Water supply control system based on Internet of Things water meter and management platform thereof

Abstract

The invention discloses a water supply control system based on an Internet of Things water meter and a management platform thereof, and relates to the field of water supply system adjustment, and the system comprises an Internet of Things water meter module, a multi-sensor module, a data processing module, a transmission fusion module and cloud equipment. The NB-IoT module and the LoRa module are subjected to data distribution through the transmission fusion module, the reliability and the real-time performance of data transmission of the water meter of the Internet of Things are improved, and the preprocessing module is used for conducting denoising, data repairing and other processing on all data subsets in the NB-IoT module in the initial stage; a proper compression algorithm is matched through a dynamic compression module according to intervals of data subsets, a dynamic distribution transmission module constructs a distribution model of multi-data subset optimization transmission, and for simultaneous transmission of a large number of Internet of Things water meter data, it is guaranteed that transmission of multiple Internet of Things water meters is rapid and synchronous, and the transmission efficiency is improved. And rapid and accurate judgment and effective management of the system on fault problems are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water supply system regulation, and in particular to a water supply control system and a management platform thereof based on an Internet of Things water meter. Background Art

[0002] With the rapid development of IoT technology, more and more IoT devices are widely deployed to realize many application scenarios such as smart cities, industrial automation, agricultural monitoring, etc. Among them, IoT water meters are intelligent metering devices that combine traditional water meters with IoT technology. They receive transmitted data through cloud control systems and realize remote control.

[0003] For example, Chinese patent CN112269343A discloses an intelligent NB Internet of Things water meter control system, which includes: an MCU control unit, an NB communication unit, a valve control unit, a remote server and a remote control terminal. The MCU control unit is connected to the NB communication unit and the valve control unit respectively, wherein: the MCU control unit is used to upload the water meter detection data to the remote server through the NB communication unit; the remote control terminal is used to log in to the remote server, view the water meter detection data, and issue valve control instructions based on the water meter detection data; the NB communication unit is used to send the valve control instructions to the MCU control unit; the valve control unit is used to control the base meter to open and close the valve according to the valve control instructions received by the MCU control unit.

[0004] However, in the prior art, in some remote rural areas, mountainous areas or islands, the construction of communication infrastructure is relatively backward, and the network signal coverage is insufficient or the signal strength is weak. This will cause the data of the IoT water meter to be unable to be transmitted to the management platform in a timely and stable manner, resulting in data loss or delay; many IoT water meters are battery-powered for easy installation and maintenance. However, a certain amount of electricity is consumed during the data transmission process. In order to extend the battery life, the existing IoT water meters usually adopt a low-power communication mode. However, this mode will reduce the rate and efficiency of data transmission, resulting in delayed or untimely data transmission; these situations seriously affect the cloud system's status monitoring of many areas and complex water supply networks and a large number of IoT water meters, resulting in the inability to timely detect faults in the water supply network and the Internet of Things, and thus the inability to effectively control them. Summary of the invention

[0005] The purpose of the present invention is to provide a water supply control system based on an Internet of Things water meter and a management platform thereof to solve the problems raised by the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: a water supply control system based on an Internet of Things water meter, comprising an Internet of Things water meter module, a multi-sensor module, a data processing module, a transmission fusion module and a cloud device; the Internet of Things water meter module comprises an Internet of Things water meter body, an NB-IoT module, a LoRa module and a signal monitoring module;

[0007] The data collected by the IoT water meter body and the multi-sensor module are transmitted to the transmission fusion module through at least one of the NB-IoT module and the LoRa module, wherein the data transmitted through the NB-IoT module first passes through the data processing module and then is transmitted to the transmission fusion module;

[0008] The data processing module includes a pre-processing module, a dynamic compression module and a dynamic allocation transmission module. After the pre-processing module processes the data transmitted by the NB-IoT module, the dynamic compression module compresses and transmits the data to the dynamic allocation transmission module according to the interval matching of the data subset, so as to perform synchronous transmission of the data of multiple IoT water meters;

[0009] The transmission fusion module includes a fusion gateway unit and a core network fusion unit. The fusion gateway receives two types of data, the NB-IoT module and the LoRa module. The two types of data are centrally allocated and managed through the core network fusion unit and transmitted to the cloud device for unified processing and management.

[0010] Preferably, the pre-processing module performs denoising, data repair and other processing on each data subset in the initial NB-IoT module to provide standardized input for subsequent compression coding.

[0011] Preferably, the compression algorithm in the dynamic compression module includes Huffman coding, LZW algorithm and Deflate algorithm, and the matching method is as follows:

[0012] S1. Calculate the distribution histogram of the sample data and divide the data value range into custom values ​​according to the cumulative distribution function;

[0013] S2. For data subsets in different intervals, match the appropriate compression encoding method by calculating their information entropy;

[0014] S3. Set a first threshold and a second threshold, where the first threshold is smaller than the second threshold. Huffman coding is used for a data subset whose information entropy is lower than the first threshold. LZW algorithm is used for a data subset between the first threshold and the second threshold. Deflate algorithm is directly used for a data subset whose entropy is higher than the second threshold.

[0015] Preferably, the dynamic allocation transmission module constructs an allocation model for optimizing transmission of multiple data subsets;

[0016] In a typical NB-IoT module uplink transmission scenario, the system uses a single carrier frequency with a bandwidth of B. A scheduling cycle contains t time slots, each time slot contains m resource blocks. The system provides data transmission services for n IoT water meters with NB-IoT modules. The data arrival rate of IoT water meter i is λ i , the amount of data to be transmitted during the allocation period is L i ,remember is a scheduling variable, indicating whether to allocate m resource blocks to IoT water meter i in time slot t, and then define is the transmission rate of IoT water meter i in time slot t, and the Shannon formula can be used to obtain formula 1:

[0017]

[0018] Where B is the bandwidth in Hertz; is the interference of device i on m resource blocks; n 0 is the power spectral density of Gaussian white noise; is the channel gain; is the transmission power of IoT water meter i. While the optimization goal is to maximize the system throughput, the transmission delay and fairness of each IoT water meter are taken into account. Therefore, the following multi-objective optimization model is constructed, as shown in Formula 2:

[0019]

[0020] Formula 3:

[0021]

[0022] Among them, j represents another IoT water meter, R i represents the average transmission rate of IoT water meter i, R j It represents the average transmission rate of IoT water meter j. The main function of formula 2 is to maximize the system throughput. Formula 3 reflects the fairness of system transmission. Both formulas are used for synchronous transmission of multiple data among multiple IoT water meters.

[0023] Preferably, the fusion gateway unit integrates the network access functions of the NB-IoT module and the LoRa module, parses and converts data of different protocols, and uniformly encapsulates them into a format suitable for transmission to the core network.

[0024] Preferably, the core network fusion unit integrates the network functions of the NB-IoT module and the LoRa module, and centrally allocates and manages the two network resources.

[0025] The management platform of the water supply control system based on the Internet of Things water meter includes a real-time monitoring module, a scheduling management module, an equipment management module, a user management module, a report statistics module, a prediction module and a mobile application module.

[0026] Preferably, the real-time monitoring module is used to check the operating status of each link of the water supply system. When an abnormal situation occurs in the system, an alarm will be immediately issued, the abnormal position will be automatically located, and abnormal related information will be displayed;

[0027] The equipment management module is used to manage various equipment in the water supply system and perform maintenance management throughout the entire life cycle;

[0028] The user management module is used to manage water supply user information and to collect statistics and analyze user water use conditions;

[0029] The mobile application module is used by management personnel to view the operating status of the water supply system in real time, receive alarm information from the system, and perform remote control operations.

[0030] Preferably, the report statistics module generates various types of reports according to requirements and supports exporting and printing;

[0031] The prediction module is connected to the device management module and the user management module at the same time, and uses a random forest algorithm to establish a user water use model, a pipe network leakage model and a water quality change model based on the device historical data and the user historical data;

[0032] The scheduling management module is connected to the real-time monitoring module and the prediction module at the same time, and formulates a water supply scheduling plan by establishing a dynamic programming model based on the real-time collected data and water demand prediction.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. In this control system, data is transmitted through the adaptive switching mechanism of the NB-IoT module and the LoRa module. The data diversion mechanism in the transmission fusion module improves the reliability and real-time performance of the data transmission of the IoT water meter. The pre-processing module is used to perform denoising and data repair on each data subset in the initial NB-IoT module. The dynamic compression module matches the appropriate compression algorithm according to the interval of the data subset to reduce the size of the transmitted data. The dynamic allocation transmission module constructs an allocation model for optimizing the transmission of multiple data subsets to reduce the delay of data transmission. For the simultaneous transmission of a large number of IoT water meter data, it ensures that multiple IoT water meters transmit quickly and synchronously, thereby improving the system's rapid and accurate judgment and effective management of fault problems.

[0035] 2. This management platform realizes the real-time collection and transmission of water supply data. Managers can understand the operating status of the water supply system through cloud devices or mobile phone APP anytime and anywhere, and promptly discover and deal with problems. Through the analysis of real-time data and water demand forecasting, water supply scheduling is more scientific and reasonable. It can adjust the water supply strategy in time according to the actual water use situation, avoid insufficient water supply or excessive water pressure, and improve the reliability and stability of water supply. At the same time, the management platform has accumulated a large amount of historical data. Through the analysis and mining of these data, it provides a scientific basis for the decision-making of water supply companies. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of a water supply control system based on an Internet of Things water meter according to the present invention;

[0037] Figure 2 The present invention is a flow chart of a management platform of a water supply control system based on an Internet of Things water meter. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1: Reference Figure 1 As shown: A water supply control system based on an IoT water meter, including an IoT water meter module, a multi-sensor module, a data processing module, a transmission fusion module and a cloud device;

[0040] The IoT water meter module includes the IoT water meter body, NB-IoT module (narrowband IoT), LoRa module (long-range radio) and signal monitoring module.

[0041] The IoT water meter module is based on the IoT water meter body. As the core equipment for water supply data collection, it adopts advanced ultrasonic or electromagnetic measurement technology. The ultrasonic water meter uses the principle that when ultrasonic waves propagate in water, their propagation speed will be affected by the water flow speed. By accurately measuring the time difference between the propagation of ultrasonic waves in the downstream and upstream directions, the speed and flow of the water flow are calculated, and then the user's water consumption is obtained. This measurement method has the advantages of high precision, no mechanical wear, and small pressure loss, which can ensure long-term stable and accurate measurement of water consumption. The electromagnetic water meter is based on Faraday's law of electromagnetic induction. When conductive water flows in a magnetic field, an induced electromotive force proportional to the flow rate is generated, and the water flow rate is measured by detecting the electromotive force. It has the characteristics of wide measurement range, good linearity, and high stability, and can adapt to various complex water use environments.

[0042] The NB-IoT module operates in the licensed frequency band and has the characteristics of low power consumption, wide coverage, low cost and massive connections. It can achieve long-distance communication under low bandwidth conditions, which is very suitable for devices such as IoT water meters that need to transmit small amounts of data stably for a long time. The LoRa module operates in the unlicensed frequency band and has the characteristics of long distance, low power consumption and low cost. It is especially suitable for IoT water meter data transmission in remote or rural areas with poor network coverage. At the same time, the IoT water meter has also been designed with low power consumption, using energy-efficient chips and circuits, and cooperating with intelligent sleep and wake-up mechanisms to effectively extend the battery life, reduce the frequency of battery replacement, and reduce maintenance costs.

[0043] The multi-sensor module includes a pressure sensor, a water level sensor, and a water quality sensor:

[0044] Pressure sensors are distributed at key nodes of the water supply network to monitor the water pressure in the network in real time. By detecting changes in pressure, abnormal pressure in the network can be detected in time. For example, excessive pressure may cause pipe rupture, while low pressure may affect the normal water use of users.

[0045] Water level sensors are installed in water sources such as reservoirs and pools to accurately monitor water levels. This is of great significance for water supply companies to rationally dispatch water sources and ensure the continuity of water supply.

[0046] Water quality sensors are used to detect key indicators of water quality, including pH value, turbidity, residual chlorine, heavy metal content, etc. These sensors can collect water quality data in real time, providing strong support for ensuring the safety of water supply quality.

[0047] These sensors are equipped with corresponding communication interfaces, which can transmit the collected data to the IoT water meter module via wired means;

[0048] The signal monitoring module monitors signal strength, signal-to-noise ratio and other parameters in real time to determine the quality of the current network environment. When the signal quality of the NB-IoT module is poor and meets the communication conditions of the LoRa module, it automatically switches to the LoRa module (LoRa transmission is the main transmission, NB-IoT transmission is the auxiliary transmission); conversely, when the NB-IoT network recovers to a good state, it switches back to the NB-IoT module (NB-IoT transmission is the main transmission, LoRa transmission is the auxiliary transmission). This adaptive switching mechanism can ensure that the terminal device is always in the best communication state and improve the reliability and stability of data transmission.

[0049] The data processing module includes a pre-processing module, a dynamic compression module and a dynamic allocation and transmission module.

[0050] The pre-processing module performs denoising and data repair on each data subset in the initial NB-IoT module to provide standardized input for subsequent compression coding. Among them, the Kalman filter algorithm is used to perform adaptive denoising on the data subset, which can significantly improve the data quality. The denoised data subset is interpolated for missing values ​​and outliers are removed to obtain a complete and clean data subset.

[0051] Traditional general compression algorithms include Huffman coding, LZW algorithm and Deflate algorithm. Since the compression performance is limited by the non-stationary characteristics of data distribution, the dynamic compression module matches the appropriate compression algorithm according to the interval of the data subset, as follows:

[0052] First, the distribution histogram of the sample data is statistically calculated, and the data value range is customized according to the cumulative distribution function; then, for data subsets in different intervals, the information entropy is calculated to match the appropriate compression encoding method; finally, the first threshold and the second threshold are set, where the first threshold is smaller than the second threshold, and Huffman coding is used for the data subset with information entropy lower than the first threshold, and the LZW algorithm is used for the data subset between the first threshold and the second threshold, and the Deflate algorithm is directly used for the data subset with entropy higher than the second threshold;

[0053] Taking into account the data transmission requirements of the IoT water meter module, the dynamic allocation transmission module constructs an allocation model for optimizing transmission of multiple data subsets;

[0054] In a typical NB-IoT module uplink transmission scenario, the system uses a single carrier frequency with a bandwidth of B. A scheduling cycle contains t time slots, and each time slot contains m resource blocks. The system provides data transmission services for n IoT water meters with NB-IoT modules. The data arrival rate of IoT water meter i is λ i , the amount of data to be transmitted during the allocation period is L i ,remember is a scheduling variable, indicating whether m resource blocks are allocated to IoT water meter i in time slot t. is the transmission rate of IoT water meter i in time slot t, and the Shannon formula can be used to obtain formula 1:

[0055]

[0056] Where B is the bandwidth in Hertz; is the interference of device i on m resource blocks; n 0 is the power spectral density of Gaussian white noise; is the channel gain; is the transmission power of IoT water meter i. While the optimization goal is to maximize the system throughput, the transmission delay and fairness of each IoT water meter are taken into account. Therefore, the following multi-objective optimization model is constructed, as shown in Formula 2:

[0057]

[0058] Formula 3:

[0059]

[0060] Among them, j represents another IoT water meter, R i represents the average transmission rate of IoT water meter i, R j represents the average transmission rate of IoT water meter j. The main function of formula 2 is to maximize the system throughput. Through a series of specific parameter settings and calculation logic, this formula can enable the system to achieve the highest possible throughput level in terms of data transmission, etc., to meet the system's demand for data processing.

[0061] At the same time, Formula 3 assumes the important responsibility of reflecting the fairness of system transmission. It uses the network minimization potential model, which outputs a corresponding value by comprehensively considering the resource allocation of each part of the system. This value has a special meaning, that is, the smaller its value, the more balanced the resources obtained by each part in the system during resource allocation, and each IoT water meter can obtain resources more fairly during the transmission process. The failure between the water supply network and the IoT water meter is often not independent. The pipeline network and multiple IoTs near a certain fault point will have problems. If one of the IoT water meters transmits faster and another IoT water meter transmits slower, it will affect the system's rapid and accurate judgment. Therefore, the model avoids the situation where the transmission network is overly concentrated on some IoT water meters, resulting in a lack of transmission of other IoT water meters, thereby ensuring the fairness and stability of the entire system transmission.

[0062] The transmission fusion module includes a fusion gateway unit and a core network fusion unit.

[0063] The fusion gateway unit is the key hub for the IoT water meter module to connect to the cloud device. It integrates the network access functions of the NB-IoT module and the LoRa module, and can receive data from both modules at the same time. In terms of data processing, the fusion gateway unit parses and converts data of different protocols and encapsulates them into a format suitable for transmission to the core network. At the same time, it is also responsible for managing the access, authentication and authorization of the IoT water meter.

[0064] During the data transmission process, different data of the IoT water meter are allocated to the NB-IoT module and LoRa module network for transmission according to the type and urgency of the data. For example, key data such as the control instructions of the IoT water meter are quickly transmitted through the NB-IoT module network to ensure real-time performance; while a large amount of historical data or non-critical monitoring data is transmitted through the LoRa module network to save costs and bandwidth. In different network environments, the proportion of data allocated to the NB-IoT module and the LoRa module is different.

[0065] In the core network integration unit, the network functions of the NB-IoT module and the LoRa module are integrated. By establishing a unified network management platform, the centralized deployment and management of the two network resources are realized. For example, in terms of traffic management, the bandwidth resources of the NB-IoT module and the LoRa module network are reasonably allocated according to the priority and real-time needs of the business to ensure the smooth operation of important businesses.

[0066] The fusion gateway receives data from the NB-IoT module and the LoRa module, centrally allocates and manages the two types of data through the core network fusion unit, and transmits them to the cloud device.

[0067] In this embodiment, the IoT water meter calculates the water flow velocity and flow rate, combines the multi-sensor module to measure the water pressure in the water supply network, the water level at the water source, and the water quality index, and transmits data through the adaptive switching mechanism of the NB-IoT module and the LoRa module. The reliability and real-time performance of the IoT water meter data transmission are improved through the data diversion mechanism in the transmission fusion module. At the same time, the resources of the two technologies are reasonably utilized to reduce the power consumption and cost of the system, improve the overall performance, and combine the operator network coverage of NB-IoT and the long-distance communication advantages of LoRa to achieve comprehensive coverage of various complex environments such as cities, remote areas, and indoors, ensuring that the IoT water meter can operate stably in any location. Among them, the NB-IoT module, due to its advantage of long-term stable transmission, uses the pre-processing module to perform denoising and data repair on each data subset in the initial NB-IoT module. The dynamic compression module matches the appropriate compression algorithm according to the interval of the data subset to reduce the size of the transmitted data. The dynamic allocation transmission module constructs an allocation model for optimizing the transmission of multiple data subsets to reduce the delay of data transmission. For the simultaneous transmission of a large number of IoT water meter data, it ensures that multiple IoT water meters transmit quickly and synchronously, thereby improving the system's rapid and accurate judgment and effective management of fault problems.

[0068] Embodiment 2: According to Figure 2 As shown: The management platform of the water supply control system based on the Internet of Things water meter includes a real-time monitoring module, a scheduling management module, an equipment management module, a user management module, a report statistics module, a prediction module and a mobile application module.

[0069] The real-time monitoring module is an important display window of the water supply management platform. Through this module, managers can view the operating status of each link of the water supply system in real time and comprehensively. The monitoring content covers the real-time changes of the water level at the water source, the production situation of the water plant, including the treatment process of raw water, the production progress of clean water, the distribution of pipe network pressure, whether the various indicators of water quality meet the standards, and the real-time readings of the user's IoT water meter. The monitoring interface adopts an intuitive graphical design to display data in various forms such as maps, charts, and dashboards. Through the pipe network map, you can intuitively see the distribution of pipe network pressure in each area, and the areas with abnormal pressure will be marked with eye-catching colors; the line chart can clearly show the trend of water level changes over time at the water source; the dashboard can display the key indicators of water quality in real time.

[0070] When an abnormal situation occurs in the system, such as low water pressure in the pipe network, excessive water quality, water meter failure, etc., the real-time monitoring module will immediately sound an alarm. The alarm methods include sound prompts, pop-up reminders, SMS notifications and other forms to ensure that managers can detect it in time. At the same time, the system will automatically locate the abnormal location and display detailed abnormal related information, such as the type of abnormality, time of occurrence, possible causes, etc. Managers can respond quickly based on this information and take appropriate measures to deal with it, such as adjusting the operating parameters of the water pump, activating the emergency plan, arranging maintenance personnel to carry out maintenance, etc., to ensure the normal operation of the water supply system.

[0071] The scheduling management module formulates a scientific and reasonable water supply scheduling plan based on real-time collected data and accurate water demand forecasts, combined with factors such as water level at the water source, pipe network pressure, and user water consumption, by establishing a dynamic programming model. The dynamic programming model comprehensively considers factors such as the physical characteristics of the water supply system, user water use patterns, and equipment operating parameters, and can accurately simulate the operation of the water supply system under different working conditions. By establishing a pipe network hydraulic model, the water flow velocity, pressure distribution and other parameters of different pipe sections are accurately calculated to provide a basis for the reasonable allocation of water supply flow; the user water demand prediction model is used to predict the user water consumption in different time periods and different areas, so as to adjust the water supply strategy in advance.

[0072] The water supply scheduling plan covers key parameters such as the start and stop time of the water pump, the operating frequency, and the switch status of the valve. During the peak water consumption period, according to the predicted increase in water consumption, the scheduling management module will start the backup water pump in advance, increase the operating frequency of the water pump, increase the water supply flow, and ensure that the user's water demand is met; during the low water consumption period, the number and frequency of water pumps will be appropriately reduced to reduce energy consumption and achieve energy-saving operation. At the same time, through remote control technology, managers can remotely control the operation of water supply equipment in real time through the management platform, and adjust the water supply strategy in time according to actual conditions. When it is found that the pipe network pressure in a certain area is too high or too low, the manager can remotely adjust the opening of the relevant valves, optimize the hydraulic distribution of the pipe network, and ensure the stability of water supply.

[0073] The equipment management module is responsible for comprehensive and detailed management of various equipment in the water supply system, including IoT water meters, pressure sensors, water level sensors, water quality sensors, water pumps, valves, etc. This module records the basic information of the equipment in detail, such as equipment model, manufacturer, installation location, purchase time, equipment number, etc., and establishes an independent equipment file for each device. The equipment file not only contains the basic information of the equipment, but also records the technical parameters, maintenance records, fault repair records, operation status monitoring data, etc. of the equipment.

[0074] The equipment management module enables the maintenance and management of the equipment throughout its life cycle. First, according to the type of equipment, use environment, operating time and other factors, set a reasonable equipment maintenance cycle and a detailed maintenance plan. When the equipment needs maintenance, the system will automatically issue a reminder to notify the maintenance personnel to perform the corresponding maintenance work. After completing the maintenance work, the maintenance personnel need to record the specific content of the maintenance, the replaced parts, the maintenance time and other information in the equipment management module. When the equipment fails, the system can quickly locate the faulty equipment based on the information in the equipment archive and real-time monitoring data, and analyze the possible causes of the failure through the fault diagnosis algorithm, and provide corresponding maintenance suggestions. For example, when the data of the IoT water meter is abnormal, the system can determine whether it is caused by a communication module failure, a measurement component failure or other reasons by analyzing its communication log, the change trend of the measurement data, etc., and provide targeted maintenance solutions.

[0075] The user management module realizes the all-round and refined management of water supply users. The management of user information covers the entry, modification and query of basic information such as user name, address, contact information, water meter number, water use nature (such as residential water use, commercial water use, industrial water use, etc.). Through in-depth statistics and analysis of user water use, such as statistics on users' monthly water use, quarterly water use, and annual water use, analysis of users' water use peak and trough periods, and water use changes in different seasons, etc., it can provide users with personalized water use suggestions and services. For example, for commercial users with large water consumption, water-saving transformation plan suggestions can be provided; for residential users, reasonable water use time arrangement suggestions can be provided according to their water use habits to help users save water.

[0076] At the same time, the user management module is deeply connected with banks, third-party payment platforms, etc., supporting users to conveniently check water bills and pay online through the management platform or mobile APP. Users only need to bind their water meter information and payment methods on the platform to check the current water bill balance, arrears, etc. at any time, and complete the payment of water bills through various payment channels, such as bank card payment, WeChat payment, Alipay payment, etc. After the payment is successful, the payment information will be updated to the user management module in real time, which is convenient for users to check payment records at any time, and also provides convenience for the financial management of water supply companies.

[0077] The report statistics module generates various types of reports according to the diverse needs of users, including daily reports, weekly reports, monthly reports, annual reports, etc. The report content is rich and comprehensive, covering the operation data of the water supply system, such as the total water consumption per day, week, month, and year, the water consumption distribution in each area, the water pressure changes in different time periods, the statistical analysis of water quality monitoring data, etc.; equipment management data, such as the equipment operation time, maintenance times, failure rate, etc.; user water use data, such as the water consumption ranking of each user, water use growth trend, etc.

[0078] The report statistics module supports the export and printing functions of reports. The exported report formats can be Excel, PDF and other common formats, which is convenient for managers to conduct further data analysis and archiving. Through in-depth analysis of report data, it can help water supply companies understand the operating trends and laws of the water supply system and discover potential problems and optimization space. By analyzing water consumption data in different time periods, water supply scheduling plans can be reasonably arranged and energy allocation can be optimized; by analyzing the operating data of equipment, potential equipment failure hazards can be discovered in time, maintenance can be carried out in advance, and equipment failure rates can be reduced; by analyzing user water consumption data, more reasonable water fee policies can be formulated to promote water conservation.

[0079] The prediction module uses a random forest algorithm to establish a user water use model, a pipe network leakage model, and a water quality change model through in-depth analysis of a large amount of historical data. The user water use model analyzes the user's historical water use data, water use habits, life patterns, seasonal factors, weather changes and other multi-dimensional data to explore the potential laws and patterns of user water use and predict the user's future water consumption. Through analysis, it is found that the water consumption of residential users in summer is usually higher than that in winter, and the water consumption of commercial users on weekends and holidays will change significantly. This can more accurately predict the user's water demand and provide a scientific basis for water supply scheduling.

[0080] The pipeline network leakage model uses the random forest algorithm to establish a model through real-time monitoring and analysis of pipeline network pressure, flow, flow velocity and other data, combined with information such as the topological structure of the pipeline network and pipe material characteristics, and can accurately identify leakage points in the pipeline network and predict the extent and development trend of leakage. For example, when the pressure in the pipeline network drops abnormally and the flow rate fluctuates abnormally, the model can determine the possible location of leakage through analysis and predict the expansion speed of leakage based on the trend of data changes, so as to arrange maintenance personnel for repair in time and reduce the waste of water resources and economic losses.

[0081] The water quality change model uses the random forest algorithm to predict water quality changes based on the water quality monitoring data collected in real time by water quality sensors, as well as water quality characteristics of the water source, water treatment processes, seasonal changes and other factors. For example, in the hot summer season, algae growth may cause certain water quality indicators to change. The water quality change model can predict this change trend in advance, and water supply companies can adjust the water treatment process in advance to ensure the safety of water supply quality. By continuously optimizing and updating these models, they can better adapt to the dynamic changes of the water supply system and improve the accuracy and reliability of predictions.

[0082] In order to facilitate managers and users to operate and query anytime and anywhere, the water supply management platform has developed a powerful mobile application module, namely the mobile phone APP. For managers, the APP can be used to view the operating status of the water supply system in real time, receive alarm information from the system, and perform remote control operations at the first time. For example, when a manager receives an alarm of abnormal pipe network pressure while on the way out, he can immediately view detailed abnormal information through the APP, and remotely adjust the operating parameters of the relevant water pumps or valves to solve the problem in time and ensure the normal operation of the water supply system.

[0083] For users, the mobile phone APP can conveniently check their water usage, including real-time water consumption, historical water consumption, water consumption details, etc.; they can check the water bill at any time, understand the arrears, and pay online; if they find that the water equipment has a fault or other problems, they can also report the fault through the APP, fill in the repair information and upload relevant photos or videos, so that the water supply company can understand the situation in time and arrange maintenance personnel to deal with it. The APP interface is simple in design and easy to operate. It adopts a humanized interactive design, which is easy for users to use. At the same time, the APP also supports the push function. The water supply company can push water outage notifications, water quality announcements, water-saving publicity and other information to users, strengthen communication and interaction with users, and improve user satisfaction and participation.

[0084] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The water supply control system based on the Internet of Things water meter is characterized by: It includes an IoT water meter module, a multi-sensor module, a data processing module, a transmission fusion module and a cloud device; the IoT water meter module includes an IoT water meter body, an NB-IoT module, a LoRa module and a signal monitoring module; The data collected by the IoT water meter body and the multi-sensor module are transmitted to the transmission fusion module through at least one of the NB-IoT module and the LoRa module, wherein the data transmitted through the NB-IoT module first passes through the data processing module and then is transmitted to the transmission fusion module; The data processing module includes a pre-processing module, a dynamic compression module and a dynamic allocation transmission module. After the pre-processing module processes the data transmitted by the NB-IoT module, the dynamic compression module compresses and transmits the data to the dynamic allocation transmission module according to the interval matching of the data subset, so as to perform synchronous transmission of the data of multiple IoT water meters; The transmission fusion module includes a fusion gateway unit and a core network fusion unit. The fusion gateway receives two types of data, the NB-IoT module and the LoRa module. The two types of data are centrally allocated and managed through the core network fusion unit and transmitted to the cloud device for unified processing and management.

2. The water supply control system based on the Internet of Things water meter according to claim 1 is characterized in that: The pre-processing module performs denoising, data repair and other processing on each data subset in the initial NB-IoT module to provide standardized input for subsequent compression coding.

3. The water supply control system based on the Internet of Things water meter according to claim 1 is characterized in that: The compression algorithms in the dynamic compression module include Huffman coding, LZW algorithm and Deflate algorithm, and the matching method is as follows: S1. Calculate the distribution histogram of the sample data and divide the data value range into custom values ​​according to the cumulative distribution function; S2. For data subsets in different intervals, match the appropriate compression encoding method by calculating their information entropy; S3. Set a first threshold and a second threshold, where the first threshold is smaller than the second threshold. Huffman coding is used for a data subset whose information entropy is lower than the first threshold. LZW algorithm is used for a data subset between the first threshold and the second threshold. Deflate algorithm is directly used for a data subset whose entropy is higher than the second threshold.

4. The water supply control system based on the Internet of Things water meter according to claim 1 is characterized in that: The dynamic allocation transmission module constructs an allocation model for optimizing transmission of multiple data subsets; In a typical NB-IoT module uplink transmission scenario, the system uses a single carrier frequency with a bandwidth of B. A scheduling cycle contains t time slots, each time slot contains m resource blocks. The system provides data transmission services for n IoT water meters with NB-IoT modules. The data arrival rate of IoT water meter i is λ i , the amount of data to be transmitted during the allocation period is L i ,remember is a scheduling variable, indicating whether to allocate m resource blocks to IoT water meter i in time slot t, and then define is the transmission rate of IoT water meter i in time slot t, and the Shannon formula can be used to obtain formula 1: Where B is the bandwidth, in Hertz; is the interference of device i on m resource blocks; n0 is the power spectral density of Gaussian white noise; is the channel gain; is the transmission power of IoT water meter i. While the optimization goal is to maximize the system throughput, the transmission delay and fairness of each IoT water meter are taken into account. Therefore, the following multi-objective optimization model is constructed, as shown in Formula 2: Formula 3: Among them, j represents another IoT water meter, R i represents the average transmission rate of IoT water meter i, R j It represents the average transmission rate of IoT water meter j. The main function of formula 2 is to maximize the system throughput. Formula 3 reflects the fairness of system transmission. Both formulas are used for synchronous transmission of multiple data among multiple IoT water meters.

5. The water supply control system based on the Internet of Things water meter according to claim 1 is characterized in that: The fusion gateway unit integrates the network access functions of the NB-IoT module and the LoRa module, parses and converts data of different protocols, and uniformly encapsulates them into a format suitable for transmission to the core network.

6. The water supply control system based on the Internet of Things water meter according to claim 1 is characterized in that: The core network integration unit integrates the network functions of the NB-IoT module and the LoRa module, and centrally allocates and manages the two network resources.

7. The management platform of water supply control system based on IoT water meter is characterized by: A water supply control system based on an Internet of Things water meter using any one of claims 1 to 6 includes a real-time monitoring module, a scheduling management module, a device management module, a user management module, a report statistics module, a prediction module and a mobile application module.

8. The management platform of the water supply control system based on the Internet of Things water meter according to claim 7 is characterized in that: The real-time monitoring module is used to check the operating status of each link of the water supply system. When an abnormal situation occurs in the system, an alarm will be immediately issued, the abnormal position will be automatically located, and abnormal related information will be displayed; The equipment management module is used to manage various equipment in the water supply system and perform maintenance management throughout the entire life cycle; The user management module is used to manage water supply user information and to collect statistics and analyze user water use conditions; The mobile application module is used by management personnel to view the operating status of the water supply system in real time, receive alarm information from the system, and perform remote control operations.

9. The management platform of the water supply control system based on the Internet of Things water meter according to claim 8 is characterized in that: The report statistics module generates various types of reports according to requirements and supports export and printing; The prediction module is connected to the device management module and the user management module at the same time, and uses a random forest algorithm to establish a user water use model, a pipe network leakage model and a water quality change model based on the device historical data and the user historical data; The scheduling management module is connected to the real-time monitoring module and the prediction module at the same time, and formulates a water supply scheduling plan by establishing a dynamic programming model based on the real-time collected data and water demand prediction.

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