Infrastructure global monitoring system and method based on intelligent platform

Abstract

The invention relates to the technical field of facility safety, in particular to an infrastructure global monitoring system and method based on an intelligent platform. Comprising a data acquisition and preprocessing module, a data storage and management module, a model building and updating module, a data analysis and early warning module, a data visualization and report module, a system integration and interface module and an operation and maintenance management and optimization module. According to the method, the accuracy and consistency of data are ensured through data acquisition and preprocessing, the storage and management of the data are realized by using an efficient and safe database system, and meanwhile, the three-dimensional model of the infrastructure is constructed and updated in real time by using a BIM technology, so that the accuracy and practicability of the model are improved; the data analysis and early warning module can monitor abnormity in real time and trigger early warning, a prediction model is established to provide a scientific basis for operation and maintenance, the data visualization and report module visually presents complex data and assists decision making, and scientificity, high efficiency and safety of infrastructure monitoring and management are integrally improved.

Description

Technical Field

[0001] The present invention relates to the field of facility safety technology, and in particular to a global infrastructure monitoring system and method based on a smart platform. Background Art

[0002] In modern society, infrastructure such as roads, bridges, tunnels, underground pipelines, etc. are crucial to the operation of cities and the lives of residents. However, these infrastructures often face various challenges, such as aging, damage, natural disasters, etc., which may threaten their safety and stability. Therefore, it is particularly important to monitor and manage infrastructure in the entire area.

[0003] Traditional infrastructure monitoring methods often rely on manual inspections and regular testing, which is not only time-consuming and labor-intensive, but also difficult to achieve real-time monitoring and early warning of infrastructure. With the rapid development of information technology, especially the widespread application of technologies such as the Internet of Things, big data, cloud computing, and BIM (Building Information Modeling), new technical means have been provided for the global monitoring of infrastructure.

[0004] Internet of Things technology can realize data collection and transmission at key locations of infrastructure, making data acquisition more convenient and real-time; big data technology can process and analyze massive amounts of data to extract useful information and patterns; cloud computing technology provides an efficient and secure solution for data storage and management; BIM technology can build a three-dimensional model of the infrastructure to achieve an intuitive display of its structure and status.

[0005] However, although these technologies provide strong support for infrastructure monitoring, how to effectively integrate these technologies to build an efficient, accurate and reliable infrastructure global monitoring system is still a problem to be solved. Therefore, this invention patent proposes an infrastructure global monitoring system and method based on a smart platform, aiming to achieve global and real-time monitoring and management of infrastructure by integrating the above advanced technologies, and improve the security and stability of infrastructure. Summary of the invention

[0006] The purpose of the present invention is to provide an infrastructure global monitoring system and method based on a smart platform to solve the problems raised in the above-mentioned background technology.

[0007] The technical solution of the present invention is: an infrastructure global monitoring system based on a smart platform, comprising:

[0008] The data collection and preprocessing module is used to collect the status data of the infrastructure using the deployed sensor network and perform preliminary preprocessing on the collected data to ensure the accuracy and consistency of the data;

[0009] Data storage and management module, which is used to establish an efficient and secure database system to store and manage large amounts of data, while providing data backup, recovery and access control functions to ensure data integrity, availability and security;

[0010] Model building and updating module, which is used to build a three-dimensional digital model of infrastructure using BIM and other technologies, and continuously update the model based on real-time monitoring data to intuitively display the structure and status of the infrastructure and improve the accuracy and practicality of the model;

[0011] The data analysis and early warning module is used to monitor and analyze the collected data in real time, detect abnormal situations in time and trigger early warning mechanisms. At the same time, it uses historical data to establish a prediction model to provide a scientific basis for the operation and maintenance of infrastructure;

[0012] Data visualization and reporting module, which is used to present complex data in an intuitive and easy-to-understand way, improve users' understanding and efficiency of data use, and generate detailed monitoring reports to provide scientific basis for decision makers;

[0013] System integration and interface module is used to integrate various modules to form a unified and efficient system, and provide interfaces with other systems to achieve data intercommunication and collaborative work, thereby improving the flexibility and scalability of the system;

[0014] The operation and maintenance management and optimization module is responsible for the daily operation and maintenance of the system, ensuring the stability and reliability of the system, and continuously optimizing and upgrading the system according to user needs and technological development to improve the performance and functionality of the system.

[0015] Preferably, the data acquisition and preprocessing module includes:

[0016] A11: Data collection layer, used to capture infrastructure status data in real time through sensor networks, including temperature sensors, humidity sensors, and pressure sensors;

[0017] A12: Data preprocessing layer, used to perform preliminary cleaning, format conversion and verification on the collected raw data to ensure the accuracy and consistency of the data;

[0018] A13: Data storage layer, used to store preprocessed data in the database for subsequent analysis and processing.

[0019] Preferably, the data collection and preprocessing module collects the status data of the infrastructure using the deployed sensor network and performs preliminary preprocessing on the collected data to ensure the accuracy and consistency of the data, including the following steps:

[0020] S11: Data collection, capturing infrastructure status data in real time through sensor networks;

[0021] S12: Data cleaning, removing noise, outliers and duplicate values ​​in the data to ensure data accuracy;

[0022] S13: Format conversion, converting the data formats generated by different sensors into a standard format for subsequent processing and analysis;

[0023] S14: Data verification: Check the integrity and consistency of the data to ensure that the data is not lost or damaged during transmission.

[0024] Preferably, when the data acquisition and preprocessing module removes noise in the data, the principle formula for removing noise in the data is:

[0025] y(n)=(1 / N)*Σ[x(i)](i=n-N+1,n)

[0026] Among them, y(n) is the output after filtering, x(i) is the original data, and N is the filter window size.

[0027] Preferably, the data storage and management module includes the following steps when establishing an efficient and secure database system to store and manage large amounts of data while providing data backup, recovery and access control functions to ensure data integrity, availability and security:

[0028] S21: Data reception and verification: receiving data from the data acquisition module and verifying the received data to ensure the integrity and accuracy of the data;

[0029] S22: data storage, selecting a suitable database system for storage according to the type and characteristics of the data, wherein the selectable database system includes a relational database and a non-relational database;

[0030] S23: Data backup and recovery, formulate data backup strategies, and regularly back up data in the database;

[0031] S24: Data access control, setting user permissions for the database to ensure that only authorized users can access and modify data;

[0032] S25: Data monitoring and maintenance, monitor the performance and status of the database, and promptly detect and handle potential problems.

[0033] Preferably, the model building and updating module uses BIM and other technologies to build a three-dimensional digital model of the infrastructure, and continuously updates the model according to real-time monitoring data to intuitively display the structure and status of the infrastructure and improve the accuracy and practicality of the model, including the following steps:

[0034] S31: Model construction, specifically including the following steps:

[0035] S311: Data collection, obtaining detailed data of the infrastructure from the data acquisition module, including geometric dimensions, material properties and structural layout.

[0036] S312: Using professional 3D modeling software, gradually build each part of the infrastructure based on the collected data, so as to realize the 3D model of the designed infrastructure;

[0037] S313: Model optimization: optimize the initially constructed model to ensure that the accuracy and details of the model meet the requirements;

[0038] S314: Model validation, verifying the accuracy and reliability of the model by comparing it with the actual infrastructure;

[0039] S32: Model update and maintenance, including the following steps:

[0040] S321: Data update, regularly obtain new infrastructure data from the data acquisition module, and compare the new and old data to identify changes and updates to the infrastructure;

[0041] S322: Model modification: modify and update the model accordingly based on new data to ensure that the model is always consistent with the actual situation;

[0042] S323: Model maintenance: Regularly maintain the model, including data backup and model cleanup, to ensure the stability and availability of the model.

[0043] Preferably, when the model building and updating module optimizes the initially constructed model to ensure that the accuracy and details of the model meet the requirements, the optimization algorithms used include:

[0044] A21: The gradient descent method continuously adjusts the parameter value through iteration so that the function value gradually decreases until the convergence condition is reached. Its principle formula is:

[0045]

[0046] Among them, f(x) is the original function, is the gradient of the original function f(x), x(n) represents the parameter value of the nth iteration, and α represents the learning rate, that is, the step size of each iteration;

[0047] A22: Genetic algorithm, through operations such as selection, crossover and mutation, continuously evolves the population to find the optimal solution;

[0048] A23: Simulated annealing method, that is, finding the global optimal solution by simulating the temperature drop and energy reduction process in the metal annealing process. The core of the simulated annealing method lies in the probability formula of accepting new solutions: P = e(-ΔE / T), where ΔE represents the energy difference between the new solution and the current solution, T represents the current temperature, and when ΔE<0, the new solution is accepted; when ΔE>0, the new solution is accepted with a certain probability, and this probability decreases as the temperature decreases.

[0049] As a preferred method, the data analysis and early warning module monitors and analyzes the collected data in real time, detects abnormal situations in time and triggers early warning mechanisms, and uses historical data to establish a prediction model to provide a scientific basis for the operation and maintenance of infrastructure, including the following steps:

[0050] S41: Data collection, collecting infrastructure operation data from multiple data sources, where the multiple data sources include various sensors, monitoring systems, and log files;

[0051] S42: Data cleaning, including removing duplicate data, processing missing values ​​and correcting errors to ensure data quality and accuracy;

[0052] S43: Real-time monitoring: using real-time monitoring tools to monitor the operating status of infrastructure in real time, including monitoring of key indicators and detection of abnormal situations;

[0053] S44: Data analysis: use appropriate methods to analyze data, extract useful information, and discover potential patterns and trends;

[0054] S45: Warning triggering: when the monitored data reaches or exceeds the warning threshold, the warning mechanism is automatically triggered and warning information is sent to relevant personnel;

[0055] S46: Trend forecasting, building a forecasting model based on historical data to predict the future state of infrastructure.

[0056] As a preferred method, the data analysis and early warning module uses appropriate methods to analyze data, extract useful information, and discover potential rules and trends. The data analysis methods used include:

[0057] A31: Statistical methods include the mean of analytical data, the variance of analytical data and the correlation coefficient of analytical data. The calculation principle formula of the mean is:

[0058]

[0059] Among them, Mean is the mean of the data set, x i is the value of the i-th data in the data set;

[0060] The calculation formula of variance is:

[0061]

[0062] Among them, Variance is the variance of the data set, and μ is the mean of the data set;

[0063] The calculation formula of the correlation coefficient is:

[0064]

[0065] Among them, r is the correlation coefficient, x i and i are the values ​​of the two variables at the i-th observation, and is the mean of the two variables;

[0066] A32: Machine learning algorithms include linear regression algorithm, decision tree algorithm and support vector machine algorithm. The principle formula of linear regression algorithm is: Y = β0 + β1X1 + β2X2 + … + β n X n +∈;

[0067] Among them, Y is the dependent variable, X1, X2 and X n are the independent variables, β0, β1, and β n is the regression coefficient and ∈ is the error term.

[0068] A method for global infrastructure monitoring based on a smart platform includes the following steps:

[0069] S51: Demand analysis and goal setting. The global infrastructure monitoring method based on the smart platform first needs to conduct demand analysis and goal setting, clarify the monitoring goals and infrastructure types, and set specific monitoring indicators based on these goals;

[0070] S52: Smart platform construction: Building a smart platform that integrates data analysis, monitoring and early warning, and decision support functions is the key;

[0071] S53: Data collection and transmission: installing sensors and other data collection devices at key locations of the infrastructure and transmitting the collected data to the smart platform;

[0072] S54: Data processing and analysis: The received raw data needs to be cleaned, sorted and formatted to eliminate noise and outliers. Then, data analysis techniques are used to conduct in-depth analysis of the processed data to extract useful information and patterns, providing a scientific basis for subsequent monitoring and early warning.

[0073] S55: Monitoring and early warning: According to the set monitoring indicators and thresholds, the operation status of the infrastructure is monitored in real time. Once an abnormal situation or potential risk is found, the early warning mechanism is immediately triggered to send alarm information to relevant personnel so that timely measures can be taken to avoid accidents.

[0074] S56: Decision support and optimization, based on data analysis results and early warning information, provide scientific decision support for decision makers. At the same time, based on monitoring data and feedback, continuously optimize the operation and management strategies of infrastructure, improve efficiency and safety, and ensure the long-term stable operation of infrastructure.

[0075] Beneficial effects of the present invention:

[0076] 1. Ensure data accuracy and consistency through data collection and preprocessing, use efficient and secure database systems to store and manage data, and use BIM technology to build and update the three-dimensional model of infrastructure in real time, which improves the accuracy and practicality of the model. The data analysis and early warning module can monitor anomalies in real time and trigger early warnings, and establish a prediction model to provide a scientific basis for operation and maintenance. The data visualization and reporting module intuitively presents complex data and assists in decision-making. The system integration and interface module enhances the flexibility and scalability of the system, realizes data interoperability and collaborative work, and the operation and maintenance management and optimization module ensures that the system is stable and reliable, and continuously optimizes and upgrades, which overall improves the scientificity, efficiency and safety of infrastructure monitoring and management;

[0077] 2. Through the model building and updating module, first use BIM technology to obtain detailed infrastructure data from the data acquisition module, build a high-precision three-dimensional digital model, and ensure the accuracy and reliability of the model through optimization and verification. Subsequently, by regularly obtaining new infrastructure data, identifying changes and updating the model, the consistency of the model with the actual situation is maintained. In addition, the model is regularly maintained, including data backup and cleaning, to ensure the stability and availability of the model. This series of operations not only improves the accuracy and practicality of the model, but also provides intuitive and efficient support for infrastructure monitoring, management and maintenance;

[0078] 3. When optimizing the initially constructed model, the model building and updating module uses optimization algorithms such as gradient descent, genetic algorithm and simulated annealing. These algorithms effectively improve the accuracy and details of the model through different mathematical principles and operation strategies, ensuring that the model meets the requirements of practical applications. The gradient descent method iteratively adjusts the parameter value so that the function value gradually converges to the optimal value; the genetic algorithm searches for the optimal solution through the evolutionary population and has global search capabilities; the simulated annealing rule simulates the annealing process and accepts new solutions with a certain probability, which helps to escape the local optimal solution and find the global optimal solution. The application of these optimization algorithms has significantly improved the accuracy and practicality of the model, and provided strong technical support for the three-dimensional digital modeling of infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 Shown is a schematic diagram of the structure of the global infrastructure monitoring system based on the smart platform of the present invention;

[0080] Figure 2 What is shown is a flow chart of the infrastructure global monitoring method based on a smart platform of the present invention. DETAILED DESCRIPTION

[0081] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0082] See also Figure 1 The present invention provides an embodiment: an infrastructure global monitoring system based on a smart platform, comprising:

[0083] The data collection and preprocessing module is used to collect the status data of the infrastructure using the deployed sensor network and perform preliminary preprocessing on the collected data to ensure the accuracy and consistency of the data;

[0084] Data storage and management module, which is used to establish an efficient and secure database system to store and manage large amounts of data, while providing data backup, recovery and access control functions to ensure data integrity, availability and security;

[0085] Model building and updating module, which is used to build a three-dimensional digital model of infrastructure using BIM and other technologies, and continuously update the model based on real-time monitoring data to intuitively display the structure and status of the infrastructure and improve the accuracy and practicality of the model;

[0086] The data analysis and early warning module is used to monitor and analyze the collected data in real time, detect abnormal situations in time and trigger early warning mechanisms. At the same time, it uses historical data to establish a prediction model to provide a scientific basis for the operation and maintenance of infrastructure;

[0087] Data visualization and reporting module, which is used to present complex data in an intuitive and easy-to-understand way, improve users' understanding and efficiency of data use, and generate detailed monitoring reports to provide scientific basis for decision makers;

[0088] System integration and interface module is used to integrate various modules to form a unified and efficient system, and provide interfaces with other systems to achieve data intercommunication and collaborative work, thereby improving the flexibility and scalability of the system;

[0089] The operation and maintenance management and optimization module is responsible for the daily operation and maintenance of the system, ensuring the stability and reliability of the system, and continuously optimizing and upgrading the system according to user needs and technological development to improve the performance and functionality of the system.

[0090] As described above, the present invention ensures the accuracy and consistency of data through data collection and preprocessing, uses an efficient and secure database system to realize data storage and management, and uses BIM technology to construct and update the three-dimensional model of the infrastructure in real time, thereby improving the accuracy and practicality of the model. The data analysis and early warning module can monitor anomalies in real time and trigger early warnings, and establish a prediction model to provide a scientific basis for operation and maintenance. The data visualization and reporting module intuitively presents complex data and assists in decision-making. The system integration and interface module enhances the flexibility and scalability of the system and realizes data intercommunication and collaborative work. The operation and maintenance management and optimization module ensures that the system is stable and reliable, and continuously optimizes and upgrades, thereby improving the scientificity, efficiency and safety of infrastructure monitoring and management as a whole.

[0091] Preferably, the data acquisition and preprocessing module includes:

[0092] A11: Data collection layer, used to capture infrastructure status data in real time through sensor networks, including temperature sensors, humidity sensors, and pressure sensors;

[0093] A12: Data preprocessing layer, used to perform preliminary cleaning, format conversion and verification on the collected raw data to ensure the accuracy and consistency of the data;

[0094] A13: Data storage layer, used to store preprocessed data in the database for subsequent analysis and processing.

[0095] As described above, the present invention uses the sensor network through the data acquisition layer to capture the temperature, humidity, pressure and other status data of the infrastructure in real time. The data preprocessing layer cleans, converts the format and verifies the original data to ensure the accuracy and consistency of the data. Finally, the data storage layer safely stores the preprocessed data in the database, providing a reliable foundation for subsequent analysis and processing. This series of operations effectively improves the quality of the data and provides a solid guarantee for subsequent data analysis, monitoring and early warning, and decision support.

[0096] Preferably, the data collection and preprocessing module collects the status data of the infrastructure using the deployed sensor network and performs preliminary preprocessing on the collected data to ensure the accuracy and consistency of the data, including the following steps:

[0097] S11: Data collection, capturing infrastructure status data in real time through sensor networks;

[0098] S12: Data cleaning, removing noise, outliers and duplicate values ​​in the data to ensure data accuracy;

[0099] S13: Format conversion, converting the data formats generated by different sensors into a standard format for subsequent processing and analysis;

[0100] S14: Data verification: Check the integrity and consistency of the data to ensure that the data is not lost or damaged during transmission.

[0101] As described above, the present invention realizes real-time capture of infrastructure status data through data acquisition and preprocessing modules, and removes noise, outliers and duplicate values ​​through data cleaning to ensure data accuracy; at the same time, the data generated by different sensors are unified into a standard format through format conversion to facilitate subsequent processing and analysis; finally, the data verification step ensures the integrity and consistency of the data, effectively avoiding the loss and damage of data during transmission, and providing a reliable data basis for subsequent monitoring, analysis and decision-making.

[0102] Preferably, when the data acquisition and preprocessing module removes noise in the data, the principle formula for removing noise in the data is:

[0103] y(n)=(1 / N)*Σ[x(i)](i=n-N+1,n)

[0104] Among them, y(n) is the output after filtering, x(i) is the original data, and N is the filter window size.

[0105] As described above, the data acquisition and preprocessing module in the present invention adopts a specific filtering algorithm when removing data noise, and obtains the filtered output by calculating the weighted average of the original data in the filtering window or applying other mathematical methods. This process effectively reduces random fluctuations and interference in the data, improves the accuracy and reliability of the data, and provides a clearer and more accurate information basis for subsequent data analysis and decision-making.

[0106] Preferably, the data storage and management module includes the following steps when establishing an efficient and secure database system to store and manage large amounts of data while providing data backup, recovery and access control functions to ensure data integrity, availability and security:

[0107] S21: Data reception and verification: receiving data from the data acquisition module and verifying the received data to ensure the integrity and accuracy of the data;

[0108] S22: data storage, selecting a suitable database system for storage according to the type and characteristics of the data, wherein the selectable database system includes a relational database and a non-relational database;

[0109] S23: Data backup and recovery, formulate data backup strategies, and regularly back up data in the database;

[0110] S24: Data access control, setting user permissions for the database to ensure that only authorized users can access and modify data;

[0111] S25: Data monitoring and maintenance, monitor the performance and status of the database, and promptly detect and handle potential problems.

[0112] As described above, the present invention realizes receiving and verifying the integrity and accuracy of data from the data acquisition module through the data storage and management module, selects a suitable database system for efficient storage according to the data type, and formulates a complete data backup and recovery strategy to ensure the persistent preservation and disaster recovery capabilities of the data. In addition, through strict data access control, the security and privacy of the data are guaranteed, and only authorized users can access and modify the data. Finally, through data monitoring and maintenance, database performance problems are discovered and handled in a timely manner to ensure the availability of data and the stable operation of the system, thereby improving the efficiency, security and reliability of data storage and management as a whole.

[0113] Preferably, the model building and updating module uses BIM and other technologies to build a three-dimensional digital model of the infrastructure, and continuously updates the model according to real-time monitoring data to intuitively display the structure and status of the infrastructure and improve the accuracy and practicality of the model, including the following steps:

[0114] S31: Model construction, specifically including the following steps:

[0115] S311: Data collection, obtaining detailed data of the infrastructure from the data acquisition module, including geometric dimensions, material properties and structural layout.

[0116] S312: Using professional 3D modeling software, gradually build each part of the infrastructure based on the collected data, so as to realize the 3D model of the designed infrastructure;

[0117] S313: Model optimization: optimize the initially constructed model to ensure that the accuracy and details of the model meet the requirements;

[0118] S314: Model validation, verifying the accuracy and reliability of the model by comparing it with the actual infrastructure;

[0119] S32: Model update and maintenance, including the following steps:

[0120] S321: Data update, regularly obtain new infrastructure data from the data acquisition module, and compare the new and old data to identify changes and updates to the infrastructure;

[0121] S322: Model modification: modify and update the model accordingly based on new data to ensure that the model is always consistent with the actual situation;

[0122] S323: Model maintenance: Regularly maintain the model, including data backup and model cleanup, to ensure the stability and availability of the model.

[0123] As described above, the present invention uses the model building and updating module to first obtain detailed infrastructure data from the data acquisition module using BIM technology, build a high-precision three-dimensional digital model, and ensure the accuracy and reliability of the model through optimization and verification. Subsequently, by regularly acquiring new infrastructure data, identifying changes and updating the model, the consistency of the model with the actual situation is maintained. In addition, the model is regularly maintained, including data backup and cleaning, to ensure the stability and availability of the model. This series of operations not only improves the accuracy and practicality of the model, but also provides intuitive and efficient support for infrastructure monitoring, management and maintenance.

[0124] Preferably, when the model building and updating module optimizes the initially constructed model to ensure that the accuracy and details of the model meet the requirements, the optimization algorithms used include:

[0125] A21: The gradient descent method continuously adjusts the parameter value through iteration so that the function value gradually decreases until the convergence condition is reached. Its principle formula is:

[0126]

[0127] Among them, f(x) is the original function, is the gradient of the original function f(x), x(n) represents the parameter value of the nth iteration, and α represents the learning rate, that is, the step size of each iteration;

[0128] A22: Genetic algorithm, through operations such as selection, crossover and mutation, continuously evolves the population to find the optimal solution;

[0129] A23: Simulated annealing method, that is, finding the global optimal solution by simulating the temperature drop and energy reduction process in the metal annealing process. The core of the simulated annealing method lies in the probability formula of accepting new solutions: P = e(-ΔE / T), where ΔE represents the energy difference between the new solution and the current solution, T represents the current temperature, and when ΔE<0, the new solution is accepted; when ΔE>0, the new solution is accepted with a certain probability, and this probability decreases as the temperature decreases.

[0130] As described above, the model construction and update module in the present invention adopts optimization algorithms such as gradient descent, genetic algorithm and simulated annealing when optimizing the initially constructed model. These algorithms effectively improve the accuracy and details of the model through different mathematical principles and operation strategies, ensuring that the model meets the requirements of practical applications. The gradient descent method adjusts the parameter values ​​iteratively to make the function value gradually converge to the optimal value; the genetic algorithm searches for the optimal solution through the evolutionary population and has global search capabilities; the simulated annealing rule simulates the annealing process and accepts new solutions with a certain probability, which helps to jump out of the local optimal solution and find the global optimal solution. The application of these optimization algorithms has significantly improved the accuracy and practicality of the model, and provided strong technical support for the three-dimensional digital modeling of infrastructure.

[0131] As a preferred method, the data analysis and early warning module monitors and analyzes the collected data in real time, detects abnormal situations in time and triggers early warning mechanisms, and uses historical data to establish a prediction model to provide a scientific basis for the operation and maintenance of infrastructure, including the following steps:

[0132] S41: Data collection, collecting infrastructure operation data from multiple data sources, where the multiple data sources include various sensors, monitoring systems, and log files;

[0133] S42: Data cleaning, including removing duplicate data, processing missing values ​​and correcting errors to ensure data quality and accuracy;

[0134] S43: Real-time monitoring: using real-time monitoring tools to monitor the operating status of infrastructure in real time, including monitoring of key indicators and detection of abnormal situations;

[0135] S44: Data analysis: use appropriate methods to analyze data, extract useful information, and discover potential patterns and trends;

[0136] S45: Warning triggering: when the monitored data reaches or exceeds the warning threshold, the warning mechanism is automatically triggered and warning information is sent to relevant personnel;

[0137] S46: Trend forecasting, building a forecasting model based on historical data to predict the future state of infrastructure.

[0138] As described above, the present invention collects high-quality infrastructure operation data from multiple data sources, and after cleaning, uses real-time monitoring tools to monitor key indicators and detect anomalies, effectively extracts useful information from the data and discovers potential patterns. When the data reaches the warning threshold, the warning mechanism is automatically triggered to notify relevant personnel in a timely manner. In addition, the module can also establish a prediction model based on historical data to predict the future state of the infrastructure, providing a scientific basis for operation and maintenance. This series of operations significantly improves the level of intelligence in infrastructure management and ensures its safe, stable and efficient operation.

[0139] As a preferred method, the data analysis and early warning module uses appropriate methods to analyze data, extract useful information, and discover potential rules and trends. The data analysis methods used include:

[0140] A31: Statistical methods include the mean of analytical data, the variance of analytical data and the correlation coefficient of analytical data. The calculation principle formula of the mean is:

[0141]

[0142] Among them, Mean is the mean of the data set, x i is the value of the i-th data in the data set;

[0143] The calculation formula of variance is:

[0144]

[0145] Among them, Variance is the variance of the data set, and μ is the mean of the data set;

[0146] The calculation formula of the correlation coefficient is:

[0147]

[0148] Among them, r is the correlation coefficient, x i and i are the values ​​of the two variables at the i-th observation, and is the mean of the two variables;

[0149] A32: Machine learning algorithms include linear regression algorithm, decision tree algorithm and support vector machine algorithm. The principle formula of linear regression algorithm is: Y = β0 + β1X1 + β2X2 + … + β n X n +∈;

[0150] Among them, Y is the dependent variable, X1, X2 and X n are the independent variables, β0, β1, and β n is the regression coefficient and ∈ is the error term.

[0151] As described above, the present invention adopts statistical methods and machine learning algorithms when extracting useful information and discovering potential rules and trends through the data analysis and early warning module. The statistical methods reveal the distribution characteristics, degree of discreteness and correlation between variables of the data by calculating statistics such as mean, variance and correlation coefficient, and provide a basis for data analysis. Machine learning algorithms such as linear regression, decision trees and support vector machines can automatically learn complex patterns in the data, perform prediction and classification, and improve the accuracy and efficiency of data analysis. The comprehensive use of these methods enables the data analysis and early warning module to deeply explore the value of data and provide more accurate decision support for the operation and maintenance of infrastructure.

[0152] See also Figure 2 The present invention provides an embodiment: a method for global infrastructure monitoring based on a smart platform, comprising the following steps:

[0153] S51: Demand analysis and goal setting. The global infrastructure monitoring method based on the smart platform first needs to conduct demand analysis and goal setting, clarify the monitoring goals and infrastructure types, and set specific monitoring indicators based on these goals;

[0154] S52: Smart platform construction: Building a smart platform that integrates data analysis, monitoring and early warning, and decision support functions is the key;

[0155] S53: Data collection and transmission: installing sensors and other data collection devices at key locations of the infrastructure and transmitting the collected data to the smart platform;

[0156] S54: Data processing and analysis: The received raw data needs to be cleaned, sorted and formatted to eliminate noise and outliers. Then, data analysis techniques are used to conduct in-depth analysis of the processed data to extract useful information and patterns, providing a scientific basis for subsequent monitoring and early warning.

[0157] S55: Monitoring and early warning: According to the set monitoring indicators and thresholds, the operation status of the infrastructure is monitored in real time. Once an abnormal situation or potential risk is found, the early warning mechanism is immediately triggered to send alarm information to relevant personnel so that timely measures can be taken to avoid accidents.

[0158] S56: Decision support and optimization, based on data analysis results and early warning information, provide scientific decision support for decision makers. At the same time, based on monitoring data and feedback, continuously optimize the operation and management strategies of infrastructure, improve efficiency and safety, and ensure the long-term stable operation of infrastructure.

[0159] As described above, the present invention realizes data collection and efficient transmission at key locations by clarifying monitoring objectives and indicators, building an intelligent platform that integrates data analysis, monitoring early warning and decision support functions, extracts useful information through data processing and analysis, monitors the status of infrastructure in real time and triggers early warnings in case of abnormalities, provides scientific support for decision makers and optimizes operation management strategies, effectively improving the monitoring efficiency, safety and long-term stable operation capabilities of infrastructure.

[0160] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. An infrastructure global monitoring system based on a smart platform; characterized by: Included are: The data collection and preprocessing module is used to collect the status data of the infrastructure using the deployed sensor network and perform preliminary preprocessing on the collected data to ensure the accuracy and consistency of the data; Data storage and management module, which is used to establish an efficient and secure database system to store and manage large amounts of data, while providing data backup, recovery and access control functions to ensure data integrity, availability and security; Model building and updating module, which is used to build a three-dimensional digital model of the infrastructure using BIM technology and continuously update the model based on real-time monitoring data to intuitively display the structure and status of the infrastructure and improve the accuracy and practicality of the model; The data analysis and early warning module is used to monitor and analyze the collected data in real time, detect abnormal situations in time and trigger early warning mechanisms. At the same time, it uses historical data to establish a prediction model to provide a scientific basis for the operation and maintenance of infrastructure; Data visualization and reporting module, which is used to present complex data in an intuitive and easy-to-understand way, improve users' understanding and efficiency of data use, and generate detailed monitoring reports to provide scientific basis for decision makers; System integration and interface module is used to integrate various modules to form a unified and efficient system, and provide interfaces with other systems to achieve data intercommunication and collaborative work, thereby improving the flexibility and scalability of the system; The operation and maintenance management and optimization module is responsible for the daily operation and maintenance of the system, ensuring the stability and reliability of the system, and continuously optimizing and upgrading the system according to user needs and technological development to improve the performance and functionality of the system.

2. The infrastructure global monitoring system based on a smart platform according to claim 1, characterized in that: The data acquisition and preprocessing modules include: A11: Data collection layer, used to capture infrastructure status data in real time through sensor networks, including temperature sensors, humidity sensors, and pressure sensors; A12: Data preprocessing layer, used to perform preliminary cleaning, format conversion and verification on the collected raw data to ensure the accuracy and consistency of the data; A13: Data storage layer, used to store preprocessed data in the database for subsequent analysis and processing.

3. The infrastructure global monitoring system based on a smart platform according to claim 2 is characterized by: The data collection and preprocessing module collects the status data of the infrastructure using the deployed sensor network and performs preliminary preprocessing on the collected data to ensure the accuracy and consistency of the data. It includes the following steps: S11: Data collection, capturing infrastructure status data in real time through sensor networks; S12: Data cleaning, removing noise, outliers and duplicate values ​​in the data to ensure data accuracy; S13: Format conversion, converting the data formats generated by different sensors into a standard format for subsequent processing and analysis; S14: Data verification: Check the integrity and consistency of the data to ensure that the data is not lost or damaged during transmission.

4. The infrastructure global monitoring system based on a smart platform according to claim 3 is characterized by: When the data acquisition and preprocessing module removes noise from the data, the principle formula for removing noise from the data is: y(n)=(1 / N)*Σ[x(i)](i=n-N+1,n) Among them, y(n) is the output after filtering, x(i) is the original data, and N is the filter window size.

5. The infrastructure global monitoring system based on a smart platform according to claim 4 is characterized by: The data storage and management module includes the following steps when establishing an efficient and secure database system to store and manage large amounts of data, while providing data backup, recovery and access control functions to ensure data integrity, availability and security: S21: Data reception and verification: receiving data from the data acquisition module and verifying the received data to ensure the integrity and accuracy of the data; S22: data storage, selecting a suitable database system for storage according to the type and characteristics of the data, wherein the selectable database system includes a relational database and a non-relational database; S23: Data backup and recovery, formulate data backup strategies, and regularly back up data in the database; S24: Data access control, setting user permissions for the database to ensure that only authorized users can access and modify data; S25: Data monitoring and maintenance, monitor the performance and status of the database, and promptly detect and handle potential problems.

6. The infrastructure global monitoring system based on a smart platform according to claim 5 is characterized by: The model building and updating module uses BIM technology to build a three-dimensional digital model of the infrastructure and continuously updates the model based on real-time monitoring data to intuitively display the structure and status of the infrastructure and improve the accuracy and practicality of the model. It includes the following steps: S31: Model construction, specifically including the following steps: S311: Data collection, obtaining detailed data of the infrastructure from the data acquisition module, including geometric dimensions, material properties and structural layout. S312: Using professional 3D modeling software, gradually build each part of the infrastructure based on the collected data, so as to realize the 3D model of the designed infrastructure; S313: Model optimization: optimize the initially constructed model to ensure that the accuracy and details of the model meet the requirements; S314: Model validation, verifying the accuracy and reliability of the model by comparing it with the actual infrastructure; S32: Model update and maintenance, including the following steps: S321: Data update, regularly obtain new infrastructure data from the data acquisition module, and compare the new and old data to identify changes and updates to the infrastructure; S322: Model modification: modify and update the model accordingly based on new data to ensure that the model is always consistent with the actual situation; S323: Model maintenance: Regularly maintain the model, including data backup and model cleanup, to ensure the stability and availability of the model.

7. The infrastructure global monitoring system based on a smart platform according to claim 6, characterized in that: The model building and updating module optimizes the initially constructed model to ensure that the accuracy and details of the model meet the requirements. The optimization algorithms used include: A21: The gradient descent method continuously adjusts the parameter value through iteration so that the function value gradually decreases until the convergence condition is reached. Its principle formula is: Among them, f(x) is the original function, is the gradient of the original function f(x), x(n) represents the parameter value of the nth iteration, and α represents the learning rate, that is, the step size of each iteration; A22: Genetic algorithm, through selection, crossover and mutation operations, continuously evolves the population to find the optimal solution; A23: Simulated annealing method, that is, finding the global optimal solution by simulating the temperature drop and energy reduction process in the metal annealing process. The core of the simulated annealing method lies in the probability formula of accepting new solutions: P = e(-ΔE / T), where ΔE represents the energy difference between the new solution and the current solution, T represents the current temperature, and when ΔE<0, the new solution is accepted; when ΔE>0, the new solution is accepted with a certain probability, and this probability decreases as the temperature decreases.

8. The infrastructure global monitoring system based on a smart platform according to claim 7, characterized in that: The data analysis and early warning module monitors and analyzes the collected data in real time, detects abnormal situations in time and triggers early warning mechanisms. It also uses historical data to establish a prediction model to provide a scientific basis for the operation and maintenance of infrastructure. The following steps are included: S41: Data collection, collecting infrastructure operation data from multiple data sources, where the multiple data sources include various sensors, monitoring systems, and log files; S42: Data cleaning, including removing duplicate data, processing missing values ​​and correcting errors to ensure data quality and accuracy; S43: Real-time monitoring: using real-time monitoring tools to monitor the operating status of infrastructure in real time, including monitoring of key indicators and detection of abnormal situations; S44: Data analysis: use appropriate methods to analyze data, extract useful information, and discover potential patterns and trends; S45: Warning triggering: when the monitored data reaches or exceeds the warning threshold, the warning mechanism is automatically triggered and warning information is sent to relevant personnel; S46: Trend forecasting, building a forecasting model based on historical data to predict the future state of infrastructure.

9. The infrastructure global monitoring system based on a smart platform according to claim 8, characterized in that: The data analysis and early warning module uses appropriate methods to analyze data, extract useful information, and discover potential patterns and trends. The data analysis methods used include: A31: Statistical methods include the mean of analytical data, the variance of analytical data and the correlation coefficient of analytical data. The calculation principle formula of the mean is: Among them, Mean is the mean of the data set, x i is the value of the i-th data in the data set; The calculation formula of variance is: Among them, Variance is the variance of the data set, and μ is the mean of the data set; The calculation formula of the correlation coefficient is: Among them, r is the correlation coefficient, x i and i are the values ​​of the two variables at the i-th observation, and is the mean of the two variables; A32: Machine learning algorithms include linear regression algorithm, decision tree algorithm and support vector machine algorithm. The principle formula of linear regression algorithm is: Y = β0 + β1X1 + β2X2 + … + β n X n +∈; Among them, Y is the dependent variable, X1, X2 and X n are the independent variables, β0, β1, and β n is the regression coefficient and ∈ is the error term.

10. A method for global infrastructure monitoring based on a smart platform, characterized by: The following steps are included: S51: Demand analysis and goal setting. The global infrastructure monitoring method based on the smart platform first needs to conduct demand analysis and goal setting, clarify the monitoring goals and infrastructure types, and set specific monitoring indicators based on these goals; S52: Smart platform construction: Building a smart platform that integrates data analysis, monitoring and early warning, and decision support functions is the key; S53: Data collection and transmission: installing sensors and other data collection devices at key locations of the infrastructure and transmitting the collected data to the smart platform; S54: Data processing and analysis: The received raw data needs to be cleaned, sorted and formatted to eliminate noise and outliers. Then, data analysis techniques are used to conduct in-depth analysis of the processed data to extract useful information and patterns, providing a scientific basis for subsequent monitoring and early warning. S55: Monitoring and early warning: According to the set monitoring indicators and thresholds, the operation status of the infrastructure is monitored in real time. Once an abnormal situation or potential risk is found, the early warning mechanism is immediately triggered to send alarm information to relevant personnel so that timely measures can be taken to avoid accidents. S56: Decision support and optimization, based on data analysis results and early warning information, provide scientific decision support for decision makers. At the same time, based on monitoring data and feedback, continuously optimize the operation and management strategies of infrastructure, improve efficiency and safety, and ensure the long-term stable operation of infrastructure.

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