Leakage-proof intelligent control water dispenser connector

Abstract

The invention discloses a leakage-proof intelligent control water dispenser connector, and belongs to the technical field of intelligent home control. The device solves the problems that water leakage of a traditional water dispenser connector depends on manual handling, response lags behind, and water flow cannot be automatically blocked. The connector comprises a connection sensing unit which comprises a connector body, a sensor cover internally provided with a pressure sensor and a control block internally provided with a control chip. The execution unit comprises a main body structure with a cavity, an anti-explosion movable joint, a leakage-proof valve core and a gasket; and the driving unit comprises a stepping motor, a connecting rod and a coupling. The control chip receives signals of the pressure sensor and controls the stepping motor to drive the leakage-proof valve element to rotate through the coupler and the connecting rod, and therefore the water flow channel is automatically opened or closed. The joint is mainly used for a water inlet joint of a water dispenser, can monitor pressure change in real time and can be automatically turned off when water leakage is detected, intelligent control over water leakage prevention is achieved, water resources are saved, and use safety is improved.

Description

Technical Field

[0001] This invention belongs to the field of smart home control technology, specifically relating to a leak-proof smart control water dispenser connector. Background Technology

[0002] In the past, resolving water dispenser leaks required receiving feedback about the leak and then manually repairing or replacing the faucet. However, this process was often time-consuming and resulted in significant waste of water resources. Furthermore, the system suffered from low automation, low safety, and high time costs, making it impossible to guarantee timely resolution of water dispenser leaks. Summary of the Invention

[0003] One object of the present invention is to solve at least the above-mentioned problems and / or defects, and to provide at least the advantages described below.

[0004] One objective of this invention is to address the passive problem of relying on manual detection of leaks in traditional water dispenser connectors, resulting in delayed response and the inability to automatically stop leaks, leading to water waste, untimely repairs, and safety hazards.

[0005] One objective of this invention is to address the problem that simple threshold judgments are prone to misjudgment or omission in complex water usage scenarios.

[0006] One objective of this invention is to solve the problem that leakage detection systems have difficulty effectively distinguishing between normal water usage behavior and actual leakage signals.

[0007] One objective of this invention is to solve the problem of system reference inaccuracy caused by differences in installation environment, long-term drift of water source pressure, or time-varying sensor performance.

[0008] One objective of this invention is to solve the problems of inconvenient operation and maintenance caused by the opaque status of smart connectors, the inability to remotely detect water leakage events, and the inability to remotely adjust system parameters.

[0009] One objective of this invention is to address the problem that dynamic thresholds may oscillate violently during adaptive adjustment due to short-term interference or data fluctuations, leading to unstable system judgment and decreased reliability.

[0010] One objective of this invention is to address the response delay problem in judgment methods based on continuous trend analysis.

[0011] One objective of this invention is to address the problem that the pre-stored water usage pattern library is insufficient to cover the diverse actual water usage habits of users, leading to new normal water usage patterns being misjudged as leaks.

[0012] One objective of this invention is to solve the problem that multiple adaptive processes may interfere with each other when running simultaneously, leading to oscillations or instability in system parameters.

[0013] One object of the present invention is to provide a leak-proof intelligent control water dispenser connector, comprising: The connection sensing unit includes: A connector used to connect to an external water source; A sensor cover is attached to the outside of the connector and encapsulates a pressure sensor inside. The cavity inside the sensor cover is connected to the internal channel of the connector, enabling the pressure sensor to detect the pressure of the water flowing through the connector in real time. A control block is disposed on the connector, and a control chip is disposed therein. The control chip is electrically connected to the pressure sensor. Execution unit, comprising: The main structure is used to connect the water dispenser, and the main structure has a cavity inside; An explosion-proof flexible joint is connected between the joint and the main structure, allowing the joint to communicate with the cavity of the main structure and forming a water flow channel; The leak-proof valve core is rotatably disposed within the cavity of the main structure; A gasket is disposed between the leak-proof valve core and the main structure; The drive unit includes: A stepper motor is fixedly mounted on the main structure. The connecting rod is connected at one end to the anti-leakage valve core; A coupling is connected between the other end of the connecting rod and the output shaft of the stepper motor, and the control chip in the control block is electrically connected to the stepper motor; The control chip is configured to receive the detection signal from the pressure sensor and control the forward / reverse rotation of the stepper motor according to the signal; the stepper motor drives the anti-leakage valve core to rotate through the coupling and the connecting rod, thereby opening or closing the water flow channel.

[0014] Preferably, in the aforementioned leak-proof intelligent control water dispenser connector, the control chip is further configured to perform the following leak detection steps: The pressure signal from the pressure sensor is continuously sampled, and its pressure value sequence over time is calculated. Based on the pressure value sequence, a basic pressure reference value that characterizes the normal pressure fluctuation range of the current system is dynamically updated and maintained. The instantaneous deviation of the current pressure signal from the baseline pressure reference value is calculated in real time, and the intensity of the continuous downward trend of the pressure signal within a predetermined time window is calculated. A leak is determined to have occurred and the stepper motor is triggered to shut down only if both of the following conditions are met simultaneously: (a) The instantaneous deviation exceeds the first dynamic threshold, and (b) The intensity of the sustained downward trend exceeds the second dynamic threshold; The first dynamic threshold and the second dynamic threshold are both adaptively adjusted based on the baseline pressure reference value and the historical pressure fluctuation statistics.

[0015] Preferably, in the aforementioned leak-proof intelligent control water dispenser connector, The control chip has a pre-stored typical pressure change pattern during normal water use. The control chip monitors the signal changes of the pressure sensor in real time and compares the current pressure change curve with the typical pressure change pattern. When the matching degree between the current pressure change curve and any of the typical pressure change patterns exceeds a preset similarity threshold, it is determined to be normal water use interference and the shutdown action is not triggered. The stepper motor is only triggered to shut down when the matching degree between the current pressure change curve and all pre-stored typical pressure change patterns is lower than the similarity threshold and the leakage judgment condition is met.

[0016] Preferably, in the leak-proof intelligent control water dispenser connector, the control chip is further configured to perform the following adaptive maintenance steps: During a preset learning period in which the system is running stably and no shutdown action is triggered, the pressure data of the pressure sensor is continuously recorded; Based on the pressure data during the learning period, statistical analysis is performed to obtain the static pressure benchmark value and normal pressure fluctuation range in the system installation environment, and the reference baseline for leak detection is updated accordingly. In subsequent operation, the static pressure reference value is smoothly tracked and updated at a first preset time interval to compensate for the effects caused by long-term slow changes in water source pressure or sensor zero-point drift. When a sudden change in the pressure signal is detected that exceeds the normal pressure fluctuation range, and the change is not caused by an identified normal water use event, a rapid recalibration process is initiated: within a short time window in which the system determines that there is no water leakage and the pressure is stable, the static pressure reference value is resampled and updated.

[0017] Preferably, the leak-proof intelligent control water dispenser connector further includes: The communication module is electrically connected to the control chip; The control chip is further configured as follows: When the stepper motor is triggered to shut down in response to a water leakage event, an alarm message containing the event type, occurrence time, and connector identifier is generated and sent to the designated remote monitoring terminal via the communication module. The system continuously or at a preset cycle collects and stores the working data of the pressure sensor, the action record of the stepper motor, and the system self-test status. In response to an instruction received from the remote monitoring terminal via the communication module, perform at least one of the following operations: upload historical working data, perform system self-test, reset the leak-proof valve core to the open state, or adjust the leakage judgment parameters.

[0018] Preferably, in the leak-proof intelligent control water dispenser connector, the control chip is configured to apply stability constraints to the adaptive adjustment process of the first dynamic threshold and the second dynamic threshold: A variable calculated value determined by the baseline pressure reference value is set for the first dynamic threshold. At the same time, an absolute lower limit and an absolute upper limit are preset for the first dynamic threshold, so that the first dynamic threshold applied in the end is always limited to the closed interval formed by the absolute lower limit and the absolute upper limit. A baseline value is set for the second dynamic threshold based on historical pressure fluctuation statistics, and a maximum allowable fluctuation percentage is set for the baseline value, so that the change range of the second dynamic threshold applied in any adjustment period does not exceed the maximum allowable fluctuation percentage. When updating the first dynamic threshold or the second dynamic threshold, a weighted smoothing algorithm is used to average the new calculated value with the threshold of the previous period to generate the final threshold applied to the next judgment period, thereby avoiding a step change in the threshold.

[0019] Preferably, in the aforementioned leak-proof intelligent control water dispenser connector, After determining that the instantaneous deviation exceeds the first dynamic threshold, and before the intensity of the continuous downward trend reaches the second dynamic threshold, a pre-emptive rapid response procedure is executed: Immediately initiate a rapid verification cycle with a duration shorter than the predetermined time window; Within the rapid verification cycle, pressure data is collected at a higher frequency and short-term downward trends are calculated; If the slope of the short-term downward trend is greater than the preset emergency slope threshold, the stepper motor will be immediately triggered to shut down without waiting for the intensity of the continuous downward trend to reach the second dynamic threshold. If the slope of the short-term downward trend does not exceed the emergency slope threshold, the determination process based on the strength of the continuous downward trend continues.

[0020] Preferably, in the aforementioned leak-proof intelligent control water dispenser connector, When the matching degree between the current pressure change curve and all the pre-stored typical pressure change patterns is lower than the similarity threshold, but it is ultimately determined to be a normal water use event, the current pressure change curve that was not identified is recorded as a new candidate pattern. When the candidate pattern appears repeatedly a preset number of times within a preset period, it is automatically added as a new pre-stored typical pressure change pattern. Meanwhile, after each successful matching of a pre-stored typical pressure change pattern, the current pressure change curve of this matching is fused with that pattern for calculation, so as to fine-tune and update the parameters of the pattern to adapt to minor changes in water usage habits or system parameters.

[0021] Preferably, in the aforementioned leak-proof intelligent control water dispenser connector, All adaptive processes are divided into two categories: the first type of adaptive process that affects the core judgment criteria, and the second type of adaptive process that affects the auxiliary judgment parameters; The update of the static pressure reference value is defined as a first-type adaptive process; During the execution of update operations of the first type of adaptive process, all update operations of the second type of adaptive process are paused; After completing the first type of adaptive process update, the system enters a parameter stabilization period. During this stabilization period, the second type of adaptive process is only allowed to record data and is not allowed to modify its parameters based on the newly recorded data. The duration of the parameter stabilization period shall cover at least three complete normal water use cycles after the first type of adaptive process update.

[0022] The present invention has at least the following beneficial effects: This invention integrates a pressure sensor, a control chip, and an electromechanical drive mechanism to construct a water dispenser connector with autonomous sensing and execution capabilities. This structure enables real-time detection and automatic shutdown of leaks, changing the traditional approach of passively responding to leaks and relying on manual intervention. Its advantages include improved timeliness of leak response, reduced unnecessary water consumption, and reduced potential equipment damage or safety hazards caused by untimely leak handling, while also reducing the cost of manual inspection and maintenance.

[0023] This invention optimizes the leakage signal identification logic by introducing a dynamic pressure reference value and a dual-threshold collaborative judgment mechanism. Its beneficial effects include improved system adaptability to complex pressure fluctuation scenarios, effectively distinguishing between instantaneous interference and continuous leakage trends. This method enhances the accuracy of leakage detection, reduces malfunctions caused by normal water pressure fluctuations or brief disturbances, and improves the reliability of the intelligent control system and the user experience.

[0024] This invention enhances the system's ability to identify normal water usage behavior by pre-storing and comparing typical water pressure patterns. Its beneficial effect lies in effectively filtering pressure change signals caused by routine user operations such as filling water, identifying these signals as legitimate interference rather than leakage events. This significantly reduces the system's false alarm rate, avoiding frequent unnecessary shutdown actions due to misjudgments, thereby ensuring the continuous availability of the water dispenser connectors and the smoothness of user operation.

[0025] This invention establishes a pressure benchmark through self-learning and implements tracking updates and rapid recalibration, enabling the system to adapt to environmental changes. Its beneficial effect lies in compensating for the impact of differences in installation conditions, long-term drift in water pressure, or slow changes in sensor performance. This mechanism maintains the accuracy of the leakage detection benchmark during long-term system operation, avoids the risk of decreased sensitivity or misjudgment due to benchmark drift, and extends the effective service life of the system.

[0026] This invention, by integrating a communication module and configuring corresponding data reporting and command response functions, enables remote monitoring and manageability of equipment. Its beneficial effects include the timely transmission of information such as water leakage alarms and equipment operating status to the management terminal, facilitating early warning and remote diagnostics. Simultaneously, it supports remote parameter adjustment and system reset, improving equipment operation and maintenance efficiency, reducing the frequency and cost of on-site maintenance, and providing a foundation for intelligent management.

[0027] This invention enhances the stability of the threshold update process by applying boundary constraints, rate-of-change limits, and smoothing treatments to the adaptive adjustment process of the dynamic threshold. Its beneficial effect lies in preventing the threshold from drastically oscillating or deviating abnormally due to short-term data fluctuations, ensuring a smooth transition of the system's decision logic. This improves the robustness of the entire adaptive control system, enabling the threshold to remain within a reasonable and reliable operating range while adapting to environmental changes.

[0028] This invention achieves early response to rapid leakage events by adding a rapid verification step before the trend confirmation process, performing rapid analysis of short-term pressure slopes. Its advantage lies in compensating for the response delay problem caused by relying solely on long-term window trend analysis. This mechanism accelerates the judgment and shutdown speed of rapidly developing leakage events without affecting the accuracy of slow leakage detection, thereby reducing the potential leakage volume in such cases.

[0029] This invention records and learns water usage curves that were not initially identified by the pattern library but were ultimately determined to be normal, thus giving the system the ability to dynamically expand its pattern library. Its beneficial effect is that it enables the system to adapt to the diverse water usage habits of different users, continuously improving the feature library of their normal water usage behavior. This increases the coverage and practicality of pattern matching, further reduces misjudgments caused by the absence of new normal water usage patterns, and enhances the system's adaptability and learning ability.

[0030] This invention achieves coordinated management of multiple parameter update mechanisms by classifying different adaptive processes, setting priorities, and scheduling time-sharing execution. Its beneficial effect lies in avoiding potential interference and conflicts that may arise when core parameters (such as stress benchmarks) and auxiliary parameters (such as modes and thresholds) are updated simultaneously. This coordination mechanism ensures the orderliness and stability of the system during adaptive learning, preventing system performance oscillations or instability caused by disordered parameter adjustments.

[0031] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the structure of the leak-proof intelligent control water dispenser connector provided by the present invention.

[0033] Figure 2 for Figure 1 A sectional view.

[0034] Figure 3 for Figure 1 A schematic diagram of the leak-proof intelligent control water dispenser connector with the connecting parts removed.

[0035] Figure 4 for Figure 1 A schematic diagram of the leak-proof intelligent control water dispenser connector structure after removing the main structure.

[0036] In the diagram: 1. Stepper motor; 2. Control block; 3. Faucet connection component; 4. Sensor cover; 5. Upper part of the main body; 6. Coupling; 7. Connecting rod; 8. Valve core; 9. Explosion-proof union; 10. Gasket; 11. Lower part of the main body; 12. Connector; 13. Water dispenser connection component. Detailed Implementation

[0037] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0038] like Figures 1 to 4As shown, this invention provides a leak-proof intelligent control water dispenser connector, comprising: a connection sensing unit, which includes: a connector for connecting to an external water source; a sensor cover connected to the outside of the connector, the sensor cover encapsulating a pressure sensor inside; a cavity inside the sensor cover communicating with an internal channel of the connector, enabling the pressure sensor to detect the water flow pressure flowing through the connector in real time; a control block disposed on the connector, which contains a control chip, the control chip being electrically connected to the pressure sensor; and an execution unit, comprising: a main structure for connecting to the water dispenser, the main structure having an internal cavity; and an explosion-proof flexible connector connected between the connector and the main structure, connecting the connector to the cavity of the main structure. The system comprises: a water flow channel; a leak-proof valve core rotatably disposed within the cavity of the main structure; a gasket disposed between the leak-proof valve core and the main structure; and a drive unit comprising: a stepper motor fixedly disposed on the main structure; a connecting rod connected at one end to the leak-proof valve core; a coupling connecting the other end of the connecting rod to the output shaft of the stepper motor; and a control chip within the control block electrically connected to the stepper motor. The control chip is configured to receive a detection signal from the pressure sensor and control the forward / reverse rotation of the stepper motor based on the signal. The stepper motor drives the leak-proof valve core to rotate via the coupling and the connecting rod, thereby opening or closing the water flow channel.

[0039] The connector mainly consists of three functional units. The connection sensing unit is located at the water inlet end (i.e., the side closest to the external water source), and its core is a connector for connecting to the external water source. A sensor cover is installed on the outside of the connector, and a pressure sensor is integrated inside to sense the water pressure in real time. A control block is also fixed on the connector housing, and a control chip is installed inside the control block. This chip is electrically connected to the pressure sensor through wires and is responsible for signal processing and logic judgment.

[0040] The actuator includes a main structure for connecting to the water dispenser, with a through-flow water chamber machined inside. An explosion-proof connector detachably connects the upstream connector to the main structure, allowing the internal chambers of both to communicate and form a complete water flow channel. Inside the chamber of the main structure, a rotatable leak-proof valve core is installed, with a gasket between the valve core and the chamber wall for dynamic sealing during rotation.

[0041] The drive unit provides the shutdown power. A stepper motor is fixedly mounted on the main structure. The motor's output shaft is connected to one end of a connecting rod via a coupling, and the other end of the connecting rod is fixedly connected to the anti-leakage valve core. The control chip within the control block is connected to the stepper motor via another wire, enabling it to drive the motor to rotate forward or in reverse.

[0042] Under normal operating conditions, the control chip continuously receives voltage signals from the pressure sensor, which are proportional to the real-time pressure within the water circuit. The control chip has preset pressure reference values ​​and fluctuation thresholds. When water is used or there is a minor leak, the pressure will change. The control chip analyzes the acquired pressure signal sequence; if it determines that the fluctuation is a temporary fluctuation caused by normal water use, it keeps the valve open.

[0043] Once the detected pressure change characteristics match the preset leakage model—for example, a sustained abnormal drop in pressure that deviates from the baseline value by a certain range and persists for a specific period—the control chip determines that a leakage event has occurred. Subsequently, the chip sends a control command to the stepper motor, driving it to rotate. The motor's rotation is transmitted to the connecting rod via a coupling, which in turn drives the anti-leak valve core to rotate within the cavity, mechanically cutting off the water flow and achieving automatic shut-off. After the leakage risk is eliminated, the motor can be reversed via system reset or remote command to reset the valve core and reopen the water circuit.

[0044] The closest existing technology is a water dispenser anti-overflow device controlled by a mechanical float valve or a simple solenoid valve. Compared to this existing technology, the specific embodiments of the present invention differ in the following ways: 1. Difference in Sensing and Judgment Mechanisms: Existing technologies typically use water level sensors (such as floats) or simple timed controls to indirectly assess overflow risk, reflecting the result state of "full water" or "time expired," rather than the leakage process itself. This invention directly installs pressure sensors at key nodes in the water system (joints) to monitor dynamic water pressure in real time. The control chip intelligently analyzes pressure change trends, distinguishing between normal water use, water pressure fluctuations, and actual leaks, achieving early and direct detection of the leakage process.

[0045] 2. Differences in Actuator and Control Logic: Existing actuators (such as float valves) are mostly purely mechanically linked, or operated by solenoid valves based on simple on / off signals. Their operational logic is simplistic, shutting off only when the water level reaches its physical limit or at the end of a set time, failing to address leaks caused by damage to joints or pipelines. The actuator of this invention consists of a stepper motor, a precision coupling, and a dedicated valve core, driven by a control chip. Its control logic, based on pressure signal analysis, can autonomously determine and initiate shutdown during leak detection, resulting in a more proactive and precise response.

[0046] 3. Differences in Function and Adaptability: Existing technologies primarily aim to prevent water dispensers from overflowing, offering limited functionality. Furthermore, their mechanical structures are susceptible to damage from scale and impurities. This invention directly addresses the risk of leakage at connections, providing end-to-end protection from the water source to the dispenser. Its electronic intelligent control avoids the jamming issues of purely mechanical mechanisms and, through pressure pattern analysis, better adapts to different installation environments and water pressure conditions, reducing the possibility of malfunctions.

[0047] In summary, this specific implementation method provides a direct solution to the risk of leakage at pipe connections, which is different from traditional overflow protection, by adopting intelligent judgment based on real-time pressure trend analysis and an active shutdown mechanism driven by a motor. It has substantial improvements in detection principle, response mechanism and functional targeting.

[0048] like Figures 1 to 4 As shown, the leak-proof intelligent control water dispenser connector includes a connector 12 with a chip installed, a sensor cover 4 connected to the connector 12, a control block 2 on the connector 12 for placing the control chip, an explosion-proof flexible connector 9 connected to the main body, a gasket 10 for connecting the upper part 5 and the lower part 11 of the main body to the leak-proof valve core 8, a rod 7 on the leak-proof valve core 8 connected to the control device, and a coupling 6 on the rod 7 connected to the stepper motor 1. The stepper motor 1 is linked with the corresponding rod 7 and drives the corresponding leak-proof valve core 8 to open and close.

[0049] like Figure 3 As shown, the main body of the leak-proof intelligent control connector includes an upper part 5 and a lower part 11 for connecting to a water dispenser, a stepper motor 1 for transmission, and a leak-proof valve core 8. The leak-proof valve core 8 contacts the upper part 5 and the lower part 11 of the main body through a gasket 10.

[0050] like Figure 4 As shown, the connection part consists of an explosion-proof flexible connector 9, a faucet connection component 3, a connector 12, and a control block 2. The explosion-proof flexible connector 9 and the faucet connection component 3 are connected through the connector 12.

[0051] When it is necessary to prevent water leakage from the water dispenser connector, the pressure sensor located inside the sensor cover 4 detects changes in the water pressure flowing through the connector 12 and transmits this pressure signal to the control chip in the control block 2. The control block 2 then transmits this signal to the stepper motor 1 and the maintenance worker, controlling the stepper motor 1 to take... Figure 2 The central connecting rod 7 controls the anti-leakage valve core 8 to close the water flow path, thus preventing water leakage from the water dispenser.

[0052] Preferably, the transmission of the main body is controlled by a stepper motor 1 through a coupling 6 to rotate the connecting rod 7 and the anti-leakage valve core 8, which can make the anti-leakage valve core 8 rotate better in the upper part 5 and the lower part 11 of the main body.

[0053] The joint and the connector are sealed with an O-ring, and the main body is connected to the connecting structure and the connecting component through the connecting component and the O-ring.

[0054] In a preferred embodiment, in the leak-proof intelligent control water dispenser connector, the control chip is further configured to perform the following leak detection steps: continuously sampling the pressure signal of the pressure sensor and calculating its pressure value sequence over time; dynamically updating and maintaining a baseline pressure reference value characterizing the normal pressure fluctuation range of the current system based on the pressure value sequence; calculating the instantaneous deviation of the current pressure signal relative to the baseline pressure reference value in real time, and calculating the intensity of the continuous downward trend of the pressure signal within a predetermined time window; only when both of the following conditions are met simultaneously is a leak detected and the stepper motor is triggered to shut down: (a) the instantaneous deviation exceeds a first dynamic threshold, and (b) the intensity of the continuous downward trend exceeds a second dynamic threshold; wherein, the first dynamic threshold and the second dynamic threshold are both adaptively adjusted based on the baseline pressure reference value and historical pressure fluctuation statistics.

[0055] The control chip has multiple preset or configurable key judgment parameters, including a predetermined time window (e.g., 30 seconds) for analyzing the continuous downward trend of pressure, a 'first dynamic threshold' for calculating the instantaneous deviation, and a 'second dynamic threshold' for evaluating the strength of the trend. These parameters can be set to default values ​​at the factory and optimized through system adaptive learning or remote commands to ensure accurate and reliable leakage detection under different installation environments and water pressure conditions.

[0056] The control chip executes the following control method to achieve intelligent leakage detection. The specific implementation steps are as follows: Signal Acquisition and Sequence Construction: The control chip continuously reads the analog voltage values ​​from the pressure sensor at a fixed sampling frequency, such as 10 times per second, and converts them into digital pressure signals through an internal analog-to-digital converter. These pressure values, arranged in chronological order, constitute a real-time updated pressure value sequence.

[0057] Dynamic baseline maintenance: Instead of using a fixed value as the benchmark, the control chip dynamically maintains a parameter called the "baseline pressure reference value." This parameter is obtained by statistically analyzing a series of pressure values ​​collected over a recent period, such as the past 5 minutes, calculating its moving average, to characterize the normal pressure level of the current system under stable, leak-free conditions. This reference value is slowly updated over time to track the daily, gradual changes in ambient water pressure.

[0058] Dual Feature Calculation: The control chip performs two key calculations in real time. The first is to calculate the "instantaneous deviation," which is the absolute value of the difference between the latest pressure sample value and the dynamically updated baseline pressure reference value, reflecting immediate pressure anomalies. The second is to calculate the strength of the continuous downward trend, which requires analyzing a pressure value sequence within a pre-defined time window (i.e., a predetermined time window, e.g., 30 seconds). The duration of this window can be determined based on system default settings, post-installation learning calibration, or remote parameter configuration to adapt to leakage feature identification needs under different environments. By linearly fitting this sequence or calculating the statistical characteristics of its difference values, the system quantifies whether the pressure exhibits a continuous and stable downward trend and the rate of decline.

[0059] Dual-threshold collaborative judgment: The system introduces two dynamic thresholds for collaborative judgment. The first dynamic threshold is used to constrain the instantaneous deviation, and the second dynamic threshold is used to constrain the strength of the continuous downward trend. These two thresholds are not fixed, but are adaptively adjusted based on the current baseline pressure reference value and the statistical variance of historical pressure fluctuations. For example, when the system is operating smoothly and pressure fluctuations are small, the thresholds can be set relatively sensitively; during peak water usage periods when background fluctuations are large, the thresholds will automatically be increased to enhance anti-interference capabilities.

[0060] Logical Decision-Making and Action Triggering: The control chip's decision-making logic requires two conditions to be met simultaneously before a leak is identified and a shutdown action is triggered: Condition 1 is that the instantaneous deviation exceeds the first dynamic threshold, indicating a significant immediate pressure anomaly; Condition 2 is that the intensity of the continuous downward trend exceeds the second dynamic threshold, indicating that the anomaly is showing a trend of continuous deterioration. This "AND" logic ensures that the system will not be falsely triggered by a single instantaneous spike interference (such as a ruptured water pipe air bladder or a sudden water usage). Only when the anomaly continues to develop will a leak be confirmed and the stepper motor controlled to close the valve core.

[0061] In some embodiments, a smart water valve or alarm may be used to determine leakage using a single fixed threshold. Compared to this prior art, this specific embodiment differs in the following ways: 1. Difference in Logic Complexity: Existing technologies typically compare real-time pressure with a pre-set fixed threshold, triggering an alarm or shutting down the system once the pressure falls below the threshold. This logic cannot distinguish between slow, minor leaks, temporary pressure drops during normal water use, and persistent, severe leaks. This implementation employs dual feature analysis, incorporating both immediate status and historical trends, and sets dual judgment conditions that must be met simultaneously. Its judgment logic is upgraded from a one-dimensional "point judgment" to a two-dimensional "spatiotemporal correlation judgment," significantly improving the dimensionality and depth of the analysis.

[0062] 2. System Adaptability Differences: The fixed thresholds in existing technologies cannot adapt to different installation environments (such as different static pressures between high-rise and low-rise buildings) and seasonal and temporal fluctuations in water pressure, easily leading to false alarms during low-pressure periods and missed alarms during high-pressure periods. This implementation method, through dynamically updated baseline pressure reference values ​​and correspondingly adjusted dynamic thresholds, enables the system to automatically adapt to different scenarios and environmental changes during long-term operation, maintaining the real-time rationality of the judgment benchmark.

[0063] 3. Difference in Anti-interference Capability: Existing single-threshold methods are equally sensitive to any pressure drop below the threshold, including instantaneous large pressure drops caused by normal faucet operation, which easily leads to frequent false alarms. This implementation introduces the condition of "intensity of continuous downward trend," effectively filtering out normal water usage disturbances that are large in magnitude but short in duration. Only pressure changes that are both instantaneous abnormalities and exhibit a continuous deterioration trend are identified as leaks, thus demonstrating stronger anti-interference capability and a lower false alarm rate in complex water usage scenarios.

[0064] In summary, this specific implementation provides a leak identification method that differs from simple threshold comparison and has environmental adaptability and stronger anti-interference capabilities by implementing an intelligent algorithm based on dynamic benchmarks, dual feature extraction, and dual threshold collaborative judgment.

[0065] In a preferred embodiment, in the leak-proof intelligent control water dispenser connector, the control chip pre-stores typical pressure change patterns during normal water use; the control chip monitors the signal changes of the pressure sensor in real time and compares the current pressure change curve with the typical pressure change patterns; when the matching degree between the current pressure change curve and any of the typical pressure change patterns exceeds a preset similarity threshold, it is determined to be normal water use interference, and the shutdown action is not triggered; the shutdown action of the stepper motor is only triggered when the matching degree between the current pressure change curve and all the pre-stored typical pressure change patterns is lower than the similarity threshold and the leakage determination condition is met.

[0066] Based on the dual threshold determination method, to further reduce false positives, this specific implementation introduces a normal water usage pattern identification and filtering mechanism, the specific steps of which are as follows: Establishment of a Typical Pressure Pattern Library: During the control system development or equipment initialization phase, pressure change data under various common water usage scenarios are collected experimentally. For example, the complete signal curves generated by the pressure sensor are recorded when a user fills a glass of water, fills it halfway, or briefly turns the faucet on and off. After feature extraction and data cleaning of these raw curves, a series of standardized "typical pressure change patterns" are formed and pre-stored in the non-volatile memory of the control chip, constituting the initial pattern library. Each pattern can be characterized as a specific shape of pressure change over time, including key features such as the descent slope, the duration of the trough, and the recovery speed.

[0067] Real-time monitoring and pattern matching: During normal equipment operation, the control chip not only calculates the pressure threshold but also monitors the signals from the pressure sensors in real time and plots them as a current pressure change curve. A pattern matching algorithm runs within the control chip, comparing the current real-time curve with all typical pressure change patterns pre-stored in a pattern library. The core of the comparison is calculating the "matching degree" between the two, which can be achieved by calculating metrics such as the similarity of curve shapes and the alignment of feature points.

[0068] Interference filtering decision: The control chip sets a preset similarity threshold. In the leak detection process, a pre-judgment step is added: the current pressure change curve is compared with patterns in the pattern library. If the matching degree between the current curve and any typical pressure change pattern in the library exceeds the preset similarity threshold, the control chip determines that the current event is a "known normal water use event," belonging to system interference. At this time, even if the instantaneous deviation of the current pressure drop might trigger a threshold alarm, the system will prioritize classifying this event as normal water use and suppress subsequent leak shutdown actions.

[0069] Composite Judgment Process: The system will only consider the current pressure change curve as an "unknown or abnormal change" and allow it to enter the dual-threshold leakage judgment process if the matching degree between the current pressure change curve and all pre-stored patterns in the pattern library is lower than the similarity threshold, meaning that the pressure change pattern does not belong to any known normal water use behavior. Only when both "unknown pattern" and "meeting the leakage judgment conditions" are met will the stepper motor be finally triggered to shut down.

[0070] In some embodiments, a leak detection device that relies solely on pressure amplitude or flow rate threshold for judgment can be used. Compared to this prior art, this specific embodiment differs in the following ways: 1. Differences in the Target of Judgment: Existing technologies focus on identifying "what constitutes a leak," with algorithms and thresholds set around the characteristics of leak signals. One of the core innovations of this implementation is that it first identifies "what does not constitute a leak," proactively identifying and eliminating normal water use as the primary source of interference. Its pattern library aims to define the scope of normal behavior, rather than merely defining anomalies.

[0071] 2. Differences in Information Utilization Dimensions: Existing technologies primarily utilize one-dimensional information such as signal amplitude and whether a threshold is exceeded. This implementation fully utilizes the two-dimensional "time-amplitude" morphological information of pressure changes. By comparing the complete curve shape, the system can distinguish between two events with similar amplitudes but vastly different temporal characteristics: for example, a rapid decrease followed by a rapid recovery (normal water use) versus a slow, continuous decrease (minor leaks), which cannot be achieved using amplitude thresholds alone.

[0072] 3. Differences in System Logic Architecture: In existing technologies, once a pressure signal triggers a threshold, the system typically proceeds directly to an alarm or shutdown process, exhibiting a linear logic. This implementation constructs a layered filtering logic architecture. The first layer is the "behavior recognition layer," which filters out known normal behaviors through pattern matching; only signals that fail to be recognized enter the second layer, the "anomaly analysis layer," i.e., dual-threshold leakage determination. This architecture essentially adds an experience-based "whitelist" filter to the system, prioritizing the unimpeded passage of common normal operations, thereby significantly reducing the most common causes of false triggers at the source.

[0073] In summary, this specific implementation method constructs a pre-decision layer centered on identifying and filtering interference from normal water use by pre-storing typical water usage patterns and implementing real-time pattern matching. This method expands the judgment logic from simple "anomaly detection" to a combination of "normal identification and anomaly detection," effectively solving the major problem of normal operation being misjudged as leakage in real, complex water usage environments.

[0074] In a preferred embodiment, in the leak-proof intelligent control water dispenser connector, the control chip is further configured to perform the following adaptive maintenance steps: continuously recording the pressure data of the pressure sensor during a preset learning period when the system is running stably and no shutdown action is triggered; based on the pressure data during the learning period, statistically analyzing the static pressure reference value and normal pressure fluctuation range under the system installation environment, and updating the reference baseline for leak detection accordingly; in subsequent operation, smoothly tracking and updating the static pressure reference value at a first preset time interval to compensate for the effects caused by long-term slow changes in water source pressure or sensor zero-point drift; when a sudden change in the pressure signal exceeding the normal pressure fluctuation range is detected, and the sudden change is not caused by an identified normal water use event, a rapid recalibration process is initiated: within a short time window when the system determines that there is no leak and the system is stable, the static pressure reference value is resampled and updated.

[0075] Furthermore, the system establishes and maintains two levels of pressure benchmarks for leak detection. The first level is the static pressure benchmark value, which represents a relatively stable absolute pressure benchmark point established by the system through learning or initialization under a specific installation environment. The second level is the base pressure reference value, a dynamically maintained parameter used to characterize the real-time center level of normal pressure fluctuations in the current system. The base pressure reference value is typically calculated and slowly updated based on the static pressure benchmark value, combined with the moving average or filtered results of recent (e.g., the past 5 minutes) pressure data. This dynamic reference value can more sensitively track the slow daily changes and short-term stable fluctuations in water pressure, serving as a direct comparison benchmark for instantaneous deviation calculation.

[0076] To improve the long-term stability and adaptability of the system, the following adaptive maintenance process has been added to this specific implementation: 1. Learning cycle and initial baseline establishment After the equipment is initially installed and connected to water, or after the user performs a "factory reset" operation, the control chip enters a preset learning cycle, for example, lasting 24 hours. During this cycle, the system assumes no continuous leakage and its main task is to learn and adapt to the installation environment. The control chip continuously records the pressure sensor readings and discards significant abrupt changes caused by occasional water usage. After the learning cycle ends, the chip performs statistical analysis on the recorded valid data to calculate the "static pressure reference value" representing the system's static water pressure, and the "normal pressure fluctuation range" reflecting normal background fluctuations under this environment. This static pressure reference value will serve as the long-term anchor point and initial value for the system's pressure reference system. Based on this, the system will generate and enter the dynamic parameters required for normal operation, namely the initial baseline pressure reference value (which can usually be set to be equal to or calculated based on the initial static pressure reference value). These parameters constitute the initial reference baseline for subsequent leakage detection and are stored.

[0077] 2. Smooth tracking updates Once in normal operation, the control chip performs baseline fine-tuning at relatively long intervals, such as every 12 hours. It collects stable pressure data from a recent period (e.g., the past 2 hours) that is determined to be neither water-using nor leaking, calculates its average value, and performs a weighted average with the currently stored static pressure reference value. The result is then used to slowly update the reference value. This process is gradual and minute, designed to smoothly compensate for extremely slow drifts that may occur due to slow changes in water pressure (such as daytime fluctuations in municipal water pressure) or the sensor's zero point. This allows the reference value to "follow" the long-term, gradual changes in the environment, preventing the sensitivity from gradually becoming inaccurate due to a fixed reference.

[0078] 3. Triggered fast recalibration In addition to periodic smooth updates, the system also features event-triggered rapid recalibration. When the control chip detects a sharp change in the pressure signal that exceeds the recorded "normal pressure fluctuation range"—such as a sudden drop or rise—and the pattern recognition module determines that this change does not belong to any known normal water usage event, the system assumes this may indicate a significant change in the installation environment (such as pressure changes after upstream main pipeline maintenance). In this case, the system will not immediately classify it as a leak but will initiate a rapid recalibration process. The control chip will wait and confirm that the pressure does not change drastically within a short stabilization time window (e.g., 3 minutes) and that no leak detection has been triggered. If the conditions are met, the system determines that the current state is a new stable state and then quickly recalculates and updates the "static pressure reference value" based on the pressure data collected within that window, rapidly aligning the system reference to the new operating pressure point and restoring effective monitoring capabilities.

[0079] Preferably, in the intelligent control system of the present invention, the pressure determination benchmark system is composed of a static pressure benchmark value and a basic pressure reference value. The static pressure benchmark value, as a long-term, stable environmental pressure characteristic, is slowly updated or recalibrated through adaptive maintenance steps to cope with changes in the installation environment or sensor drift. The basic pressure reference value, serving as a short-term, dynamic operational reference center, is based on the static pressure benchmark value and combines real-time pressure fluctuations for smooth tracking, directly serving the instantaneous deviation calculation in the dual-threshold leakage determination logic. The two work in a layered collaboration to jointly ensure the accuracy and adaptability of the pressure determination benchmark at different time scales.

[0080] In some embodiments, a leak detection device with factory-fixed parameters or relying on manual calibration by the user can be used. Compared to this method, this specific embodiment differs in the following ways: 1. Differences in the initialization process: Existing technologies typically set a universal pressure threshold at the factory or require users to manually test and set a baseline value after installation. This approach cannot adapt to the diverse installation environments. This implementation uses an automated "learning cycle" to enable the device to autonomously sense and establish a precise baseline suitable for its own environment after installation. This achieves out-of-the-box usability and personalized initial calibration, lowering the installation and commissioning threshold for users and improving the accuracy of initial judgments.

[0081] 2. Differences in Long-Term Maintenance Mechanisms: Existing technologies fix parameters once set, or only offer a manual recalibration option. Over years of use, sensor characteristic drift or changes in water supply pressure can cause fixed parameters to gradually deviate from reality, resulting in either decreased sensitivity and increased false alarms, or excessive sensitivity and frequent false alarms. This implementation introduces periodic "smooth tracking updates," enabling the system to self-adjust, gently tracking long-term, gradual environmental changes, much like a system with a "slow metabolism," thus maintaining stable monitoring accuracy over a longer timescale.

[0082] 3. Addressing Differences in Sudden Environmental Changes: When a significant adjustment to the water supply system causes a step change in the pressure baseline, existing systems relying on fixed parameters or slow tracking may completely fail within a period of time. This implementation method adds a "reflective" capability to the system in response to sudden environmental changes through a "trigger-based rapid recalibration" mechanism. It can identify those severe pressure surges that are neither due to leaks nor normal water usage, and treat them as signals that the environment may have changed. After confirming safety, it quickly rebuilds the operating baseline, enabling the system to recover from environmental changes more quickly and enhancing its overall environmental adaptability and resilience.

[0083] In summary, this specific implementation method constructs an intelligent system capable of autonomously adapting to dynamic environmental changes throughout the entire process from initial installation to long-term operation by combining a multi-level adaptive strategy that integrates a personalized baseline established through a learning cycle, smooth updates to track long-term gradual changes, and rapid recalibration to cope with sudden and drastic changes. This distinguishes it from existing technologies with fixed parameters or those that can only be manually adjusted, in terms of automation, long-term reliability, and environmental adaptability.

[0084] In a preferred embodiment, the leak-proof intelligent control water dispenser connector further includes: a communication module electrically connected to the control chip; the control chip is further configured to: when the stepper motor is triggered to close in response to a leak event, generate alarm information including the event type, occurrence time, and connector identifier, and send it to a designated remote monitoring terminal via the communication module; continuously or at a preset cycle collect and store the working data of the pressure sensor, the action record of the stepper motor, and the system self-test status; and in response to an instruction received from the remote monitoring terminal via the communication module, perform at least one of the following operations: upload historical working data, perform system self-test, reset the leak-proof valve core to the open state, or adjust the leak judgment parameters.

[0085] Based on the connector with local intelligent control capabilities, this specific embodiment extends the remote interaction function, and its structure and operation are as follows: Hardware Expansion: A communication module, such as a low-power Wi-Fi module or a cellular mobile communication module, is integrated onto the control block's circuit board. This module is electrically connected to the control chip via a serial port or SPI bus. The communication module is responsible for wireless data exchange with the external network and has independent network protocol stack processing capabilities to reduce the communication processing burden on the control chip. The device's power supply system must also provide stable power to the communication module.

[0086] Event-triggered reporting: When the control chip, based on its judgment logic, confirms a water leak and triggers the stepper motor's shutdown action, this event is marked as a valid "water leak alarm event." The control chip immediately generates a structured alarm message. This message includes at least the event type (e.g., "connector leak shutdown"), the precise timestamp of the event, and the device's unique connector identifier. After generation, the control chip transmits this message to the communication module via the bus. The communication module then uses its wireless connection to send the alarm message to a pre-configured remote monitoring terminal, such as a cloud server or a manager's mobile application. This enables remote real-time notification of water leak events.

[0087] Data logging and periodic reporting: The control chip allocates a local non-volatile storage space for continuously or periodically recording system operation data. Recorded data typically includes: periodic sampling values ​​from the pressure sensor, the direction and time of each stepper motor movement, and system self-test status (such as voltage stability). This data can be stored in log files. The communication module proactively connects to the server at a preset period, such as once a day or during off-peak network hours in the early morning, and uploads this historical data to the remote monitoring terminal, creating an operational archive for long-term analysis.

[0088] Remote Command Response and Control: Administrators of the remote monitoring terminal can proactively send commands to specific devices. Upon receiving a command data packet from the remote device, the communication module parses it and transmits it to the control chip. The control chip then executes corresponding operations based on the command content. These operations include, but are not limited to: responding to data upload requests and immediately sending specified historical data segments; performing a system self-test and returning the result; receiving a reset command and controlling the stepper motor to rotate the anti-leakage valve core back to the open state, restoring water flow; or receiving parameter adjustment commands and, after safety verification, updating its internal leakage detection parameters, such as dynamic thresholds or learning cycle duration.

[0089] In some embodiments, a mechanical or simple electronic leak detector with only local audible and visual alarm function is used. This specific embodiment differs from the method in the following ways: 1. Differences in Information Transmission Methods: Existing local alarm systems can only alert to anomalies by emitting a buzzer or flashing lights on-site, limiting their information transmission range to the physically audible and visible distance. If unattended, leaks cannot be detected in a timely manner. This implementation integrates a wireless communication module, converting alarm information from the physical site and transmitting it to the digital network space, achieving remote and asynchronous information transmission. This allows managers to transcend geographical limitations and receive alarms from anywhere with a network connection, greatly expanding the scope and timeliness of event notification.

[0090] 2. Differences in Data Value and Utilization: Existing technologies typically only provide immediate status information for "existence" or "absence" of leaks, lacking process data and historical records. Their data is transient and unrecorded. This implementation not only reports the event outcome but also systematically records and uploads rich operational process data. This makes leak events no longer isolated points but can be traced and analyzed within historical data curves. Managers can assess pressure trends before leaks occur, analyze the accuracy of equipment operation, and even predict equipment health based on long-term data, achieving a shift from simple event response to data-driven equipment management and preventative maintenance.

[0091] 3. Differences in System Manageability: Existing technologies typically require on-site personnel to reset, repair, or verify parameters after an alarm is triggered; all operations are local and manual. This implementation supports remote commands, enabling some critical maintenance operations to be completed remotely. For example, after confirming a false alarm or brief interference, the equipment can be remotely reset, avoiding unnecessary on-site travel; or, based on data analysis results, the sensitivity parameters of equipment in different areas can be remotely fine-tuned to optimize overall performance. This gives the system remote configurability, controllability, and recoverability, partially upgrading the traditional "on-site maintenance" model to "remote maintenance," reducing the manpower and time costs of long-term maintenance.

[0092] In summary, this specific implementation transforms a smart connector, originally limited to local automatic shutdown, into an IoT node capable of remote monitoring, data traceability, and online management by adding wireless communication and remote interaction capabilities. Compared to existing technologies that only possess local alarm functions or rely on complex wired systems, this offers a different solution in terms of information accessibility, data depth, and system manageability.

[0093] In a preferred embodiment, in the leak-proof intelligent control water dispenser connector, the control chip is configured to apply stability constraints to the adaptive adjustment process of the first dynamic threshold and the second dynamic threshold: a variable calculated value determined by the baseline pressure reference value is set for the first dynamic threshold, and an absolute lower limit and an absolute upper limit are preset for the first dynamic threshold, so that the first dynamic threshold applied in the end is always limited to the closed interval formed by the absolute lower limit and the absolute upper limit; a benchmark value calculated based on historical pressure fluctuation statistics is set for the second dynamic threshold, and a maximum allowable fluctuation percentage is set for the benchmark value, so that the change range of the second dynamic threshold applied in the end does not exceed the maximum allowable fluctuation percentage in any adjustment cycle; when updating the first dynamic threshold or the second dynamic threshold, a weighted smoothing algorithm is used to perform a weighted average of the new calculated value and the threshold of the previous cycle to generate the threshold finally applied to the next judgment cycle, thereby avoiding a step change in the threshold.

[0094] This specific implementation method, when implementing the dynamic threshold adaptive adjustment function, focuses on adding stability constraints to the threshold adjustment process to prevent unreasonable fluctuations in the threshold. The specific implementation steps are as follows: 1. Constraints on the first dynamic threshold The first dynamic threshold is primarily used to determine whether the instantaneous pressure deviation is significant. In the control chip, this threshold is calculated based on a dynamically updated baseline pressure reference value, for example, set as a fixed percentage of the reference value. To prevent this threshold from changing drastically when there are temporary abnormal fluctuations in the baseline pressure reference value, the system sets absolute lower and absolute upper limits for it.

[0095] The chip first calculates an initial threshold value based on the current baseline pressure reference value.

[0096] The chip compares the calculated value with a preset absolute lower limit. If it is lower than the lower limit, the absolute lower limit is used as a temporary threshold. Then it compares the calculated value with a preset absolute upper limit. If it is higher than the upper limit, the absolute upper limit is used. This ensures that the first dynamic threshold applied in the final application is always constrained within a closed interval consisting of the absolute lower limit and the upper limit.

[0097] This ensures that even if the baseline pressure reference value is temporarily abnormal due to occasional disturbances, the first dynamic threshold will not exceed a reasonable range of physical perception, avoiding the risk of false alarms due to a threshold that is too low and therefore sensitive to any small fluctuations, or the risk of false alarms due to a threshold that is too high and therefore unable to detect obvious leaks.

[0098] 2. Constraints on the second dynamic threshold The second dynamic threshold is used to determine whether the intensity of the sustained downward pressure trend has reached a dangerous level. Its benchmark value is derived from the calculation of historical pressure fluctuation statistics, such as a certain multiple of the variance of pressure changes over a past period.

[0099] The system sets a maximum permissible percentage fluctuation for this baseline value, for example, the change cannot exceed ±10% within each judgment cycle. When a new baseline value is calculated based on new data, the chip checks its change relative to the baseline value of the previous cycle. If the change exceeds the maximum permissible percentage fluctuation, the new baseline value will not be directly adopted, but will be adjusted according to the maximum permissible fluctuation to generate the second dynamic threshold for the final application.

[0100] This rate of change limit effectively prevents the second dynamic threshold from undergoing abrupt and significant adjustments due to short-term data fluctuations, ensuring the smoothness and continuity of changes in the trend judgment criteria.

[0101] 3. Smoothing of threshold updates Neither the first nor the second dynamic threshold will immediately replace the currently used threshold after a new target value is calculated based on new data.

[0102] The control chip employs a weighted smoothing algorithm to calculate a weighted average between the newly calculated target value and the currently used threshold. For example, the weight of the new value is set to 0.3, and the weight of the old value is set to 0.7. The weighted sum of the two is calculated, and the result is used as the new threshold to be used in the next judgment cycle.

[0103] This smooth transition mechanism avoids abrupt changes in the threshold between adjacent periods. Even if the calculated target value changes reasonably due to new data, the threshold used in practice gradually transitions to the new value. This greatly enhances the immunity of the entire judgment system to short-term data noise and improves the stability of the system output.

[0104] In some embodiments, a smart monitoring algorithm for adaptive threshold adjustment is employed using a simple recursive update or direct replacement method. Compared to this method, this specific embodiment differs in the following ways: 1. Differences in Constraint Mechanisms: Existing technologies typically rely solely on algorithmic calculations when adjusting thresholds, lacking external constraints. This can lead to extreme values ​​being calculated for thresholds under specific data sequences (such as sudden sharp fluctuations after a long period of inactivity), temporarily deviating from the reasonable operating range. This implementation introduces explicit boundary constraints and rate-of-change constraints into the threshold adjustment process. These constraints, acting as safety boundaries independent of the core algorithm, ensure that the adaptive process always operates within a pre-defined safety corridor, preventing the risk of parameter runaway.

[0105] 2. Differences in Dynamic Update Characteristics: Existing technologies often use a direct "calculation-replacement" approach to threshold updates, where the new threshold may take effect immediately. In this mode, the threshold trajectory may exhibit jagged jumps. This implementation alters the threshold update dynamics through forced smoothing, transforming it from a "jump" to a "gradual" change. This makes the system's sensitivity changes continuous and predictable, avoiding drastically different judgments on the same type of pressure signal at adjacent times due to sudden threshold changes, thus improving the consistency of system behavior.

[0106] 3. Differences in Anti-interference Focus: Existing technologies primarily rely on the robust design of front-end signal filtering and the judgment algorithm itself for anti-interference capabilities. This implementation embeds anti-interference considerations directly into the parameter management level. Through constraints and smoothing, the system absorbs new information and adapts to the environment while exhibiting inertia towards short-term, potentially noisy, data shocks. This is equivalent to adding an "inertia" to the parameter adjustment process, making it focus more on medium- to long-term trends rather than short-term disturbances, thus achieving a better balance between dynamic adaptability and short-term stability.

[0107] In summary, this specific implementation constructs a more robust parameter update mechanism by setting three "filters"—boundary, floating limit, and inertial smoothing—on the path of dynamic threshold adaptive adjustment. Compared with existing technologies that rely solely on the internal logic of the algorithm for adaptive adjustment, this reduces oversensitivity to data quality and enhances the long-term operational stability of the system in complex and ever-changing environments.

[0108] In a preferred embodiment, in the leak-proof intelligent control water dispenser connector, after determining that the instantaneous deviation exceeds the first dynamic threshold and before waiting for the intensity of the continuous downward trend to reach the second dynamic threshold, a pre-emptive rapid response procedure is executed: immediately initiating a rapid verification cycle with a duration shorter than the predetermined time window; within the rapid verification cycle, collecting pressure data at a higher frequency and calculating the short-term downward trend; if the slope of the short-term downward trend is greater than a preset emergency slope threshold, then without waiting for the intensity of the continuous downward trend to reach the second dynamic threshold, immediately triggering the stepper motor to shut down; if the slope of the short-term downward trend does not exceed the emergency slope threshold, then continuing to execute the determination process based on the intensity of the continuous downward trend.

[0109] This specific implementation addresses the potential response delay issue in the dual-threshold determination process by adding a pre-emptive fast response channel to the determination process. The specific implementation is as follows: Triggering of the rapid verification mechanism: When the control chip completes the calculation of the instantaneous deviation and finds that the instantaneous deviation of the current pressure signal has exceeded the first dynamic threshold, this indicates that a potential abnormal event has been initially triggered. According to the original procedure, the system would enter a preset, relatively long observation window to wait for the calculation of the sustained downward trend strength. However, before entering this waiting phase, the system does not passively wait but immediately and synchronously initiates a "rapid verification cycle." The duration of this rapid verification cycle is significantly shorter than the main observation window used to calculate the sustained downward trend; for example, the main window is 30 seconds, while the rapid verification cycle is set to 5 seconds.

[0110] High-frequency sampling and short-term trend analysis: At the moment the fast verification cycle begins, the control chip temporarily switches to a higher-frequency pressure signal sampling mode. For example, it increases from the usual 1 sample per second to 5 samples per second. Using the high-density data points collected within these 5 seconds, the chip quickly calculates a pressure change curve on a short timescale. The core of this calculation is the instantaneous slope of the pressure drop within this short window, i.e., the rate of change of the "short-term downward trend," used to quantify the abruptness of the pressure drop.

[0111] Grading and Quick Action: The system performs a quick fork determination based on the calculation results of short-term trends. Scenario 1: Rapid Leak Identification: If the calculated short-term downward trend slope is very large, with its absolute value exceeding a preset "emergency slope threshold," this indicates that the pressure is rapidly collapsing, consistent with the characteristics of a rapid pipe burst or severe rupture. In this case, the system no longer waits for the 30-second master observation window to end, nor does it rely on the complete long-term trend strength value. The control chip immediately identifies it as a high-risk emergency leak and bypasses all subsequent conventional judgment steps, directly issuing an emergency shutdown command to the stepper motor.

[0112] Scenario 2: Continue with standard procedure: If the calculated short-term downward trend slope does not exceed the emergency slope threshold, it indicates that the current pressure drop rate is still within a relatively gentle range, possibly representing slow leakage or a disturbance that does not pose an emergency threat. In this case, the system determines that no emergency shutdown is required and continues with the original decision-making logic. After the rapid verification cycle ends, the system resumes the normal sampling frequency and continues to collect data within the remaining main observation window, ultimately calculating the complete sustained downward trend strength and making a final decision based on the second dynamic threshold.

[0113] The entire pre-emptive rapid response procedure is seamlessly integrated into the existing dual-threshold judgment process. It is only activated when the instantaneous deviation exceeds the limit, serving as a rapid "initial screening" for potential emergencies. If no emergency occurs, the system smoothly transitions back to the original precise judgment mode. The entire process has no negative impact on routine slow leakage detection. On the contrary, by filtering out some scenarios that require emergency response, the existing process can focus on handling slow leakage situations with ambiguous boundaries that require longer confirmation.

[0114] In some embodiments, a leak detection algorithm relies entirely on a fixed-duration window for trend confirmation. Compared to this method, this specific implementation differs in the following ways: 1. Differences in Time Scale and Decision Path: Existing technologies typically use a single time scale for judgment, requiring the same observation period before a final decision is made, regardless of whether the leak is fast or slow. The decision path is linear and singular. This implementation introduces a dual-time-scale mechanism, simultaneously confirming long-term trends and opening a short-term rapid analysis channel. This forms two parallel decision paths: a fast but high-threshold emergency channel and a slower but more accurate conventional channel. The system dynamically selects different decision paths based on the speed of event development, achieving multi-scale judgment.

[0115] 2. Response Differences Based on Event Type: Existing single-window settings essentially strike a trade-off between response speed and confirmation accuracy. For rapidly developing events, the response speed is insufficient; while for slowly developing events, the observation window may be shorter. This implementation clearly distinguishes the "urgency" of events, utilizing a fast track specifically to handle rapid leaks requiring immediate attention. It reduces response delays from fixed tens of seconds to within seconds, specifically optimizing response performance for the most dangerous type of leaks.

[0116] 3. Differences in Algorithm Complexity and Resource Scheduling: Existing technologies have relatively simple algorithm flows and stable resource consumption. This implementation adds extra logical judgments and short-term high-frequency sampling calculations, increasing algorithm complexity and instantaneous computational load. However, it uses an "event-triggered" approach, initiating the high-load rapid verification process only when necessary (instantaneous deviation exceeds the limit), representing a targeted, on-demand strategy for allocating computational resources. During most periods without anomalies or with only minor fluctuations, the system operates in a lightweight state similar to existing technologies; therefore, this increased complexity does not lead to sustained power consumption or performance burden.

[0117] In summary, this specific implementation adds a "reflective" response capability to smart connectors for rapid leakage events by grafting a parallel, rapid verification channel based on short-term precipitousness analysis to the front end of the original judgment process. Compared with existing technologies that use a single fixed observation window, this provides a more refined time response strategy, significantly improving the response speed to rapid and severe leakage events without drastically altering slow seepage detection performance.

[0118] In a preferred embodiment, in the leak-proof intelligent control water dispenser connector, when the matching degree between the current pressure change curve and all pre-stored typical pressure change patterns is lower than the similarity threshold, but is ultimately determined to be a normal water use event, the unidentified current pressure change curve is recorded as a new candidate pattern. When the candidate pattern recurs a preset number of times within a preset period, it is automatically added as a new pre-stored typical pressure change pattern. At the same time, after each successful matching of a pre-stored typical pressure change pattern, the current pressure change curve matched this time is fused with the pattern to fine-tune and update the parameters of the pattern, so as to adapt to minor changes in water use habits or system parameters.

[0119] This specific implementation method aims to enable the system to dynamically learn users' new water usage habits. The specific working method is as follows: Candidate Pattern Learning and Capture: During system operation, the pattern matching process is executed normally. When a pressure change event occurs, and its signal curve matches all pre-stored typical patterns in the pattern library below the similarity threshold (i.e., not identified as known normal water use), but the control chip ultimately determines that the event is not a leak through other auxiliary information or processes—for example, the pressure recovers smoothly after the event and no leak determination conditions are triggered—the event is marked as an "unidentified normal water use event." Subsequently, the system records and temporarily stores the complete pressure change curve of this event as a new "candidate pattern." The recorded information includes the time series of its pressure change and key characteristics.

[0120] Pattern Confirmation and Storage: The system does not immediately add an occasional candidate pattern to the core pattern library. To this end, it employs a repeatability verification mechanism, including a preset observation period (e.g., one week) and a repetition threshold (e.g., three times). The control chip continuously tracks whether the same or highly similar candidate patterns recur within the next observation period. When the system records that the number of times the same type of candidate pattern occurs reaches the preset repetition threshold, it determines that this water usage behavior has formed a stable user habit and is no longer an accidental operation. At this point, the control chip transfers the candidate pattern from the temporary storage area to the formal "pre-stored typical pressure change pattern" library, making it a new basis for the system to identify normal water usage in the future.

[0121] Dynamic Fine-tuning of Existing Patterns: In addition to adding new patterns, the system also focuses on maintaining the accuracy of existing patterns. Each time a real-time pressure curve successfully matches a pre-stored pattern in the pattern library and is determined to be normal water usage, the control chip does not simply end the process. It performs a fusion calculation with the matched pattern data, for example, by weighted averaging the corresponding feature points of both. This process slowly updates the pattern's parameters with small increments, such as fine-tuning the slope of its falling edge or the width of its valley. The purpose of this fine-tuning is to allow the pattern library to slowly adapt to subtle changes, such as a slight blockage in the faucet causing a slower water flow or minor changes in user water usage habits, thereby maintaining the long-term effectiveness and accuracy of pattern matching.

[0122] In some embodiments, a pattern recognition system with a fixed pattern library has a pattern library that remains unchanged after factory shipment or initialization. This specific embodiment differs from the method in the following ways: 1. Differences in Pattern Library Attributes: Existing pattern libraries are static, containing a closed and predefined range of normal water usage patterns. They are based on assumptions about "general" water usage behavior. The pattern library in this implementation is dynamic and extensible, with an open and evolving scope. It is built upon the actual behavior of "specific users" and can grow as user habits change. This fundamentally changes the nature of the pattern library, transforming it from a general knowledge base into a personalized knowledge base that grows alongside specific users and installation environments.

[0123] 2. Differences in the Source of Adaptability: The adaptability (if it can be called adaptation) of the existing system relies entirely on the breadth of the initial pattern library, whose performance ceiling is determined at deployment. When faced with new water usage patterns outside the library, the system can only classify them as "unknown" and rely on subsequent processes (such as leak detection) for identification, which undoubtedly increases the risk of misjudgment. The adaptability of this implementation comes from a continuous learning process. The system transforms each "misjudgment" of an unknown normal event (unrecognized from the perspective of the initial pattern library) into a potential learning opportunity. Through a repeatability verification mechanism, the system can proactively discover and absorb stable new normal states, continuously adapting its recognition capabilities to actual usage scenarios.

[0124] 3. Differences in Maintenance Methods: Existing pattern libraries, once deployed, typically cannot be adjusted or optimized, and their matching performance is prone to gradual decline due to aging water appliances or shifting user habits. This implementation introduces a continuous optimization capability for existing knowledge through a "fine-tuning after successful matching" mechanism. Instead of a large-scale reconstruction of the entire pattern library, it performs subtle iterative updates on successfully used knowledge entries. This is a low-cost, incremental knowledge maintenance strategy that helps combat performance degradation over time.

[0125] In summary, this specific implementation method, by introducing three mechanisms—candidate pattern capture, repeatability verification and database entry, and fine-tuning of existing patterns—injects dynamic learning and incremental optimization capabilities into a static pattern recognition system. This allows the system to learn from actual use, continuously enriching and improving its knowledge system regarding "what is normal," thereby better adapting to the ever-changing real-world environment and improving long-term accuracy and user satisfaction. This distinguishes it from existing technologies that rely on a fixed pattern library.

[0126] In a preferred embodiment, the leak-proof intelligent control water dispenser connector divides all adaptive processes into two categories: a first type of adaptive process affecting the core judgment benchmark, and a second type of adaptive process affecting the auxiliary judgment parameters; wherein, the update of the static pressure benchmark value is defined as the first type of adaptive process; during the update operation of the first type of adaptive process, all update operations of the second type of adaptive process are suspended; after the update of the first type of adaptive process is completed, the system enters a parameter stabilization period, during which the second type of adaptive process is only allowed to record data, and its parameters are not allowed to be modified based on the newly recorded data; the duration of the parameter stabilization period covers at least three complete normal water use cycles after the update of the first type of adaptive process.

[0127] This specific implementation aims to manage multiple adaptive processes, ensuring their coordinated and orderly operation and avoiding mutual interference. Its core management strategy is implemented as follows: Adaptive Process Classification: The control chip explicitly classifies all processes within the system that can automatically modify its internal parameters into two categories. The first category of adaptive processes is defined as those directly affecting the core benchmark for leak detection, primarily referring to the "update of the static pressure benchmark value." This parameter is the absolute zero point for the system to determine whether the pressure is abnormal, and its accuracy is crucial. The second category of adaptive processes is defined as those affecting auxiliary judgment parameters, mainly including "dynamic threshold updates," "updates to the pre-stored typical pressure change pattern library," and other possible auxiliary parameter learning processes. The purpose of adjusting these parameters is to optimize performance, but they do not directly affect the absolute definition of the pressure benchmark.

[0128] Execution control of update operations: The first type of process execution: When the system determines that the first type of adaptive process (i.e., updating the static pressure reference value) needs to be executed, the control chip sends a global control signal before performing this update operation. This signal forcibly pauses all running or waiting second type of adaptive processes, placing them in a waiting state. After the static pressure reference value is updated and stored, the system then enters a preset "parameter stabilization period".

[0129] The second type of process is controlled: During this "parameter stabilization period," the control chip allows the second type of adaptive processes to continue their routine data acquisition and recording work, such as continuing to statistically analyze pressure fluctuations to calculate dynamic thresholds and continuing to record new water usage curves to learn candidate patterns. However, the system locks the "parameter modification permissions" of these processes. This means that during the stabilization period, no matter how much new data these processes acquire, they are not allowed to modify any existing auxiliary parameters based on this new data. All learning results and data are marked as pending processing and are temporarily inactive.

[0130] Definition of Stabilization Period: The duration of the stabilization period for this parameter is not fixed but is linked to the actual operating state of the system. The principle for setting it is that it must cover at least three clearly identified and recorded "normal water usage cycles" from the completion of the static pressure baseline update. A "normal water usage cycle" typically refers to the period from the identification of a normal water usage event by the system until the system returns to a stable state after that event. This ensures that the new core baseline has sufficient opportunity to be verified and stabilized in real-world operating scenarios.

[0131] Recovery after the stabilization period: Once the system has successfully passed the parameter stabilization period, meaning the new static pressure benchmark value has remained stable in several actual water usage tests, the control chip will release the lock on the second type of adaptive process. At this time, suspended operations such as threshold updates and pattern library updates can be executed sequentially or according to priority. They will adjust parameters based on the new data accumulated during the stabilization period, thereby completing the collaborative optimization of auxiliary parameters on the established new benchmark.

[0132] In some embodiments, a complex controller operates multiple independent adaptive modules in parallel within the system. These modules are typically designed to update their parameters independently and asynchronously according to their respective algorithms. This specific implementation differs from the prior art in the following ways: 1. Differences in Parameter Update Timing Control: In existing technologies, multiple adaptive modules often operate based on their own independent trigger conditions and clocks, resulting in dispersed and random update times. This can lead to core parameters and auxiliary parameters updating simultaneously or sequentially within a very short period, easily resulting in inconsistent states. This implementation enforces a serialized or time-sharing update timing logic through explicit classification and priority relationships: auxiliary parameters are only allowed to be updated after the core parameters have been updated and stabilized. This introduces mandatory temporal isolation and order, transforming potential asynchronous conflicts into an ordered, phased update process.

[0133] 2. Differences in Validation Methods: In existing technologies, once a module updates its parameters, the changes typically take effect immediately and are put into use. The effectiveness depends on other modules' immediate adaptation to the new parameters. This implementation adds a mandatory "verification observation period," or parameter stabilization period, to the updates of the most critical core parameters. During this period, the system essentially operates in "observation mode," verifying the performance of the new baseline value under actual load, while freezing adjustments to other parameters. This isolates variables, allowing the true effect of the core parameter updates to be clearly evaluated and avoiding the difficulty in locating the root cause of problems when multiple parameters change simultaneously.

[0134] 3. Differences in System State Management Complexity: Existing technologies suffer from management complexity in handling the real-time scheduling and resource allocation of concurrently running modules. This implementation shifts the management complexity from "handling concurrent conflicts" to "executing predetermined phased protocols" by introducing global coordination logic. It uses a relatively simple state machine (whether core parameters are being updated, or whether the system is in a stable period) to globally coordinate multiple complex learning processes, resulting in higher predictability and orderliness in the adaptive behavior of the entire system and reducing the risk of uncertain oscillations caused by multi-module interactions.

[0135] In summary, this specific implementation provides a clear management framework for the collaborative operation of multiple adaptive mechanisms by establishing classification, prioritization, and a time-sharing execution protocol based on a stable period for the adaptive process. This changes the loosely coupled state of existing adaptive modules operating independently, and achieves serialization and stability verification of update operations through centralized coordination, which helps improve the overall consistency and reliability of complex intelligent systems in long-term operation.

[0136] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Other modifications can be readily made by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. A leakage-proof intelligent control water dispenser connector, characterized in that, include: The connection sensing unit includes: A connector used to connect to an external water source; A sensor cover is attached to the outside of the connector and encapsulates a pressure sensor inside. The cavity inside the sensor cover is connected to the internal channel of the connector, enabling the pressure sensor to detect the pressure of the water flowing through the connector in real time. A control block is disposed on the connector, and a control chip is disposed therein. The control chip is electrically connected to the pressure sensor. Execution unit, comprising: The main structure is used to connect the water dispenser, and the main structure has a cavity inside; An explosion-proof flexible joint is connected between the joint and the main structure, allowing the joint to communicate with the cavity of the main structure and forming a water flow channel; The leak-proof valve core is rotatably disposed within the cavity of the main structure; A gasket is disposed between the leak-proof valve core and the main structure; The drive unit includes: A stepper motor is fixedly mounted on the main structure. The connecting rod is connected at one end to the anti-leakage valve core; A coupling is connected between the other end of the connecting rod and the output shaft of the stepper motor, and the control chip in the control block is electrically connected to the stepper motor; The control chip is configured to receive the detection signal from the pressure sensor and control the forward / reverse rotation of the stepper motor according to the signal; the stepper motor drives the anti-leakage valve core to rotate through the coupling and the connecting rod, thereby opening or closing the water flow channel.

2. The leak-proof smart control drinking water machine connector according to claim 1, characterized in that, The control chip is further configured to perform the following leakage detection steps: The pressure signal from the pressure sensor is continuously sampled, and its pressure value sequence over time is calculated. Based on the pressure value sequence, a basic pressure reference value that characterizes the normal pressure fluctuation range of the current system is dynamically updated and maintained. The instantaneous deviation of the current pressure signal from the baseline pressure reference value is calculated in real time, and the intensity of the continuous downward trend of the pressure signal within a predetermined time window is calculated. A leak is determined to have occurred and the stepper motor is triggered to shut down only if both of the following conditions are met simultaneously: (a) The instantaneous deviation exceeds the first dynamic threshold, and (b) The intensity of the sustained downward trend exceeds the second dynamic threshold; The first dynamic threshold and the second dynamic threshold are both adaptively adjusted based on the baseline pressure reference value and the historical pressure fluctuation statistics.

3. The leak-proof intelligent control water dispenser connector according to claim 2, characterized in that, The control chip has a pre-stored typical pressure change pattern during normal water use. The control chip monitors the signal changes of the pressure sensor in real time and compares the current pressure change curve with the typical pressure change pattern. When the matching degree between the current pressure change curve and any of the typical pressure change patterns exceeds a preset similarity threshold, it is determined to be normal water use interference and the shutdown action is not triggered. The stepper motor is only triggered to shut down when the matching degree between the current pressure change curve and all pre-stored typical pressure change patterns is lower than the similarity threshold and the leakage judgment condition is met.

4. The leak-proof smart control drinking water machine connector according to claim 3, characterized in that, The control chip is further configured to perform the following adaptive maintenance steps: In a preset learning period during which the system is stable and no shutdown action is triggered, pressure data of the pressure sensor is continuously recorded; Based on the pressure data in the learning period, a static pressure reference value and a normal pressure fluctuation range in the system installation environment are statistically analyzed, and the reference baseline for water leakage determination is updated; In subsequent operation, the static pressure reference value is updated at a first preset time interval to compensate for the effects caused by long-term slow changes in water source pressure or sensor zero drift; When a sudden change in pressure signal is monitored, which exceeds the normal pressure fluctuation range and is not caused by a normal water use event, a rapid recalibration process is started: in a short time window during which the system determines no water leakage and is stable, the static pressure reference value is resampled and updated.

5. The leak-proof smart control drinking water machine connector according to claim 1, characterized in that, Further comprising: a communication module electrically connected to the control chip; The control chip is further configured to: When the shutdown action of the stepper motor is triggered in response to a water leakage event, alarm information containing the event type, occurrence time, and connector identification is generated and sent to a designated remote monitoring terminal through the communication module; The working data of the pressure sensor, the action record of the stepper motor, and the system self-checking state are continuously or periodically collected and stored; In response to instructions received from the remote monitoring terminal through the communication module, at least one of the following operations is performed: uploading historical working data, performing system self-checking, resetting the leak-proof valve core to an open state, or adjusting water leakage determination parameters.

6. The leak-proof smart control drinking water machine connector according to claim 2, wherein, The control chip is configured to impose stability constraints on the adaptive adjustment process of the first dynamic threshold and the second dynamic threshold: A variable calculation value determined by the base pressure reference value is set for the first dynamic threshold, and an absolute lower limit value and an absolute upper limit value are preset for the first dynamic threshold, so that the final applied first dynamic threshold is always limited within the closed interval formed by the absolute lower limit value and the absolute upper limit value; A reference value calculated based on historical pressure fluctuation statistical values is set for the second dynamic threshold, and a maximum allowed floating percentage is set for the reference value, so that the change amplitude of the final applied second dynamic threshold does not exceed the maximum allowed floating percentage in any adjustment period; When updating the first dynamic threshold or the second dynamic threshold, a weighted smoothing algorithm is used to weight and average the new calculation value and the threshold value of the last period to produce the threshold value applied in the next determination period, thereby avoiding stepwise mutation of the threshold value.

7. The leak-proof intelligent control faucet connector according to claim 3, wherein, After determining that the instantaneous deviation exceeds the first dynamic threshold, and before waiting for the sustained downward trend strength to reach the second dynamic threshold, a pre-fast response program is executed: A fast verification period with a duration shorter than the predetermined time window is immediately started; In the fast verification period, pressure data is collected at a higher frequency and a short-term downward trend is calculated; If the slope of the short-term downward trend is greater than a preset emergency slope threshold, the waiting for the intensity of the sustained downward trend to reach the second dynamic threshold is no longer performed, and the closing action of the stepper motor is triggered immediately; If the slope of the short-term downward trend does not exceed the emergency slope threshold, the determination process based on the intensity of the sustained downward trend is continued. 8.The intelligent control water leakage prevention faucet according to claim 3, wherein, When the matching degree of the current pressure change curve with all the pre-stored typical pressure change patterns is lower than the similarity threshold, but it is finally determined as a normal water use event, the current pressure change curve that is not identified this time is recorded as a new candidate pattern; When the candidate pattern appears repeatedly for a preset number of times within a preset period, it is automatically added as a new pre-stored typical pressure change pattern; At the same time, after successfully matching each time, the current pressure change curve matched this time is fused and calculated with the pattern to fine-tune and update the parameters of the pattern, so that it can adapt to small changes in water use habits or system parameters. 9.The intelligent control water leakage prevention faucet according to claim 8, wherein, All adaptive processes are divided into two categories: the first category of adaptive processes that affect the core determination reference, and the second category of adaptive processes that affect auxiliary judgment parameters; The update of the static pressure reference value is defined as the first category of adaptive processes; During the update operation of the first category of adaptive processes, the update operation of all second category adaptive processes is suspended; After the update of the first category of adaptive processes is completed, the system enters a parameter stabilization period, during which the second category of adaptive processes is only allowed to record data, and is not allowed to modify its parameters based on newly recorded data; The length of the parameter stabilization period covers at least three complete normal water use cycles after the update of the first category of adaptive processes.

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