Outlet water temperature control method for pet water dispenser

Abstract

The invention relates to the technical field of intelligent pet equipment temperature control, and discloses an outlet water temperature control method for a pet water dispenser. The method comprises the following steps: acquiring temperature and environment sensing information through a supervision control and data acquisition system, and carrying out preliminary evaluation; and according to an evaluation result, selecting a corresponding control mode from a multi-mode control strategy library, and generating a preliminary control decision. In the control process according to the decision, real-time outlet water temperature data are synchronously collected and compared with the target temperature, and a temperature error analysis result is generated. And based on a temperature error analysis result, combining with a change trend of environment sensing information, carrying out dynamic correction on the preliminary control decision, and generating an optimization control strategy. Finally, the working state of the heating or cooling element is dynamically adjusted according to the optimization strategy. According to the method, the response speed, the adaptability and the stability of temperature control are improved by preselecting the control mode and combining dynamic correction of the environment trend.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology for intelligent pet devices, specifically a method for controlling the outlet water temperature of a pet water dispenser. Background Technology

[0002] Current pet water fountains mostly employ simple feedback mechanisms based on fixed thresholds for temperature control. A common approach involves using a single temperature sensor to monitor the water temperature and, based on its deviation from the set value, driving the heating or cooling elements via on / off or fixed-parameter PID control. This method relies solely on the hysteresis feedback of the outlet water temperature, resulting in simplistic control logic and a lack of targeted strategies for complex operating conditions such as ambient temperature, seasonal changes, and initial water temperature differences. When the system starts up or experiences sudden environmental changes, it often requires a long adjustment period to reach stability, and its ability to handle disturbances throughout operation is limited, making it difficult to maintain accurate and stable outlet water temperature across different usage scenarios.

[0003] A further drawback is that existing technology cannot effectively cope with dynamic disturbances from the external environment. Conventional control only focuses on the current temperature error value and lacks the ability to perceive and predict trends in environmental factors. When the ambient temperature continues to rise or fall, the system can only compensate and adjust after the outlet water temperature has deviated significantly, resulting in delayed control and large water temperature fluctuations. This passive, reactive control makes the outlet water temperature susceptible to continuous and persistent changes in the environment, failing to achieve smooth and stable temperature maintenance, thus affecting the pet's drinking experience and the equipment's energy efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a method for controlling the water temperature of a pet water fountain, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for controlling the outlet water temperature of a pet water fountain, the method comprising: The temperature and environmental sensor information of the pet water fountain is obtained through a monitoring and control and data acquisition system. A preliminary assessment is performed on the temperature sensing information and the environmental sensing information to obtain a preliminary assessment result related to the current operating status of the pet water fountain. Based on the preliminary evaluation results, the corresponding control mode is selected from the predefined multi-mode control strategy library to generate preliminary control decision results for the pet water fountain. During the process of controlling the pet water fountain based on the preliminary control decision results, the real-time water temperature data of the pet water fountain is collected simultaneously and compared with the preset target temperature to generate temperature error analysis results. Based on the temperature error analysis results and the changing trend of the environmental sensing information, the preliminary control decision results are dynamically corrected to generate an optimized control strategy for the operation of the pet water fountain. According to the optimized control strategy, the working status of the heating or cooling elements of the pet water fountain is dynamically adjusted to control the water temperature.

[0006] Preferably, the acquisition of temperature and environmental sensor information from the pet water fountain through the monitoring, control, and data acquisition system includes: The temperature of the water outlet of the pet water fountain is periodically monitored and sampled to form the first temperature time-series data stream; The ambient temperature of the pet water fountain is monitored and sampled synchronously and periodically to form the first environmental time-series data stream; The first temperature time-series data stream is subjected to noise filtering and data smoothing to generate a second temperature time-series data stream. The first environmental time-series data stream is subjected to noise filtering and data smoothing to generate the second environmental time-series data stream.

[0007] Preferably, the preliminary evaluation of the temperature sensing information and the environmental sensing information to obtain a preliminary evaluation result related to the current operating status of the pet water fountain includes: Feature extraction was performed on the second temperature time-series data stream to analyze the temperature distribution and temperature fluctuation characteristics of the pet water dispenser's outlet temperature within the most recent time window. Feature extraction is performed on the second environmental time-series data stream to analyze and obtain the environmental temperature change trend characteristics within the corresponding time window; Establish a correlation analysis model between the temperature fluctuation characteristics and the environmental temperature change trend characteristics; The correlation analysis model is used to calculate a quantitative assessment index of the impact of environmental changes on the effluent temperature. This quantitative assessment index constitutes the core part of the preliminary assessment results.

[0008] Preferably, the step of selecting a corresponding control mode from a predefined multi-mode control strategy library based on the preliminary evaluation results to generate preliminary control decision results for the pet water fountain includes: The quantitative evaluation index is compared with multiple control mode trigger thresholds pre-set in the multi-mode control strategy library; Based on the comparison results, determine the control mode identifier that should be enabled at the moment; Based on the determined control mode identifier, load the corresponding basic control parameters from the multi-mode control strategy library; Based on the current values ​​of the second temperature time-series data stream, the loaded basic control parameters are initially calculated to generate preliminary control decision results containing the target temperature value and initial power parameters.

[0009] Preferably, during the process of controlling the pet water fountain based on the preliminary control decision results, real-time water temperature data of the pet water fountain is collected simultaneously and compared with a preset target temperature to generate temperature error analysis results, including: While implementing the preliminary control decision results, actual temperature data of the pet water fountain outlet is collected at a higher frequency than the preliminary monitoring sampling to form a high-frequency actual temperature dataset. The target temperature value is extracted from the preliminary control decision results and used as a comparison benchmark; Calculate the instantaneous temperature difference between the data points in the high-frequency actual temperature dataset and the comparison benchmark; Statistical analysis was performed on multiple instantaneous temperature differences over a period of time to calculate the average temperature difference, the standard deviation of the temperature difference, and the rate of change of the temperature difference. The average temperature difference, the standard deviation of the temperature difference, and the rate of change of the temperature difference are integrated to form a temperature error analysis result describing the control deviation.

[0010] Preferably, the step of dynamically correcting the preliminary control decision based on the temperature error analysis results and the changing trend of the environmental sensing information to generate an optimized control strategy for the operation of the pet water fountain includes: Based on the rate of change of temperature difference in the temperature error analysis results, determine whether the response speed of the current control process is lagging. Based on the latest changes in the second environmental time-series data stream, predict the possible direction and magnitude of changes in environmental temperature in the next time period; Based on the judgment of whether the response speed is lagging and the predicted change in ambient temperature, the correction amount of the initial power parameter in the preliminary control decision result is calculated. The correction amount is applied to the initial power parameters to obtain the updated power control parameters; The updated power control parameters are bound to the target temperature value to form an optimized control strategy.

[0011] Preferably, the step of dynamically adjusting the operating state of the heating or cooling elements of the pet water fountain according to the optimized control strategy to control the outlet water temperature includes: The updated power control parameters in the optimized control strategy are converted into duty cycle commands for pulse width modulation signals or voltage and current adjustment commands for heating elements. The heating module or semiconductor cooling module of the pet water fountain is driven to work according to the duty cycle command or the voltage and current adjustment command. While executing the drive, the high-frequency actual temperature dataset is continuously acquired to determine whether the outlet water temperature is approaching the target temperature value. If it is determined that the approach is not effective, the process of generating the temperature error analysis results will be retried.

[0012] Preferably, the process of establishing the predefined multi-mode control strategy library includes: Under different simulated environmental conditions of the pet water fountain, various preset basic control programs are executed; Record the water temperature response curve of the pet water fountain, the time required to reach stability, and energy consumption data under each of the aforementioned basic control programs; The recorded water temperature response curve, the time required to reach stability, and energy consumption data are classified and labeled. A corresponding control mode file is created for each type of data, and a control mode trigger threshold is set. The collection of all control mode files constitutes the multi-mode control strategy library.

[0013] Preferably, selecting the corresponding control mode from the predefined multi-mode control strategy library includes: When the preliminary assessment results indicate that the environmental changes have a relatively small impact on the effluent temperature, the steady-state maintenance control mode is selected. When the preliminary assessment results indicate that environmental changes have a significant impact on the effluent temperature, a rapid response control mode is selected. When the preliminary assessment results indicate that the ambient temperature and the target effluent temperature differ greatly, the high-power start-up control mode is selected.

[0014] Preferably, the method further includes recording the control process and optimizing the strategy library, including: Completely record each control process from generating the initial control decision results to executing the optimized control strategy, including the temperature error analysis results, the correction amounts used, and the final temperature control performance score; Regularly perform statistical analysis on all complete control processes recorded over a period of time to identify control pattern files with poor control performance; Based on the identification results, the basic control parameters or trigger thresholds of the corresponding control mode files in the multi-mode control strategy library are adjusted.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By constructing a predefined multi-mode control strategy library and actively selecting the corresponding control mode based on the preliminary assessment of temperature and environmental information in the initial control phase, the starting point of conventional single feedback control is changed. This mechanism enables the system to quickly match specific operating conditions such as the current season, room temperature, or initial state of the equipment, providing a better initial decision-making framework for subsequent precise control. It shortens the initial response time for the system to reach the target temperature, improves the overall adaptability and control accuracy under different typical operating scenarios, and avoids the problem of poor performance of a single control mode in complex and changing environments.

[0016] In the process of feedback correction based on real-time outlet water temperature error, the changing trend of environmental sensor information is simultaneously introduced as a dynamic correction variable, achieving deep integration of feedforward control and feedback control. The system not only corrects existing temperature deviations but also predicts the potential impact on outlet water temperature based on the real-time direction and rate of change of environmental parameters such as ambient temperature. This trend-predictive feedforward compensation allows control decisions to precede significant temperature deviations, weakening the impact of external environmental disturbances on outlet water temperature, reducing temperature fluctuations, and achieving a more stable and interference-resistant dynamic temperature balance. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the working principle of the water outlet temperature control method for pet water fountains according to the present invention. Figure 2 A flowchart for generating preliminary control decision results; Figure 3 To generate a flowchart for the optimized control strategy; Figure 4 Heatmaps comparing energy consumption of different basic control programs under various environmental simulation conditions; Figure 5 Trend chart of temperature error analysis indicators during the dynamic correction phase of the rapid response control mode. Detailed Implementation

[0018] The technical solutions of 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.

[0019] Please see Figure 1This invention provides a method for controlling the outlet water temperature of a pet water fountain. The method includes: acquiring temperature sensing information and environmental sensing information of the pet water fountain through a supervisory control and data acquisition system, including the outlet water temperature and the ambient temperature; performing a preliminary evaluation of the temperature sensing information and the environmental sensing information to obtain a preliminary evaluation result related to the current operating state of the pet water fountain, reflecting the correlation between the current temperature state and environmental influences; selecting a corresponding control mode from a predefined multi-mode control strategy library based on the preliminary evaluation result to generate a preliminary control decision result for the pet water fountain, which includes a target temperature value and initial power parameters; during the control operation of the pet water fountain based on the preliminary control decision result, simultaneously acquiring real-time outlet water temperature data of the pet water fountain and comparing it with a preset target temperature to generate a temperature error analysis result, which includes the average temperature difference, the standard deviation of the temperature difference, and the rate of change of the temperature difference; and dynamically correcting the preliminary control decision result based on the temperature error analysis result and the changing trend of the environmental sensing information to generate an optimized control strategy for the operation of the pet water fountain, which includes updated power control parameters. Based on the optimized control strategy, the working status of the heating or cooling elements of the pet water fountain is dynamically adjusted to control the water temperature. Specifically, this is achieved by adjusting the duty cycle of the pulse width modulation signal or the voltage and current of the heating element.

[0020] Example 1: The temperature of the pet water fountain's outlet is periodically monitored and sampled to form a first temperature time-series data stream. Simultaneously, the ambient temperature of the pet water fountain is periodically monitored and sampled to form a first environmental time-series data stream. Noise filtering and data smoothing are performed on the first temperature time-series data stream to generate a second temperature time-series data stream. Noise filtering and data smoothing are also performed on the first environmental time-series data stream to generate a second environmental time-series data stream. Feature extraction is performed on the second temperature time-series data stream to analyze the temperature distribution and temperature fluctuation characteristics of the pet water fountain's outlet temperature within the most recent time window. Feature extraction is also performed on the second environmental time-series data stream to analyze the environmental temperature change trend characteristics within the corresponding time window. A correlation analysis model is established between the temperature fluctuation characteristics and the environmental temperature change trend characteristics. Through the correlation analysis model, a quantitative assessment index of the degree of influence of environmental changes on the outlet temperature is calculated. The quantitative assessment index constitutes the core part of the preliminary assessment results.

[0021] In implementation, the temperature of the pet water fountain's outlet is periodically monitored and sampled to form a first temperature time-series data stream. The sampling interval can be set to 5 seconds, and 120 data points are collected continuously to form a first temperature time-series data stream within a 10-minute time window. The ambient temperature of the pet water fountain is simultaneously and periodically monitored and sampled to form a first environmental time-series data stream. The sampling period for the ambient temperature is consistent with that for the outlet temperature to ensure data alignment at specific times. The first temperature time-series data stream undergoes noise filtering and data smoothing to generate a second temperature time-series data stream. Noise filtering can be achieved using a first-order low-pass digital filter, and data smoothing can be achieved using a moving average algorithm with a window length of 5. The first environmental time-series data stream undergoes noise filtering and data smoothing to generate a second environmental time-series data stream. The processing method is consistent with that used for the first temperature time-series data stream to ensure the synchronicity of subsequent processing of the two types of data.

[0022] In some embodiments, feature extraction is performed on the second temperature time-series data stream to analyze the temperature distribution and temperature fluctuation characteristics of the pet water dispenser outlet temperature within the most recent time window. The temperature distribution characteristics are characterized by calculating the arithmetic mean and standard deviation of all sampled values ​​within the time window, and the temperature fluctuation characteristics are characterized by calculating the average absolute value of the temperature change rate between adjacent sampling points. Feature extraction is performed on the second environmental time-series data stream to analyze the environmental temperature change trend characteristics within the corresponding time window. The environmental temperature change trend characteristics are quantified by performing linear regression analysis on the second environmental time-series data stream, using the slope of the resulting linear equation as a quantitative indicator. A correlation analysis model is established between the temperature fluctuation characteristics and the environmental temperature change trend characteristics. This correlation analysis model is used to evaluate the contribution of the environmental temperature change trend characteristics to the outlet temperature fluctuation characteristics. The specific construction process of the correlation analysis model begins with a deep analysis of the processed second temperature time-series data stream and the second environmental time-series data stream. The temperature fluctuation characteristics are quantified by calculating the average absolute value of the temperature change rate between adjacent sampling points, and the environmental temperature change trend characteristics are characterized by the slope obtained from linear regression analysis to represent its direction and rate of change. When constructing the model, the ambient temperature series and the outlet temperature series within the time window are first aligned at the time point to ensure data synchronization. Then, the statistical correlation method is used to evaluate the co-variation relationship between the two series. By analyzing the co-variation pattern of the ambient temperature change trend characteristics and the outlet temperature fluctuation characteristics within the corresponding time window, the influence intensity of the former on the latter is quantified.

[0023] In practical implementation, a correlation analysis model is used to calculate quantitative assessment indicators of the impact of environmental changes on effluent temperature. These quantitative assessment indicators constitute the core part of the preliminary assessment results. One calculation method for the correlation analysis model can be expressed by the following formula: in: Indicates quantitative evaluation indicators, Indicates the first A sampled ambient temperature value, This represents the average value of the ambient temperature samples within the corresponding time window. Indicates and The outlet temperature sample value was collected at the same time. This represents the average value of the outlet temperature samples within the corresponding time window. This indicates the number of sampling points within the time window. This represents the preset environmental impact weighting coefficient. It can be understood that the formula calculates the Pearson correlation coefficient between the ambient temperature series and the outlet temperature series within a specific time window, and then uses the weighting coefficient... Adjustments were made, and the final result was achieved. This serves as a quantitative assessment indicator to measure the impact of ambient temperature changes on the outlet water temperature. In an example scenario, when the ambient temperature rises by 2 degrees Celsius over 10 minutes, and the outlet water temperature passively rises by 0.5 degrees Celsius without the heating element being activated, a high positive correlation quantitative assessment indicator is calculated using the above formula. Value. In another data comparison scenario, when the ambient temperature remains stable, but the water outlet temperature changes due to the operation of the heating element inside the pet water fountain, the calculated quantitative evaluation index... The value will be close to zero, indicating that environmental changes have a low impact on the current outlet water temperature.

[0024] Example 2: See Figure 2 The quantitative evaluation indicators are compared with multiple pre-set control mode trigger thresholds in the multi-mode control strategy library. Based on the comparison results, the identifier of the control mode to be activated is determined. According to the determined control mode identifier, the corresponding basic control parameters are loaded from the multi-mode control strategy library. Combined with the current values ​​of the second temperature time-series data stream, the loaded basic control parameters are initially calculated to generate preliminary control decision results containing the target temperature value and initial power parameters. Simultaneously with the execution of the preliminary control decision results, actual temperature data of the pet water dispenser outlet is collected at a frequency higher than the initial monitoring sampling frequency, forming a high-frequency actual temperature dataset. The target temperature value is extracted from the preliminary control decision results as a comparison benchmark. The instantaneous temperature difference between the data points in the high-frequency actual temperature dataset and the comparison benchmark is calculated. Statistical analysis is performed on multiple instantaneous temperature differences over a period of time, calculating the average temperature difference, the standard deviation of the temperature difference, and the rate of change of the temperature difference. The average temperature difference, the standard deviation of the temperature difference, and the rate of change of the temperature difference are integrated to form a temperature error analysis result describing the control deviation.

[0025] In practical implementation, the quantitative evaluation index is compared with multiple pre-set control mode trigger thresholds in the multi-mode control strategy library. The quantitative evaluation index is derived from the calculation output of the correlation analysis model. The pre-set control mode trigger thresholds in the multi-mode control strategy library include the upper limit of the steady-state maintenance control mode threshold, the lower limit of the rapid response control mode threshold, and the temperature difference threshold of the high-power start-up control mode. Based on the comparison results, the control mode identifier to be activated is determined. For example, when the quantitative evaluation index is less than 0.15, the control mode identifier is determined to be "steady-state maintenance mode"; when the quantitative evaluation index is greater than or equal to 0.15 and the difference between the ambient temperature and the target temperature is less than 10 degrees Celsius, the control mode identifier is determined to be "rapid response mode". Based on the determined control mode identifier, the corresponding basic control parameters are loaded from the multi-mode control strategy library. The basic control parameters include the proportional coefficient, the integral time constant, and the basic heating power percentage. Combined with the current value of the second temperature time-series data stream, the loaded basic control parameters are initially calculated to generate a preliminary control decision result containing the target temperature value and the initial power parameter. The initial power parameter is obtained by multiplying the basic heating power percentage by the rated power of the pet water dispenser heating element.

[0026] In some embodiments, while implementing the preliminary control decision results, actual temperature data of the pet water fountain outlet is collected at a higher frequency than the preliminary monitoring sampling frequency, forming a high-frequency actual temperature dataset. The preliminary monitoring sampling frequency is once every 5 seconds, and the high-frequency collection can be set to once every 1 second. A target temperature value is extracted from the preliminary control decision results as a comparison benchmark; the target temperature value can be, for example, 25 degrees Celsius. The instantaneous temperature difference between the data points in the high-frequency actual temperature dataset and the comparison benchmark is calculated; the instantaneous temperature difference is equal to each actual temperature sample value minus the target temperature value. Statistical analysis is performed on multiple instantaneous temperature differences over a period of time to calculate the average temperature difference, the standard deviation of the temperature difference, and the rate of change of the temperature difference. The rate of change of the temperature difference can be calculated using the following formula: in: Indicates the rate of change of temperature difference. Indicates the first A momentary temperature difference, Indicates the first The time corresponding to each sampling point This indicates the number of instantaneous temperature difference data points used for statistical analysis. The formula calculates the average absolute change in instantaneous temperature difference per unit time, used to describe the severity of temperature fluctuations. Integrating the average temperature difference, standard deviation of the temperature difference, and rate of change of the temperature difference forms the temperature error analysis result, which describes the control deviation. The temperature error analysis result is a data structure containing three numerical elements.

[0027] In practical implementation, an example scenario demonstrates the process from quantifying the evaluation index to generating preliminary control decision results. Assuming the quantified evaluation index is 0.1, which is lower than the upper threshold of 0.15 for the steady-state maintenance control mode, the control mode is identified as "steady-state maintenance mode." The basic control parameters corresponding to "steady-state maintenance mode" are loaded from the multi-mode control strategy library: a proportional gain of 0.8, an integral time constant of 120 seconds, and a basic heating power percentage of 30%. Combining the latest outlet water temperature of 22 degrees Celsius from the second temperature time-series data stream with the preset target temperature of 25 degrees Celsius, the initial power parameter is calculated to be 30% of the rated power, generating a preliminary control decision result containing the target temperature of 25 degrees Celsius and the initial power parameter of 30%. In another data comparison scenario, if the quantified evaluation index is 0.25, the ambient temperature is 15 degrees Celsius, and the target temperature remains 25 degrees Celsius, the temperature difference reaches 10 degrees Celsius, triggering the "high-power start-up control mode." The basic heating power percentage is 80% from the basic control parameters loaded from the strategy library, and the initial power parameter in the generated preliminary control decision results is 80% of the rated power.

[0028] Optionally, an example scenario for generating temperature error analysis results is as follows: During the execution of the initial control decision results of the "steady-state maintenance mode," actual outlet water temperature data is collected for 10 seconds at a frequency of once per second, resulting in a high-frequency actual temperature dataset [24.7, 24.8, 24.9, 25.0, 25.1, 25.0, 24.9, 25.0, 25.0, 25.1] degrees Celsius. The target temperature value is 25 degrees Celsius, and the calculated instantaneous temperature difference sequence is [-0.3, -0.2, -0.1, 0.0, 0.1, 0.0, -0.1, 0.0, 0.0, 0.1] degrees Celsius. The calculated average temperature difference is -0.05 degrees Celsius, and the standard deviation of the temperature difference is approximately 0.12 degrees Celsius, calculated according to the temperature difference change rate formula. The value is approximately 0.05 degrees Celsius per second. In a data comparison scenario, if the high-frequency actual temperature dataset initially collected during the "high-power start-up control mode" is [16.0, 16.5, 17.2, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0] degrees Celsius, and the target temperature is 25 degrees Celsius, then the instantaneous temperature difference sequence is [-9.0, -8.5, -7.8, -7.0, -6.0, -5.0, -4.0, -3.0, -2.0, -1.0] degrees Celsius. The calculated average temperature difference is -5.33 degrees Celsius, the standard deviation of the temperature difference is approximately 2.67 degrees Celsius, and the rate of change of temperature difference is... The value is approximately 0.89 degrees Celsius per second.

[0029] Example 3: See Figure 3Based on the temperature difference change rate in the temperature error analysis results, it is determined whether the response speed of the current control process is lagging. Based on the latest changes in the second environmental time-series data stream, the direction and magnitude of the environmental temperature change in the next time period are predicted. Based on the judgment of whether the response speed is lagging and the predicted change in environmental temperature, the correction amount for the initial power parameters in the preliminary control decision results is calculated. The correction amount is applied to the initial power parameters to obtain updated power control parameters. The updated power control parameters are bound to the target temperature value to form an optimized control strategy. The updated power control parameters in the optimized control strategy are converted into duty cycle commands for pulse width modulation signals or voltage and current adjustment commands for the heating element. Based on the duty cycle commands or voltage and current adjustment commands, the heating module or semiconductor cooling module of the pet water dispenser is driven to work. While executing the drive, a high-frequency actual temperature dataset is continuously acquired to determine whether the outlet water temperature is approaching the target temperature value. If it is determined that it is not effectively approaching, the temperature error analysis result generation process is re-triggered.

[0030] In practical implementation, based on the rate of change of temperature difference in the temperature error analysis results, it is determined whether the response speed of the current control process is lagging. The criterion is that when the absolute value of the rate of change of temperature difference is less than a preset response speed threshold, the response speed is considered lagging. Based on the latest changes in the second environmental time-series data stream, the direction and magnitude of the environmental temperature change in the next time period are predicted. The prediction method involves linear extrapolation of the last few data points of the second environmental time-series data stream. The slope of the linear extrapolation multiplied by the prediction step size yields the predicted magnitude of the environmental temperature change. Based on the determination of whether the response speed is lagging and the predicted change in environmental temperature, the correction amount for the initial power parameters in the preliminary control decision results is calculated. The correction amount can be calculated using the following formula: in: This indicates the amount of correction to the initial power parameters; This represents the quantitative output based on the judgment result of the temperature difference change rate. When it is judged as lagging, Take a positive value; its magnitude is positively correlated with the degree of lag. When it is determined to be non-lagful... Take zero or a negative value; It indicates the predicted range of change in ambient temperature, and its sign indicates the direction of change; Indicates the target temperature value; and These are the response lag compensation gain and the environmental change pre-compensation gain, respectively. The calculated correction amounts... Applying this to the initial power parameters yields updated power control parameters, which are equal to the initial power parameters plus a correction amount. However, the calculation results are limited to the percentage of power allowed by the heating or cooling element. The updated power control parameters are then bound to the target temperature value to form an optimized control strategy. This optimized control strategy is a data structure that includes the target temperature setpoint and the execution instructions for the updated power control parameters.

[0031] In some embodiments, the updated power control parameters in the optimized control strategy are converted into duty cycle commands for pulse width modulation signals or voltage and current adjustment commands for the heating element. The conversion relationship is that the percentage of the updated power control parameters directly corresponds to the duty cycle percentage of the pulse width modulation signal. Based on the duty cycle command or voltage and current adjustment command, the heating module or semiconductor cooling module of the pet water dispenser is driven to operate, and the power drive circuit adjusts its output according to the command. While executing the drive, a high-frequency actual temperature dataset is continuously acquired to determine whether the outlet water temperature is approaching the target temperature value. The judgment logic is to check whether the absolute value sequence of the difference between the values ​​of several recent consecutive high-frequency actual temperature data points and the target temperature value shows a monotonically decreasing trend. If it is determined that there is no effective approach, the temperature error analysis result generation process is retried, that is, the average temperature difference, standard deviation of temperature difference, and rate of change of temperature difference are recalculated based on the latest acquired high-frequency actual temperature dataset.

[0032] In practical implementation, an example scenario illustrates the process from judgment and prediction to generating an optimized control strategy. Assuming the temperature difference change rate in the temperature error analysis results is 0.05 degrees Celsius / second, and the preset response speed threshold is 0.1 degrees Celsius / second, since 0.05 is less than 0.1, the current control process is judged to have a lagging response speed, and a quantified output is generated. It is set to +1.2. Assuming that based on linear extrapolation of the second environmental time-series data stream, the environmental temperature is predicted to decrease by 2 degrees Celsius over a future period, i.e., the predicted environmental temperature change range... The target temperature is -2 degrees Celsius. The temperature is 25 degrees Celsius. Assume the response lag compensation gain. The pre-compensation gain for environmental changes is 0.5. The value is 0.8. The correction amount is calculated according to the formula. If the initial power parameter in the preliminary control decision is 30% of the rated power, then the updated power control parameter is 30% + 0.536% = 30.536%, which remains 30.536% after limiting. In a data comparison scenario, if the temperature difference change rate is 0.15 degrees Celsius / second, then the response is considered lag-free, and the output is quantized. Set to 0. Predict an ambient temperature rise of 1.5 degrees Celsius, i.e. The target temperature is +1.5 degrees Celsius. The temperature remains at 25 degrees Celsius. Calculate the correction amount. If the initial power parameter is 30%, then the updated power control parameter is 30.048%.

[0033] Optionally, an example of execution and feedback is as follows: After obtaining the updated power control parameter of 30.536%, this parameter is converted into a duty cycle instruction for a pulse width modulation signal. The microcontroller outputs a pulse width modulation signal with the corresponding duty cycle to drive the power switch in the heating module. Simultaneously with the drive execution, the system continuously collects the outlet temperature once per second, forming a new high-frequency actual temperature dataset. It can be understood that if the collected temperature value sequence in the next 10 seconds is [24.5, 24.7, 24.8, 24.9, 24.8, 24.7, 24.6, 24.5, 24.4, 24.3] degrees Celsius, while the target temperature is 25 degrees Celsius, then the absolute value sequence of the temperature difference is [0.5, 0.3, 0.2, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7] degrees Celsius. This sequence does not show a monotonically decreasing trend, therefore the system determines that it has not effectively approached the target temperature. At this point, the system will pause the execution of the current optimized control strategy and immediately re-trigger the temperature error analysis process. Based on the latest high-frequency actual temperature dataset [24.5, 24.7, 24.8, 24.9, 24.8, 24.7, 24.6, 24.5, 24.4, 24.3] degrees Celsius, it will recalculate and generate new temperature error analysis results, thereby initiating a new round of dynamic correction calculations.

[0034] Example 4: Under different simulated environmental conditions of the pet water fountain, various preset basic control programs were executed. The water temperature response curve, the time required to reach stability, and energy consumption data were recorded for each basic control program. The recorded water temperature response curve, time required to reach stability, and energy consumption data were categorized and labeled. A corresponding control mode file was created for each type of data, and a control mode trigger threshold was set. The collection of all control mode files constitutes a multi-mode control strategy library. When the preliminary evaluation results indicate that the environmental change has a small impact on the water temperature, the steady-state maintenance control mode is selected. When the preliminary evaluation results indicate that the environmental change has a large impact on the water temperature, the fast response control mode is selected. When the preliminary evaluation results indicate that the ambient temperature differs greatly from the target water temperature, the high-power start-up control mode is selected.

[0035] In practical implementation, under different environmental simulation conditions of the pet water fountain, various preset basic control programs are executed. These environmental simulation conditions include a constant low-temperature environment, a constant high-temperature environment, a temperature step-up environment, a temperature step-down environment, and a temperature periodic fluctuation environment. The preset basic control programs include a fixed duty cycle heating program, a proportional-integral-derivative (PID) control program, and a feedforward-based power compensation program. The water outlet temperature response curve, the time required to reach stability, and energy consumption data are recorded for each basic control program. The water outlet temperature response curve is obtained by recording the complete temperature-time sequence from program start to end. The time required to reach stability is defined as the duration required for the water outlet temperature to enter and remain within the target temperature range of ±0.5 degrees Celsius. Energy consumption data is obtained by integral calculation of the cumulative electrical energy consumption of the heating element during the working period. The recorded water outlet temperature response curve, time required to reach stability, and energy consumption data are classified and labeled. The classification criteria include response speed category, overshoot category, and energy efficiency category. Each data record is labeled with the corresponding environmental condition and control program tags (see Table 1).

[0036] Table 1: Basic Control Program Execution Record Table In some embodiments, a corresponding control mode profile is established for each type of data, and a control mode trigger threshold is set. The collection of all control mode profiles constitutes a multi-mode control strategy library. The control mode profile includes a profile number, a description of applicable environmental conditions, a recommended set of basic control parameters, and expected performance indicators. Setting the control mode trigger threshold relies on statistical analysis of historical data. One method for setting the trigger threshold involves calculating the statistical distribution of a quantitative evaluation index of the impact of environmental changes on the outlet water temperature under different categories. It can be understood that by analyzing historical data under the "fast response" category, it is found that when the quantitative evaluation index is greater than 0.15, the system requires faster adjustment capabilities in most cases. Therefore, the lower limit of the threshold for the fast response control mode can be set to 0.15. The quantitative calculation of the control mode trigger threshold can refer to the following formula: in: This indicates the trigger threshold for a specific control mode to be set. This represents the average value of historical quantitative evaluation index data corresponding to this type of control mode. This represents the standard deviation of this type of historical quantitative evaluation indicator data. It is an adjustable sensitivity parameter used to control the leniency of the threshold. For example, for steady-state maintenance control modes, the historical quantitative evaluation index data is usually small, and the calculated... It is 0.05. The value is 0.03. If the value is 1.0, then the upper limit of the steady-state maintenance control mode threshold is... It can be set to 0.08. When the preliminary assessment results indicate that the environmental change has a small impact on the outlet water temperature, the steady-state maintenance control mode is selected. Specifically, this mode is triggered when the quantitative assessment index is less than or equal to the upper limit of the steady-state maintenance control mode threshold. When the preliminary assessment results indicate that the environmental change has a large impact on the outlet water temperature, the fast response control mode is selected. Specifically, this mode is triggered when the quantitative assessment index is greater than the upper limit of the steady-state maintenance control mode threshold and simultaneously less than the quantitative assessment index mapping value corresponding to the temperature difference threshold of the high-power start-up control mode. When the preliminary assessment results indicate that the difference between the ambient temperature and the target outlet water temperature is extremely large, the high-power start-up control mode is selected. Here, "extremely large difference" is determined by an independent temperature difference threshold. For example, when the absolute value of the difference between the ambient temperature and the target outlet water temperature is greater than 10 degrees Celsius, the high-power start-up control mode is directly triggered regardless of the quantitative assessment index.

[0037] In practical implementation, an example scenario illustrates the logic of control mode selection. Assume the preliminary quantitative evaluation index is 0.06, the upper limit of the steady-state maintenance control mode threshold is 0.08, the temperature difference threshold of the high-power start-up control mode is 10 degrees Celsius, the current ambient temperature is 22 degrees Celsius, the target outlet water temperature is 25 degrees Celsius, and the temperature difference is 3 degrees Celsius. Since the quantitative evaluation index of 0.06 is less than 0.08, and the temperature difference of 3 degrees Celsius is less than 10 degrees Celsius, the system selects the steady-state maintenance control mode. In a data comparison scenario, if the quantitative evaluation index is 0.20, the ambient temperature is 15 degrees Celsius, the target outlet water temperature is 28 degrees Celsius, and the temperature difference is 13 degrees Celsius. Although the quantitative evaluation index of 0.20 falls within the range of the fast response control mode, the temperature difference of 13 degrees Celsius exceeds the temperature difference threshold of 10 degrees Celsius for the high-power start-up control mode; therefore, the system will prioritize the high-power start-up control mode. Another data comparison scenario is as follows: the quantitative evaluation index is 0.12, the ambient temperature is 24 degrees Celsius, the target outlet water temperature is 25 degrees Celsius, and the temperature difference is 1 degree Celsius. The quantitative evaluation index of 0.12 is greater than the upper limit of the steady-state maintenance control mode threshold of 0.08, but the temperature difference of 1 degree Celsius is far from reaching the temperature difference threshold of the high-power start-up control mode. Therefore, the system selects the fast response control mode.

[0038] See Figure 4The data, presented in heatmap form, visually represents the energy consumption (in watt-hours) of a pet water fountain under five simulated environmental conditions (constant low temperature, constant high temperature, temperature step increase, temperature step decrease, and temperature periodic fluctuation) for three basic control programs (fixed duty cycle (50%), PID control, and feedforward power compensation). Specifically, the heatmap uses color gradients (from light yellow to dark red) to correspond to differences in energy consumption values ​​(0.4~1.6 watt-hours), achieving quantitative visualization of energy consumption distribution. Data characteristics show that in the temperature step decrease environment, the PID control energy consumption is only 0.3 watt-hours, significantly lower than other programs and scenarios, demonstrating its energy efficiency advantage under decreasing temperature conditions. In the constant high temperature environment, the feedforward power compensation program consumes 1.7 watt-hours, the highest value across all scenarios, reflecting the energy cost of this program in maintaining a stable high-temperature state. In the temperature periodic fluctuation environment, the PID control energy consumption (1.6 watt-hours) is higher than that of the fixed duty cycle and feedforward power compensation, indicating its weaker energy control performance under dynamic temperature fluctuation scenarios. The core value of the graph lies in providing a quantitative basis for the labeling of "energy efficiency category" in the multi-mode control strategy library through energy consumption mapping of multi-dimensional operating conditions, which can support the energy efficiency priority screening of control programs under different environmental conditions.

[0039] Example 5: Completely record each control process from generating initial control decisions to executing optimized control strategies, including temperature error analysis results, correction amounts used, and the final temperature control performance score. Periodically perform statistical analysis on all recorded complete control processes over a period of time to identify control mode files with poor control performance. Based on the identification results, adjust the basic control parameters or trigger thresholds of the corresponding control mode files in the multi-mode control strategy library.

[0040] In practice, each complete control process, from generating initial control decisions to executing optimized control strategies, is fully recorded. The records include the average temperature difference, standard deviation of the temperature difference, and rate of change of the temperature difference from the temperature error analysis results. The records also include the correction amounts used in the dynamic correction phase and the final temperature control performance score. The temperature control performance score is calculated comprehensively and used to quantitatively evaluate the final effectiveness of a single control process. Statistical analysis is performed periodically on all complete control processes recorded within a certain period, which can be daily, weekly, or after every 100 control processes. The statistical analysis includes calculating the average and variance of the temperature control performance scores of all records under the same control mode identifier, identifying control mode files with poor control performance. The identification criteria are that when the average score corresponding to a control mode file is lower than a preset pass threshold or the score variance is higher than a preset fluctuation threshold, the file is judged to have poor control performance. Based on the identification results, the basic control parameters or trigger thresholds of the corresponding control mode files in the multi-mode control strategy library are adjusted. Adjustments can be made by refitting parameters based on historically successful data or by fine-tuning the trigger thresholds.

[0041] In some embodiments, the calculation of the temperature control effectiveness score relies on the integration of multiple recorded indicators, and one feasible calculation method uses the following formula: in: This indicates the score for temperature control effectiveness. This represents the average temperature difference recorded. This represents the standard deviation of the recorded temperature difference. This represents the ratio of the total energy consumption required to reach a steady temperature to the baseline energy consumption. and These are the temperature difference and standard deviation reference constants used for normalization. , and It is the weight coefficient of each indicator and satisfies It can be understood that the formula quantifies and evaluates a single control process from three dimensions: control accuracy, control stability, and control efficiency, and assigns a score. A higher value indicates a better overall control effect.

[0042] In practical implementation, an example of periodic statistical analysis is as follows: After the system completes 50 control processes identified as "fast response control mode," it initiates periodic statistical analysis, calculating the average temperature control effect score of these 50 records as 0.45, with a variance of 0.08. The preset acceptable threshold is 0.6, and the fluctuation threshold is 0.05. Since the average value of 0.45 is lower than the acceptable threshold of 0.6 and the variance of 0.08 is higher than the fluctuation threshold of 0.05, the "fast response control mode" file is judged to have poor control effect and needs optimization. Based on the identification results, the multi-mode control strategy library is adjusted. One method to adjust the basic control parameters is to analyze the actual correction amount and environmental conditions used in several records with higher scores among the 50 records, and reverse-engineer better basic control parameters. For example, through analysis, it was found that in scenarios where the ambient temperature rises rapidly, the proportional coefficient in the original basic control parameters caused overshoot, so the proportional coefficient was reduced from 1.2 to 1.0. In data comparison scenarios, if a file marked as "steady-state maintenance control mode" has an average score of 0.7 and a variance of 0.03 in statistical analysis, both of which are better than the preset threshold, then the file is judged to be effective and does not need to be adjusted immediately. However, its data will be added to the historical success record library for future threshold calibration reference.

[0043] Optionally, an example of adjusting the control mode trigger threshold is as follows: After identifying that the "fast response control mode" was ineffective, further analysis revealed that a large number of records with quantitative evaluation indicators between 0.15 and 0.18 were categorized into this mode but performed poorly, while records with indicators greater than 0.2 performed reasonably well. This indicates that the original fast response control mode threshold lower limit of 0.15 was set too sensitively.

[0044] See Figure 5 In the dynamic correction phase of the rapid response control mode for pet water fountains, the evolution characteristics of average temperature difference, standard deviation of temperature difference, temperature difference warning threshold, and rate of change of temperature difference over control time were simultaneously displayed: the average temperature difference (red line) initially fluctuated around 3.0℃, gradually decreasing as the control progressed, with the overall trend approaching the temperature difference warning threshold (red dashed line, 1.0℃), reflecting the convergence effect of dynamic correction on temperature deviation; the standard deviation of temperature difference (orange line) showed a continuous downward trend, decreasing from approximately 1.5℃ initially to around 0.5℃ later, reflecting the improved stability of temperature fluctuations during control; the rate of change of temperature difference (blue line) corresponded to the high-frequency dynamic adjustment of the average temperature difference fluctuations, with its value range gradually narrowing over time, indicating that the response speed and adjustment amplitude of the control strategy to temperature deviation tended to be stable. This intuitively presented the multi-dimensional optimization effect of temperature error indicators during the dynamic correction process under the rapid response control mode, providing a quantitative basis for subsequent parameter iteration of the control strategy library.

[0045] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the outlet water temperature of a pet water fountain, characterized in that, include: The temperature and environmental sensor information of the pet water fountain is obtained through a monitoring and control and data acquisition system. A preliminary assessment is performed on the temperature sensing information and the environmental sensing information to obtain a preliminary assessment result related to the current operating status of the pet water fountain. Based on the preliminary evaluation results, the corresponding control mode is selected from the predefined multi-mode control strategy library to generate preliminary control decision results for the pet water fountain. During the process of controlling the pet water fountain based on the preliminary control decision results, the real-time water temperature data of the pet water fountain is collected simultaneously and compared with the preset target temperature to generate temperature error analysis results. Based on the temperature error analysis results and the changing trend of the environmental sensing information, the preliminary control decision results are dynamically corrected to generate an optimized control strategy for the operation of the pet water fountain. According to the optimized control strategy, the working status of the heating or cooling elements of the pet water fountain is dynamically adjusted to control the water temperature.

2. The method for controlling the outlet water temperature of a pet water dispenser as described in claim 1, characterized in that, The monitoring, control, and data acquisition system acquires temperature and environmental sensor information from the pet water fountain, including: The temperature of the water outlet of the pet water fountain is periodically monitored and sampled to form the first temperature time-series data stream; The ambient temperature of the pet water fountain is monitored and sampled synchronously and periodically to form the first environmental time-series data stream; The first temperature time-series data stream is subjected to noise filtering and data smoothing to generate a second temperature time-series data stream. The first environmental time-series data stream is subjected to noise filtering and data smoothing to generate the second environmental time-series data stream.

3. The method for controlling the outlet water temperature of a pet water dispenser as described in claim 2, characterized in that, The preliminary evaluation of the temperature sensing information and the environmental sensing information to obtain preliminary evaluation results related to the current operating status of the pet water fountain includes: Feature extraction was performed on the second temperature time-series data stream to analyze the temperature distribution and temperature fluctuation characteristics of the pet water dispenser's outlet temperature within the most recent time window. Feature extraction is performed on the second environmental time-series data stream to analyze and obtain the environmental temperature change trend characteristics within the corresponding time window; Establish a correlation analysis model between the temperature fluctuation characteristics and the environmental temperature change trend characteristics; The correlation analysis model is used to calculate a quantitative assessment index of the impact of environmental changes on the effluent temperature. This quantitative assessment index constitutes the core part of the preliminary assessment results.

4. The method for controlling the outlet water temperature of a pet water fountain as described in claim 3, characterized in that, Based on the preliminary evaluation results, the corresponding control mode is selected from a predefined multi-mode control strategy library to generate preliminary control decision results for the pet water fountain, including: The quantitative evaluation index is compared with multiple control mode trigger thresholds pre-set in the multi-mode control strategy library; Based on the comparison results, determine the control mode identifier that should be enabled at the moment; Based on the determined control mode identifier, load the corresponding basic control parameters from the multi-mode control strategy library; Based on the current values ​​of the second temperature time-series data stream, the loaded basic control parameters are initially calculated to generate preliminary control decision results containing the target temperature value and initial power parameters.

5. The method for controlling the outlet water temperature of a pet water dispenser as described in claim 4, characterized in that, During the process of controlling the pet water fountain based on the preliminary control decision results, real-time water temperature data of the pet water fountain is collected simultaneously and compared with the preset target temperature to generate temperature error analysis results, including: While implementing the preliminary control decision results, actual temperature data of the pet water fountain outlet is collected at a higher frequency than the preliminary monitoring sampling to form a high-frequency actual temperature dataset. The target temperature value is extracted from the preliminary control decision results and used as a comparison benchmark; Calculate the instantaneous temperature difference between the data points in the high-frequency actual temperature dataset and the comparison benchmark; Statistical analysis was performed on multiple instantaneous temperature differences over a period of time to calculate the average temperature difference, the standard deviation of the temperature difference, and the rate of change of the temperature difference. The average temperature difference, the standard deviation of the temperature difference, and the rate of change of the temperature difference are integrated to form a temperature error analysis result describing the control deviation.

6. The method for controlling the outlet water temperature of a pet water dispenser as described in claim 5, characterized in that, Based on the temperature error analysis results and combined with the changing trends of the environmental sensor information, the preliminary control decision results are dynamically corrected to generate an optimized control strategy for the operation of the pet water fountain, including: Based on the rate of change of temperature difference in the temperature error analysis results, determine whether the response speed of the current control process is lagging. Based on the latest changes in the second environmental time-series data stream, predict the possible direction and magnitude of changes in environmental temperature in the next time period; Based on the judgment of whether the response speed is lagging and the predicted change in ambient temperature, the correction amount of the initial power parameter in the preliminary control decision result is calculated. The correction amount is applied to the initial power parameters to obtain the updated power control parameters; The updated power control parameters are bound to the target temperature value to form an optimized control strategy.

7. The method for controlling the outlet water temperature of a pet water dispenser as described in claim 6, characterized in that, The step of dynamically adjusting the operating state of the heating or cooling elements of the pet water fountain according to the optimized control strategy to control the outlet water temperature includes: The updated power control parameters in the optimized control strategy are converted into duty cycle commands for pulse width modulation signals or voltage and current adjustment commands for heating elements. The heating module or semiconductor cooling module of the pet water fountain is driven to work according to the duty cycle command or the voltage and current adjustment command. While executing the drive, the high-frequency actual temperature dataset is continuously acquired to determine whether the outlet water temperature is approaching the target temperature value. If it is determined that the approach is not effective, the process of generating the temperature error analysis results will be retried.

8. The method for controlling the outlet water temperature of a pet water fountain as described in claim 1, characterized in that, The process of establishing the predefined multi-mode control strategy library includes: Under different simulated environmental conditions of the pet water fountain, various preset basic control programs are executed; Record the water temperature response curve of the pet water fountain, the time required to reach stability, and energy consumption data under each of the aforementioned basic control programs; The recorded water temperature response curve, the time required to reach stability, and energy consumption data are classified and labeled. A corresponding control mode file is created for each type of data, and a control mode trigger threshold is set. The collection of all control mode files constitutes the multi-mode control strategy library.

9. The method for controlling the outlet water temperature of a pet water dispenser as described in claim 8, characterized in that, The step of selecting the corresponding control mode from the predefined multi-mode control strategy library includes: When the preliminary assessment results indicate that the environmental changes have a relatively small impact on the effluent temperature, the steady-state maintenance control mode is selected. When the preliminary assessment results indicate that environmental changes have a significant impact on the effluent temperature, a rapid response control mode is selected. When the preliminary assessment results indicate that the ambient temperature and the target effluent temperature differ greatly, the high-power start-up control mode is selected.

10. A method for controlling the outlet water temperature of a pet water fountain as described in claim 7, characterized in that, The method also includes the recording of the control process and the optimization of the strategy library, including: Completely record each control process from generating the initial control decision results to executing the optimized control strategy, including the temperature error analysis results, the correction amounts used, and the final temperature control performance score; Regularly perform statistical analysis on all complete control processes recorded over a period of time to identify control pattern files with poor control performance; Based on the identification results, the basic control parameters or trigger thresholds of the corresponding control mode files in the multi-mode control strategy library are adjusted.

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