Intelligent fluid heating preheating energy-saving control method based on historical water consumption data

Abstract

This invention discloses an intelligent fluid heating preheating energy-saving control method based on historical water usage data, belonging to the field of intelligent fluid heating control technology. This method is implemented based on water usage point trigger sensors and a central control unit. The sensor only sends a unique water usage point ID to the central control unit, without collecting redundant data. The central control unit continuously records historical water usage data for each water usage point at a configurable preset period. Based on the frequency distribution pattern of historical water usage periods, it predicts the user's next water usage time, calculates the target heating temperature and minimum heating duration in conjunction with environmental parameters, determines the preheating start node, and initiates preheating. This invention completely breaks the long-standing technical bias in the field that "accurate preheating requires the collection of multi-dimensional data at the end point," solving the industry pain points of users waiting for hot water and high energy consumption from ineffective heating. Actual measurements show a water usage prediction accuracy of ≥98.7% and a comprehensive energy saving rate of ≥20.2%. It is compatible with all types of household / commercial fluid heating equipment and has strong practicality and applicability.

Description

[0001] This application is a divisional application of the invention patent application with application number 202610244827X, application date March 2, 2026, entitled "A self-powered multi-point identification intelligent fluid heating system with multi-module deep collaboration and its control method". Technical Field

[0002] This invention belongs to the field of intelligent fluid heating control technology, specifically relating to an intelligent fluid heating preheating energy-saving control method based on historical water usage data. Background Technology

[0003] Currently, all categories of fluid heating equipment, including storage-type electric water heaters, gas water heaters, air source water heaters, and wall-hung boilers, face four common technological bottlenecks in their intelligent control systems:

[0004] ① Lack of multi-point water use identification: It can only determine the overall water use based on the main unit's flow rate, and cannot distinguish specific water use points such as showers, kitchens, and washbasins. The temperature control logic is one-size-fits-all and cannot achieve differentiated and precise temperature control.

[0005] ② Limited power supply for end sensors: Battery power requires regular replacement, wired power supply cannot be adapted to old buildings without power supply, multi-node recognition solutions cannot be deployed on a large scale, and user acceptance is extremely low;

[0006] ③ Lack of water usage prediction and preheating capabilities: There has long been a technical bias in this field that "to achieve accurate prediction of water usage time and preheating control, it is necessary to collect detailed data from multiple dimensions such as water temperature, flow rate, and user behavior at the point of use." Existing solutions cannot achieve accurate prediction based on the extremely simple water point ID signal. Users need to wait for a long time for cold water to be drained each time they use water, resulting in high energy consumption from ineffective heating and limiting both energy saving effect and user experience.

[0007] ④ Rigid prediction logic and lack of abnormal data filtering mechanism: The existing fixed-time preheating scheme cannot adapt to different water usage habits such as weekdays / holidays. Invalid data such as occasional water usage and test water temperature seriously interfere with the accuracy of prediction, which is prone to false prediction and missed prediction, and cannot be implemented on a large scale. Summary of the Invention

[0008] This invention provides an intelligent fluid heating preheating energy-saving control method based on historical water usage data, which completely breaks the long-standing technical prejudice in the field that "precise preheating must collect multi-dimensional data at the end point". Through the combined innovation of "unique ID triggering of water usage point + continuous recording of historical water usage data + prediction of frequency distribution patterns + preheating temperature control + deep energy saving in different scenarios", it solves the industry pain points of users waiting for hot water, one-size-fits-all temperature control logic, and high energy consumption of ineffective heating, and achieves precise preheating, deep energy saving and full-scenario home adaptability.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A smart fluid heating preheating energy-saving control method based on historical water usage data is implemented using water point trigger sensors and a central control unit of the fluid heating host. The water point trigger sensors are deployed in the fluid pipes behind the faucets at each water point and are used only to detect the hot water activation status and send an ID signal uniquely bound to that water point to the central control unit. The method includes the following steps:

[0011] S1. Water usage trigger and historical data synchronous recording: After receiving the unique water usage point ID sent by the water usage point trigger sensor, the central control unit synchronously collects the current water temperature, inlet water temperature and real-time fluid flow data of the inner tank of the fluid heating host, and continuously records and updates the historical water usage data corresponding to the unique water usage point at a configurable preset period. The historical water usage data includes the historical water usage period, water usage duration and water usage of the water usage point.

[0012] S2. Water usage prediction and parameter calculation based on historical data: The central control unit matches the user's current water usage behavior scenario based on the unique water point ID, real-time fluid flow, and historical water usage data of the water point; at the same time, based on the frequency distribution pattern of historical water usage periods within the preset period of the water point, it extracts the concentrated water usage period, predicts the user's next water usage time, and combines the comfortable water temperature, inlet water temperature, and ambient temperature and humidity of the water point in history to calculate the target heating temperature and minimum heating time in advance, determine the preheating start node, and ensure that heating to the target temperature is completed before the predicted water usage time;

[0013] S3. Preheating and Scenario-based Energy-saving Temperature Control: At the preheating start-up node, the central control unit controls the heating components of the fluid heating host to start running in advance. After heating to the target heating temperature, it enters the heat preservation state adapted to the water use scenario, achieving precise preheating and deep energy saving.

[0014] Furthermore, the water point trigger sensor is a distributed temperature difference self-powered trigger sensor. The specific triggering logic is as follows: if the temperature rise of the outer wall of the fluid pipe is ≥8℃ within 3 seconds, it is determined that the water point is in use for hot water. The sensor is only awakened when it detects that the hot water is in use. After sending the water point ID, it immediately enters deep sleep mode and is completely powered off when not triggered. The power generation of a single water flow for 1 second can support ≥30 signal transmissions.

[0015] Furthermore, in step S2, the concentrated water use period is the continuous period with the highest proportion of water use triggering frequency within a preset period. The central control unit counts the frequency distribution of historical water use periods according to weekdays, holidays, and rest days, and predicts the next water use time for different dates accordingly.

[0016] Furthermore, in step S2, the central control unit filters out outliers from the historical water usage data, removing test water temperature data with a single water output duration of ≤2 seconds and occasional water usage data that is not in a fixed time period, and statistically analyzes the frequency distribution pattern based on the filtered valid data.

[0017] Furthermore, in step S2, the preheating start node is dynamically adjusted according to the inlet water temperature and the ambient temperature. The lower the inlet water temperature and the lower the ambient temperature, the earlier the preheating start node is, ensuring that the target heating temperature is reached before the predicted water usage time.

[0018] Furthermore, in step S3, the preheating adopts a stepped heating logic, first preheating the inner tank water temperature to a preheating threshold 5-8℃ lower than the target temperature, and then heating it to the target temperature 1-3 minutes before the predicted water usage time to reduce heat loss during insulation.

[0019] Furthermore, in step S2, when multiple water usage points have historical water usage records at the same time, the central control unit calculates a unified preheating start node and target heating temperature based on the historical water usage priority of each water usage point, so as to realize synchronous preheating of multiple water usage points.

[0020] Furthermore, in step S2, the water usage behavior scenarios and the calculation logic are pre-defined and have a one-to-one correspondence, including four scenarios: showering, washing vegetables and hands, testing water temperature, and no water usage when away from home.

[0021] Furthermore, different water usage scenarios correspond to independent temperature control models. Based on historical comfortable water temperature, inlet water temperature, and ambient temperature and humidity, the target heating temperature and minimum heating time are calculated to achieve precise preheating and deep energy saving.

[0022] Furthermore, the central control unit performs dual-redundancy bidirectional calibration using water point trigger signals and fluid flow data. Signals that do not match are discarded directly to avoid false triggering or missed triggering. Beneficial effects

[0023] 1. Outstanding creativity, completely breaking industry technical bias: For the first time, it has achieved a combination of "minimalist ID signal triggering + historical data frequency prediction + scenario-based precise preheating", which completely breaks the long-standing technical bias in this field that "in order to achieve accurate water usage time prediction and preheating control, it is necessary to collect multi-dimensional detailed data at the water end". It is not a simple patchwork of existing technologies and has outstanding non-obviousness.

[0024] 2. High prediction accuracy and excellent user experience: After 60 days of testing with 5 households, the accuracy of this method in predicting water usage time reached 98.7%, with no missed or incorrect predictions. Users can get hot water immediately after turning on the tap, without having to wait for the cold water to drain, completely solving the core pain point of waiting for hot water.

[0025] 3. Significant energy saving effect: The measured overall energy saving rate is 20.2% compared with the traditional fixed-time preheating solution. Among them, the standby power consumption in the away mode is reduced by 83%, with no ineffective heating, and the deep energy saving effect is outstanding.

[0026] 4. Highly adaptable to local conditions: The end-point sensor requires no wiring or maintenance, can be installed directly in old houses, has an extremely low BOM cost per sensor, does not require modification of the core structure of the water heater unit, and has virtually no barrier to large-scale promotion.

[0027] 5. High adaptability: It can be directly connected to all types of household / commercial fluid heating equipment, and is compatible with various self-powered methods and installation scenarios, making it widely applicable. Attached Figure Description

[0028] Figure 1 is an overall structural block diagram of the intelligent fluid heating system of the present invention.

[0029] Figure 2 is an overall flowchart of the intelligent fluid heating control method of the present invention.

[0030] In the diagram: 1. Central control unit; 2. User terminal; 3. Heating component; 4. Kitchen sensor; 5. Shower sensor; 6. Washbasin sensor; 11. Sensor sends signal; 12. Precise scene prediction; 13. Precise scene temperature control; 14. Heating is executed; 15. Away mode is determined; 16. Sleep mode is activated. Detailed Implementation

[0031] This embodiment is applicable to all fluid heating hosts such as electric water heaters, gas water heaters, air source water heaters, and wall-hung boilers. The sensor is installed on the outer wall of the pipe at the back end of the faucet at various water points such as shower, kitchen, washbasin, and balcony. The present invention will be further described in detail below with reference to specific embodiments. All implementation methods are derived from the original disclosure of the original application date.

[0032] Example 1: Historical data prediction and preheating temperature control for home shower scenarios.

[0033] This embodiment is applied to a household storage-type electric water heater. The sensor is installed on the outer wall of the shower pipe in the secondary bathroom. It is a TEG temperature difference self-powered trigger sensor (corresponding to shower sensor 5). It only sends the water point ID and does not collect any additional data.

[0034] Users typically shower between 21:00 and 22:00. The central control unit 1 continuously records historical water usage data for the water point corresponding to the sensor 5 in the shower room, including daily water usage time, water usage duration, and the comfortable water temperature of 40℃ for more than 80% of the user's historical water usage needs. The preset recording period is 30 days.

[0035] Based on the frequency distribution of 30 days of historical water usage data, the central control unit 1 identified that the water usage frequency between 21:00 and 22:00 accounted for 92%, which was extracted as the concentrated water usage period. It predicted that the user would take a shower around 21:00 on the same day, and calculated that the preheating start node was 20:50, the minimum heating time was 10 minutes, and the target heating temperature was the user's historically commonly used comfortable water temperature of 40℃.

[0036] At 20:50, the central control unit 1 performs scene precision prediction 12, determines it to be a shower scene, performs scene precision temperature control 13, calls the independent temperature control model for the shower scene, calculates the target temperature Ttarget = 40℃ + 3℃ (correction for inlet water temperature of 10℃) + 2℃ (correction for ambient temperature of 15℃) + 0℃ (correction for ambient humidity of 55%) = 45℃, calculates the minimum heating time according to the formula, and controls the heating component 3 to perform heating 14, preheating the inner tank water temperature to the target temperature, and enters the heat preservation state after heating is completed.

[0037] At 21:00, when the user turns on the hot water tap in the shower, the shower room sensor 5 detects a temperature rise of ≥8℃ in the pipes within 3 seconds and triggers a signal 11, sending the unique ID of the shower water point to the central control unit 1. Upon receiving the signal, the central control unit 1 fine-tunes the heating parameters based on the real-time inlet water temperature and ambient temperature and humidity, ensuring a constant water temperature throughout the process. Hot water is available immediately upon turning on the tap, without waiting or ineffective heating. After the user turns off the tap, the system stops heating and enters a normal heat preservation and sleep waiting state 16, while simultaneously updating the historical water usage data for that water point.

[0038] Example 2: Implementation of weekday / holiday prediction by dimension.

[0039] Users' water usage for washing up is concentrated between 7:00 and 7:30 on weekdays and between 9:00 and 10:00 on weekends. The central control unit 1 collects historical water usage data from the corresponding water points of the handwashing station sensor 6 for weekdays and weekends respectively, and extracts the concentrated water usage periods.

[0040] On weekdays, the central control unit 1 predicts that a user will use water at 7:00 AM and starts preheating 10 minutes in advance, heating the water to the user's preset temperature of 38℃. On weekends, the prediction time is automatically adjusted to 9:00 AM, and preheating starts 15 minutes in advance to avoid ineffective heating. Simultaneously, the central control unit 1 filters outliers from historical data, removing invalid data such as users testing water temperature for 1.5 seconds during a single water tap and occasional abnormal data from early morning water use on weekends. Based on the frequency distribution of the filtered valid data, the prediction accuracy is improved to over 99%.

[0041] Example 3: Implementation of stepped preheating energy-saving control.

[0042] Users typically use water for washing vegetables in their kitchens between 6:00 PM and 7:00 PM daily. Based on historical data, the central control unit 1 predicts when users will use water at 6:00 PM and employs a stepped heating logic: at 5:30 PM, the water temperature in the inner tank is preheated to 32°C (below the preheating threshold of the target temperature of 39°C), and then the heating is started at 5:57 PM to reach 39°C. This significantly reduces heat loss during prolonged heat preservation and improves energy efficiency by more than 12% compared to continuous heat preservation solutions.

[0043] Example 4: Simultaneous preheating at multiple water points.

[0044] The user's master bathroom and secondary bathroom both have daily water usage records around 21:00. Based on the historical water usage priorities of the two water points, the central control unit 1 calculates a unified preheating start point of 20:45, with a target heating temperature of 45℃. This preheating is synchronized in advance to ensure that the water temperature remains stable and without fluctuations when the two water points are in use at the same time, eliminating the need for waiting.

[0045] Example 5: Deep energy-saving implementation in the away-from-home mode.

[0046] If there is no trigger signal at any water point for 24 consecutive hours, the system will execute the "away mode 15" judgment, automatically determining it as an away-from-home no-water scenario, and enter the sleep waiting 16 and deep energy-saving state: non-essential function modules such as scene recognition and adaptive learning are turned off, and only the anti-freeze monitoring and trigger signal receiving functions are retained. The standby power consumption is reduced from the usual 5W to below 1W; the inner tank heat preservation threshold is lowered from the usual 40℃ to the anti-freeze low temperature threshold of 5℃. Heating is only started to 8℃ when the water temperature is below 5℃, and heating is completely stopped at other times to achieve deep energy saving.

[0047] Example 6: Implementation of dual-redundancy calibration to prevent false triggering.

[0048] The corresponding sensor erroneously sends signal 11 due to pipeline vibration, sending a trigger signal to the central control unit 1. The central control unit 1 synchronously detects that the fluid flow rate is 0L / min. Since the trigger signal and flow data do not match, the signal is discarded directly, and the heating component 3 is not controlled to perform heating 14, thus completely eliminating false triggering and maximizing system stability.

[0049] Test data: After 60 days of actual testing in 5 households, the invention achieved an overall energy saving rate of 20.2% compared to traditional water heater solutions. Among them, the standby power consumption in the away-from-home mode was reduced by 83%, the accuracy rate of water usage time prediction reached 98.7%, the scene recognition accuracy rate reached 98.7%, the sensor trigger success rate was 100%, with no false triggers or missed triggers. The user comfort satisfaction rate reached 98%, demonstrating its practicality and technical effectiveness.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart fluid heating preheating energy-saving control method based on historical water usage data, characterized in that, This method is implemented based on water point trigger sensors and a central control unit for the fluid heating host. The water point trigger sensors are installed in the fluid pipes behind the faucets at each water point and are used only to detect the hot water activation status and send an ID signal uniquely bound to that water point to the central control unit. The method includes the following steps: S1. Water usage trigger and historical data synchronous recording: After receiving the unique water usage point ID sent by the water usage point trigger sensor, the central control unit synchronously collects the current water temperature, inlet water temperature and real-time fluid flow data of the inner tank of the fluid heating host, and continuously records and updates the historical water usage data corresponding to the unique water usage point at a configurable preset period. The historical water usage data includes the historical water usage period, water usage duration and water usage of the water usage point. S2. Water usage prediction and parameter calculation based on historical data: The central control unit matches the user's current water usage behavior scenario based on the unique water point ID, real-time fluid flow, and historical water usage data of the water point; at the same time, based on the frequency distribution pattern of historical water usage periods within the preset period of the water point, it extracts the concentrated water usage period, predicts the user's next water usage time, and combines the comfortable water temperature, inlet water temperature, and ambient temperature and humidity of the water point in history to calculate the target heating temperature and minimum heating time in advance, determine the preheating start node, and ensure that heating to the target temperature is completed before the predicted water usage time; S3. Preheating and Scenario-based Energy-saving Temperature Control: At the preheating start-up node, the central control unit controls the heating components of the fluid heating host to start running in advance. After heating to the target heating temperature, it enters the heat preservation state adapted to the water use scenario, achieving precise preheating and deep energy saving.

2. The control method according to claim 1, characterized in that, The water point trigger sensor is a distributed temperature difference self-powered trigger sensor. The specific triggering logic is as follows: if the temperature rise of the outer wall of the fluid pipe is ≥8℃ within 3 seconds, it is determined that the water point is in use for hot water. The sensor is only awakened when it detects that the hot water is in use. After sending the water point ID, it immediately enters deep sleep mode and is completely powered off when not triggered. The power generation of a single water flow for 1 second can support ≥30 signal transmissions.

3. The control method according to claim 1, characterized in that, In step S2, the concentrated water use period is the continuous period with the highest proportion of water use triggering frequency within a preset period. The central control unit counts the frequency distribution of historical water use periods according to weekdays, holidays and rest days, and predicts the next water use time for different dates.

4. The control method according to claim 1, characterized in that, In step S2, the central control unit filters out outliers from historical water usage data, removing test water temperature data with a single water output duration of ≤2 seconds and occasional water usage data that is not in a fixed time period, and statistically analyzes the frequency distribution pattern of the filtered valid data.

5. The control method according to claim 1, characterized in that, In step S2, the preheating start node is dynamically adjusted according to the inlet water temperature and the ambient temperature. The lower the inlet water temperature and the lower the ambient temperature, the earlier the preheating start node is, to ensure that the target heating temperature is reached before the predicted water usage time.

6. The control method according to claim 1, characterized in that, In step S3, the preheating adopts a stepped heating logic. First, the water temperature in the inner tank is preheated to a preheating threshold that is 5-8°C lower than the target temperature. Then, it is heated to the target temperature 1-3 minutes before the predicted water usage time to reduce heat loss during insulation.

7. The control method according to claim 1, characterized in that, In step S2, when multiple water points have historical water usage records at the same time, the central control unit calculates a unified preheating start node and target heating temperature based on the historical water usage priority of each water point, so as to realize synchronous preheating of multiple water points.

8. The control method according to claim 1, characterized in that, In step S2, the water use behavior scenarios and the calculation logic have a one-to-one preset binding relationship, specifically as follows: (1) Shower water point ID trigger + continuous water supply for ≥5 minutes + fluid flow rate ≥8L / min, determined to be a shower / bathing scenario; (2) If the water point ID in the kitchen / washbasin is triggered, the water output duration is ≤60 seconds, and the fluid flow rate is ≤2L / min, it is determined to be a vegetable washing and hand washing scenario; (3) If any water point ID triggers the water flow for ≤2 seconds, it is determined to be a water temperature test scenario; (4) If there is no trigger signal at any water point for 24 consecutive hours, it is determined to be a scenario where there is no water use when away from home.

9. The control method according to claim 8, characterized in that, In step S3, the formula for calculating the target heating temperature for the shower / bathing scenario is: Ttarget = Tbase + ΔTin + ΔTamb + ΔThum; Where: Tbase is the comfortable water temperature for more than 80% of the historical water demand at this shower point, ΔTin is the inlet water temperature correction, ΔTamb is the bathroom ambient temperature correction, ΔThum is the bathroom ambient humidity correction, ΔThum=-2℃ when humidity>80%, ΔThum=-1℃ when 60%<humidity≤80%, and ΔThum=0℃ when humidity≤60%; The formula for calculating the minimum heating time is: tmin = [C×m×(Ttarget-Tin)] / (P×η) × khum, where khum is the heat loss coefficient after humidity correction.

10. The control method according to claim 1, characterized in that, In step S1, the central control unit performs dual-redundancy bidirectional calibration using the water point trigger signal and fluid flow data. Signals that do not match are discarded directly to avoid false triggering or missed triggering.

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