Water saving control valve with water anomaly alarm

Abstract

This invention relates to the field of water use monitoring and water-saving equipment technology, and discloses a water-saving control valve with an anomaly alarm, comprising: a valve body assembly, an actuator assembly, a control component, a buzzer alarm, an L-shaped mounting plate, and multiple U-shaped connectors; the control component integrates data acquisition, multi-dimensional data analysis, data fusion, comprehensive judgment, and closed-loop correction modules to collect real-time data on water supply pipeline flow, pressure, duration, and temperature. After noise reduction preprocessing, the abnormal characteristics are analyzed from multiple dimensions, and the overall anomaly degree is quantified through dynamic weight fusion. The water use status is graded and the root cause is traced, and a differentiated control scheme is formulated. Closed-loop correction promotes the return of water use to stability, and the buzzer alarm is triggered when an anomaly occurs. This invention can comprehensively capture water use anomalies, significantly reduce the false judgment rate, accurately balance the control intensity and water demand, ensure the stable and efficient operation of the water use system, achieve the dual goals of water saving and safety protection, and is suitable for various water use scenarios.

Description

Technical Field

[0001] This invention relates to the field of water use monitoring and water-saving equipment technology, specifically a water-saving control valve with an alarm for abnormal water use. Background Technology

[0003] Currently, water systems typically involve monitoring multiple water usage parameters such as flow rate, pressure, and temperature. Real-time monitoring of these parameters allows for timely detection of changes in water usage status. Furthermore, with the development of intelligent technologies, timely response and control of water usage anomalies based on monitoring data has become a trend in water conservation control. The application of related technologies can effectively improve water resource management efficiency, ensure the stable operation of water systems, and align with the overall requirements of current green and low-carbon development.

[0004] However, it still has some drawbacks in practical use, such as: 1. Limited monitoring dimensions: Most existing technologies only monitor a single parameter such as flow rate or pressure, which cannot comprehensively capture abnormal water usage characteristics and is prone to missing anomalies. For example, when only monitoring flow rate, it is difficult to identify water safety hazards caused by abnormal temperatures, and it cannot meet the accurate monitoring needs in multiple scenarios; 2. The anomaly detection logic is simple, relying heavily on fixed thresholds to trigger alarms without considering the correlation between parameters, resulting in a high false alarm rate. When water usage parameters fluctuate normally, they are easily misjudged as abnormal states. Frequent alarms not only affect the user experience but also reduce the system's reliability. 3. The control methods are rigid and lack a tiered control mechanism, with a uniform control strategy applied regardless of the severity of the anomaly. Over-controlling minor anomalies can disrupt normal water use, while insufficient control for severe anomalies fails to promptly curb water waste or safety risks. 4. The system lacks a closed-loop verification mechanism. After the control actions are executed, the water usage status is not monitored and evaluated again, making it impossible to confirm whether the anomaly has been resolved. This could lead to situations where the system fails to respond promptly after a control action fails, resulting in continuous water waste or unstable operation of the water system. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a water-saving control valve with an alarm for abnormal water usage, which solves the problems of missed anomalies, high false alarm rates, inaccurate control, and water waste caused by existing water monitoring systems, which suffer from single monitoring dimensions, simple judgment logic, rigid control methods, and lack of closed-loop verification.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a water-saving control valve with an alarm for abnormal water usage, comprising: The valve body assembly, installed on the water supply pipeline, is used to control the flow of water. It includes a Hall flow sensor installed at the inlet, a pressure sensor installed in the flow channel, and an NTC thermistor installed at the outlet, which are used to collect pipeline flow, pressure and water temperature data, respectively. An actuation component, connected to the valve body assembly, is used to drive the valve body assembly to open or close; The control component is electrically connected to the actuation component and is used to monitor water usage parameters in real time, and output a control signal to the actuation component when an abnormal water usage is detected, so as to close the valve body assembly; A buzzer alarm is connected to the side of the control unit and electrically connected to the control unit, and is used to issue an audible alarm when there is an abnormality in water usage; An L-shaped mounting plate is detachably connected to the side of the actuator via multiple U-shaped connectors for mounting and fixing the controller and the buzzer alarm. The control components include: a data acquisition module, a multi-dimensional data analysis module, a data fusion module, a comprehensive judgment module, and a closed-loop correction module; The data acquisition module collects real-time data on flow rate, pressure, duration, and temperature of the water supply pipeline and related components, performs noise reduction preprocessing on the collected raw data, and outputs multi-dimensional data sets. The multidimensional data analysis module: Based on multidimensional data groups, it conducts analysis from the dimensions of flow status, pressure stability, duration rationality, and temperature safety, and outputs single-dimensional anomaly analysis results and corresponding warning indicators; Data fusion module: Classifies and integrates the anomaly analysis results of various dimensions, conducts multi-dimensional correlation verification, quantifies the overall anomaly degree through dynamic weight fusion, resolves logically conflicting data, and outputs a unified dataset containing correlation analysis conclusions, comprehensive anomaly quantification values, and overall early warning indicators.

[0007] Preferably, the comprehensive judgment module: assesses the current state level of the water use process based on a unified dataset, predicts the future trend of parameter changes, calculates the degree of deviation between the predicted value and the target value, traces the root cause of the anomaly by combining the correlation analysis conclusion, and outputs judgment information including the state level, prediction result, deviation value and root cause conclusion; The closed-loop correction module: according to the preset control priority, it formulates a graded control scheme for the execution components based on the root cause of the abnormality and the degree of deviation, and applies control actions. After control, it re-triggers the data acquisition, analysis, fusion and judgment process, repeats the control until the water use process returns to a stable state, and synchronously feeds back the control execution results.

[0008] Preferably, the multi-dimensional data set includes: Data includes flow rate, pressure, duration, and temperature. Flow rate data includes instantaneous flow rate, average flow rate, and cumulative flow rate. Pressure data includes real-time pressure, average pressure, and pressure fluctuation frequency. Duration data includes continuous water usage duration and water usage interval frequency. Temperature data includes real-time water temperature, real-time ambient temperature, and heating rate.

[0009] Preferably, the analysis of the traffic status dimension includes: Based on a multi-dimensional data set, the average flow rate, rate of change of flow rate, and duration of flow rate are extracted to determine whether the average flow rate is within the normal range, whether the rate of change of flow rate exceeds the allowable range, and whether the low flow rate condition has reached the set duration. Based on the judgment results, two types of abnormal conditions are identified: large flow surge and low flow drip. When the average flow rate exceeds the normal upper limit and the rate of change exceeds the standard, it is determined to be a large flow surge. When the low flow rate condition reaches the set duration, it is determined to be a low flow drip. Finally, the abnormal analysis results of the flow condition dimension and the corresponding warning indicators are output.

[0010] Preferably, the analysis of the pressure stability dimension includes: Based on multi-dimensional data sets, the average pressure, frequency of pressure fluctuations, and rate of pressure change are extracted. It is then determined whether the average pressure is within the normal range, whether the frequency of pressure fluctuations exceeds a set threshold, and whether the rate of pressure change exceeds the allowable range. Based on these determinations, three abnormal states are identified: overpressure, water shortage, and frequent fluctuations. Overpressure is defined as the average pressure exceeding the upper limit of normal, water shortage as the average pressure falling below the lower limit of normal, and frequent fluctuations as the frequency or rate of pressure fluctuation exceeding the standard. Finally, the abnormal analysis results for the pressure stability dimension and corresponding warning indicators are output.

[0011] Preferably, the analysis of the duration reasonableness dimension includes: Based on multi-dimensional data sets, the continuous water usage duration and water usage interval frequency information are extracted; then, it is determined whether the continuous water usage duration exceeds the set duration threshold and whether the water usage interval frequency exceeds the set frequency threshold; based on the judgment results, two abnormal states are identified: excessive water usage and frequent start-stop. When the continuous water usage duration exceeds the set threshold, it is determined to be excessive water usage, and when the water usage interval frequency exceeds the set threshold, it is determined to be frequent start-stop; finally, the abnormal analysis results of the duration rationality dimension and the corresponding warning indicators are output.

[0012] Preferably, the analysis of the temperature safety dimension includes: Based on a multi-dimensional data set, information on water flow temperature, ambient temperature, and heating rate is extracted. Then, it is determined whether the water flow temperature is within the normal range and whether the heating rate exceeds the allowable range. Based on the judgment results, two abnormal states are identified: freezing crack risk and scalding risk. When the water flow temperature is below the lower limit of normal, it is determined to be freezing crack risk. When the water flow temperature is above the upper limit of normal or the heating rate exceeds the standard, it is determined to be scalding risk. Finally, the abnormal analysis results of the temperature safety dimension and the corresponding warning indicators are output.

[0013] Preferably, the step of quantifying the overall anomaly level through dynamic weight fusion includes: First, obtain the anomaly quantification values ​​corresponding to the anomaly analysis results of each dimension; then, allocate dynamic weights according to the magnitude of the anomaly quantification values ​​of each dimension; subsequently, multiply the anomaly quantification values ​​of each dimension with the corresponding dynamic weights, and sum all the product results to obtain the basic fusion value; finally, combine the maximum value among the anomaly quantification values ​​of each dimension for superposition and correction to obtain the overall anomaly quantification value.

[0014] Preferably, the assessment of the current status level of the water use process includes: Based on the overall anomaly quantification value, a threshold range of quantification values ​​corresponding to multiple state levels is set; then, the overall anomaly quantification value is matched with each threshold range, and the corresponding state level is determined according to the matching result. When the overall anomaly quantification value is less than or equal to 0.3, it is judged as a normal state; when it is greater than 0.3 and less than or equal to 0.6, it is judged as a warning state; when it is greater than 0.6, it is judged as a dangerous state; finally, the current state level of the water use process obtained by matching is output.

[0015] Preferably, the tiered control scheme and the application of control actions include: Based on the judgment information, combined with the status level, the root cause of the anomaly and the degree of deviation, a graded control scheme with corresponding intensity is formulated according to the preset control priority; then, according to the graded control scheme, the corresponding control action is applied to the execution component, the execution status of the control action is recorded and feedback is provided.

[0016] This invention provides a water-saving control valve with an alarm for abnormal water usage. It has the following beneficial effects: 1. This invention, through a multi-dimensional collaborative monitoring design, simultaneously covers four core water usage parameters: flow rate, pressure, duration, and temperature. It can comprehensively capture various abnormal water usage characteristics, effectively avoid the problem of missed detection caused by single-dimensional monitoring, and accurately adapt to the monitoring needs of various water usage scenarios such as residential life and commercial buildings.

[0017] 2. This invention combines the correlation between various water parameters to carry out comprehensive anomaly judgment, abandons the traditional fixed threshold triggering mode, improves the judgment accuracy through multi-dimensional data cross-validation, significantly reduces the anomaly false judgment rate, ensures the reliability of alarm signals, and significantly improves user experience and system credibility.

[0018] 3. This invention constructs a graded control mechanism, which formulates differentiated control strategies based on the severity of the anomaly and the state level. In the early warning state, conventional intensity control is used to ensure normal water use, while in the dangerous state, emergency intensity control is used to quickly contain the risk, thereby achieving a precise balance between control effect and water demand.

[0019] 4. This invention establishes a closed-loop control system of "monitoring-analysis-control-re-monitoring". After the control action is executed, the water use status is evaluated in a timely manner. If the abnormality is confirmed to be resolved, the control is terminated. If the problem is not resolved, the control is repeated to ensure that the abnormality is thoroughly dealt with and to ensure the stable and efficient operation of the water use system. Attached Figure Description

[0020] Figure 1 This is a three-dimensional structural diagram of the present invention; Figure 2 This is a front view structural diagram of the present invention; Figure 3 This is a rear-view three-dimensional structural diagram of the present invention; Figure 4 This is a schematic diagram of the control component architecture of the present invention.

[0021] The components include: 1. Valve body assembly; 101. Hall effect flow sensor; 102. Pressure sensor; 103. NTC thermistor; 2. Actuation assembly; 3. U-shaped connector; 4. L-shaped mounting plate; 5. Control unit; 6. Buzzer alarm. Detailed Implementation

[0022] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see the appendix Figure 1 - Appendix Figure 3 This invention provides a water-saving control valve with an abnormal water usage alarm, including a valve body assembly 1, an actuation assembly 2, a control component 5, a buzzer alarm 6, a U-shaped connector 3, and an L-shaped mounting plate 4. The components are mechanically and electrically connected to form a water-saving system that integrates monitoring, control, and alarm functions. The valve body assembly 1 is installed in the water supply pipeline to control the flow of water. The actuator 2 is mechanically connected to the valve body assembly 1 and is used to receive control signals and drive the valve body assembly 1 to open or close. The control component 5 is electrically connected to the actuator 2 and is used to monitor and logically determine the water usage status. The buzzer alarm 6 is electrically connected to the control component 5 and is used to receive alarm commands and emit an audible alarm. The L-shaped mounting plate 4 is detachably connected to the side of the actuator 2 through multiple U-shaped connectors 3, forming a stable mounting platform. The control component 5 and the buzzer alarm 6 are both fixedly installed on the L-shaped mounting plate 4, thus integrating with the actuator 2 into a compact whole, facilitating installation, maintenance, and replacement in the water supply pipeline.

[0024] In this embodiment, the valve body assembly 1 (adopting the common valve structure of ball valve, gate valve and butterfly valve) and the actuator assembly 2 (adopting the gear transmission mechanism driven by micro motor and the direct-acting structure driven by electromagnetic solenoid) are both existing technologies or well-known general components in the field of valves; their core mechanical structure, driving principle and manufacturing process are widely known and applied by those skilled in the art, and will not be described in detail in this embodiment.

[0025] The control unit 5 uses an industrial-grade embedded controller as its core hardware platform. It features multi-channel analog / digital signal input / output interfaces and a high-speed data bus (such as CAN or Ethernet), and can efficiently adapt to peripherals such as the Hall effect flow sensor 101, diffused silicon pressure sensor 102, NTC thermistor 103, actuator 2 driver, and buzzer alarm 6, meeting the needs of real-time multi-dimensional data acquisition and high-response alarm control in water usage scenarios. The software system running on this hardware platform adopts a modular design, forming a complete closed loop of sensing, processing, fusion, judgment, execution, and feedback, such as... Figure 4 As shown, it includes: a data acquisition module, a multi-dimensional data analysis module, a data fusion module, a comprehensive judgment module, and a closed-loop correction module. These modules communicate with each other through clearly defined data interfaces, enabling integrated management and control of accurate water usage anomalies, tiered alarm triggering, closed-loop control execution, pipeline safety protection, and water conservation.

[0026] The data acquisition module is used to collect raw data on flow, pressure, duration, and temperature of water supply pipelines, core components, and the surrounding environment in real time. Through moving average filtering and noise reduction processing, interference signals are eliminated, and an accurate and interference-free effective data array is output. In one specific embodiment, the data acquisition module is directly connected to the Hall flow sensor 101, the diffused silicon pressure sensor 102, the crystal oscillator timing unit, the NTC thermistor 103, and the ambient temperature sensor, and processes the data according to an integrated "acquisition-denoising-output" process, as follows: The instantaneous flow rate of the pipeline is collected by the Hall flow sensor 101 (waterproof rating IP67) at the water inlet end of valve body assembly 1, and the 10-second average flow rate is calculated simultaneously. The cumulative flow rate was collected at a frequency of 10Hz (100 data points in 10 seconds), with the unit being liters per minute (L / min). The real-time pressure of the pipeline is collected by the diffused silicon pressure sensor 102 (measurement accuracy ±0.01MPa) in the flow channel of valve body assembly 1, and the 10-second average pressure is calculated simultaneously. With pressure fluctuation frequency (pressure exceeding within 10 seconds) The number of times (50 data points were collected at a frequency of 5Hz, with a total of 50 data points in 10 seconds) is measured in megapascals (MPa). The continuous water usage duration and the frequency of water usage interruptions (start and stop times) within 1 hour are collected by the crystal oscillator timing unit (timing accuracy ±0.1s) built into the control unit 5. The collection frequency is 1Hz, the duration unit is minutes (min), and the frequency unit is times / hour. The NTC thermistor 103 (measuring range) at the outlet of valve body assembly 1 5 Real-time water flow temperature is collected at 100℃ (accuracy ±0.5℃), and the 10-second average temperature is calculated simultaneously. The heating rate was measured at a frequency of 2Hz (20 data points in 10 seconds), and the unit was degrees Celsius (°C). The device collects real-time ambient temperature data from an external environmental temperature sensor at a frequency of 5 Hz, with the unit being degrees Celsius (°C).

[0027] The raw data was denoised using a moving average filter to prevent interference signals from affecting subsequent analysis. The filtering formula is as follows: ; Where N=10 is the size of the sliding window. This represents the acquisition period for the corresponding parameter. These are the original collected values. This represents the preprocessed valid data, where i is the index. For the current moment, a unified timestamp (accurate to 0.1s) is set for all valid data.

[0028] The data acquisition module outputs a multi-dimensional valid data array and timestamps, which are then transmitted to the multi-dimensional data analysis module.

[0029] The multidimensional data analysis module, based on the effective data array output by the data acquisition module, identifies abnormal features in the water use process through quantitative function calculations and logical correlation mining in four dimensions: flow status, pressure stability, duration rationality, and temperature safety. This provides basic analysis material for the data fusion module and triggers abnormal warnings in the corresponding dimensions.

[0030] In one specific embodiment, the multidimensional data analysis module receives a valid data array via a high-speed data bus and extracts physical parameters to perform multidimensional analysis, as follows: Flow status analysis: By analyzing the mean flow rate, rate of change, and duration, two types of anomalies are characterized: large flow surges and small flow drips. The quantification function and anomaly criteria are as follows: in, =3L / min is the standard flow rate =1L / min is the allowable deviation. =4L / min is the limit deviation. =0.3L / min is the dripping threshold. =0.5L / (min*s) is the allowable rate of change. =2L / (min*s) is the limiting rate of change. =5 minutes is the drip detection time. This represents the rate of change of flow rate, i.e., the slope of the linear regression of the flow rate sequence. Anomaly detection: When When the value is greater than 0.3, it is determined to be an abnormal flow status, specifically divided into two categories: large flow surges and small flow drips, triggering a flow anomaly warning. The value ranges from [0, 1], and the larger the value, the more severe the anomaly.

[0031] Pressure stability analysis: By analyzing the average pressure, fluctuation frequency, and rate of change, the analysis characterizes pipeline overpressure, water shortage, and abnormal pressure changes. The quantification function and anomaly determination are as follows: in, Standard water pressure, Minimum normal water pressure, (Maximum normal water pressure), f is the frequency of pressure fluctuations within 10 seconds (exceeding) (number of times) 10 times per 10 seconds is the extreme fluctuation frequency. Let be the rate of change of pressure, and let the ultimate rate of change of pressure be... ; Anomaly detection: When When the pressure value is greater than 0.3, it is considered an abnormal pressure stability, specifically categorized into three types: overpressure, water shortage, and frequent fluctuations, triggering a pressure anomaly warning. The value ranges from [0, 1], and the larger the value, the more severe the anomaly.

[0032] Duration Reasonableness Analysis: By combining continuous water usage duration and intermittent frequency, abnormalities such as forgetting to close the valve and frequent start-stop cycles of the pipeline are characterized. The quantification function and abnormality judgment are as follows: Where t is the duration of continuous water use. Here, n is the exponential decay coefficient, and n is the frequency of intermittent events within one hour. =20 times / hour is the maximum intermittent frequency; Anomaly detection: When When the value is greater than 0.3, it is judged as an abnormal duration, specifically divided into two categories: excessive water usage and frequent start-stop cycles, triggering an abnormal duration warning. The value ranges from [0, 1], and the larger the value, the more severe the anomaly.

[0033] Temperature safety analysis: By analyzing water flow temperature and heating rate, the risk of pipe freezing and scalding from hot water is characterized. The quantification function and anomaly detection are as follows: in, For standard water temperature, Minimum normal water temperature, The maximum normal water temperature is given by v, where v is the heating rate. This is the limiting heating rate; Anomaly detection: When A temperature reading >0.3 is considered an abnormal temperature safety level, specifically categorized into two types: risk of frostbite and risk of burns, triggering a temperature anomaly warning. The value ranges from [0, 1], and the larger the value, the more severe the anomaly.

[0034] The multidimensional data analysis module outputs a combination of single-dimensional function values, anomaly types, and anomaly warning indicators, which is then transmitted to the data fusion module.

[0035] The data fusion module, based on the single-dimensional function values, anomaly types, and anomaly warning indicators output by the multi-dimensional data analysis module, mines the causal relationships between anomalies through multi-dimensional correlation verification, dynamic weight fusion, conflict data resolution, and data normalization processing, forming a unified dataset that reflects the overall state of the water use process. This provides comprehensive and coherent analytical support for the comprehensive judgment module and triggers overall state warnings.

[0036] In one specific embodiment, the data fusion module receives the analysis result array via a high-speed data bus, extracts key information from each dimension, and performs comprehensive fusion, as follows: Based on the four dimensions of flow, pressure, duration, and temperature output by the multidimensional data analysis module, the data is first classified and sorted according to "flow status, pressure status, duration status, and temperature status" to clarify the anomaly types, function values, and impact range of each dimension. Then, multidimensional correlation verification is carried out to explore the causal transmission and superposition effects between anomalies. The overall anomaly degree is quantified through dynamic weight fusion. Data with logical conflicts are resolved according to the priority of anomaly impact. Finally, the data is normalized to form a unified dataset containing classified and integrated data, correlation analysis conclusions, and comprehensive fusion values, and the corresponding level of overall status warning is triggered simultaneously. It should be further explained that the aforementioned classification and sorting involves classifying and integrating the categorized data, specifically including: Flow status: includes flow function value The types of anomalies (none / sudden large flow / dripping small flow) and flow anomaly warning indicators are directly related to the risk of pipeline leakage. Pressure status: includes pressure function values The abnormality type (no pressure / overpressure / water shortage / frequent fluctuation) and pressure abnormality warning indicators are directly related to the safe operation status of the pipeline; Duration status: includes duration function value The abnormality type (none / excessive water usage / frequent start-stop) and abnormal duration warning indicators are directly related to abnormal user usage habits; Temperature status: includes temperature function values Abnormality type (no risk / freezing / burn risk), temperature abnormality warning sign, directly related to water safety risks.

[0037] It should be further explained that the multi-dimensional correlation verification analysis analyzes the causal relationships among the four dimensions to verify whether there is a transmission or cumulative effect of the anomaly, and forms a correlation analysis conclusion: Correlation between flow rate and pressure: If the flow rate status is determined to be "sudden change in flow rate" and the pressure status is determined to be "sudden drop in pressure", then it is determined to be "abnormal flow-pressure linkage caused by pipeline damage", and the abnormal transmission path is identified. Correlation between duration and flow rate: If the duration status is determined to be "excessive water usage" and the flow rate status is determined to be "continuous low flow rate", then it is determined to be "forgot to turn off the valve, resulting in abnormal duration-flow rate superposition", clarifying the abnormal superposition relationship; The relationship between pressure and temperature: If the pressure status is determined to be "overpressure" and the temperature status is determined to be "high temperature", then it is determined that "the thermal expansion of the hot water pipeline causes an abnormal pressure-temperature relationship", clarifying the influence relationship between the environment and the pipeline.

[0038] It should be further explained that the calculation of the comprehensive fusion value adopts a dynamic weight fusion strategy, highlighting the influence of the dominant anomaly dimension. Its specific mathematical function is as follows: Among them, m is the number of enabled dimensions, and m = {1, 2, 3, 4}; Corresponding respectively to , , , , when the temperature dimension is not enabled = 0; And , , , correspond one by one; h = 0.2 is the leading anomaly enhancement coefficient, which amplifies the influence of the most severe anomaly dimension; is the dynamic weight, and its specific calculation formula is: , where , to avoid the denominator being 0, and the weights are allocated according to the anomaly degree of each dimension; The value range of the comprehensive fusion value F is [0, 1]. The larger the value, the more severe the overall anomaly. Correspondingly, warnings are triggered according to the value: F ≤ 0.3, no warning; 0.3 < F ≤ 0.6, trigger the overall status warning; F > 0.6, trigger the overall status danger warning.

[0039] It should be further noted that conflict data resolution includes when logical conflicts occur in the correlation analysis between dimensions, that is, when a single dimension determines an anomaly and triggers a warning, and the remaining three dimensions are all determined to be normal and there is no warning, the core anomaly conclusion is retained according to the "anomaly impact priority" (flow anomaly > pressure anomaly > temperature anomaly > duration anomaly), and at the same time, "unrelated anomaly" is marked, for example, "flow status anomaly (unrelated pressure / duration / temperature anomaly)", ensuring that the data set focuses on key issues and does not miss core anomaly information.

[0040] The data fusion module outputs a unified data set including classified and integrated data, correlation analysis conclusions, comprehensive fusion values, and overall warning flags, and transmits it to the comprehensive determination module.

[0041] Based on the unified data set output by the data fusion module (including classified and integrated data, correlation analysis conclusions, comprehensive fusion values, and overall warning flags), the comprehensive determination module clarifies the operating state and regulation direction of the water use process through current state level assessment, key parameter trend prediction, comparison of the predicted value with the target value deviation, and anomaly root cause location, provides accurate decision-making basis for the closed-loop correction module, and at the same time outputs the final state level warning.

[0042] In a specific embodiment, the comprehensive determination module receives the integrated data set through a high-speed data bus, extracts the core information and performs a full-process determination, specifically as follows: Based on the standardized data set output by the data fusion module, first determine the current normal, warning or dangerous state level of the water use process according to the comprehensive fusion value, then use the weighted exponential smoothing method to predict the change trend of the pipeline flow rate and pressure parameters within the next 3 minutes, quantify the deviation degree by calculating the deviation between the predicted value and the target safety value, and finally trace the core root cause of the abnormality in combination with the conclusion of the correlation analysis, form combined information including the state level, predicted parameter value, deviation value, correlation analysis conclusion and root cause analysis conclusion, clarify the control direction, and synchronously output the final state level warning.

[0043] It should be further noted that the current state level assessment determines the current state level of the water treatment process based on the comprehensive fusion value F output by the data fusion module, and the level division and warnings are as follows: Normal state: F ≤ 0.3, indicating that there is no significant abnormality in the water use process, and the correlations in all dimensions are coordinated, and no state warning is output; Warning state: 0.3 < F ≤ 0.6, indicating that there are local abnormalities or single - dimension abnormalities, without serious superimposed effects, and a state warning is output; Dangerous state: F > 0.6, indicating that there are multi - dimension superimposed abnormalities or serious abnormalities in the core dimension, which may lead to water resource waste or equipment damage, and a state danger warning is output.

[0044] It should be further noted that the trend prediction predicts the parameters within the next 3 minutes, and the prediction time window , and its specific calculation function is as follows: Flow prediction: Among them, α = 0.7 is the smoothing coefficient, is the average flow rate in the current 10 seconds, is the flow rate prediction value of the previous cycle, = 0.8 is the flow trend correction coefficient, is the flow rate change rate; Pressure prediction: Among them, is the average pressure in the current 10 seconds, is the pressure prediction value of the previous cycle, = 0.9 is the pressure trend correction coefficient, is the pressure change rate.

[0045] It should be further noted that the deviation comparison and analysis of the abnormal root cause specifically include: Target value setting: The target safe flow rate = 3L / min, determined based on the balance between water conservation and usage requirements, and the target safe pressure = 0.35MPa, determined based on the safe operation of the pipeline; Deviation Calculation: The deviation value directly reflects the degree of deviation between the predicted state and the ideal state. The specific calculation is as follows: Flow deviation: ; Pressure deviation: ; Root cause analysis: Based on the correlation analysis conclusions of the data fusion module, the root cause of the anomaly is accurately located. If the correlation analysis conclusion is "pipeline damage causing abnormal flow-pressure linkage", then the core root cause is "pipeline damage and leakage". If the correlation analysis conclusion is "forgot to close the valve causing abnormal time-flow superposition", then the core root cause is "user not closing the valve". If the correlation analysis conclusion is "thermal expansion of hot water pipes causing abnormal pressure-temperature linkage", then the core root cause is "hot water temperature too high". If there is no correlation, the root cause is directly located for the core anomaly dimension. When there is no correlation for flow abnormality, the root cause is "valve body sealing failure". When there is no correlation for pressure abnormality, the root cause is "pipeline pressure regulation imbalance".

[0046] The comprehensive judgment module outputs a combination of information including the status level, predicted parameter value, deviation value, correlation analysis conclusion, root cause analysis conclusion, and final warning indicator, which is then transmitted to the closed-loop correction module.

[0047] The closed-loop correction module, based on the combined information output by the comprehensive judgment module (including status level, root cause analysis conclusion, and final warning indicator), formulates targeted control schemes for the execution components according to the principle of "safety first, hierarchical control". It corrects anomalies through quantitative parameter calculation and precise action output, promotes the water use process to return to a stable state, and at the same time provides feedback on the control execution results.

[0048] In one specific embodiment, the closed-loop correction module receives the judgment result data through a high-speed data bus, extracts the root cause analysis conclusions and deviation values, and performs closed-loop control, as follows: Following the control priority of safety (leakage prevention) > pipeline protection (overpressure / water shortage prevention) > water conservation (flow control) > ease of use (duration control), based on the abnormal root causes and deviation quantification values ​​identified by the comprehensive judgment module, control logic is designed for valve body component 1, buzzer alarm 6, and water supply valve. The optimal control parameters are calculated using mathematical formulas and actions are applied. After control, the data acquisition module is triggered to re-collect pipeline operating parameters after 5 seconds. The status is re-evaluated by the multi-dimensional data analysis, data fusion, and comprehensive judgment module. If a stable state is not reached or the deviation does not meet the requirements, the control process is repeated until the water use process returns to stability, ensuring that the deviation of key parameters is controlled within the allowable range, and the execution results and parameter changes of each control are fed back synchronously.

[0049] It should be further explained that the specific implementation of the control scheme for the execution components includes: Valve body assembly 1 regulation: Corresponding to the root cause analysis conclusion of "pipe breakage and water leakage / valve body seal failure / user did not close the valve", the regulation goal is to quickly cut off the water flow to avoid water resource waste or equipment damage. The regulation actions are: Warning state (0.3 < F ≤ 0.6): The execution component 2 drives the valve body to close slowly (closing time is 3 seconds to avoid water flow impact), and the buzzer alarm 6 emits a continuous alarm simultaneously (frequency 800 Hz, volume 75 dB); Dangerous state (F > 0.6): The execution component 2 drives the valve body to close quickly (closing time is 1 second), the buzzer alarm 6 emits a high-frequency intermittent alarm (frequency 1.2 kHz, lasting for 1 second, with an interval of 0.2 seconds), and if the wireless communication module is enabled, an emergency alarm is pushed to the mobile APP simultaneously.

[0050] Constraint condition: After the valve body is closed, it needs to maintain a locked state until the user manually resets it or it is automatically unlocked after the root cause of the abnormality is repaired.

[0051] Make-up water valve regulation (for water shortage abnormality): Corresponding to the root cause analysis conclusion of "pipe water shortage / low pressure", the regulation goal is to supplement the water volume in the pipeline and restore the normal pressure.

[0052] The regulation formula is: ; where is the make-up water flow rate, = 2 L / min is the basic make-up water flow rate; Constraint condition: During the make-up water process, the pipeline pressure is monitored in real time, and when the pipeline pressure reaches 0.3 MPa, the make-up water stops to avoid overpressure.

[0053] Temperature regulation (for temperature abnormality): Corresponding to the root cause analysis conclusion of "hot water temperature is too high / risk of scalding", the regulation goal is to reduce the hot water temperature to the safe range.

[0054] The regulation formula is: ; where is the target temperature after regulation, is the current hot water temperature (unit: °C), = 0.8 is the temperature adjustment coefficient, is the temperature deviation ( ); Constraint condition: The temperature regulation range is 30 45 °C, to avoid freezing due to too low temperature or scalding due to too high temperature.

[0055] Duration-based reminder control (for abnormally long water usage): Based on the root cause analysis conclusion of "user not closing the valve / excessive water usage", the control objective is to remind the user to close the valve. The control action is as follows: the buzzer alarm 6 emits an intermittent alarm (frequency 500Hz, duration 0.5 seconds, interval 1 second), the orange LED indicator of the control component 5 remains constantly lit, and if the user does not respond within 3 minutes, the valve body will be automatically triggered to close.

[0056] It should be further explained that closed-loop regulation specifically includes: After the control action is executed, the data acquisition module is triggered to re-acquire pipeline flow, pressure, and temperature parameters after a 5-second interval. The acquired data is analyzed by the multi-dimensional data analysis module, fused by the data fusion module, and judged by the comprehensive judgment module. If the judgment result is "normal state" and the flow deviation is within acceptable limits, the system will determine the next step. ≤5%, pressure deviation ≤5%, temperature deviation If the deviation is ≤5%, the current control status is maintained and the control result is recorded; if the stable state is not reached or the deviation exceeds the standard, the above control process is repeated based on the new judgment result until the water use process meets the stability requirements.

[0057] The closed-loop correction module outputs the control action type, control parameters, execution result, and current status feedback to complete the closed-loop control.

[0058] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A water-saving control valve with an alarm for abnormal water usage, characterized in that, include: The valve body assembly (1) is installed on the water supply pipeline and is used to control the flow of water. It includes a Hall flow sensor (101) installed at the inlet end, a pressure sensor (102) installed in the flow channel, and an NTC thermistor (103) installed at the outlet end, which are used to collect pipeline flow, pressure and water temperature data respectively. An execution component (2) is connected to the valve body assembly (1) and is used to drive the valve body assembly (1) to open or close. The control unit (5) is electrically connected to the execution component (2) for real-time monitoring of water usage parameters and outputting a control signal to the execution component (2) when water usage is determined to be abnormal, so as to close the valve body assembly (1). A buzzer alarm (6) is connected to the side of the control unit (5) and electrically connected to the control unit (5) for issuing an audible alarm when water usage is abnormal; L-shaped mounting plate (4), which is detachably connected to the side of the actuator (2) via multiple U-shaped connectors (3), is used to install and fix the controller (5) and the buzzer alarm (6). The control component (5) includes: a data acquisition module, a multidimensional data analysis module, a data fusion module, a comprehensive judgment module, and a closed-loop correction module; The data acquisition module collects real-time data on flow rate, pressure, duration, and temperature of the water supply pipeline and related components, performs noise reduction preprocessing on the collected raw data, and outputs multi-dimensional data sets. The multidimensional data analysis module: Based on multidimensional data groups, it conducts analysis from the dimensions of flow status, pressure stability, duration rationality, and temperature safety, and outputs single-dimensional anomaly analysis results and corresponding warning indicators; Data fusion module: Classifies and integrates the anomaly analysis results of various dimensions, conducts multi-dimensional correlation verification, quantifies the overall anomaly degree through dynamic weight fusion, resolves logically conflicting data, and outputs a unified dataset containing correlation analysis conclusions, comprehensive anomaly quantification values, and overall early warning indicators.

2. A water-saving control valve with an abnormal water usage alarm according to claim 1, characterized in that, The comprehensive judgment module assesses the current state level of the water use process based on a unified dataset, predicts the future trend of parameter changes, calculates the degree of deviation between the predicted value and the target value, traces the root cause of the anomaly by combining the correlation analysis conclusion, and outputs judgment information including the state level, prediction results, deviation value and root cause conclusion. The closed-loop correction module: according to the preset control priority, formulates a graded control scheme for the execution component (2) based on the abnormal root cause and the degree of deviation, and applies control actions. After control, the data acquisition, analysis, fusion and judgment process is re-triggered, and the control is repeated until the water use process returns to a stable state, and the control execution results are fed back synchronously.

3. A water-saving control valve with an abnormal water usage alarm according to claim 2, characterized in that, The multi-dimensional data set includes: Data includes flow rate, pressure, duration, and temperature. Flow rate data includes instantaneous flow rate, average flow rate, and cumulative flow rate. Pressure data includes real-time pressure, average pressure, and pressure fluctuation frequency. Duration data includes continuous water usage duration and water usage interval frequency. Temperature data includes real-time water temperature, real-time ambient temperature, and heating rate.

4. A water-saving control valve with an abnormal water usage alarm according to claim 3, characterized in that, The analysis of the traffic state dimension includes: Based on a multi-dimensional data set, the average flow rate, rate of change of flow rate, and duration of flow rate are extracted to determine whether the average flow rate is within the normal range, whether the rate of change of flow rate exceeds the allowable range, and whether the low flow rate condition has reached the set duration. Based on the judgment results, two types of abnormal conditions are identified: large flow surge and low flow drip. When the average flow rate exceeds the normal upper limit and the rate of change exceeds the standard, it is determined to be a large flow surge. When the low flow rate condition reaches the set duration, it is determined to be a low flow drip. Finally, the abnormal analysis results of the flow condition dimension and the corresponding warning indicators are output.

5. A water-saving control valve with an abnormal water usage alarm according to claim 4, characterized in that, The analysis of the pressure stability dimension includes: Based on multi-dimensional data sets, the average pressure, frequency of pressure fluctuations, and rate of pressure change are extracted. It is then determined whether the average pressure is within the normal range, whether the frequency of pressure fluctuations exceeds a set threshold, and whether the rate of pressure change exceeds the allowable range. Based on these determinations, three abnormal states are identified: overpressure, water shortage, and frequent fluctuations. Overpressure is defined as the average pressure exceeding the upper limit of normal, water shortage as the average pressure falling below the lower limit of normal, and frequent fluctuations as the frequency or rate of pressure fluctuation exceeding the standard. Finally, the abnormal analysis results for the pressure stability dimension and corresponding warning indicators are output.

6. A water-saving control valve with an abnormal water usage alarm according to claim 5, characterized in that, The analysis of the reasonableness of the duration includes: Based on multi-dimensional data sets, the continuous water usage duration and water usage interval frequency information are extracted; then, it is determined whether the continuous water usage duration exceeds the set duration threshold and whether the water usage interval frequency exceeds the set frequency threshold; based on the judgment results, two abnormal states are identified: excessive water usage and frequent start-stop. When the continuous water usage duration exceeds the set threshold, it is determined to be excessive water usage, and when the water usage interval frequency exceeds the set threshold, it is determined to be frequent start-stop; finally, the abnormal analysis results of the duration rationality dimension and the corresponding warning indicators are output.

7. A water-saving control valve with an abnormal water usage alarm according to claim 6, characterized in that, The analysis of the temperature safety dimension includes: Based on a multi-dimensional data set, information on water flow temperature, ambient temperature, and heating rate is extracted. Then, it is determined whether the water flow temperature is within the normal range and whether the heating rate exceeds the allowable range. Based on the judgment results, two abnormal states are identified: freezing crack risk and scalding risk. When the water flow temperature is below the lower limit of normal, it is determined to be freezing crack risk. When the water flow temperature is above the upper limit of normal or the heating rate exceeds the standard, it is determined to be scalding risk. Finally, the abnormal analysis results of the temperature safety dimension and the corresponding warning indicators are output.

8. A water-saving control valve with an abnormal water usage alarm according to claim 7, characterized in that, The method of quantifying the overall anomaly level through dynamic weight fusion includes: First, obtain the anomaly quantification values ​​corresponding to the anomaly analysis results of each dimension; then, allocate dynamic weights according to the magnitude of the anomaly quantification values ​​of each dimension; subsequently, multiply the anomaly quantification values ​​of each dimension with the corresponding dynamic weights, and sum all the product results to obtain the basic fusion value; finally, combine the maximum value among the anomaly quantification values ​​of each dimension for superposition and correction to obtain the overall anomaly quantification value.

9. A water-saving control valve with an abnormal water usage alarm according to claim 8, characterized in that, The assessment of the current status level of the water use process includes: Based on the overall anomaly quantification value, a threshold range of quantification values ​​corresponding to multiple state levels is set; then, the overall anomaly quantification value is matched with each threshold range, and the corresponding state level is determined according to the matching result. When the overall anomaly quantification value is less than or equal to 0.3, it is judged as a normal state; when it is greater than 0.3 and less than or equal to 0.6, it is judged as a warning state; when it is greater than 0.6, it is judged as a dangerous state; finally, the current state level of the water use process obtained by matching is output.

10. A water-saving control valve with an abnormal water usage alarm according to claim 9, characterized in that, The tiered control scheme and the application of control actions include: Based on the judgment information, combined with the status level, the root cause of the anomaly and the degree of deviation, a graded control scheme with corresponding intensity is formulated according to the preset control priority; then, according to the graded control scheme, the corresponding control action is applied to the execution component, the execution status of the control action is recorded and feedback is provided.

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