An intelligent water control system for swimming pools integrating artificial intelligence technology

Abstract

The present invention proposes an intelligent water control system for a swimming pool that integrates artificial intelligence technology, aiming to optimize the water resource management, energy efficiency and safety monitoring of swimming pools. The system consists of four modules: data monitoring, processing, automatic control and user interaction. It monitors water quality and usage in real time through high-precision sensors, and uses deep learning algorithms to analyze data and predict trends. The automatic control module adjusts the water flow, water temperature and energy efficiency equipment accordingly to achieve water and energy saving. The user interaction interface provides real-time monitoring and feedback optimization. In addition, the system also includes intelligent water saving, lane utilization efficiency optimization, energy efficiency management and water quality monitoring and prediction methods, as well as artificial intelligence-assisted safety monitoring to ensure the safety of swimmers. The application of this system improves the intelligence level of swimming pools and meets the requirements of sustainable development and environmental protection.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent water control systems, and in particular to an intelligent water control system for a swimming pool integrated with artificial intelligence technology. Background Art

[0002] Traditional water control systems in swimming pools are mostly mechanical or simple electronic control systems. These systems usually lack the ability to collect and analyze real-time data and cannot dynamically adjust the use and allocation of water resources according to actual usage, resulting in waste of water and energy. At the same time, water quality monitoring mostly relies on manual sampling and testing, which cannot achieve real-time monitoring, and safety monitoring mainly relies on video monitoring and manual inspections, which have slow response speeds and cannot effectively prevent and promptly handle emergencies. Summary of the invention

[0003] In order to solve the above problems, the present invention provides an intelligent water control system based on artificial intelligence, which can monitor, analyze and predict the water usage, water quality and safety conditions in swimming pools in real time, and automatically adjust the operation of water control facilities accordingly to achieve the purposes of water saving, improving utilization efficiency, energy efficiency management and safety monitoring.

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] Intelligent water control system includes:

[0006] - At least one data monitoring module for real-time monitoring of water usage, water quality and safety in the swimming pool, including but not limited to high-precision measurement of the water level, water flow, water temperature, pH value, dissolved oxygen, oxidation-reduction potential (ORP), chlorine concentration, turbidity and temperature of the swimming pool;

[0007] - a data processing module that uses artificial intelligence technology to analyze and predict data, wherein the artificial intelligence technology includes deep learning algorithms and data mining technology;

[0008] - an automatic control module, which automatically adjusts the operation of the water control facility according to the analysis results of the data processing module, and the adjustment includes but is not limited to dynamically adjusting the water flow and water temperature to achieve the purpose of water saving;

[0009] - A user interaction module that allows managers to monitor system status and adjust control strategies, including water use management strategies generated based on real-time data, forecast data, and historical data.

[0010] The data monitoring module further includes: a high-precision sensor unit for monitoring parameters such as the water level, water flow, and water temperature of the swimming pool, wherein the sensor unit includes a pressure sensor, a flow meter, a temperature sensor, etc., and has a measurement accuracy of ±0.1%; a data security transmission unit, which uses encryption technology to protect the security of data during transmission, wherein the encryption technology includes at least the AES encryption standard.

[0011] The data processing module further includes: a deep learning processing unit, which is used to learn and extract patterns from historical data, identify water use patterns and the impact of special events on water consumption, wherein the deep learning model includes a convolutional neural network (CNN) and a recurrent neural network (RNN); a control strategy generation unit, which is used to generate a water management strategy based on real-time data, predicted data and historical data, wherein the water management strategy includes a lane usage efficiency optimization strategy based on user behavior analysis.

[0012] The user interaction module further includes: a graphical display unit for intuitively displaying the water usage status, water quality parameters and safety monitoring status of the swimming pool, wherein the graphical display includes real-time data charts, historical data trend charts and early warning information prompts; a user feedback collection unit for collecting swimmers' feedback and further optimizing the control strategy, wherein the feedback includes a water temperature comfort survey and a water flow suitability survey.

[0013] An intelligent water-saving water control module comprises the following steps:

[0014] -Use a data monitoring unit to monitor the use of swimming pools in real time and predict peak and off-peak periods, wherein the prediction model includes time series analysis and machine learning prediction models;

[0015] -Use the automatic control unit to dynamically adjust the water flow and water temperature according to the analysis results of the data processing unit to achieve the purpose of water saving, wherein the adjustment strategy includes feedforward control based on the prediction results and feedback control based on real-time monitoring data.

[0016] The AI-based swimming pool efficiency optimization module includes the following steps:

[0017] - The data monitoring unit monitors the use of the swimming lane in real time, including the swimming lane occupancy rate, the activity intensity of the swimmer, etc., wherein the monitoring data includes the swimming lane occupancy time, the swimmer's exercise frequency and swimming style recognition;

[0018] - The data processing unit uses a deep learning algorithm to analyze the lane usage data to identify swimmers' behavior habits and peak lane usage periods, wherein the deep learning algorithm includes behavior pattern recognition and cluster analysis;

[0019] -Based on the lane utilization efficiency evaluation model constructed by the control strategy generation unit, the automatic control unit automatically adjusts the lane allocation to optimize the swimmer's experience and the utilization efficiency of the swimming pool, wherein the evaluation model includes a multi-objective optimization algorithm for the lane utilization efficiency.

[0020] The AI-driven swimming pool energy efficiency management module includes the following steps:

[0021] -The data monitoring unit comprehensively collects energy consumption data related to the swimming pool, wherein the energy consumption data includes electricity consumption, gas consumption and energy consumption of the water circulation system;

[0022] -The data processing unit uses machine learning and data mining techniques to analyze energy consumption data and identify energy consumption patterns and anomalies, wherein the analysis methods include anomaly detection algorithms and association rule learning;

[0023] -The automatic control unit automatically generates and executes an adaptive control strategy based on the analysis results of the data processing unit to adjust the operating status of the heating, filtering and other equipment, wherein the control strategy includes equipment operation scheduling based on energy consumption prediction and equipment operation optimization based on real-time energy consumption data.

[0024] The artificial intelligence-based swimming pool water quality monitoring and prediction module includes the following steps:

[0025] -The data monitoring unit monitors key water quality parameters using high-precision sensors, including pH meters, dissolved oxygen sensors, oxidation-reduction potential (ORP) sensors, and chlorine concentration sensors;

[0026] - The data processing unit uses artificial intelligence algorithms to perform water quality data analysis and trend prediction, wherein the algorithms include neural networks and support vector machines (SVM);

[0027] -The automatic control unit automatically adjusts water treatment measures, such as pH adjustment and chlorine addition, according to the prediction results of the data processing unit to ensure water quality safety, wherein the adjustment measures include an automatic dosing system based on the prediction results and a circulation filtration system control based on real-time monitoring data.

[0028] AI-assisted swimming pool safety monitoring module, including components and steps:

[0029] -High-definition camera units fully cover the pool area to capture swimmers' behavior in real time, including wide-angle cameras and night vision cameras;

[0030] -The real-time behavior analysis unit uses a deep learning model to analyze video data and identify abnormal behaviors, wherein the model includes a behavior recognition network and an anomaly detection network;

[0031] -The early warning mechanism unit responds quickly when abnormal behavior is detected and notifies lifeguards or management personnel through a user interaction interface or directly, wherein the early warning mechanism includes sound alarms and visual prompts.

[0032] The intelligent water control system further comprises:

[0033] - a continuous optimization unit, for regularly evaluating the historical data and control effects generated by the data processing unit to identify deficiencies in the control strategy and make improvements based on feedback collected through the user interaction interface, wherein the evaluation method includes strategy effect evaluation and user satisfaction survey;

[0034] - A self-learning unit, configured to continuously optimize the control strategy based on real-time data and user feedback, wherein the self-learning unit utilizes the deep learning algorithm of the data processing unit for self-optimization, wherein the optimization algorithm includes reinforcement learning and strategy iteration.

[0035] Through the above technical solution, the intelligent water control system based on artificial intelligence of the present invention is realized with artificial intelligence technology as the core, which improves the intelligence level of swimming pools and the utilization efficiency of water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to make the embodiments, technical solutions and technical advantages of the present application clearer, the following drawings provide non-limiting views of the present application:

[0037] Figure 1 The system architecture diagram of the intelligent water control system is shown.

[0038] Figure 2 A flow chart showing the intelligent water-saving water control method.

[0039] Figure 3 A flow chart showing the AI-based method for optimizing pool usage efficiency.

[0040] Figure 4 A flow chart showing an AI-driven pool energy efficiency management system.

[0041] Figure 5 A flow chart showing the AI-based method for monitoring and predicting swimming pool water quality.

[0042] Figure 6 A flow chart showing the AI-assisted pool safety monitoring system. DETAILED DESCRIPTION

[0043] In order to elaborate on the embodiments, technical solutions and technical advantages of the present application, a detailed and complete description will be provided below in conjunction with the accompanying drawings. It should be clear that the described embodiments only represent some application scenarios of the present application, not all possibilities. According to the embodiments of the present disclosure, those skilled in the art can obviously obtain other possible embodiments without performing creative work, and these embodiments also belong to the scope of protection of the present application.

[0044] 1. Intelligent water-saving water control method

[0045] The core of intelligent water-saving water control methods lies in accurate data collection. This system uses high-precision sensors to monitor the water level, water flow, water temperature of the swimming pool, as well as environmental parameters such as humidity and temperature in the venue in real time. These sensors are located around the swimming pool and at key nodes, and can capture small changes to ensure the comprehensiveness and accuracy of the data. The frequency of data collection can be adjusted according to the actual needs of different venues to achieve the best monitoring effect.

[0046] The data monitoring module is the foundation of the intelligent water control system and is responsible for real-time collection of various water quality and environmental parameters in the swimming pool. The module uses a high-precision sensor array, including but not limited to water level, water flow, water temperature, pH value, dissolved oxygen, oxidation-reduction potential (ORP), chlorine concentration, turbidity and temperature. To improve creativity, the module uses adaptive sampling frequency technology to dynamically adjust the data collection frequency based on the real-time monitoring of the pool usage.

[0047] Taking swimming pool water temperature monitoring as an example, the sensor uses a thermistor, whose resistance value changes with the water temperature. The relationship can be expressed as:

[0048]

[0049] Where, R(t): resistance value at time t.

[0050] R0: Initial resistance value, refers to the resistance value at reference temperature T0.

[0051] T(t): water temperature at time t.

[0052] T0: Reference temperature, usually a standard temperature such as freezing point or room temperature.

[0053] ΔT: Temperature sensitivity coefficient, which indicates the sensitivity of resistance value to temperature changes.

[0054] The collected data is transmitted to the central processing unit via a secure wireless network. Encryption technology is used during the transmission process to ensure that the data is not intercepted or tampered with during the transmission process. After reaching the central processing unit, the data is stored in a highly secure database that has anti-attack, data backup and rapid recovery capabilities to ensure data security and reliability.

[0055] The data processing module uses artificial intelligence algorithms to conduct in-depth analysis and prediction of the data collected by the data monitoring module. The fuzzy logic control algorithm is creatively introduced, combined with deep learning technology, to conduct comprehensive evaluation and trend prediction of water quality parameters, improving the accuracy of prediction and the adaptability of the system.

[0056] Taking water quality prediction as an example, time series analysis methods, such as the ARIMA model, are used to predict changes in water quality parameters over a period of time in the future. The model can be expressed as:

[0057] Y t =φ1Y t-1 +φ2Y t-2 +…+θ1∈ t-1 +θ2∈ t-2 +…+∈ t

[0058] Where, Yt: observed value of water quality parameter at time t.

[0059] φ1, φ2, …: Autoregressive coefficients of the ARIMA model, representing the impact of past values ​​on current values.

[0060] θ1,θ2,…: Difference coefficients of the ARIMA model, used to make the time series stationary.

[0061] ∈ t : White noise, representing the prediction error at time t.

[0062] The AI ​​algorithm on the central processing unit has deep learning capabilities and can continuously learn and extract patterns from historical data. By analyzing a large amount of historical data, the algorithm can identify water usage patterns in different time periods, such as peak water usage during peak hours, the impact of specific activities on water consumption, etc. In addition, the algorithm can also learn and predict the impact of special events (such as pool parties, competitions, etc.) on water consumption.

[0063] Based on the results of AI analysis, the system can automatically generate control strategies that adapt to the current water use situation. These strategies take into account not only the current real-time data, but also the forecast data and historical data to achieve more accurate water management. For example, when a low-peak period is predicted, the system will automatically reduce the water flow and lower the water temperature. Before the peak period arrives, the system will gradually adjust the water temperature and flow to a suitable level for swimming.

[0064] The control strategy is sent to water control devices such as pumps, valves and heaters in real time through the execution unit. After receiving the control signal, these devices can respond quickly and adjust to the predetermined working state. The design of the execution unit ensures the accuracy of control, ensuring that the amplitude and speed of water adjustment meet the requirements of the control strategy, avoiding water waste or swimmer discomfort caused by improper adjustment.

[0065] The automatic control module automatically adjusts the water control facilities of the swimming pool according to the control strategy generated by the data processing module. The adaptive control algorithm is introduced to dynamically optimize the control strategy based on the real-time monitored energy consumption data and user usage patterns.

[0066] Taking the swimming pool heating system as an example, the power input of the heater is automatically adjusted according to the water temperature control strategy. The control strategy can be expressed as:

[0067] P(t)=k p ·(T sct -T(t)

[0068] Where, P(t): heater power input at time t.

[0069] k p : Proportional control coefficient, used to adjust the response speed of the control strategy.

[0070] T set : Set the water temperature, that is, the target water temperature that the system hopes to achieve.

[0071] T(t): Current water temperature, that is, the water temperature monitored in real time.

[0072] The system provides a user interface that allows managers to monitor water usage and control strategy execution in real time. Through the graphical interface, managers can intuitively understand the water usage status of the swimming pool and fine-tune the control strategy to meet special needs. In addition, the system can also collect feedback from swimmers, such as the comfort of water temperature and the suitability of water flow, to further optimize the control strategy.

[0073] The user interaction module provides an intuitive graphical interface that allows managers and swimmers to monitor the pool status in real time. The voice interaction function is creatively integrated, allowing users to control the pool facilities through voice commands.

[0074] The intelligent water-saving water control method also includes a continuous optimization mechanism. The system regularly evaluates historical data and control effects to identify deficiencies in the control strategy. Through continuous evaluation and adjustment, the system can gradually improve its water-saving efficiency and response accuracy to achieve long-term water-saving goals.

[0075] The intelligent water-saving water control module is a key component of the present invention. It monitors the use of the swimming pool in real time through the data monitoring unit, predicts the peak and off-peak periods, and dynamically adjusts the water flow and water temperature to achieve the purpose of water saving. During the off-peak period of the swimming pool, the system automatically reduces the water flow and lowers the water temperature by monitoring the lower lane occupancy rate, thereby reducing unnecessary waste of water resources. The environmental factor prediction model is creatively introduced, combined with the actual use of the swimming pool, the water flow is intelligently adjusted to adapt to possible weather changes, and the water-saving effect is further optimized.

[0076] During the off-peak hours of the swimming pool, the system automatically reduces the water flow and lowers the water temperature by monitoring the lower lane occupancy rate to reduce unnecessary water waste. Assume that the off-peak water flow of the swimming pool is set to Q low , and the peak water flow Q high The ratio is r, then:

[0077] Q low = r·Q high

[0078] Among them, r is the water-saving ratio coefficient.

[0079] The system is designed to be adaptable to different environmental conditions. Whether it is an indoor or outdoor swimming pool, whether it is in the hot summer or the cold winter, the system can adjust the control strategy according to environmental changes to ensure that water is saved while meeting the needs of swimmers.

[0080] Through intelligent water conservation, the system can not only reduce water usage, but also reduce energy consumption in heating and filtration, achieving the dual benefits of energy conservation and environmental protection. The system is designed in line with the concept of sustainable development and helps swimming venues enhance their social responsibility and environmental protection image.

[0081] 2. Method for optimizing swimming pool efficiency based on artificial intelligence

[0082] The system uses high-resolution cameras and multiple sensors to monitor the use of swimming lanes in real time, including lane occupancy, swimmers' activity intensity and swimming style, etc. These data are transmitted to the central processing unit in real time via a high-speed network, providing the system with basic data for lane use efficiency analysis.

[0083] Using machine learning algorithms in artificial intelligence, the system conducts deep learning analysis on swimmers’ behavior patterns. By analyzing swimmers’ lane selection, swimming speed, rest frequency and other behaviors at different time periods, the system can identify swimmers’ behavioral habits and peak lane usage times.

[0084] The system builds a lane utilization efficiency evaluation model that takes into account factors such as lane occupancy, swimmer density, and type of swimming activity. By comprehensively evaluating these factors, the system is able to quantify lane utilization efficiency and identify inefficient areas and time periods.

[0085] Based on the evaluation results of lane efficiency, the system uses an AI-driven optimization algorithm to automatically adjust lane allocation. The algorithm takes into account swimmers’ behavior patterns, lane usage history, and expected change trends to achieve optimal allocation of lane resources.

[0086] The system can dynamically adjust the lane status based on real-time monitoring data and prediction models. During periods when there are fewer swimmers, the system can close some lanes to save resources; when the usage rate is expected to increase, the system can open more lanes in advance to meet the needs of swimmers.

[0087] The system analyzes the swimmer's long-term behavior data to predict his or her lane usage preferences and provide personalized lane recommendations. For example, for swimmers who like to swim fast, the system can recommend the fast lane; for beginners, the system can recommend the teaching lane.

[0088] The system integrates an intelligent guidance system to provide swimmers with lane usage suggestions and instructions through display screens, mobile applications or voice prompts. The intelligent guidance system helps to disperse the pressure of lane usage and improve the overall efficiency of lane usage. The pool efficiency optimization module monitors the use of lanes in real time through the data monitoring unit, including lane occupancy rate and swimmer activity intensity. A deep learning algorithm is used to analyze lane usage data to identify swimmers' behavioral habits and peak lane usage hours. The user behavior learning algorithm is creatively introduced. The system learns and predicts users' lane usage preferences through long-term collected user data, realizes personalized lane recommendations and allocations, and improves lane usage efficiency and swimmer satisfaction.

[0089] The efficiency optimization module builds a lane efficiency evaluation model that comprehensively considers the lane occupancy rate, swimmer density, and swimming activity type. Assume that the lane efficiency is E, the occupancy rate is O, the swimmer density is D, and the activity type influencing factor is A, then:

[0090] E=ω1·O+ω2·D+ω3·A

[0091] Among them, ω1, ω2, ω3 are weight coefficients.

[0092] The system not only considers the improvement of lane efficiency, but also comprehensively considers factors such as swimmer experience, safety management and energy consumption. The multi-objective optimization algorithm can balance these factors and provide comprehensive decision support for managers.

[0093] The system is self-learning and can continuously optimize lane allocation strategies based on real-time data and user feedback. Through continuous learning and improvement, the system can adapt to changes in swimmer behavior and the needs of venue operations.

[0094] The system provides a highly integrated user interface that allows managers and swimmers to view lane usage status, lane allocation strategies, and personalized recommendations in real time. The interactive interface is friendly, intuitive, and easy to operate, which improves the usability of the system.

[0095] The system design takes into account swimming venues of different sizes and types, and has good environmental adaptability and scalability. Whether it is a small community pool or a large water sports center, the system can be adjusted and expanded according to actual needs.

[0096] During lane monitoring and user behavior analysis, the system strictly abides by relevant laws and regulations on privacy protection and anonymizes data involving personal privacy. At the same time, the system has a complete security mechanism to ensure data security and system stability.

[0097] 3. Artificial Intelligence-driven Swimming Pool Energy Efficiency Management System

[0098] The AI-driven swimming pool energy efficiency management system first needs to comprehensively collect all energy consumption-related data of the swimming pool. This includes but is not limited to the power consumption of the swimming pool water circulation system, the gas or electricity consumption of the heating equipment, the power consumption of the lighting system, and the energy consumption of the facilities surrounding the swimming pool. By installing smart meters and sensors, the system can monitor the energy consumption data of each energy consumption point in real time to ensure that there are no data blind spots.

[0099] The collected energy consumption data is transmitted to the central processing unit in real time via a high-speed network. Here, the data is first cleaned and standardized to eliminate possible errors and outliers. Subsequently, the data is stored in a central database for further analysis and processing by artificial intelligence algorithms. Artificial intelligence algorithms are able to identify energy consumption patterns, detect energy consumption anomalies, and predict future energy consumption trends.

[0100] The system's built-in artificial intelligence algorithm uses advanced machine learning and data mining technology to conduct in-depth analysis of energy consumption data. The algorithm takes into account a variety of factors such as pool usage patterns, seasonal changes, weather conditions, etc. to develop the best energy management strategy. These strategies include adjusting the operating parameters of heating equipment, optimizing the operating time and intensity of the water circulation system, and intelligently controlling the switching of the lighting system.

[0101] Based on the decision-making of artificial intelligence algorithms, the system can automatically generate adaptive control strategies. These strategies can be dynamically adjusted based on real-time data and prediction models to respond to changes in pool usage. For example, when it is predicted that the pool usage rate will decrease, the system will automatically reduce the operating power of the heating equipment to reduce energy consumption.

[0102] The system optimizes energy efficiency by intelligently scheduling and managing various equipment in the swimming pool. This includes intelligent control of equipment such as heaters, water pumps, filters and lighting. The equipment automatically adjusts its operating status according to the control strategy generated by the system to ensure that energy consumption is minimized while meeting usage needs.

[0103] The system provides customized energy efficiency management functions, allowing managers to set energy efficiency management goals and parameters based on the specific needs and operation strategies of the swimming pool. The system can adjust the decision parameters of the intelligent algorithm according to these customized needs and generate energy efficiency management strategies that meet the expectations of managers.

[0104] The system provides a visual display function for energy efficiency management, which can intuitively display energy consumption data and energy efficiency management strategies to managers through charts, dashboards, etc. This enables managers to quickly understand the energy efficiency status of the swimming pool and make timely adjustments.

[0105] The system has the ability to self-learn and can continuously optimize the artificial intelligence algorithm based on the results of energy efficiency management. Through continuous learning and optimization, the system can adapt to changes in pool operations and improve the accuracy and effectiveness of energy efficiency management.

[0106] The system can evaluate the energy efficiency of the swimming pool from multiple dimensions and generate detailed energy efficiency reports. These reports include energy consumption analysis, energy saving effect evaluation, equipment operation efficiency analysis, etc., providing managers with comprehensive energy efficiency management information. The swimming pool energy efficiency management module uses artificial intelligence algorithms to conduct in-depth analysis of energy consumption data, identify energy consumption patterns and anomalies, and automatically generate and execute adaptive control strategies based on the analysis results to adjust the operating status of heating, filtering and other equipment. The system creatively introduces a predictive maintenance algorithm for the operating status of the equipment. Based on the energy consumption data and operating status of the equipment, the system can predict possible equipment failures, perform maintenance in advance, reduce unexpected downtime, and improve the intelligence level of energy efficiency management.

[0107] The energy efficiency management module optimizes energy efficiency by intelligently scheduling and managing various equipment in the swimming pool. Suppose the total energy consumption of the swimming pool is E total , the energy consumption of the heating equipment is E heat , the energy consumption of the water circulation system is E circ , the energy consumption of the lighting system is E light ,but:

[0108] Etotal =E heat +E circ +E light

[0109] The system can also intelligently detect equipment failures and anomalies and issue maintenance reminders to managers in a timely manner. This helps to promptly discover and solve equipment problems and avoid energy efficiency reduction and additional energy waste caused by equipment failures.

[0110] The system takes into account the impact of swimming pool operations on the environment and provides an environmental impact assessment function. By analyzing the energy consumption structure and emissions of the swimming pool, the system can put forward optimization suggestions to reduce environmental impact, such as using clean energy and improving water treatment processes.

[0111] The system can also help managers ensure that the energy efficiency management of the swimming pool complies with relevant regulations and standards. The system provides a regulatory compliance check function that can evaluate the energy efficiency management status of the swimming pool according to the latest regulatory requirements and make improvement suggestions.

[0112] 4. Pool water quality monitoring and prediction method based on artificial intelligence

[0113] This system uses high-precision sensors to monitor multiple key quality parameters of swimming pool water in real time, including but not limited to pH, dissolved oxygen, oxidation-reduction potential (ORP), chlorine concentration, turbidity and temperature. These sensors are evenly distributed in different areas of the swimming pool to ensure the representativeness and accuracy of the monitoring results.

[0114] The monitored water quality parameters are sent to the central processing unit in real time through a reliable data transmission network. Before the data arrives, necessary preprocessing is performed, including data cleaning, denoising and standardization, to improve the accuracy and efficiency of subsequent analysis.

[0115] The system uses advanced artificial intelligence algorithms, such as neural networks, support vector machines (SVMs), and random forests, to conduct in-depth analysis of water quality data. These algorithms can identify complex relationships between water quality parameters, predict water quality trends, and identify factors that may affect water quality.

[0116] Based on historical data and real-time monitoring data, artificial intelligence algorithms can predict the changing trends of water quality parameters in the future. This prediction is crucial for timely adjusting water treatment strategies to prevent water quality deterioration.

[0117] Based on the predictions of the artificial intelligence algorithm, the system automatically adjusts the water treatment strategy, such as automatically adjusting the pH value, adjusting the amount of chlorine, starting or shutting down the circulating filtration system, etc. This intelligent optimization ensures that the water quality always meets safety standards while reducing the excessive use of chemicals.

[0118] The system has a real-time early warning function. When the monitored water quality parameters exceed the preset safety range or predict an impending water quality problem, the system will immediately issue an early warning to prompt managers to take appropriate measures. The swimming pool water quality monitoring and prediction module uses high-precision sensors to monitor key water quality parameters and uses artificial intelligence algorithms to analyze water quality data and predict trends. The system creatively introduces a water quality adjustment algorithm based on user feedback. The system dynamically adjusts water quality parameters based on real-time feedback from swimmers, such as comfort survey results, to improve swimmers' experience and the personalized level of water quality management.

[0119] The water quality monitoring module ensures water quality safety by monitoring parameters such as pH value, dissolved oxygen, and oxidation-reduction potential (ORP). min To pH max ,but:

[0120] pH min ≤ pH value monitored ≤ pH max

[0121] The system allows users to customize water quality management goals and parameters based on specific needs and preferences. For example, a professional training pool may require higher water quality standards, while a children's paddling pool may require a gentler water treatment method.

[0122] The system provides a visual interface for water quality management, which intuitively displays real-time data and forecast trends of water quality parameters through charts, dashboards, etc. This enables managers to quickly grasp the water quality status and make timely decisions.

[0123] The system has the ability to self-learn and can continuously optimize the artificial intelligence algorithm based on the results of water quality management. Through continuous learning and improvement, the system can adapt to the water quality management needs of different swimming pools and improve the accuracy of prediction and the efficiency of water treatment.

[0124] The system can evaluate the water quality of the swimming pool from multiple dimensions and generate detailed water quality reports. These reports include water quality parameter analysis, water quality trend prediction, water treatment effect evaluation, etc., providing managers with comprehensive water quality management information.

[0125] The system can also intelligently manage the pool's water treatment equipment, such as automatic dosing pumps, filters, etc. The system automatically adjusts the operating status of the equipment based on water quality monitoring results and predicted trends to ensure efficient operation of the equipment and stable water quality.

[0126] The system takes into account the impact of environmental factors on pool water quality, such as weather changes, seasonal changes, etc. Artificial intelligence algorithms are able to analyze the relationship between these environmental factors and water quality parameters and take them into account when predicting and optimizing water treatment strategies.

[0127] The system ensures that the pool water quality management complies with local regulations and health and safety standards. The system provides a compliance check function that can evaluate the pool water quality management status according to the latest regulatory requirements and make improvement suggestions.

[0128] The system also provides education and training modules to help swimmers and managers understand the importance of pool water quality and how to maintain water quality through daily behavior.

[0129] 5. AI-assisted swimming pool safety monitoring system

[0130] The AI-assisted pool safety monitoring system uses high-definition cameras to fully cover the pool area, ensuring no blind spots. Cameras are deployed in reasonable locations around the pool, including the starting platform, the boundary between deep and shallow water areas, and the corners of the pool, to fully capture the behavior of swimmers.

[0131] The system receives and processes the video stream from the camera in real time through an efficient video processing unit. The video stream is transmitted to the central processing unit via a wired or wireless network to ensure the stability and real-time nature of data transmission.

[0132] The system has built-in deep learning models that analyze swimmers’ behavior in real time. These models can identify normal swimming behaviors and potentially dangerous behaviors, such as swimmers suddenly coming to a stop, floating abnormally, or struggling excessively.

[0133] Once the deep learning model detects abnormal behavior, the system will immediately trigger an early warning mechanism. The early warning information will quickly notify lifeguards or managers through sound, light or mobile applications so that they can respond in time.

[0134] The pool safety monitoring module uses high-definition cameras to fully cover the pool area, capture swimmers' behavior in real time, and analyze video data through deep learning models to identify abnormal behavior, quickly respond and notify lifeguards or managers. Creatively introducing multimodal safety analysis, the system not only analyzes video data, but also integrates other sensor data such as sound sensors and underwater sensors to improve the accuracy of abnormal behavior identification and the system's response speed.

[0135] The safety monitoring module uses a real-time behavior analysis unit to analyze the swimmer's behavior using a deep learning model. The abnormal behavior recognition rate is set to P abnormal ,but:

[0136]

[0137] The system not only analyzes video data, but also integrates other sensor data, such as sound sensors, underwater sensors, etc., to conduct multimodal security analysis. This fusion analysis can improve the accuracy of abnormal behavior recognition and the response speed of the system.

[0138] The system intelligently dispatches the nearest lifeguard to respond based on the location and severity of the abnormal behavior. This intelligent dispatch ensures that lifeguards arrive at the scene in the shortest possible time, improving rescue efficiency.

[0139] The system has the ability to self-learn, learn from historical safety events, and continuously optimize behavior recognition models and early warning strategies. This continuous learning and optimization helps the system better adapt to different monitoring environments and swimmer behavior patterns.

[0140] The system integrates a safety education module to provide swimmers with safety tips and guidance through displays around the pool or mobile applications. In addition, the system can also provide personalized safety advice based on the swimmer's swimming skills and behavioral habits.

[0141] The system supports emergency drills, allowing managers to simulate safety incidents and test the response capabilities of lifeguards and safety monitoring systems. Through regular emergency drills, the professional skills of lifeguards and the reliability of the system can be improved.

[0142] When conducting video surveillance and behavior analysis, the system strictly abides by relevant laws and regulations on privacy protection. For video data involving personal privacy, the system uses encrypted storage and access control to ensure data security.

[0143] The system is designed with multiple redundancy and fault-tolerant mechanisms to ensure that the monitoring and early warning functions can be maintained continuously when some equipment or networks fail. This high reliability design ensures the stability of swimming pool safety monitoring.

[0144] The system supports cross-platform operation, and managers can view monitoring images and receive warning information in real time through different devices such as computers, tablets or smart phones. This cross-platform support improves the flexibility and convenience of the system.

[0145] The system provides community participation and information sharing functions, allowing swimmers, lifeguards and managers to jointly participate in pool safety management. Through information sharing, pool safety awareness can be improved and a good safety culture can be formed.

[0146] The design and operation of the system complies with national and local regulations and standards on swimming pool safety. The system can adjust monitoring and early warning strategies according to the latest regulatory requirements to ensure compliance with swimming pool safety management.

[0147] The system supports continuous technical updates and upgrades to adapt to changing security needs and technological developments. Through regular software updates and hardware upgrades, the system can maintain the most advanced security monitoring capabilities.

Claims

1. An intelligent water control system, characterized in that: include: - At least one data monitoring module for real-time monitoring of water usage, water quality and safety in the swimming pool, including high-precision measurement of the pool's water level, water flow, water temperature, pH value, dissolved oxygen, redox potential (ORP), chlorine concentration, turbidity and temperature; - A data processing module, which uses artificial intelligence technology to analyze and predict data, wherein the artificial intelligence technology includes deep learning algorithms and data mining technology; wherein the data processing module further includes: A deep learning processing unit is used to learn and extract patterns from historical data, identify water use patterns and the impact of special events on water consumption. The deep learning model includes convolutional neural networks (CNN) and recurrent neural networks (RNN); A control strategy generating unit is used to generate a water management strategy based on real-time data, forecast data and historical data, wherein the water management strategy includes a lane usage efficiency optimization strategy based on user behavior analysis; wherein the lane usage efficiency optimization strategy includes: Real-time monitoring of lane usage, including lane occupancy rate, swimmer activity intensity, lane occupancy time, swimmer movement frequency and swimming style recognition; Using a deep learning algorithm to analyze lane usage data to identify swimmers' behavioral habits and peak lane usage hours, wherein the deep learning algorithm includes behavioral pattern recognition and cluster analysis; According to the lane utilization efficiency evaluation model constructed by the control strategy generation unit, the lane allocation is adjusted to optimize the swimmer's experience and the utilization efficiency of the swimming pool, wherein the evaluation model is: E=ω1·O+ω2·D+ω3·A, wherein the lane utilization efficiency is E, the lane occupancy rate is O, the swimmer density is D, the activity type influencing factor is A, and ω1, ω2, and ω3 are weight coefficients; By analyzing swimmers’ long-term behavior data, we can predict their lane usage preferences and provide personalized lane recommendations. - an automatic control module, which automatically adjusts the operation of the water control facilities according to the analysis results of the data processing module, wherein the adjustment includes dynamically adjusting the water flow and water temperature to achieve the purpose of water saving, wherein the use of the swimming pool is monitored in real time, and the peak and off-peak periods are predicted, wherein the prediction model includes time series analysis and machine learning prediction model; the water flow and water temperature are dynamically adjusted according to the analysis results to achieve the purpose of water saving, wherein the dynamic adjustment includes feedforward control based on the prediction results and feedback control based on the real-time monitoring data; The adjustment also includes equipment operation scheduling based on energy consumption prediction and equipment operation optimization based on real-time energy consumption data; wherein, comprehensive energy consumption-related data of the swimming pool is collected, including electricity consumption, gas consumption and energy consumption of the water circulation system; machine learning and data mining techniques are used to analyze energy consumption data to identify energy consumption patterns and anomalies, wherein the analysis includes anomaly detection algorithms and association rule learning; adaptive adjustment is performed according to the analysis results, including equipment operation scheduling based on energy consumption prediction and equipment operation optimization based on real-time energy consumption data; - A user interaction module that allows managers to monitor system status and adjust control strategies, including water management strategies generated based on real-time data, forecast data, and historical data; wherein the user interaction module further includes: A graphical display unit, used to graphically display the water usage status, water quality parameters and safety monitoring status of the swimming pool, wherein the graphical display includes real-time data charts, historical data trend charts and early warning information prompts; The user feedback collection unit is used to collect swimmers' feedback and further optimize the control strategy, wherein the feedback includes a water temperature comfort level survey and a water flow rate suitability survey.

2. The intelligent water control system according to claim 1, characterized in that: The data monitoring module further comprises: - A high-precision sensor unit for monitoring swimming pool parameters, including water level, water flow, and water temperature, wherein the sensor unit includes a pressure sensor, a flow meter, and a temperature sensor, and has a measurement accuracy of ±0.1%; - A data security transmission unit, which uses encryption technology to protect the security of data during transmission, wherein the encryption technology at least includes the AES encryption standard.

3. The intelligent water control system according to claim 1, further comprising a swimming pool water quality monitoring and prediction module based on artificial intelligence, further comprising: -The data monitoring unit uses high-precision sensors to monitor key water quality parameters, including pH meters, dissolved oxygen sensors, ORP sensors, and chlorine concentration sensors; - The data processing unit uses artificial intelligence algorithms to analyze water quality data and predict trends, wherein the algorithms include neural networks and support vector machines (SVMs); - The automatic control unit automatically adjusts water treatment measures, including pH adjustment and chlorine administration, according to the prediction results of the data processing unit to ensure water quality safety, wherein the adjustment measures include an automatic dosing system based on the prediction results and a circulation filtration system control based on real-time monitoring data.

4. The intelligent water control system according to claim 1, further comprising an artificial intelligence-assisted swimming pool safety monitoring module, further comprising: -High-definition camera units fully cover the pool area to capture swimmers' behavior in real time, including wide-angle cameras and night vision cameras; -The real-time behavior analysis unit uses a deep learning model to analyze video data and identify abnormal behaviors, wherein the model includes a behavior recognition network and an anomaly detection network; -The early warning mechanism unit responds quickly when abnormal behavior is detected and notifies lifeguards or management personnel directly through a user interaction interface, wherein the early warning mechanism includes sound alarms and visual prompts.

5. The intelligent water control system according to claim 1, characterized in that: Further including: -Continuous optimization unit, used to regularly evaluate the historical data and control effects generated by the data processing unit to identify deficiencies in the control strategy and make improvements based on feedback collected through the user interaction interface, where the evaluation methods include strategy effect evaluation and user satisfaction survey; - A self-learning unit, which is used to continuously optimize the control strategy based on real-time data and user feedback, wherein the self-learning unit uses the deep learning algorithm of the data processing unit to perform self-optimization, wherein the optimization algorithm includes reinforcement learning and policy iteration.

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