Intelligent power consumption statistical control method based on heat pump system

Abstract

This invention proposes an intelligent power consumption statistical control method based on a heat pump system. The method includes: real-time acquisition of current, voltage, frequency signals and operating status flags of the compressor, fan and water pump in the heat pump system; calculation of the frequency change rate of each module; identification of the operating status of each module as start-up, frequency ramp-up, steady state, frequency ramp-down or stop state based on a preset rule base, and generation of combined state labels; calculation of system disturbance score based on the combined state labels, combined with the disturbance impact weight of each module and the state scoring function, and dynamic generation of the current sampling period and sampling accuracy threshold; calculation of disturbance change trend index based on historical disturbance score sequence to determine whether there is a state transition trend; execution of voltage and current sampling according to the current sampling strategy, calculation of average power, and dynamic correction of power consumption; and forced activation of prediction strategy when a specific state transition is detected, finally outputting cumulative power consumption data.

Description

Technical Field

[0001] The present invention belongs to the field of power control, and particularly relates to an intelligent power consumption statistics control method based on a heat pump system. Background Art

[0002] As an efficient energy utilization device, heat pumps have been widely used in scenarios such as heating, hot water supply, and industrial and agricultural process control. The energy consumption performance of the device has become one of the core indicators to measure its performance. The current mainstream power consumption statistics methods still mainly rely on external electricity meters or the default linear estimation method in the controller. However, these methods generally have a fundamental limitation, that is, they cannot dynamically adapt to the non-linear, high-frequency changes, and multi-component alternating start-stop characteristics shown by the heat pump system during actual operation. Although the external electricity meter has high accuracy, its sampling granularity and data interface are difficult to perform linkage control according to behaviors such as compressor frequency changes and intermittent operation of water pumps, resulting in statistical deviations in key operation stages, and at the same time increasing the installation complexity and the overall machine integration difficulty. Although the built-in algorithm has lower cost, its fixed sampling interval and the statistical logic that ignores the load state seriously limit its application value in complex working conditions. Especially during load dynamic processes such as compressor frequency increase, intermittent operation of water pumps, and critical start-stop of electric auxiliary heating, the system energy consumption fluctuates violently and the duration is extremely short. Traditional methods are prone to problems such as missed sampling, mis-sampling, and sampling point misalignment, making the final statistical result distorted and difficult to meet the user's needs for energy-saving performance visualization and behavior diagnosis. Summary of the Invention

[0003] The object of the present invention is to design an intelligent power consumption statistics control method based on a heat pump system, which can sense changes in the system operation state, adjust the sampling strategy in advance according to the fluctuation trend, and introduce a fluctuation correction mechanism during the sampling process, so as to make it have the application capabilities of being deployable, scalable, low-cost, and high-precision.

[0004] In order to achieve the above object, the present invention provides an intelligent power consumption statistics control method based on a heat pump system, and the method includes:

[0005] Real-time collect the current, voltage, frequency signals and operation state flags of the compressor, fan, and water pump in the heat pump system, calculate the frequency change rate of each module, identify the operation state of each module as start, frequency increase, steady state, frequency decrease, or stop state based on a preset rule library, and generate a system-level combined state label;

[0006] According to the combined state label, combine the disturbance influence weight and state scoring function of each module, calculate the system disturbance score, and dynamically generate the current sampling period and sampling accuracy threshold to adapt to the system power fluctuation;

[0007] Based on the historical disturbance score sequence, the disturbance change trend index is calculated to determine whether there is a state transition trend, and the sampling strategy parameters for the next period are set in advance.

[0008] Voltage and current sampling is performed according to the current sampling strategy to calculate the average power. A correction factor based on power fluctuation rate is introduced to dynamically correct the power consumption. At the same time, when a specific state transition is detected, the prediction strategy is forcibly activated, and finally the cumulative power consumption data is output.

[0009] Furthermore, the rate of frequency change is obtained by calculating the difference between the frequency value at the current moment and the frequency value at the previous sampling moment, wherein the interval between the previous sampling moment and the current moment is a fixed 1 second.

[0010] Furthermore, the combined status label is a tuple composed of the status labels of the compressor module, the fan module, and the water pump module; the status label of each module is generated independently based on its own operating vector, which includes frequency value, frequency change rate, current value, voltage value, and binary operating status flag.

[0011] Furthermore, the disturbance impact weights of each module are pre-set fixed values, with the compressor module having the highest weight, the water pump module having the second highest weight, and the fan module having the lowest weight.

[0012] Furthermore, the state scoring function is a mapping from state labels to numerical scores, where the score mapped to the start-up state is higher than the score mapped to the frequency increase or decrease state, the score mapped to the frequency increase or decrease state is higher than the score mapped to the steady-state operation state, and the score mapped to the stop state is zero.

[0013] Furthermore, the sampling period is obtained by dividing the maximum allowed sampling period of the system by a term that is linearly related to the system disturbance score, so that the sampling period decreases as the system disturbance score increases; the sampling accuracy threshold is obtained by adding a linear compensation term that is negatively related to the system disturbance score to the system minimum resolution threshold, so that the sampling accuracy threshold decreases as the system disturbance score increases.

[0014] Furthermore, the disturbance change trend index is calculated as follows: First, the rate of change of the historical disturbance score sequence at consecutive time points is calculated and averaged to obtain the average slope term representing the disturbance growth trend; then, the average value of the historical sampling period is calculated and multiplied by a suppression factor to obtain the suppression term; the final disturbance change trend index is the result of subtracting the suppression term from the average slope term.

[0015] Furthermore, when the final disturbance change trend index exceeds a preset positive threshold, it is determined that the system has a state transition trend; the sampling period of the next cycle will be compressed proportionally based on the current sampling period according to the degree to which the trend index exceeds the threshold, but the compressed result shall not be less than the minimum sampling period allowed by the system platform.

[0016] Furthermore, the correction factor is obtained by multiplying the standard deviation of the power value calculated within the current sampling window to the average power within that window by a sensitivity coefficient and applying an upper limit constraint. The function of this factor is to proportionally adjust the power estimation result when the power fluctuation is severe, so as to more accurately capture transient energy consumption.

[0017] Furthermore, the specific state transition conditions for forcibly enabling the prediction strategy include any one of the following three situations: the state of the compressor module changes to start or frequency increase; the state of the water pump module changes from stop to start; the trend indicator calculated by the trend analysis step exceeds the preset threshold for two consecutive sampling periods and maintains an upward trend.

[0018] The beneficial technical effects of the present invention are at least as follows:

[0019] To address the aforementioned issues, this invention provides an intelligent power statistical control method based on a heat pump system. Starting from the structural characteristics of equipment operation behavior, a state label recognition mechanism is constructed. First, the frequency, current, and voltage signals of key modules such as compressors, fans, and water pumps are converted into semantically meaningful state labels, forming a standardized input expression for system operation. Based on this, this invention designs a disturbance weight scoring model, combining the power influence factors of different modules with the current state to form a quantitative scoring result for dynamically scheduling the sampling period and sampling accuracy, thereby achieving accurate tracking of the sampling behavior to the degree of disturbance in the real system. Building upon strategy generation, this invention further introduces a disturbance gradient trend recognition mechanism, constructing a sampling scheduling trend prediction channel based on historical state scoring sequences. This allows the system to perceive upcoming changes before the actual state occurs, compressing the sampling period or improving sampling accuracy in advance, solving the response lag problem commonly found in traditional solutions. To address the integral deviation caused by power fluctuations within the sampling period during variable frequency operation, this invention constructs a structure-sensitive correction factor based on the current-power fluctuation coefficient in the power calculation stage. This guides structural amplification of the power consumption in each cycle during periods of high fluctuation intensity, ensuring that the system possesses complete high-dynamic energy capture capabilities while maintaining sampling resource control capabilities. The overall approach constructs a complete four-step sampling control closed loop, from operating status identification to sampling strategy generation, trend prediction scheduling, and integral correction output. In the heat pump operating environment with multiple components running in parallel, multiple operating conditions alternating, and unstable frequency changes, it has the advantages of high adaptability, high precision, low error accumulation, and low control cost, and can significantly improve the accuracy of heat pump system power statistics and the user's energy consumption perception experience. Attached Figure Description

[0020] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0021] Figure 1 This is a flowchart of the intelligent power consumption statistical control method based on a heat pump system according to the present invention. Detailed Implementation

[0022] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0023] In one or more embodiments, such as Figure 1 As shown, an intelligent power consumption statistical control method based on a heat pump system is disclosed, the method comprising the following:

[0024] S1: Real-time acquisition of current, voltage, frequency signals and operating status indicators of compressor, fan and water pump in heat pump system, calculation of frequency change rate of each module, identification of the operating status of each module as start-up, frequency increase, steady state, frequency decrease or stop based on preset rule base, and generation of system-level combined status label;

[0025] Specifically, this step involves identifying the current operating status of the main load modules of the heat pump and converting them into structured labels for use in the next step of selecting a sampling strategy. The heat pump contains several key power-consuming modules, such as the compressor, fan, and water pump. Their start-up, shutdown, and operation phases directly determine the dynamic pattern of the system's total power change. Therefore, high-precision power consumption statistical control requires clear, phased identification of the behavior of these modules and expression in a unified data format. Traditionally, equipment operating status is often interpreted based on human experience rather than real-time analysis of operating signals. This approach lacks scalability and the ability to dynamically link sampling behavior. This step, however, relies entirely on equipment operating data to automatically determine the status, without depending on the internal interfaces of the control logic.

[0026] The first step in identification is to collect the core operating data of each module in the heat pump system at the current moment. Taking the compressor as an example, the required data includes its current value. Voltage value Frequency signals With running flag The current signal is acquired by a Hall current sensor connected internally to the main control board. The sensor output is an analog voltage, which is digitized by the ADC module on the MCU. The sampling frequency is typically set to 1kHz. The voltage value is obtained by a voltage divider sampling circuit connected to the main control board and digitized in the same way as the current. The PWM output feedback signal from the control board driver module is expressed as a dimensionless normalized frequency ratio; running flags. The control signal is set by the compressor, taking only 0 or 1 to represent not running or running, respectively. The data structure of the fan and water pump is the same as that of the compressor, and they are named as follows: as well as .

[0027] To accurately describe the dynamic changes of frequency over time, it is necessary to introduce a quantity representing the rate of frequency change. The calculation method is as follows:

[0028] ;

[0029] in, The sampling interval is fixed at 1 second. This variable is used to determine whether the module is in a frequency ramp-up or ramp-down process. For example, if the compressor frequency has increased from 10% to 50% in the past second, then... It can be considered to be in a state of rapid frequency increase.

[0030] The module operation vector is based on the above five-dimensional features. The system will match the status pattern in the device's preset rule base. The rule base is compiled from a large number of stable samples collected during factory testing and expert experience, specifically including:

[0031] If at a certain moment and This indicates that the compressor has just been connected to the drive, but has not yet output a valid frequency, and is therefore determined to be in "start-up state".

[0032] like and If so, it is judged as a "rapid frequency increase" state;

[0033] like If the frequency fluctuation is stable, it is determined to be "steady-state operation";

[0034] like This indicates that the system has entered a "rapid frequency reduction" process;

[0035] like If the compressor is stopped, it will be in a "stopped" state.

[0036] These rules are calculated in real time in the main control chip through conditional statements, with a response latency of less than 20ms, ensuring that sudden changes in state can be captured in a timely manner.

[0037] Status recognition is not limited to a single module, but rather applies to the compressor ( ), fan ( ), water pump ( After the three modules make independent judgments, a combined status label is formed:

[0038] ;

[0039] in Representation module At any moment The system's states are enumerated as: Start, Frequency Increase, Steady State, Frequency Decrease, and Stop. For example, at a certain moment, the system label is... This indicates that the compressor is increasing its frequency, the fan is maintaining a steady state, and the water pump has just started running.

[0040] This combined state label will serve as the sole basis for selecting the sampling strategy in the next step, eliminating the need to read any additional low-level signals. This semantic-level state representation significantly reduces the complexity of subsequent control modules and avoids the problem of redundant data being repeatedly transmitted across multiple modules.

[0041] This status label can be implemented in the project by combining three methods: Boolean logic, numerical difference, and threshold judgment. It does not require neural network or model training, making the entire module stable, reliable, and easy to maintain in the embedded environment.

[0042] S2: Based on the combined state label, combined with the disturbance impact weight of each module and the state scoring function, calculate the system disturbance score, dynamically generate the current sampling period and sampling accuracy threshold, so as to adapt to system power fluctuations;

[0043] Specifically, this step aims to dynamically generate the optimal sampling strategy based on the current actual operating status of the heat pump system, thereby achieving more sensitive and faster-responding power statistical sampling control to power fluctuations without increasing the burden on system resources. This module is the key hub connecting "sensing" and "sampling behavior," and its input depends on the system status label constructed in step one. The output sampling strategy parameters will directly affect the frequency and accuracy settings of voltage and current sampling in subsequent steps, and are the decision-making link of high-precision intelligent power statistical control.

[0044] In practical heat pump applications, the impact of different operating modules on overall power fluctuations varies. Compressor frequency fluctuations have the greatest impact on power consumption statistics errors, followed by pump start-up and shutdown behavior, and then fan steady-state operation. Therefore, traditional "one state, one strategy" or "three-module equal-weight strategy" cannot reflect the differences in module impact, easily leading to low-priority modules masking high-dynamic state signals. This step introduces a dynamic module weight scoring mechanism during the sampling strategy construction process, enabling the system to obtain the most critical energy consumption fluctuation data with minimal sampling resource configuration.

[0045] First, sampling period The calculation does not depend on the state labels of a single module, but rather on global state labels. Calculate a state perturbation score The definition is as follows:

[0046] ;

[0047] in, The score representing the overall disturbance level of the current system is the main basis for controlling the sampling period. For module The disturbance influence weight is set to the compressor. The water pump is set to The fan is .function The state scoring function is designed with the following values:

[0048] Startup status: ;

[0049] Upclocking / Downclocking Status: ;

[0050] Steady-state operation: ;

[0051] Stopped state: ;

[0052] This scoring function is built based on actual measured current fluctuation data. For example, during the heat pump startup process, the compressor current jumps by an average of about 13A, and the average power increase rate exceeds 1kW / s when the frequency increases, thus earning a high score. In contrast, the fluctuation range of the fan during steady-state operation is less than 0.1A, resulting in the lowest score. This scoring function is driven by engineering data, does not rely on a learning model, and has reliable deployment.

[0053] By rating Sampling period The settings are no longer generated using a table lookup method, but rather using the following formula:

[0054] ；

[0055] in, This is the maximum sampling period allowed by the system (set to 5 seconds). This is the sampling period adjustment factor, with a value range of [2, 5]. Its value can be set according to the system energy consumption statistical accuracy target. The design of this formula ensures that the higher the system disturbance score, the shorter the sampling period. For example, when... and hour, Seconds; when hour, Second.

[0056] Sampling accuracy threshold A compensation control is set to be positively correlated with the degree of system disturbance, with the following specific settings:

[0057] ;

[0058] in, This is the minimum resolution threshold that the system can support (e.g., 0.01). This is a sampling precision adjustment factor (e.g., 0.04). This is the theoretical maximum disturbance score. This formula ensures that sampling accuracy automatically improves when the system is under large disturbance, and relieves the pressure on sampling accuracy when the system tends to stabilize, thereby reducing energy consumption and computational load.

[0059] Specifically, under typical operating conditions, if ,but Assuming The sampling period is seconds, with a precision threshold of This strategy setting satisfies both the accuracy and energy consumption control constraints.

[0060] Compared with the traditional lookup table mapping strategy, this method has the following two advantages: (1) It introduces a weight system based on the layering of module disturbance impact, so that the system perception granularity focuses on the main cause of power consumption change and improves the efficiency of sampling resource utilization; (2) The sampling control strategy achieves continuous response through disturbance scoring, avoids the control mutation problem caused by "strategy jump" under the lookup table strategy, and improves the stability of the actual deployed system operation.

[0061] S3: Based on the historical disturbance score sequence, calculate the disturbance change trend index, determine whether there is a state transition trend, and pre-set the sampling strategy parameters for the next period.

[0062] Specifically, this step is an auxiliary control module designed to further address the problem of "mis-sampling of critical power segments due to sampling response lag," building upon the previous two steps of state identification and strategy generation. During the operation of a heat pump system, the start-up, shutdown, and frequency changes of loads such as compressors and water pumps often exhibit a gradual trend; relying solely on the current state label... It is impossible to predict upcoming power fluctuations. Therefore, if the system only generates a sampling strategy based on the current state... This will inevitably cause us to miss the critical leading edge of power changes, especially in typical scenarios such as the compressor frequency increase start-up stage, the load transition from stop to operation stage, and the sudden jump in electric auxiliary heating power. The accumulation of errors will be serious, leading to systematic deviations in power statistics.

[0063] To address this issue, this step introduces a trend-assisted scheduling mechanism. By analyzing the changing trends of historical state labels and disturbance scores, it predicts whether the system has a tendency to transition to a new state in the next cycle, and pre-sets the sampling parameters for the next cycle if the prediction is correct. This operation does not directly control the current sampling behavior, but rather provides a more forward-looking strategy reference for subsequent sampling, enhancing the system's ability to follow operational dynamics.

[0064] The first step in trend judgment is to analyze the disturbance score. The direction and magnitude of the change. Unlike conventional methods, this scheme considers the characteristics of heat pump operation and uses an "acceleration-weighted difference form" with a suppression term to construct a trend change index. Its definition is as follows:

[0065] ;

[0066] Here, the first term is the "average slope" of the disturbance change, which is the degree of continuous increase in the disturbance score per unit time, and can measure the trend of the system disturbance growth; the second term is the average of the current and historical sampling periods multiplied by the suppression factor. This structure is designed to suppress false positives caused by "oversparse sampling." It innovatively incorporates the "aggressiveness of control behavior" into trend prediction, ensuring timely responses to rapidly changing trends while suppressing unnecessary repeated compression sampling when the strategy itself is already aggressive.

[0067] Trend Indicators Essentially, this constitutes a control signal that represents the "increased disturbance signal strength," which the system then compares with a set threshold. Compare, if satisfied If this happens, the system is considered to be about to undergo a transition, and it enters the rapid sampling preparation phase ahead of schedule. Based on this, the sampling period for the next cycle is preset as follows:

[0068] ;

[0069] in The strategy compression factor controls the magnitude of the sampling period contraction (e.g.) This indicates a maximum compression of 60%. This function design has two key features:

[0070] when Or when the trend is not obvious, The system maintains the original sampling strategy;

[0071] when Furthermore, when growth is rapid, the sampling period is compressed proportionally, but not less than the minimum value allowed by the platform, to ensure stable system operation without interference.

[0072] Specifically, assuming The disturbance scores are as follows: , , , The corresponding sampling periods are all Substituting into the formula, the slope term is approximately The average sampling period is ,set up Then there is If set , This triggers pre-sampling compression. Seconds. It can be seen that after predicting the trend of a rapid increase in power, the system automatically compresses the sampling period of the next cycle to a minimum value to ensure close tracking of load changes.

[0073] It should be noted that this compression mechanism only affects the next cycle. The sampling control does not affect the current policy execution; nor does it permanently change the policy settings, but rather serves as a temporary scheduling mechanism embedded in the policy execution process. The system will reassess the state in the next cycle. and rating If no significant changes occur, the original sampling strategy is restored, providing complete reversibility and control redundancy management capabilities.

[0074] S4: Perform voltage and current sampling according to the current sampling strategy, calculate the average power, and introduce a correction factor based on power fluctuation rate to dynamically correct the power consumption. At the same time, when a specific state transition is detected, the prediction strategy is forcibly activated, and finally the cumulative power consumption data is output.

[0075] Specifically, in heat pump systems, scenarios such as compressor frequency fluctuations, intermittent pump starts, and coordinated fan switching can cause irregular and sudden changes in current and voltage signals within a short period. Continuing to use a traditional integral model based on a constant sampling period will severely miss these critical energy consumption transition points, leading to systematic errors in hourly or daily energy consumption accumulation. This step uses the sampling strategy output from step three. To control the input, the sampling rhythm is dynamically adjusted and a fluctuation-sensitive integral correction mechanism is introduced to improve the power capture accuracy in the non-steady-state range while ensuring the system's timely response.

[0076] Additional input for this step: Current With voltage The data is collected by the autonomous control board's ADC module, with a sampling frequency adjustable between 10 and 50 Hz, and a sampling window length that is [not specified]. control.

[0077] In the control logic of heat pump equipment, most power surges occur in the "transition zone" of state changes, and the current signal in these transition zones often exhibits nonlinear oscillations. If accumulated at fixed intervals... Multiply by When electrical energy is obtained, slight waveform variations are ignored. Therefore, this step introduces a correction mechanism based on the ratio of power variation amplitude to integration time, adding a variation-sensitive term to the single-cycle power estimation. The complete calculation formula is as follows:

[0078] ;

[0079] in, In the current The first sampling window Voltage and current values ​​at each moment; The actual number of sampling points within this sampling period is determined by... It is determined together with the sampling frequency. The current-power variation sensitive term is constructed as follows:

[0080] ;

[0081] Here This represents the average power during the sampling period. The standard deviation of the power samples is calculated using a sliding window. To adjust the scaling factor of sensitivity (e.g.) ), Maximum correction limit (e.g.) (This indicates a maximum dynamic correction of 20%). This design demonstrates significant adaptability to heat pump scenarios: for example, during the compressor's frequency increase from 12Hz to 20Hz, the current fluctuates greatly, but the energy consumption center remains stable. Maintain small adjustments; however, during the initial stage of pump operation from start-up to shutdown, the power surge may be concentrated in the first 500ms. Extremely high, A 20% upward adjustment is made upon reaching the peak, effectively capturing peak power levels.

[0082] At the same time, in order to avoid If the value is too large, the fluctuation range will not be filled, and the status label will be affected. The system will force adoption when any of the following three conditions are met. :

[0083] The compressor enters the "start" or "frequency increase" state;

[0084] The water pump transitions from "stop" to "start" mode;

[0085] In trend analysis Exceeding the predicted threshold And it has maintained a positive upward trend for two consecutive cycles.

[0086] Once any of the above conditions are met, the current period will enter the preset sampling mode for the next period ahead of schedule, and its flag variable will be determined accordingly. This information is recorded in the control log for subsequent policy analysis.

[0087] The final output is the single-cycle power consumption. This data is accumulated into the daily / hourly / module-level energy consumption array for backend display and energy efficiency assessment.

[0088] This invention also provides an intelligent power consumption statistics and control device based on a heat pump system, 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 embodiments of the intelligent power consumption statistics and control method based on a heat pump system. 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.

[0089] 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 intelligent power consumption statistical control device based on the heat pump system.

[0090] The intelligent power consumption statistics and control device based on a heat pump system 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, and buses.

[0091] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASACs), 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. The processor is the control center of the intelligent power statistics and control device based on the heat pump system, connecting all parts of the device via various interfaces and lines.

[0092] The memory can be used to store the computer program and / or modules. The processor implements various functions of the intelligent power consumption statistical control device based on the heat pump system by running or executing the computer program and / or modules stored in the memory, and by 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), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0093] The module integrated into the intelligent power consumption statistics and control device based on the heat pump system, 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, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0094] 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.

[0095] 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. A method for intelligent power metering control based on a heat pump system, characterized in that, The method comprises: The heat pump comprises a plurality of key power consumption modules, including a compressor, a fan and a water pump; the current, voltage, frequency signals and running state flags of the compressor, fan and water pump in the heat pump system are collected in real time, the frequency change rate of each module is calculated, the running state of each module is identified as a starting, frequency increasing, steady state, frequency decreasing or stopping state based on a preset rule library, and a system-level combined state label is generated; According to the combined state label, the system disturbance score is calculated by combining the disturbance influence weight of each module and the state score function, and the current sampling period and sampling accuracy threshold are dynamically generated to adapt to the system power fluctuation; the combined state label is a multi-element group composed of the state label of the compressor module, the state label of the fan module and the state label of the water pump module; wherein the state label of each module is independently generated according to its own running vector, and the running vector includes the frequency value, the frequency change rate, the current value, the voltage value and the binary running state flag; the state score function is a mapping from the state label to the numerical score, wherein the score mapped by the starting state is higher than the score mapped by the frequency increasing or frequency decreasing state, the score mapped by the frequency increasing or frequency decreasing state is higher than the score mapped by the steady state running state, and the score mapped by the stopping state is zero; Based on the historical disturbance score sequence, the disturbance change trend index is calculated to judge whether there is a state transition trend, and the sampling strategy parameters of the next period are set in advance; According to the current sampling strategy, voltage and current sampling is performed, the average power is calculated, a correction factor based on the power fluctuation rate is introduced to dynamically correct the electricity, and a prediction strategy is forcibly enabled when a specific state transition is detected, and finally the cumulative electricity data is output.

2. The method of claim 1, wherein, The frequency change rate is obtained by calculating the difference between the current frequency value and the frequency value at the previous sampling time, wherein the interval between the previous sampling time and the current time is fixed at 1 second.

3. The method of claim 1, wherein the method further comprises: The disturbance influence weight of each module is a fixed value preset in advance, wherein the weight of the compressor module is the highest, the weight of the water pump module is the second highest, and the weight of the fan module is the lowest.

4. The method of claim 1, wherein the method further comprises: The sampling period is obtained by dividing the maximum sampling period allowed by the system by a term linearly related to the system disturbance score, so that the sampling period decreases as the system disturbance score increases; the sampling accuracy threshold is obtained by adding a linear compensation term negatively related to the system disturbance score to the system minimum resolution threshold, so that the sampling accuracy threshold decreases as the system disturbance score increases.

5. The method of claim 1, wherein the method further comprises: The disturbance change trend index is calculated by the following method: first, calculate the change rate of the historical disturbance score sequence at consecutive time points and take the average to obtain the average slope term representing the disturbance growth trend; then, calculate the average value of the historical sampling period and multiply it by a suppression factor to obtain the suppression term; and finally, the disturbance change trend index is the result of subtracting the suppression term from the average slope term.

6. The method of claim 5, wherein the method further comprises: When the final perturbation change trend index exceeds a preset positive threshold, it is determined that there is a state transition trend in the system; the sampling period of the next cycle is compressed in proportion to the extent to which the trend index exceeds the threshold based on the sampling period of the current cycle, but the compressed result must not be less than the minimum value of the sampling period allowed by the system platform.

7. The method of claim 1, wherein the method further comprises: The correction factor is obtained based on the ratio of the standard deviation of the power value calculated in the current sampling window to the average power in the window, multiplied by a sensitivity coefficient and subjected to an upper limit constraint; the role of the correction factor is to proportionally adjust the power estimation result when the power fluctuates sharply, so as to more accurately capture the transient energy consumption. 8.The method of claim 1, wherein, The specific state transition condition for forcibly enabling the prediction strategy includes any one of the following three situations: the state of the compressor module changes to start or frequency increase; the state of the water pump module changes from stop to start; The trend index calculated by the trend analysis step exceeds the preset threshold for two consecutive sampling periods and maintains an upward trend.

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