Intelligent monitoring and maintenance management platform for municipal facilities

Abstract

The present invention relates to the field of urban infrastructure management technology, and specifically to an intelligent monitoring and maintenance management platform for municipal facilities. The invention collects data in real time through liquid level, flow, pressure and water quality sensors, and uses communication technologies such as NB‑IoT and LoRa for data transmission. The data processing layer monitors the drainage status in real time and warns of potential faults in advance through cleaning, analysis and machine learning models. The application layer provides functions such as real-time monitoring, early warning and alarm, fault diagnosis, maintenance management and report generation, and supports desktop and mobile access. The implementation of this platform can not only effectively prevent water pollution and promote environmental protection, but also improve the intelligence level of maintenance management and promote the sustainable development of urban infrastructure.

Description

Technical Field

[0001] The present invention relates to the technical field of urban infrastructure management, and in particular to an intelligent monitoring and maintenance management platform for municipal facilities. Background Art

[0002] With the continuous acceleration of urbanization, the construction and maintenance of urban infrastructure are facing increasingly severe challenges. As an important part of urban infrastructure, the normal operation of the drainage system directly affects the city's environmental sanitation and the quality of life of residents. However, traditional drainage system monitoring and maintenance methods mostly rely on manual inspections and regular inspections, which have the following major problems:

[0003] Insufficient real-time monitoring: Traditional monitoring methods lack real-time performance and cannot promptly detect potential problems in the drainage system, such as pipe blockages and leaks. This delay not only leads to increased environmental pollution, but may also cause more serious consequences, such as floods and damage to infrastructure.

[0004] High labor costs: Manual inspections require a lot of human resources, and the professional knowledge and experience of inspectors are crucial to the accuracy of monitoring. Due to insufficient personnel or substandard skills, inspection results are often inaccurate, which in turn affects the maintenance decision of the drainage system.

[0005] Limited data processing capabilities: Traditional methods often rely on manual analysis to process collected data, resulting in slow information processing and difficulty in effectively mining and utilizing historical data, and unable to provide a scientific basis for the optimization of the drainage system.

[0006] Lack of intelligent decision support: In traditional drainage system management, there is a lack of intelligent decision support tools based on data analysis. Managers often make decisions based on experience rather than based on real-time data and model analysis. This approach is prone to decision errors, further increasing maintenance costs and risks.

[0007] In order to solve the above problems, many cities have begun to explore intelligent monitoring and maintenance management platforms for drainage systems based on the Internet of Things (IoT), big data and artificial intelligence (AI) technologies. These emerging technologies can achieve real-time monitoring, fault prediction and intelligent decision support for drainage systems, thereby improving management efficiency and maintenance effects.

[0008] For example, the Internet of Things technology can realize real-time data collection and transmission of various sensors in the drainage system, ensuring the timeliness and accuracy of the data; big data analysis technology can conduct in-depth mining of massive monitoring data to provide a scientific basis for the optimization of the drainage system; and machine learning and artificial intelligence technologies can quickly diagnose and predict faults, improving the scientificity and effectiveness of decision-making.

[0009] Therefore, designing and developing an intelligent monitoring and maintenance management platform for drainage systems that comprehensively utilizes advanced technologies not only has important practical significance, but can also effectively improve the management level of urban drainage systems, reduce maintenance costs, and improve the reliability and safety of urban infrastructure. Summary of the invention

[0010] In view of this, the purpose of the present invention is to propose an intelligent monitoring and maintenance management platform for municipal facilities to solve the above-mentioned problems.

[0011] Based on the above objectives, the present invention provides an intelligent monitoring and maintenance management platform for municipal facilities.

[0012] Intelligent monitoring and maintenance management platform for municipal facilities, including:

[0013] The data collection layer is used to collect various data of the drainage system in real time through liquid level sensors, flow sensors, pressure sensors and water quality sensors;

[0014] The data transmission layer uses NB-IoT and LoRa networks to transmit the collected data to the cloud server;

[0015] The data processing layer uses data cleaning, preprocessing, real-time data analysis, prediction models, and fault diagnosis models to analyze and process the collected data;

[0016] The application layer provides functional modules for real-time monitoring, early warning and alarm, fault diagnosis, maintenance management and report generation.

[0017] Furthermore, the specific operation process of the data cleaning and preprocessing module is as follows:

[0018] Step 1: Fill in missing values

[0019] Extract sensor data from the database and determine the location of missing values ​​in each sensor dataset.

[0020] Calculate the mean of the data set and fill the missing values ​​with the mean;

[0021] Step 2: Outlier Detection

[0022] Calculate the Z score for each data point;

[0023] If the Z score exceeds the set threshold, the data point is marked as an outlier and recorded.

[0024] Step 3: Data Normalization

[0025] Calculate the maximum and minimum values ​​of each sensor data;

[0026] Use the min-max normalization formula: All data are linearly transformed to the interval [0,1].

[0027] Furthermore, the specific operation process of the fault diagnosis module is as follows:

[0028] Step 1: Logistic regression model

[0029] Collect sensor data and clean it;

[0030] Divide the dataset into training set and test set;

[0031] Use the training set to train the model and optimize the model parameters;

[0032] Input the test set into the model and output the prediction result;

[0033] Step 2: Support Vector Machine (SVM) Model

[0034] Use the same training and testing datasets;

[0035] Select a suitable kernel function and train the model;

[0036] The new sensor data is input into the SVM model for multi-classification diagnosis.

[0037] Furthermore, the composition and operation process of the real-time monitoring module are as follows: Desktop application

[0038] Get the latest sensor data from the cloud server;

[0039] Display data in the form of charts, including water level, flow, and pressure; provide a user interface that allows users to customize the way data is displayed; mobile applications

[0040] Push monitoring data and early warning information in real time;

[0041] Allow on-site maintenance personnel to view work orders and upload maintenance records. Further, the specific operation process of the early warning and alarm module is as follows: Step 1: Monitoring data analysis

[0042] Collect the latest data from sensors regularly;

[0043] Compare current data with set thresholds to detect whether they are beyond the normal range;

[0044] Step 2: Generate an alarm notification

[0045] If an abnormality is found, an early warning message is automatically generated;

[0046] Notify relevant managers via SMS, email and mobile push notifications. Furthermore, the workflow of the maintenance management module is as follows:

[0047] Step 1: Generate a maintenance work order

[0048] The system automatically generates a work order based on the fault diagnosis results, recording the fault details and recommended maintenance measures;

[0049] Step 2: Work Order Assignment

[0050] Send maintenance work orders to on-site maintenance personnel via mobile applications;

[0051] After receiving the work order, the maintenance personnel will process it on site and update the maintenance status.

[0052] Furthermore, the functions and processes of the report generation module are as follows:

[0053] Step 1: Data aggregation

[0054] Regularly extract monitoring data and maintenance records from the database;

[0055] Step 2: Report generation

[0056] Automatically generate monitoring reports, which include various monitoring data, early warning information, fault diagnosis results and maintenance records;

[0057] Format reports as PDF or Excel and email them to management.

[0058] Furthermore, the specific operation process of the data transmission layer is as follows:

[0059] Step 1: Sensor data collection

[0060] Each sensor collects data at a set period;

[0061] Step 2: Data Transfer

[0062] Data is transmitted to the cloud server via NB-IoT or LoRa network;

[0063] The server confirms data receipt and stores and backs it up.

[0064] Beneficial effects of the present invention:

[0065] 1. Through real-time data collection of liquid level, flow, pressure and water quality sensors, the system can timely detect potential problems, such as pipe blockage and leakage, and quickly notify relevant personnel through an automated early warning mechanism, thereby reducing the risk of accidents. The integrated data processing layer provides managers with accurate prediction and fault diagnosis through cleaning, analysis and modeling, helps to formulate scientific maintenance plans and resource allocation, and improves operational efficiency. The system can automatically generate maintenance work orders and fault reports, optimize the scheduling and maintenance process of maintenance personnel, and ensure that problems are handled in a timely manner, thereby extending the service life of facilities and reducing maintenance costs. By real-time monitoring of water quality and drainage conditions, the platform can effectively prevent water pollution and resource waste, promote sustainable development, and meet environmental protection requirements. The introduction of advanced machine learning and data analysis technologies enables the system to not only respond to current problems, but also learn and adapt to future needs through historical data, thereby improving the overall intelligence level. User-friendly interface: The design of desktop and mobile applications enables users to easily access monitoring data and maintenance information, improve operating experience and response speed. In summary, the technical solution significantly improves the operational safety and maintenance efficiency of the drainage system through intelligent monitoring and management, and provides strong support for the sustainable management of urban infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 This is the system architecture diagram process of the present invention;

[0068] Figure 2 The data processing flow of the present invention;

[0069] Figure 3 The early warning and alarm process of the present invention;

[0070] Figure 4 This is the maintenance management process of the present invention. DETAILED DESCRIPTION

[0071] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0072] Intelligent monitoring and maintenance management platform for municipal facilities, including

[0073] 1. System architecture

[0074] This platform consists of data acquisition layer, data transmission layer, data processing layer and application layer, and aims to achieve comprehensive monitoring and management of the drainage system.

[0075] 2. Data Collection Layer

[0076] 2.1 Installation and configuration of liquid level sensor

[0077] Installation location: Install liquid level sensors at key nodes of drainage pipes, such as drainage wells, sump tanks, and drainage outlets. These sensors are used to monitor water level changes in real time.

[0078] Sensor type: Ultrasonic liquid level sensor is used to improve measurement accuracy.

[0079] Data transmission: The sensor transmits data to the cloud server in real time through the LoRa network to ensure the timeliness of the data.

[0080] 2.2 Installation and configuration of flow sensor

[0081] Installation location: Install the flow sensor in the main drainage pipe to measure the water flow through the pipe.

[0082] Sensor type: Select electromagnetic flowmeter, which is suitable for flow measurement of various liquids.

[0083] Data transmission: The flow sensor sends data to the cloud via the NB-IoT network to ensure a stable connection.

[0084] 2.3 Installation and configuration of pressure sensor

[0085] Installation location: Install pressure sensors at key points in the pipeline, such as bends and outlets, to monitor pressure changes in the pipeline.

[0086] Sensor type: Uses high-precision pressure sensor, capable of coping with different water pressure conditions.

[0087] Data transmission: The sensor transmits monitoring data to the cloud via the LoRa network.

[0088] 2.4 Installation and configuration of water quality sensors

[0089] Installation location: Install a water quality sensor at the outlet of the drainage system to monitor the quality of the outlet water.

[0090] Sensor Type: Use a multi-parameter water quality sensor capable of simultaneously measuring pH, turbidity, and pollutant concentration.

[0091] Data transmission: Water quality sensors send data to the cloud in real time via the NB-IoT network.

[0092] 3. Data transmission layer

[0093] 3.1 Network Settings

[0094] Base station deployment: NB-IoT and LoRa base stations are deployed in the drainage system coverage area to ensure stable transmission of sensor data.

[0095] Communication configuration: Configure the communication module of the sensor to connect it to the corresponding network to ensure timely upload of data.

[0096] 4. Data processing layer

[0097] 4.1 Data cleaning and preprocessing

[0098] Missing value filling: Use mean filling method to handle missing values ​​in sensor data to ensure data integrity.

[0099] In sensor data collection, missing values ​​may occur due to various reasons (such as sensor failure, network delay, etc.). The handling of missing values ​​is an important step in data cleaning to ensure the accuracy of data analysis and modeling. Here is how missing value filling works:

[0100] 1.1 Method selection

[0101] Mean filling method:

[0102] Calculate the mean of a feature (such as water level, flow, etc.) in the current data set.

[0103] Replace missing values ​​with the mean of the feature.

[0104] formula:

[0105]

[0106] in, is the mean of the feature.

[0107] Forward Fill:

[0108] Replace missing values ​​with their preceding non-missing value.

[0109] Suitable for time series data and maintains data continuity.

[0110] Backward Fill:

[0111] Replace missing values ​​with the next non-missing value.

[0112] Also works well with time series data to help fill in future information.

[0113] 1.2 Implementation steps

[0114] Read sensor dataset.

[0115] Check for missing values ​​for each column (feature).

[0116] Select an appropriate filling method (mean filling, forward filling, backward filling, etc.).

[0117] Apply the filling method and generate a new dataset

[0118] Outlier detection: Use the Z-score method to detect and process outliers in the data to ensure data reliability;

[0119] Outlier Detection

[0120] Outlier detection is an important part of data cleaning, the purpose is to identify and deal with extreme values ​​in the data, which may be caused by sensor errors, data transmission problems, etc. Here is how outlier detection works:

[0121] Method selection

[0122] Z-score method:

[0123] For each data point, calculate its Z score.

[0124] Z-score formula:

[0125] Interquartile Range (IQR):

[0126]

[0127] Where X is the data point, μ is the mean of the data, and σ is the standard deviation. · Generally, if the Z score is greater than 3 or less than -3, it can be considered an outlier. · Calculate the first quartile (Q1) and the third quartile (Q3) of the data.

[0128] Calculate the interquartile range (IQR):

[0129] IQR=Q3-Q1

[0130] Define the range of outliers: Lower Bound = Q1-1.5×IQR Upper Bound = Q3+1.5×IQR

[0131] Values ​​outside this range are considered outliers.

[0132] Implementation steps

[0133] Read sensor dataset.

[0134] Select an appropriate outlier detection method (Z-score or quartiles).

[0135] Calculate the desired statistics (mean, standard deviation, quartiles, etc.).

[0136] Outliers were marked and recorded for consideration for deletion, replacement, or retention for subsequent analysis.

[0137] Data normalization: Normalize the data from different sensors so that they can be compared on the same scale. Use the formula:

[0138]

[0139] Data Normalization

[0140] Data normalization is an important step in data preprocessing, especially when processing data from different sensors. It helps convert data of different scales to the same scale for comparison and analysis. Here is how data normalization works:

[0141] 3.1 Method selection Min-Max Normalization:

[0142] Scale the data to the [0, 1] interval. Normalization formula:

[0143]

[0144] Among them, X is the original data, X min and X max are the minimum and maximum values ​​of the feature respectively. z-sec normalization:

[0145] Transform the data to a standard normal distribution.

[0146] Normalization formula:

[0147]

[0148] Where μ is the mean and σ is the standard deviation.

[0149] Read sensor dataset.

[0150] For each feature, select a normalization method (min-max normalization or Z-score normalization).

[0151] Calculate the minimum, maximum, mean, and standard deviation of the features.

[0152] Apply the normalization formula to generate a normalized data set.

[0153] 4.2 Real-time data analysis

[0154] Sliding window averaging: Perform sliding window averaging on real-time data to smooth data fluctuations and reduce noise.

[0155] Weighted moving average: weighted processing of flow and pressure data based on changes in historical data to reflect the latest trends in a timely manner.

[0156] 4.3 Prediction Model

[0157] ARIMA model: Use the ARIMA model to perform time series analysis on historical water level and flow data to provide early warning of possible overflow risks.

[0158] LSTM model: The LSTM neural network is implemented through the Keras framework to predict flow and pressure data and predict future trends.

[0159] 4.4 Fault Diagnosis

[0160] Logistic regression model: The logistic regression model is trained with historical data to diagnose the risk of pipeline blockage.

[0161] Logistic regression is a statistical model widely used in classification problems, especially in binary classification problems. Its core idea is to predict the probability of an event through a linear model. Logistic regression converts the linear combination of input features into output probability through a logistic function (sigmoid function).

[0162] 2. Mathematical Model

[0163] 2.1 Linear combination: The logistic regression model can be expressed as:

[0164] z=w T x+b, where w is the weight vector, x is the input feature vector, and b is the bias term. 2.2 Sigmoid function: The sigmoid function maps the result of the linear combination to between 0 and 1:

[0165]

[0166] The output probability P(y=1|x) represents the probability that the input feature x belongs to the positive class.

[0167] Loss Function

[0168] Logistic regression uses the cross entropy loss function to evaluate the performance of the model:

[0169]

[0170] Support Vector Machine (SVM) Model: Use the SVM model to analyze monitoring data, identify the risk of pipeline leakage, and generate fault reports.

[0171] Support vector machine (SVM) is a powerful supervised learning algorithm mainly used for classification and regression tasks. The goal of SVM is to find an optimal hyperplane to maximize the margin between categories.

[0172] Mathematical model

[0173] 2.1 Hyperplane definition: In two-dimensional space, a hyperplane can be expressed as:

[0174] w T x+b=0

[0175] Among them, w is the normal vector, x is the input feature, and b is the bias.

[0176] 2.2 Maximum Margin: SVM improves the robustness of classification by maximizing the margin:

[0177]

[0178] Among them, y i is the sample label, x i is the sample feature.

[0179] 3. Loss Function

[0180] SVM usually uses Hinge Loss to evaluate model performance:

[0181] L(y,f(x))=max(0,1-yf(x))

[0182] Where f(x) = w T x+b.

[0183] 4. Model Training

[0184] The training of the SVM model is achieved by solving convex optimization problems. Commonly used algorithms include SMO (Sequential Minima Optimization) and gradient descent.

[0185] 5. Kernel Methods

[0186] For nonlinearly separable data, SVM maps the data to a high-dimensional space by introducing kernel functions (such as RBF kernel, polynomial kernel, etc.) to make it linearly separable. The form of the kernel function is:

[0187] K(x i , x j )=φ(x i ) T φ(x j )

[0188] 5. Application layer

[0189] 5.1 Real-time monitoring module

[0190] In the user's desktop application, the drainage system's water level, flow, pressure, water quality and other monitoring data are displayed in real time, and the data change trend is displayed graphically.

[0191] 5.2 Early warning and alarm module

[0192] When the monitoring data exceeds the set normal range, the system will automatically issue early warning and alarm notifications, including SMS and email notifications.

[0193] 5.3 Fault diagnosis module

[0194] The system automatically generates fault reports and maintenance recommendations by analyzing abnormal data, helping managers develop maintenance plans.

[0195] 5.4 Maintenance Management Module

[0196] Generate maintenance work orders based on fault diagnosis results, arrange maintenance personnel to perform on-site maintenance, and track maintenance progress in real time.

[0197] 5.5 Report Generation Module

[0198] The system will regularly generate monitoring reports and maintenance reports for managers' reference and decision-making. The reports include monitoring data, early warning information, fault diagnosis results and maintenance records.

[0199] 6. User Interface

[0200] 6.1 Desktop Application

[0201] Provides detailed monitoring data and analysis results display, supports multi-dimensional data query and graphical display, including heat maps and time series graphs.

[0202] 6.2 Mobile Application

[0203] Maintenance personnel can view work orders, upload maintenance photos and record maintenance status through mobile applications on site, and receive monitoring data and early warning information in real time.

[0204] Summarize

[0205] This implementation plan can effectively prevent potential failures and risks and improve the operating efficiency and safety of the drainage system through comprehensive monitoring, data analysis and intelligent management of the drainage system. The combination of various technologies ensures the reliability and real-time performance of the system, providing an innovative solution for urban drainage management.

[0206] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Intelligent monitoring and maintenance management platform for municipal facilities, characterized by: include: The data collection layer is used to collect various data of the drainage system in real time through liquid level sensors, flow sensors, pressure sensors and water quality sensors; The data transmission layer uses NB-IoT and LoRa networks to transmit the collected data to the cloud server; The data processing layer uses data cleaning, preprocessing, real-time data analysis, prediction models, and fault diagnosis models to analyze and process the collected data; The application layer provides functional modules for real-time monitoring, early warning and alarm, fault diagnosis, maintenance management and report generation.

2. The municipal facility intelligent monitoring and maintenance management platform according to claim 1 is characterized in that: The specific operation process of the data cleaning and preprocessing module is as follows: Step 1: Fill in missing values: Extract sensor data from the database and determine the location of missing values ​​in each sensor data set; Calculate the mean of the data set and fill the missing values ​​with the mean; Step 2: Outlier Detection: Calculate the Z score for each data point; If the Z score exceeds the set threshold, the data point is marked as an outlier and recorded; Step 3: Data normalization: Calculate the maximum and minimum values ​​of each sensor data; Use the min-max normalization formula: , linearly transform all data to the interval [0, 1].

3. The municipal facility intelligent monitoring and maintenance management platform according to claim 1 or 2, characterized in that: The specific operation process of the fault diagnosis module is as follows: Step 1: Logistic regression model: Collect sensor data and clean it; Divide the dataset into training set and test set; Use the training set to train the model and optimize the model parameters; Input the test set into the model and output the prediction result; Step 2: Support Vector Machine (SVM) Model: Use the same training and testing datasets; Select a suitable kernel function and train the model; The new sensor data is input into the SVM model for multi-classification diagnosis.

4. The municipal facility intelligent monitoring and maintenance management platform according to claim 1 is characterized in that: The composition and operation process of the real-time monitoring module are as follows: Desktop application: Get the latest sensor data from the cloud server; Display data in graphs and charts, including water level, flow, and pressure; Provide a user interaction interface, allowing users to customize the way data is displayed; Mobile applications: Push monitoring data and early warning information in real time; Allows field maintenance personnel to view work orders and upload maintenance records.

5. The municipal facility intelligent monitoring and maintenance management platform according to claim 1 is characterized in that: The specific operation process of the early warning and alarm module is as follows: Step 1: Monitoring data analysis: Collect the latest data from sensors regularly; Compare current data with set thresholds to detect whether they are beyond the normal range; Step 2: Generate an alarm notification: If an abnormality is found, an early warning message is automatically generated; Notify relevant managers via SMS, email and mobile push.

6. The municipal facility intelligent monitoring and maintenance management platform according to claim 1 is characterized in that: The workflow of the maintenance management module is as follows: Step 1: Generate a maintenance work order: The system automatically generates a work order based on the fault diagnosis results, recording the fault details and recommended maintenance measures; Step 2: Work Order Assignment: Send maintenance work orders to on-site maintenance personnel via mobile applications; After receiving the work order, the maintenance personnel will process it on site and update the maintenance status.

7. The municipal facility intelligent monitoring and maintenance management platform according to claim 1 is characterized in that: The functions and processes of the report generation module are as follows: Step 1: Data aggregation: Regularly extract monitoring data and maintenance records from the database; Step 2: Report generation: Automatically generate monitoring reports, which include various monitoring data, early warning information, fault diagnosis results and maintenance records; Format reports as PDF or Excel and email them to management.

8. The municipal facility intelligent monitoring and maintenance management platform according to claim 1 is characterized in that: The specific operation process of the data transmission layer is as follows: Step 1: Sensor data collection: Each sensor collects data at a set period; Step 2: Data transmission: Data is transmitted to the cloud server via NB-IoT or LoRa network; The server confirms data receipt and stores and backs it up.

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