Intelligent water affair management analysis system

Abstract

The invention discloses an intelligent water affair management and analysis system, and belongs to the technical field of water affair treatment. An intelligent water affair management analysis system comprises a data acquisition and sensor module, a big data analysis and prediction module, a real-time monitoring and remote control module, an intelligent optimization module, a user participation and social module and a safety and privacy protection module. In the invention, the intelligent optimization module is utilized to more comprehensively know the relationship between water consumption and discharge, so that effective measures are taken to improve the water consumption efficiency, reduce the discharge and realize sustainable development, and the system can dynamically adjust the control parameters according to the actual operation condition by using the self-adaptive PID control algorithm, so that the system is more stable in operation. Therefore, the system change and the external disturbance can be better adapted.

Description

Technical Field

[0001] The present invention relates to the field of water treatment technology, and more specifically, to a smart water management and analysis system. Background Art

[0002] Water management refers to the process of planning, monitoring, allocating, utilizing and protecting water resources. This process aims to achieve sustainable use of water resources while ensuring that social, economic and environmental needs are met. Water management requires the processing of huge amounts of data information, which in turn requires the use of smart water management and analysis systems. Common smart water management and analysis systems are usually applicable to the coordinated management of regional water use by water bureaus, and are often used to manage and calculate the water use of regional users. However, there is a lack of actual comparison in the overall data processing of water consumption and drainage, which affects subsequent regional water use planning and analysis. Summary of the invention

[0003] The purpose of the present invention is to provide a smart water management and analysis system to solve the problems raised in the above-mentioned background technology: the common smart water management and analysis system is usually suitable for the coordinated management of regional water use by the water bureau, and is often a management calculation for the water use of regional users, but lacks actual comparison in the overall data processing of water consumption and drainage, which affects the subsequent regional water use planning analysis.

[0004] A smart water management and analysis system comprises a data acquisition and sensor module, a big data analysis and prediction module, a real-time monitoring and remote control module, an intelligent optimization module, a user participation and social module and a security and privacy protection module. The data acquisition and sensor module integrates multi-source data, and then analyzes the collected data through the big data analysis and prediction module, and uses the real-time monitoring and remote control module to perform real-time monitoring and remote control. The data acquisition and sensor module combines the Internet of Things to monitor the water flow in urban areas in real time, so as to detect leaks and make up for losses in a timely manner. The intelligent optimization module integrates a machine learning algorithm, and combines the Internet of Things to detect the ratio of urban water consumption and discharge, and establishes a prediction model. The intelligent optimization module automatically adjusts system parameters according to real-time changes in collected data to improve the automation level of the system. Afterwards, user feedback is collected through the user participation and social module, and the user participation and social module is provided with user-oriented reminders. The security and privacy protection module is provided with an alarm function and data encryption.

[0005] Preferably, the data acquisition and sensor module includes multi-source data integration and local data processing, the multi-source data integration collects data from different sources including sensor data, social media data and meteorological data, and the sensor data includes water quality sensor collected data, water level sensor collected data and flow sensor collected data, and monitors key parameters of the water system in real time, the local data processing sets up edge computing nodes near the water quality sensor, the water level sensor and the flow sensor, and the local data processing uses anomaly detection algorithms based on the Internet of Things to identify abnormal water use of local users, including water leakage and water quality abnormalities.

[0006] By adopting the above technical solution, data collection and sensor modules are combined with the Internet of Things, so that water usage statistics can be based on actual data, improving the accuracy and timeliness of statistics, rather than relying solely on periodic meter readings. In addition, based on the Internet of Things, it can help water departments to promptly detect water pipe leakage and equipment failures, thereby reducing losses in the water system, improving the stability and efficiency of the water supply system, and achieving more comprehensive and accurate water system monitoring and analysis.

[0007] Preferably, the big data analysis and prediction module includes data storage and management and primary data analysis. The data storage and management uses the Cassandra distributed database system to store data on multiple nodes. The primary data analysis performs feature engineering on the collected data, including feature extraction, conversion and selection, to extract useful information from the data and prepare for the subsequent modeling of the intelligent optimization module.

[0008] By adopting the above technical solution, the Cassandra distributed database system is used to improve the scalability and fault tolerance of the system, helping the big data analysis and prediction module to effectively process large-scale real-time data. Through primary data analysis, the water data is preliminarily analyzed and features are extracted, which facilitates the establishment of accurate and efficient prediction models, providing more accurate decision support and optimization opportunities for water management.

[0009] Preferably, the real-time monitoring and remote control module includes real-time monitoring and remote control. The real-time monitoring is provided with a dashboard, and the key indicators of the water system, including the current water quality index, water level and total water consumption, are displayed through the dashboard. The real-time monitoring is provided with a visual geographic information display platform, and the real-time status of each monitoring point is displayed using the visual geographic information display platform. The remote control provides control buttons for remotely turning on and off the water pump function and adjusting the valve function, and for adjustable parameters, including the water pump speed and valve opening, corresponding sliders and input boxes are provided for precise parameter adjustment.

[0010] By adopting the above technical solutions, an intuitive, practical and user-friendly real-time monitoring interface can be created, allowing water management personnel to quickly understand the system status, identify problems and make real-time decisions, and effectively implement a safe and efficient remote control interface for the water system, allowing remote operators to easily monitor and control the operation of the water system.

[0011] Preferably, the intelligent optimization module includes a water use prediction model, calculation of the water use to emission ratio and adaptive control. The water use prediction model uses time series analysis and regression algorithm to establish a prediction model to predict the future trend of key parameters such as water quality, water level and water use. The prediction model of water quality and water use is established through time series analysis and regression algorithm, and the ratio of urban water use to emission is calculated by using the monitored real-time water use data and environmental emission data. An intuitive visualization interface is provided, and the real-time data of the city's water use, water quality and emission are displayed through the visualization interface. The adaptive control uses an adaptive PID controller to optimize parameters according to real-time feedback.

[0012] By adopting the above technical solution and using the intelligent optimization module, we can have a more comprehensive understanding of the relationship between water use and emissions, so that effective measures can be taken to improve water use efficiency, reduce emissions, and achieve sustainable development. By using the adaptive PID control algorithm, the system can dynamically adjust the control parameters according to the actual operating conditions, so as to better adapt to system changes and external disturbances.

[0013] Preferably, the user participation and social module includes user feedback and user-targeted reminders. The user feedback provides a variety of user feedback collection channels, including online forms, feedback buttons within mobile applications, and social media platforms, and allows users to provide text feedback, pictures, and voice messages. The user feedback provides an anonymous feedback option and introduces a user reward mechanism, including points and coupons. The user-targeted reminder is provided with a personalized reminder setting, and the user sets personalized reminder options according to personal preferences and concerns. The user can choose to receive water information and water quality status reminders related to themselves, and the user-targeted reminder is provided with message permissions, and the user can set the monthly water use control limit and reminder cycle by himself.

[0014] By adopting the above technical solution, the user participation and social module set up can comprehensively consider user feedback and targeted reminders, so that the system can better meet the personalized needs of users, improve users' participation and experience in the water system, and help users better understand and control the actual water usage of individual households.

[0015] Preferably, the alarm function sets up a real-time alarm notification mechanism to send security event alarms to relevant personnel via SMS, email and push, and the alarm function is integrated with the security monitoring system to monitor network traffic, system performance and firewall logs in real time. The data encryption uses the AES encryption algorithm to encrypt and store sensitive information during data collection and data storage to prevent the leakage of sensitive information when the database is illegally accessed, and the data encryption establishes a fine-grained access control mechanism to ensure that only authenticated users can access the encrypted data.

[0016] By adopting the above technical solutions, the set alarm function can detect potential security threats in a timely manner, and the set data encryption can enhance the stability and security of the smart water management and analysis system.

[0017] Compared with the prior art, the advantages of the present invention are: (1) In the present invention, the intelligent optimization module is used to more comprehensively understand the relationship between water use and emissions, so that effective measures can be taken to improve water use efficiency, reduce emissions, and achieve sustainable development. By using an adaptive PID control algorithm, the system can dynamically adjust control parameters according to actual operating conditions, thereby better adapting to system changes and external disturbances.

[0018] (2) In the present invention, the user participation and social module can comprehensively consider user feedback and targeted reminders, so that the system can better meet the personalized needs of users, improve the user's participation and experience in the water system, and help users better understand and control the actual water use of individual households.

[0019] (3) In the present invention, the data acquisition and sensor module are combined with the Internet of Things, so that the water consumption statistics table can be based on actual data, improving the accuracy and timeliness of the statistics, rather than relying solely on periodic meter reading. In addition, based on the Internet of Things, it can help the water department to promptly detect water pipe leakage and equipment failure, thereby reducing the loss of the water system, improving the stability and efficiency of the water supply system, and realizing more comprehensive and accurate water system monitoring and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall system of the present invention. DETAILED DESCRIPTION

[0021] Example: See Figure 1A smart water management and analysis system includes a data acquisition and sensor module, a big data analysis and prediction module, a real-time monitoring and remote control module, an intelligent optimization module, a user participation and social module, and a security and privacy protection module. The data acquisition and sensor module integrates multi-source data, and then analyzes the collected data through the big data analysis and prediction module, and uses the real-time monitoring and remote control module for real-time monitoring and remote control. The data acquisition and sensor module combines the Internet of Things to monitor the water flow in urban areas in real time, so as to detect leaks and make up for losses in a timely manner. The intelligent optimization module integrates a machine learning algorithm, and combines the Internet of Things to detect the ratio of urban water consumption and emissions, and establishes a prediction model. The intelligent optimization module automatically adjusts system parameters according to the real-time changes of the collected data to improve the automation level of the system. After that, user feedback is collected through the user participation and social module, and the user participation and social module is provided with user-oriented reminders. The security and privacy protection module is provided with an alarm function and data encryption.

[0022] The data acquisition and sensor module includes multi-source data integration and local data processing. The multi-source data integration collects data from different sources including sensor data, social media data and meteorological data, and the sensor data includes water quality sensor data, water level sensor data and flow sensor data, and monitors the key parameters of the water system in real time. Local data processing sets edge computing nodes near water quality sensors, water level sensors and flow sensors, and local data processing uses anomaly detection algorithms based on the Internet of Things to identify abnormal water use of local users, including water leakage and water quality abnormalities. The data collected by the data acquisition and sensor module can be transmitted to the central server through the Internet of Things to monitor the system status in real time. In addition, the edge computing nodes set near the water quality sensors, water level sensors and flow sensors can be used for local data processing and preliminary analysis to reduce data transmission to the central server. The water quality sensors, water level sensors and flow sensors combined with the Internet of Things can monitor the water quality, water level and flow key parameters of the water system in real time, which enables the water use statistics table to be based on actual data, improve the accuracy and timeliness of statistics, rather than relying solely on periodic meter readings.

[0023] By adopting the above technical solution, data collection and sensor modules are combined with the Internet of Things, so that water usage statistics can be based on actual data, improving the accuracy and timeliness of statistics, rather than relying solely on periodic meter readings. In addition, based on the Internet of Things, it can help water departments to promptly detect water pipe leakage and equipment failures, thereby reducing losses in the water system, improving the stability and efficiency of the water supply system, and achieving more comprehensive and accurate water system monitoring and analysis.

[0024] The big data analysis and prediction module includes data storage and management and primary data analysis. Data storage and management uses the Cassandra distributed database system to store data on multiple nodes. Primary data analysis performs feature engineering on the collected data, including feature extraction, conversion and selection, to extract useful information from the data and prepare for the subsequent intelligent optimization module modeling. If you want to use the Cassandra distributed database system, you need to download and install the Cassandra database system first, and configure the Cassandra cluster to ensure that each node can communicate with each other. The Cassandra distributed database system uses a distributed hashing algorithm to determine the partitioning of data to ensure that the data is evenly distributed in the cluster, improve load balancing and performance, and then monitor the node status in the cluster to ensure the normal operation of each node, and add or remove nodes when necessary to achieve dynamic expansion and contraction of the cluster. The collected water data is then imported into the Cassandra distributed database system.

[0025] By adopting the above technical solutions, the Cassandra distributed database system is used to improve the scalability and fault tolerance of the system, helping the big data analysis and prediction module to effectively process large-scale real-time data. Through primary data analysis, the water data is initially analyzed and features are extracted, which facilitates the establishment of accurate and efficient prediction models, providing more accurate decision support and optimization opportunities for water management.

[0026] The real-time monitoring and remote control module includes real-time monitoring and remote control. The real-time monitoring is provided with a dashboard, and the key indicators of the water system, including the current water quality index, water level and total water consumption, are displayed through the dashboard. The real-time monitoring is provided with a visual geographic information display platform, and the real-time status of each monitoring point is displayed using the visual geographic information display platform. The remote control provides control buttons for remotely switching the water pump function and adjusting the valve function, and for adjustable parameters, including the water pump speed and the valve opening, corresponding sliders and input boxes are provided for precise parameter adjustment. The visual geographic information display platform indicates different water quality levels by different colors or signs, and the visual geographic information display platform provides interactivity, allowing users to click on points on the map to obtain detailed information, and the remote control provides real-time feedback to display the results of the remote control operation, such as whether the water pump is turned on and whether the valve is adjusted. The remote control integrates a remote troubleshooting tool, allowing the remote operator to solve some faults through remote diagnosis and analysis, and provides real-time support, such as online chat or voice call, to assist the operator in troubleshooting.

[0027] By adopting the above technical solutions, an intuitive, practical and user-friendly real-time monitoring interface can be created, allowing water management personnel to quickly understand the system status, identify problems and make real-time decisions, and effectively implement a safe and efficient remote control interface for the water system, allowing remote operators to easily monitor and control the operation of the water system.

[0028] The intelligent optimization module includes water consumption prediction model, calculation of water consumption and emission ratio and adaptive control. The water consumption prediction model uses time series analysis and regression algorithm to establish a prediction model to predict the future trend of key parameters such as water quality, water level and water consumption. It also uses time series analysis and regression algorithm to establish water quality and water consumption prediction models, and uses the monitored real-time water consumption data and environmental emission data to calculate the ratio of urban water consumption and emission. It is equipped with an intuitive visualization interface and displays the real-time data of urban water consumption, water quality and emission through the visualization interface. Adaptive control uses an adaptive PID controller to optimize parameters according to real-time feedback. The time series analysis and regression algorithm used for water consumption prediction and water consumption and emission ratio calculation need to collect historical water consumption and emission data, and ensure that the data contains a long enough time series to capture potential seasonality and trends. Then select ARIMA (AutoRegressive Integrated Moving Average). Average) model is used as the time series model, and the linear regression algorithm is selected to train the time series model and the linear regression algorithm in advance. Then, the user's water consumption and discharge are calculated and analyzed regularly every month and every quarter. When the adaptive PID controller is used initially, the initial values ​​of the proportional coefficient (P), integral coefficient (I), and differential coefficient (D) of the PID controller are set, and the error between the current system output and the expected output, that is, the control error, is calculated based on the real-time collected data. Then, the model reference adaptive control is used to dynamically update the PID parameters through the real-time monitored control error and other related information. Then, according to the updated PID parameters, the new controller output is calculated, and the calculated controller output is applied to the system to achieve system regulation.

[0029] By adopting the above technical solution and using the intelligent optimization module, we can have a more comprehensive understanding of the relationship between water use and emissions, so that effective measures can be taken to improve water use efficiency, reduce emissions, and achieve sustainable development. By using the adaptive PID control algorithm, the system can dynamically adjust the control parameters according to the actual operating conditions, so as to better adapt to system changes and external disturbances.

[0030] The user engagement and social module includes user feedback and user-targeted reminders. User feedback provides a variety of user feedback collection channels, including online forms, feedback buttons within mobile applications, and social media platforms, and allows users to provide text feedback, pictures, and voice messages. User feedback provides anonymous feedback options and introduces a user reward mechanism, including points and coupons. User-targeted reminders have personalized reminder settings. Users can set personalized reminder options based on their personal preferences and concerns, and users can choose to receive water information and water quality status reminders related to themselves. User-targeted reminders have message permissions, and users can set their own monthly water use control limits and reminder cycles. A real-time feedback processing mechanism is designed to ensure that user feedback can be quickly received and processed by the system, and user feedback is classified and screened to remove duplicate and irrelevant information and extract relevant and useful information. This can be done through offline manual form screening or online message screening. The user-oriented reminders set up are user-oriented and provide a variety of reminder methods, including SMS, push notifications, emails and social media messages to ensure that users can receive reminders in time in different scenarios. Reminders are combined with real-time monitoring data to provide timely information on the status of the water system, helping users to keep abreast of real-time water changes, including water outage notifications and water consumption, and provide information reminders when user usage exceeds preset values.

[0031] By adopting the above technical solution, the user participation and social module set up can comprehensively consider user feedback and targeted reminders, so that the system can better meet the personalized needs of users, improve users' participation and experience in the water system, and help users better understand and control the actual water usage of individual households.

[0032] The alarm function sets up a real-time alarm notification mechanism to send security incident alarms to relevant personnel via SMS, email, and push notifications. The alarm function is integrated with the security monitoring system to monitor network traffic, system performance, and firewall logs in real time. Data encryption uses the AES encryption algorithm to encrypt and store sensitive information during data collection and data storage to prevent sensitive information leakage when the database is illegally accessed. Data encryption establishes a fine-grained access control mechanism to ensure that only authenticated users can access encrypted data. The alarm content should include the event type, occurrence time, and related user information, which can better protect the smart water management and analysis system. In remote access and remote control, data encryption uses the SSH secure transmission protocol to ensure the security of remote communications. By implementing these security and privacy protection measures, we can better respond to potential threats and ensure the security and privacy of user and system data.

[0033] By adopting the above technical solutions, the set alarm function can detect potential security threats in a timely manner, and the set data encryption can enhance the stability and security of the smart water management and analysis system.

[0034] The specific workflow of this system is as follows: first, data is collected through the data acquisition and sensor module and transmitted to the central server through the Internet of Things. After that, it is preliminarily processed through the big data analysis and prediction module, and the Cassandra distributed database system is used to store the data on multiple nodes. The real-time monitoring and remote control module set up in the system can monitor and remotely control the user's water use in real time, and the intelligent optimization module includes a water use prediction model, calculation of the water use and emission ratio, and adaptive control, which can analyze and calculate based on the collected information. The data processed by the big data analysis and prediction module can be further processed in the intelligent optimization module and intelligently regulated through adaptive control. In addition, the user participation and social modules set up can also collect user feedback and provide personalized reminders to users.

[0035] 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. A smart water management and analysis system, characterized by: It includes a data acquisition and sensor module, a big data analysis and prediction module, a real-time monitoring and remote control module, an intelligent optimization module, a user participation and social module, and a security and privacy protection module. The data acquisition and sensor module integrates multi-source data, and then analyzes the collected data through the big data analysis and prediction module, and uses the real-time monitoring and remote control module for real-time monitoring and remote control. The data acquisition and sensor module combines with the Internet of Things to monitor the water flow in urban areas in real time, so as to detect leaks and repair losses in time. The intelligent optimization module integrates a machine learning algorithm, and combines with the Internet of Things to detect the ratio of urban water consumption and emissions, and establishes a prediction model. The intelligent optimization module automatically adjusts system parameters according to real-time changes in collected data to improve the automation level of the system. Afterwards, user feedback is collected through the user participation and social module, and the user participation and social module is provided with user-oriented reminders. The security and privacy protection module is provided with an alarm function and data encryption.

2. The smart water management and analysis system according to claim 1, characterized in that: The data acquisition and sensor module includes multi-source data integration and local data processing. The multi-source data integration collects data from different sources including sensor data, social media data and meteorological data, and the sensor data includes water quality sensor collection data, water level sensor collection data and flow sensor collection data, and monitors key parameters of the water system in real time. The local data processing sets up edge computing nodes near the water quality sensor, the water level sensor and the flow sensor, and the local data processing uses anomaly detection algorithms based on the Internet of Things to identify abnormal water use of local users, including water leakage and water quality abnormalities.

3. The smart water management and analysis system according to claim 1, characterized in that: The big data analysis and prediction module includes data storage and management and data primary analysis. The data storage and management uses the Cassandra distributed database system to store data on multiple nodes. The primary data analysis performs feature engineering on the collected data. It includes feature extraction, transformation and selection to extract useful information from the data and prepare for the modeling of the intelligent optimization module described later.

4. The smart water management and analysis system according to claim 1, characterized in that: The real-time monitoring and remote control module includes real-time monitoring and remote control. The real-time monitoring is provided with a dashboard, and the key indicators of the water system, including the current water quality index, water level and total water consumption, are displayed through the dashboard. The real-time monitoring is provided with a visual geographic information display platform, and the real-time status of each monitoring point is displayed using the visual geographic information display platform. The remote control provides control buttons for remotely turning on and off the water pump function and adjusting the valve function, and for adjustable parameters, including the water pump speed and valve opening, corresponding sliders and input boxes are provided for precise parameter adjustment.

5. The smart water management and analysis system according to claim 1, characterized in that: The intelligent optimization module includes a water use prediction model, calculation of the water use and emission ratio and adaptive control. The water use prediction model uses time series analysis and regression algorithm to establish a prediction model to predict the future trend of key parameters such as water quality, water level and water use. The prediction model of water quality and water use is established through time series analysis and regression algorithm, and the ratio of urban water use and emission is calculated by using the monitored real-time water use data and environmental emission data. An intuitive visualization interface is provided, and the real-time data of the city's water use, water quality and emission are displayed through the visualization interface. The adaptive control uses an adaptive PID controller to optimize parameters according to real-time feedback.

6. The smart water management and analysis system according to claim 1, characterized in that: The user participation and social module includes user feedback and user-targeted reminders. The user feedback provides a variety of user feedback collection channels, including online forms, feedback buttons within mobile applications, and social media platforms, and allows users to provide text feedback, pictures, and voice messages. The user feedback provides an anonymous feedback option and introduces a user reward mechanism, including points and coupons. The user-targeted reminder is set with a personalized reminder setting. Users can set personalized reminder options based on their personal preferences and concerns, and users can choose to receive water information and water quality status reminders related to themselves. The user-targeted reminder is set with message permissions, and users can set the monthly water use control limit and reminder cycle by themselves.

7. The smart water management and analysis system according to claim 1, characterized in that: The alarm function sets up a real-time alarm notification mechanism to send security incident alarms to relevant personnel via SMS, email and push, and the alarm function is integrated with the security monitoring system to monitor network traffic, system performance and firewall logs in real time. The data encryption uses the AES encryption algorithm to encrypt and store sensitive information during data collection and data storage to prevent the leakage of sensitive information when the database is illegally accessed, and the data encryption establishes a fine-grained access control mechanism to ensure that only authenticated users can access the encrypted data.

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