A method and system for intelligent control of a heat pump with step defrosting

Abstract

The application provides a kind of hierarchical defrosting heat pump intelligent control method and system, method includes: collecting fin import and export temperature calculation temperature difference and its change rate, assesses the initial state of frost formation;Then combine water side heat exchange efficiency and its attenuation rate, further analyze the influence of frost layer on system performance.Based on this information, the controller uses the frost formation level score function to accurately determine the mild, moderate and severe frost formation state, and automatically matches the corresponding defrosting strategy: maintain heating mode and online defrosting when mild;Progressive bypass defrosting is used when moderate;Switch to refrigeration mode and dynamically adjust the expansion valve opening when severe to enhance the defrosting effect.The application not only improves the defrosting accuracy and system stability, but also improves the operating efficiency and user experience of heat pump equipment in low temperature environment.

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 in severe cases, may 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 heating interruption during execution, and users will feel obvious cold wind or hot water temperature drop in the heating state, which seriously affects comfort. In a complex environment with variable weather conditions, traditional methods lack the ability to dynamically identify the degree of frosting and cannot implement graded response 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 water side heat exchange, and failing to reflect the real impact of frosting on energy transfer efficiency. These problems collectively result in low operating efficiency, frequent defrosting, shortened service life, and poor user experience of heat pump equipment in low-temperature environments. SUMMARY

[0003] The present application aims 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-mentioned purpose, in the first aspect of the present application, a heat pump intelligent control method for graded defrosting is provided, which comprises:

[0005] Obtaining the temperatures at both ends of the heat pump outdoor fin heat exchanger, calculating the temperature difference and the change rate of the temperature difference per unit time;

[0006] Synchronously obtaining the inlet water temperature, outlet water temperature and water flow of the heat pump water side heat exchanger, and calculating the current water side heat exchange and its change rate per unit time in combination with the temperature difference;

[0007] Based on the temperature difference, temperature difference change rate, water side heat exchange amount and its change rate, a frosting grade scoring model is constructed by fusion to distinguish mild, moderate and severe frosting states;

[0008] For different frosting grades, online defrosting in heating mode, progressive bypass defrosting or switching to refrigeration mode dynamic intensity defrosting strategies are triggered respectively, wherein the opening degree of the main road electronic expansion valve is dynamically adjusted according to the frosting severity when severe defrosting.

[0009] 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.

[0010] Further, the calculation window length of the heat exchange amount per unit time is shortened as the temperature difference change rate increases, to improve the response speed to sudden frosting.

[0011] Further, the frosting grade scoring model includes a linear weighting term, a nonlinear trend enhancement term and a condition triggered regularization correction term, which is used to improve the stability of the frosting grade determination under critical working conditions.

[0012] Further, in the mild frosting state, the system maintains the heating mode and opens the bypass valve for online defrosting, and the main road electronic expansion valve remains normal operation control logic without forced intervention.

[0013] Further, in the moderate frosting state, if it is determined again that the frosting is moderate within a preset time interval, the bypass defrosting operation is repeated once.

[0014] Further, in the severe frosting state, the opening degree of the main road electronic expansion valve is dynamically adjusted according to the frosting score value between the set basic opening degree and the maximum allowable opening degree to match the actual defrosting demand.

[0015] Further, the output of the frosting grade scoring model is mapped after threshold segmentation to determine the frosting grade, and the threshold value can be dynamically calibrated according to the actual operation data.

[0016] 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.

[0017] In a second aspect of the present application, a hierarchical defrosting intelligent control system of a heat pump is provided, the system comprising:

[0018] A signal acquisition module is used to acquire the temperature of the fin heat exchanger at both ends, the water inlet / outlet temperature and water flow of the water side;

[0019] The control module is configured to execute the hierarchical defrosting control method as described above.

[0020] The execution module includes a four-way valve, a main path electronic expansion valve, and a bypass valve, and is used 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.

[0021] The present application has at least the following beneficial technical effects:

[0022] To solve the above problems, the present application provides a hierarchical defrosting intelligent control method and system for a heat pump, which constructs the temperature difference and its time change rate characteristics by collecting the inlet and outlet temperatures of the fins, establishes the spatial and dynamic trend description of the 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 grade scoring function with a regularization correction term and a nonlinear response structure in the controller, which can accurately determine the frosting state in the light, medium and heavy frosting states. According to the different grade frost states, the controller automatically matches the control strategy of the core components of the heat pump: when the frosting is light, the heating mode is maintained and the one-way bypass valve is opened to realize online defrosting; when the frosting is moderate, progressive double-cycle bypass defrosting is performed; when the frosting is heavy, the four-way valve is switched to the refrigeration mode and the main path electronic expansion valve opening is dynamically adjusted according to the scoring result to control the defrosting intensity. The present application first introduces the integrated design of "hierarchical judgment + hierarchical execution" in the defrosting control of the heat pump, directly associates the physical signals and control actions through linear and nonlinear combined criteria, and realizes the transformation of the 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 in low temperature complex working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0023] The present application is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by those skilled in the art without creative labor on the premise of not paying any creative labor.

[0024] Figure 1 A hierarchical defrosting intelligent control method flowchart of the present application.

[0025] Figure 2 A hierarchical defrosting intelligent control system framework diagram of the present application. DETAILED DESCRIPTION

[0026] Embodiments of the present application are described below in detail with reference to the drawings, examples of which are shown in the drawings, wherein the same or similar notations 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 only, and are for the purpose of explanation of the present application, and are not to be understood as a limitation of the present application.

[0027] In one or more embodiments, as shown in Figure 1 A method for intelligent control of a heat pump with staged defrosting is disclosed, the method comprising the following:

[0028] 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;

[0029] Specifically, this step is used to identify in real time whether the heat pump fin heat exchanger is in the early stage of the frosting development stage. Frost usually first appears at a location where the heat exchanger surface temperature is low, and gradually spreads over time, so by reasonably arranging temperature sensors, collecting and analyzing the temperature changes at key points, the early stage of frosting can be judged sensitively without affecting the operation of the system. The ultimate goal of this step is to output two variables: the current fin two-point temperature difference and the change rate of the temperature difference over time These two variables will participate in the comprehensive judgment of the frost level in the subsequent steps.

[0030] 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 air flow channel at the inlet and outlet ends 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 by a spring sheet, and fixed in position by 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.

[0031] After the sensor data is collected, the fin temperature difference at the current time is first calculated by the following formula:

[0032] ;

[0033] Wherein, represents the fin temperature difference at the current time, which is used to preliminarily determine whether there are signs of local condensation and frosting; The temperature value collected by the sensor at the inlet end of the heat exchanger is collected in real time by a resistance temperature sensor installed at the center of the air inlet surface of the heat exchanger. The temperature value collected at the outlet is obtained by installing a similar sensor at the center of the air outlet surface. For example, if a heat pump unit is installed... ℃ In an outdoor environment with relative humidity, samples were collected. ℃, ℃, then the current temperature is calculated. ℃, which is within the critical range where mild frost may occur.

[0034] To determine whether the temperature difference is widening, i.e., whether the frost layer is developing further, it is necessary to introduce an index of the rate of change of temperature difference based on a fixed time window:

[0035] ;

[0036] in, This represents the rate of change of temperature difference per unit time. The recommended setting for the time window length is [length]. seconds; this value should be the typical frosting response time of a heat pump. The fin temperature difference at the current moment. This is the current sampling time point; for The temperature difference value from a second ago is stored in the buffer register within the controller.

[0037] In actual deployment, if observations are made within multiple monitoring periods... ℃ and If the temperature is measured in °C / s, it indicates that frost buildup on the fins is gradually worsening. The system needs to be assessed to determine if this has affected overall heat exchange performance, and a defrosting strategy should be triggered when conditions are suitable. The update cycle of all variables should be consistent with the controller's calculation cycle to avoid misjudgments due to data asynchrony. It is recommended to filter the collected raw data using a moving average window (e.g., 5 minutes) to further improve the system's anti-interference capability; however, this filtering logic is not implemented in this step but is handled by the overall system integration section.

[0038] Ultimately, the two variables output by this step are and The former indicates the non-uniformity of heat exchange on the current fin surface, while the latter reflects the rate at which this non-uniformity changes over time. These two indicators will be used in subsequent steps to assess whether frost has caused a decline in system performance, and will ultimately participate in the comprehensive judgment logic for the frost level.

[0039] S2: Synchronize to obtain the water inlet temperature, water outlet temperature and water flow of the water side heat exchanger of the heat pump, and combine the temperature difference to calculate the current water side heat exchange and its unit time change rate;

[0040] Specifically, the purpose 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 a key basis for subsequent judgment whether to enter the defrost process. It is based on the two variables output in step one, i.e. fin temperature difference and temperature difference change rate These two quantities respectively represent the frosting trend in space dimension and the growth rate in time dimension, but they cannot directly reflect the actual impact of frost layer on system performance, so this step needs to introduce a feature index in "energy dimension", i.e. water side heat exchange capacity and its change trend , to judge whether the frost layer has hindered the normal transfer of heat.

[0041] In terms of input, the heat related input is obtained from the water side system of the shell and tube heat exchanger, and the data includes: and are the water inlet temperature and water outlet temperature of the heat pump system waterway, respectively, which are collected by NTC temperature sensors consistent with the air side, fixedly installed on the center line of the inlet and outlet water pipes; represents the real-time flow of water side, which is collected from the turbine flow meter installed in the main waterway, in L / min unit, and is used after 10 seconds of sliding average processing.

[0042] Since frost formation mainly occurs at the cold end of the fin, its direct impact is the heat exchange between the refrigerant and the gas, and this heat is then transferred to the shell and tube side waterway through the refrigerant, so the change of water side heat exchange capacity has a certain hysteresis. This step makes use of this physical response characteristic to build an improved heat exchange model containing a lag term and a temperature difference driving term:

[0043] ;

[0044] Where, is the water side heat exchange capacity of the system per unit time; is the specific heat capacity constant of water, which is preset as a constant term in the controller; , , are the current water flow, outlet water temperature, and inlet water temperature, respectively; is the fin temperature difference calculated in the previous step; is a lag term adjustment coefficient, which is used to reflect the inhibitory effect of fin frosting on the shell and tube heat exchange capacity, and is recommended to be initially set between 0.3~0.5, which can be fine-tuned according to the response characteristics of the equipment. This formula introduces The quantitative coupling relationship between the separated frost state on the air side and the heat exchange performance on the water side is established, and the engineering rule that "the greater the frost layer temperature difference, the more limited the water side heat exchange capacity" is explicitly expressed. Unlike the traditional model which estimates the water temperature difference multiplied by the flow rate, this model is more suitable for the analysis of the relationship between the frost layer space development and system energy efficiency.

[0045] To further characterize the performance degradation trend caused by frosting, a sliding time window (recommended length 300 seconds) is retained in the controller to continuously record each calculation of And its rate of change is calculated as follows:

[0046] ;

[0047] Where, is the rate of change of the system's 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 the adjustment factor, used to adjust the calculation span of the rate of change according to the growth rate of the fin temperature difference. Its physical meaning is: when the growth rate of the frost layer temperature difference is faster, the calculation window of the system heat exchange capacity change should tend to be short-term response to enhance the system's sensitivity to sudden frosting. Variable comes from step one and does not need to be calculated again. This formula uses a "dynamic adjustment difference expression" in structure, which can automatically adjust the accuracy of the heat decline trend judgment.

[0048] For example, if the following data is collected in a certain period: L / min, ℃, ℃, ℃, then after substituting the previous formula, we get: W (this is a simplified example calculation, without units), if the record 5 minutes ago is W, and ℃ / s, seconds, then

[0049] ;

[0050] This value indicates that the current system water side heat exchange capacity 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 stages of moderate frosting process, and whether it enters the defrosting stage needs to be determined in the subsequent steps.

[0051] This step is from the space frost state transition to energy performance analysis, using the known fin temperature difference variable 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 the frost layer development and the 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.

[0052] S3: Based on the temperature difference, temperature difference change rate, water side heat transfer amount and its change rate, a frost grade scoring model is constructed by fusion to distinguish between mild, moderate and severe frost states;

[0053] Specifically, this step is the core hub of the entire "graded defrosting control" method, and the main task is to fuse the frost layer development trend and system performance decline signal extracted in the previous two steps, and based on this multi-dimensional index comprehensive analysis result, determine whether the current heat pump system is in a mild, moderate or severe frost state, thereby providing 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 uses a fusion scoring model, which introduces four variables of fin space temperature difference characteristics, temperature difference change trend, heat transfer capacity absolute value and its unit time decline rate to construct a "frost layer grade comprehensive scoring system" that can run in real time and be embedded in the controller. In particular, the scoring system not only combines the physical thermodynamic mechanism, but also introduces inhibition correction items and prior compensation items in the index expression, further enhancing the judgment stability and anti-interference ability of the system in "critical working conditions" (such as short-term rapid frosting, high humidity and weak wind).

[0054] The input variables are the output variables of the previous two steps, a total of four, which are: fin temperature difference , its unit time change rate , current water side heat transfer capacity , and its change rate . Four variables have been collected and calculated in steps one and two, and are cached as floating point numbers in the controller without additional collection or preprocessing operations.

[0055] In the fusion judgment logic, a frost layer grade scoring function is first constructed, and its expression is as follows:

[0056] ;

[0057] The scoring function combines linear indicator terms, nonlinear trend response terms and conditional prior suppression terms, forming a comprehensive grading model with interpretability, physical rationality and practical engineering value. The definitions of each variable and coefficient are as follows:

[0058] The first term is the main driving term, representing the temperature difference between the inlet and outlet of the fin, reflecting the formation amplitude of the frost layer, is its weight, and the recommended value is 1.2;

[0059] The second term is the trend enhancement term, representing the temperature difference growth rate, controlling its sensitivity in the overall judgment, and the recommended value is 80;

[0060] The third term is the energy decay term, representing the heat exchange rate, is the current heat exchange, and the denominator adds a constant (suggested value 0.01) to prevent 0 value; this term reflects whether the frost layer has substantially affected the system heat exchange capacity, and the recommended value is 1500;

[0061] The fourth term is the regularization response modification term introduced, which is an S-shaped function multiplied by a hard decision function, used to increase the stability of the score in the following two cases:

[0062] When is large, i.e., the temperature difference growth suddenly accelerates, the S-shaped function tends to 1, indicating that the system needs to improve the frost sensitivity;

[0063] If at this time is lower than a certain empirical threshold (such as 2000W), it means that the performance has been significantly limited, is the indicator function, and the value of this product term is 1, activating the additional score;

[0064] If the condition is not met, the product term is 0, automatically suppressing the impact, is the maximum influence weight of the modification term (recommended value is 0.5), controls the shape of the S-shaped function (recommended ); this regularization term is an enhancement term introduced by this step to adapt to the "light load but rapid frosting" scenario of the actual heat pump, making the judgment more close to the actual industrial operation.

[0065] After the scoring function is calculated, the output value in the controller is mapped by piecewise judgment:

[0066] ;

[0067] where, The current frost level label is represented, taking the value 0 for light frost, 1 for moderate, and 2 for heavy; With is an empirical adjustment threshold, and the recommended value is 2.5 to 5.5. The output value is used for system log analysis and does not participate in direct control; the label will be used as the control instruction entry variable for the defrosting strategy in the next step.

[0068] Through this combination of linear weighting and regular inhibition term level mapping mechanism, this step not only inherits the reliability of the two basic indicators of temperature difference driving and energy efficiency decline, but also effectively makes up for the judgment lag of traditional methods in complex climate conditions or light load running of heat pump, and can realize more accurate and flexible frost level identification, which has a structural support role for the realization of "graded control and defrosting", and constitutes the most critical decision node of the intelligent control method.

[0069] S4: For different frost levels, trigger online defrosting in heating mode, progressive bypass defrosting, or switch to dynamic intensity defrosting strategy in cooling mode, and dynamically adjust the opening degree of the main electronic expansion valve according to the severity of the heavy frost.

[0070] Specifically, this step is the key bridge that converts the judgment results into actual control actions in the entire scheme. Its role is to drive specific control components in the heat pump system, such as the four-way valve, the main electronic expansion valve, and the one-way bypass valve, based on the frost level label and score value output in step three to execute the graded 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 actual application, the defrosting path and control strategy required by different frost levels differ significantly, and this step structures the mapping of control parameters into control parameter groups and introduces a dynamic adjustment mechanism based on the score value.

[0071] The input data are the two variables output in the previous step: the frost level label and the score value . Output by the controller's internal logic judgment module, 0 represents light frost, 1 represents moderate, and 2 represents heavy; 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 intensity of the control parameters.

[0072] In the control output design, first, the control logic of each component and its interface data structure need to be clearly defined. The four-way valve The control system switches operating modes, and the control signal is a digital switching quantity, output from the controller to the solenoid valve actuator. When the four-way valve switches to cooling mode, it is used for heavy defrosting; when At this time, it remains in heating mode. The control quantity of the main circuit electronic expansion valve is a step opening. The unit is steps. The controller uses a PWM driver to control the stepper motor to adjust the opening, typically with an effective range of 0 to 400 steps. One-way bypass valve. It is an electromagnetically controlled structure that only supports two states: switch and on. This indicates that the circuit is open, allowing liquid refrigerant to be channeled to the fin side for thermal flushing and defrosting. This indicates that the system is off and will maintain its normal heat exchange mode.

[0073] To achieve differentiated control, the following control strategy mapping relationship was designed in this step. First, the control path is selected based on the frost level:

[0074] when When there is slight frost, the system maintains heating mode. At the same time, open the one-way bypass valve ( This does not affect the main circuit heat exchange and heating operation; the main circuit electronic expansion valve... Normal PID control is applied without forced intervention.

[0075] when (During moderate frosting), the system remains in heating mode. The bypass valve is open. If moderate frost is detected again within 20 minutes after the initial defrosting, the bypass defrosting process is repeated. During this time, the main circuit electronic expansion valve also maintains PID control.

[0076] when When there is heavy frost, the system switches to cooling mode. The bypass valve is closed. The forced opening degree of the main circuit electronic expansion valve is calculated using the following formula:

[0077] ;

[0078] in, This indicates the base opening for severe frosting (320 steps is recommended). To adjust the slope factor (recommended setting: 20). The frost layer score is given. The maximum opening limit of the electronic expansion valve is fixed at 400 steps. This formula is designed with explicit graded adjustment: when entering the heavy defrost logic, according to... The opening degree of the expansion valve is gradually increased from 320 steps to 400 steps, without using a fixed strong defrosting opening degree, so that the problems of energy consumption increase and compressor overload caused by excessive defrosting in the traditional scheme are avoided.

[0079] For example, if and , then:

[0080] ;

[0081] The controller finally outputs steps, and drives the electronic expansion valve to be fully opened to realize strong defrosting. If , then steps, which indicates that although it belongs to heavy frosting, the score is not extreme, the defrosting intensity can be controlled in the medium-high range, and the defrosting accuracy is effectively improved.

[0082] The entire control strategy is refreshed and executed by the controller master control logic according to a timing judgment period (recommended 60 seconds). 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, which meets the real-time requirements of the heat pump control system.

[0083] In one or more embodiments, as shown in Figure 2 , a heat pump intelligent control system with hierarchical defrosting is disclosed, and the system comprises:

[0084] A signal acquisition module is configured to acquire the temperature at both ends of the finned heat exchanger, the water inlet / outlet temperature and water flow rate of the water side;

[0085] A control module is configured to execute the hierarchical defrosting control method as described above;

[0086] An execution module comprises a four-way valve, a main route electronic expansion valve and a bypass valve, and is configured 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.

[0087] It is worth noting that the specific working process of the heat pump intelligent control system with hierarchical defrosting provided by the embodiment of the present application is the same as the process of the heat pump intelligent control method with hierarchical defrosting described in the above embodiment, and will not be repeated here.

[0088] The embodiment of the present application also provides a heat pump intelligent control device with hierarchical defrosting, which comprises 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, the steps in the above-mentioned heat pump intelligent control method with hierarchical defrosting embodiment are implemented, such as the steps S1-S4 described in Figure 1 , or the processor executes the computer program to realize the functions of the modules in the above-mentioned system embodiments.

[0089] For example, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the hierarchical defrosting heat pump intelligent control device.

[0090] The hierarchical defrosting heat pump intelligent control device can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The hierarchical defrosting heat pump intelligent control device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the hierarchical defrosting heat pump intelligent control device can also include input / output devices, network access devices, buses and the like.

[0091] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASAC), field programmable gate arrays (FPGA) 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, etc. The processor is the control center of the hierarchical defrosting heat pump intelligent control device, and connects all parts of the hierarchical defrosting heat pump intelligent control device through various interfaces and lines.

[0092] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the intelligent control device for a heat pump with staged defrosting by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, and the like; and the data storage area can store data created according to the running of the air conditioner controller, and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a SmartMediaCard (SMC), a Secure Digital Card (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0093] The modules integrated in the intelligent control device for a heat pump with staged defrosting can be stored in a computer-readable storage medium if they are realized in the form of software function units and sold or used as independent products. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. The computer program can implement the steps of the above-mentioned various method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, 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 of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium, and the program can include the processes of the above-mentioned various method embodiments when executed. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0095] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements are also considered to be within the scope of the present application.

Claims

1. A method of intelligent control of a heat pump with defrosting stages, 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. The calculation of the water-side heat exchange explicitly introduces the fin temperature difference as an inhibition factor to quantify the hindering effect of frost on the refrigerant-water side heat transfer. The calculation window length of the rate of change per unit time of the water-side heat exchange is shortened as the rate of change of temperature difference increases, so as to improve the response speed to sudden frost formation. Based on the temperature difference, the rate of temperature change, the heat exchange on the water side and its rate of change, a frosting level scoring model is constructed by fusion to distinguish between light, moderate and heavy frosting states. The frosting 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 frosting level determination under critical operating 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, 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.

3. 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.

4. The intelligent control method for a heat pump with graded 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.

5. 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.

6. The intelligent control method for a heat pump with staged 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.

7. 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 6; 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.

Images