Air inlet cooling control system for air-cooled heat pump of air conditioner

Abstract

The invention discloses an air-conditioner air-cooled heat pump inlet air cooling control system, and relates to the technical field of air-cooled heat pump control. According to the air-conditioner air-cooled heat pump inlet air cooling control system, an air-conditioner monitoring area is set, historical measurement data in the area are collected and processed through a data processing method, and the processed historical measurement data are obtained; based on historical measurement data and a linear programming algorithm, an initial model for predicting the cooling effect is constructed, meanwhile, a temperature compensation correction mechanism is introduced to train the model, a final cooling effect prediction model is obtained, a cooling control working mode is set based on the final prediction model, and the current cooling requirement is judged through the model; and finally, in the continuous operation process of the system, key data are monitored in real time, if abnormal information is monitored, the system automatically records and generates a log file, control intervention is performed according to log feedback, and the operation performance and reliability of the air-cooled heat pump are comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of air-cooled heat pump control technology, specifically to an air-cooled heat pump intake cooling control system for air conditioning. Background Technology

[0002] Air-cooled heat pump air conditioning units are widely used in various buildings due to their elimination of cooling towers, ease of installation, and ability to provide both cooling and heating in winter and summer. However, the outdoor intake air temperature and humidity directly affect the efficiency and operating performance of their heat exchangers, and existing technologies have significant drawbacks. In the high temperatures of summer, the intake air temperature approaches the refrigerant condensation temperature, causing a sharp drop in heat exchange efficiency. High-load operation of the compressor leads to soaring energy consumption or even shutdown. In the low-temperature and high-humidity environment of winter, the heat exchanger is prone to frosting. Existing defrosting technologies consume extra electricity and have poor heating reliability. Meanwhile, most units rely solely on fan speed to regulate air intake, which cannot adapt to complex operating conditions such as day-night temperature differences and sudden weather changes. Traditional auxiliary cooling methods such as spraying and misting are either water-intensive and prone to scaling or costly and easily freeze and crack, making it difficult to operate stably throughout the year. There is an urgent need for a highly efficient and energy-saving air intake cooling control solution to break through the bottleneck. Summary of the Invention

[0003] The purpose of this invention is to provide an air-cooled heat pump intake cooling control system for air conditioning, which solves the problems existing in the background art.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an air-cooled heat pump air intake cooling control system for air conditioning, specifically including the following steps: S1, set the air conditioning monitoring area and collect historical measurement data within the set air conditioning monitoring area. After the data collection is completed, the collected historical measurement data is processed by the data processing method to obtain the processed historical measurement data. The historical measurement data includes: historical indoor and outdoor ambient temperature, historical relative humidity, and historical condenser condensing temperature data; S2, based on the processed historical measurement data and linear programming algorithm, construct an initial model to predict the cooling effect; S3, based on the constructed initial model, introduces a temperature compensation correction mechanism to train the model and obtain the final cooling effect prediction model; S4 sets the cooling control working mode based on the final prediction model, determines the current cooling demand through the model, selects the corresponding operating mode, and drives the air-cooled heat pump equipment. S5 monitors key data in real time during continuous system operation. If abnormal information is detected, the system automatically records and generates a log file, and intervenes based on the log feedback.

[0005] Preferably, the process of setting up an air conditioning monitoring area and collecting historical measurement data within that area, followed by processing the collected historical measurement data using a data processing method to obtain processed historical measurement data, includes the following steps: Based on the actual installation scenario of the air-cooled heat pump, an air conditioning monitoring area is set up, and humidity sensors and humidity detection equipment are installed within the area. Based on humidity sensors and temperature monitoring equipment, historical measurement data is collected, including historical indoor and outdoor ambient temperatures, historical relative humidity, and historical condenser condensing temperature data. Based on the collected historical data and actual needs, the following data ranges are set: indoor temperature and humidity regulation must meet user needs, condenser condensing temperature must be controlled within a safe threshold range, and when the collected data exceeds the range, it is judged as invalid data and removed, thus obtaining the filtered historical data. Based on the filtered historical data, the data is processed using data standardization methods to obtain the processed historical measurement data; The standardization formula is as follows: ; in The values ​​are standardized. For the collected parameter data, This is the average of all data for this parameter. This represents the standard deviation of all data for this parameter.

[0006] Preferably, the initial model for predicting the cooling effect based on processed historical measurement data and a linear programming algorithm includes the following steps: S21, Establish a dataset based on the processed historical data. ,in Outdoor temperature Indoor temperature, This refers to the indoor and outdoor relative humidity. This refers to the condenser's condensing temperature. S22. Based on the influence of indoor and outdoor temperature and humidity on air-cooled heat pumps, a temperature and humidity relationship function is established, and the comprehensive influence value between temperature and humidity is obtained based on the relationship function. The temperature and humidity relationship function is shown below: ; in The value represents the overall impact of temperature. and These are the weighting coefficients for the effects of temperature difference and humidity, respectively. This is the equivalent conversion factor between humidity and temperature; S23, based on the data in the dataset and the linear programming algorithm, sets the linear correlation variable between indoor and outdoor temperatures as... The combined influence of temperature and humidity and the linear correlation between condensation temperature and other variables are: And assign weight coefficients to each type of data in the dataset, and set Outdoor temperature is the weighting factor. Indoor temperature weighting coefficient, The weighting coefficient is used to determine the overall impact value. Let be the weighting coefficient for the condenser condensing temperature, and satisfy . And set the target temperature as ; The variables with linear correlation between indoor and outdoor temperatures are shown below: ; The system parameter-related variables are shown below: ; S24, based on the linearly correlated variables of indoor and outdoor temperatures and the related variables of system parameters, sets a comprehensive linear programming function with the objectives of maximizing cooling efficiency and minimizing energy consumption, and sets the weight of cooling efficiency as follows: Energy consumption weight is ; The comprehensive linear programming function is shown below: ; in To predict the cooling effect value; S25, substitute the data from the dataset into the linear programming function mentioned above, and establish the mapping relationship between the data and the cooling effect based on the linear programming function to form an initial model for predicting the cooling effect.

[0007] Preferably, the step of training the model based on the constructed initial model and introducing a temperature compensation correction mechanism includes the following steps: S31: Collect real-time environmental data, including dynamically changing indoor and outdoor temperatures, relative humidity, and condenser condensing temperatures, and process the data according to step S1 to form a training sample dataset. S32, Set the outdoor temperature compensation coefficient The indoor temperature compensation coefficient is Its value is determined by the deviation between the indoor and outdoor ambient temperatures and the actual required temperature; The outdoor temperature compensation mechanism is as follows: when the actual outdoor temperature is higher than the required temperature, a positive value is taken to improve the overall cooling effect; when the actual outdoor temperature is lower than the required temperature, a negative value is taken to reduce excessive cooling caused by excessively low outdoor temperature. The indoor temperature compensation mechanism is as follows: when the actual indoor temperature is higher than the required temperature, a positive value is taken to strengthen cooling compensation; when the actual indoor temperature is equal to the required temperature, a zero value is taken to pause cooling compensation and maintain the current temperature stability; when the actual indoor temperature is lower than the required temperature, a negative value is taken to weaken indoor cooling compensation. S33 trains the model based on the set temperature compensation mechanism and training sample set data.

[0008] Preferably, training the model based on the established temperature compensation mechanism and training sample set data includes the following steps: Based on the training sample set data, the predicted cooling effect value is obtained by substituting the data from the training sample set into the initial model, and then compared with the actual cooling effect value to obtain the deviation value. ; Based on the deviation value and the set temperature compensation coefficient, a comprehensive correction function that integrates the deviation value and temperature compensation is established; The correction function is as follows: ; in This is the model correction amount. , Divided into outdoor and indoor temperature compensation weighting coefficients, Let be the weighting coefficient for the deviation value, and satisfy . ; Based on the correction function and the established initial model, the two are combined to obtain the corrected initial model function for predicting the cooling effect; The corrected initial model function for predicting the cooling effect is shown below: ; in This is the corrected cooling effect value; Repeat the above steps, set a threshold for the deviation between the model's predicted value and the actual value, and iterate the training until the deviation between the model's predicted value and the actual value reaches the set threshold. The model obtained at this point is the final cooling effect prediction model.

[0009] Preferably, the step of setting the cooling control operating mode based on the final prediction model, determining the current cooling demand through the model, selecting the corresponding operating mode, and driving the air-cooled heat pump equipment to adjust includes the following steps: Real-time data collection includes outdoor temperature, indoor temperature, indoor and outdoor relative humidity, and the current condenser temperature. The data is then processed according to step S1 to obtain the processed data. The processed data is then fed into the final cooling effect prediction model to obtain the current cooling effect value. Based on the obtained cooling effect value and the actual cooling requirements, the cooling effect range and operating mode are set: the low cooling threshold is set as less than... The corresponding operating mode is energy-saving mode, and the medium cooling threshold judgment range is... The corresponding working mode is normal mode, and the high cooling threshold determination condition is greater than The corresponding working mode is the enhanced mode; Based on the obtained cooling effect value and the set cooling effect judgment range, select the current working mode of the air-cooled heat pump and drive the air-cooled heat pump to execute according to the selected working mode. During the cooling process of the air-cooled heat pump, the system continuously collects data and feeds it back to the final prediction model, and dynamically adjusts the working mode based on the real-time data.

[0010] Preferably, during the continuous operation of the system, real-time monitoring of key data is performed. If abnormal information is detected, the system automatically records and generates a log file, and control intervention is based on log feedback, including the following steps: During system operation, indoor and outdoor temperature and humidity, as well as condenser temperature data are monitored in real time and compared with two preset thresholds. If the data exceeds the range, it is judged as abnormal information and uploaded to the system. Based on the received abnormal information, the system records the abnormal value and runtime data, generates a log file, and notifies the administrator to handle the abnormality control. During the anomaly handling process, the system records the processing and recovery status, and incorporates the anomaly data and processing records into the model optimization library to generate processing logs, providing a basis for system optimization.

[0011] This embodiment also discloses an air conditioning air-cooled heat pump intake air cooling control system, including: a data acquisition module, a central control module, an intake air cooling execution module, and a monitoring and maintenance module; The data acquisition module is used to collect indoor and outdoor temperature and humidity data, condenser condensing temperature data, and transmit the data to the central control module after standardization processing. The central control module is used to build and train a cooling effect prediction model based on the received data and linear programming algorithm, combine the model to determine the current cooling demand, and select the appropriate working mode to issue instructions to the air intake cooling execution module. The air intake cooling execution module is used to receive control commands from the central control module, drive the air-cooled heat pump equipment to adjust the operating mode, and execute the air intake cooling operation. The monitoring and maintenance module is used to monitor system operation data in real time, identify and record abnormal information, notify the administrator to handle abnormal situations, and incorporate relevant data into the model optimization library to assist in system iterative optimization.

[0012] The beneficial effects of this invention are as follows: By measuring indoor and outdoor ambient temperature, relative humidity, and condenser condensing temperature data, and processing the data using data processing methods, processed measurement data is obtained. Then, based on the measurement data and linear programming algorithm, an initial model for predicting the cooling effect is constructed. Simultaneously, based on the constructed initial model, a temperature compensation correction mechanism is introduced to train the model, resulting in a final cooling effect prediction model. Then, based on the final prediction model, a cooling control working mode is set. The model determines the current cooling demand and selects the corresponding operating mode to drive the air-cooled heat pump equipment. Finally, during the continuous operation of the system, key data is monitored in real time. If abnormal information is detected, the system automatically records and generates a log file, and controls and intervenes based on log feedback, comprehensively improving the operating performance and reliability of the air-cooled heat pump. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the air intake cooling control method for an air-cooled heat pump in this invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Example 1 Please see Figure 1 This embodiment discloses an air-cooled heat pump intake air cooling control system, which specifically includes the following steps: S1, set the air conditioning monitoring area and collect historical measurement data within the set air conditioning monitoring area. After the data collection is completed, the collected historical measurement data is processed by the data processing method to obtain the processed historical measurement data. The historical measurement data includes: historical indoor and outdoor ambient temperature, historical relative humidity, and historical condenser condensing temperature data; The process involves setting up an air conditioning monitoring area and collecting historical measurement data within that area. After data collection, the historical measurement data is processed using data processing methods to obtain the processed historical measurement data, including the following steps: Based on the actual installation scenario of the air-cooled heat pump, an air conditioning monitoring area is set up, and humidity sensors and humidity detection equipment are installed within the area. Based on humidity sensors and temperature monitoring equipment, historical measurement data is collected, including historical indoor and outdoor ambient temperatures, historical relative humidity, and historical condenser condensing temperature data. Based on the collected historical data and actual needs, the following data ranges are set: indoor temperature and humidity regulation must meet user needs, condenser condensing temperature must be controlled within a safe threshold range, and when the collected data exceeds the range, it is judged as invalid data and removed, thus obtaining the filtered historical data. Based on the filtered historical data, the data is processed using data standardization methods to obtain the processed historical measurement data; The standardization formula is as follows: ; in The values ​​are standardized. For the collected parameter data, This is the average of all data for this parameter. This represents the standard deviation of all data for this parameter.

[0016] S2, based on the processed measurement data and linear programming algorithm, construct an initial model to predict the cooling effect; The initial model for predicting cooling effects, based on processed historical measurement data and a linear programming algorithm, includes the following steps: S21, Establish a dataset based on the processed historical data. ,in Outdoor temperature Indoor temperature, This refers to the indoor and outdoor relative humidity. This refers to the condenser's condensing temperature. S22. Based on the influence of indoor and outdoor temperature and humidity on air-cooled heat pumps, a temperature and humidity relationship function is established, and the comprehensive influence value between temperature and humidity is obtained based on the relationship function. The temperature and humidity relationship function is shown below: ; in The value represents the overall impact of temperature. and These are the weighting coefficients for the effects of temperature difference and humidity, respectively. This is the equivalent conversion factor between humidity and temperature; S23, based on the data in the dataset and the linear programming algorithm, sets the linear correlation variable between indoor and outdoor temperatures as... The combined influence of temperature and humidity and the linear correlation between condensation temperature and other variables are: And assign weight coefficients to each type of data in the dataset, and set Outdoor temperature is the weighting factor. Indoor temperature weighting coefficient, The weighting coefficient is used to determine the overall impact value. Let be the weighting coefficient for the condenser condensing temperature, and satisfy . And set the target temperature as ; The variables with linear correlation between indoor and outdoor temperatures are shown below: ; The system parameter-related variables are shown below: ; S24, based on the linearly correlated variables of indoor and outdoor temperatures and the related variables of system parameters, sets a comprehensive linear programming function with the objectives of maximizing cooling efficiency and minimizing energy consumption, and sets the weight of cooling efficiency as follows: Energy consumption weight is ; The comprehensive linear programming function is shown below: ; in To predict the cooling effect value; S25, substitute the data from the dataset into the linear programming function mentioned above, and establish the mapping relationship between the data and the cooling effect based on the linear programming function to form an initial model for predicting the cooling effect.

[0017] S3, based on the constructed initial model, introduces a temperature compensation correction mechanism to train the model and obtain the final cooling effect prediction model; The training of the model based on the constructed initial model, incorporating a temperature compensation correction mechanism, includes the following steps: S31: Collect real-time environmental data, including dynamically changing indoor and outdoor temperatures, relative humidity, and condenser condensing temperatures, and process the data according to step S1 to form a training sample dataset. S32, Set the outdoor temperature compensation coefficient The indoor temperature compensation coefficient is Its value is determined by the deviation between the indoor and outdoor ambient temperatures and the actual required temperature; The outdoor temperature compensation mechanism is as follows: when the actual outdoor temperature is higher than the required temperature, a positive value is taken to improve the overall cooling effect; when the actual outdoor temperature is lower than the required temperature, a negative value is taken to reduce excessive cooling caused by excessively low outdoor temperature. The indoor temperature compensation mechanism is as follows: when the actual indoor temperature is higher than the required temperature, a positive value is taken to strengthen cooling compensation; when the actual indoor temperature is equal to the required temperature, a zero value is taken to pause cooling compensation and maintain the current temperature stability; when the actual indoor temperature is lower than the required temperature, a negative value is taken to weaken indoor cooling compensation. S33 trains the model based on the set temperature compensation mechanism and training sample set data.

[0018] The training of the model based on the established temperature compensation mechanism and training sample set data includes the following steps: Based on the training sample set data, the predicted cooling effect value is obtained by substituting the data from the training sample set into the initial model, and then compared with the actual cooling effect value to obtain the deviation value. ; Based on the deviation value and the set temperature compensation coefficient, a comprehensive correction function that integrates the deviation value and temperature compensation is established; The correction function is as follows: ; in This is the model correction amount. , Divided into outdoor and indoor temperature compensation weighting coefficients, Let be the weighting coefficient for the deviation value, and satisfy . ; Based on the correction function and the established initial model, the two are combined to obtain the corrected initial model function for predicting the cooling effect; The corrected initial model function for predicting the cooling effect is shown below: ; in This is the corrected cooling effect value; Repeat the above steps, set a threshold for the deviation between the model's predicted value and the actual value, and iterate the training until the deviation between the model's predicted value and the actual value reaches the set threshold. The model obtained at this point is the final cooling effect prediction model.

[0019] S4 sets the cooling control working mode based on the final prediction model, determines the current cooling demand through the model, selects the corresponding operating mode, and drives the air-cooled heat pump equipment. The process of setting the cooling control operating mode based on the final prediction model, determining the current cooling demand through the model, selecting the corresponding operating mode, and driving the air-cooled heat pump equipment to adjust includes the following steps: Real-time data collection includes outdoor temperature, indoor temperature, indoor and outdoor relative humidity, and the current condenser temperature. The data is then processed according to step S1 to obtain the processed data. The processed data is then fed into the final cooling effect prediction model to obtain the current cooling effect value. Based on the obtained cooling effect value and the actual cooling requirements, the cooling effect range and operating mode are set: the low cooling threshold is set as less than... The corresponding operating mode is energy-saving mode, and the medium cooling threshold judgment range is... The corresponding working mode is normal mode, and the high cooling threshold determination condition is greater than The corresponding working mode is the enhanced mode; Based on the obtained cooling effect value and the set cooling effect judgment range, select the current working mode of the air-cooled heat pump and drive the air-cooled heat pump to execute according to the selected working mode. During the cooling process of the air-cooled heat pump, the system continuously collects data and feeds it back to the final prediction model, and dynamically adjusts the working mode based on the real-time data.

[0020] S5 monitors key data in real time during continuous system operation. If abnormal information is detected, the system automatically records and generates a log file, and intervenes based on the log feedback.

[0021] During continuous system operation, key data is monitored in real time. If abnormal information is detected, the system automatically records and generates a log file, and controls and intervenes based on the log feedback, including the following steps: During system operation, indoor and outdoor temperature and humidity, as well as condenser temperature data are monitored in real time and compared with two preset thresholds. If the data exceeds the range, it is judged as abnormal information and uploaded to the system. Based on the received abnormal information, the system records the abnormal value and runtime data, generates a log file, and notifies the administrator to handle the abnormality control. During the anomaly handling process, the system records the processing and recovery status, and incorporates the anomaly data and processing records into the model optimization library to generate processing logs, providing a basis for system optimization.

[0022] Example 2 This embodiment also discloses an air conditioning air-cooled heat pump intake air cooling control system, including: a data acquisition module, a central control module, an intake air cooling execution module, and a monitoring and maintenance module; The data acquisition module is used to collect indoor and outdoor temperature and humidity data, condenser condensing temperature data, and transmit the data to the central control module after standardization processing. The central control module is used to build and train a cooling effect prediction model based on the received data and linear programming algorithm, combine the model to determine the current cooling demand, and select the appropriate working mode to issue instructions to the air intake cooling execution module. The air intake cooling execution module is used to receive control commands from the central control module, drive the air-cooled heat pump equipment to adjust the operating mode, and execute the air intake cooling operation. The monitoring and maintenance module is used to monitor system operation data in real time, identify and record abnormal information, notify the administrator to handle abnormal situations, and incorporate relevant data into the model optimization library to assist in system iterative optimization.

[0023] It should be noted that The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. An air-cooled heat pump intake air cooling control system for an air conditioner, characterized in that, Includes the following steps: S1, set the air conditioning monitoring area and collect historical measurement data within the set air conditioning monitoring area. After the data collection is completed, the collected historical measurement data is processed by the data processing method to obtain the processed historical measurement data. The historical measurement data includes: historical indoor and outdoor ambient temperature, historical relative humidity, and historical condenser condensing temperature data; S2, based on the processed historical measurement data and linear programming algorithm, construct an initial model to predict the cooling effect; S3, based on the constructed initial model, introduces a temperature compensation correction mechanism to train the model and obtain the final cooling effect prediction model; S4 sets the cooling control working mode based on the final prediction model, determines the current cooling demand through the model, selects the corresponding operating mode, and drives the air-cooled heat pump equipment. S5 monitors key data in real time during continuous system operation. If abnormal information is detected, the system automatically records and generates a log file, and intervenes based on the log feedback.

2. The air-cooled heat pump intake cooling control system for an air conditioner according to claim 1, characterized in that, The process involves setting up an air conditioning monitoring area and collecting historical measurement data within that area. After data collection, the historical measurement data is processed using data processing methods to obtain the processed historical measurement data, including the following steps: Based on the actual installation scenario of the air-cooled heat pump, an air conditioning monitoring area is set up, and humidity sensors and humidity detection equipment are installed within the area. Based on humidity sensors and temperature monitoring equipment, historical measurement data is collected, including historical indoor and outdoor ambient temperatures, historical relative humidity, and historical condenser condensing temperature data. Based on the collected historical data and actual needs, the following data ranges are set: indoor temperature and humidity regulation must meet user needs, condenser condensing temperature must be controlled within a safe threshold range, and when the collected data exceeds the range, it is judged as invalid data and removed, thus obtaining the filtered historical data. Based on the filtered historical data, the data is processed using data standardization methods to obtain the processed historical measurement data; The standardization formula is as follows: ； in The values ​​are standardized. For the collected parameter data, This is the average of all data for this parameter. This represents the standard deviation of all data for this parameter.

3. The air-cooled heat pump intake cooling control system for an air conditioner according to claim 1, characterized in that, The initial model for predicting cooling effects, based on processed historical measurement data and a linear programming algorithm, includes the following steps: S21, Establish a dataset based on the processed historical data. ,in Outdoor temperature Indoor temperature, This refers to the indoor and outdoor relative humidity. This refers to the condenser's condensing temperature. S22. Based on the influence of indoor and outdoor temperature and humidity on air-cooled heat pumps, a temperature and humidity relationship function is established, and the comprehensive influence value between temperature and humidity is obtained based on the relationship function. The temperature and humidity relationship function is shown below: ; in The value represents the overall impact of temperature. and These are the weighting coefficients for the effects of temperature difference and humidity, respectively. This is the equivalent conversion factor between humidity and temperature; S23, based on the data in the dataset and the linear programming algorithm, sets the linear correlation variable between indoor and outdoor temperatures as... The combined influence of temperature and humidity and the linear correlation between condensation temperature and other variables are: And assign weight coefficients to each type of data in the dataset, and set Outdoor temperature is the weighting factor. Indoor temperature weighting coefficient, The weighting coefficient is used to determine the overall impact value. Let be the weighting coefficient for the condenser condensing temperature, and satisfy . And set the target temperature as ; The variables with linear correlation between indoor and outdoor temperatures are shown below: ； The system parameter-related variables are shown below: ； S24, based on the linearly correlated variables of indoor and outdoor temperatures and the related variables of system parameters, sets a comprehensive linear programming function with the objectives of maximizing cooling efficiency and minimizing energy consumption, and sets the weight of cooling efficiency as follows: Energy consumption weight is ; The comprehensive linear programming function is shown below: ； in To predict the cooling effect value; S25, substitute the data from the dataset into the linear programming function mentioned above, and establish the mapping relationship between the data and the cooling effect based on the linear programming function to form an initial model for predicting the cooling effect.

4. The air-cooled heat pump intake cooling control system for an air conditioner according to claim 1, characterized in that, The training of the model based on the constructed initial model, incorporating a temperature compensation correction mechanism, includes the following steps: S31: Collect real-time environmental data, including dynamically changing indoor and outdoor temperatures, relative humidity, and condenser condensing temperatures, and process the data according to step S1 to form a training sample dataset. S32, Set the outdoor temperature compensation coefficient The indoor temperature compensation coefficient is Its value is determined by the deviation between the indoor and outdoor ambient temperatures and the actual required temperature; The outdoor temperature compensation mechanism is as follows: when the actual outdoor temperature is higher than the required temperature, a positive value is taken to improve the overall cooling effect; when the actual outdoor temperature is lower than the required temperature, a negative value is taken to reduce excessive cooling caused by excessively low outdoor temperature. The indoor temperature compensation mechanism is as follows: when the actual indoor temperature is higher than the required temperature, a positive value is taken to strengthen cooling compensation; when the actual indoor temperature is equal to the required temperature, a zero value is taken to pause cooling compensation and maintain the current temperature stability; when the actual indoor temperature is lower than the required temperature, a negative value is taken to weaken indoor cooling compensation. S33 trains the model based on the set temperature compensation mechanism and training sample set data.

5. The air-cooled heat pump intake cooling control system for an air conditioner according to claim 4, characterized in that, The training of the model based on the established temperature compensation mechanism and training sample set data includes the following steps: Based on the training sample set data, the predicted cooling effect value is obtained by substituting the data from the training sample set into the initial model, and then compared with the actual cooling effect value to obtain the deviation value. ; Based on the deviation value and the set temperature compensation coefficient, a comprehensive correction function that integrates the deviation value and temperature compensation is established; The correction function is as follows: ； in This is the model correction amount. , Divided into outdoor and indoor temperature compensation weighting coefficients, Let be the weighting coefficient for the deviation value, and satisfy . ; Based on the correction function and the established initial model, the two are combined to obtain the corrected initial model function for predicting the cooling effect; The corrected initial model function for predicting the cooling effect is shown below: ； in This is the corrected cooling effect value; Repeat the above steps, set a threshold for the deviation between the model's predicted value and the actual value, and iterate the training until the deviation between the model's predicted value and the actual value reaches the set threshold. The model obtained at this point is the final cooling effect prediction model.

6. The air-cooled heat pump intake cooling control system for an air conditioner according to claim 1, characterized in that, The process of setting the cooling control operating mode based on the final prediction model, determining the current cooling demand through the model, selecting the corresponding operating mode, and driving the air-cooled heat pump equipment to adjust includes the following steps: Real-time data collection includes outdoor temperature, indoor temperature, indoor and outdoor relative humidity, and the current condenser temperature. The data is then processed according to step S1 to obtain the processed data. The processed data is then fed into the final cooling effect prediction model to obtain the current cooling effect value. Based on the obtained cooling effect value and the actual cooling requirements, the cooling effect range and operating mode are set: the low cooling threshold is set as less than... The corresponding operating mode is energy-saving mode, and the medium cooling threshold judgment range is... The corresponding working mode is normal mode, and the high cooling threshold determination condition is greater than The corresponding working mode is the enhanced mode; Based on the obtained cooling effect value and the set cooling effect judgment range, select the current working mode of the air-cooled heat pump and drive the air-cooled heat pump to execute according to the selected working mode. During the cooling process of the air-cooled heat pump, the system continuously collects data and feeds it back to the final prediction model, and dynamically adjusts the working mode based on the real-time data.

7. The air-cooled heat pump intake cooling control system for an air conditioner according to claim 1, characterized in that, During continuous system operation, key data is monitored in real time. If abnormal information is detected, the system automatically records and generates a log file, and controls and intervenes based on the log feedback, including the following steps: During system operation, indoor and outdoor temperature and humidity, as well as condenser temperature data are monitored in real time and compared with two preset thresholds. If the data exceeds the range, it is judged as abnormal information and uploaded to the system. Based on the received abnormal information, the system records the abnormal value and runtime data, generates a log file, and notifies the administrator to handle the abnormality control. During the anomaly handling process, the system records the processing and recovery status, and incorporates the anomaly data and processing records into the model optimization library to generate processing logs, providing a basis for system optimization.

8. A control system for air-cooled heat pump intake cooling according to any one of claims 1-7, characterized in that, include: The system includes a data acquisition module, a central control module, an air intake cooling execution module, and a monitoring and maintenance module. The data acquisition module is used to collect indoor and outdoor temperature and humidity data, condenser condensing temperature data, and transmit the data to the central control module after standardization processing. The central control module is used to build and train a cooling effect prediction model based on the received data and linear programming algorithm, combine the model to determine the current cooling demand, and select the appropriate working mode to issue instructions to the air intake cooling execution module. The air intake cooling execution module is used to receive control commands from the central control module, drive the air-cooled heat pump equipment to adjust the operating mode, and execute the air intake cooling operation. The monitoring and maintenance module is used to monitor system operation data in real time, identify and record abnormal information, notify the administrator to handle abnormal situations, and incorporate relevant data into the model optimization library to assist in system iterative optimization.

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