A waterway detection and control method, circuit and application

Abstract

The present invention provides a waterway detection and control method, circuit and application. The method includes arranging various types of sensors on a water tank, realizing multi-dimensional acquisition of the water level in the water tank through the sensors, fusing and calibrating the acquired water level data to obtain pre-processed water level data; obtaining the pre-processed water level data and inputting it into a dynamic waterway control adjustment strategy, and dynamically adjusting the waterway according to the water level change trend and waterway state; at the same time, comparing the water level data with a preset multi-level water level threshold, and automatically triggering an alarm mechanism when the water level data is lower than the safety threshold in the multi-level water level threshold; obtaining water quality data from the water level data and comparing it with a preset safe range, and automatically performing water quality cleaning when it exceeds the safe range; adjusting the dynamic waterway control adjustment strategy. The system includes a data acquisition module, a dynamic adjustment module and an iterative learning module. The present invention realizes all-round monitoring and control of the water level and water quality.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent waterway control, and particularly relates to a waterway detection and control method, circuit and application. Background Art

[0002] A water dispenser is an intelligent terminal device for providing drinking water. A water tank is a container for storing water sources in the water dispenser, and a waterway is a pipeline system connecting the water tank and the water dispenser; the water tank and the pipeline together constitute the water supply system of the water dispenser. The reliability and safety of the waterway are crucial for the function of the water dispenser and directly affect the user experience; the control of the waterway is crucial for the normal operation of the water dispenser and the user experience. By controlling the water flow of the waterway, it can ensure that the water dispenser can provide a stable water flow under different usage scenarios; the water dispenser usually needs to provide cold water, normal temperature water and hot water; the control of the waterway can ensure that water at different temperatures can be accurately transported from the water tank to the water dispenser and reach the temperature required by the user through a heating or cooling system; the control of the waterway can also help monitor and maintain water quality; for example, by setting a filtering device in the waterway, impurities and harmful substances in the water can be removed to ensure that the water drunk by the user is clean. Reasonable waterway control can reduce energy waste; for example, through an intelligent control system, the water dispenser can automatically shut down or reduce energy consumption when not in use, so as to achieve the purpose of energy conservation and environmental protection. The control of the waterway can also help monitor the operating state of the system; when there is a blockage, leakage or other abnormal conditions in the waterway, the control system can issue a warning in time to remind the user to perform maintenance and avoid greater failures. In short, the control of the waterway is not only related to the normal operation of the water dispenser, but also directly affects the drinking experience and health and safety of the user. Through scientific and reasonable design and control, it can ensure that the water dispenser can work efficiently and stably in various environments. However, the existing technology has relatively single means for waterway detection and does not perform a full range of detection on the waterway, resulting in the need to further improve the accuracy of the output waterway detection results.

[0003] Prior art one, application number: CN201711042578.3 discloses a waterway control system and method for a drinking water device on a beauty device, including a water tank, a heating device, a display temperature device, an alarm device, a heating device, a microprocessor. The microprocessor is respectively connected to the heating device, the display temperature device, the alarm device and a temperature sensor, and the heating device and the heating device are respectively connected to the water tank. Although it saves the water level gauge and reduces the cost of the drinking water device on the beauty device, it can more accurately detect whether the waterway control system of the drinking water device on the beauty device is normal. However, it only realizes whether the waterway control is normal, and its function is relatively single, and its function needs to be further enriched and improved.

[0004] Prior Art 2, Application No.: CN201210135031.9 discloses an intelligent water machine detection device, including a device housing. A circuit control system and a waterway execution system controlled by the electrical signals of the circuit control system are arranged in the device housing. The circuit control system includes a power supply line for providing power transmission to the product to be detected and a control circuit connected in parallel with the power supply line. The control circuit is equipped with an intelligent controller, which can manually set the detection parameters inside the intelligent controller according to the detection requirements. The intelligent controller controls the on-off of the electric execution element in the waterway execution system through electrical signals, so as to control the on-off of the water flow between the waterway execution system and the product to be detected, meeting the test requirements. Although it has high detection accuracy, a wide range of applications, is safe and convenient to operate, and can effectively improve the detection rate of products and the factory pass rate of products. However, it lacks alarm prompt and cleaning prompt functions, resulting in the need to further improve the intelligence level of detection.

[0005] Prior Art 3, Application No.: CN201410584895.8 discloses an intelligent Internet of Things system adapted to a water purification device, including: a water quality pH value detection module, a water quality TDS and hardness detection module, a flow statistics module, a waterway control module, a water leakage detection module, a core operation processing module, a data storage module, a screen display control module, a safety alarm module, a sensor error calibration module, a mobile terminal communication configuration module, a cloud service communication module, and an intelligent consumable life calculation module. Although it can be adapted to most water purification machines on the market and can complete functions such as intelligent water quality detection, intelligent calculation of consumable life, kitchen water leakage detection and protection, water flow consumption and pattern statistics, kitchen combustible gas detection and alarm, and intelligent calibration of detection sensors without relying on the water purification machine itself. However, it is not integrated with the water dispenser, increasing the use cost of the water dispenser and being inconvenient to operate.

[0006] Currently, Prior Art 1, Prior Art 2, and Prior Art 3 have the problems of low intelligence level in waterway detection, simple functions, and inability to comprehensively and effectively reflect the waterway detection results. Therefore, the present invention provides a waterway detection and control method, circuit, and application. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides a waterway detection and control method, including the following steps:

[0008] Deploy various types of sensors on the water tank, realize multi-dimensional acquisition of the water level in the water tank through the sensors, fuse and calibrate the collected water level data, and obtain the preprocessed water level data;

[0009] Obtain the preprocessed water level data, input it into the dynamic waterway control adjustment strategy, and dynamically adjust the waterway according to the water level change trend and the waterway state;

[0010] Input the data after dynamically adjusting the water path into a machine learning model to learn the data and the dynamic water path control adjustment strategy. Through continuous iterative learning, adjust the dynamic water path control adjustment strategy.

[0011] Optionally, the process of deploying various types of sensors on the water tank includes the following steps:

[0012] Obtain the geometric characteristics of the water tank, including the height, diameter, wall thickness, and inlet and outlet positions of the water tank. According to the height and diameter of the water tank, preliminarily determine the number and distribution of monitoring points, divide the water tank into upper, middle, and lower three regions, and set several monitoring points in each region;

[0013] Analyze the inlet and outlet positions of the water tank to determine the water flow path and stagnant areas; according to the geometric characteristics of the water tank and the water flow path, set multiple monitoring points, and each monitoring point should include at least one type of sensor;

[0014] Input the preprocessed water level data into the dynamic water path control adjustment strategy, and dynamically adjust the position and parameters of the sensors according to the water level change trend and the water path state;

[0015] Water level change trend prediction formula:

[0016]

[0017] In the formula, represents the predicted water level change, represents the observed water level change, represents the water level change predicted last time, represents the smoothing coefficient;

[0018] Sensor position adjustment:

[0019]

[0020] In the formula, represents the adjusted sensor position, represents the original sensor position, represents the position adjustment amount.

[0021] Optionally, compare the water level data with the preset multi-level water level thresholds at the same time. When the water level data is lower than the safety threshold in the multi-level water level thresholds, automatically trigger the alarm mechanism; obtain the water quality data from the water level data and compare it with the preset safety range. When it exceeds the safety range, automatically perform water quality cleaning.

[0022] Optionally, the process of dynamically adjusting the water path includes the following steps:

[0023] Perform time series analysis on the water level data to extract the changing trend and periodic fluctuation target features of the water level; use the target feature time series prediction model to predict the future water level change trend and generate a prediction curve;

[0024] According to the designed capacity of the water tank and the current water level, dynamically evaluate the waterway capacity through fuzzy inference; based on the stability evaluation method, conduct dynamic evaluation of the waterway through probabilistic inference; combine the data of the water quality sensor to evaluate whether the water quality meets the safety standards and whether water quality cleaning is required;

[0025] Set multi-level water level thresholds according to the water level change trend and waterway status, including safety thresholds and warning thresholds; adopt adaptive threshold setting to dynamically adjust the thresholds by combining historical water level data and real-time water level data; generate corresponding dynamic waterway control adjustment strategies according to the comparison result of the current water level and the thresholds; at the same time, when the water level is lower than the safety threshold, automatically trigger the alarm mechanism; perform real-time adjustment of the waterway according to the generated dynamic waterway control adjustment strategies; evaluate the execution effect of the adjustment strategies by real-time monitoring of the water level and water quality data.

[0026] Optionally, the process of dynamically evaluating the waterway includes the following steps:

[0027] Obtain the designed capacity data of the water tank as the basic parameter for evaluation; collect the current water level data in real time as the dynamic input for evaluation; perform fuzzification processing on the current water level data to convert it into a fuzzy linguistic variable; perform fuzzification processing on the designed capacity of the water tank to convert it into a fuzzy linguistic variable;

[0028] Based on expert knowledge and historical data, construct a fuzzy rule base; input the fuzzified water level and capacity into the fuzzy inference engine, and perform inference through the fuzzy rule base to obtain the fuzzy evaluation result of the waterway capacity; perform defuzzification processing on the fuzzy evaluation result to convert it into a specific numerical value as the dynamic evaluation result of the waterway capacity;

[0029] Obtain historical and real-time water level data as the basic input for evaluation; combine the data of the water quality sensor to obtain the water quality status as the auxiliary input for evaluation; define the nodes of the Bayesian network as the variables for evaluation;

[0030] Construct a conditional probability table to describe the conditional probability relationship between each node; input the water level and water quality data into the Bayesian network inference engine, and perform probabilistic inference to obtain the stability evaluation result of the waterway; output the stability evaluation result of the waterway, and generate corresponding decision support information based on the evaluation result.

[0031] Optionally, the process of defining the nodes of the Bayesian network includes the following steps:

[0032] Node definitions of the Bayesian network: a first-level water level variable that affects the waterway capacity; a second-level water quality variable that indirectly affects the waterway capacity through the waterway stability; a middle variable of waterway stability that connects water quality and waterway capacity; a third-level waterway capacity variable that is the final output result.

[0033] Optionally, in a directed acyclic graph, nodes represent different entities or states, edges represent the dependency or causal relationship between nodes, and each edge has a direction indicating a one-way relationship from one node to another; this edge indicates that node H has a direct impact on node C, and a path indicates that node Q indirectly affects node C through node S.

[0034] Optionally, establish a conditional probability table. The conditional probability table of waterway stability based on water quality describes the probability distribution of waterway stability under different water quality conditions; the conditional probability table of waterway capacity based on water level H and waterway stability describes the probability distribution of waterway capacity under different water levels and waterway stability conditions.

[0035] A waterway detection and control circuit provided by the present invention includes:

[0036] A data acquisition module responsible for deploying various types of sensors on the water tank, realizing multi-dimensional acquisition of the water level in the water tank through the sensors, fusing and calibrating the collected water level data, and obtaining preprocessed water level data;

[0037] A dynamic adjustment module responsible for obtaining the preprocessed water level data, inputting it into the dynamic waterway control adjustment strategy, dynamically adjusting the waterway according to the water level change trend and waterway state; at the same time, comparing the water level data with a preset multi-level water level threshold, and automatically triggering an alarm mechanism when the water level data is lower than the safety threshold in the multi-level water level threshold; obtaining water quality data from the water level data, comparing it with the preset safe range, and automatically performing water quality cleaning when it exceeds the safe range;

[0038] An iterative learning module responsible for inputting the data after dynamically adjusting the waterway into a machine learning model, learning the data and the dynamic waterway control adjustment strategy, and adjusting the dynamic waterway control adjustment strategy through continuous iterative learning.

[0039] The application of a waterway detection and control method provided by the present invention: deploy an ultrasonic sensor and a capacitive sensor in the water tank of a water dispenser; the ultrasonic sensor is used to measure the water level height, and the capacitive sensor is used to detect the liquid level change; fuse and calibrate the data of the two sensors to obtain water level data; input the preprocessed water level data into the dynamic waterway control adjustment strategy; dynamically adjust the waterway according to the water level change trend and waterway state.

[0040] The multi-dimensional water level data acquisition and preprocessing of the present invention uses various types of sensors (such as ultrasonic sensors, pressure sensors, capacitive sensors, and water quality sensors), which can collect water level data from different dimensions, improving the accuracy and reliability of the data; by fusing and calibrating the collected data, sensor errors are eliminated, and more accurate preprocessed water level data is obtained. Significance achieved: Through multi-sensor fusion and data calibration, the change of water level can be monitored more accurately, providing a reliable data basis for subsequent dynamic control; the addition of water quality sensors enables drinking water equipment (such as water dispensers) to monitor water quality in real time, promptly discover and handle water quality problems, and ensure the stability and safety of water quality. The dynamic waterway control and alarm mechanism enables the drinking water equipment to dynamically adjust the waterway according to the water level change trend and waterway state to ensure the stability of the water level; by comparing the water level data with preset multi-level water level thresholds, the drinking water equipment can automatically trigger the alarm mechanism when the water level is lower than the safety threshold, promptly reminding the operator; when the water quality data exceeds the safe range, the drinking water equipment can automatically clean the water quality to ensure that the water quality is always in a safe state. Significance achieved: The dynamic adjustment of the waterway and the automatic alarm mechanism enable the drinking water equipment to respond to changes in water level and water quality in real time, ensuring the stable operation of the waterway; the automatic alarm and water quality cleaning functions effectively reduce human operation errors and improve the safety and reliability of the drinking water equipment. The machine learning model optimizes the control strategy. The data after dynamically adjusting the waterway is input into the machine learning model, and through continuous iterative learning, the dynamic waterway control adjustment strategy is optimized; the machine learning model can adaptively adjust the control strategy according to historical data and real-time data, improving the intelligent level of the drinking water equipment. Significance achieved: Through the application of the machine learning model, the drinking water equipment can continuously optimize the control strategy, achieve intelligent management, reduce human intervention, and improve management efficiency; the machine learning model enables the drinking water equipment to continuously improve and adapt to different waterway environments and requirements, improving the adaptability and flexibility of the drinking water equipment.

[0041] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be learned by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structure specifically pointed out in the written specification and the drawings.

[0042] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0043] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0044] Figure 1Flowchart of the waterway detection and control method in Embodiment 1 of the present invention;

[0045] Figure 2 Process diagram of arranging various types of sensors on the water tank in Embodiment 2 of the present invention;

[0046] Figure 3 Process diagram of dynamically adjusting the waterway in Embodiment 3 of the present invention;

[0047] Figure 4 Process diagram of dynamically evaluating the waterway in Embodiment 4 of the present invention;

[0048] Figure 5 Process diagram of defining nodes of the Bayesian network in Embodiment 5 of the present invention;

[0049] Figure 6 Process diagram of obtaining the stability evaluation result of the waterway in Embodiment 6 of the present invention;

[0050] Figure 7 Process diagram of outputting the evaluation result of the waterway stability in Embodiment 7 of the present invention;

[0051] Figure 8 Process diagram of adjusting the dynamic waterway control adjustment strategy in Embodiment 8 of the present invention;

[0052] Figure 9 Block diagram of the waterway detection and control circuit in Embodiment 9 of the present invention. Detailed implementation manners

[0053] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0055] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0056] Embodiment 1: As Figure 1 shown, an embodiment of the present invention provides a waterway detection and control method, which includes the following steps:

[0057] S100: Deploy various types of sensors on the water tank, and through the sensors, multi-dimensional acquisition of the water level in the water tank is realized, and the collected water level data is fused and calibrated to obtain the preprocessed water level data;

[0058] Among them, various types of sensors include ultrasonic sensors, pressure sensors, capacitive sensors, and water quality sensors, etc.; ultrasonic sensors are suitable for water level measurement, pressure sensors are suitable for depth measurement, capacitive sensors are suitable for liquid level detection, and water quality sensors are used to monitor water quality parameters such as pH value, dissolved oxygen, etc.; the selection of each sensor needs to be based on its functional characteristics and applicable scenarios;

[0059] S200: Obtain the preprocessed water level data, input it into the dynamic waterway control adjustment strategy, and dynamically adjust the waterway according to the water level change trend and waterway state; at the same time, compare the water level data with the preset multi-level water level thresholds. When the water level data is lower than the safety threshold in the multi-level water level thresholds, the alarm mechanism is automatically triggered; obtain the water quality data from the water level data, compare it with the preset safety range, and when it exceeds the safety range, automatically perform water quality cleaning;

[0060] S300: Input the data after dynamically adjusting the waterway into the machine learning model, learn the data and the dynamic waterway control adjustment strategy, and adjust the dynamic waterway control adjustment strategy through continuous iterative learning.

[0061] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, various types of sensors are first arranged on the water tank, and multi-dimensional acquisition of the water level in the water tank is realized through the sensors. The collected water level data is fused and calibrated to obtain preprocessed water level data. Among them, various types of sensors include ultrasonic sensors, pressure sensors, capacitive sensors, water quality sensors, etc. Secondly, the preprocessed water level data is obtained and input into the dynamic waterway control adjustment strategy. According to the water level change trend and the waterway state, the waterway is dynamically adjusted. At the same time, the water level data is compared with the preset multi-level water level thresholds. When the water level data is lower than the safety threshold among the multi-level water level thresholds, the alarm mechanism is automatically triggered. The water quality data is obtained from the water level data and compared with the preset safe range. When it exceeds the safe range, the water quality is automatically cleaned. Finally, the data after dynamically adjusting the waterway is input into the machine learning model to learn the data and the dynamic waterway control adjustment strategy. Through continuous iterative learning, the dynamic waterway control adjustment strategy is adjusted. In step S100 of the above solution, multi-dimensional water level data acquisition and preprocessing use various types of sensors (such as ultrasonic sensors, pressure sensors, capacitive sensors, and water quality sensors), which can collect water level data from different dimensions, improve the accuracy and reliability of the data. By fusing and calibrating the collected data, sensor errors are eliminated, and more accurate preprocessed water level data is obtained. The achieved significance is that through multi-sensor fusion and data calibration, the water level change can be monitored more accurately, providing a reliable data basis for subsequent dynamic control. The addition of the water quality sensor enables drinking water equipment (such as water dispensers) to monitor the water quality in real time, timely discover and handle water quality problems, and ensure the stability and safety of the water quality. In step S200, dynamic waterway control and alarm mechanism, according to the water level change trend and the waterway state, the drinking water equipment can dynamically adjust the waterway to ensure the stability of the water level. By comparing the water level data with the preset multi-level water level thresholds, the drinking water equipment can automatically trigger the alarm mechanism when the water level is lower than the safety threshold, timely reminding the operator. When the water quality data exceeds the safe range, the drinking water equipment can automatically clean the water quality to ensure that the water quality is always in a safe state. The achieved significance is that the dynamic adjustment of the waterway and the automatic alarm mechanism enable the drinking water equipment to respond to the changes in water level and water quality in real time, ensuring the stable operation of the waterway. The automatic alarm and water quality cleaning functions effectively reduce human operation errors and improve the safety and reliability of the drinking water equipment. In step S300, the machine learning model optimizes the control strategy. The data after dynamically adjusting the waterway is input into the machine learning model. Through continuous iterative learning, the dynamic waterway control adjustment strategy is optimized. The machine learning model can adaptively adjust the control strategy according to historical data and real-time data, improving the intelligent level of the drinking water equipment.Achieved Significance: Through the application of machine learning models, the drinking water equipment can continuously optimize control strategies, achieve intelligent management, reduce human intervention, and improve management efficiency; the machine learning model enables the drinking water equipment to continuously improve and adapt to different water circuit environments and requirements, enhancing the adaptability and flexibility of the drinking water equipment.

[0062] In summary, through multi-sensor fusion, dynamic control, automatic alarm, and machine learning optimization, this embodiment realizes the comprehensive monitoring and control of water level and water quality. It can improve the monitoring accuracy, respond to water level changes in real time, and ensure water quality safety, and continuously improve the performance of the drinking water equipment through intelligent management. It not only improves the safety and reliability of the water circuit drinking water equipment but also provides strong support for intelligent water resource management.

[0063] Embodiment 2: As Figure 2 shown, on the basis of Embodiment 1, the process of arranging various types of sensors on the water tank provided by the embodiment of the present invention includes the following steps:

[0064] S101: Obtain the geometric characteristics of the water tank, including the height, diameter, wall thickness, inlet and outlet positions, etc. of the water tank. According to the height and diameter of the water tank, initially determine the number and distribution of monitoring points, divide the water tank into upper, middle, and lower three regions, and set several monitoring points in each region;

[0065] S102: Analyze the inlet and outlet positions of the water tank to determine the water flow path and stagnant areas; according to the geometric characteristics of the water tank and the water flow path, set multiple monitoring points, and each monitoring point should include at least one type of sensor;

[0066] Set monitoring points at the bottom of the water tank for monitoring water level and water quality; a pressure sensor is used to measure the water pressure at the bottom of the water tank, indirectly reflecting the water level height; a water quality sensor is used to monitor the water quality parameters at the bottom, such as pH value, dissolved oxygen, and conductivity, etc.;

[0067] Set monitoring points in the middle of the water tank for monitoring the dynamic changes of the water level; an ultrasonic sensor is used to measure the water level height, with high accuracy and response speed; a temperature sensor is used to monitor the water temperature to help analyze the water flow path and water quality changes;

[0068] Set monitoring points at the top of the water tank, mainly for monitoring the upper limit of the water level and water quality; a capacitive sensor is used to measure the water level height, suitable for high-precision liquid level detection; a gas sensor is used to monitor the gas components at the top of the water tank, such as oxygen and carbon dioxide, etc., to help analyze water quality changes;

[0069] S103: Input the preprocessed water level data into the dynamic water circuit control adjustment strategy, and dynamically adjust the position and parameters of the sensors according to the water level change trend and water circuit state.

[0070] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the geometric characteristics of the water tank are first obtained, including the height, diameter, wall thickness, inlet and outlet positions, etc. of the water tank. According to the height and diameter of the water tank, the number and distribution of monitoring points are initially determined, and the water tank is divided into three regions: upper, middle, and lower. Several monitoring points are set in each region; secondly, the inlet and outlet positions of the water tank are analyzed to determine the water flow path and stagnant areas; according to the geometric characteristics of the water tank and the water flow path, multiple monitoring points are set, and each monitoring point should include at least one type of sensor; finally, the preprocessed water level data is input into the dynamic water path control adjustment strategy, and according to the water level change trend and water path state, the positions and parameters of the sensors are dynamically adjusted. In step S101 of the above solution, the geometric characteristics of the water tank are obtained. By obtaining the geometric characteristics of the water tank, such as height, diameter, wall thickness, and inlet and outlet positions, the number and distribution of monitoring points can be accurately determined; the water tank is divided into three regions: upper, middle, and lower, and several monitoring points are set in each region to ensure full coverage of each key part of the water tank. The achieved significance is to ensure that each region of the water tank can be effectively monitored, improving the comprehensiveness and accuracy of monitoring; through reasonable zoning settings, the layout of sensors is optimized, improving the monitoring efficiency and data reliability. In step S102, the inlet and outlet positions of the water tank are analyzed. By analyzing the inlet and outlet positions, the water flow path and stagnant areas are determined, so as to reasonably set the monitoring points; various types of sensors, such as pressure sensors, water quality sensors, ultrasonic sensors, temperature sensors, capacitive sensors, and gas sensors, are configured at each monitoring point to ensure the acquisition of multi-dimensional data. The achieved significance is to realize the dynamic monitoring of the water level, water quality, temperature, and gas composition in the water tank through multi-sensor configuration, improving the real-time and accuracy of monitoring; multi-sensor configuration can provide rich data to help comprehensively analyze the operating state of the water tank and water quality changes. In step S103, the dynamic water path control adjustment strategy preprocesses the collected water level data to improve the accuracy and reliability of the data; according to the water level change trend and water path state, the positions and parameters of the sensors are dynamically adjusted to ensure the flexibility and adaptability of the monitoring system. The achieved significance is that through the dynamic adjustment strategy, the changes in the water tank can be responded to in real time, improving the response speed and accuracy of the monitoring system; the dynamic adjustment strategy helps to optimize the operating state of the water tank, improving the operating efficiency and safety of the water tank.

[0071] In this embodiment, S101 obtains the geometric characteristics of the water tank:

[0072] Calculation formula for the number of monitoring points:

[0073]

[0074] In the formula, represents the number of monitoring points, represents the height of the water tank, represents the diameter of the water tank, Indicates the monitoring point spacing;

[0075] Calculation formula for the number of monitoring points in each zone:

[0076]

[0077] In the formula, Indicates the number of monitoring points in each area;

[0078] S102 indicates the inlet and outlet positions of the analysis water tank:

[0079] Water flow path analysis formula:

[0080]

[0081] In the formula, Indicates the water flow path length, , , Indicates the inlet coordinate, , , Indicates the outlet coordinate;

[0082] Stagnant area calculation formula:

[0083]

[0084] In the formula Indicates the volume of the stagnant area, Indicates the height of the stagnant area;

[0085] S102 indicates the sensor settings:

[0086] Water pressure calculation formula for the pressure sensor:

[0087]

[0088] In the formula, Indicates the water pressure, Indicates the density of water, Indicates the acceleration due to gravity, Indicates the water level height;

[0089] Water level calculation formula for the ultrasonic sensor:

[0090]

[0091] In the formula, Indicates the water level height measured by the ultrasonic sensor, Indicates the speed of sound, Indicates the ultrasonic propagation time;

[0092] Water level calculation formula for the capacitive sensor:

[0093]

[0094] In the formula, represents the capacitance value, represents the vacuum permittivity, represents the relative permittivity, represents the electrode area, represents the electrode spacing;

[0095] Gas sensor concentration calculation formula:

[0096]

[0097] In the formula, represents the gas concentration, represents the number of moles of gas, represents the volume of the water tank;

[0098] S103 Dynamic water path control adjustment strategy

[0099] Water level change trend prediction formula:

[0100]

[0101] In the formula, represents the predicted water level change, represents the observed water level change, represents the previously predicted water level change, represents the smoothing coefficient;

[0102] Sensor position adjustment:

[0103]

[0104] In the formula, represents the adjusted sensor position, represents the original sensor position, represents the position adjustment amount.

[0105] In summary, through the accurate acquisition of geometric characteristics, reasonable analysis of the water flow path, and dynamic water path control adjustment strategy in this embodiment, comprehensive, dynamic, and multi-dimensional monitoring of the water tank is achieved. It not only improves the accuracy and real-time performance of monitoring, but also optimizes the operating state of the water tank, ensuring the safe and efficient operation of the water tank.

[0106] Embodiment 3: As Figure 3 shown, on the basis of Embodiment 1, the process of dynamically adjusting the water path provided by the embodiment of the present invention includes the following steps:

[0107] S201: Conduct time series analysis on the water level data to extract target features such as the change trend and periodic fluctuations of the water level; use the target feature time series prediction model to predict the future water level change trend and generate a prediction curve;

[0108] S202: Dynamically evaluate the waterway capacity through fuzzy inference based on the designed capacity of the water tank and the current water level; conduct dynamic evaluation of the waterway through probability inference based on the stability evaluation method; combine the data of the water quality sensor to evaluate whether the water quality meets the safety standards and whether water quality cleaning is required;

[0109] S203: Set multiple water level thresholds according to the water level change trend and the waterway state, including safety thresholds and warning thresholds, etc.; use adaptive threshold setting to dynamically adjust the thresholds by combining historical water level data and real-time water level data; generate corresponding dynamic waterway control adjustment strategies according to the comparison result between the current water level and the thresholds; at the same time, automatically trigger the alarm mechanism when the water level is lower than the safety threshold; conduct real-time adjustment of the waterway according to the generated dynamic waterway control adjustment strategies; evaluate the execution effect of the adjustment strategy by real-time monitoring of the water level and water quality data.

[0110] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, time series analysis is first performed on the water level data to extract target features such as the change trend and periodic fluctuations of the water level; the target feature time series prediction model is used to predict the future water level change trend and generate a prediction curve; secondly, according to the designed capacity of the water tank and the current water level, the waterway capacity is dynamically evaluated through fuzzy inference; the waterway is dynamically evaluated through probability inference based on the stability evaluation method; combined with the data of the water quality sensor, it is evaluated whether the water quality meets the safety standards and whether water quality cleaning is required; finally, according to the water level change trend and the waterway state, multi-level water level thresholds are set, including safety thresholds and warning thresholds, etc.; the adaptive threshold setting is used to dynamically adjust the threshold by combining historical water level data and real-time water level data; according to the comparison result between the current water level and the threshold, a corresponding dynamic waterway control adjustment strategy is generated; at the same time, when the water level is lower than the safety threshold, the alarm mechanism is automatically triggered; according to the generated dynamic waterway control adjustment strategy, the waterway is adjusted in real time; the execution effect of the adjustment strategy is evaluated by real-time monitoring of the water level and water quality data. In step S201, data analysis and prediction of the above solution, through time series analysis, extracts target features such as the change trend and periodic fluctuations of the water level, providing basic data for prediction and adjustment; using time series prediction models such as ARIMA and LSTM to predict the future water level change trend and generate a prediction curve, improving the accuracy and reliability of the prediction. The achieved significance: Through accurate prediction, it is possible to early warn of the water level change trend, providing sufficient time and data support for the adjustment strategy; based on the prediction results, the allocation of water resources can be optimized, avoiding waste and shortage of resources, and improving the operation efficiency of the waterway. In step S202, waterway state evaluation, the waterway capacity is dynamically evaluated through fuzzy inference, improving the flexibility and accuracy of the evaluation and adapting to complex waterway states; based on the stability evaluation method of Bayesian network, the stability of the waterway is dynamically evaluated through probability inference, improving the scientificity and reliability of the evaluation; combined with the data of the water quality sensor, the water quality state is evaluated in real time to ensure that the water quality meets the safety standards and avoid the impact of water quality problems on the waterway. The achieved significance: By dynamically evaluating the waterway capacity and stability, the safe operation of the waterway is ensured, avoiding risks such as overflow and dryness; the water quality state is monitored in real time, and water quality cleaning is carried out in a timely manner to ensure water quality safety and improve the overall health level of the waterway.Step S203: Generate and execute dynamic adjustment strategies. An adaptive threshold setting method is adopted to dynamically adjust the threshold by combining historical water level data and real-time water level data, improving the system's adaptability. According to the comparison result between the current water level and the threshold, corresponding dynamic waterway control adjustment strategies are generated, such as increasing or decreasing the water inflow, starting the drainage system, etc., improving the accuracy and efficiency of the adjustment. When the water level is lower than the safety threshold, the alarm mechanism is automatically triggered to notify relevant personnel for handling in a timely manner, improving the system's emergency response ability. By real-time monitoring the water level and water quality data, the execution effect of the adjustment strategy is evaluated to ensure the effectiveness and timeliness of the adjustment strategy. Significance achieved: Through dynamic adjustment strategies, the waterway can be adjusted in real time to ensure that the water level is within the safe range, improving the stability and reliability of the system; the alarm mechanism is automatically triggered to ensure timely response in case of emergencies, reducing losses and improving the safety and reliability of the system; through real-time monitoring and feedback, the adjustment strategy is continuously optimized to improve the system's adaptability and operation efficiency, ensuring the long-term stable operation of the waterway.

[0111] In summary, in this embodiment, through data analysis and prediction, early warning of water level changes is carried out, resource allocation is optimized, and operation efficiency is improved; through the evaluation of the waterway state, the safe operation of the waterway of the drinking water equipment and water quality safety are ensured, improving the overall health level of the system; through the generation and execution of dynamic adjustment strategies, the waterway is adjusted in real time to ensure that the water level is within the safe range, improving the stability and reliability of the system; through real-time monitoring and feedback, the adjustment strategy is continuously optimized to improve the system's adaptability and operation efficiency, ensuring the long-term stable operation of the waterway.

[0112] Embodiment 4: As Figure 4 shown, on the basis of Embodiment 3, the process of dynamically evaluating the waterway provided by the embodiment of the present invention includes the following steps:

[0113] S2021: Obtain the design capacity data of the water tank as the basic parameter for evaluation; real-time collect the current water level data as the dynamic input for evaluation; perform fuzzy processing on the current water level data to convert it into fuzzy linguistic variables, such as "low water level", "medium water level", "high water level", etc.; perform fuzzy processing on the water tank design capacity to convert it into fuzzy linguistic variables, such as "small capacity", "medium capacity", "large capacity", etc.

[0114] S2022: Based on expert knowledge and historical data, construct a fuzzy rule base, such as "if the water level is low and the capacity is small, then the waterway capacity is low", etc.; input the fuzzified water level and capacity into the fuzzy inference engine, and perform inference through the fuzzy rule base to obtain the fuzzy evaluation result of the waterway capacity; perform defuzzification processing on the fuzzy evaluation result to convert it into a specific value as the dynamic evaluation result of the waterway capacity.

[0115] S2023: Obtain historical and real-time water level data as the basic input for evaluation; combine the data from water quality sensors to obtain the water quality status as the auxiliary input for evaluation; define the nodes of the Bayesian network, such as water level, water quality, and waterway capacity, as the variables for evaluation;

[0116] Among them, there is a direct causal relationship between water level and waterway capacity: the water level H directly affects the waterway capacity C,

[0117] Description: The higher the water level, the greater the waterway capacity; the lower the water level, the smaller the waterway capacity;

[0118] Mathematical expression: C = f(H), where f is a function describing the relationship between water level and waterway capacity; Example: When the water level is "high water level", the waterway capacity is "high capacity"; when the water level is "low water level", the waterway capacity is "low capacity";

[0119] There is an indirect causal relationship between water quality and waterway capacity: the water quality Q indirectly affects the waterway capacity C by affecting the waterway stability S; Description: The better the water quality, the higher the waterway stability and the more stable the waterway capacity; the worse the water quality, the lower the waterway stability and the more unstable the waterway capacity;

[0120] Mathematical expression: S = g(Q), where g is a function describing the relationship between water quality and waterway stability; then C = h(S), where h is a function describing the relationship between waterway stability and waterway capacity; Example: When the water quality is "high-quality water quality", the waterway stability is "high stability" and the waterway capacity is "high capacity"; when the water quality is "poor water quality", the waterway stability is "low stability" and the waterway capacity is "low capacity";

[0121] S2024: Construct a conditional probability table to describe the conditional probability relationships between nodes; input the water level and water quality data into the Bayesian network inference engine, and through probability inference, obtain the evaluation result of the waterway stability; output the evaluation result of the waterway stability, such as "stable", "unstable", etc., and based on the evaluation result, generate corresponding decision support information, such as "need to increase the water inflow", "need to conduct water quality cleaning", etc.

[0122] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, first, the designed capacity data of the water tank is obtained as the basic parameter for evaluation; the current water level data is collected in real time as the dynamic input for evaluation; the current water level data is fuzzified and transformed into fuzzy linguistic variables such as "low water level", "medium water level", "high water level", etc.; the designed capacity of the water tank is fuzzified and transformed into fuzzy linguistic variables such as "small capacity", "medium capacity", "large capacity", etc.; secondly, based on expert knowledge and historical data, a fuzzy rule base is constructed, such as "if the water level is low and the capacity is small, then the waterway capacity is low", etc.; the fuzzified water level and capacity are input into the fuzzy inference engine, and through the fuzzy rule base, an inference is made to obtain the fuzzy evaluation result of the waterway capacity; the fuzzy evaluation result is defuzzified and transformed into a specific value as the dynamic evaluation result of the waterway capacity; then, the historical and real-time water level data is obtained as the basic input for evaluation; combined with the data of the water quality sensor, the water quality status is obtained as the auxiliary input for evaluation; the nodes of the Bayesian network are defined, such as water level, water quality, and waterway capacity, etc., as the variables for evaluation; finally, a conditional probability table is constructed to describe the conditional probability relationship between each node; the water level and water quality data are input into the Bayesian network inference engine, and through probability inference, the stability evaluation result of the waterway is obtained; the stability evaluation result of the waterway is output, such as "stable", "unstable", etc., and based on the evaluation result, corresponding decision support information is generated, such as "need to increase the water inflow", "need to clean the water quality", etc. In the step S2021 of data acquisition and fuzzification processing of the above solution, by obtaining the designed capacity and real-time water level data of the water tank, it provides basic data support for the evaluation; transforming the specific water level data into fuzzy linguistic variables (such as "low water level", "medium water level", "high water level") makes the evaluation process more flexible and adaptable, and can handle uncertainties; transforming the designed capacity of the water tank into fuzzy linguistic variables (such as "small capacity", "medium capacity", "large capacity") provides a basis for fuzzy inference. Significance: Through fuzzification processing, it can better cope with the uncertainty of water level changes, improve the accuracy and robustness of the evaluation; provide the necessary input for fuzzy inference, and ensure the continuity and consistency of the evaluation process. In step S2022 of fuzzy inference and defuzzification, based on expert knowledge and historical data, a fuzzy rule base is constructed, making the evaluation process more intelligent and scientific; through the fuzzy inference engine, using the fuzzy rule base for inference, the fuzzy evaluation result of the waterway capacity is obtained; transforming the fuzzy evaluation result into a specific value makes the evaluation result more intuitive and easy to understand. Significance: Through fuzzy inference, various factors can be comprehensively considered, improving the comprehensiveness and accuracy of the evaluation; defuzzification processing makes the evaluation result more specific and practical, facilitating subsequent decision support.Step S2023 Data Acquisition and Bayesian Network Construction: Obtain historical and real-time water level data, and combine it with data from water quality sensors to provide comprehensive data support for evaluation; define the nodes of the Bayesian network (such as water level, water quality, and waterway capacity, etc.), which provides a basis for probabilistic inference. Significance: Through the Bayesian network, the relationships between multiple variables can be comprehensively considered, improving the complexity and comprehensiveness of the evaluation; it provides the necessary structure and data support for probabilistic inference. Step S2024 Probabilistic Inference and Decision Support: Describe the conditional probability relationships between nodes, providing a basis for probabilistic inference; through the Bayesian network inference engine, use the conditional probability table to perform probabilistic inference to obtain the evaluation result of the waterway stability; based on the evaluation result, generate corresponding decision support information, such as "need to increase the water inflow", "need to conduct water quality cleaning", etc. Significance: Through probabilistic inference, the stability of the waterway can be evaluated more scientifically, improving the accuracy and reliability of the evaluation; the generated decision support information provides guidance for actual operations, improving the efficiency and effectiveness of waterway management.

[0123] In summary, the waterway dynamic evaluation process of this embodiment not only realizes the comprehensive acquisition and processing of data, but also improves the accuracy and scientificity of the evaluation through fuzzy inference and probabilistic inference. The finally generated decision support information provides strong guidance for actual operations, improving the efficiency and effectiveness of waterway management. Through the above hierarchical fuzzy inference and probabilistic inference process, the dynamic evaluation of the waterway capacity and state is realized; the specific steps include input data preparation, fuzzification processing, fuzzy rule base construction, fuzzy inference engine, defuzzification processing, stability evaluation model construction, probabilistic inference process, and dynamic evaluation result output. The steps are interconnected to form a complete dynamic evaluation process, ensuring the safe operation and efficient management of the waterway.

[0124] Embodiment 5: As Figure 5 shown, on the basis of Embodiment 4, the process of defining the nodes of the Bayesian network provided by the embodiment of the present invention includes the following steps:

[0125] S20231: Definition of the nodes of the Bayesian network, the water level is a first-level variable that affects the waterway capacity, the water quality is a second-level variable that indirectly affects the waterway capacity through the waterway stability, the waterway stability is an intermediate variable that connects the water quality and the waterway capacity, and the waterway capacity is a third-level variable that is the final output result;

[0126] S20232: In a directed acyclic graph, the nodes represent different entities or states, and the edges represent the dependency or causal relationships between the nodes. Each edge has a direction, representing a one-way relationship from one node to another; this edge indicates that node H has a direct impact on node C, and the path indicates that node Q indirectly affects node C through node S;

[0127] S20233: Establish a conditional probability table, a conditional probability table of waterway stability based on water quality, which describes the probability distribution of waterway stability under different water quality conditions; a conditional probability table of waterway capacity based on water level H and waterway stability, which describes the probability distribution of waterway capacity under different water levels and waterway stability conditions.

[0128] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, first, the nodes of the Bayesian network are defined. The water level is a first-level variable that affects the waterway capacity. The water quality is a second-level variable that indirectly affects the waterway capacity through waterway stability. The waterway stability is an intermediate variable that connects water quality and waterway capacity. The waterway capacity is a third-level variable, which is the final output result. Secondly, in the directed acyclic graph, the nodes represent different entities or states, and the edges represent the dependence relationship or causal relationship between the nodes. Each edge has a direction, indicating a one-way relationship from one node to another. This edge indicates that node H has a direct impact on node C, and the path indicates that node Q indirectly affects node C through node S. Finally, a conditional probability table is established. The conditional probability table of waterway stability based on water quality describes the probability distribution of waterway stability under different water quality conditions; the conditional probability table of waterway capacity based on water level H and waterway stability describes the probability distribution of waterway capacity under different water levels and waterway stability conditions. In step S20231 of the above solution, the nodes of the Bayesian network are defined. By defining the nodes and their hierarchical relationships, the direct and indirect influence relationships between various variables are clarified; the complex system is decomposed into multiple levels of nodes, which is convenient for understanding and analysis. Significance: It helps users to understand the roles and mutual relationships of various variables in the system as a whole; decomposes complex problems into multiple sub-problems, which is convenient for step-by-step solution and optimization. In step S20232, in the directed acyclic graph, the nodes represent different entities or states, and the edges represent the dependence relationship or causal relationship between the nodes. By graphically representing the relationship between nodes and edges through the DAG, the dependence and causal relationship between variables are intuitively displayed; the direction of each edge is clarified to ensure that the dependence relationship is one-way and avoid circular dependence. Significance: Through graphical display, users can intuitively understand the dependence relationship between variables; ensure that the system logic is clear, which is convenient for the subsequent establishment of conditional probability tables and reasoning. In step S20233, a conditional probability table is established. Through the conditional probability table, the dependence relationship between nodes is quantified, and a specific probability distribution is provided; it provides basic data for the reasoning of the Bayesian network, making probability-based reasoning possible. Significance: Through the conditional probability table, the states and changes of each variable can be predicted more accurately; it provides a probability-based basis for decision-making, helping users to make more reasonable decisions in uncertainty.

[0129] In summary, in this embodiment, by defining nodes, the levels and relationships of various variables are clarified, laying a foundation for subsequent graphical representation and probability modeling; by graphically representing the relationships between nodes and edges using DAGs, the dependencies and causal relationships between variables are intuitively shown, ensuring clear waterway control logic; by establishing conditional probability tables, the dependencies between nodes are quantified, providing basic data for the inference of Bayesian networks and making accurate prediction and decision support possible. Through these steps, the complex waterway control is decomposed into nodes and dependencies at multiple levels, facilitating the analysis and understanding of waterway control; by quantifying the dependencies between nodes, a probability-based inference foundation is provided, making it possible to make accurate predictions and reasonable decisions under uncertainty; a probability-based basis for decision-making is provided to help users make better decisions in complex and uncertain environments.

[0130] Embodiment 6: As Figure 6 shown, on the basis of Embodiment 4, the process for obtaining the stability evaluation result of the waterway provided by the embodiment of the present invention includes the following steps:

[0131] S20241: Normalize the water level and water quality data to the interval [0, 1], and input the normalized real-time water level and water quality data into the Bayesian network inference engine;

[0132] S20242: The Bayesian network inference engine calculates the posterior probability of waterway stability according to the input data and the existing conditional probability table; according to the input water level and water quality data, look up the corresponding probability values in the conditional probability table;

[0133] S20243: Use Bayes' theorem to calculate the posterior probability of waterway stability, and output the evaluation result of waterway stability according to the calculated posterior probability; if the waterway stability evaluation is unstable, take measures.

[0134] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the water level and water quality data are first normalized to the interval [0, 1], and the normalized real-time water level and water quality data are input into the Bayesian network inference engine. Secondly, the Bayesian network inference engine calculates the posterior probability of the waterway stability according to the input data and the existing conditional probability table; according to the input water level and water quality data, the corresponding probability values in the conditional probability table are found. Finally, the posterior probability of the waterway stability is calculated using Bayes' theorem, and according to the calculated posterior probability, the evaluation result of the waterway stability is output; if the waterway stability evaluation is unstable, measures are taken. In step S20241 of the above solution, data normalization and input, the water level and water quality data are normalized to the interval [0, 1] to ensure that data with different dimensions are compared on the same scale and avoid inference errors caused by data magnitude differences; the normalized data is input into the Bayesian network inference engine to provide an accurate data basis for probability calculation. Significance: The normalization process can eliminate the influence of data magnitude differences on the inference results and improve the accuracy of Bayesian network inference; ensure that the formats and ranges of the input data are consistent, which is convenient for the Bayesian network model to perform unified inference calculations. In step S20242, conditional probability query and posterior probability calculation, according to the input water level and water quality data, the corresponding probability values in the conditional probability table (CPT) are found to obtain the prior probability of the waterway stability under specific conditions; Bayes' theorem is used to calculate the posterior probability of the waterway stability, and combined with the prior probability and the observed data, a more accurate evaluation result is obtained. Significance: Through real-time query and calculation, the waterway stability can be dynamically evaluated, and the stability state under the current water level and water quality conditions can be reflected in a timely manner; provide an evaluation result based on probability, provide a scientific basis for decision-making, and enhance the reliability and effectiveness of decision-making. In step S20243, output the evaluation result and take measures, according to the calculated posterior probability, output the evaluation result of the waterway stability, and clarify the current state of the waterway stability; according to the evaluation result, take corresponding measures, such as increasing the water inflow or cleaning the water quality, to maintain or improve the waterway stability. Significance: Through real-time evaluation and taking measures, the changes in waterway stability can be responded to in a timely manner, avoiding potential risks and losses; the measures taken based on the evaluation results contribute to optimizing waterway management and improving the utilization efficiency and safety of water resources.

[0135] In summary, this embodiment realizes the real-time, dynamic and scientific evaluation of the waterway stability, providing strong decision-making support for waterway management. The specific significance includes: the evaluation result based on probability reasoning makes the decision-making more scientific and reasonable; through real-time monitoring and dynamic evaluation, the reliability and stability of the waterway management system are enhanced; the measures taken based on the evaluation result contribute to optimizing the utilization of water resources and improving the efficiency and effectiveness of waterway management. The Bayesian network inference engine can provide accurate and reliable evaluation results in a complex waterway management environment, providing strong support for the maintenance and optimization of waterway stability.

[0136] Example 7: As Figure 7 shown, based on Example 6, the process of evaluating the output waterway stability provided by the embodiments of the present invention includes the following steps:

[0137] S202431: Calculate the probability of the observed data under a given waterway stability condition, reflecting the possibility of the observed data in different stability states;

[0138] S202432: Calculate the marginal probability of the observed data, which is the joint probability of the observed data under all possible waterway stability states; include all states of waterway stability and calculate the joint probability of the observed data; first calculate the probability of the observed data under stable conditions and multiply it by the prior probability of stability; then calculate the probability of the observed data under unstable conditions and multiply it by the prior probability of instability; finally, add these two results to obtain the marginal probability of the observed data;

[0139] S202433: According to Bayes' theorem, combine the prior probability, likelihood function, and marginal probability to calculate the posterior probability of waterway stability, reflecting the update of the waterway stability after observing the current data;

[0140] Among them, multiply the likelihood function by the prior probability to obtain an intermediate result; divide the intermediate result by the marginal probability to obtain the posterior probability.

[0141] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, first, the probability of the observed data is calculated under the given waterway stability conditions, which reflects the possibility of the observed data under different stability states; second, the marginal probability of the observed data is calculated, which is the joint probability of the observed data under all possible waterway stability states; all states of waterway stability are included, and the joint probability of the observed data is calculated; first, the probability of the observed data under stable conditions is calculated and multiplied by the prior probability of stability; then, the probability of the observed data under unstable conditions is calculated and multiplied by the prior probability of instability; finally, these two results are added together to obtain the marginal probability of the observed data; finally, according to Bayes' theorem, the prior probability, likelihood function, and marginal probability are combined to calculate the posterior probability of waterway stability, which reflects the update of the waterway stability after observing the current data. In step S202431 of the above solution, the probability of the observed data is calculated under the given waterway stability conditions. By calculating the probability of the observed data under the known waterway stability conditions, a likelihood function is generated; the likelihood function reflects the possibility of the observed data under different stability states; using the conditional probability table, the probability of the observed data under different waterway stability states is queried to provide basic data for the calculation. Significance: By calculating the likelihood function, the influence of the observed data on waterway stability is quantified, providing a basis for subsequent Bayesian updates; the accurate calculation of the likelihood function helps to improve the accuracy and reliability of the Bayesian network model. In step S202432, the marginal probability of the observed data is calculated. The marginal probability of the observed data is calculated, that is, the joint probability of the observed data under all possible waterway stability states. This step involves weighted summation of the probabilities of the observed data under different stability states; by first calculating the probability of the observed data under stable conditions and multiplying it by the prior probability of stability; then calculating the probability of the observed data under unstable conditions and multiplying it by the prior probability of instability; finally, these two results are added together to obtain the marginal probability of the observed data. Significance: The calculation of the marginal probability comprehensively considers all possible waterway stability states, ensuring that when calculating the posterior probability, the influence of the observed data can be fully reflected; by calculating the marginal probability, the robustness of the Bayesian network model is improved, ensuring that the model can operate stably under different conditions. In step S202433, according to Bayes' theorem, the prior probability, likelihood function, and marginal probability are combined to calculate the posterior probability of waterway stability. By Bayes' theorem, the prior probability, likelihood function, and marginal probability are combined to calculate the posterior probability of waterway stability; the update of the belief in waterway stability is realized; using the Bayes' theorem formula, substituting the known prior probability, likelihood function, and marginal probability, the posterior probability is calculated. Significance: The posterior probability reflects the updated belief in waterway stability after observing the current data; the update process enables the model to dynamically adapt to new observed data, improving the accuracy of prediction; by calculating the posterior probability, a scientific basis is provided for waterway management, supporting decision-makers to make more reasonable decisions under different conditions.

[0142] In summary, this embodiment can systematically evaluate the waterway stability and dynamically update the waterway stability according to the observed data; calculate the likelihood function to quantify the impact of the observed data and improve the model accuracy; calculate the marginal probability to comprehensively consider all possibilities and improve the model robustness; apply Bayes' theorem to update the posterior probability to support decision-making and improve the prediction accuracy. Together, they constitute a complete Bayesian network inference process, ensuring the scientificity and reliability of the waterway stability evaluation.

[0143] Embodiment 8: As Figure 8 shown, based on Embodiment 7, the process of adjusting the dynamic waterway control adjustment strategy provided by the embodiment of the present invention includes the following steps:

[0144] S301: Preprocess the input dynamic adjustment waterway data, including data cleaning, normalization processing, and feature extraction; through feature extraction, extract key features such as the water level change trend, water quality parameter change, and sensor response time from the original water level data, and use the features as the input of the machine learning model;

[0145] S302: Select a deep neural network model for initialization, and input the preprocessed features into the deep neural network model for training; during the training process, the deep neural network model optimizes the parameters of the deep neural network model by learning the association between the water level change and the control strategy in the historical data;

[0146] S303: Decompose the dynamic waterway control adjustment strategy into three levels: underlying strategy learning, middle-level strategy learning, and high-level strategy learning; during the strategy learning process of each level, evaluate according to the actual waterway control effect and generate a feedback signal; through continuous iterative learning, optimize the dynamic waterway control adjustment strategy;

[0147] Among them, underlying strategy learning: mainly focuses on the real-time response to the water level change trend. By training the model, it can quickly adjust the waterway control parameters according to the change of the current water level data, such as the start and stop of the water pump, the opening and closing of the valve, etc.; middle-level strategy learning: on the basis of the underlying strategy, consider the change of water quality parameters; by training the model, it can dynamically adjust the operation state of the water quality cleaning equipment according to the real-time change of the water quality data to ensure that the water quality is always within the safe range; high-level strategy learning: on the basis of the middle-level strategy, comprehensively consider the long-term change trends of the water level change trend and water quality parameters; by training the model, it can predict the future water level and water quality changes and adjust the waterway control strategy in advance to cope with potential risks and challenges.

[0148] Among them, in step S303, the optimization of the dynamic waterway control adjustment strategy is a multi-level and multi-dimensional complex process, involving the comprehensive effects of underlying, middle-level, and high-level strategy learning;

[0149]

[0150] In the formula, represents the comprehensive optimization value of the dynamic waterway control adjustment strategy at time t , reflecting the effectiveness of the overall strategy; k represents the hierarchical index of strategy learning ( k =1) is the underlying strategy, ( k =2) is the middle-level strategy, ( k =3) is the high-level strategy; represents the importance weight coefficient of the k -th layer of strategy learning, reflecting the contribution weight of this level in the overall strategy; represents the basic effectiveness value of the t -th layer of strategy at time k , reflecting the execution effect of this level of strategy at the current time; represents the water level change trend influence factor of the k -th layer of strategy, reflecting the sensitivity of this level of strategy to water level changes; represents the water level change trend intensity of the t -th layer of strategy at time k , reflecting the dynamic change characteristics of the current water level; represents the water quality parameter influence factor of the k -th layer of strategy, reflecting the sensitivity of this level of strategy to water quality changes; represents the water quality parameter deviation value of the t -th layer of strategy at time k , reflecting the deviation degree between the current water quality and the preset safety range; represents the comprehensive feedback adjustment coefficient at time t , reflecting the dynamic adjustment ability of the overall strategy; represents the sensor response time influence factor at time t , reflecting the real-time influence of the sensor on strategy adjustment; represents the sensor response time deviation value at time t , reflecting the dynamic change of the sensor response time; represents the external environment change influence factor at time t , reflecting the influence of the external environment (such as temperature, pressure, etc.) on strategy adjustment; represents the external environment change intensity at time t , reflecting the fluctuation characteristics of the external environment at the current time.

[0151] Hierarchical strategy learning part:

[0152] Reflects the combined effects of underlying, middle, and high-level policy learning. The basic effectiveness value of each level interacts with the water level change trend and the deviation of water quality parameters and combines their respective weight coefficients 、 and to obtain the optimized contribution value of this level;

[0153] The underlying policy represents and and is mainly based on the short-term response to real-time water level changes and water quality parameters; The middle-level policy represents and and further considers the medium-term change trend of water quality parameters on the basis of the underlying level; The high-level policy represents and and makes predictive adjustments by comprehensively considering the long-term change trends of water level and water quality;

[0154] Comprehensive feedback regulation part:

[0155] Comprehensively considers the feedback effects of sensor response time and external environment changes; and reflects the real-time impact of sensor response time on policy adjustment; and reflects the continuous impact of external environment changes on policy adjustment; Dynamically adjusts the intensity of comprehensive feedback according to the current state to ensure the flexibility and adaptability of policy adjustment.

[0156] Hierarchy realizes the unified modeling of underlying, middle, and high-level policy learning, reflecting the multi-level characteristics of policy optimization; Multi-dimensionality means including multiple dimensions such as water level change, water quality parameters, sensor response time, and external environment changes to ensure the comprehensiveness and accuracy of the formula; Dynamics means realizing the dynamic feedback regulation of policy optimization to ensure the real-time nature and adaptability of the policy; The underlying, middle, and high-level policy learning are closely combined, which not only reflects the independence of each level of policy, but also realizes the dynamic optimization of the overall policy through the comprehensive feedback regulation part, ensuring that the waterway control adjustment policy always remains efficient and accurate in complex and changeable scenarios.

[0157] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the input dynamic adjustment waterway data is first preprocessed, including data cleaning, normalization processing, and feature extraction; through feature extraction, key features such as the key water level change trend, water quality parameter change, and sensor response time are extracted from the original water level data, and these features are used as the input of the machine learning model; secondly, a deep neural network model is selected for initialization, and the preprocessed features are input into the deep neural network model for training; during the training process, the deep neural network model learns the association between the water level change and the control strategy in the historical data to optimize the parameters of the deep neural network model; finally, the dynamic waterway control adjustment strategy is decomposed into three levels: low-level strategy learning, middle-level strategy learning, and high-level strategy learning; during the strategy learning process of each level, it is evaluated according to the actual waterway control effect and a feedback signal is generated; through continuous iterative learning, the dynamic waterway control adjustment strategy is optimized; among them, low-level strategy learning: mainly focuses on the real-time response to the water level change trend. By training the model, it can quickly adjust the waterway control parameters according to the change of the current water level data, such as the start and stop of the water pump, the opening and closing of the valve, etc.; middle-level strategy learning: on the basis of the low-level strategy, consider the change of water quality parameters; by training the model, it can dynamically adjust the operation state of the water quality cleaning equipment according to the real-time change of the water quality data to ensure that the water quality is always within the safe range; high-level strategy learning: on the basis of the middle-level strategy, comprehensively consider the long-term change trends of the water level change trend and water quality parameters; by training the model, it can predict the future water level and water quality changes and adjust the waterway control strategy in advance to cope with potential risks and challenges. In step S301 of the above solution, data preprocessing and feature extraction are carried out to remove noise and outliers, ensuring the accuracy and reliability of the input data; unify data with different dimensions to the same scale to avoid the influence of data deviation on model training; extract key features such as the key water level change trend, water quality parameter change, and sensor response time from the original data to provide high-quality input data for model training. The achieved significance: Through data preprocessing and feature extraction, the accuracy and consistency of the input data are ensured, laying a solid foundation for subsequent model training; it helps to improve the training efficiency and prediction accuracy of the model, thus better realizing dynamic waterway control. In step S302, the initialization and training of the deep neural network model are carried out. By selecting the deep neural network model and using its powerful non-linear fitting ability, it can better capture the complex relationship between the water level change and the control strategy; by learning the association between the water level change and the control strategy in the historical data, the parameters of the deep neural network model are optimized so that it can more accurately predict the future water level change and water quality parameters.Achieved Significance: The application of deep neural network models enables the system to learn complex control strategies from a large amount of historical data, improving the intelligent level of waterway control. It not only helps to respond to water level changes in real time but also can predict future water level and water quality changes and take control measures in advance to ensure the stable operation of the waterway system. In step S303, hierarchical strategy learning and optimization are carried out. By training the model, it can quickly adjust waterway control parameters according to the changes in current water level data, such as starting and stopping pumps, opening and closing valves, etc. On the basis of the underlying strategy, further consider the changes in water quality parameters. By training the model, it can dynamically adjust the operating state of water quality cleaning equipment according to the real-time changes in water quality data to ensure that the water quality is always within a safe range. On the basis of the middle-level strategy, comprehensively consider the water level change trend and the long-term change trend of water quality parameters. By training the model, it can predict future water level and water quality changes and adjust the waterway control strategy in advance to cope with potential risks and challenges. Achieved Significance: The hierarchical strategy learning method enables the system to comprehensively optimize the waterway control strategy from multiple dimensions. The underlying strategy ensures real-time response to water level, the middle-level strategy focuses on water quality safety guarantee, and the high-level strategy provides prediction and response capabilities for long-term trends. The multi-level control strategy not only improves the response speed and control accuracy of the system but also can cope with the complex and changeable waterway environment to ensure the stable operation of the waterway system and water quality safety.

[0158] In summary, the adjustment process of the dynamic waterway control adjustment strategy in this embodiment not only realizes real-time response and prediction of water level and water quality changes but also comprehensively optimizes the waterway control strategy through a hierarchical strategy learning method. It not only improves the intelligent level of the waterway system but also ensures the stable operation of the waterway system and water quality safety, having important practical application value. Through the hierarchical learning process, the comprehensive optimization of the dynamic waterway control adjustment strategy is realized. From the response to water level changes at the bottom layer, to the adjustment of water quality parameters at the middle layer, and then to the prediction of long-term trends at the high layer, the learning at each layer provides a solid foundation for the final control strategy. The hierarchical learning method not only improves the accuracy and efficiency of waterway control but also can cope with the complex and changeable waterway environment to ensure the stable operation of the waterway system.

[0159] Embodiment 9: As Figure 9 shown, on the basis of Embodiments 1 - 8, the waterway detection and control circuit provided by the embodiment of the present invention includes:

[0160] A data acquisition module, responsible for arranging various types of sensors on the water tank, realizing multi-dimensional acquisition of the water level in the water tank through the sensors, fusing and calibrating the collected water level data, and obtaining preprocessed water level data;

[0161] Among them, multiple types of sensors include ultrasonic sensors, pressure sensors, capacitive sensors, and water quality sensors, etc.; ultrasonic sensors are suitable for water level measurement, pressure sensors are suitable for depth measurement, capacitive sensors are suitable for liquid level detection, and water quality sensors are used to monitor water quality parameters such as pH value, dissolved oxygen, etc.; the selection of each sensor needs to be based on its functional characteristics and applicable scenarios;

[0162] The dynamic adjustment module is responsible for obtaining the preprocessed water level data, inputting it into the dynamic waterway control adjustment strategy, and dynamically adjusting the waterway according to the water level change trend and waterway state; at the same time, comparing the water level data with the preset multi-level water level thresholds, and automatically triggering the alarm mechanism when the water level data is lower than the safety threshold in the multi-level water level thresholds; obtaining water quality data from the water level data, comparing it with the preset safe range, and automatically performing water quality cleaning when it exceeds the safe range;

[0163] The iterative learning module is responsible for inputting the data after dynamically adjusting the waterway into the machine learning model, learning the data and the dynamic waterway control adjustment strategy, and adjusting the dynamic waterway control adjustment strategy through continuous iterative learning.

[0164] The working principle and beneficial effects of the above technical solution are as follows: The data acquisition module in this embodiment arranges various types of sensors on the water tank, and realizes multi-dimensional acquisition of the water level in the water tank through the sensors, fuses and calibrates the acquired water level data, and obtains the pre-processed water level data; among them, various types of sensors include ultrasonic sensors, pressure sensors, capacitive sensors, and water quality sensors, etc.; ultrasonic sensors are suitable for water level measurement, pressure sensors are suitable for depth measurement, capacitive sensors are suitable for liquid level detection, and water quality sensors are used to monitor water quality parameters such as pH value and dissolved oxygen; the selection of each sensor is based on its functional characteristics and applicable scenarios; the dynamic adjustment module obtains the pre-processed water level data and inputs it into the dynamic waterway control adjustment strategy, and dynamically adjusts the waterway according to the water level change trend and the waterway state; at the same time, compares the water level data with the preset multi-level water level thresholds, and when the water level data is lower than the safety threshold in the multi-level water level thresholds, automatically triggers the alarm mechanism; obtains the water quality data from the water level data, compares it with the preset safety range, and automatically performs water quality cleaning when it exceeds the safety range; the iterative learning module inputs the data after dynamically adjusting the waterway into the machine learning model, learns the data and the dynamic waterway control adjustment strategy, and adjusts the dynamic waterway control adjustment strategy through continuous iterative learning. The data acquisition module in the above solution realizes multi-dimensional acquisition of water level and water quality parameters by arranging various types of sensors (such as ultrasonic sensors, pressure sensors, capacitive sensors, and water quality sensors) on the water tank; fuses and calibrates the acquired water level data to ensure the accuracy and consistency of the data, and obtains the pre-processed water level data. The achieved significance: Multi-dimensional data acquisition and fusion calibration ensure the accuracy and reliability of water level and water quality data, providing high-quality input data for subsequent dynamic adjustment and control; helps to improve the accuracy and stability of the waterway control system and ensure the normal operation of the waterway system. The dynamic adjustment module inputs the pre-processed water level data into the dynamic waterway control adjustment strategy, and dynamically adjusts the waterway control parameters according to the water level change trend and the waterway state; compares the water level data with the preset multi-level water level thresholds, and when the water level data is lower than the safety threshold, automatically triggers the alarm mechanism to ensure that the water level is within the safe range; obtains the water quality data from the water level data, compares it with the preset safety range, and automatically performs water quality cleaning when the water quality parameters exceed the safety range to ensure water quality safety. The achieved significance: The dynamic adjustment module realizes real-time monitoring and control of water level and water quality changes, ensuring that the water level and water quality are always within the safe range; not only improves the safety and reliability of the waterway system, but also can timely respond to changes in water level and water quality, reducing potential risks and losses.The iterative learning module inputs the data with the dynamically adjusted water path into the machine learning model to learn the data and the dynamic water path control adjustment strategy. Through continuous iterative learning, the dynamic water path control adjustment strategy is optimized; through the training of the machine learning model, the water path control strategy is continuously optimized, and the response speed and control accuracy of the system are improved. Significance achieved: Through the application of the machine learning model, the iterative learning module enables the system to learn the optimal control strategy from historical data, improving the intelligent level of water path control; it not only helps to respond to changes in water level and water quality in real time, but also can predict future change trends and take control measures in advance to ensure the stable operation of the water path system and water quality safety.

[0165] In summary, the water path detection and control circuit of this embodiment not only realizes multi-dimensional monitoring and control of water level and water quality, but also comprehensively optimizes the water path control strategy through dynamic adjustment and iterative learning; it not only improves the safety and reliability of the water path system, but also ensures that the water level and water quality are always within the safe range, having important practical application value.

[0166] Embodiment 10: On the basis of Embodiment 9, the application of the water path detection and control circuit provided by the embodiment of the present invention includes: arranging ultrasonic sensors and capacitive sensors in the water tank of the water dispenser, etc.; the ultrasonic sensor is used to measure the water level height, and the capacitive sensor is used to detect the liquid level change; the data of the two sensors are fused and calibrated to obtain accurate water level data; the preprocessed water level data is input into the dynamic water path control adjustment strategy; according to the water level change trend and the water path state, the water path is dynamically adjusted to ensure that the water level is always within the safe range; when the water level data is lower than the safety threshold in the multi-level water level threshold, the water replenishment mechanism is automatically triggered to ensure that the water dispenser always has sufficient water supply;

[0167] A water quality sensor is arranged in the water tank of the water dispenser; the water quality sensor is used to monitor water quality parameters such as pH value and dissolved oxygen; the collected water quality data is fused and calibrated to obtain accurate water quality data; the preprocessed water quality data is input into the dynamic water path control adjustment strategy; according to the water quality change trend and the water path state, the water path is dynamically adjusted to ensure that the water quality always meets the safety standards; when the water quality data exceeds the preset safe range, the cleaning program is automatically started to ensure that the water in the water dispenser is always clean and hygienic;

[0168] The data with the dynamically adjusted water path is input into the machine learning model of the water dispenser controller; through continuous iterative learning, the data and the dynamic water path control adjustment strategy are learned; according to the learning results, the dynamic water path control adjustment strategy is adjusted to improve the intelligent level of the water dispenser and ensure its long-term stable operation.

[0169] This embodiment can also be applied to the urban water supply system to monitor the water level in the urban water supply system in real time, ensure the normal operation of the water supply network; monitor the water quality of the water supply to ensure that the water quality meets the drinking water standards; dynamically adjust the water supply strategy according to the changes in water level and water quality, optimize the operation efficiency of the water supply network, ensure the water demand of urban residents; improve the intelligent level of the urban water supply system, ensure the safety and stability of the water supply, reduce the occurrence of water supply accidents, and improve the quality of life of residents.

[0170] The working principle and beneficial effects of the above technical solutions are as follows: This embodiment provides high-precision water level measurement and liquid level change detection; ensures the accuracy and reliability of water level data; adjusts the water path in real time according to the water level change trend and water path state to ensure that the water level is always within the safe range; when the water level is lower than the safety threshold, automatic water replenishment is triggered to ensure that the water dispenser always has sufficient water supply. Significance: Through real-time monitoring and automatic control, ensure that the water level is always within the safe range, avoiding dry burning or other safety hazards caused by too low water level; the automatic water replenishment mechanism ensures that users always have water available, improving the convenience and satisfaction of use. Water quality monitoring and cleaning, real-time monitoring of water quality parameters such as pH value, dissolved oxygen, etc., to ensure that the water quality meets the safety standards; ensures the accuracy and reliability of water quality data; adjusts the water path in real time according to the water quality change trend and water path state to ensure that the water quality always meets the safety standards; when the water quality exceeds the safe range, the cleaning program is automatically started to ensure that the water in the water dispenser is always clean and hygienic. Significance: Through real-time monitoring and automatic cleaning, ensure that the water quality always meets the drinking water standards, protecting the health and safety of users; regular cleaning can reduce the accumulation of scale and bacteria, extending the service life of the water dispenser. Dynamic adjustment and optimization, through continuous iterative learning, optimize the water path control strategy to improve the intelligent level of the water dispenser; dynamically adjust the water path according to the learning results to ensure the long-term stable operation of the water dispenser. Significance: Through the machine learning model, continuously optimize the control strategy to make the water dispenser have a higher intelligent level and adapt to various complex environments; dynamically adjust the water path strategy to ensure that the water dispenser maintains an efficient and stable operation state during long-term use. Applied to the urban water supply system, ensure the normal operation of the water supply network and the water quality meets the drinking water standards; optimize the operation efficiency of the water supply network according to the changes in water level and water quality to ensure the water demand of urban residents; optimize the water supply strategy through the machine learning model to ensure the safety and stability of the water supply. Significance: Through real-time monitoring and dynamic adjustment, ensure the safety and stability of the urban water supply system, reduce the occurrence of water supply accidents; optimize the water supply strategy to ensure the water demand of urban residents and improve the quality of life of residents.

[0171] In summary, the application of the water path detection and control circuit of the water dispenser in this embodiment not only improves the intelligent level and safety of the device, but also ensures the long-term stable operation of the water quality and water supply system, which has important practical significance and broad application prospects.

[0172] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of equivalent technologies of the present invention, the present invention is also intended to include these modifications and variations.

Claims

1. A waterway detection and control method, characterized in that: The following steps are involved: Various types of sensors are placed on the water tank to collect the water level in multiple dimensions. The collected water level data are fused and calibrated to obtain pre-processed water level data. Obtain pre-processed water level data and input it into the dynamic waterway control adjustment strategy to dynamically adjust the waterway according to the water level change trend and waterway status; The data after the dynamic adjustment of the waterway is input into the machine learning model to learn the data and the dynamic waterway control adjustment strategy, and the dynamic waterway control adjustment strategy is adjusted through continuous iterative learning; The process of placing various types of sensors on the water tank includes the following steps: Obtain the geometric characteristics of the water tank, including the height, diameter, wall thickness and inlet and outlet positions of the water tank. According to the height and diameter of the water tank, preliminarily determine the number and distribution of monitoring points, divide the water tank into three areas: upper, middle and lower, and set several monitoring points in each area; Analyze the inlet and outlet locations of the water tank to determine the water flow path and retention area; set up multiple monitoring points based on the geometric characteristics of the water tank and the water flow path, and each monitoring point should contain at least one type of sensor; The pre-processed water level data is input into the dynamic waterway control adjustment strategy, and the position and parameters of the sensor are dynamically adjusted according to the water level change trend and waterway status; Water level change trend prediction formula: In the formula, represents the predicted water level change, represents the observed water level change, represents the last predicted water level change, represents the smoothing coefficient; Sensor position adjustment: In the formula, represents the adjusted sensor position, Indicates the original sensor position, Indicates the position adjustment amount.

2. The waterway detection and control method according to claim 1, characterized in that: At the same time, the water level data is compared with the preset multi-level water level thresholds. When the water level data is lower than the safety threshold in the multi-level water level threshold, the alarm mechanism is automatically triggered; water quality data is obtained from the water level data and compared with the preset safety range. When it exceeds the safety range, the water quality is automatically cleaned.

3. The waterway detection and control method according to claim 1, characterized in that: The process of dynamically adjusting the waterway includes the following steps: Perform time series analysis on water level data to extract water level change trends and periodic fluctuation target features; use the target feature time series prediction model to predict future water level change trends and generate prediction curves; According to the design capacity of the water tank and the current water level, the waterway capacity is dynamically evaluated through fuzzy reasoning; based on the stability evaluation method, the waterway is dynamically evaluated through probabilistic reasoning; combined with the data from the water quality sensor, it is evaluated whether the water quality meets the safety standards and whether water cleaning is required; According to the water level change trend and waterway status, multi-level water level thresholds are set, including safety thresholds and warning thresholds; adaptive threshold settings are used in combination with historical water level data and real-time water level data to dynamically adjust the thresholds; based on the comparison results between the current water level and the threshold, the corresponding dynamic waterway control adjustment strategy is generated; at the same time, when the water level is lower than the safety threshold, the alarm mechanism is automatically triggered; according to the generated dynamic waterway control adjustment strategy, the waterway is adjusted in real time; and the implementation effect of the adjustment strategy is evaluated by real-time monitoring of water level and water quality data.

4. The waterway detection and control method according to claim 3, characterized in that: The process of dynamic assessment of waterways includes the following steps: Obtain the design capacity data of the water tank as the basic parameter for evaluation; collect the current water level data in real time as the dynamic input for evaluation; perform fuzzy processing on the current water level data and convert it into fuzzy language variables; perform fuzzy processing on the design capacity of the water tank and convert it into fuzzy language variables; Based on expert knowledge and historical data, a fuzzy rule base is constructed; the fuzzified water level and capacity are input into the fuzzy reasoning engine, and the fuzzy evaluation results of the waterway capacity are obtained through reasoning through the fuzzy rule base; the fuzzy evaluation results are defuzzified and converted into specific values ​​as the dynamic evaluation results of the waterway capacity; Obtain historical and real-time water level data as the basic input for evaluation; combine the data from water quality sensors to obtain water quality status as an auxiliary input for evaluation; define the nodes of the Bayesian network as variables for evaluation; Construct a conditional probability table to describe the conditional probability relationship between nodes; input water level and water quality data into the Bayesian network inference engine, and obtain the stability assessment results of the waterway through probabilistic reasoning; Output the stability assessment results of the waterway and generate corresponding decision support information based on the assessment results.

5. The waterway detection and control method according to claim 4, characterized in that: The process of defining the nodes of a Bayesian network consists of the following steps: The node definition of the Bayesian network is: water level is a primary variable, affecting the waterway capacity; water quality is a secondary variable, indirectly affecting the waterway capacity through waterway stability; waterway stability is an intermediate variable, connecting water quality and waterway capacity; waterway capacity is a tertiary variable, and the final output is the result.

6. The waterway detection and control method according to claim 5, characterized in that: In a directed acyclic graph, nodes represent different entities or states, and edges represent dependencies or causal relationships between nodes. Each edge has a direction, indicating a unidirectional relationship from one node to another; this edge indicates that node H has a direct impact on node C, and the path indicates that node Q indirectly affects node C through node S.

7. The waterway detection and control method according to claim 6, characterized in that: A conditional probability table is established. The conditional probability table of waterway stability is based on water quality, which describes the probability distribution of waterway stability under different water quality conditions. The conditional probability table of waterway capacity is based on water level H and waterway stability, which describes the probability distribution of waterway capacity under different water levels and waterway stability conditions.

8. A waterway detection and control circuit, characterized in that: Include: The data acquisition module is responsible for placing various types of sensors on the water tank, realizing multi-dimensional collection of the water level in the water tank through the sensors, fusing and calibrating the collected water level data, and obtaining the pre-processed water level data; The dynamic adjustment module is responsible for obtaining the pre-processed water level data and inputting it into the dynamic waterway control adjustment strategy. It dynamically adjusts the waterway according to the water level change trend and waterway status. At the same time, it compares the water level data with the preset multi-level water level threshold. When the water level data is lower than the safety threshold in the multi-level water level threshold, the alarm mechanism is automatically triggered. The water quality data is obtained from the water level data and compared with the preset safety range. When it exceeds the safety range, the water quality is automatically cleaned. The iterative learning module is responsible for inputting the data after the dynamic adjustment of the waterway into the machine learning model, learning the data and the dynamic waterway control adjustment strategy, and adjusting the dynamic waterway control adjustment strategy through continuous iterative learning; The process of placing various types of sensors on the water tank includes: Obtain the geometric characteristics of the water tank, including the height, diameter, wall thickness and inlet and outlet positions of the water tank. According to the height and diameter of the water tank, preliminarily determine the number and distribution of monitoring points, divide the water tank into three areas: upper, middle and lower, and set several monitoring points in each area; Analyze the inlet and outlet locations of the water tank to determine the water flow path and retention area; set up multiple monitoring points based on the geometric characteristics of the water tank and the water flow path, and each monitoring point should contain at least one type of sensor; The pre-processed water level data is input into the dynamic waterway control adjustment strategy, and the position and parameters of the sensor are dynamically adjusted according to the water level change trend and waterway status; Water level change trend prediction formula: In the formula, represents the predicted water level change, represents the observed water level change, represents the last predicted water level change, represents the smoothing coefficient; Sensor position adjustment: In the formula, represents the adjusted sensor position, Indicates the original sensor position, Indicates the position adjustment amount.

9. An application of a waterway detection and control method, characterized in that: Ultrasonic sensors and capacitive sensors are placed in the water tank of the water dispenser; the ultrasonic sensor is used to measure the water level height, and the capacitive sensor is used to detect the change of the liquid level; the data of the two sensors are fused and calibrated to obtain the water level data; the pre-processed water level data is input into the dynamic water channel control adjustment strategy; the water channel is dynamically adjusted according to the water level change trend and the water channel status; The process of placing various types of sensors on the water tank includes: Obtain the geometric characteristics of the water tank, including the height, diameter, wall thickness and inlet and outlet positions of the water tank. According to the height and diameter of the water tank, preliminarily determine the number and distribution of monitoring points, divide the water tank into three areas: upper, middle and lower, and set several monitoring points in each area; Analyze the inlet and outlet locations of the water tank to determine the water flow path and retention area; set up multiple monitoring points based on the geometric characteristics of the water tank and the water flow path, and each monitoring point should contain at least one type of sensor; The pre-processed water level data is input into the dynamic waterway control adjustment strategy, and the position and parameters of the sensor are dynamically adjusted according to the water level change trend and waterway status; Water level change trend prediction formula: In the formula, represents the predicted water level change, represents the observed water level change, represents the last predicted water level change, represents the smoothing coefficient; Sensor position adjustment: In the formula, represents the adjusted sensor position, Indicates the original sensor position, Indicates the position adjustment amount.

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