A proactive early warning intelligent service system of a community intelligent drinking water equipment

Abstract

The application relates to the technical field of drinking water equipment, and discloses an active early warning intelligent service system of a community intelligent drinking water equipment. An edge master control module receives original data of a sensor group and identifies a steady-state operation window of the equipment. In the steady-state operation window, a flow-electricity coupling impedance characteristic value is extracted, and a stagnation heat risk integral quantity and an ultraviolet attenuation penalty factor are calculated. The stagnation heat risk integral quantity and the ultraviolet attenuation penalty factor are multiplied to generate a biological pollution risk criterion. When the criterion exceeds a preset trigger boundary, a cleaning instruction is generated according to the flow-electricity coupling impedance characteristic value, an optimal oscillation frequency is matched to drive a booster pump to output a pulsating water flow to execute cleaning, and a correction action is executed according to an impedance recovery difference after the cleaning. The application filters external environmental interference, improves internal pipeline state sensing accuracy, avoids biological pollution caused by heat accumulation of retained water in the pipeline, and adaptively adjusts the cleaning intensity to balance the stripping effect and operation noise.

Description

Technical Field

[0001] This invention relates to the field of drinking water equipment technology, specifically to an active early warning intelligent service system for community smart drinking water equipment. Background Technology

[0002] With the increasing prevalence of direct drinking water equipment in communities, ensuring water quality and stable operation has become crucial. While existing technologies (such as CN120378832A IoT management, CN110162015B fault diagnosis, CN120578052B intelligent control, and CN107121960A big data monitoring) have improved overall operational capabilities, they still have shortcomings in addressing the problem of heat accumulation in stagnant water in pipelines due to environmental factors, leading to the proliferation of internal microorganisms.

[0003] For example, Turkish patent TR201720859A2 discloses a method for detecting algae, bacteria, etc., using sensors and issuing audible and visual alarms via LEDs or smart terminals. However, this technology only remains at the passive response stage of alarming after exceeding the standard, lacking proactive prediction and early warning based on multi-dimensional data (such as water quality, water temperature, equipment electrical parameters, etc.), and unable to provide proactive intervention and intelligent services (such as automatically starting cleaning, shutting off water supply, etc.) before pollution or malfunction occurs.

[0004] In addition, the existing timed continuous cleaning mode for dealing with pollution is difficult to effectively remove stubborn deposits, and full-load rinsing is prone to water hammer effect and noise. At the same time, the system lacks a parameter correction mechanism for the aging of disinfection components such as ultraviolet sterilization components and changes in pipeline resistance, resulting in low sensing accuracy and a decline in the long-term early warning accuracy and cleaning effectiveness of the equipment. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an active early warning intelligent service system for community smart drinking water equipment. This system solves the problems of low accuracy in sensing the internal pipeline status due to external environmental interference such as fluctuations in pipeline pressure and temperature changes during the long-term operation of drinking water equipment, potential biological pollution caused by heat accumulation in stagnant water in the pipeline, and the inability of conventional cleaning modes to adaptively adjust the cleaning intensity to balance stripping effect and operating noise.

[0006] To address the above problems, the present invention provides the following technical solution: This invention provides an active early warning intelligent service system for community smart drinking water equipment, comprising: a drinking water equipment entity, a sensor group, an actuator, and an edge control module. The sensor group and the actuator are disposed on the drinking water equipment entity, and the edge control module is communicatively connected to the sensor group and the actuator. The edge control module is used to: receive raw sampling data output by the sensor group and identify the steady-state operation window of the drinking water equipment entity; based on the raw sampling data, extract current coupling impedance characteristic values ​​within the steady-state operation window, and calculate the stagnation heat risk integral and the ultraviolet attenuation penalty factor; multiply the stagnation heat risk integral and the ultraviolet attenuation penalty factor to generate a biological contamination risk criterion; when the biological contamination risk criterion exceeds a preset trigger boundary, generate a cleaning command based on the current coupling impedance characteristic value and send it to the actuator to drive the drinking water equipment entity to perform a cleaning action.

[0007] Furthermore, the drinking water equipment includes a booster pump, a filter assembly, a wastewater bypass valve, an ultraviolet sterilization assembly, and pipelines; the actuator specifically includes the booster pump, the wastewater bypass valve, and the ultraviolet sterilization assembly; the sensor group includes a flow sensor, a temperature sensor, a pressure sensor, a current sampling circuit, a voltage sampling circuit, and a photosensitive sensor.

[0008] Furthermore, the step of receiving the raw sampling data output by the sensor group and identifying the steady-state operating window of the drinking water device specifically includes: constructing a sampling matrix from the raw sampling data within the same time period. ; in, , , , , and These are parameter vectors representing flow rate, temperature, pressure, current, voltage, and illumination data collected by a flow sensor, temperature sensor, pressure sensor, current sampling circuit, voltage sampling circuit, and photosensitive sensor, respectively. The edge control module performs low-pass filtering on the original sampled data to obtain a filtered sequence. The edge control module calculates the flow rate dispersion slope and the current dispersion slope based on the filtered sequence. When the absolute value of the flow rate dispersion slope is less than a preset flow rate slope threshold and the current dispersion slope is less than a preset current slope threshold, and the filtered instantaneous flow rate value obtained based on the filtered sequence is greater than a preset minimum effective flow rate, the edge control module verifies and confirms the steady-state point and extracts the steady-state operating window.

[0009] Furthermore, the edge master control module is also used to: calculate the average parameters of inlet pressure, bus voltage and water temperature based on the original sampling data within the steady-state operation window; perform dimensionality reduction transformation on the average parameters using a discrete mapping function to generate an operating condition index, and use the operating condition index as the benchmark constraint condition for subsequent data processing.

[0010] Further, the step of extracting the current-electric coupling impedance characteristic value within the steady-state operation window based on the original sampled data specifically includes: extracting the average operating current and average flow rate of the booster pump within the steady-state operation window; retrieving the preset reference current and preset reference flow rate corresponding to the operating condition index, and dividing the ratio of the average operating current to the reference current by the ratio of the average flow rate to the reference flow rate to obtain the current-electric coupling impedance characteristic value.

[0011] Furthermore, the calculation of the stagnation heat risk integral specifically includes: when the instantaneous flow rate is identified as being lower than a preset stagnation threshold based on the original sampling data, accumulating the stagnation duration; when the stagnation duration exceeds a preset judgment duration, calculating the stagnation heat risk integral based on the stagnation duration and the water temperature in the pipeline collected by the temperature sensor, combined with a preset risk reference temperature and temperature sensitivity coefficient; when the instantaneous flow rate recovers, integrating and accumulating the instantaneous flow rate to obtain the cumulative replacement volume, and when the cumulative replacement volume reaches a preset replacement condition, forcibly resetting the stagnation heat risk integral to zero.

[0012] Further, the calculation of the ultraviolet attenuation penalty factor specifically includes: within the window when the ultraviolet sterilization component is lit and in a stable state, calculating the average electrical power and average irradiance based on the original sampling data; obtaining the reference power and reference irradiance corresponding to the ultraviolet sterilization component; and calculating the ultraviolet attenuation penalty factor based on the ratio of the average electrical power to the reference power, the ratio of the average irradiance to the reference irradiance, and a preset penalty upper limit.

[0013] Furthermore, the step of generating a cleaning command based on the current coupling impedance characteristic value and issuing it to the actuator specifically includes: acquiring the valid flags of the booster pump, the sewage bypass valve, the ultraviolet sterilization component, and the sensor group respectively; multiplying the valid flags to generate a comprehensive execution permission quantity; generating the cleaning command based on the current coupling impedance characteristic value when the comprehensive execution permission quantity meets a preset value; otherwise, sending a degraded operation code.

[0014] Furthermore, the edge master control module is also used to: respond to the cleaning command, control the sewage bypass valve to open and maintain the operation of the ultraviolet sterilization component; obtain the reference impedance value corresponding to the operating condition index, calculate the impedance severity based on the current coupling impedance characteristic value and the reference impedance value, and match the optimal cleaning oscillation frequency based on the impedance severity; generate a pulsating duty cycle sequence based on the optimal cleaning oscillation frequency, and drive the booster pump to output continuously oscillating water to perform the cleaning action.

[0015] Furthermore, the edge control module is also configured to: after the cleaning action is completed, keep the drain bypass valve open, perform time integration calculation based on the instantaneous flow rate obtained by the flow sensor to obtain the net air exhaust volume, and control the drain bypass valve to close after confirming that the net air exhaust volume has reached a preset dead zone volume threshold; after controlling the drain bypass valve to close, identify and extract the post-measured steady-state window, and extract the current coupling impedance characteristic value in the post-measured steady-state window to calculate the impedance recovery difference before and after cleaning; perform a correction action based on the distribution range of the impedance recovery difference, the correction action including updating the temperature sensitivity coefficient used to calculate the integral of the stagnant heat risk or overwriting the reference impedance value.

[0016] Furthermore, the verification and confirmation of the steady-state point and the extraction of the steady-state operating window specifically include: constructing a sliding buffer queue that stores the filtered sequence in time series; configuring a steady-state counter, performing an accumulation operation on the steady-state counter when the current sampling point meets the primary steady-state characteristics, and clearing the steady-state counter to zero when a non-steady-state point occurs; when the accumulated value of the steady-state counter reaches the set minimum number of steady-state points, triggering a steady-state locking mechanism, and recording the starting point of continuous counting and the current time coordinate when the minimum number of steady-state points is reached, thus forming the steady-state operating window.

[0017] Furthermore, the step of using a discrete mapping function to perform dimensionality reduction transformation on the average parameters to generate the operating condition index specifically includes: subtracting the corresponding lower limit of the average inlet pressure, average bus voltage, and average water temperature from their respective reference values, dividing by the corresponding interval step size, and then performing a rounding operation to obtain the initial pressure interval number, voltage interval number, and temperature interval number; performing a boundary limiting mechanism on the initial interval numbers to obtain the limited interval numbers; and merging the limited pressure interval numbers, voltage interval numbers, and temperature interval numbers based on multi-base combinational logic to generate the one-dimensional operating condition index.

[0018] Furthermore, in response to the cleaning command, the system performs pipeline reconfiguration and status feedback confirmation, specifically including: starting the booster pump at a set safe test speed and reading instantaneous flow data and pipeline pressure data; when the instantaneous flow data is greater than the set lower limit threshold for flushing flow and the pipeline pressure data is less than the set upper limit threshold for pressure relief, and this condition is maintained for a set determination time, the system confirms that the sewage bypass valve is in the open state, and determines that the low-resistance path has been successfully established; otherwise, it switches to a degraded operation mode.

[0019] Furthermore, the step of generating a pulsating duty cycle sequence based on the optimal cleaning oscillation frequency specifically includes: setting the base sustain duty cycle and the pulsating peak duty cycle of the driving signal; constructing a low-frequency modulation envelope function in combination with the optimal cleaning oscillation frequency; and using the output value of the low-frequency modulation envelope function as a weight to perform a linear mapping between the base sustain duty cycle and the pulsating peak duty cycle to generate a real-time driving duty cycle that changes continuously with time.

[0020] Furthermore, before extracting the current-electric coupling impedance characteristic value within the post-test steady-state window, the system performs a comparability verification of the operating conditions before and after cleaning. Specifically, this includes: retrieving the baseline operating condition parameter set before cleaning and the current post-test operating condition parameter set after cleaning; calculating the absolute values ​​of the differences between the average temperature, average pressure, and average driving voltage of the two respectively; when the absolute values ​​of the above differences are all less than or equal to their respective tolerance thresholds, the post-cleaning effect evaluation process is allowed; otherwise, the current-electric coupling impedance characteristic value after cleaning is written into the historical sample sequence as a temporary baseline sample.

[0021] Furthermore, the correction action based on the distribution range of the impedance recovery difference specifically includes: when the impedance recovery difference is greater than or equal to the effective stripping threshold, updating the temperature sensitivity coefficient used to calculate the integral of the stagnant heat risk; when the impedance recovery difference is greater than or equal to the abnormal change threshold and less than the effective stripping threshold, overwriting the reference impedance value corresponding to the current operating condition index through a first-order exponential smoothing filter mechanism; when the impedance recovery difference is less than the abnormal change threshold, maintaining the original reference impedance value unchanged, and generating a maintenance log containing current operating condition parameters and fault codes.

[0022] Furthermore, as a preferred technical solution, the edge master control module includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the functions of the edge master control module.

[0023] This invention provides a proactive early warning intelligent service system for community smart drinking water equipment, which has the following beneficial effects: This invention eliminates the interference of transient fluctuations on sampling data by identifying a steady-state operating window, and extracts the current-electric coupling impedance characteristic value under this steady-state constraint to characterize the pipeline flow state. Simultaneously, the system multiplies the stagnation heat risk integral calculated based on the water stagnation state with the ultraviolet attenuation penalty factor reflecting the aging state of the sterilization components to generate a comprehensive biological contamination risk criterion as a trigger condition. When the trigger condition is met, a cleaning command is generated based on the aforementioned current-electric coupling impedance characteristic value. This multi-dimensional synergistic coupling of the physical pipeline impedance of the drinking water equipment, the risk of the stagnant water environment, and the aging state of the sterilization components avoids misjudgments caused by single threshold judgments or transient interference, improving the accuracy of early warning triggering and cleaning execution, and reducing the problems of over-cleaning or under-cleaning.

[0024] In the process of identifying the steady-state operating window, this system calculates the discrete slope of flow rate and current and combines it with a filtering mechanism. At the same time, it uses a discrete mapping function to reduce the dimension of inlet pressure, voltage and water temperature to generate an operating condition index. This eliminates hydraulic interference caused by pump start-up or valve operation, reduces the impact of temperature changes and pipeline pressure fluctuations on the reference offset of sensor data, and improves the reliability of internal pipeline status perception.

[0025] During cleaning, this system drives the booster pump to output pulsating water flow by matching the appropriate cleaning oscillation frequency according to the severity of pipeline impedance. After cleaning, it calculates the impedance recovery difference to update the temperature sensitivity coefficient or overwrite the reference impedance value. It uses fluid shear force to peel off the deposits on the pipe wall, which helps reduce the peak pressure of the pipeline and operating noise. At the same time, the automatic parameter correction process after cleaning enables the system to adapt to the aging process of equipment components, maintaining the effectiveness of early warning and cleaning strategies under long-term operating conditions. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall architecture of the proactive early warning intelligent service system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the physical structure of the drinking water device of the present invention; Figure 3 This is a flowchart illustrating the overall workflow of the proactive early warning intelligent service system according to an embodiment of the present invention. Figure 4 This is a control flowchart for the multi-dimensional physical state perception and preprocessing stage in an embodiment of the present invention. Figure 5 This is a flowchart of the parallel calculation and early warning determination of the dual-track bottom-level feature parameters in an embodiment of the present invention; Figure 6 This is a state transition diagram of the active early warning triggering mechanism according to an embodiment of the present invention; Figure 7 This is a flowchart of envelope-limited continuous pulsation control according to an embodiment of the present invention; Figure 8 The diagram shows the system control and response curves for the flexible oscillation cleaning stage of the present invention. (a) is the continuous pulse duty cycle envelope curve of the system output to the booster pump drive circuit during the flexible oscillation cleaning stage, and (b) is the smooth transition response curve of the flow field of the measured pressure in the internal pipeline of the equipment under the drive of the above control law.

[0027] The components include: 1. UV sterilization component; 2. filtration component; 3. flow sensor; 4. booster pump; 5. pressure sensor; 6. inlet pipe; 7. sewage bypass valve; and 8. outlet pipe. Detailed Implementation

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] See Figure 1 and Figure 2 This invention provides an active early warning intelligent service system, mainly comprising a drinking water equipment entity, a sensor group, an edge control module, and a cloud-based intelligent service platform. The drinking water equipment entity provides the basic topology for fluid processing, including an inlet pipe 6, a booster pump 4, a filter assembly 2, a sewage bypass valve 7, an ultraviolet sterilization assembly 1, and an outlet pipe 8. Specifically, the output end of the inlet pipe 6 is connected to the input end of the booster pump 4, the output end of the booster pump 4 is connected to the input end of the filter assembly 2, and the output end of the filter assembly 2 is connected to the input end of the ultraviolet sterilization assembly 1. The output end of the ultraviolet sterilization assembly 1 is connected to a common main channel, at the end of which a branching node is set. The branching node is connected to the input ends of the outlet pipe 8 and the sewage bypass valve 7, respectively. The output end of the sewage bypass valve 7 is connected to an external sewage discharge channel.

[0030] The sensor array is installed at various operating nodes of the drinking water equipment to collect comprehensive low-level operating data. Specifically, the sensor array includes at least: a flow sensor 3 installed on the common main channel to collect flow data during the regular water supply and sewage cleaning stages; a temperature sensor and a pressure sensor 5 installed on the inlet pipe 6 to collect inlet water pressure; a photosensitive sensor installed inside the ultraviolet sterilization component 1; and current sampling circuits and voltage sampling circuits electrically connected to the booster pump 4 and the ultraviolet sterilization component 1. As a preferred embodiment, the sensor array also includes a back pressure sensor, which is installed at the output end of the booster pump 4 or on the upstream main pipe of the sewage bypass valve 7 to detect the pressure relief state after the sewage bypass valve 7 is opened.

[0031] The edge control module is communicatively connected to the aforementioned sensor group, booster pump 4, sewage bypass valve 7, and ultraviolet sterilization component 1. It receives various physical quantity signals output by the sensor group and outputs execution drive signals to the booster pump 4, sewage bypass valve 7, and ultraviolet sterilization component 1. Furthermore, the edge control module establishes a data interaction link with the cloud-based intelligent service platform via a wireless communication network to upload status characteristic data. The cloud-based intelligent service platform receives the status characteristic data, stores the baseline data of each device, and generates corresponding maintenance dispatch instructions based on the status characteristic data.

[0032] See Figure 3 The proactive early warning intelligent service system executes five consecutive process stages in chronological order.

[0033] S1 is the multi-dimensional physical state perception and preprocessing stage. The edge control module synchronously acquires signals from the sensor group. Specifically, the raw sampling data within the same time period is constructed into a sampling matrix. ;in, , , , , and These are parameter vectors representing the flow rate, temperature, pressure, current, voltage, and illumination data collected by the flow sensor 3, temperature sensor, pressure sensor 5, current sampling circuit, voltage sampling circuit, and photosensitive sensor, respectively.

[0034] The edge control module performs low-pass filtering on the raw sampled data. It then extracts the slope characteristics of the flow and current parameters to identify the steady-state operating window of the water supply equipment. Based on the ambient temperature and input pressure within this steady-state operating window, the edge control module constructs the corresponding operating condition index.

[0035] S2 represents the parallel computation stage for the dual-track bottom-level characteristic parameters. The first type represents current-electric coupling impedance characteristics, and the second type represents biocontamination risk-related characteristics, including the stagnation heat risk integral. and UV attenuation penalty factor The edge control module extracts the current coupling impedance characteristic value within the steady-state operation window. It calculates the stagnation heat risk integral based on the duration of the stagnation time and the water temperature. Simultaneously, the edge control module calculates the UV attenuation penalty factor for UV sterilization component 1.

[0036] S3 is the active biocontamination early warning and judgment stage based on boundary conditions. The edge master control module outputs biocontamination risk criteria by combining the stagnation heat risk integral and the ultraviolet attenuation penalty factor. When the biocontamination risk criteria exceed the trigger boundary and the status flags of all electrical nodes in the system meet the conditions, the edge master control module generates an active cleaning trigger command. When the status flags do not meet the conditions, the edge master control module sends a degradation operation code to the cloud intelligent service platform. The degradation operation code includes at least the device number, faulty component identifier, fault timestamp, and current operating condition index. Key sensor snapshots and anomaly type encoding.

[0037] S4 is the flexible oscillating cleaning execution stage based on envelope constraints. The edge master control module controls the opening of the sewage bypass valve 7 and maintains the operation of the ultraviolet sterilization component 1. The edge master control module matches the target cleaning frequency according to the current coupling impedance characteristic value. The edge master control module generates a pulsating duty cycle sequence with amplitude limiting mechanism and rate of change limiting mechanism to drive the booster pump 4 to output continuously oscillating fluid to perform cleaning.

[0038] S5 is the post-cleaning physical identification and baseline parameter closed-loop self-correction stage. After the flexible oscillation cleaning is completed, the edge control module keeps the drain bypass valve 7 open and controls the booster pump 4 to run at the set base speed, executing the exhaust delay clearance constraint. Once the set dead zone volume threshold is reached, the bypass valve 7 is closed and the post-measured steady-state characteristic parameters are extracted. The edge control module calculates the impedance recovery difference before and after cleaning. Based on the distribution range of the impedance recovery difference, the edge control module performs correction actions. Action branches include updating the sensitive constant of the stagnant heat risk integral, overwriting the reference impedance set stored in the cloud intelligent service platform, or pushing an abnormal status code to the cloud intelligent service platform to trigger it to issue an equipment maintenance work order.

[0039] See Figure 4 The specific steps for the edge control module to perform the multi-dimensional physical state perception and preprocessing stage may include: S11: In this embodiment, the edge master control module performs sampling at a set frequency during the operating cycle of the drinking water device entity. The system drives the sensor array to acquire data. During this process, the system operates according to a set sampling period. Perform discrete sampling.

[0040] S12: During data acquisition, the edge control module receives hydraulic and environmental parameters output by the sensor group. The continuous time variable is set as... The aforementioned hydraulic and environmental parameters specifically include the instantaneous flow data output by flow sensor 3. The water temperature inside the pipeline output by the temperature sensor. and the inlet water pressure output by pressure sensor 5. .

[0041] S13: Combining hydraulic and environmental parameters, the edge control module synchronously receives electrical and actuation parameters output from the sensor group. These electrical and actuation parameters include the booster pump operating current output from the current sampling circuit. and the working current of the ultraviolet sterilization component The driving bus voltage output by the voltage sampling circuit and the operating voltage of the ultraviolet sterilization component and the irradiance of the ultraviolet sterilization component output by the photosensitive sensor. As a preferred approach, the electrical and actuation parameters also relate to the feedback status of the drain bypass valve 7. This is used to monitor the actual opening and closing status of physical valves.

[0042] S14: To filter out interference from the external electromagnetic environment on weak sensing signals, the edge master control module performs filtering processing on the received raw sampling data. The edge master control module uses a discrete first-order low-pass filtering algorithm with a forgetting factor to calculate the filtered sequence. This is done to smooth out data features. The specific calculation formula is as follows: ; In the formula, This is the filtered value at the current sampling time; Indicates the discrete sampling sequence number; This is the filtered value from the previous sampling time. The filter coefficients for the corresponding sampling channels have a range of values ​​that satisfy... The filtering coefficient can be calibrated based on the inherent response time of each sensor hardware and the characteristics of external interference. For any original sampling channel at discrete sampling number The original sampled values ​​at the location. After calculation, the edge control module will filter the sequence. It is stored in internal memory for subsequent operations.

[0043] S15: Based on the data smoothing, the edge control module further extracts the slope characteristics of the flow and current parameters. Because the internal flow field of the drinking water equipment is prone to pulsating disturbances when the booster pump 4 is started or the sewage bypass valve 7 is activated, causing instantaneous physical quantities to deviate from the true resistance characteristics, the edge control module, based on the aforementioned filtered sequence... The flow rate dispersion slope and current dispersion slope are calculated separately. Specifically, the edge control module extracts the filtered instantaneous flow rate value at the current moment and the historical filtered instantaneous flow rate values ​​separated by a set flow rate difference span, calculates the difference between the two and divides it by the corresponding time span (i.e., the flow rate difference span and the sampling period). The product of the current and current is used to obtain the flow dispersion slope. Similarly, the edge control module calculates the difference between the current filtered booster pump operating current value at the current moment and the historical filtered booster pump operating current value at a set current difference span, and divides it by the corresponding time span to obtain the current dispersion slope. The flow difference span and current difference span mentioned above are both positive integers, which can be combined with the sampling frequency. Configure with pipeline voltage regulation delay characteristics.

[0044] S16: To pinpoint a reliable data analysis interval, the edge control module determines whether the drinking water equipment is in steady-state operation based on slope characteristics. Steady-state operation requires simultaneously meeting the following three basic criteria: First, the absolute value of the flow dispersion slope must be less than or equal to the set flow slope threshold. Second, the absolute value of the current dispersion slope must be less than or equal to the set current slope threshold. Third, the current filtered instantaneous flow rate must be greater than or equal to the set minimum effective flow rate to eliminate invalid operating conditions such as air resistance or severe leakage inside the pipeline. The edge control module needs to verify this steady-state operating condition in a continuous discrete sampling sequence. The number of sampling points that continuously meet the condition is calculated by dividing the set minimum steady-state duration by the sampling period. The result is then rounded up. When a detection point does not meet the above steady-state operating conditions, the edge control module continues to perform the judgment according to the time-series sliding sampling window.

[0045] S17: When the above steady-state operation conditions are met, the edge master control module successfully extracts the steady-state operation window. At this time, the start time of this steady-state operation window is recorded. and end time and through the end time and the start time The difference determines the window length of the steady-state operating window. .

[0046] S18: After establishing the steady-state operating window, the edge master control module calculates the average environmental parameters within the steady-state operating window. Since outdoor temperature differences and fluctuations in municipal pipeline pressure can alter the fluid's dynamics, to reduce errors introduced by external variables, the edge master control module considers the inlet water pressure... Drive bus voltage and the water temperature inside the pipeline The mean value is calculated in the time domain. In the engineering implementation of the microprocessor, the edge control module obtains the average inlet pressure, average bus voltage, and average water temperature by accumulating the corresponding discrete sampled data sequences within the steady-state operation window and dividing by the total amount of sequence data.

[0047] S19: Finally, the edge master control module constructs the current operating condition index based on the acquired average environmental parameters. This establishes a unified comparison benchmark in a multi-dimensional environmental space. Specifically, the edge control module uses a discrete mapping function to reduce the dimensionality of the average environmental parameters. This discrete mapping function includes pressure mapping, voltage mapping, and temperature mapping mechanisms. The edge control module converts the average inlet pressure, average bus voltage, and average water temperature into corresponding pressure range numbers. Voltage range numbering and temperature range number And further generate a working condition index. Operating Condition Index Integrating external environmental factors related to hydraulics and electricity, this system provides unique parametric coordinates for subsequent condition assessments and historical benchmark comparisons of drinking water equipment. Operating Condition Index For the specific construction process, please refer to steps S191 to S194.

[0048] In this embodiment, to reduce data deviation caused by transient disturbances, the edge master control module performs adaptive identification of the steady-state operating window during the data preprocessing stage. To further illustrate the steady-state operating condition identification process in step S16, the edge master control module may execute the following sub-steps.

[0049] In practice, during the operation of the drinking water equipment, the startup process of the booster pump 4 and the switching action of the sewage bypass valve 7 cause flow field reconstruction within the pipeline. When the fluid accelerates from rest or changes direction, pressure fluctuations and backflow oscillations easily occur within the pipeline. This change in fluid state causes the initial data collected by the sensor group to exhibit nonlinear pulsating characteristics, and the extracted data cannot stably reflect the steady-state impedance performance of the filter component 2 and the system. Therefore, the edge control module uses the following control steps to eliminate transient interference intervals.

[0050] S161: The edge master control module constructs a sliding buffer queue in its internal memory. As a preferred approach, this sliding buffer queue employs a first-in, first-out (FIFO) mechanism, temporarily storing discrete data sequences, including filtered instantaneous flow rate values ​​and filtered booster pump operating current values, in a time-series manner. To ensure that historical data can be effectively retrieved, the depth of the sliding buffer queue is pre-allocated based on the set flow rate differential span, current differential span, and sampling period.

[0051] S162: After establishing the data buffering mechanism, the edge master control module obtains the changing trends of characteristic parameters based on the sliding buffer queue, and calculates the flow dispersion slope and current dispersion slope of the current sampling point respectively. In the actual calculation, the edge master control module recalculates the flow dispersion slope and current dispersion slope of the current sampling point based on the latest and historical filtered data with differential spans in the sliding buffer queue.

[0052] S163: After acquiring the aforementioned slope characteristics, the edge control module performs a multi-dimensional conditional joint judgment. At this time, the edge control module extracts the absolute values ​​of the calculated flow rate discrete slope and current discrete slope, and compares them with the set flow rate slope threshold and current slope threshold. Furthermore, the filtered instantaneous flow rate value at the current sampling point is also simultaneously compared with the set minimum effective flow rate. The aforementioned thresholds can be calibrated based on the conventional tolerance range under normal and stable water production conditions of the drinking water equipment.

[0053] S164: If the absolute value of the flow rate dispersion slope is not greater than the flow rate slope threshold, and the absolute value of the current dispersion slope is not greater than the current slope threshold, and the instantaneous flow rate after filtering is not less than the minimum effective flow rate, the edge master control module determines that the current sampling point meets the primary steady-state characteristics and marks it as a candidate steady-state point. Conversely, if any of the above determination conditions are not met, it indicates that the system is in the flow field oscillation period or there is an abnormally low flow condition, and the edge master control module will mark the current sampling point as a non-steady-state point.

[0054] S165: Considering that occasional flatness in local data can easily lead to misjudgments, the edge master control module introduces a time-resident mechanism for continuous verification. Through an internally configured steady-state counter, the edge master control module controls the steady-state counter to perform an accumulation operation when a candidate steady-state point is detected; if a non-steady-state point appears in the detection sequence, the edge master control module clears the steady-state counter to zero and restarts counting.

[0055] S166: As the sampling sequence progresses, the edge control module continuously monitors the value of the steady-state counter. When the accumulated value of the steady-state counter reaches the set minimum steady-state point count, the edge control module confirms that the water supply device meets the set requirements for a stable water production state, thereby triggering the steady-state locking mechanism. Simultaneously, to avoid falling into a logic dead zone when the water supply device experiences severe leakage or continuous abnormal fluctuations, the edge control module is configured with a timeout exit mechanism. If the number of continuously sliding sampling points exceeds the set maximum detection step count within the current operating cycle, and the steady-state counter has not yet reached the minimum steady-state point count, the edge control module interrupts the identification of this steady-state operating window and outputs a non-steady-state abnormality flag to terminate the current stage of the process.

[0056] S167: After triggering the steady-state locking mechanism, the edge master control module records the time coordinate corresponding to the starting point of this continuous counting as the start time of the steady-state operation window, and simultaneously records the current time coordinate when the minimum number of steady-state points is reached as the end time of the steady-state operation window. Relying on processing logic combining slope constraints and time dwell, the edge master control module can adaptively avoid transient interference regions and obtain a steady-state operation window that meets the algorithm evaluation requirements, thereby providing reliable data support for subsequent feature parameter calculations.

[0057] In this embodiment, after successfully extracting the steady-state operating window, the edge master control module further executes the dynamic operating condition index construction to establish an alignment benchmark under multi-dimensional environmental parameters. To further illustrate the operating condition index construction process in step S19, the edge master control module may execute the following sub-steps.

[0058] In outdoor or semi-outdoor scenarios, the operating status of drinking water equipment is easily affected by various external environmental factors. For example, diurnal fluctuations in the inlet pressure of the municipal water supply network can alter the system's basic hydraulic back pressure; voltage fluctuations in the drive bus caused by changes in power grid load can affect the actual output power of the booster pump 4; furthermore, changes in outdoor temperature can alter the dynamic viscosity of the fluid within the pipeline. Without decoupling these external variables, the system's assessment of internal fluid impedance changes often fails to objectively reflect the true state of the filter component 2. Therefore, the edge control module constructs a dynamic operating condition index through the following steps.

[0059] S191: After obtaining the average value of each feature, the edge control module discretizes it using a discrete mapping function. Specifically, during execution, the edge control module will average the inlet water pressure... Subtract the preset lower limit of the reference inlet water pressure Then divide by the set pressure range step size. The calculation results are then rounded down to obtain the initial pressure interval number. Similarly, the edge master control module will average the bus voltage. Subtract the preset lower limit of the reference bus voltage Divide by the voltage range step size Then round down to obtain the initial voltage range number. Average water temperature Subtract the preset lower limit of the reference water temperature Divide by the temperature range step size Then round down to obtain the initial temperature range number. Based on this, the aforementioned lower limit of the benchmark inlet water pressure Lower limit of reference bus voltage and the lower limit of the reference water temperature It can be pre-configured in the internal memory based on historical extreme environmental data of the physical deployment location of the drinking water equipment.

[0060] S192: To prevent data addressing out-of-bounds errors under extreme weather or abnormal pipeline conditions, the edge master control module executes a boundary limiting mechanism after obtaining the initial interval numbers. If the interval number calculated for any dimension is less than zero, the edge master control module sets it to zero; if the calculated interval number is greater than or equal to the total number of discrete intervals corresponding to that dimension, the edge master control module overwrites it with the maximum sequence number corresponding to the total number of discrete intervals minus one, thereby obtaining the pressure interval number after limiting. Voltage range numbering and temperature range number .

[0061] S193: After calculating the interval numbers for each dimension and completing the amplitude limiting, the edge control module integrates multi-dimensional parameters to construct a one-dimensional operating condition index. To create unique address coordinates in internal memory without carry overlap, the edge master control module generates the operating condition index based on multi-base combinational logic. The calculation formula is as follows: ; In the formula, For operating condition index; Number the pressure ranges; The total number of pressure discrete intervals allocated by the system based on the extreme range of inlet pressure; Number the voltage ranges; The total number of voltage discrete intervals allocated based on the span of voltage fluctuation extreme values; The temperature range is numbered.

[0062] S194: The edge master control module will generate the operating condition index. This serves as a baseline constraint for subsequent data processing. When the system performs internal state assessment and baseline parameter comparison, the edge control module sets the current operating data to be indexed against the same operating condition. The system performs matching calculations based on historical benchmark data. Relying on this environmental alignment mechanism, the proactive early warning intelligent service system can effectively isolate interference introduced by outdoor temperature and pressure fluctuations from multi-source heterogeneous data without adding additional hardware temperature and pressure regulation devices, ensuring the reliability of parameter sensing.

[0063] See Figure 5 In this embodiment, after extracting the steady-state operating window and operating condition index, the edge master control module performs a parallel calculation phase of the dual-track low-level feature parameters. In a specific embodiment, the edge master control module extracts the current coupling impedance characteristics, stagnant heat risk integral, and ultraviolet attenuation performance of the drinking water equipment entity in parallel. The specific steps include the following.

[0064] S21: The edge control module extracts the average value of the corresponding feature parameters within a defined steady-state operating window. In digital domain computation, the edge control module retrieves the discretely sampled booster pump operating current sequence and instantaneous flow rate sequence within the steady-state operating window, sums them, and divides by the total number of data points in the sequence to obtain the smoothed average pump current value. and average flow .

[0065] S22: When the filter component 2 inside the drinking water equipment becomes clogged, the water flow resistance increases, causing the booster pump 4 to consume more electrical energy to maintain the set output. To reduce the impact of different pipe network water pressure environments on the fluid foundation resistance assessment, the edge control module retrieves the internally stored index related to the current operating condition. Corresponding reference current With reference flow Current operating condition index When no corresponding reference parameters are available, the edge control module uses the factory calibration benchmark, the interpolation benchmark of the adjacent operating conditions, or the initial installation benchmark as temporary reference parameters. Based on the retrieved reference parameters, the edge control module calculates the average pump current. and average flow Normalization is performed to construct dimensionless current-current coupling impedance eigenvalues. The calculation formula is as follows: ; In the formula, This is the characteristic value of the current coupling impedance. Used to objectively reflect the degree of system obstruction under the current environment; Indicates the current operating condition index; This represents the average value of the pump current. The reference current corresponding to the current operating condition index; This represents the average flow rate. This is the reference traffic volume corresponding to the current operating condition index.

[0066] S23: The flow resistance of the drinking water equipment may change slowly over long-term operation. The edge control module further establishes a dynamic benchmark based on historical data. The edge control module extracts indices belonging to the same operating condition. The recent The characteristic value of current coupling impedance calculated within a historical operating cycle; when the number of historical samples under the same operating condition is insufficient. When the existing sample mean or temporary reference benchmark is used as a transitional benchmark, it switches to a dynamic benchmark after the sample accumulation meets the set number, and the arithmetic mean of the historical feature sequence is calculated and used as the index of the current working condition. Corresponding reference impedance value .for The value of can be set by those skilled in the art based on the daily usage frequency of the equipment, for example, by selecting a positive integer between 5 and 20. The reference impedance values ​​corresponding to each operating condition index are summarized to form a reference impedance set. Ideally, the data should be stored simultaneously in the internal memory of the edge control module and in the cloud-based intelligent service platform. The edge control module is used for real-time access, while the cloud-based intelligent service platform is used for long-term archiving, cross-device comparison, and maintenance analysis. After updating the baseline data on the edge side, an update message is simultaneously sent to the cloud-based intelligent service platform to complete consistent overwriting.

[0067] S24: In low-frequency water intake scenarios, the internal pipes of drinking water equipment are prone to prolonged periods without water flow. Stagnant water within the pipes is susceptible to heat accumulation due to ambient temperature, creating conditions conducive to microbial growth. Therefore, the edge control module performs an integral assessment of the stagnation heat risk. Specifically, when the instantaneous flow rate falls below a preset stagnation threshold... When the value is slightly larger than the zero-point noise error of flow sensor 3, the edge control module starts its internal timer to accumulate the dwell time. If the cumulative value of the stagnation time exceeds the preset judgment time... (The time can be set to 2 to 4 hours depending on the season and environment). The edge control module uses the water temperature collected in real time by the temperature sensor as the input parameter, calculates the difference between the actual water temperature and the preset risk reference temperature, and combines the difference with the preset temperature sensitivity coefficient to perform integral accumulation calculation in the time dimension to generate the stagnant heat risk integral quantity.

[0068] Furthermore, when the instantaneous flow rate is restored to supply water, the edge control module integrates and accumulates the instantaneous flow rate to obtain the cumulative replacement volume. When the cumulative replacement volume reaches the preset replacement condition, the aforementioned stagnation heat risk integral is forcibly reset to zero. This stagnation heat risk integral serves as the basis for subsequently generating biological contamination risk criteria.

[0069] S25: Based on this, the edge master control module combines the water temperature in the pipeline. Assess the catalytic impact of heat accumulation on biofouling to calculate the stagnation heat risk integral. : ; In the formula, This is the integral of the stagnation heat risk. This is a time constant used to prevent data overflow; Duration of stagnation; The temperature sensitivity coefficient is used to characterize the correlation between water temperature changes and microbial growth. Its value can be preset based on the growth curve characteristics of microorganisms in conventional water bodies. The temperature of the water inside the pipeline; It is an exponential function; The maximum function represents taking The larger of the values ​​within the parentheses; The risk reference temperature can be set to 20°C to 25°C. For integration time variable, It is a time infinitesimal element.

[0070] S26: When the water supply equipment initiates a water intake or performs internal cleaning, the flowing purified water gradually replaces the stagnant water in the pipeline. Starting from the moment the water flow resumes, the edge control module integrates and accumulates the instantaneous flow rate over time to obtain the cumulative replacement volume. Simultaneously, the edge control module monitors whether the instantaneous flow rate reaches the set flushing flow rate threshold and maintains the set flushing duration. When the above flow rate and duration conditions are met, and the cumulative replacement volume reaches a certain threshold... To achieve the preset pipeline inner cavity volume With substitution coefficient When multiplying, the edge control module confirms that the replacement of stagnant water is complete, and then executes the risk release action, integrating the stagnant heat risk. Forced zeroing. Replacement coefficient. It is set to a constant between 1.5 and 3. This mechanism helps the drinking water equipment to safely exit the associated risk-suspended state after the water replacement is completely completed.

[0071] When the instantaneous flow rate recovers but the cumulative displacement volume... The preset pipeline internal volume was not reached. With substitution coefficient When multiplying, the edge master control module pauses or stagnates for a certain duration. The cumulative amount of stagnant heat risk integral. This is the current value; the edge control module will only execute the risk release action and integrate the stagnant heat risk after the set flushing flow rate threshold, flushing duration, and cumulative replacement volume conditions are met. Forced reset to zero.

[0072] S27: As a core component for preventing microbial risks, the ultraviolet sterilization component 1 experiences aging and degradation after long-term operation. In this embodiment, within the window where the ultraviolet sterilization component 1 is lit and in a stable state, the edge control module calculates the average electrical power within that window based on the input signals from the voltage sampling circuit, current sampling circuit, and photosensor. and average irradiance .

[0073] S28: The edge control module retrieves the pre-stored reference power corresponding to the ultraviolet sterilization component 1. and reference irradiation Calculate the ultraviolet attenuation penalty factor The calculation formula is as follows: ; In the formula, This is the ultraviolet attenuation penalty factor; The maximum penalty to be set; Reference power; Average electrical power; The protection constant of the denominator of the electric power; For reference irradiation; The average irradiance; The denominator of the irradiance is the protection constant. It is the minimum function. It is the maximum function.

[0074] By introducing this ultraviolet attenuation penalty factor The edge control module can quantify the impact of the aging state of the ultraviolet sterilization component 1 on its sterilization performance.

[0075] See Figure 6 After acquiring the aforementioned characteristic parameters, the edge control module executes a proactive biological pollution early warning determination phase based on boundary conditions to decide whether to trigger autonomous intervention. The specific steps include the following.

[0076] S31: To improve the safety early warning sensitivity of aging equipment, the edge control module integrates the water stagnation state and the hardware aging state to construct a unified assessment criterion. In specific operation, the edge control module integrates the stagnation heat risk. UV attenuation penalty factor Direct multiplication generates a criterion for assessing the risk of biological contamination. Through the modulation and amplification effect of this ultraviolet attenuation penalty factor, equipment with more severe attenuation will generate a higher risk of biocontamination when facing the same stagnant accumulated temperature environment. The calculation formula is as follows: ; In the formula, As a criterion for assessing the risk of biological contamination; This is the integral of the stagnation heat risk. This is the ultraviolet attenuation penalty factor.

[0077] S32: Before triggering the cleaning intervention, to prevent system jamming due to actuator failure, the edge control module performs a hardware health check on the relevant actuators. The edge control module obtains the valid flag of the booster pump. Valid markings of sewage bypass valve UV effective mark and valid sensor chain identifier Comprehensive execution of license volume The calculation formula is as follows: ; In the formula, To comprehensively implement the license volume; The indicator for the effectiveness of the booster pump is set to 1 if the booster pump drive response is normal, the operating current does not exceed the overcurrent threshold, and no stall condition is detected within the set detection time window; otherwise, it is set to 0. The effective indicator of the sewage bypass valve is set to 1 if the valve opening / closing command and the feedback status are consistent within the set consistency time limit, otherwise it is set to 0. As an effective indicator of ultraviolet radiation, the value is 1 when the working voltage, working current and irradiation intensity of the ultraviolet sterilization component 1 are all within their respective preset ranges, and 0 otherwise. This is a valid indicator for the sensor chain. It is set to 1 if there is no disconnection, saturation, exceeding limit, or communication abnormality in any of its sensor channels; otherwise, it is set to 0.

[0078] S33: Edge control module's judgment criteria for generated biological contamination risk Perform boundary condition determination. The edge control module compares it with the set trigger upper boundary. A comparison was made. To reduce the probability of false triggers caused by transient data jitter, the edge control module determined the biological contamination risk criteria. Greater than the upper trigger boundary And the duration reaches the set risk time threshold. At that time, the conditions for issuing a warning were officially confirmed.

[0079] S34: When the warning conditions are met, the edge master control module checks the comprehensive execution license quantity. The system calculates the value of the comprehensive execution permission quantity to determine whether the conditions are met and controls the system to proceed to the corresponding processing branch. If the value of the comprehensive execution permission quantity is 1, it indicates that the drinking water equipment entity has a complete functional link (i.e., the conditions are met), and the system proceeds to step S35; otherwise, if .... If the value is 0, it indicates that there is a hardware failure in the system that prevents the physical cleaning loop from being completed (i.e., the condition is not met), then the system proceeds to step S36.

[0080] S35: The edge master control module temporarily suspends the regular water intake command initiated by the external user and generates an active cleaning trigger command to enter the subsequent flexible oscillation cleaning execution stage.

[0081] S36: The edge control module then enters degraded operation mode, terminating water intake locally and uploading degraded operation code containing hardware failure location information to the cloud intelligent service platform via the wireless communication network. Upon receiving the degraded operation code, the cloud intelligent service platform generates a maintenance work order based on the fault information it carries and promptly dispatches it to service personnel, thereby achieving a complete closed-loop business process from proactive front-end warning to intelligent back-end maintenance. The current judgment process ends upon completion.

[0082] S37: System pre-sets trigger upper boundary and release the lower boundary And release the lower boundary Less than the upper boundary This establishes a hysteresis judgment interval. After generating the active cleaning trigger command, the edge control module continuously monitors the biocontamination risk criteria. Determine whether the criterion falls back and below the lower release boundary. If the criterion is not below the lower release boundary, the suspension status is maintained (return to S35); if the biocontamination risk criterion is determined... If the price falls back below the lower release boundary, the system proceeds to step S38.

[0083] S38: The edge master control module releases the system from the suspended state and restores normal water supply response.

[0084] In this embodiment, after the edge control module generates an active cleaning trigger command and temporarily suspends the regular water intake command, the system enters the initial process of the flexible oscillation cleaning execution phase. The purpose of this phase is to change the regular water supply flow direction to establish a pipeline environment suitable for cleaning. The specific steps include the following.

[0085] S41: The edge control module sends an opening command to the sewage bypass valve 7 to execute a pipeline reconfiguration operation. Under normal water intake conditions, water flows sequentially through the filter assembly 2 and the terminal pipeline to the user's water intake, where there is high fluid resistance. When the sewage bypass valve 7 is open, the internal fluid is guided to a dedicated sewage discharge pipeline, bypassing the terminal flow restriction node. This action creates a low-resistance path dedicated to sewage discharge within the drinking water equipment, thus providing the basic operating conditions for the subsequent generation of high-flow flushing water.

[0086] S42: To prevent mechanical jamming of valves from causing internal pressure buildup and damage to the pipeline, the edge control module provides feedback confirmation on the establishment status of the low-resistance path. Specifically, the edge control module starts the booster pump 4 at a set safe test speed and simultaneously reads the instantaneous flow data output by the flow sensor 3 and the pipeline pressure data output by the back pressure sensor. As a preferred method, this safe test speed can be set to 20% to 30% of the rated operating speed of the booster pump 4 to provide test power while avoiding excessive back pressure.

[0087] Because the fluid resistance of the low-resistance path is less than that of the conventional water intake path, the instantaneous flow rate in the pipeline will show an upward trend under the same pumping output, while the pipeline pressure will decrease accordingly. The edge control module compares the read instantaneous flow rate data with the set lower limit threshold of the flushing flow rate and the read pipeline pressure data with the set upper limit threshold of the pressure relief. The lower limit threshold of the flushing flow rate and the upper limit threshold of the pressure relief can be pre-calibrated based on the hydraulic characteristics of the equipment under no-load sewage discharge conditions. When the instantaneous flow rate data is greater than the lower limit threshold of the flushing flow rate and the pipeline pressure data is less than the upper limit threshold of the pressure relief, and this condition is maintained for the set judgment time, the edge control module confirms that the sewage bypass valve 7 is in the open state, and determines that the low-resistance path has been successfully established. The above judgment time can be set to 3 to 5 seconds.

[0088] Furthermore, if the instantaneous flow rate data and pipeline pressure data fail to meet the above-mentioned judgment conditions within the set detection period, it indicates that the sewage bypass valve 7 is abnormally open. In this case, the edge control module cuts off the drive power of the booster pump 4, causing the system to switch to degraded operation mode and terminate the subsequent cleaning process to prevent the equipment from continuing to operate in an abnormal state.

[0089] S43: After confirming the successful establishment of the low-resistance path, the edge control module sends a drive signal to the UV sterilization component 1 for coordinated lighting. During the subsequent flushing process, impurities adhering to the inner wall of the pipe are easily shed by the water flow and discharged with the wastewater. By synchronously activating the UV sterilization component 1, the system can simultaneously perform antibacterial treatment on the fluid before it enters the diversion node, thereby reducing the risk of microbial contamination during the sewage discharge and water supply restoration processes. Establishing this antibacterial collaborative environment helps reduce the probability of secondary cross-contamination caused by detached material remaining in the drainage pipe.

[0090] In this embodiment, during the flexible oscillation cleaning process, to balance the effect of deposit removal and operating noise, the system dynamically adjusts the oscillation characteristics of the cleaning water flow based on the blockage status of the internal pipelines. Specifically, the edge control module performs impedance-driven adaptive pulsation frequency mapping, including the following steps.

[0091] S44: The edge master control module retrieves the current coupling impedance characteristic value stored in the previous operation phase, corresponding to the current operating condition. and reference impedance value Based on the acquired data, the edge control module calculates the impedance severity. The calculation formula is as follows: ; In the formula, Impedance severity is used to characterize the relative increase in resistance of a pipeline due to the adhesion of impurities. This represents the characteristic value of the current-current coupling impedance corresponding to the current operating condition. This is the reference impedance value.

[0092] By introducing The limiting function, which takes the larger of 0 and the value within parentheses, can reduce the characteristic value of the current coupling impedance caused by transient errors. Slightly below the reference impedance value The calculation result is set to zero at that time, thus preventing calculation anomalies caused by passing negative numerical parameters to subsequent stages.

[0093] S45: During the cleaning process, the pulsation characteristics of the water flow must be adapted to the state of the deposits inside the pipe. When the deposits are thick and the resistance is severe... When the contaminant is large, short-duration, high-frequency water flow is unlikely to penetrate the contaminant layer. In this case, a lower-frequency water flow pulsation is suitable to prolong the duration of a single water flow impact, while also reducing the probability of excessive back pressure accumulation in the pipeline. Conversely, when the contaminant is thin, a higher pulsation frequency is beneficial for generating high-frequency shear force, thereby oscillating and peeling off the surface contaminant. Based on this principle, the system establishes an impedance severity rating. The negative correlation mapping logic between the cleaning oscillation frequency and the cleaning oscillation frequency.

[0094] S46: As a preferred embodiment, the edge master module's internal memory pre-stores a frequency mapping table. This mapping table contains multiple sets of preset impedance severity nodes and their corresponding reference frequency nodes. This node data can be obtained in advance through bench calibration testing. The edge master module will then calculate the impedance severity... Input frequency mapping table for interval matching. To ensure logic integrity, if impedance severity is... If the value is less than or equal to the smallest node in the mapping table, the edge master control module directly outputs the corresponding highest reference frequency; if it is greater than or equal to the largest node, it directly outputs the corresponding lowest reference frequency.

[0095] When impedance severity Falling on two adjacent impedance severity nodes During this period, the edge control module uses a linear interpolation algorithm to calculate the optimal cleaning oscillation frequency. The corresponding calculation formula is: ; In the formula, The optimal cleaning oscillation frequency; For impedance severity nodes The corresponding reference frequency node; The calculated impedance severity; It is one of the adjacent impedance severity nodes; This is the second of two adjacent impedance severity nodes; For impedance severity nodes The corresponding reference frequency node.

[0096] S47: To ensure the safety of the booster pump 4 and the pipeline structure, the edge main control module calculates the optimal cleaning oscillation frequency using interpolation. Perform boundary limiting operations. Specifically, the edge master control module will optimize the cleaning oscillation frequency. The frequency is compared with preset minimum and maximum safe frequencies, and values ​​exceeding these ranges are clamped to the corresponding boundary frequencies. For example, the minimum safe frequency can be set to 1Hz to 2Hz, and the maximum safe frequency can be set to 8Hz to 10Hz. The frequency after limiting is determined as the final oscillation execution parameter, which is applied to the control pulse modulation of the booster pump 4 drive circuit, thereby achieving adaptive setting of the flushing water flow pulsation period.

[0097] See Figure 7 In this embodiment, the optimal cleaning oscillation frequency is obtained. Subsequently, to reduce mechanical noise caused by water hammer during nighttime cleaning, the system employs a continuously pulsating PWM control law with limited envelope to drive the booster pump 4. If a conventional square wave start-stop control mode is used, the water pressure in the pipeline is prone to sudden abrupt changes, leading to fluid knocking noises. To address this issue, the edge control module outputs continuously adjusted, smooth control signals to execute the cleaning process, the specific steps of which include the following:

[0098] S48: The edge master control module sets the duty cycle boundary parameters of the drive signal, specifically including the basic sustain duty cycle. With peak duty cycle Introducing a base to maintain duty cycle This system can maintain a basic continuous flow velocity in the internal pipes during the troughs of water flow pulsations, reducing the probability of hard fluid impacts when the next wave peak arrives due to a brief period of complete stagnation. The aforementioned boundary parameters can be pre-calibrated based on the effective operating range of the booster pump 4 and the pressure resistance limit of the internal pipes. As a preferred approach, a basic maintenance duty cycle is used. The peak pulsation duty cycle can be set to 30% to 40%. It can be set to 85% to 95%.

[0099] S49: Optimal cleaning oscillation frequency obtained from the previous steps The edge master control module is constructed as a time variable Continuously varying low-frequency modulation envelope function To maintain the continuity of the first derivative of the control variable and prevent sudden changes in system acceleration, the system uses a cosine function to construct the waveform. The corresponding calculation formula is as follows: ; In the formula, It is a low-frequency modulation envelope function; It is a cosine function; Pi; The optimal cleaning oscillation frequency; The time variable is used. This low-frequency modulation envelope function... The range of values ​​is normalized to be limited to the interval between 0 and 1, exhibiting smooth periodic oscillation characteristics over time.

[0100] S410: Based on the generated waveform data, the edge master control module will apply the low-frequency modulation envelope function. The output value is used as the weight to maintain the duty cycle. With peak duty cycle A linear mapping is performed between them to calculate the real-time drive duty cycle. This mapping mechanism drives the duty cycle in real time. The transition between peaks and troughs is smooth. Because the rate of change of the control parameters remains continuous, it helps to suppress sudden changes in the rotor angular acceleration inside the booster pump 4.

[0101] S411: The edge master control module drives the duty cycle according to the calculated real-time parameters. The corresponding PWM electrical signal is generated and continuously output to the drive circuit of the booster pump 4. Under the action of the above control law, the fluid inside the pipeline will form a continuous pulse scouring. This scouring method, while using the velocity difference and shear force between the peaks and troughs to peel off the deposits on the pipe wall, effectively reduces the transient changes in fluid pressure, thereby reducing pipeline resonance and water hammer noise, making the equipment operation meet the community's need for undisturbed use at night.

[0102] S412: To prevent the algorithm from getting stuck in the cleaning dead zone, the edge control module continuously monitors the cumulative replacement volume established in the preceding process while maintaining the above-mentioned dynamic pulse flushing state. And determine the cumulative replacement volume. Has the set replacement condition been met? If not, maintain the above dynamic pulse flushing state; if it is determined that the cumulative replacement volume has reached the set replacement condition, resulting in the stagnation heat risk integral... After being forcibly reset, the system proceeds to step S413.

[0103] S413: The edge master control module cuts off the output of the PWM electrical signal, ends the flexible oscillation cleaning state, and prepares to enter the subsequent exhaust delay stage.

[0104] In this embodiment, after the flexible oscillation cleaning process is completed, the system needs to perform post-cleaning effect assessment on the pipeline. Because cavitation may occur inside the pipeline and microbubbles may form when the pulsating flushing stops, directly extracting feature data at this time can easily cause transient distortion of the impedance signal as the microbubble cluster flows through the water quality monitoring node. To reduce such gas phase interference, the edge control module implements an exhaust delay clearance constraint mechanism, the specific steps of which include the following.

[0105] S51: After the cleaning termination condition is met, the edge control module stops outputting the aforementioned PWM electrical signal. At this time, to provide an internal channel for bubble discharge, the edge control module keeps the drain bypass valve 7 open and sends a constant duty cycle drive signal to the booster pump 4, allowing it to continue running smoothly at the set base speed. The system then enters the exhaust dead zone stage, during which the edge control module temporarily suspends the acquisition of aftereffect impedance data.

[0106] S52: Because a simple time-delay strategy is susceptible to fluctuations in inlet water pressure, the system employs a venting dead-zone algorithm based on volume integration. During execution, the edge control module reads the instantaneous flow data output by the flow sensor in real time. And perform time integration on it to obtain the net exhaust volume during the exhaust phase. The calculation formula is as follows: ; In the formula, This refers to the net exhaust volume; This is the starting moment when the system enters the exhaust dead zone phase; This is the current calculation time; This is instantaneous flow data; It is a time infinitesimal element.

[0107] S53: The edge master control module will calculate the net exhaust volume. The dead zone volume threshold is compared with a preset threshold. As a preferred method, this dead zone volume threshold can be set to 1.5 to 2.0 times the actual internal volume of the pipeline between the equipment inlet and the monitoring node. When the net air venting volume... When the volume of the dead zone is greater than or equal to the threshold, the edge control module determines that the pipeline has returned to a stable continuous liquid phase state, and then releases the exhaust dead zone constraint state.

[0108] To prevent the system from entering an exhaust dead zone due to abnormal sensor sampling or pipeline obstruction, the edge control module synchronously performs forced timeout monitoring. If the continuous operation time of the exhaust dead zone exceeds the set maximum tolerance duration, and the net exhaust volume... If the dead zone volume threshold is not reached, the edge control module will forcibly interrupt the venting process, cut off the power supply to booster pump 4, and report a fault message indicating abnormal hydraulic operation of the equipment. The maximum tolerance time can be set to 20 to 30 seconds, based on the time required for normal venting.

[0109] S54: After successfully releasing the exhaust dead zone constraint, the edge control module first controls the drain bypass valve 7 to reset and close, restoring the normal water circuit. Then, while maintaining stable water flow, it triggers the steady-state detection logic and opens the post-measurement steady-state window. Within this window, the system performs feature extraction, collecting and calculating the current-electric coupling impedance characteristic values ​​after cleaning. Once data acquisition is complete, the edge control module cuts off the drive power to the booster pump 4, restoring the water circuit to its normal standby state. This mechanism helps isolate the sweeping gas phase interference left by the pulsed cleaning, ensuring the reliability of the evaluation benchmark data in the post-effect identification stage.

[0110] In this embodiment, after obtaining the current coupling impedance characteristic value after cleaning within the post-test steady-state window, the system needs to perform differential comparison with the baseline data extracted before cleaning in order to evaluate the actual pipeline cleaning effect.

[0111] Because the cleaning process spans a certain time period, external water supply conditions or equipment electrical status may change during this time. Fluctuations in inlet water temperature, pipeline pressure, or drive voltage can easily cause deviations between the fluid's fundamental characteristics and the sensor's sampling reference. If feature comparison is directly performed under unequal operating conditions before and after cleaning, environmental background noise can easily be introduced, leading to biased evaluation results. Based on this, the edge control module executes a comparability verification mechanism for operating conditions before and after cleaning, and the specific steps include the following.

[0112] S55: The edge control module retrieves the set of baseline operating condition parameters recorded during the regular water intake phase. This parameter set specifically includes the pre-measured average temperature, pre-measured average pressure, and pre-measured average drive voltage. Simultaneously, within the current post-measured steady-state window, the edge control module reads and calculates the current operating condition parameter set after cleaning through the corresponding sensor nodes, specifically including the post-measured average temperature, post-measured average pressure, and post-measured average drive voltage.

[0113] S56: To quantify the changes in operating conditions before and after cleaning, the edge control module calculates the absolute values ​​of the differences in average temperature, average pressure, and average drive voltage before and after cleaning, and defines them as absolute temperature deviation, absolute pressure deviation, and absolute voltage deviation, respectively. Through this calculation, the system can objectively measure the drift of each environmental parameter before and after cleaning.

[0114] S57: The edge control module compares the obtained absolute deviations in each dimension with the internally pre-stored tolerance thresholds. As a preferred method, these tolerance thresholds can be calibrated based on the sensitivity test data of various environmental factors according to the current coupling impedance characteristics of the equipment. Specifically, the temperature tolerance threshold can be set to 0.5℃ to 1.0℃, the pressure tolerance threshold to 0.02MPa to 0.05MPa, and the voltage tolerance threshold to 3% to 5% of the nominal operating voltage.

[0115] S58: Based on the comparison results, the edge control module executes the corresponding logic branch judgment. When the absolute deviations of temperature, pressure, and voltage are all less than or equal to their respective tolerance thresholds, the edge control module determines that the environment within the current post-measurement steady-state window is consistent with that before cleaning, and the environmental parameters have not fluctuated beyond the allowable range. Under this condition, the current-electric coupling impedance characteristic values ​​extracted before and after are comparable in terms of operating conditions, and the system then proceeds with the subsequent quantitative evaluation process of the cleaning effect.

[0116] If any of the above absolute deviations exceeds the corresponding tolerance threshold, it indicates that the operating conditions of the equipment have changed. To ensure the completeness of the algorithm logic and the accuracy of the evaluation, the edge control module determines that the data before and after the changes are not directly comparable, and therefore abandons the current post-effect comparison operation. Simultaneously, to maintain the continuity of system monitoring, the edge control module uses the current set of operating parameters and its corresponding post-cleaning current coupling impedance characteristic value as the current operating condition index. Temporary benchmark samples are written into the historical sample sequence; when the number of historical samples under the same working condition reaches a set number... Then, update the reference impedance value according to the mean value rule in step S23. Then the system enters the next routine operation and monitoring cycle. This verification mechanism helps reduce misjudgments of cleaning effectiveness caused by drift in basic environmental parameters and improves the reliability of the system's self-assessment.

[0117] In this embodiment, after confirming the comparability of environmental conditions before and after cleaning, the system enters a self-learning closed-loop phase. Differences in water quality and pipe materials in different areas affect the formation rate and peeling characteristics of deposits. When water flow washes away deposits from the pipe wall surface, the equivalent flow resistance of the pipe and the load state of the booster pump change, which in turn manifests as a change in the characteristic value of the current-electric coupling impedance through the combination of the booster pump's operating current and flow rate. If a fixed evaluation model is used, it is difficult to maintain the accuracy of judging the risk of stagnant heat over a long period. Based on this, the edge control module adaptively corrects the built-in model parameters according to the recovery of characteristic values ​​before and after cleaning. The specific execution steps include the following:

[0118] S59: The edge master control module retrieves the characteristic value of the current coupling impedance obtained during the preceding normal operation phase before cleaning. And the characteristic value of the current coupling impedance after cleaning obtained within the post-test steady-state window. By analyzing the characteristic value of the current coupling impedance before cleaning... Characteristic value of current coupling impedance after cleaning By performing a differential calculation, the system obtains the corresponding impedance recovery difference value. The impedance recovery difference It can reflect, to some extent, the actual amount of material removed from the pipe wall by flexible oscillation cleaning.

[0119] S510: To achieve targeted model correction and avoid algorithm logic dead zones, the edge master control module will recover the impedance difference. The comparison is performed against preset judgment thresholds. These thresholds include the effective stripping threshold. With abnormal mutation threshold As a preferred approach, the aforementioned threshold can be determined based on the device's lifecycle calibration data under typical local water quality conditions. In practice, the effective stripping threshold... The threshold for abnormal changes can be set to 5% of the factory-nominal reference impedance. It can be set to -2% of the factory nominal reference impedance. Based on the comparison results, the system enters one of the following three mutually exclusive logic branches.

[0120] S511: When impedance recovery difference Greater than or equal to the preset effective stripping threshold The readings indicate a significant decrease in the current-electric coupling impedance characteristic value after cleaning, suggesting that the deposits inside the pipeline are mostly loose, reversible attachments that can be broken down by the shear force of the water flow. To make the calculation results of the stagnant heat risk integral more closely reflect the actual physical attachment state of the pipeline, the edge master control module utilizes the impedance recovery difference. An adaptive correction model is established to dynamically update the temperature sensitivity coefficient used to calculate the integral of stagnant heat risk. The corresponding correction formula is: ; In the formula, To update the cycle number; The updated temperature sensitivity coefficient; The temperature sensitivity coefficient used in the current cycle; The learning rate factor is used to control the update step size (its value can be set from 0.01 to 0.05). This represents the impedance recovery difference. The effective stripping threshold; The characteristic value of the current coupling impedance before cleaning.

[0121] S512: When impedance recovery difference Greater than or equal to the abnormal mutation threshold And less than the effective stripping threshold At this point, the system enters an irreversible attenuation logic branch. This indicates that the characteristic value of the current-electric coupling impedance decreases only slightly after cleaning, or there is a slight reverse fluctuation. This phenomenon reflects the formation of permanent mineral deposits on the pipe wall surface that are difficult to remove by hydraulic scouring, or electrochemical aging of the pipe material. Continuing to use the original benchmark for comparison at this time can easily lead to misjudgment. Therefore, the edge control module overwrites the system's benchmark impedance value under this operating condition using a first-order exponential smoothing filter mechanism. The update formula is: ; In the formula, To update the cycle number; This is the updated reference impedance value; This is the current reference impedance value; The smoothing filter coefficients are set to a value range of 0.1 to 0.2. This is the characteristic value of the current coupling impedance after cleaning; to ensure data consistency in subsequent evaluation logic, the edge master control module, after calculating the updated reference impedance value, simultaneously applies the corresponding most recent value under this operating condition. The characteristic sequences stored within each historical operating cycle are uniformly refreshed to the updated reference impedance value. This mechanism allows the system evaluation baseline to smoothly follow the long-term aging state of the equipment.

[0122] S513: When impedance recovery difference Less than the abnormal mutation threshold At this point, the system enters an abnormal isolation logic branch. This data indicates an abnormal increase in the current-current coupling impedance characteristic value after cleaning, attributed to flow channel blockage caused by detached material inside the pipeline, secondary contamination of the sensor, or poor contact in the hardware circuitry. To prevent erroneous data from contaminating the adaptive correction model, the edge master control module forcibly blocks the aforementioned parameter learning and baseline overwrite process, maintaining the original model parameters unchanged, and simultaneously generates a maintenance log containing current operating parameters and fault codes. The edge master control module uploads this maintenance log to the cloud intelligent service platform via an external communication interface to trigger manual maintenance services.

[0123] A specific application example uses a semi-outdoor drinking water station in a community as an example. The average ambient temperature during the test period was set at 30℃. From 0:00 to 5:00, the drinking water equipment was in a state of no user water intake, and the water flow in the pipeline was stagnant. The system performed monitoring and intervention according to the following procedure: Acquisition of characteristic parameters and calculation of current-electric coupling impedance: During the daytime steady-state operation window before any user water intake, the edge control module measured the average operating current of the booster pump. A, Average flow rate reported by the flow sensor L / min. The system retrieves the reference current corresponding to the current operating condition. A and reference flow L / min. Substitute into the formula to calculate the characteristic value of the current current coupling impedance: ; System retrieves historical reference impedance Calculate the impedance severity: ; Based on the calculation results, the system determined that the presence of deposits in the pipeline caused an increase in fluid resistance.

[0124] Calculation of stagnation heat risk and ultraviolet decay parameters: The system recorded a continuous nighttime pipeline stagnation time of up to [number missing]. h, during which the average water temperature inside the pipe was The set risk reference temperature Let the time constant be... Current temperature sensitivity coefficient The integral of stagnant heat risk is: ; The average electrical power of the ultraviolet sterilization component was detected simultaneously. W (corresponding reference power) W), average irradiance uW / cm 2 (Corresponding reference irradiation) uW / cm 2 ). Calculate the ultraviolet attenuation penalty factor: ; Proactive early warning and flexible cleaning execution: System calculation of biological contamination risk criteria .because If the value exceeds the set trigger upper boundary (set value is 8.0), the system confirms the early warning condition is met and generates an active cleaning trigger command. When calculating the cleaning frequency, the system queries the internal mapping table: The reference frequency is 4Hz when the impedance severity node is 0.2, and 2Hz when the node is 0.3. The optimal cleaning oscillation frequency is calculated using linear interpolation. Hz. The system sets the base hold duty cycle of the booster pump to Hz. The peak duty cycle of the pulsation is set to The edge control module calculates and outputs a PWM signal with a frequency of 2.86Hz based on the cosine envelope function, which controls the booster pump to output a continuous pulsating water flow to clean the pipeline.

[0125] After performing cleaning and exhaust delay clearance constraints, the steady-state window was measured after the system was started. The measured characteristic value of the current-electric coupling impedance after cleaning was reduced to Calculate the impedance recovery difference The system has a preset effective stripping threshold. .because This indicates that the deposits on the pipe wall have been removed. The system utilizes a self-learning factor. Update the temperature sensitivity coefficient: ; The corrected parameters are overwritten into internal memory for calculation in the next cycle.

[0126] To verify the effectiveness of the above solution, two identical direct drinking water devices were connected to the same municipal water source and deployed in the same outdoor environment for a 60-day comparative test. The control group used the conventional logic of flushing for 2 minutes at a fixed time and speed every day at 03:00. The experimental group used the active early warning and adaptive flexible oscillation cleaning method provided by this invention.

[0127] The test data statistics are as follows: Regarding water quality monitoring indicators, during the testing period, the peak total bacterial count of water samples extracted from the control group pipeline reached 85 CFU / mL; the experimental group, due to the implementation of proactive early warning cleaning based on the risk of stagnant heat, had a maximum total bacterial count of 14 CFU / mL throughout the entire testing period.

[0128] Regarding water consumption and noise levels, the control group generated a total of 240L of cleaning wastewater over 60 days, and the average operating noise measured during regular full-load flushing was 76dB. The experimental group's total water consumption for adaptive cleaning was 105L (a decrease of 56.25%), and because it adopted envelope-limited continuous pulsation control, there was no abnormal water hammer noise in the pipeline, with the highest measured operating noise being 52dB.

[0129] See Figure 8 , Figure 8 In (a), the horizontal axis represents execution time (s), and the vertical axis represents real-time duty cycle (%). As shown in the figure, the duty cycle does not use a step-type square wave, but rather follows... The frequency Hz undergoes a smooth, continuous transition between a set baseline duty cycle (30%) and a pulsating peak duty cycle (90%) based on a cosine function.

[0130] Figure 8 In (b), the horizontal axis represents the execution time (s), and the vertical axis represents the measured pressure in the pipeline (MPa). Since the rate of change of the input power of the booster pump remains continuous, coupled with the inertial damping characteristics of the fluid itself, the water pressure in the pipeline fluctuates gently with the duty cycle, avoiding mechanical impact (water hammer phenomenon) caused by pressure transients, thus explaining the reason for the decrease in noise in the aforementioned experimental comparison.

[0131] The above description is merely some specific implementations of this application and is not intended to limit the scope of protection of this application. Any variations or substitutions easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A proactive early warning intelligent service system for community smart drinking water equipment, characterized in that, The device includes a drinking water equipment entity, a sensor group, an actuator, and an edge control module. The sensor group and the actuator are disposed on the drinking water equipment entity, and the edge control module is communicatively connected to the sensor group and the actuator. The edge master control module is used for: Receive the raw sampling data output by the sensor group and identify the steady-state operating window of the drinking water equipment entity; Based on the original sampling data, the current coupling impedance characteristic value is extracted within the steady-state operation window, and the stagnation heat risk integral and ultraviolet attenuation penalty factor are calculated. The integral of the stagnant heat risk is multiplied by the ultraviolet attenuation penalty factor to generate a biological contamination risk criterion. When the biological contamination risk criterion exceeds the preset trigger boundary, a cleaning command is generated based on the current coupling impedance characteristic value and sent to the actuator to drive the drinking water equipment to perform a cleaning action.

2. The proactive early warning intelligent service system for community intelligent drinking water equipment according to claim 1, characterized in that, The drinking water equipment includes a booster pump (4), a filter assembly (2), a sewage bypass valve (7), an ultraviolet sterilization assembly (1), and pipelines; The implementing agency specifically includes: The booster pump (4), the sewage bypass valve (7), and the ultraviolet sterilization component (1); The sensor group includes a flow sensor (3), a temperature sensor, a pressure sensor (5), a current sampling circuit, a voltage sampling circuit, and a photosensitive sensor.

3. The proactive early warning intelligent service system for community intelligent drinking water equipment according to claim 2, characterized in that, Receiving the raw sampling data output by the sensor group and identifying the steady-state operating window of the drinking water equipment entity specifically includes: The original sampled data within the same time period are constructed into a sampling matrix. ; in, , , , , and These are parameter vectors representing the flow rate, temperature, pressure, current, voltage, and illumination data collected by the flow sensor (3), temperature sensor, pressure sensor (5), current sampling circuit, voltage sampling circuit, and photosensitive sensor, respectively. The original sampled data is subjected to low-pass filtering to obtain the filtered sequence; The flow dispersion slope and current dispersion slope are calculated based on the filtered sequence. When the absolute value of the flow dispersion slope is less than a preset flow slope threshold and the current dispersion slope is less than a preset current slope threshold, and the filtered instantaneous flow value obtained based on the filtered sequence is greater than a preset minimum effective flow, the steady-state point is verified and confirmed, and the steady-state operating window is extracted.

4. The proactive early warning intelligent service system for community intelligent drinking water equipment according to claim 3, characterized in that, The edge master control module is also used for: Within the steady-state operation window, the average parameters of inlet pressure, bus voltage, and water temperature are calculated based on the original sampled data. The average parameter is reduced in dimension using a discrete mapping function to generate a working condition index, which is then used as a baseline constraint for subsequent data processing.

5. The proactive early warning intelligent service system for community intelligent drinking water equipment according to claim 4, characterized in that, Based on the original sampled data, the current coupling impedance characteristic values ​​are extracted within the steady-state operating window, specifically including: Extract the average operating current and average flow rate of the booster pump (4) within the steady-state operation window; Retrieve the preset reference current and preset reference flow rate corresponding to the operating condition index, and divide the ratio of the average operating current to the reference current by the ratio of the average flow rate to the reference flow rate to obtain the current coupling impedance characteristic value.

6. The proactive early warning intelligent service system for community intelligent drinking water equipment according to claim 4, characterized in that, The calculation of the stagnant heat risk integral specifically includes: When the instantaneous flow rate is identified as being lower than a preset stagnation threshold based on the original sampled data, the cumulative stagnation time is recorded. When the stagnation time exceeds the preset judgment time, the stagnation heat risk integral is calculated based on the stagnation time and the water temperature in the pipeline collected by the temperature sensor, combined with the preset risk reference temperature and temperature sensitivity coefficient. When the instantaneous flow rate recovers, the instantaneous flow rate is integrated and accumulated to obtain the cumulative replacement volume. When the cumulative replacement volume reaches the preset replacement condition, the integral amount of stagnant heat risk is forcibly cleared to zero.

7. The proactive early warning intelligent service system for community intelligent drinking water equipment according to claim 2, characterized in that, The calculation of the ultraviolet attenuation penalty factor specifically includes: Within the window when the ultraviolet sterilization component (1) is lit and in a stable state, the average electrical power and average irradiance are calculated based on the original sampling data; Obtain the reference power and reference irradiance corresponding to the ultraviolet sterilization component (1); The ultraviolet attenuation penalty factor is calculated based on the ratio of the average electric power to the reference power, the ratio of the average irradiance to the reference irradiance, and a preset penalty upper limit.

8. The proactive early warning intelligent service system for community intelligent drinking water equipment according to claim 5, characterized in that, A cleaning command is generated based on the current coupling impedance characteristic value and sent to the actuator, specifically including: The valid marks of the booster pump (4), the sewage bypass valve (7), the ultraviolet sterilization component (1), and the sensor group are obtained respectively. The valid flags are multiplied together to generate a total execution license quantity; When the total execution allowable quantity meets the preset value, the cleaning command is generated based on the current coupling impedance characteristic value; Otherwise, send downgrade execution code.

9. The proactive early warning intelligent service system for community intelligent drinking water equipment according to claim 8, characterized in that, The edge master control module is also used for: In response to the cleaning command, the sewage bypass valve (7) is opened and the ultraviolet sterilization component (1) is kept running; Obtain the reference impedance value corresponding to the working condition index, calculate the impedance severity based on the current coupling impedance characteristic value and the reference impedance value, and match the optimal cleaning oscillation frequency based on the impedance severity. Based on the optimal cleaning oscillation frequency, a pulsating duty cycle sequence is generated, which drives the booster pump (4) to output continuously oscillating water to perform the cleaning action.

10. The proactive early warning intelligent service system for community intelligent drinking water equipment according to claim 9, characterized in that, The edge master control module is also used for: After the cleaning action is completed, the sewage bypass valve (7) is kept open. The net air volume is obtained by performing time integration calculation based on the instantaneous flow rate obtained by the flow sensor (3). After confirming that the net air volume has reached the preset dead zone volume threshold, the sewage bypass valve (7) is controlled to close. After the sewage bypass valve (7) is closed, the post-measured steady-state window is identified and extracted, and the current coupling impedance characteristic value in the post-measured steady-state window is extracted to calculate the impedance recovery difference before and after cleaning. A correction action is performed based on the distribution range of the impedance recovery difference, the correction action including updating the temperature sensitivity coefficient used to calculate the integral of stagnant heat risk or overwriting the reference impedance value.

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