.js, and set the display frequency to update once per second to ensure real-time data. Provide a historical data query interface, support filtering by time (such as the past 1 hour, 24 hours, 7 days), parameter type, and event level. Report generation supports PDF and CSV formats, and users can customize the report cycle, such as daily, weekly, or monthly. User configuration is stored in NoSQL databases such as MongoDB in JSON format, and the size of each user's configuration file is limited to 10KB to ensure fast reading and writing. Establish a feedback system where users can evaluate the usability of the interface and the completeness of functions, with a rating of 1 to 5 stars, and the collection cycle is once a quarter. Provide online tutorials and user manuals, and the document update cycle does not exceed 1 month. The customer support team responds to user inquiries within 24 hours. Implement role-based access control (RBAC) and define permissions for different roles, such as administrators, ordinary users, and visitors, to ensure that users can only access authorized resources. Adopt agile development methods, iterate every 2 weeks, and update the user interface and customized services based on user feedback and market trends.

[0060] The customized report generation module allows users to set parameters according to specific needs and automatically generate detailed reports containing key monitoring indicators such as bridge stress (unit: microstrain), displacement (unit: millimeter), and temperature change (unit: Celsius). Users can specify the report frequency (such as generating every Monday at 8 o'clock), select specific monitoring points of concern, define the data display method (such as time series graph), and determine the level of detail of early warning records and maintenance recommendations. Finally, the system will send reports in the format selected by the user (PDF, HTML, Excel) via email or system message based on these settings, ensuring that users obtain comprehensive and personalized information in a timely manner to meet their specific needs for urban infrastructure monitoring.

[0061] Cloud platform deployment and management: AWS was selected as the cloud service provider. The cloud platform infrastructure was configured, including EC2 compute instances (type: m5.large, quantity: 3, deployed across 3 availability zones) and S3 storage services. VPC networks and security groups were set up. Inbound and outbound rules were configured to allow only specific ports (such as HTTP 80, HTTPS 443). A microservice architecture was designed. Each service ran in a Docker container and was orchestrated using Kubernetes to ensure high availability and scalability of the service. The minimum and maximum number of instances for automatic capacity expansion were set to 2 and 10 respectively. Jenkins was used to implement CI / CD, and Jenkinsfile was configured to implement the code pull, build, test, and deploy processes. The deployment trigger condition was set to the update of the master branch of the code repository. CloudWatch was configured to monitor CPU usage (threshold set to 70%) and memory usage (threshold set to 80%), triggering the automatic expansion policy to increase or decrease the number of compute instances to cope with load changes. ELB was used to distribute traffic to the compute instances, and the health check interval was configured to 30 seconds and the unhealthy threshold to 3. The load balancer's listening port was set to 443 and the forwarding rule was set to polling. Use S3 storage service to store monitoring data, set lifecycle policies, and set the data retention period to 1 year. Configure S3 version control and cross-region replication to ensure the security of data backup. Deploy CloudWatch monitoring service and set alarms when system indicators exceed preset thresholds (such as CPU utilization exceeding 80%). Integrate CloudWatch Logs to collect system and application logs, and set the log retention period to 3 months. Configure IAM roles and policies to strictly control access to cloud resources. Implement key management and use AWS KMS to encrypt sensitive data to ensure the security of data transmission and storage. Use AWS CostExplorer to monitor costs and set budget warnings when monthly costs exceed predetermined limits (such as $1000). Use Auto Scaling and reserved instances to optimize computing resource costs. Develop a maintenance plan and perform security audits and performance assessments on the system every quarter. Use AWS Systems Manager for system updates and patch management to ensure that system components are kept up to date.

[0062] System initialization and feedback loop are key links to ensure the efficient operation of the urban infrastructure monitoring system. In the initialization phase, we first deploy sensor hardware, such as installing stress sensors on bridges, set the sampling frequency to 100Hz, and configure the data acquisition module to read data once per second. Then, initialize the software environment on the cloud platform, install the Ubuntu Server 20.04LTS operating system, configure the PostgreSQL database, and set up the RabbitMQ message queue to support the system design of the microservice architecture. Next, during the operation of the system, actively collect user feedback through the user interface module. For example, users can report accuracy issues of the early warning system or request new reporting functions through the feedback form on the interface. The adaptive optimization module dynamically adjusts the early warning threshold and resource allocation based on these feedbacks and system performance indicators such as CPU usage and response time. For example, if the CPU usage is detected to exceed 80%, the system will automatically optimize the resource allocation strategy to maintain stable performance. Finally, the implementation of the closed-loop feedback mechanism ensures the continuous improvement and adaptability of the system. Review user feedback regularly (such as quarterly), analyze system logs and performance reports, and determine the direction of system optimization. Using agile development methods, the system is updated every two weeks to develop new features and improve existing features. At the same time, tools such as AWS CloudWatch are used to continuously monitor system performance to ensure that the system response time is kept within 2 seconds, ensuring the smoothness of the user experience and the reliability of the system. Through this meticulous management and optimization, the system can continue to evolve to meet the complex needs of urban infrastructure monitoring.

[0063] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent monitoring and early warning system for urban infrastructure, characterized in that: include: Sensor integration modules to: connect and coordinate multiple types of urban infrastructure sensors; Data acquisition module, used to: collect sensor data in real time and perform preliminary processing; The communication module is used to: safely and efficiently transmit the collected data to the central processing unit; Data fusion and processing module, used to: integrate data from different sources and perform advanced data analysis; Intelligent diagnostic module, which is used to: analyze the processed data and identify the operating status and potential problems of the infrastructure; The early warning decision module is used to: generate early warning information based on the diagnosis results and classify them according to the risk level; Adaptive optimization module, used to automatically adjust monitoring parameters based on early warning feedback and system performance; A user interface module is used to: provide a user interaction interface, allowing the user to configure monitoring options and access reports; Report generation module, used to: generate customized reports based on user requirements and monitoring data; The cloud service management module is used to deploy, manage and maintain the entire monitoring system on the cloud platform.

2. According to claim 1, an intelligent monitoring and early warning system for urban infrastructure is characterized in that: The data acquisition module has data synchronization and timing control functions.

3. According to claim 1, an intelligent monitoring and early warning system for urban infrastructure is characterized in that: The sensor integration module includes but is not limited to bridge monitoring, traffic monitoring, environmental monitoring and pipe network monitoring sensors.

4. According to claim 1, an intelligent monitoring and early warning system for urban infrastructure is characterized in that: The communication module uses secure encryption technology to ensure the security of data transmission.

5. According to claim 1, the intelligent monitoring and early warning system for urban infrastructure is characterized in that: The data fusion and processing module uses machine learning and data mining techniques.

6. According to claim 1, the intelligent monitoring and early warning system for urban infrastructure is characterized in that: The intelligent diagnosis module can monitor the health status of urban infrastructure in real time.

7. According to claim 1, the intelligent monitoring and early warning system for urban infrastructure is characterized in that: The early warning decision module includes a multi-level early warning mechanism to adapt to different risk situations.

8. According to claim 1, the intelligent monitoring and early warning system for urban infrastructure is characterized in that: The adaptive optimization module uses a deep learning algorithm to continuously optimize the early warning model.

9. The intelligent monitoring and early warning system for urban infrastructure according to claim 1 is characterized in that: The user interface module supports multi-terminal access, including desktop and mobile devices.

10. A monitoring method applied to claim 1, characterized in that: The following steps are involved: S1: Configure the sensor integration module to connect with sensors of urban infrastructure; S2: collect sensor data in real time through the data acquisition module and perform preliminary processing; S3: using the communication module to transmit the data to the central processing unit; S4: Integrate and analyze data in the data fusion and processing module; S5: Smart diagnostic module evaluates infrastructure status and identifies problems; S6: Based on the diagnosis results, the warning decision module generates and distributes warning information; S7: The adaptive optimization module adjusts the monitoring strategy based on system performance and warning feedback; S8: The user configures monitoring options and views reports through the user interface module; S9: The report generation module generates customized monitoring reports according to requirements; S10: The cloud service management module is responsible for the deployment, management and maintenance of the system; S11: The system continuously improves monitoring performance based on user feedback and warning effects.

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