Air energy heat pump sensor anti-interference control method based on user water use habit prediction

Abstract

The invention provides an air energy heat pump sensor anti-interference control method based on user water consumption habit prediction. The method comprises the steps that a water consumption habit prediction result in a current time window is generated according to historical water consumption event records; dynamically determining an anti-interference strategy boundary for limiting the change amplitude of the sensor signal based on the prediction result; in each control period, according to the anti-interference strategy boundary, restraining the variable quantity between the original sampling value of the sensor and the effective sensor signal of the previous control period, and generating the effective sensor signal of the current control period; and operation control of the air energy heat pump is executed based on the effective sensor signal. According to the method and the system, user water consumption behavior prediction and sensor signal processing are combined, so that the self-adaptive anti-interference effect of suppressing interference in a non-water consumption period and reserving a real signal in a water consumption period is realized, and the stability and response timeliness of system control are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of heat pump control, and particularly relates to an air energy heat pump sensor anti-interference control method based on user water use habit prediction. BACKGROUND

[0002] Air energy heat pumps are usually installed outdoors or in semi-open spaces and are subjected to complex working conditions such as high humidity, alternating cold and heat, electromagnetic interference and mechanical vibration for a long time. Sensor signals are prone to abnormal conditions such as transient jumps, short-term jitter or slow drift. Such abnormalities are often highly similar to the state changes caused by real water use in numerical performance. The control system cannot accurately determine the cause of the abnormality only by the instantaneous value of the sensor, and thus is prone to misjudgment of non-real disturbance as water use demand and triggering of operation adjustment. In the prior art, fixed parameter filtering, threshold judgment, time delay confirmation or multi-sensor redundancy are usually used to process sensor signals. These methods have certain effect in inhibiting random noise, but lack the ability to distinguish the use scene and time characteristics. When the user suddenly uses water, the response is often slow, and during the non-water use period, the system is prone to false operation due to abnormal fluctuations. At the same time, the air energy heat pump system itself has large thermal inertia and start-stop cost. False control decisions not only reduce the overall energy efficiency of the system, but also increase the mechanical loss caused by frequent start-stop of the compressor and shorten the service life of the equipment. Most of the existing solutions independently deal with the above problems from the aspects of signal processing or control rules, and fail to fully utilize the stable rules of user water use behavior in time distribution. Therefore, there is a lack of an overall control idea that can constrain the reliability of sensor signals from the perspective of time semantics, and it is difficult to achieve an ideal balance between anti-interference ability and water use response timeliness. SUMMARY

[0003] The purpose of the present application is to design an air energy heat pump sensor anti-interference control method based on user water use habit prediction, which can naturally integrate the long-term stable external constraint of user water use habit into the sensor anti-interference and operation control link, and realize the pre-inhibition of sensor abnormal disturbance and the timely response to real water use behavior.

[0004] In order to achieve the above purpose, the present application provides an air energy heat pump sensor anti-interference control method based on user water use habit prediction, which comprises the following steps: generating a water use habit prediction result reflecting the water use possibility in a current time window according to historical water use event records; determining an anti-interference strategy boundary for limiting the change amplitude of the sensor signal based on the water use habit prediction result; in each control period, the change amount between the original sampling value of the sensor and the effective sensor signal of the last control period is constrained according to the anti-interference strategy boundary to generate the effective sensor signal of the current control period. perform operation control of the air energy heat pump based on the effective sensor signal.

[0005] Further, the water usage habit prediction result is generated by the following way: dividing a day into a plurality of time windows with fixed length; In the operation process of the air energy heat pump, a water usage event is identified based on the combination condition of continuous temperature drop on the hot water side and rising load of the compressor, and the time window to which the water usage event belongs is recorded; statistically counting the occurrence frequency of the water usage event in each time window to form a time water usage statistical value; generating a recent operation correction result in combination with the water usage event identification in the recent plurality of time windows; weighting the time water usage statistical value and the recent operation correction result to obtain the water usage habit prediction result.

[0006] Further, the division of the time window is completed in the device initialization stage and remains unchanged in the subsequent operation process.

[0007] Further, the anti-interference strategy boundary is generated from the water usage habit prediction result by a nonlinear mapping method, so that the strategy boundary is quickly tightened when the water usage possibility is low, and the strategy boundary is slowly relaxed when the water usage possibility is high.

[0008] Further, the anti-interference strategy boundary is fused with the strategy boundary that has taken effect in the last control cycle in each control cycle to obtain a smooth and continuous current strategy boundary.

[0009] Further, when the effective sensor signal is generated, the change amount between the current sensor original sampling value and the effective sensor signal in the last control cycle is calculated; when the absolute value of the change amount does not exceed the anti-interference strategy boundary, the current sensor original sampling value is taken as the effective sensor signal; when the absolute value of the change amount exceeds the anti-interference strategy boundary, the update amplitude is compressed in proportion to generate an effective sensor signal between the effective sensor signal in the last control cycle and the current sensor original sampling value.

[0010] Further, the operation control includes: calculating a state deviation according to the effective sensor signal and a target state value; converting the state deviation into a normalized control intensity; determining an operation phase of the air energy heat pump according to a preset interval in which the control intensity is located.

[0011] Further, the operation phase includes a maintenance phase, a low-intensity operation phase and a high-intensity operation phase, and each operation phase corresponds to different compressor start-stop instructions and operation intensity instructions.

[0012] Further, the air energy heat pump needs to run continuously for at least one minimum holding period after entering any operation stage, and can be switched to other operation stages.

[0013] Further, the sensor is a temperature sensor for detecting hot water side temperature, and the effective sensor signal is used to represent the hot water side temperature state.

[0014] The beneficial technical effects of the present application are at least the following points: To solve the above problems, the present application provides an air energy heat pump sensor anti-interference control method based on user water usage habit prediction. By modeling the water usage time regularity formed during the long-term operation of the air energy heat pump, a prediction result reflecting the water usage possibility in the current time window is generated, and the result is further converted into an anti-interference strategy boundary for constraining the change amplitude of the sensor signal, so that the sensor signal is constrained by time rationality at the numerical value update level. On this basis, the original sampling signal of the sensor is gradually updated according to the strategy boundary in each control period, generating an effective sensor signal that can maintain time continuity and suppress abnormal jumps. Then, the effective signal is directly used for the operation control decision of the air energy heat pump, so that the adjustment process of the compressor start-stop and operation intensity is consistent with the real water demand in the time structure. Through the above method, without changing the existing basic control architecture of the air energy heat pump, the user water usage habit, a long-term stable external constraint, is naturally integrated into the sensor anti-interference and operation control link, realizing the pre-inhibition of sensor abnormal disturbance and the timely response to real water usage behavior, thereby improving the stability, energy efficiency and equipment reliability of the system operation at the engineering level. BRIEF DESCRIPTION OF DRAWINGS

[0015] The present application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application. For ordinary skilled in the art, other drawings can be obtained without creative labor on the basis of the following drawings.

[0016] Figure 1 A flowchart of an air energy heat pump sensor anti-interference control method based on user water usage habit prediction. DETAILED DESCRIPTION

[0017] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation on the present application.

[0018] In one or more embodiments, as shown in Figure 1 A user water habit prediction based air-to-air heat pump sensor anti-interference control method is disclosed, which comprises the following steps: S1: generating a water habit prediction result reflecting the possibility of water use in the current time window according to historical water use event records; Specifically, this step is used to generate a water habit prediction result during the actual operation of the air-to-air heat pump, thereby depicting the possibility of real water use behavior in the current time window. This prediction result serves as the time semantic basis for subsequent sensor anti-interference control, and its core purpose is to convert the water use time regularity implied in long-term operation into a numerical result that can be directly used by the control system, so that the subsequent steps can distinguish between "signal changes consistent with water use habits" and "abnormal disturbances inconsistent with water use habits". In specific implementation, the air-to-air heat pump control system first determines the time window it is currently in based on the time acquisition module inside the control board. This time acquisition module can use a common real-time clock circuit or time information converted from the system running counter. The control system divides the running time of a day into several fixed-length time windows according to pre-set time division rules, for example, half an hour or an hour as a window, and assigns a unique index number to each time window. This time window division method is determined during device initialization or installation and debugging, and remains unchanged during subsequent operation, thereby ensuring the consistency of the statistical results.

[0019] After the time window is divided, the control system identifies and records water use events in daily operation. The identification of water use events does not rely on external water meters or user input, but is based on the changes in the running state of the air-to-air heat pump. Taking a hot water type air-to-air heat pump as an example, when the system detects that the hot water side temperature continuously decreases in a short period of time, and the compressor load or heating demand significantly increases, it can be determined that there is a water use behavior. This determination logic can be achieved by combining the temperature change trend and heating state in multiple consecutive sampling periods, thereby avoiding misjudgment of transient noise as a water use event. Whenever a water use event is identified, the control system only records the time window index of the event, without recording the specific water use amount or other detailed information.

[0020] As the device keeps running, the control system accumulates the occurrence of water usage events in each time window and gradually forms the corresponding time water statistics. The statistics can take the form of simple cumulative number or smoothed occurrence frequency and are stored in the device local storage in a lookup table manner. For example, in an actual home use scenario, if the device identifies water usage events multiple times in the morning period and almost no water usage events in the late night period, the statistics corresponding to the morning time window will be significantly higher than that of the late night time window. In this way, the device can naturally form statistical characteristics reflecting long-term water usage time patterns without introducing complex behavior analysis.

[0021] To enable the prediction result to reflect the change trend of the recent running state, the control system introduces a recent running correction mechanism on the basis of the above-mentioned long-term time water statistics. Specifically, the system maintains a water usage event counting logic in a sliding time range during operation to count the number of water usage events identified in the recent time windows and their distribution. When water usage events are continuously identified in adjacent or close time windows, the control system maps this state to a higher recent running correction result; when no water usage events are identified in consecutive time windows, the control system maps this state to a lower recent running correction result. The correction process can be completed through simple counting and threshold judgment, ensuring stable implementation and easy deployment in an embedded controller.

[0022] After obtaining the time water statistics corresponding to the current time window and the recent running correction result, the control system combines the two to generate the water usage habit prediction result. The combination adopts a fixed weight weighting method, so that the prediction result reflects both the long-term time pattern and the recent running state, and the calculation relationship is as follows: ; wherein, represents the water usage habit prediction result, which characterizes the possibility of real water usage behavior occurring in the current time window; represents the time water statistics corresponding to the current time window, which is obtained from the statistical results stored in the device local storage through a lookup table manner; represents the recent running correction result, which is calculated by the control system based on the counting of water usage events in the recent time windows; is a weight parameter set in the device configuration stage, which is used to adjust the relative influence degree of the long-term time water pattern and the recent running state in the prediction result. Through proportional processing of the calculation result, Within a preset range, it is easy to use in subsequent steps. Through the above implementation method, the air source heat pump transforms the water usage time patterns and recent usage trends that naturally form during operation into a stable and actionable water usage habit prediction result without introducing additional sensors or increasing the types of data.

[0023] S2: Based on the water usage habit prediction results, determine the boundary of the anti-interference strategy to limit the amplitude of sensor signal changes; Specifically, after obtaining the water usage habit prediction results generated in step one... This step then revolves around the result, with the aim of further transforming the judgment of water use rationality in the time dimension into an anti-interference strategy boundary that can be directly applied to the processing logic of the air source heat pump sensor, so that the subsequent processing of the sensor signal is no longer a fixed rule, but is consistent with the probability of water use within the current time window.

[0024] In the actual operation of air source heat pumps, the signal changes collected by sensors may originate from actual water usage behavior, as well as from environmental disturbances, electrical interference, or measurement jitter. These variations from different sources are often difficult to distinguish numerically, but they differ significantly in their temporal distribution: actual water usage behavior is typically concentrated in long-term user water usage periods, while interference signals are more random in time. Therefore, this step focuses on... As a quantitative expression of time rationality, it is mapped to a strategy boundary used to limit the range of sensor signal changes, so that the system can more strictly constrain sensor signal changes in time windows with low water usage probability, and reserve more room for maneuver for changes caused by actual water usage in time windows with high water usage probability.

[0025] To achieve the above objectives, the control system first... Generate a candidate anti-interference policy boundary. This candidate boundary is not derived using a linear mapping, but rather through... A nonlinear transformation is performed to make the boundary tighten faster in the low water usage probability range and widen more gradually in the high water usage probability range, thus better aligning with the control requirements for misjudgment risk in the actual operation of air source heat pumps. The calculation relationship of the candidate boundary is as follows: ; in, This represents the boundary of the candidate anti-interference strategy calculated based on the current time window; This indicates the most stringent boundary to be used under conditions of extremely low water usage probability. This parameter is preset and stored in the controller during the equipment configuration phase. This indicates the maximum allowable boundary under conditions of extremely high water usage probability, and is also set during the configuration phase; This is the water usage habit prediction result output from step one. By introducing the squared term, when... When smaller, the boundary rapidly moves towards By converging, the suppression of untimely mutations is strengthened; when When it is large, the boundary follows The increase is greater but the change is more gradual, avoiding excessive relaxation of constraints on the real signal during periods of high water usage.

[0026] Considering that the switching of the time window during the operation of the air source heat pump may lead to When there are phased changes, use directly. Using the policy boundary as a basis can cause significant fluctuations in the anti-interference strength between adjacent control cycles, thus affecting the stability of the control. Therefore, the control system performs a fusion process between the candidate boundary and the historical boundary in each control cycle, ensuring that the final effective anti-interference policy boundary has continuous change characteristics. Specifically, the controller stores the boundary values ​​that have taken effect in the previous control cycle in its internal state area and combines them with the candidate boundary calculated in the current cycle to obtain the actual policy boundary used for subsequent steps. The calculation relationship is as follows: ; in, This indicates the boundary of the anti-interference strategy ultimately adopted within this control cycle; This indicates the boundary values ​​that have taken effect and been stored in the previous control cycle; The candidate boundary is calculated for the current cycle. This fusion method is achieved through simple numerical averaging, which allows the anti-interference strategy boundary to evolve smoothly over time. It can follow the changing trend of water use time rationality, while avoiding abrupt policy jumps caused by time window switching.

[0027] Through the above processing, the water usage habit prediction results output in step one It is completely transformed into an executable and continuously updatable anti-interference strategy boundary. This boundary parameter is directly used in subsequent steps to limit the allowable range of sensor signal variation within a short period of time, providing a clear and adjustable control basis for the air source heat pump's suppression of sensor interference during different water usage periods. This lays a crucial strategic constraint layer for the entire sensor anti-interference control scheme based on the rationality of water usage time.

[0028] S3: In each control cycle, constrain the change between the original sensor sample value and the effective sensor signal of the previous control cycle according to the boundary of the anti-interference strategy, and generate the effective sensor signal of the current control cycle. Specifically, in obtaining the anti-interference strategy boundary generated in step two... Next, this step processes the raw sampling signals from the air source heat pump sensor around this boundary to generate effective sensor signals that can be directly used for control decisions. The core of this process is to incorporate the "water usage time rationality constraint" determined in the previous stage into the signal update of each control cycle, ensuring that the sensor output is consistent with the user's water usage habits in the time dimension, thereby providing a stable and reliable input for subsequent control actions.

[0029] In actual operation, the air source heat pump sensor outputs a raw sample value in each control cycle. The sampled value is obtained by the sensor through a sampling circuit and an analog-to-digital converter module, and stored in the controller's sampling buffer. Simultaneously, the controller stores valid sensor signals generated and active in the previous control cycle in its internal state area. This is used to characterize the sensor states recognized by the system in the previous cycle. This step uses... , and the anti-interference strategy boundary output in step two Based on this, constrained updates are performed on signal changes between adjacent periods.

[0030] Interference in the operating environment of air source heat pumps typically manifests as anomalous jumps between adjacent sampling periods, such as instantaneous changes caused by electrical interference, poor contact, or environmental disturbances. In contrast, signal changes resulting from actual water usage tend to have a smoother temporal structure and unfold gradually over multiple consecutive control cycles. Based on this characteristic, this step focuses on the "change in the current raw sample value relative to the effective value of the previous period," and uses anti-interference strategies to define the boundaries... Limits are imposed on this change to ensure that the update magnitude within a single cycle is consistent with the reasonableness of the current water usage time.

[0031] In practical implementation, the controller first calculates an update coefficient to control the amplitude of the control signal update. This coefficient is determined by the relationship between the amplitude of the current change and the policy boundary. When the change is small, the update coefficient takes a larger value, ensuring that the effective signal fully follows the original sample; when the change is large, the update coefficient automatically decreases, constraining the update amplitude by the policy boundary, thereby suppressing sudden jumps. The calculation relationship of the update coefficient is as follows: ; in, This represents the update coefficient used to adjust the signal update amplitude within the current control cycle; This represents the boundary parameters of the anti-interference strategy determined in step two; These are the raw sensor sample values ​​acquired during the current control cycle; This refers to valid sensor signals that have already taken effect in the previous control cycle; This indicates that the magnitude of the change is taken. Using this calculation method, when... and When the difference is within the allowable boundary range, Setting it to 1 allows for unrestricted signal updates; when the difference exceeds the boundary, A value less than 1 causes the update amplitude to be compressed according to boundary requirements.

[0032] After obtaining the updated coefficients, the controller generates the valid sensor signals for the current control cycle in a coefficient-progressive manner, with the following update relationship: ; in, This indicates the valid sensor signals generated in this control cycle, which are used for subsequent control logic. , and The meaning is consistent with the above. In engineering, this update relationship corresponds to a "boundary-constrained incremental update" process: when the equipment is in a time window with a high probability of water use, the update relationship generated in step two... Larger, making It is easier to approach 1, and the effective signal can quickly follow changes caused by actual water usage; when the device is in a time window where water usage is less likely. Smaller, making It is easier to reduce the impact of anomalous jumps on control decisions, as the effective signal changes gradually over multiple cycles. At the end of each control cycle, the controller will calculate the... Write to the status area as the data for the next control cycle. Use, while The operating control logic of the air source heat pump is provided as a direct input. Through this cycle-by-cycle update and state transfer method, the boundary of the anti-interference strategy is defined. The sensor signal generation process is continuously applied to form a continuous link from water usage habit prediction and strategy boundary determination to effective signal generation, so that the sensor output maintains a dynamic balance between time continuity and anti-interference capability that matches the user's water usage habits.

[0033] S4: Execute the operation control of the air source heat pump based on the effective sensor signal; Specifically, in step three, the effective sensor signal is obtained. Subsequently, this step uses this signal as the sole sensing basis for the operation control of the air source heat pump, ultimately translating the processing results from the previous steps regarding the rationality of water usage time and the sensor's anti-interference capabilities into specific operational actions of the compressor and related actuators. Due to the engineering characteristics of air source heat pump systems, such as high thermal inertia and high start-up and shutdown costs, the operation control does not pursue rapid tracking of instantaneous states but emphasizes a stable response to "continuous state deviations." Therefore, this step focuses on... Construct a control decision-making process centered on state alignment to ensure that the equipment operation rhythm is consistent with the actual water demand in terms of time structure.

[0034] In the specific implementation, the controller reads the data generated and stored in step three from the state area at the beginning of each control cycle. Simultaneously, it reads the target state corresponding to the current operating mode from the parameter area or the operating mode state machine. . The target state is determined by user settings or system operating strategies. For example, in domestic hot water mode, the target state is written by the user and automatically selected by the controller's internal logic when switching between different operating modes. Within the current control cycle, the controller calculates the system's state deviation based on these two quantities to characterize the difference between the current operating state and the target state. The calculation relationship is as follows: ; in, It represents the state deviation of the current control cycle and is used to drive operational control decisions; This represents the target state value in the current operating mode, which is read by the controller. This represents the valid sensor signal output from step three. This deviation is updated once in each control cycle and serves as the basis for subsequent control strength determination.

[0035] Considering that air source heat pumps should not be frequently started and stopped or have their power drastically adjusted during operation, this step does not directly use... Instead of controlling the execution components, it will... Convert to a normalized control strength This is used to uniformly characterize the "intensity of adjustment demand corresponding to the current deviation." The control intensity is calculated by combining proportional amplification with interval constraints, so that small deviations correspond to low-intensity maintenance or fine-tuning, while large deviations correspond to more aggressive operational adjustments. The calculation relationship is as follows: ; in, This indicates the normalized control strength, which is used to drive the selection of subsequent operation phases; This represents the control gain parameter, which is preset and stored during the equipment commissioning phase based on the heat exchange capacity of the model and the system inertia. This refers to the aforementioned state deviation; This indicates an interval restriction operation, used to limit the calculation results within a preset range. Through this calculation method, the control strength changes continuously with the deviation, while always remaining at a uniform scale that the controller can handle.

[0036] When the control strength is obtained Then, the controller will use the running state machine to... This is mapped to specific actions to be performed. The state machine uses... The operating interval serves as a stage criterion, dividing the operation process into a maintenance stage, a low-intensity operation stage, and a high-intensity operation stage. Corresponding compressor start / stop commands and operating intensity commands are output within each stage. For example, when... When in a lower range, the controller maintains its current operating state or enters a maintenance phase; when When entering the medium-intensity range, the controller generates a compressor start-up or low-intensity operation command; when When entering a higher range, the controller increases its operating intensity to accelerate state recovery. To adapt to the engineering characteristics of air source heat pumps, such as high start-up and shutdown costs and frequent switching leading to wear and tear, a minimum hold period is set inside the state machine. This ensures that after entering a certain operating phase, the system will remain in that phase for at least a certain number of control cycles before switching to other phases is allowed. This hold period is implemented through the controller's internal counting logic and increases with the control cycle.

[0037] because It is obtained by incrementally updating the boundary of the anti-interference strategy in the preceding steps, and its changes are continuous in time, making it possible to... and It also changes smoothly with the control cycle. Thus, during peak water usage periods as users are accustomed to, the changes in state caused by actual water usage will continue to drive the process across multiple control cycles. The rise in temperature causes the controller to enter the operating phase in a predetermined sequence and stably output heating capacity; short-term abnormal changes that occur during non-water usage periods are difficult to detect. This creates a continuous driving force, which is then naturally absorbed by the control logic, avoiding frequent start-stop cycles of the compressor.

[0038] At the end of each control cycle, the controller writes the current operating stage, the hold count status, and the executed commands into the status area, serving as the basis for the next control cycle. The final output of this step is the operating control result of the air source heat pump within the current control cycle. This result is implemented as executed commands to the compressor and related actuators, and its timing is consistent with the water usage prediction and anti-interference processing results, thus completing the final implementation of the entire control chain.

[0039] This invention also provides an anti-interference control device for an air source heat pump sensor based on user water usage habit prediction, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above embodiment of an anti-interference control method for an air source heat pump sensor based on user water usage habit prediction. Figure 1 The steps S1 to S4 described above; or, when the processor executes the computer program, it implements the functions of each module in the above system embodiments.

[0040] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the air-source heat pump sensor anti-interference control device based on user water usage habit prediction.

[0041] The aforementioned air source heat pump sensor anti-interference control device based on user water usage habit prediction can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the device may also include input / output devices, network access devices, buses, etc.

[0042] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the air-source heat pump sensor anti-interference control device based on user water usage habit prediction, connecting all parts of the device via various interfaces and lines.

[0043] The memory can be used to store the computer program and / or modules. The processor implements various functions of the air source heat pump sensor anti-interference control device based on user water usage habit prediction by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the operation of the air conditioner controller, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0044] The module integrated into the air-source heat pump sensor anti-interference control device based on user water usage habit prediction, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0045] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0046] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. An air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction, characterized in that, The method comprises: generating a water use habit prediction result reflecting water use possibility in a current time window according to historical water use event records; determining an anti-interference strategy boundary for limiting the change range of a sensor signal based on the water use habit prediction result; in each control period, constraining the change between a sensor raw sampling value and an effective sensor signal of the previous control period according to the anti-interference strategy boundary to generate an effective sensor signal of the current control period; and performing operation control of the air energy heat pump based on the effective sensor signal.

2. The air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction according to claim 1, characterized in that, The water use habit prediction result is generated by: dividing a day into a plurality of time windows of fixed length; identifying a water use event based on the combined condition of continuous temperature drop on the hot water side and compressor load rise during operation of the air energy heat pump, and recording the time window to which the water use event belongs; counting the occurrence frequency of water use events in each time window to form a time water use statistical value; generating a recent operation correction result in combination with the identification of water use events in the recent time windows; weighting the time water use statistical value and the recent operation correction result to obtain the water use habit prediction result.

3. The air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction according to claim 2, characterized in that, The division of the time window is completed in the device initialization stage and remains unchanged in the subsequent operation process.

4. The air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction according to claim 1, characterized in that, The anti-interference strategy boundary is generated from the water use habit prediction result by a nonlinear mapping method, so that the strategy boundary is quickly tightened when the water use possibility is low, and the strategy boundary is slowly relaxed when the water use possibility is high.

5. The air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction according to claim 1, characterized in that, The anti-interference strategy boundary is fused with the strategy boundary that has taken effect in the previous control period in each control period to obtain a smooth and continuous current strategy boundary.

6. The air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction according to claim 1, characterized in that, When generating the effective sensor signal, the change between the current sensor raw sampling value and the effective sensor signal of the previous control period is calculated; when the absolute value of the change does not exceed the anti-interference strategy boundary, the current sensor raw sampling value is taken as the effective sensor signal; when the absolute value of the change exceeds the anti-interference strategy boundary, the update range is compressed in proportion to generate an effective sensor signal between the effective sensor signal of the previous control period and the current sensor raw sampling value.

7. The air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction according to claim 1, characterized in that, The operation control comprises: calculating a state deviation according to the effective sensor signal and a target state value; converting the state deviation into a normalized control strength; determining an operation stage of the air energy heat pump according to the preset interval in which the control strength is located. 8.The air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction of claim 1, wherein, The operation stage comprises a maintenance stage, a low-intensity operation stage and a high-intensity operation stage, each operation stage corresponding to different compressor start-stop instructions and operation intensity instructions. 9.The air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction of claim 1, wherein, After entering any operation stage, the air energy heat pump needs to be continuously operated for at least one minimum maintenance period before switching to other operation stages.

10. The air-to-water heat pump sensor anti-interference control method based on user water usage habit prediction according to claim 1, characterized in that, The sensor is a temperature sensor for detecting the temperature of the hot water side, and the effective sensor signal is used to represent the temperature state of the hot water side.

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