Solar energy assisted air source heat pump greenhouse intelligent management system

Abstract

The present application relates to the technical field of intelligent analysis, and more particularly to a solar energy assisted air source heat pump greenhouse intelligent management system, which is provided with a multi-source environment sensing module, a feature acquisition module, an auxiliary analysis module, a partition management module and a collaborative regulation module, analyzes the influence of temperature, humidity and environmental wind on the frosting tendency of the heat pump according to the auxiliary analysis module, calculates the frosting risk specific value, determines the frosting tendency of the heat pump, analyzes the running state and the influence of particle adhesion on the heat pump fins based on the partition management module, determines the abnormal characteristic value of each fin, determines the abnormal area of the heat pump, analyzes each area, issues a pre-warning signal for the abnormal area, and performs defrosting treatment on the abnormal area by calling solar energy. The present application performs abnormal analysis on frosting in advance, locates the abnormal area, and performs early warning on the abnormal area, so as to complete the intelligent control of the greenhouse.

Description

Technical Field

[0001] This invention relates to the field of intelligent analysis technology, and in particular to an intelligent management system for a solar-assisted air source heat pump greenhouse. Background Technology

[0002] Traditional greenhouses suffer from high energy consumption, high operating costs, and significant environmental pressures in winter heating and summer cooling. Their reliance on manual experience for environmental control also fails to meet the precise temperature and humidity requirements of greenhouses. While efficient and energy-saving air-source heat pump technology and clean, renewable solar thermal utilization technology have been introduced into the greenhouse field, air-source heat pumps experience reduced heating efficiency and are prone to frosting in cold conditions, while solar energy is intermittent and unstable, unable to independently serve as a stable heat source. Therefore, the "solar-assisted air-source heat pump" system, which complements the advantages of both, has emerged. It enhances heat pump efficiency through solar energy and ensures stable operation at low temperatures. The complexity and dynamism of this multi-energy coupled system far exceed the capabilities of manual control. It must rely on an intelligent management system integrating IoT sensing, automatic control, and intelligent decision-making algorithms as the "brain" for coordinated command, optimizing energy distribution around the clock while ensuring the best growing environment for crops, thereby maximizing the overall energy efficiency and economy of the system.

[0003] Chinese Patent Publication No. CN117331392A discloses an environmental management system for intelligent greenhouses, including a multi-source environmental sensing module, an agricultural brain environmental control module, a 3D animation display module, and a user adjustment interface. This invention uses environmental monitoring sensors to monitor environmental parameters within the intelligent greenhouse in real time. Through 3D animation demonstrations of control and management processes and packaging procedures, it simulates the growth status of tomato plants. Based on an optimal growth environment index management model, it determines the optimal growth environment conditions for tomato plants and can automatically control intelligent ventilation windows, automated shading curtains, and automated heat insulation curtains. This helps provide stable growth conditions without human intervention, optimizing growth conditions within the greenhouse to maximize plant growth and yield, thereby improving agricultural production efficiency and reducing resource waste.

[0004] Chinese Patent Publication No. CN109906833A discloses a greenhouse intelligent management system based on big data, which addresses the problems of judging plant growth status by leaf color and compressing and storing big data to facilitate the storage of more data. The system includes a plant detection subsystem, a big data platform center, an environmental monitoring subsystem, a wireless transmission module, an analysis module, a processor, a display module, an alarm module, an actuator, and a control subsystem. This greenhouse intelligent management system uses the formula Zs = YL*K1 + Hp*K2 to obtain the plant development comparison value Zs; a larger plant development comparison value Zs indicates better plant development. The system deletes the originally stored environmental and plant parameter information. By periodically compressing and storing data within the big data platform center, the storage space of the big data platform center is increased, thus facilitating the storage of more data.

[0005] However, the following problems still exist in the existing technology.

[0006] Heat pumps in greenhouse applications face the problem of frosting caused by ambient temperature and humidity. Abnormal ambient wind, poor operating conditions, and particles on the fins can worsen frosting. In particular, the accumulation of unevenly distributed particles, if not effectively controlled, will lead to a decrease in heat exchange efficiency and a decline in operating performance, resulting in an imbalance in the greenhouse thermal environment. Summary of the Invention

[0007] To address this issue, the present invention provides a solar-assisted air-source heat pump greenhouse intelligent management system to solve the frosting problem faced by heat pumps in greenhouse applications due to environmental temperature and humidity. Factors such as abnormal ambient wind, poor operating conditions, and particulate matter on the fins can worsen frosting. In particular, the accumulation of unevenly distributed particulate matter, if not effectively controlled, will lead to a decrease in heat exchange efficiency and a decline in operating performance, resulting in an imbalance in the greenhouse thermal environment.

[0008] To achieve the above objectives, the present invention provides a solar-assisted air-source heat pump greenhouse intelligent management system, comprising:

[0009] The multi-source environmental sensing module includes a temperature monitoring unit, a humidity monitoring unit, an operation monitoring unit, an environmental wind monitoring unit, and an image monitoring unit.

[0010] The feature acquisition module, which is connected to the multi-source environmental sensing module, is used to calculate the temperature difference value based on the temperature monitoring unit, the humidity difference value based on the humidity monitoring unit, the operation characterization coefficient based on the operation monitoring unit, the environmental wind influence coefficient based on the environmental wind monitoring unit, and the particle adhesion influence coefficient based on the image monitoring unit.

[0011] An auxiliary analysis module, connected to the feature acquisition module, is used to determine the specific value of the frost risk of the heat pump based on the temperature difference value, humidity difference value and the environmental wind influence coefficient, so as to classify the frost tendency of the heat pump.

[0012] The partition management module, which is connected to the feature acquisition module and the auxiliary analysis module, calculates the abnormal feature value of the heat pump fins in response to the strong frosting tendency of the heat pump, combining the frosting risk specific value, the operation characterization coefficient and the particle adhesion influence coefficient, in order to determine the abnormal area of ​​the heat pump.

[0013] The collaborative control module, which is connected to the partition management module, is used to issue early warning signals for abnormal areas and to call solar energy according to the abnormal characteristic values ​​to perform defrosting treatment on the abnormal areas.

[0014] Furthermore, the feature acquisition module calculates temperature difference values ​​and humidity difference values, including,

[0015] The absolute value used to determine the difference between the ambient temperature and the required greenhouse temperature is the first temperature difference value;

[0016] The absolute value of the difference between the solar energy emission temperature and the greenhouse required temperature is used to determine the second temperature difference value;

[0017] The absolute value of the difference between the first temperature difference value and the second temperature difference value is used to determine the temperature difference value.

[0018] The absolute value used to determine the difference between ambient humidity and greenhouse humidity is the humidity difference value.

[0019] Further, the feature acquisition module calculates the runtime characterization coefficients, including:

[0020] The ratio of the operating start / stop value to the baseline operating start / stop value is used to determine the operating characterization coefficient.

[0021] The start-stop value is the ratio of the number of times the heat pump starts and stops within a predetermined time period to the predetermined time period.

[0022] Furthermore, the feature acquisition module calculates the environmental wind influence coefficient, including,

[0023] Used to determine the angle between the fins and the ambient wind direction when the heat pump starts up;

[0024] The first effective factor is used to determine the ratio of the included angle to the reference included angle;

[0025] The absolute value of the difference between the heat pump operating speed and the wind speed is used to calculate the wind speed difference.

[0026] The ratio of the wind speed difference to the heat pump operating speed is used as a second effective factor to determine the value.

[0027] The average of the sum of the first effective factor and the second effective factor is used to determine the environmental wind influence coefficient.

[0028] Further, the feature acquisition module calculates the particle adhesion influence coefficient, including,

[0029] Used to scan the abnormal particles on each of the heat pump fins and determine the three-dimensional coordinates of each of the abnormal particles;

[0030] Used to calculate the density of each abnormal particle on each of the heat pump fins based on the three-dimensional coordinates;

[0031] The average value of each density is used to determine the particle adhesion influence coefficient of the fin;

[0032] The abnormal particles are those with a diameter larger than a predetermined diameter.

[0033] Furthermore, the auxiliary analysis module determines the specific value of the frost risk of the heat pump, including,

[0034] The ratio of the temperature difference value to the baseline temperature difference value is used to determine the first risk factor.

[0035] The ratio of the humidity difference value to the baseline humidity difference value is used to determine the second risk factor;

[0036] The weighted sum of the first risk factor, the second risk factor, and the environmental wind influence coefficient is used to determine the frost risk specific value.

[0037] Furthermore, the auxiliary analysis module classifies the frost tendency of the heat pump, wherein,

[0038] If the specific value of the frosting risk is greater than the threshold value of the frosting risk, then the heat pump is classified as having a strong frosting tendency.

[0039] If the frost risk specific value is less than or equal to the frost risk specific value threshold, then the heat pump is classified as having a weak frost tendency.

[0040] Furthermore, the partition management module calculates the abnormal characteristic values ​​of the heat pump fins, including:

[0041] The ratio of the frost risk specific value to the benchmark frost risk specific value is used to determine the first anomaly factor;

[0042] The ratio of the particle adhesion influence coefficient to the benchmark particle adhesion influence coefficient is used to determine the second anomaly factor.

[0043] The weighted sum of the first abnormal factor, the second abnormal factor, and the running characterization coefficient is used to determine the abnormal feature value.

[0044] Furthermore, the zone management module identifies abnormal areas of the heat pump, wherein,

[0045] If the abnormal feature value is greater than the abnormal feature value threshold, then the fins of the heat pump are determined to be an abnormal area.

[0046] If the abnormal feature value is less than or equal to the abnormal feature value threshold, then the fins of the heat pump are determined to be in the normal region.

[0047] Furthermore, the coordinated control module calls upon solar energy based on the abnormal characteristic value, wherein,

[0048] The value of the solar energy invoked is proportional to the abnormal characteristic value.

[0049] Compared with existing technologies, this invention establishes a multi-source environmental sensing module, a feature acquisition module, an auxiliary analysis module, a zone management module, and a collaborative control module. The auxiliary analysis module analyzes the impact of temperature, humidity, and ambient wind on the heat pump's frosting tendency, calculates frosting risk specific values, and determines the heat pump's frosting tendency. The zone management module analyzes the operating status and the impact of particle adhesion on the heat pump fins, determines abnormal feature values ​​for each fin, and identifies abnormal areas of the heat pump. This allows for targeted analysis of each area and the issuance of early warning signals for abnormal areas, enabling defrosting treatment using solar energy. This invention achieves intelligent control of the greenhouse by performing pre-emptive frosting anomaly analysis, locating abnormal areas, and issuing early warnings for them.

[0050] In particular, by comprehensively analyzing temperature, humidity, and ambient wind, the specific value of frost risk for the heat pump is calculated. Even when no frost risk occurs, abnormal frost tendency is predicted. In reality, at the same temperature, higher humidity means more water vapor in the air available for condensation, leading to faster frost formation and growth, potentially even causing abnormal frost. At this point, ambient wind further reduces the evaporator surface temperature through convection heat transfer and continuously pushes moisture-rich air towards the cold surface, significantly exacerbating the frost tendency. Crucially, these three factors do not act independently but are coupled, producing a further superimposed effect. For example… In low-temperature, high-humidity environments, ambient wind nonlinearly amplifies the risk of frost formation, leading to rapid frost thickening. Existing passive, post-event defrosting technologies have inherent lag and struggle to cope with this multi-factor coupled frost process, resulting in increased reaction time and reduced defrosting efficiency. Therefore, this invention predicts the frost risk of heat pumps through comprehensive analysis of temperature, humidity, and ambient wind, shifting from "post-event defrosting" to "pre-event prevention." This ensures the efficient and stable operation of heat pumps under complex weather conditions, providing a data foundation for subsequent regional analysis and enabling intelligent control of greenhouses.

[0051] In particular, for heat pumps with a strong tendency to frost, further regional analysis of the frost situation is conducted to address specific issues. In practice, traditional heat pump defrosting strategies generally adopt a "global defrosting" mode, which means that once frost signs are detected, the system is processed globally. This extensive management has significant drawbacks when facing localized frost problems: on the one hand, it causes huge energy waste in the synchronous heating of frost-free or slightly frost-free areas, resulting in low defrosting efficiency; on the other hand, frequent interruptions and restarts of overall operation will exacerbate equipment wear and tear and cause periodic fluctuations in heating temperature, disrupting the stability of the greenhouse environment. Based on this, this invention considers regional analysis of frost tendency. By integrating real-time heat pump operating data with the adhesion status of fin particles, high-risk areas of frost can be identified and located, achieving targeted control from "global" to "local". Energy and actions are precisely delivered to the source of the problem, thereby maximizing resource conservation, improving defrosting efficiency, and effectively maintaining the continuity of overall system operation and the stability of the greenhouse environment, thus achieving intelligent control of the greenhouse.

[0052] In particular, based on the operation of the heat pump and the particle adhesion on the heat pump fins, this invention conducts a regional analysis of the frost tendency of the heat pump from the data. In reality, there is a significant coupling effect, which jointly induces and exacerbates the non-uniform distribution of the frost layer in the spatial dimension. Specifically, at the micro level, the particulate matter adhesion layer not only increases the heat transfer resistance of the fin surface, but more importantly, it changes the surface properties and free energy of the material, making the contaminated area preferentially become a water vapor condensation nucleus, significantly reducing the nucleation barrier of ice crystals, and thus rapidly evolving into the core area for frost growth. At the macro level, fluctuations in start-up and shutdown rates or local velocity differences caused by load changes during heat pump operation can directly lead to weak airflow organization. The sharp decline in heat exchange efficiency in this area makes the fin surface temperature more likely to drop below the dew point or even the freezing point. When this operational instability and the deterioration of surface characteristics caused by particulate matter adhesion are superimposed in space, a strong synergistic effect is produced. Ultimately, this results in the frosting rate, frost thickness, and frosting probability of the area deviating significantly from normal expectations, exhibiting an abnormal state. Based on this, this invention considers conducting regional analysis on heat pumps with strong abnormal frosting tendencies to identify abnormal areas, thereby reducing defrosting energy consumption, improving defrosting efficiency, and achieving intelligent control of the greenhouse.

[0053] In particular, for areas requiring regulation, solar energy is adaptively deployed for targeted energy replenishment, reducing the risk of frost formation in key areas and minimizing the energy consumption of the heat pump. In practice, the heat required for defrosting essentially originates from the electrical energy consumed by the heat pump system. By switching the refrigerant flow through a four-way reversing valve, the system briefly transitions from "heating mode" to "cooling mode." During this time, the indoor heat exchanger becomes a condenser, absorbing heat from the indoor air (leading to a drop in greenhouse temperature), while the outdoor heat exchanger (i.e., the evaporator that needs defrosting) becomes a condenser, using heat from the indoor environment and the heat generated by the compressor to melt the frost layer. This defrosting method can lead to greenhouse instability. Therefore, this invention considers pre-deploying and storing solar energy to reduce the heat pump system's own energy consumption and energy fluctuations during the defrosting process. This ensures defrosting efficiency while maintaining the continuous stability of the thermal environment inside the greenhouse, thus achieving intelligent control of the greenhouse. Attached Figure Description

[0054] Figure 1 A schematic diagram of the structure of the solar-assisted air-source heat pump greenhouse intelligent management system according to an embodiment of the invention;

[0055] Figure 2 A logic block diagram illustrating the frosting tendency of the heat pump according to an embodiment of the invention;

[0056] Figure 3 This is a logic block diagram for determining the abnormal region of the heat pump according to an embodiment of the invention. Detailed Implementation

[0057] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0058] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0059] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0060] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a solar-assisted air-source heat pump greenhouse intelligent management system according to an embodiment of the invention. The solar-assisted air-source heat pump greenhouse intelligent management system according to an embodiment of the invention includes:

[0061] The multi-source environmental sensing module includes a temperature monitoring unit, a humidity monitoring unit, an operation monitoring unit, an environmental wind monitoring unit, and an image monitoring unit.

[0062] The feature acquisition module, which is connected to the multi-source environmental sensing module, is used to calculate the temperature difference value based on the temperature monitoring unit, the humidity difference value based on the humidity monitoring unit, the operation characterization coefficient based on the operation monitoring unit, the environmental wind influence coefficient based on the environmental wind monitoring unit, and the particle adhesion influence coefficient based on the image monitoring unit.

[0063] An auxiliary analysis module, connected to the feature acquisition module, is used to determine the specific value of the frost risk of the heat pump based on the temperature difference value, humidity difference value and the environmental wind influence coefficient, so as to classify the frost tendency of the heat pump.

[0064] The partition management module, which is connected to the feature acquisition module and the auxiliary analysis module, calculates the abnormal feature value of the heat pump fins in response to the strong frosting tendency of the heat pump, combining the frosting risk specific value, the operation characterization coefficient and the particle adhesion influence coefficient, in order to determine the abnormal area of ​​the heat pump.

[0065] The collaborative control module, which is connected to the partition management module, is used to issue early warning signals for abnormal areas and to call solar energy according to the abnormal characteristic values ​​to perform defrosting treatment on the abnormal areas.

[0066] Specifically, the temperature monitoring unit is used to monitor the ambient temperature, the required temperature of the greenhouse, and the solar energy emission temperature. The method of monitoring the temperature is not limited. For example, a high-precision temperature sensor can be used to measure the ambient temperature in contact, a distributed digital temperature sensor can be deployed inside the greenhouse to obtain the required temperature of the greenhouse, and an infrared temperature measuring device can be used to monitor the working fluid temperature of key nodes such as the collector outlet in the solar circuit in real time to determine the solar energy emission temperature. Of course, those skilled in the art can also use other methods to obtain the required data, as long as they are reasonable, which will not be elaborated here.

[0067] Specifically, the humidity monitoring unit is used to monitor ambient humidity and greenhouse humidity. The method of humidity monitoring is not limited. For example, a humidity sensor can be used to collect data. Of course, those skilled in the art can also use other methods to obtain the required data, as long as they are reasonable. This will not be elaborated further.

[0068] Specifically, the operation monitoring unit is used to monitor the start and stop values ​​of the heat pump. The method for monitoring the start and stop status is not limited. For example, a vibration sensor installed on the heat pump can be used, and vibration is recorded as a start. Of course, those skilled in the art can also use other methods to obtain the required data, as long as they are reasonable, which will not be elaborated here.

[0069] Specifically, the environmental wind monitoring unit is used to monitor the direction and speed of the environmental wind. There are no restrictions on the method of monitoring the environmental wind. For example, it can be measured using a meteorological-grade ultrasonic anemometer or a mechanical cup sensor, or it can be obtained from publicly available monitoring data released by the meteorological department. Any reasonable method will suffice, and will not be elaborated further.

[0070] Specifically, the image monitoring unit is used to monitor the particle distribution on the heat pump fins. During monitoring, a high-definition camera can be used to capture images of the particles to determine their distribution, which will not be elaborated further.

[0071] Specifically, the warning signal can be an audio signal or a light signal, which can be determined by those skilled in the art based on the actual situation, and will not be elaborated further here.

[0072] Specifically, the feature acquisition module calculates temperature difference values ​​and humidity difference values, including,

[0073] The absolute value used to determine the difference between the ambient temperature and the required greenhouse temperature is the first temperature difference value;

[0074] The absolute value of the difference between the solar energy emission temperature and the greenhouse required temperature is used to determine the second temperature difference value;

[0075] The absolute value of the difference between the first temperature difference value and the second temperature difference value is used to determine the temperature difference value.

[0076] The absolute value used to determine the difference between ambient humidity and greenhouse humidity is the humidity difference value.

[0077] Specifically, the feature acquisition module calculates the runtime representation coefficients, including:

[0078] The ratio of the operating start / stop value to the baseline operating start / stop value is used to determine the operating characterization coefficient.

[0079] The start-stop value is the ratio of the number of times the heat pump starts and stops within a predetermined time period to the predetermined time period.

[0080] Specifically, the baseline start-stop value is calculated in advance. Several historical heat pump start-stop values ​​that have not experienced frost are obtained in advance, and the average of the sum of all start-stop values ​​is determined as the baseline start-stop value.

[0081] Specifically, the scheduled time is one operating cycle. In practice, one operating cycle is determined to be 24 hours. Of course, those skilled in the art can also determine the scheduled time according to the actual situation, which will not be elaborated here.

[0082] Specifically, the feature acquisition module calculates the environmental wind impact coefficient, including:

[0083] Used to determine the angle between the fins and the ambient wind direction when the heat pump starts up;

[0084] The first effective factor is used to determine the ratio of the included angle to the reference included angle;

[0085] The absolute value of the difference between the heat pump operating speed and the wind speed is used to calculate the wind speed difference.

[0086] The ratio of the wind speed difference to the heat pump operating speed is used as a second effective factor to determine the value.

[0087] The average of the sum of the first effective factor and the second effective factor is used to determine the environmental wind influence coefficient.

[0088] Specifically, the reference angle is calculated in advance. Several historical angles of fins that have not experienced frost formation are obtained in advance, and the average of the sum of these angles is determined as the reference angle.

[0089] Specifically, there are no restrictions on the source of the heat pump's operating speed. For example, it can be obtained directly from the set operating data, or it can be obtained from the smart sensors installed on the heat pump. Any reasonable method will suffice, and this will not be elaborated further.

[0090] Specifically, the feature acquisition module calculates the particle adhesion influence coefficient, including,

[0091] Used to scan the abnormal particles on each of the heat pump fins and determine the three-dimensional coordinates of each of the abnormal particles;

[0092] Used to calculate the density of each abnormal particle on each of the heat pump fins based on the three-dimensional coordinates;

[0093] The average value of each density is used to determine the particle adhesion influence coefficient of the fin;

[0094] The abnormal particles are those with a diameter larger than a predetermined diameter.

[0095] Specifically, there is no limitation on the method of density calculation. For example, the K-nearest neighbor method can be used to calculate the density. The K nearest neighbors of the particle are determined, the distance to the Kth neighbor is calculated, and a sphere is drawn with this distance as the radius. The reciprocal of the volume of the sphere is the density of the particle. Of course, those skilled in the art can also use other methods to determine the density, as long as they are reasonable. This will not be elaborated further.

[0096] Specifically, the predetermined diameter is calculated in advance, and several particle diameters that will not affect frosting are obtained in advance, and the average value of each particle diameter is determined as the predetermined diameter.

[0097] Specifically, the auxiliary analysis module determines the specific value of the frost risk of the heat pump, including,

[0098] The ratio of the temperature difference value to the baseline temperature difference value is used to determine the first risk factor.

[0099] The ratio of the humidity difference value to the baseline humidity difference value is used to determine the second risk factor;

[0100] The weighted sum of the first risk factor, the second risk factor, and the environmental wind influence coefficient is used to determine the frost risk specific value.

[0101] Specifically, the baseline temperature difference value is calculated in advance. Several historical temperature difference values ​​when no frost occurred are obtained in advance, and the average of the sum of the historical temperature difference values ​​is determined as the baseline temperature difference value.

[0102] Specifically, the baseline humidity difference value is determined by pre-obtaining several historical humidity difference values ​​when frost has not occurred, and then averaging the sum of these humidity difference values.

[0103] Specifically, the sum of the weighting coefficients of the first risk factor, the second risk factor, and the environmental wind influence coefficient is 1. When adjusting the weighting coefficients, considering that humidity is a key factor affecting frost and has a greater impact on frost, the weighting coefficient of the first risk factor is set to 0.3, the weighting coefficient of the second risk factor is set to 0.4, and the weighting coefficient of the environmental wind influence coefficient is set to 0.3.

[0104] Specifically, by comprehensively analyzing temperature, humidity, and ambient wind, the specific value of frost risk for the heat pump is calculated. When no frost risk occurs, abnormal frost tendency is predicted. In reality, at the same temperature, higher humidity means more water vapor in the air that can condense, leading to faster frost formation and growth, potentially even causing abnormal frost. At this time, ambient wind further reduces the evaporator surface temperature through convection heat transfer and continuously pushes moisture-rich air towards the cold surface, significantly exacerbating the frost tendency. Crucially, these three factors do not act independently but are coupled, producing a further superimposed effect, for example… In low-temperature, high-humidity environments, ambient winds nonlinearly amplify the risk of frost formation, leading to rapid frost thickening. Existing passive, post-event defrosting technologies have inherent lag and struggle to cope with this multi-factor coupled frost process, resulting in increased reaction time and reduced defrosting efficiency. Therefore, this invention predicts the frost risk of heat pumps through comprehensive analysis of temperature, humidity, and ambient winds, shifting from "post-event defrosting" to "pre-event prevention." This ensures the efficient and stable operation of heat pumps under complex weather conditions, providing a data foundation for subsequent regional analysis and enabling intelligent control of greenhouses.

[0105] Please see Figure 2 , Figure 2 This is a logic block diagram illustrating the classification of the frosting tendency of the heat pump according to an embodiment of the invention. Specifically, the auxiliary analysis module classifies the frosting tendency of the heat pump, wherein...

[0106] If the specific value of the frosting risk is greater than the threshold value of the frosting risk, then the heat pump is classified as having a strong frosting tendency.

[0107] If the frost risk specific value is less than or equal to the frost risk specific value threshold, then the heat pump is classified as having a weak frost tendency.

[0108] Specifically, the frost risk idiosyncratic threshold characterizes a boundary under certain conditions where the heat pump will frost. It is calculated in advance by obtaining several historical frost risk idiosyncratic values ​​corresponding to several frost occurrences. The average value of the sum of the historical frost risk idiosyncratic values ​​and the product of the risk coefficient are determined as the frost risk idiosyncratic threshold. The risk coefficient is selected in the range [0.8, 1.0]. In practice, to improve the identification accuracy, the risk coefficient is determined to be 0.9.

[0109] Specifically, for heat pumps with a strong tendency to frost, this invention further analyzes the frost situation regionally to address specific issues. In practice, traditional heat pump defrosting strategies generally employ a "global defrosting" approach, where the system is processed globally upon detecting frost. This extensive management approach has significant drawbacks when facing localized frost problems: firstly, it wastes a large amount of energy by simultaneously heating frost-free or slightly frost-free areas, resulting in low defrosting efficiency; secondly, frequent interruptions and restarts of overall operation exacerbate equipment wear and cause periodic fluctuations in heating temperature, disrupting the stability of the greenhouse environment. Therefore, this invention considers regional analysis of frost tendencies. By integrating real-time heat pump operating data with the adhesion status of fin particles, high-risk frost areas are identified and located, enabling targeted control from a "global" to a "local" perspective. This precisely delivers energy and action to the source of the problem, maximizing resource conservation, improving defrosting efficiency, and effectively maintaining the continuity of overall system operation and the stability of the greenhouse environment, thus achieving intelligent control of the greenhouse.

[0110] Specifically, the partition management module calculates the abnormal characteristic values ​​of the heat pump fins, including:

[0111] The ratio of the frost risk specific value to the benchmark frost risk specific value is used to determine the first anomaly factor;

[0112] The ratio of the particle adhesion influence coefficient to the benchmark particle adhesion influence coefficient is used to determine the second anomaly factor.

[0113] The weighted sum of the first abnormal factor, the second abnormal factor, and the running characterization coefficient is used to determine the abnormal feature value.

[0114] Specifically, the baseline frost risk specificity values ​​are the baseline difference, the baseline humidity difference, and the baseline environmental wind influence coefficient corresponding to the frost risk specificity values.

[0115] Specifically, the baseline particle adhesion influence coefficient is calculated in advance. Several historical particle adhesion influence coefficients corresponding to regional frost conditions are obtained in advance, and the average of the sum of the historical particle adhesion influence coefficients is determined as the baseline particle adhesion influence coefficient.

[0116] Specifically, the baseline environmental wind influence coefficient is calculated in advance. Several historical environmental wind influence coefficients for times when frost has not occurred are obtained in advance, and the average of the sum of the historical environmental wind influence coefficients is determined as the baseline environmental wind influence coefficient.

[0117] Specifically, the sum of the weighting coefficients of the first abnormal factor, the second abnormal factor, and the operational characterization coefficient is 1. When adjusting the weighting coefficients, considering that the adhesion of particles directly affects the severity of frosting and has a greater impact on causing regional frosting, the weighting coefficient of the first abnormal factor is set to 0.3, the weighting coefficient of the second abnormal factor is set to 0.4, and the weighting coefficient of the operational characterization coefficient is set to 0.3.

[0118] Specifically, this invention analyzes the frosting tendency of heat pumps regionally based on data from the operation of the heat pump and the particle adhesion on the heat pump fins. In reality, there is a significant coupling effect, which jointly induces and exacerbates the non-uniform distribution of the frost layer in the spatial dimension. Specifically, at the microscopic level, the particulate matter adhesion layer not only increases the heat transfer resistance of the fin surface, but more importantly, it changes the surface properties and free energy of the material, making the contaminated area preferentially become a water vapor condensation nucleus, significantly reducing the nucleation barrier of ice crystals, and thus rapidly evolving into the core area for frost growth. At the macro level, fluctuations in start-up and shutdown rates or local velocity differences caused by load changes during heat pump operation can directly lead to weak airflow organization. The sharp decline in heat exchange efficiency in this area makes the fin surface temperature more likely to drop below the dew point or even the freezing point. When this operational instability and the deterioration of surface characteristics caused by particulate matter adhesion are superimposed in space, a strong synergistic effect is produced. Ultimately, this results in the frosting rate, frost thickness, and frosting probability of the area deviating significantly from normal expectations, exhibiting an abnormal state. Based on this, this invention considers conducting regional analysis on heat pumps with strong abnormal frosting tendencies to identify abnormal areas, thereby reducing defrosting energy consumption, improving defrosting efficiency, and achieving intelligent control of the greenhouse.

[0119] Please see Figure 3 , Figure 3 This is a logic block diagram illustrating the determination of abnormal regions of the heat pump according to an embodiment of the invention. Specifically, the zone management module determines the abnormal regions of the heat pump, wherein...

[0120] If the abnormal feature value is greater than the abnormal feature value threshold, then the fins of the heat pump are determined to be an abnormal area.

[0121] If the abnormal feature value is less than or equal to the abnormal feature value threshold, then the fins of the heat pump are determined to be in the normal region.

[0122] Specifically, the abnormal feature value threshold characterizes the boundary value of regional frost on the heat pump. It is calculated in advance by obtaining several historical abnormal feature values ​​corresponding to regional frost occurrences in advance. The product of the average value of the sum of the historical abnormal feature values ​​and the abnormal coefficient is determined as the abnormal feature value threshold. The abnormal coefficient is selected in the range [0.8, 1.0]. In practice, in order to ensure timely response, the abnormal coefficient is determined to be 0.9.

[0123] Specifically, the coordinated control module allocates solar energy based on the abnormal characteristic values, wherein...

[0124] The value of the solar energy invoked is proportional to the abnormal characteristic value.

[0125] Specifically, solar energy can be energy stored in an energy storage system, with a pre-set baseline solar energy value, wherein...

[0126] If the abnormal feature value is greater than the abnormal feature value threshold but less than 1.5 times the abnormal feature value threshold, then the value of the solar energy called is 1.2 times the base solar energy value.

[0127] If the abnormal feature value is greater than or equal to 1.5 times the abnormal feature value threshold and less than 2 times the abnormal feature value threshold, then the value of the solar energy called is 1.6 times the base solar energy value.

[0128] It is understandable that the specific value of the reference solar energy is not limited. In practice, the reference solar energy is determined to be the value calculated in advance. Several historical energies required for defrosting are obtained in advance, and the average value of the sum of these historical energies is determined as the reference solar energy. Of course, those skilled in the art can also determine it according to the actual situation, as long as it is reasonable. This will not be elaborated further.

[0129] Specifically, the coordinated control module also includes a distributed heating unit. Based on the abnormal area location information output by the zoning management module, it controls the activation of the heating unit in the corresponding area and delivers the solar energy to the heating unit in the form of electricity or heat transfer medium to achieve targeted defrosting of the abnormal area.

[0130] Specifically, for areas requiring regulation, solar energy is adaptively deployed for targeted energy replenishment, reducing the risk of frost formation in key areas and minimizing the energy consumption of the heat pump. In practice, the heat required for defrosting essentially originates from the electrical energy consumed by the heat pump system. By switching the refrigerant flow through a four-way reversing valve, the system briefly transitions from "heating mode" to "cooling mode." During this time, the indoor heat exchanger becomes a condenser, absorbing heat from the indoor air (leading to a drop in greenhouse temperature), while the outdoor heat exchanger (i.e., the evaporator requiring defrosting) becomes a condenser, utilizing heat from the indoor environment and the heat generated by the compressor to melt the frost layer. This defrosting method can lead to greenhouse instability. Therefore, this invention considers pre-deploying and storing solar energy to reduce the heat pump system's own energy consumption and energy fluctuations during the defrosting process. This ensures defrosting efficiency while maintaining the continuous stability of the greenhouse's thermal environment, thus achieving intelligent control of the greenhouse.

[0131] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0132] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A solar-assisted air-source heat pump greenhouse intelligent management system, characterized in that, include: The multi-source environmental sensing module includes a temperature monitoring unit, a humidity monitoring unit, an operation monitoring unit, an environmental wind monitoring unit, and an image monitoring unit. The feature acquisition module, which is connected to the multi-source environmental sensing module, is used to calculate the temperature difference value based on the temperature monitoring unit, the humidity difference value based on the humidity monitoring unit, the operation characterization coefficient based on the operation monitoring unit, the environmental wind influence coefficient based on the environmental wind monitoring unit, and the particle adhesion influence coefficient based on the image monitoring unit. An auxiliary analysis module, connected to the feature acquisition module, is used to determine the specific value of the frost risk of the heat pump based on the temperature difference value, humidity difference value and the environmental wind influence coefficient, so as to classify the frost tendency of the heat pump. The partition management module, which is connected to the feature acquisition module and the auxiliary analysis module, calculates the abnormal feature value of the heat pump fins in response to the strong frosting tendency of the heat pump, combining the frosting risk specific value, the operation characterization coefficient and the particle adhesion influence coefficient, in order to determine the abnormal area of ​​the heat pump. The collaborative control module, which is connected to the partition management module, is used to issue early warning signals for abnormal areas and to call solar energy according to the abnormal characteristic values ​​to perform defrosting treatment on the abnormal areas.

2. The solar-assisted air-source heat pump greenhouse intelligent management system according to claim 1, characterized in that, The feature acquisition module calculates the temperature difference value and the humidity difference value. include, The absolute value used to determine the difference between the ambient temperature and the required greenhouse temperature is the first temperature difference value; The absolute value of the difference between the solar energy emission temperature and the greenhouse required temperature is used to determine the second temperature difference value; The absolute value of the difference between the first temperature difference value and the second temperature difference value is used to determine the temperature difference value. The absolute value used to determine the difference between ambient humidity and greenhouse humidity is the humidity difference value.

3. The solar-assisted air-source heat pump greenhouse intelligent management system according to claim 1, characterized in that, The feature acquisition module calculates the runtime characterization coefficients, including: The ratio of the operating start / stop value to the baseline operating start / stop value is used to determine the operating characterization coefficient. The start-stop value is the ratio of the number of times the heat pump starts and stops within a predetermined time period to the predetermined time period.

4. The solar-assisted air-source heat pump greenhouse intelligent management system according to claim 1, characterized in that, The feature acquisition module calculates the environmental wind influence coefficient, including: Used to determine the angle between the fins and the ambient wind direction when the heat pump starts up; The first effective factor is used to determine the ratio of the included angle to the reference included angle; The absolute value of the difference between the heat pump operating speed and the wind speed is used to calculate the wind speed difference. The ratio of the wind speed difference to the heat pump operating speed is used as a second effective factor to determine the value. The average of the sum of the first effective factor and the second effective factor is used to determine the environmental wind influence coefficient.

5. The solar-assisted air-source heat pump greenhouse intelligent management system according to claim 1, characterized in that, The feature acquisition module calculates the particle adhesion influence coefficient, including, Used to scan the abnormal particles on each of the heat pump fins and determine the three-dimensional coordinates of each of the abnormal particles; Used to calculate the density of each abnormal particle on each of the heat pump fins based on the three-dimensional coordinates; The average value of each density is used to determine the particle adhesion influence coefficient of the fin; The abnormal particles are those with a diameter larger than a predetermined diameter.

6. The solar-assisted air-source heat pump greenhouse intelligent management system according to claim 2, characterized in that, The auxiliary analysis module determines the specific value of the frost risk of the heat pump. include, The ratio of the temperature difference value to the baseline temperature difference value is used to determine the first risk factor. The ratio of the humidity difference value to the baseline humidity difference value is used to determine the second risk factor; The weighted sum of the first risk factor, the second risk factor, and the environmental wind influence coefficient is used to determine the frost risk specific value.

7. The solar-assisted air-source heat pump greenhouse intelligent management system according to claim 1, characterized in that, The auxiliary analysis module classifies the frost tendency of the heat pump, wherein, If the specific value of the frosting risk is greater than the threshold value of the frosting risk, then the heat pump is classified as having a strong frosting tendency. If the frost risk specific value is less than or equal to the frost risk specific value threshold, then the heat pump is classified as having a weak frost tendency.

8. The solar-assisted air-source heat pump greenhouse intelligent management system according to claim 1, characterized in that, The partition management module calculates the abnormal characteristic values ​​of the heat pump fins. include, The ratio of the frost risk specific value to the benchmark frost risk specific value is used to determine the first anomaly factor; The ratio of the particle adhesion influence coefficient to the benchmark particle adhesion influence coefficient is used to determine the second anomaly factor. The weighted sum of the first abnormal factor, the second abnormal factor, and the running characterization coefficient is used to determine the abnormal feature value.

9. The solar-assisted air-source heat pump greenhouse intelligent management system according to claim 1, characterized in that, The zone management module identifies the abnormal areas of the heat pump, wherein, If the abnormal feature value is greater than the abnormal feature value threshold, then the fins of the heat pump are determined to be an abnormal area. If the abnormal feature value is less than or equal to the abnormal feature value threshold, then the fins of the heat pump are determined to be in the normal region.

10. The solar-assisted air-source heat pump greenhouse intelligent management system according to claim 1, characterized in that, The coordinated control module calls upon solar energy based on the abnormal characteristic value, wherein, The value of the solar energy invoked is proportional to the abnormal characteristic value.

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