Intelligent control method and system for heat pump with hierarchical defrosting function

Abstract

The invention provides a heat pump intelligent control method and system for hierarchical defrosting, and the method comprises the steps: collecting the temperature of an inlet and an outlet of a fin, calculating the temperature difference and the change rate thereof, and evaluating the initial state of frosting; and the influence of the frost layer on the system performance is further analyzed in combination with the water side heat exchange efficiency and the attenuation rate thereof. Based on the information, the controller accurately judges mild, moderate and severe frosting states by using a frosting grade scoring function, and automatically matches corresponding defrosting strategies: when the frosting state is mild, the heating mode is maintained, and online defrosting is carried out; when the temperature is moderate, a progressive bypass is adopted for defrosting; and switching to a refrigeration mode and dynamically adjusting the opening degree of the expansion valve to strengthen the defrosting effect when the temperature is heavy. The defrosting precision and the system stability are improved, and the operation efficiency and the user experience of the heat pump equipment in the low-temperature environment are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of heat pump control, and particularly relates to a heat pump intelligent control method and system for graded defrosting. BACKGROUND

[0002] As a high-efficiency energy conversion device, heat pumps are widely used in building heating, domestic hot water and industrial waste heat recovery, etc. However, when operating in a low-temperature and high-humidity environment, the outdoor fin heat exchanger is prone to frosting. Frosting can significantly reduce the heat exchange efficiency, leading to a decrease in system heating capacity and deterioration of energy efficiency ratio, and even cause problems such as compressor liquid knock, pipeline vibration and frequent low-pressure protection shutdown. To solve this problem, existing heat pump equipment generally uses a timed defrosting or a threshold defrosting method based on the temperature difference between the environment and the fin. However, these methods have obvious limitations. Timed defrosting ignores the actual degree of frosting, which may result in "defrosting without frost" and waste energy. The fixed temperature difference threshold method is prone to start defrosting only when the frosting is severe, resulting in a significant decrease in heating performance. In addition, the commonly used four-way valve reverse refrigeration defrosting method will cause the heating to be interrupted during the execution process, and the user will feel obvious cold wind or hot water temperature drop in the heating state, which seriously affects the comfort. In a complex environment with variable climate conditions, the traditional method lacks the ability to dynamically identify the degree of frosting, and cannot implement graded response according to different frost thickness and distribution characteristics, resulting in unstable and energy-inefficient defrosting control of the system. At the same time, the existing control system usually only uses a single temperature difference or time signal to judge frosting, ignoring the coupling characteristics between the heat pump gas side and the water side heat exchange, and failing to reflect the real impact of frosting on energy transfer efficiency. These problems together result in low operating efficiency, frequent defrosting, shortened service life and poor user experience of the heat pump equipment in low-temperature environments. SUMMARY

[0003] The purpose of the present application is to design a heat pump intelligent control method and system for graded defrosting, which can judge the frosting state in real time and has the ability of dynamic graded identification and multi-component coordinated control.

[0004] To achieve the above purpose, a heat pump intelligent control method for graded defrosting is provided in the first aspect of the present application, which comprises: obtaining the temperatures at both ends of the outdoor fin heat exchanger of the heat pump, calculating the temperature difference and the change rate of the temperature difference per unit time; synchronously obtaining the inlet water temperature, outlet water temperature and water flow of the water side heat exchanger of the heat pump, and combining the temperature difference to calculate the current water side heat exchange and its change rate per unit time; based on the temperature difference, temperature difference change rate, water side heat exchange and its change rate, a frosting grade scoring model is constructed by fusion to distinguish between light, moderate and severe frosting states; For different frosting levels, the online defrosting in heating mode, progressive bypass defrosting or dynamic intensity defrosting in cooling mode is triggered, wherein the opening degree of the main route electronic expansion valve is dynamically adjusted according to the frosting severity during heavy defrosting.

[0005] Further, the calculation of the water-side heat exchange amount explicitly introduces the fin temperature difference as a suppression factor to quantify the hindering effect of the frost layer on the refrigerant-water side heat transfer.

[0006] Further, the calculation window length of the heat exchange amount per unit time is shortened as the temperature difference changes rate increases, to improve the response speed to sudden frosting.

[0007] Further, the frosting level scoring model includes a linear weighting term, a nonlinear trend enhancement term, and a condition-triggered regularization correction term to improve the stability of the frosting level determination under critical operating conditions.

[0008] Further, in the light frosting state, the system maintains the heating mode and opens the bypass valve for online defrosting, and the main route electronic expansion valve remains normal operation control logic without forced intervention.

[0009] Further, in the moderate frosting state, if the moderate frosting is determined again within the preset time interval, the bypass defrosting operation is repeated once.

[0010] Further, in the heavy frosting state, the opening degree of the main route electronic expansion valve is dynamically adjusted according to the frosting score between the set basic opening degree and the maximum allowable opening degree to match the actual defrosting demand.

[0011] Further, the output of the frosting level scoring model is mapped by threshold segmentation to determine the frosting level, and the threshold can be dynamically calibrated according to actual operating data.

[0012] Further, after executing the defrosting control strategy, the system recalculates the frosting score based on the updated temperature difference and heat exchange performance data, and dynamically adjusts the type or intensity of the subsequent defrosting action, forming an adaptive closed-loop control.

[0013] In a second aspect of the present application, a hierarchical defrosting intelligent control system for heat pumps is provided, which comprises: A signal acquisition module for acquiring the temperature difference of the fin heat exchanger, the water inlet / outlet temperature and water flow of the water side; A control module configured to execute the hierarchical defrosting control method as described above; An execution module including a four-way valve, a main route electronic expansion valve and a bypass valve, for executing corresponding defrosting actions according to the instructions of the control module, and dynamically adjusting the control strategy based on real-time monitoring data.

[0014] The beneficial technical effects of the present application are at least the following: To solve the above problems, the present application provides a heat pump intelligent control method and system for hierarchical defrosting, which constructs the temperature difference and its time change rate characteristics by collecting the fin inlet and outlet temperatures, establishes the space and dynamic trend description of fin frosting, and forms the energy dimension description of the frost layer affecting the system performance by combining the water side heat exchange capacity and its unit time decay rate. By fusing the above signals, the present application constructs a frosting level scoring function with a regularization correction term and a nonlinear response structure in the controller, which can accurately determine the three frosting states of light, medium and heavy. According to the different levels of frost layer state, the controller automatically matches the control strategy of the core components of the heat pump: keep the heating mode and open the one-way bypass valve to realize online defrosting when the frost is light; perform progressive double-cycle bypass defrosting when the frost is medium; and switch the four-way valve to the refrigeration mode and dynamically adjust the opening degree of the main road electronic expansion valve according to the scoring results to control the defrosting intensity when the frost is heavy. The present application first introduces the integrated design of "hierarchical judgment + hierarchical execution" in the heat pump defrosting control, directly links the physical signals and control actions through linear and nonlinear combined criteria, and realizes the transformation of defrosting response from passive triggering to active adjustment. This method not only effectively avoids the problems of no frost defrosting and defrosting lag, but also significantly improves the defrosting accuracy and system stability through dynamic parameter adjustment, so that the heat pump equipment has higher energy efficiency and better user experience under low temperature and complex working conditions. 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. Other drawings can also be obtained by those skilled in the art without creative labor on the basis of the following drawings.

[0016] Figure 1 A flow chart of the present application, a heat pump intelligent control method for hierarchical defrosting.

[0017] Figure 2 A framework diagram of the present application, a heat pump intelligent control system for hierarchical defrosting. DETAILED DESCRIPTION

[0018] 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 with 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.

[0019] In one or more embodiments, as Figure 1 shown, a heat pump intelligent control method for hierarchical defrosting is disclosed, which comprises the following: S1: Obtain the temperature at both ends of the heat pump outdoor fin heat exchanger, calculate the temperature difference and the change rate of the temperature difference per unit time; Specifically, this step is used to identify in real time whether the heat pump fin heat exchanger is in the early stage of frost development. Frost usually first appears at a location where the heat exchanger surface temperature is low, and gradually spreads over time. Therefore, by reasonably arranging temperature sensors and collecting and analyzing the temperature changes at key points, a sensitive judgment can be made on the initial stage of frost formation without affecting the operation of the system. The ultimate goal of this step is to output two variables: the current two-point temperature difference of the fin and the change rate of the temperature difference over time These two variables will be involved in the comprehensive judgment of the frost level in the subsequent steps.

[0020] All temperature data in this step are collected by thermistor temperature sensors (such as NTC negative temperature coefficient thermistors) installed on the surface of the fin heat exchanger. The sensor has the characteristics of fast response speed, small error, and is suitable for small space installation, and is suitable for outdoor heat pump equipment use environment. The sensor arrangement point is selected at the middle position of the inlet and outlet wind flow channels of the heat exchanger, as close as possible to the actual heat exchange path start and end point. In order to suppress local fluctuations, it is recommended that the sensor should not be close to the pipe wall but should be pressed in the center of the fin through a spring sheet, and fixed in place through thermal conductive glue. The output of each sensor is connected to the ADC interface of the microcontroller, the sampling period is recommended to be set to 30 seconds, the sampling value is converted by 12-bit resolution ADC, and after digital filtering, it is input to the algorithm processing module.

[0021] After collecting the sensor data, the temperature difference between the two ends of the fin at the current time is first calculated by the following formula: ; Wherein, represents the current fin temperature difference, which is used to preliminarily determine whether there are signs of local condensation and frost formation; is the temperature value collected by the inlet sensor of the heat exchanger, which is collected in real time by the thermistor sensor installed at the center position of the heat exchanger inlet; is the temperature value collected at the outlet, which is collected by the same type of sensor installed at the center position of the outlet. For example, if a heat pump equipment is installed in an outdoor environment with ℃, relative humidity, the collected ℃, ℃, the calculated current ℃, belongs to the critical interval with possible mild frost.

[0022] In order to judge whether the temperature difference is expanding, that is, whether the frost layer is further developing, a temperature difference change rate index based on a fixed time window is also introduced: ; wherein, represents the unit time change rate of temperature difference; is the length of the time window, it is recommended to be set as seconds, which should be the typical frost response time period of the heat pump; is the fin temperature difference at the current time, is the current sampling time point; is the temperature difference value at seconds ago, which is saved in the buffer register in the controller.

[0023] In actual deployment, if ℃ and ℃ / s are observed in multiple monitoring periods, it indicates that the fin frosting is gradually getting worse, and the subsequent system needs to evaluate whether it has affected the overall heat exchange performance, and trigger the defrosting strategy when the conditions are mature. The update period of all variables should be consistent with the calculation period of the controller to avoid misjudgment due to asynchronous data. It is recommended to filter the collected raw data through a sliding average window (such as 5 minutes) to further improve the system's anti-interference ability, but this filtering logic is not implemented in this step, but handled by the overall system integration part.

[0024] Finally, the two variables output by this step are and , the former represents the non-uniformity of heat exchange on the current fin surface, and the latter reflects the speed of change of this non-uniformity over time. These two indicators will be used in the subsequent steps to evaluate whether the frosting has caused a decline in system performance, and ultimately participate in the comprehensive judgment logic of the frost layer level.

[0025] S2: Synchronously acquire the water inlet temperature, water outlet temperature and water flow of the water side heat exchanger of the heat pump, and calculate the current water side heat exchange and its unit time change rate in combination with the temperature difference; Specifically, the goal of this step is to identify whether the frost layer has affected the overall heat exchange efficiency of the system without interrupting the heat pump heating operation, thereby providing key judgment basis for whether to enter the defrosting process subsequently. It is built on the two variables output by step one, i.e. fin temperature difference and temperature difference change rate , which respectively represent the frosting trend in the spatial dimension and the growth speed in the time dimension, but cannot directly reflect the actual impact of the frost layer on the system performance. Therefore, this step needs to introduce the "energy dimension" feature index, i.e. water side heat exchange capacity and its change trend , to judge whether the frost layer has hindered the normal transfer of heat.

[0026] On the input side, the heat-related inputs are collected from the water-side system of the shell-and-tube heat exchanger, including: Tin and Tout, respectively, are the inlet and outlet water temperatures of the water circuit of the heat pump system, which are collected by NTC temperature sensors installed on the centerline of the inlet and outlet water pipes, consistent with the air side; Qwater represents the real-time water flow rate, which is collected from a turbine flow meter installed in the main water circuit and is used after being processed by a 10-second moving average in L / min.

[0027] Since frost formation mainly occurs at the cold end of the fins, its direct impact is on the heat exchange between the refrigerant and the gas, and this heat is then transferred to the water circuit on the shell-and-tube side, so changes in water-side heat exchange capacity have a certain lag. This step takes advantage of this physical response characteristic to build an improved heat exchange model that includes a lag term and a temperature difference driving term: ; where, Qwater is the water-side heat exchange capacity of the system per unit time; Cwater is the specific heat capacity constant of water, which is preset as a constant term in the controller; , , Qwater, Tout, and Tin are the current water flow rate, outlet water temperature, and inlet water temperature, respectively; ΔTfin is the real-time calculated fin temperature difference from the previous step; K is a lag term adjustment coefficient that reflects the inhibitory effect of frost on the shell-and-tube heat exchange capacity, and is recommended to be initially set between 0.3 and 0.5, with fine-tuning possible based on the response characteristics of the device. This formula establishes a quantitative coupling relationship between the air-side frost state and the water-side heat exchange performance by introducing the term , explicitly expressing the engineering rule that "the greater the frost layer temperature difference, the more limited the water-side heat exchange capacity," which is different from the traditional model's estimation method of multiplying the water temperature difference by the flow rate, and better meets the analysis needs of the relationship between frost layer space development and system energy efficiency in this scheme.

[0028] To further characterize the performance degradation trend caused by frost, a sliding time window (recommended length: 300 seconds) is retained in the controller to continuously record each calculation of , and its change rate is calculated as follows: ; where, Qwater_rate is the change rate of the water-side heat exchange capacity per unit time, which is the second output of this step; is the historical window width (set to 300 seconds); ​is a regulating factor for adjusting the calculation span of the change rate according to the fin temperature difference growth rate, and its physical meaning is: when the frost layer temperature difference growth rate is faster, the calculation window of the system heat change should tend to be short-term response to enhance the sensitivity of the system to sudden frost. Variable From step one, no need to repeat the calculation. The formula uses a "dynamic adjustment difference expression" in structure, which can automatically adjust the accuracy of the judgment of heat decay trend.

[0029] For example, if the following data is collected in a certain period: L / min, ℃, ℃, ℃, then the previous formula is substituted to get: W (this is a simplified example calculation, no unit is shown), if the record 5 minutes ago is W, and ℃ / s, seconds, then ; This value indicates that the current water-side heat transfer capacity of the system is decreasing at a rate of 0.47 watts per second, combined with the temperature difference growth rate, it can be judged that it is in the middle and late stage of moderate frosting process, and whether it enters the defrosting stage needs to be determined in the subsequent steps.

[0030] This step transits from space frosting state to energy performance analysis, and uses known fin temperature difference variables to build a heat estimation model with "hysteresis suppression effect" and "dynamic response adjustment", so that the system can dynamically perceive the internal coupling relationship between frost layer development and energy efficiency decline without relying on fixed threshold judgment. Two output variables and will be combined with the temperature difference characteristics in the subsequent grade judgment step to form the criterion combination of graded defrosting, thereby improving the overall judgment accuracy and response efficiency, and forming a control logic closely embedded with the original system structure.

[0031] S3: Based on the temperature difference, temperature difference change rate, water-side heat transfer and its change rate, a frosting grade scoring model is built by fusion to distinguish between light, moderate and severe frosting states; Specifically, this step is the core of the entire "defrosting control by frost level" method, and its main task is to integrate the frost layer development trend extracted in the previous two steps with the system performance degradation signal, and based on the multi-dimensional index comprehensive analysis result, to determine whether the current heat pump system is in a mild, moderate or severe frosting state, so as to provide direct instruction basis for subsequent execution of different defrosting strategies. Compared with the simple method of traditional defrosting strategy based on temperature difference or fixed time judgment, this step adopts a fusion scoring model, which introduces four variables of fin space temperature difference characteristics, temperature difference change trend, heat exchange capacity absolute value and its unit time decline rate, to build a "frost level comprehensive scoring system" that can be run in real time and embedded in the controller. In particular, the scoring system not only combines the physical thermodynamic mechanism, but also introduces the suppression correction term and the prior compensation term in the index expression, further enhancing the judgment stability and anti-interference ability of the system in "critical working conditions" (such as short-time rapid frosting, high humidity and weak wind).

[0032] The input variables are the output variables of the previous two steps, a total of four, which are: the fin temperature difference , the current water-side heat exchange capacity , and its change rate . The four variables have been collected and calculated in steps one and two, and are stored as floating-point numbers in the controller, without the need for additional collection or preprocessing operations.

[0033] In the fusion judgment logic, a frost level scoring function is first constructed, and its expression is as follows: ; The scoring function integrates linear index terms, nonlinear trend response terms, and conditional prior suppression terms, forming a comprehensive level model with interpretability, physical reasonableness, and practical engineering value. The definitions of the variables and coefficients are as follows: The first term is the main driving term, which represents the fin inlet and outlet temperature difference, reflecting the formation amplitude of the frost layer, is its weight, and the recommended value is 1.2; The second term is the trend enhancement term, which represents the temperature difference growth rate, controls its sensitivity in the overall judgment, and the recommended value is 80; The third term is the energy decline term, which represents the heat exchange rate, is the current heat exchange capacity, and the constant (suggested value 0.01) in the denominator prevents the appearance of 0; this term reflects whether the frost layer has substantially affected the system heat exchange capacity, and the recommended value is 1500; The fourth is the introduced regularized response modification term, which is the structure of S-shaped function multiplied by the hard decision function, used to increase the score stability in the following two cases: When The larger, that is, the sudden increase in temperature difference, the S-shaped function tends to 1, indicating that the system needs to improve the frost sensitivity; If at this time Below a certain empirical threshold (such as 2000W), it means that the performance has been significantly limited, The product term is 1, indicating the activation of the additional score; If the condition is not met, the product term is 0, which automatically suppresses the impact, The maximum impact weight of the modification term (recommended value is 0.5), Control the shape of the S-shaped function (recommended ); The regular term is an enhancement introduced by this step to adapt to the heat pump real machine in the "light load but rapid frosting" scene, making the judgment closer to the actual industrial operation.

[0034] After the score function is calculated, the output value In the controller is segmented and judged as follows: ; Where, The current frost level label is 0, indicating light frost, 1 for moderate, and 2 for heavy; And are empirical adjustment thresholds, recommended values are 2.5 and 5.5, which can be dynamically fine-tuned through trial operation in actual deployment. The output value Is used for system log recording and analysis, and does not participate in direct control; The label Will be used as the control instruction entry variable of the defrosting strategy in the next step.

[0035] Through this level mapping mechanism that combines linear weighting and regular suppression terms, this step not only inherits the reliability of the temperature difference drive and energy efficiency decline two basic indicators, but also effectively makes up for the judgment lag of traditional methods in complex climate conditions or heat pump light load running stage, and can achieve more accurate and flexible frost level identification. It has a structural support role for the "graded control, defrosting by grade" to be achieved, and constitutes the most critical decision node of the intelligent control method.

[0036] S4: For different frost levels, respectively trigger online defrosting in heating mode, progressive bypass defrosting or switching to dynamic intensity defrosting strategy in refrigeration mode, and according to the frost severity, dynamically adjust the opening degree of the main road electronic expansion valve when heavy defrosting.

[0037] Specifically, this step is the key bridge in the whole scheme to convert the judgment result into actual control action, which is based on the frost level label and score value output in step three to drive specific control components in the heat pump system, such as the four-way valve, main route electronic expansion valve, and one-way bypass valve, to implement the staged defrosting strategy. This step not only needs to realize the correct matching of the control path, but also needs to realize the fine adjustment of the control action to ensure that the defrosting process is efficient and does not affect the heating performance. In practical applications, the defrosting path and control strategy required for different frost levels differ significantly, and this step maps them into a control parameter group and introduces a dynamic adjustment mechanism combined with the score value.

[0038] The input data are the two variables output in the previous step: frost level label and score value . Output by the controller internal logic judgment module, 0 represents light frost, 1 represents moderate frost, and 2 represents heavy frost; It is a real value, generally ranging from 0 to 10, calculated based on the temperature difference trend and heat exchange capacity decay degree integrated in the previous step, used to further adjust the strength of the control parameters.

[0039] In the control output design, first, the control logic of each component and its interface data structure need to be determined. The four-way valve controls the system operation mode switching, and its control signal is a digital switch, output by the controller to the electromagnetic valve actuator. When , the four-way valve switches to the refrigeration mode for heavy defrosting; when , it remains in the heating mode. The control quantity of the main route electronic expansion valve is the step opening , with a unit of steps, and the controller controls the step motor opening adjustment through the PWM driver, usually with an effective range of 0 to 400 steps. The one-way bypass valve is an electromagnetic control structure, only supporting two states of on and off, representing on, conducting liquid refrigerant to the fin side for thermal flushing defrosting; representing off, the system maintaining the normal heat exchange mode.

[0040] To achieve differentiated control, this step designs the following control strategy mapping relationship. First, the control path is selected according to the frost level: When (light frost), the system maintains the heating mode ( ), and the one-way bypass valve is opened ( ), without affecting the main route heat exchange and heating operation, and the main route electronic expansion valve is controlled normally without forced intervention.

[0041] When (mild frosting), the system still maintains heating mode ( ), the bypass valve opens ( ), and if the mild frosting is judged again within 20 minutes after the initial defrosting, the bypass defrosting is repeated once. At this time, the main route electronic expansion valve also maintains PID control.

[0042] When (severe frosting), the system switches to refrigeration mode ( ), the bypass valve is closed ( ), and the main route electronic expansion valve forced opening is calculated as follows: ; wherein represents the basic opening degree when severe frosting occurs (320 steps are recommended), is a regulation slope factor (20 is recommended), is a frost layer score value, is the maximum opening limit of the electronic expansion valve, which is fixed at 400 steps. The design of the formula has an explicit hierarchical regulation function: when entering the severe defrosting logic, the opening of the expansion valve is gradually increased from 320 steps to 400 steps according to the different , without using a fixed strong defrosting opening, avoiding the problems of increased energy consumption and compressor overload caused by excessive defrosting in the traditional scheme.

[0043] For example, if and , then: ; The controller finally outputs steps to drive the electronic expansion valve to be fully open to achieve strong defrosting. If , then steps, which means that although it belongs to severe frosting, the score is not extreme, and the defrosting intensity can be controlled in the medium-high range, effectively improving the defrosting accuracy.

[0044] The entire control strategy is refreshed and executed by the controller master logic according to the timing judgment period (60 seconds are recommended). All control signals are transmitted to the execution components through digital IO and PWM output interfaces, and the control response time is within 1 second, meeting the real-time requirements of the heat pump control system.

[0045] In one or more embodiments, as shown in Figure 2 , a hierarchical defrosting heat pump intelligent control system is disclosed, which comprises: a signal acquisition module for acquiring the temperatures at both ends of the finned heat exchanger, the water inlet / outlet temperature and water flow rate on the water side; The control module is configured to execute the graded defrosting control method described above; The execution module, including a four-way valve, a main electronic expansion valve, and a bypass valve, is used to execute corresponding defrosting actions according to the instructions of the control module and dynamically adjust the control strategy based on real-time monitoring data.

[0046] It is worth noting that the specific workflow of the heat pump intelligent control system for graded defrosting provided in this embodiment of the invention is the same as that of the heat pump intelligent control method for graded defrosting described in the above embodiment, and will not be repeated here.

[0047] This invention also provides a heat pump intelligent control device for graded defrosting, 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 a heat pump intelligent control method for graded defrosting, for example... 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.

[0048] 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 graded defrosting heat pump intelligent control device.

[0049] The aforementioned intelligent control device for staged defrosting heat pumps can be a desktop computer, laptop, handheld computer, or cloud server, among other computing devices. This intelligent control device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the intelligent control device may also include input / output devices, network access devices, and buses.

[0050] 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 staged defrosting heat pump intelligent control device, connecting all parts of the device via various interfaces and lines.

[0051] The memory can be used to store the computer program and / or modules. The processor implements various functions of the heat pump intelligent control device with graded defrosting 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 according to 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.

[0052] The integrated module of the heat pump intelligent control device for graded defrosting, 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.

[0053] 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.

[0054] 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 heat pump intelligent control method for staged defrosting, characterized in that, The method includes: Obtain the temperatures at both ends of the outdoor finned heat exchanger of the heat pump, and calculate the temperature difference and the rate of change of the temperature difference per unit time. The inlet water temperature, outlet water temperature and water flow rate of the heat pump water-side heat exchanger are acquired simultaneously, and the current water-side heat exchange and its rate of change per unit time are calculated in combination with the temperature difference. Based on the temperature difference, the rate of temperature change, the heat exchange on the water side and its rate of change, a frost level scoring model is constructed by fusion to distinguish between light, moderate and heavy frost conditions. For different frost levels, online defrosting, progressive bypass defrosting, or dynamic intensity defrosting strategies that switch to cooling mode are triggered respectively. In the case of heavy defrosting, the opening of the main circuit electronic expansion valve is dynamically adjusted according to the severity of frost.

2. The intelligent control method for a heat pump with graded defrosting according to claim 1, characterized in that, The calculation of the water-side heat exchange explicitly incorporates the fin temperature difference as an inhibition factor to quantify the hindering effect of frost on the refrigerant-water side heat transfer.

3. The intelligent control method for a heat pump with graded defrosting according to claim 1, characterized in that, The calculation window length for the rate of change of heat exchange per unit time is shortened as the rate of change of temperature difference increases, so as to improve the response speed to sudden frost formation.

4. The intelligent control method for a heat pump with graded defrosting according to claim 1, characterized in that, The frost level scoring model includes a linear weighting term, a nonlinear trend enhancement term, and a condition-triggered regularization correction term, which are used to improve the stability of frost level determination under critical operating conditions.

5. The intelligent control method for a heat pump with graded defrosting according to claim 1, characterized in that, In the case of light frost, the system maintains the heating mode and opens the bypass valve for online defrosting. The main circuit electronic expansion valve maintains normal operation control logic and does not perform forced intervention.

6. The intelligent control method for a heat pump with graded defrosting according to claim 1, characterized in that, If the system is in a state of moderate frost and is again determined to be in a state of moderate frost within a preset time interval, the bypass defrosting operation will be repeated once.

7. The intelligent control method for a heat pump with staged defrosting according to claim 1, characterized in that, In the case of heavy frost, the opening of the main electronic expansion valve is dynamically adjusted between the set basic opening and the maximum allowable opening based on the frost score value to match the actual defrosting needs.

8. The intelligent control method for a heat pump with graded defrosting according to claim 1, characterized in that, The output of the frost level scoring model is used to determine the frost level after threshold segmentation mapping, and the threshold can be dynamically calibrated based on actual operating data.

9. The intelligent control method for a heat pump with graded defrosting according to claim 1, characterized in that, After implementing the defrosting control strategy, the system recalculates the frost score based on the updated temperature difference and heat exchange performance data, and dynamically adjusts the type or intensity of subsequent defrosting actions to form an adaptive closed-loop control.

10. A staged defrosting intelligent control system for heat pumps, characterized in that, The system includes: The signal acquisition module is used to acquire the temperature at both ends of the finned heat exchanger, the inlet / outlet water temperature on the water side, and the water flow rate. The control module is configured to execute the heat pump intelligent control method for staged defrosting as described in any one of claims 1 to 9; The execution module, including a four-way valve, a main electronic expansion valve, and a bypass valve, is used to execute corresponding defrosting actions according to the instructions of the control module and dynamically adjust the control strategy based on real-time monitoring data.

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