A method, system, electronic device, and storage medium for locating faults in the pulse signal of a proportional valve in a water heater.

Abstract

This invention discloses a method for locating faults in the pulse signal of a proportional valve in a water heater, comprising the following steps: calculating the moving average de-jitter value of the original amplitude of the pulse signal at multiple moments of the proportional valve; determining the filtered pulse signal amplitude based on the moving average de-jitter value and an amplitude difference threshold; determining the pulse signal characteristics based on the filtered pulse signal amplitude; determining the characteristic anomaly quantization value and anomaly type based on the pulse signal characteristics and corresponding preset standard values; determining the fault severity quantization value based on the characteristic anomaly quantization value; determining the fault probability based on the fault severity quantization value; and outputting fault prompt information based on the fault severity quantization value, fault probability, and anomaly type. This invention performs detection and calculation using easily obtainable amplitude data on-site, eliminating the need for large-scale testing equipment, significantly reducing the complexity of on-site testing and equipment carrying costs, and improving on-site repair efficiency. This invention also discloses a system, electronic device, and storage medium for implementing the above method.

Description

Technical Field

[0001] This invention relates to the field of computer data processing technology, and in particular to a method, system, electronic device, and storage medium for locating faults caused by abnormal pulse signals in a proportional valve of a water heater. Background Technology

[0002] As a commonly used household hot water supply device, the stability of the combustion system of a gas water heater directly affects the user experience. The proportional valve, as the core actuator for gas flow regulation, controls the valve opening by receiving pulse drive signals (usually PWM signals) output from the controller, thereby achieving precise water temperature regulation. However, in practical applications, abnormalities in the proportional valve's pulse drive signal frequently occur, especially a less common but non-high-frequency fault that easily leads to misjudgments—pulse signal amplitude attenuation, frequency drift, and duty cycle distortion (distortion rate is typically 5%~15%).

[0003] These types of faults are characterized by their high degree of concealment and lack of intuitive manifestation: the abnormality only appears briefly during the start-up and shutdown of the water heater or during water temperature adjustment, specifically manifesting as non-specific symptoms such as sudden rises or falls in water temperature, delayed ignition, or intermittent flameout, which are easily mistaken for sensor malfunctions, water circuit blockages, or ignition system problems. Furthermore, because the fault signal is weak (amplitude attenuation is typically less than 10%, frequency drift is mostly within ±2%, and duty cycle distortion is only 5%~15%), conventional detection methods are insufficient for effective identification.

[0004] In existing technologies, when maintenance personnel conduct on-site troubleshooting, they can typically only use a multimeter to detect the continuity of pulse signals, and cannot capture changes in signal amplitude, frequency, and duty cycle parameters. To further confirm signal abnormalities, the proportional valve needs to be disassembled and connected to a professional signal testing instrument (such as an oscilloscope or signal analyzer) for waveform analysis. The disassembly of the proportional valve alone takes more than 30 minutes. Furthermore, professional testing equipment is bulky and inconvenient to carry, resulting in low on-site testing efficiency, a high misjudgment rate, and a significant reduction in maintenance efficiency. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a method, system, electronic device, and storage medium for locating faults in the pulse signal of a proportional valve in a water heater. The technical problem to be solved by this invention is achieved through the following technical solution: The first aspect of this invention provides a method for locating faults in the pulse signal of a proportional valve in a water heater, comprising the following steps: Calculate the moving average dejitter value of the original amplitude of the pulse signal of the proportional valve of the water heater at multiple moments; The amplitude of the filtered pulse signal at each moment is determined based on the original amplitude of the pulse signal at each moment, the corresponding moving average de-jitter value, and the amplitude difference threshold. Based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes, determine the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics; Based on the average amplitude feature, the pulse frequency feature, and the duty cycle feature, as well as the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value, determine the feature anomaly quantification value and anomaly type; The fault severity quantification value is determined based on the aforementioned abnormal feature quantification value, preset weight, and auxiliary weight; The probability of failure is determined based on the quantified value of the degree of failure. Based on the fault severity quantification value, the fault probability, and the anomaly type, output fault prompt information.

[0006] In one embodiment of the present invention, the moving average dejitter value The calculation formula is: in, This indicates the size of the sliding debounce window. Indicates the first Each data collection moment, Indicates the first i The original amplitude of the pulse signal at time t.

[0007] In one embodiment of the present invention, the amplitude of the filtered pulse signal The calculation formula is: in, Indicates the first t The original amplitude of the pulse signal at time 10:00. Indicates the first t The amplitude of the filter pulse signal at time -1 This represents the amplitude difference threshold.

[0008] In one embodiment of the present invention, determining the average amplitude characteristic, pulse frequency characteristic, and duty cycle characteristic based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes includes: The average amplitude characteristics and pulse frequency characteristics are determined based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes. The duty cycle characteristics are determined based on the average amplitude characteristics and the pulse signal threshold.

[0009] In one embodiment of the present invention, determining the feature anomaly quantization value and anomaly type based on the average amplitude feature, the pulse frequency feature, and the duty cycle feature, as well as the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value, includes: Based on the average amplitude feature, the pulse frequency feature, and the duty cycle feature, as well as the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value, the feature anomaly quantization value is determined; Based on the average amplitude characteristics, the pulse frequency characteristics, and the duty cycle characteristics, as well as the corresponding preset average amplitude standard values, preset pulse frequency standard values, and preset duty cycle standard values, anomaly type indicators are determined; The anomaly type is determined based on the anomaly type index and the index threshold.

[0010] In one embodiment of the present invention, the fault severity quantification value The calculation formula is: in, Indicates the quantized value of the feature anomaly. Indicates the preset weight. Indicates auxiliary weights, An anomaly type index representing the characteristics of average amplitude. An indicator representing the abnormality type of pulse frequency characteristics. An anomaly type indicator representing duty cycle characteristics.

[0011] In one embodiment of the present invention, the formula for calculating the failure probability is: in, This indicates the probability of failure.

[0012] A second aspect of this invention provides a fault location system for abnormal pulse signals of a water heater proportional valve, comprising: The calculation module is used to calculate the moving average de-jitter value of the original amplitude of the pulse signal of the proportional valve of the water heater at multiple moments; The first determining module is used to determine the amplitude of the filtered pulse signal at each moment based on the original amplitude of the pulse signal at each moment, the corresponding moving average de-jitter value, and the amplitude difference threshold. The second determining module is used to determine the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes. The third determining module is used to determine the feature anomaly quantization value and anomaly type based on the average amplitude feature, the pulse frequency feature, the duty cycle feature, and the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value. The fourth determining module is used to determine the fault degree quantification value based on the characteristic anomaly quantification value, preset weight, and auxiliary weight; The fifth determining module is used to determine the failure probability based on the quantified value of the failure severity. The output module is used to output fault prompt information based on the fault severity quantification value, the fault probability, and the anomaly type.

[0013] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a method for locating abnormal pulse signals of a proportional valve in a water heater, as provided in the first aspect of the present invention.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for locating abnormal pulse signals of a proportional valve in a water heater, as provided in the first aspect of the present invention.

[0015] The beneficial effects of this invention are: This invention utilizes readily available amplitude data for detection, avoiding the tedious 30-minute or more-than-average valve disassembly required in traditional methods. The data preprocessing stage employs debouncing logic to adapt to complex on-site interference environments (such as gas pipeline vibration and mains power fluctuations), ensuring accurate feature extraction. Through "multi-dimensional feature synchronous extraction + anomaly quantification," it can not only accurately determine the presence of a fault but also clearly distinguish anomaly types (amplitude attenuation, frequency drift, duty cycle distortion) and quantify the degree of anomaly, providing targeted repair guidance for maintenance personnel. Furthermore, it eliminates the need for large, specialized testing equipment such as oscilloscopes, significantly reducing the complexity of on-site testing and equipment carrying costs, greatly improving on-site repair efficiency, and perfectly meeting the actual needs of maintenance personnel for "rapid response and efficient handling."

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart illustrating a method for locating faults in the pulse signal of a proportional valve in a water heater, provided in an embodiment of the present invention; Figure 2 This is a block diagram of a fault location system for an abnormal pulse signal of a proportional valve in a water heater, provided as an embodiment of the present invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0020] like Figure 1 As shown, the first aspect of this invention provides a method for locating faults in the pulse signal of a proportional valve in a water heater, comprising the following steps: Step 11: Calculate the moving average dejitter value of the original amplitude of the pulse signal at multiple moments of the proportional valve of the water heater.

[0021] Step 12: Determine the amplitude of the filtered pulse signal at each moment based on the original amplitude of the pulse signal at each moment, the corresponding moving average de-jitter value, and the amplitude difference threshold.

[0022] Step 13: Determine the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes.

[0023] Step 14: Determine the characteristic anomaly quantification value and anomaly type based on the average amplitude characteristics, pulse frequency characteristics, duty cycle characteristics, and the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value.

[0024] Step 15: Determine the fault severity quantification value based on the characteristic anomaly quantification value, preset weight, and auxiliary weight.

[0025] Step 16: Determine the failure probability based on the quantified value of the failure severity.

[0026] Step 17: Output fault prompt information based on the fault severity quantification value, fault probability, and anomaly type.

[0027] In this embodiment, amplitude data readily available on-site is used for detection, avoiding the tedious operation of disassembling the proportional valve, which takes more than 30 minutes, in traditional methods. The data preprocessing stage employs debouncing logic to adapt to complex on-site interference environments (such as gas pipeline vibration and mains power fluctuations), ensuring the accuracy of feature extraction. Through "multi-dimensional feature synchronous extraction + anomaly quantification," not only can the presence or absence of a fault be accurately determined, but the types of anomalies (amplitude attenuation, frequency drift, duty cycle distortion) can also be clearly distinguished and the degree of anomaly quantified, providing targeted repair guidance for maintenance personnel. Simultaneously, it eliminates the need for large professional testing equipment such as oscilloscopes, significantly reducing the complexity of on-site testing and equipment carrying costs, greatly improving on-site repair efficiency, and fully meeting the actual needs of maintenance personnel for "rapid response and efficient handling."

[0028] Based on the first aspect of the present invention, the second aspect of the present invention provides a method for locating faults caused by abnormal pulse signals in a proportional valve of a water heater. The second aspect of the present invention provides a method for locating faults caused by abnormal pulse signals in a proportional valve of a water heater, applied to a service platform, and includes the following steps: Step 20: Obtain the original amplitude values ​​of the pulse signals at multiple moments from the proportional valve of the water heater.

[0029] In this step, the raw amplitude data of the pulse signal at multiple moments is collected from the proportional valve of the water heater. For example, 5 seconds of data are collected, resulting in 50 raw pulse signal amplitudes.

[0030] Step 21: Calculate the sliding average dejitter value of the original amplitude of the pulse signal of the proportional valve of the water heater at multiple moments.

[0031] The formula for calculating the moving average dejitter value at each time step is: in, Indicates the first The moving average de-jitter value at each acquisition time (the core is used to filter instantaneous interference signals). This indicates the size of the slide debounce window (generally, a value of...). n =5, verified by actual testing, this window can effectively filter transient signal interference caused by mains power fluctuations and pipeline vibrations, while retaining the abnormal characteristics of pulse signals. Indicates the first Each data collection moment, Indicates the first i The original amplitude of the pulse signal at each acquisition time.

[0032] Step 22: Determine the amplitude of the filtered pulse signal at each moment based on the original amplitude of the pulse signal at each moment, the corresponding moving average de-jitter value, and the amplitude difference threshold.

[0033] No. Amplitude of the filtered pulse signal at each acquisition time The calculation formula is: in, Indicates the first t The original amplitude of the pulse signal at time t, in V. Indicates the first t The amplitude of the filter pulse signal at time -1 This represents the amplitude difference threshold, used for anomaly detection (set based on the normal signal amplitude range of different brands of proportional valves).

[0034] This step employs a dual preprocessing logic of "moving average jitter removal + outlier replacement" to adapt to the complex on-site environment of on-site repairs. Moving average jitter removal filters out transient interference (such as slight touches during repair personnel's operation or fluctuations in mains voltage), preventing interference signals from being misjudged as abnormal pulse signals. Outlier replacement uses valid data from the previous moment instead of directly removing it, ensuring the continuity of signal data and supporting the accurate extraction of subsequent frequency and duty cycle. It also simplifies the calculation logic, eliminating the need for complex interference separation algorithms and adapting to the rapid calculation requirements of mobile devices.

[0035] For example, the repairman collected the raw data of the pulse signal amplitude of the proportional valve of a certain brand of gas water heater (model XXX) (10 sets selected, t=1 to t=10): [5.2, 5.3, 5.1, 7.8, 5.2, 5.3, 5.1, 5.4, 5.2, 5.3].

[0036] Moving average debouncing (n=5, taking t=5 as an example): Anomaly filtering ( (Taking t=4 as an example): calculate ( The moving average value at t=4 is calculated as follows: =5.88V): This was identified as an anomaly. Replace the outlier: ( The data after filtering at t=3 is calculated as follows. (≈5.2V); The 10 sets of final filtered data are: [5.2, 5.3, 5.2, 5.2, 5.4, 5.3, 5.2, 5.3, 5.2, 5.3]. The data is continuous and has been filtered out of interference signals, so it can be used for subsequent feature extraction.

[0037] Step 23: Determine the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes.

[0038] This step achieves the simultaneous extraction of three core features: amplitude, frequency, and duty cycle. These three features work together to cover all types of anomalies in the proportional valve pulse signal (amplitude attenuation, frequency drift, and duty cycle distortion), ensuring that no anomaly type is missed. Frequency extraction uses a simplified calculation logic of "adjacent amplitude difference + time weighting," which can accurately calculate the pulse frequency without the need for professional pulse period detection equipment, avoiding complex Fourier transform calculations and adapting to mobile scenarios. Duty cycle extraction combines an indicator function to simplify the high-level duration statistics and is linked with the average amplitude to ensure the accuracy of duty cycle calculation.

[0039] Step 23 includes steps 231-232: Step 231: Determine the average amplitude characteristics and pulse frequency characteristics based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes.

[0040] Average amplitude characteristics The calculation formula is: in, This indicates the total number of filter pulse signal amplitudes. The unit is V, and one of its core characteristics reflects the degree of amplitude attenuation.

[0041] Pulse frequency characteristics The calculation formula is: in, This represents the average period of a pulse signal (unit: seconds, inversely proportional to frequency). ); It represents the absolute value of the difference in amplitude between adjacent filtered pulse signals (used to identify the rising and falling edges of the pulse signal and to calculate the period). The unit is Hz, and its second core characteristic is that it reflects the degree of frequency drift.

[0042] Step 232: Determine the duty cycle characteristics based on the average amplitude characteristics and the pulse signal threshold.

[0043] Duty cycle characteristics The calculation formula is: in, The unit is %, which is the third core feature and reflects the degree of duty cycle distortion. This represents the threshold value of the pulse signal (generally, it takes the value of...). This is the minimum driving amplitude for the proportional valve to operate normally, used to distinguish between high and low pulse levels. Indicates the indicator function (when) When the high level is active, the value is 1; otherwise, the value is 0, used to count the percentage of high-level duration.

[0044] Example, after filtering, the result is m =50 sets of pulse signal amplitude data The average amplitude was calculated as follows , Substitute these features into the formula to extract three core features: Average amplitude : Calculated (The normal average amplitude of the proportional valve of this model of water heater is pre-stored on the platform and will be used for anomaly quantification later.) pulse frequency f : First calculate Statistical calculations yielded a sum of 122.5, which, when substituted into the formula: Duty cycle : Statistical analysis of 50 sets of data The number of groups is 38 in total. Substitute this into the formula: The three core features extracted are: average amplitude 5.25V, pulse frequency 0.204Hz, and duty cycle 76%, which are used for subsequent anomaly quantization calculations.

[0045] Step 24: Determine the characteristic anomaly quantification value and anomaly type based on the average amplitude characteristics, pulse frequency characteristics, duty cycle characteristics, and the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value.

[0046] Specifically, step 24 includes steps 241-243: Step 241: Determine the characteristic anomaly quantization value based on the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics, as well as the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value.

[0047] Feature anomaly quantification value The calculation formula is: in, k The numbers representing the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics are indicated. k =1 corresponds to the average amplitude characteristic , k =2 corresponds to pulse frequency characteristics , k =3 corresponds to the duty cycle feature , Indicates the firstk The value of each feature (i.e.) , , ); Indicates the first k Each feature corresponds to a preset standard value (pre-stored in the platform database, categorized by gas water heater brand and model, such as this model). , , ); Indicates the first k The weights of the three features (the degree of influence of the three abnormal features of the pulse signal obtained from historical fault data of the platform) , , (The sum is 1). Represents the abnormal iteration correction coefficient (typically taking a value). This is used to correct the dispersion of the three feature anomalies and improve quantization accuracy.

[0048] Indicates the first k The relative anomalous deviation of a single feature (reflecting the degree of anomalousness of a single feature). The value range is 0~1. The larger the value, the more severe the abnormality of the pulse signal.

[0049] This step employs a quantization logic of "weighted summation of relative deviations of a single feature + multi-feature deviation dispersion correction." Its core advantage lies in its ability to simultaneously quantify the anomaly levels of three features. Simultaneously, through iterative correction terms, it balances the differences in anomaly deviations among different features, preventing the over-amplification or neglect of anomalies in a single feature. The calculation method for relative anomaly deviations is adaptable to the differences in feature standard values ​​for different brands and models of gas water heaters, requiring no modification to the core algorithm logic; only updating the pre-stored standard values ​​is needed, thus improving the algorithm's adaptability. Weights... The actual impact of pulse signal anomalies (amplitude attenuation has the greatest impact on the operation of the proportional valve and has the highest weight) is considered to ensure the accuracy and relevance of the quantification results.

[0050] For example, the normal standard value for the pulse signal of the proportional valve of a certain brand of gas water heater is: , , , , , , Substitute into the formula to calculate the characteristic anomaly quantification value. : Calculate the relative outlier deviation for each feature: k =1 (amplitude): k =2 (frequency): k =3 (duty cycle): Calculate the weighted sum: Calculate the iterative correction term: Final value of the correction item: Calculate the quantification value of the feature anomaly: Step 242: Determine the corresponding anomaly type index based on the average amplitude characteristics, pulse frequency characteristics, duty cycle characteristics, and the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value.

[0051] Anomaly type index for each feature The calculation formula is: This indicator represents the first k The percentage of abnormal deviations for each feature.

[0052] Step 243: Determine the anomaly type based on the anomaly type index and the corresponding index threshold.

[0053] An anomaly type index representing the characteristics of average amplitude. An indicator representing the abnormality type of pulse frequency characteristics. These are indicators representing the anomaly type based on duty cycle characteristics. Accordingly, comparing three indicators to a value of 0 determines the type of anomaly. If the value is greater than 0, the abnormal type is amplitude attenuation; otherwise, it is normal. If the value is greater than 0, the abnormal type is frequency drift; otherwise, it is considered normal. If the value is less than 0, the abnormal type is duty cycle distortion; if it is less than 0, it is normal.

[0054] Step 25: Determine the fault severity quantification value based on the characteristic anomaly quantification value, preset weight, and auxiliary weight.

[0055] Fault severity quantification value The calculation formula is: in, This represents the preset weights (weights of quantized abnormal features, with values ​​ranging from 1 to 10). (highlighting the role of core quantitative values) This represents the auxiliary weight (maximum outlier weight, with values ​​ranging from 0 to 1). (to assist in correcting the quantification accuracy of anomalies). This represents the maximum percentage of abnormal deviations among the three features (reflecting the most severe single feature anomaly). The value of E ranges from 0 to 1. The larger the value of E, the more severe the anomaly.

[0056] Step 26: Determine the failure probability based on the quantified value of the failure severity.

[0057] Failure probability The calculation formula is: in, Indicates the probability of a fault occurring (values ​​range from 0 to 100%, used for final fault determination to avoid probability overflow caused by over-calculation).

[0058] Here, three major functions—"anomaly type determination, severity quantification, and fault probability calculation"—are integrated to form a complete fault confirmation logic. Anomaly type determination accurately distinguishes between three anomaly types—amplitude attenuation, frequency drift, and duty cycle distortion—by using the sign and magnitude of the anomaly deviation percentage, and can also identify multiple types of composite anomalies. Anomaly severity quantification combines core quantization values... The maximum deviation from the maximum abnormality ensures that the quantitative results closely match the actual fault situation; the fault probability calculation uses a minimum value function to avoid probability overflow caused by calculation errors. The logic is rigorous and easy to understand. Maintenance personnel can quickly judge the credibility of the fault through the probability value. At the same time, the quantitative value of the abnormality can guide the maintenance priority and further improve maintenance efficiency.

[0059] Example, known , Substitute the values ​​into the formula to complete the anomaly type determination, severity quantification, and fault probability calculation: Calculate the percentage of abnormal deviation for each feature. : (Negative value, determined as amplitude attenuation); (Negative values ​​are identified as frequency drift); (Positive value, determined to be duty cycle distortion); The final determination was that the pulse signal of the proportional valve of the water heater had a combined anomaly of "amplitude attenuation + frequency drift + duty cycle distortion".

[0060] Quantification of anomaly degree : Calculate the maximum percentage of outliers: (i.e., 0.184); ; Failure probability calculation ( ): .

[0061] Step 27: Output fault prompt information based on the fault severity quantification value, fault probability, and anomaly type.

[0062] In this step, fault judgment is made based on the quantified value of fault severity and the threshold for determining fault probability. Specifically, the fault severity can be divided into multiple levels from minor to severe, with different levels corresponding to different thresholds. By comparing the quantified value of fault severity with each threshold, the current fault severity level can be obtained, which serves as one of the fault indication messages.

[0063] At the same time, by comparing the fault probability with its corresponding threshold, the fault result of whether it is abnormal can be obtained, which serves as one of the fault indication messages.

[0064] At the same time, the exception type is output as one of the fault indication messages.

[0065] In addition, based on the severity of the fault, whether the fault result is abnormal, and the type of abnormality, corresponding pre-set maintenance suggestion information is output.

[0066] For example, if "P≥20% indicates an abnormal proportional valve pulse signal", then this case is considered a fault. Based on E=0.2185, the degree of abnormality is quantified as "minor abnormality". Based on the type of abnormality, the maintenance suggestion is: maintenance personnel can specifically check the proportional valve wiring and drive module without disassembling the proportional valve.

[0067] Example scenario: User feedback: A gas water heater of a certain brand (model XXX) in my home recently experienced an ignition delay (about 3 seconds) when starting and stopping, and a sudden rise and fall in water temperature (temperature difference of about 5℃) when adjusting the water temperature. There were no obvious abnormal noises or error codes. Multiple on-site repairs failed to find the fault. The platform sent a repairman with the APP to the site for troubleshooting.

[0068] On-site operation procedures for maintenance personnel: Step 1: Data Collection (completed in 5 minutes). The repair technician opens the platform APP, selects the water heater brand and model, and follows the APP prompts to collect data. Proportional valve pulse signal amplitude: Collect 50 sets of data, ranging from 5.1 to 5.4V; Pulse signal period: synchronous acquisition, range 4.8~5.2 seconds; Step 2: Automatic Algorithm Calculation (APP side, completed in 8 seconds) – The APP background calls the algorithm in the above embodiment and calculates according to the following steps: 1. Data preprocessing: Signal de-jittering and outlier replacement are completed through steps 21 and 22.

[0069] 2. Feature Extraction: Three core features are extracted in step 23, and the average amplitude is calculated. The pulse frequency is approximately 0.204 Hz, and the duty cycle is 76%.

[0070] 3. Anomaly Quantification: Calculate the anomaly feature quantification value Q≈0.227 through step 241.

[0071] 4. Fault Confirmation: Step 242 determines the anomaly type to be a composite anomaly of "amplitude attenuation + frequency drift + duty cycle distortion". 5. The anomaly level quantification value is calculated to be E≈0.219, and the failure probability is P≈21.9%.

[0072] Step 3: Fault Output - The APP pushes the results to the maintenance personnel: "Proportional valve pulse signal is abnormal, with a fault probability of 21.9%. It is a minor abnormality. The abnormality types are: amplitude attenuation (-12.5%), frequency drift (-18.4%), and duty cycle distortion (+8.57%). It is recommended to check the proportional valve wiring terminals and the output signal of the drive module."

[0073] Step 4: Targeted Repair and Feedback – Following the instructions, the repair personnel, without disassembling the proportional valve, only checked the proportional valve's wiring terminals and found that loose terminals were causing poor contact, leading to abnormal pulse signals. After tightening the wiring terminals, data was collected again. The algorithm calculated that the fault probability had decreased to 8.2%, indicating that the fault was resolved. The repair personnel uploaded the "Repair Result (Loose Wiring Terminals Causing Abnormal Pulse Signal)" to the platform, and the algorithm automatically updated the weights. Correction coefficient We will continue to optimize recognition accuracy.

[0074] In one feasible implementation, after repairs are completed, the app uploads the actual cause of the fault and the repair results. Automatic weight updates are triggered if any of the following conditions are met. Correction coefficient : Condition 1: The above method determines the abnormality and on-site maintenance confirms that the proportional valve pulse is abnormal (true positive). Condition 2: The above methods determine no abnormality and on-site maintenance confirms that it is a pulse abnormality (false negative); Condition 3: The above method determines the abnormality and on-site maintenance confirms that it is not a pulse abnormality (false positive).

[0075] 1. Feature weights Iterative update (guaranteeing sum = 1): Learning rate (controls the update step size) Deviation sign (to distinguish between attenuation and increase) Repair result correction factor True positive: False negative: (Strengthening weight) False positive: (Weakening weight) 2. Iterative update of correction coefficient θ: in, : Learning rate of coefficients; The above algorithm calculates the quantized value; : Actual fault quantification value (assigned after repair confirmation), confirming pulse abnormality: Confirming non-pulse abnormalities: .

[0076] 3. Iterative convergence rule: | | < 0.001 and | If any condition < 0.005 is met, the update will stop to avoid parameter oscillation.

[0077] This invention employs a core logic of "multi-dimensional feature synchronous extraction + anomaly quantification + iterative correction," simultaneously covering three core features of pulse signals: amplitude, frequency, and duty cycle. It can accurately identify single and compound anomalies, while quantifying the degree of anomalies, overcoming the limitation of merely determining the presence or absence of a fault. Data preprocessing utilizes a dual logic of "moving average de-jittering + anomaly point replacement," adapting to the complex on-site environment of on-site repairs, ensuring the accuracy of feature extraction, and simplifying calculations to meet the rapid calculation needs of mobile devices. Anomaly quantification uses "relative deviation weighting + dispersion correction," adapting to the differences in feature standard values ​​among different brands and models of gas water heaters without requiring algorithm logic reconstruction, demonstrating strong adaptability. Integrating anomaly type determination, degree quantification, and fault probability calculation into a closed-loop logic provides repair personnel with targeted repair guidance, improving repair efficiency.

[0078] In real-world scenarios, maintenance personnel only need to collect two easily obtainable data types—the amplitude and period of the proportional valve pulse signal—through the platform's app. There is no need to disassemble the proportional valve or carry specialized testing equipment. The data collection process takes only 5 minutes, perfectly meeting the core requirements of "efficiency and convenience" for on-site repairs. The algorithm's calculation logic is simplified, requiring no complex equipment. The app can complete the calculation and output the fault result within 8 seconds, making it highly practical and directly applicable to on-site repair scenarios.

[0079] Therefore, conventional troubleshooting of this fault requires disassembling the proportional valve and connecting a professional signal detector (which takes more than 30 minutes), while this invention only takes 13 minutes (5 minutes of data acquisition + 8 seconds of calculation + targeted testing and repair), greatly reducing the repair time.

[0080] The method of this invention does not require professional testing equipment or disassembly of parts. Maintenance personnel only need to collect data through an APP to obtain accurate fault guidance, avoid blind troubleshooting, reduce ineffective work, and reduce the difficulty of troubleshooting.

[0081] The method of this invention can quantify the degree and type of anomalies, clarify the type and degree of anomalies, and allow maintenance personnel to determine maintenance priorities in advance and prepare suitable parts (such as tightening the wiring only if the proportional valve does not need to be replaced), thereby reducing maintenance costs and improving user experience.

[0082] like Figure 2 As shown, a third aspect of the present invention provides a fault location system for abnormal pulse signals of a water heater proportional valve, comprising: Calculation module 31 is used to calculate the moving average de-jitter value of the original amplitude of the pulse signal of the proportional valve of the water heater at multiple moments; The first determining module 32 is used to determine the amplitude of the filtered pulse signal at each moment based on the original amplitude of the pulse signal at each moment, the corresponding moving average de-jitter value, and the amplitude difference threshold. The second determining module 33 is used to determine the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes. The third determining module 34 is used to determine the characteristic anomaly quantification value and anomaly type based on the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics, as well as the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value. The fourth determination module 35 is used to determine the fault degree quantification value based on the characteristic anomaly quantification value, preset weight and auxiliary weight; The fifth determining module 36 is used to determine the failure probability based on the quantified value of the failure severity. Output module 37 is used to output fault prompt information based on the fault severity quantification value, fault probability and anomaly type.

[0083] In one embodiment of the present invention, the moving average dejitter value The calculation formula is: in, This indicates the size of the sliding debounce window. Indicates the first Each data collection moment, Indicates the first iThe original amplitude of the pulse signal at time t.

[0084] In one embodiment of the present invention, the amplitude of the filtered pulse signal is... The calculation formula is: in, Indicates the first t The original amplitude of the pulse signal at time 10:00. Indicates the first t The amplitude of the filter pulse signal at time -1 This represents the amplitude difference threshold.

[0085] In one embodiment of the present invention, determining the average amplitude characteristic, pulse frequency characteristic, and duty cycle characteristic based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes includes: The average amplitude characteristics and pulse frequency characteristics are determined based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes. The duty cycle characteristics are determined based on the average amplitude characteristics and the pulse signal threshold.

[0086] In one embodiment of the present invention, based on the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics, as well as the corresponding preset average amplitude standard values, preset pulse frequency standard values, and preset duty cycle standard values, the characteristic anomaly quantization value and anomaly type are determined, including: Based on the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics, as well as the corresponding preset average amplitude standard values, preset pulse frequency standard values, and preset duty cycle standard values, the characteristic anomaly quantification values ​​are determined; Based on the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics, as well as the corresponding preset average amplitude standard values, preset pulse frequency standard values, and preset duty cycle standard values, anomaly type indicators are determined; The anomaly type is determined based on the anomaly type index and the index threshold.

[0087] In one embodiment of the present invention, the fault severity quantification value The calculation formula is: in, Indicates the quantized value of the feature anomaly. Indicates the preset weight. Indicates auxiliary weights, An anomaly type index representing the characteristics of average amplitude. An indicator representing the abnormality type of pulse frequency characteristics. An anomaly type indicator representing duty cycle characteristics.

[0088] In one embodiment of the present invention, the formula for calculating the failure probability is: in, This indicates the probability of failure.

[0089] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described method for locating abnormal pulse signals of a proportional valve in a water heater provided by the present invention.

[0090] The fifth aspect of this invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for locating abnormal pulse signals of a proportional valve in a water heater provided by this invention.

[0091] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage system located remotely from the aforementioned processor.

[0092] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware systems.

[0093] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0094] For system / electronic device embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be found in the description of the method embodiments.

[0095] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for locating faults caused by abnormal pulse signals in a proportional valve of a water heater, characterized in that, Includes the following steps: Calculate the moving average dejitter value of the original amplitude of the pulse signal of the proportional valve of the water heater at multiple moments; The amplitude of the filtered pulse signal at each moment is determined based on the original amplitude of the pulse signal at each moment, the corresponding moving average de-jitter value, and the amplitude difference threshold. Based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes, determine the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics; Based on the average amplitude feature, the pulse frequency feature, and the duty cycle feature, as well as the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value, determine the feature anomaly quantification value and anomaly type; The fault severity quantification value is determined based on the aforementioned abnormal feature quantification value, preset weight, and auxiliary weight; The probability of failure is determined based on the quantified value of the degree of failure. Based on the fault severity quantification value, the fault probability, and the anomaly type, output fault prompt information.

2. The method as described in claim 1, characterized in that, The sliding average de-jitter value The calculation formula is: in, This indicates the size of the sliding debounce window. Indicates the first Each data collection moment, Indicates the first i The original amplitude of the pulse signal at time t.

3. The method as described in claim 2, characterized in that, The amplitude of the filtering pulse signal The calculation formula is: in, Indicates the first t The original amplitude of the pulse signal at time 10:

00. Indicates the first t The amplitude of the filter pulse signal at time -1 This represents the amplitude difference threshold.

4. The method as described in claim 3, characterized in that, The step of determining the average amplitude characteristic, pulse frequency characteristic, and duty cycle characteristic based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes includes: The average amplitude characteristics and pulse frequency characteristics are determined based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes. The duty cycle characteristics are determined based on the average amplitude characteristics and the pulse signal threshold.

5. The method as described in claim 4, characterized in that, The step of determining the characteristic anomaly quantification value and anomaly type based on the average amplitude characteristic, the pulse frequency characteristic, the duty cycle characteristic, and the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value includes: Based on the average amplitude feature, the pulse frequency feature, and the duty cycle feature, as well as the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value, the feature anomaly quantization value is determined; Based on the average amplitude characteristics, the pulse frequency characteristics, and the duty cycle characteristics, as well as the corresponding preset average amplitude standard values, preset pulse frequency standard values, and preset duty cycle standard values, anomaly type indicators are determined; The anomaly type is determined based on the anomaly type index and the index threshold.

6. The method as described in claim 5, characterized in that, The fault severity quantification value The calculation formula is: in, Indicates the quantized value of the feature anomaly. Indicates the preset weight. Indicates auxiliary weights, An anomaly type index representing the characteristics of average amplitude. An indicator representing the abnormality type of pulse frequency characteristics. An anomaly type indicator representing duty cycle characteristics.

7. The method as described in claim 1, characterized in that, The formula for calculating the failure probability is: in, This indicates the probability of failure.

8. A fault location system for abnormal pulse signal of a proportional valve in a water heater, characterized in that, include: The calculation module is used to calculate the moving average de-jitter value of the original amplitude of the pulse signal of the proportional valve of the water heater at multiple moments; The first determining module is used to determine the amplitude of the filtered pulse signal at each moment based on the original amplitude of the pulse signal at each moment, the corresponding moving average de-jitter value, and the amplitude difference threshold. The second determining module is used to determine the average amplitude characteristics, pulse frequency characteristics, and duty cycle characteristics based on the amplitude of the filtered pulse signal and the total number of filtered pulse signal amplitudes. The third determining module is used to determine the feature anomaly quantization value and anomaly type based on the average amplitude feature, the pulse frequency feature, the duty cycle feature, and the corresponding preset average amplitude standard value, preset pulse frequency standard value, and preset duty cycle standard value. The fourth determining module is used to determine the fault degree quantification value based on the characteristic anomaly quantification value, preset weight, and auxiliary weight; The fifth determining module is used to determine the failure probability based on the quantified value of the failure severity. The output module is used to output fault prompt information based on the fault severity quantification value, the fault probability, and the anomaly type.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for locating abnormal pulse signals of a water heater proportional valve as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for locating abnormal pulse signals of the proportional valve of a water heater as described in any one of claims 1 to 7.

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