Method and system for intelligently adjusting and optimizing parameters of air source heat pump unit in refrigerating system

Abstract

The invention discloses a method and system for intelligently adjusting and optimizing parameters of an air source heat pump unit in a refrigerating system, and belongs to the technical field of big data model application. The first model is used for predicting a basic load and performing preliminary control on the air source heat pump unit; the second model is used for predicting a load difference by using a PSO-LSTM algorithm model and a second-class data source; the parameter adjusting and optimizing module is used for firstly superposing the basic load and the load difference to obtain the refrigerating capacity needing to be provided by the air source heat pump unit; the method comprises the steps that firstly, the refrigerating capacity is obtained, then the standard water supply temperature corresponding to the refrigerating capacity is calculated according to the relation between the refrigerating capacity and the water supply temperature, the return water temperature and the flow, finally the water supply temperature is stipulated through stipulation conditions, and parameters of preliminary control are adjusted according to stipulation results. A step-by-step control mode is adopted, large fluctuation of the refrigerating system can be avoided, and efficient operation of the air source heat pump unit can be guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data model application, and in particular relates to a method and system for intelligently adjusting parameters of an air source heat pump unit in a refrigeration system. Background Art

[0002] As we all know, with the rapid development of society, refrigeration systems have become essential basic equipment for high-rise or large buildings. At present, in order to meet the refrigeration needs in buildings, the following two methods are mainly used: Method 1: The staff manually controls the parameters of the refrigeration system based on their personal experience; this method relies on the professional knowledge and experience of the operators, who need to manually adjust the operating parameters of the refrigeration system according to factors such as ambient temperature, humidity, and changes in the heat load inside the building. Although this method can meet basic refrigeration needs to a certain extent, its level of automation is not high and it mainly stays at the on-site control stage. Due to the lack of a unified quantitative standard, the operation and management of the system is relatively extensive, which often leads to low thermal efficiency and poor refrigeration quality of the system. Especially when users take active energy-saving measures, changes in heat load inevitably cause fluctuations in pipe network pressure, which in turn puts higher requirements on the safety control indicators of the energy station. As one of the main forms of energy, the operation and regulation of air source heat pumps mainly rely on manual experience and the control logic of the unit itself. In actual operation, staff need to judge when to start or stop the heat pump based on experience, and how to adjust its operating parameters to adapt to different environmental conditions and load requirements.

[0003] Method 2: preset automatic control, that is, when the room temperature reaches the preset value, the refrigeration system automatically adjusts the parameters; this method uses pre-set control logic and parameters to enable the refrigeration system to automatically adjust the parameters when the room temperature reaches the preset value, thereby achieving control of the indoor temperature. Preset automatic control has improved the automation level of the refrigeration system to a certain extent and reduced the need for manual intervention. However, this method also has certain limitations. In actual applications, on-site operators usually set some fixed thresholds, which often fail to fully consider the actual needs of indoor loads or real-time outdoor meteorological conditions, resulting in the air source heat pump being unable to operate under optimal conditions. In addition, due to the inability to monitor indoor terminal energy consumption, even when there is no load or extremely low load at the terminal, the circulating water pump and the unit continue to operate ineffectively, lacking an effective automation solution, resulting in serious waste of energy.

[0004] In summary, under the operation mode that relies on manual experience, it is difficult to ensure that the air source heat pump unit is always in the optimal operating state under different environmental conditions, resulting in low efficiency of the entire system, serious energy waste, and poor user experience. At the same time, manual experience is difficult to replicate, and due to the different energy requirements of different building types, manual experience has great limitations. As the number of projects increases, the labor costs required also increase significantly.

[0005] In the field of research on the application of air source heat pump units in refrigeration systems, the research progress at home and abroad in recent years has mainly focused on improving system energy efficiency, optimizing control strategies and intelligent regulation, aiming to improve the energy efficiency ratio (COP) of air source heat pumps. By improving heat pump design, selecting efficient compressors and heat exchangers, and optimizing the use of refrigerants, we are committed to improving the performance of air source heat pumps.

[0006] Some researchers have explored the performance of heat pumps under different working conditions by combining experiments with simulations, which has become an important direction for technological innovation. Some patents have proposed a multi-parameter experimental platform, using precision measuring equipment to comprehensively test the energy efficiency ratio (COP) and cooling capacity of heat pump systems under different ambient temperature, humidity and load conditions. Other researchers have described a heat pump performance test device equipped with multiple sensors, which can collect system operating parameters in real time and establish a detailed performance database, providing an important experimental basis for subsequent system optimization. Computational fluid dynamics (CFD) simulation technology has also played an important role in heat pump performance research. Many patents use CFD technology to accurately simulate the fluid flow and heat exchange process inside the heat pump, analyze the impact of different heat exchanger structures on system performance through numerical simulation, and achieve accurate design and performance prediction of key components of the heat pump. Other researchers have proposed a comprehensive evaluation system for heat pump performance. By comparing and analyzing experimental test data with CFD simulation results, a more accurate performance prediction model has been established, which significantly improves the accuracy of system design.

[0007] Although some progress has been made in the research of air source heat pump refrigeration systems, existing technologies still have limitations. First, existing control algorithms are usually based on fixed models and lack the ability to adapt to environmental changes and load fluctuations in real time, resulting in poor energy efficiency performance under complex working conditions. Secondly, intelligent adjustment methods usually rely on a large amount of historical data for training and optimization, but in practical applications, data acquisition and processing may be limited, resulting in insufficient model generalization capabilities. In addition, the integration between different technologies and equipment remains a challenge, especially when multiple energy systems operate in coordination. How to achieve efficient energy management and scheduling still needs further research. Finally, although intelligent control and optimization algorithms can improve system performance, their implementation and maintenance costs may be high, limiting their application in a wider range of fields. Summary of the invention

[0008] In view of the above technical problems, the present invention provides a method and system for intelligently optimizing the parameters of an air source heat pump unit in a refrigeration system. First, a type of data source is analyzed, and then the air source heat pump unit is preliminarily controlled according to the outdoor temperature. Then, an algorithm model is used to analyze a second type of data source, and on the basis of the preliminary control, the parameters of the preliminary control are optimized. By means of distributed control, the present invention can ensure the efficient operation of the air source heat pump unit on the premise of avoiding large fluctuations in the system.

[0009] Automatically adjust the working parameters of the air source heat pump unit through intelligent means to achieve the goal of meeting user comfort and achieving energy conservation and emission reduction.

[0010] To achieve the above invention purpose, the first object of the present invention is to provide a system for intelligently optimizing the parameters of an air source heat pump unit in a refrigeration system, including: A data source acquisition module for acquiring a first type of data source and a second type of data source. The first type of data source includes the operating state, supply water temperature, return water temperature, instantaneous flow rate, and outdoor temperature of the air source heat pump unit at different times; the second type of data source includes the first type of data source, outdoor humidity, outdoor wind direction, outdoor wind speed, outdoor radiation, and the basic load output by the first model at different times. A first model for predicting the basic load and preliminarily controlling the air source heat pump unit. The first model includes: A first unit for constructing the corresponding relationship between the supply water temperature and the outdoor temperature by using the first type of data source. A second unit for calculating the basic load at different times by using the corresponding relationship and preliminarily controlling the air source heat pump unit according to the basic load. A second model for predicting the load difference by using the PSO-LSTM algorithm model and the second type of data source. A parameter optimization module. First, the basic load and the load difference are superimposed to obtain the cooling capacity required by the air source heat pump unit. Then, the standard supply water temperature corresponding to the cooling capacity is calculated according to the relationship between the cooling capacity, the supply water temperature, the return water temperature, and the flow rate. Finally, the supply water temperature is regulated by using the regulation conditions, and the parameters of the preliminary control are adjusted according to the regulation result.

[0011] Preferably, the first model includes a first preprocessing module, and the preprocessing of the first preprocessing module includes: Removing the data within a preset time period after the air source heat pump unit is started. Deleting the data when the air source heat pump unit is not started. Filtering out abnormal data and filtering out outliers for the supply water temperature, return water temperature, flow rate, and outdoor temperature.

[0012] Preferably, the use of the first type of data source to construct a corresponding relationship between the water supply temperature and the outdoor temperature includes: Using the method of mean aggregation and quadratic polynomial fitting, the mean of the corresponding water supply temperature is calculated according to the outdoor temperature range. The calculation formula is: T _供水温度 = a • (T _室外温度 )^2 + b•T _室外温度 + c; Among them, T _室外温度 is the outdoor temperature, T _供水温度 is the water supply temperature, a, b, c are constant terms in the curve equation, Output outdoor temperature and water supply temperature reference curves.

[0013] A second object of the present invention is to provide a method for intelligently optimizing parameters of an air source heat pump unit in a refrigeration system, comprising: S1. Obtain a first-class data source and a second-class data source, wherein the first-class data source includes the operating status, water supply temperature, return water temperature, instantaneous flow rate, and outdoor temperature of the air source heat pump unit at different times; the second-class data source includes the first-class data source, outdoor humidity, outdoor wind direction, outdoor wind speed, outdoor radiation, and the basic load output by the first model at different times; S2. Importing a type of data source into a first model to predict a base load; the first model is used to predict the base load and perform preliminary control on the air source heat pump unit; the first model first uses the type of data source to construct a corresponding relationship between the water supply temperature and the outdoor temperature; then uses the corresponding relationship to calculate the base load at different times, and performs preliminary control on the air source heat pump unit according to the base load; S3, importing the two types of data sources into a second model to predict the load difference; the second model includes a PSO-LSTM algorithm model; S4. Optimize the parameters of the air source heat pump unit. First, superimpose the basic load and the load difference to obtain the cooling capacity that the air source heat pump unit needs to provide. Then, calculate the standard water supply temperature corresponding to the cooling capacity based on the relationship between the cooling capacity and the water supply temperature, the return water temperature, and the flow rate. Finally, use the regulation conditions to regulate the water supply temperature. According to the regulation results, adjust the parameters of the preliminary control.

[0014] Preferably, the first model includes a first preprocessing module, and the preprocessing of the first preprocessing module includes: Remove the data within the preset time period after the air source heat pump unit is turned on; Delete the data of the air source heat pump unit not starting up; Filter out abnormal data and filter outliers for supply water temperature, return water temperature, flow rate and outdoor temperature.

[0015] Preferably, the use of the first type of data source to construct a corresponding relationship between the water supply temperature and the outdoor temperature includes: Using the method of mean aggregation and quadratic polynomial fitting, the mean of the corresponding water supply temperature is calculated according to the outdoor temperature range. The calculation formula is: T _供水温度 = a • (T _室外温度 )^2 + b•T _室外温度 + c; Among them, T _室外温度 is the outdoor temperature, T _供水温度 is the water supply temperature, a, b, c are constant terms in the curve equation, Output outdoor temperature and water supply temperature reference curves.

[0016] The third object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for intelligently adjusting the parameters of an air source heat pump unit in the above-mentioned refrigeration system.

[0017] A fourth object of the present invention is to provide a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned method for intelligently adjusting the parameters of an air source heat pump unit in a refrigeration system.

[0018] The advantages and positive effects of this application are: The present invention firstly uses the first model to analyze a type of data source, and then performs preliminary control on the air source heat pump unit according to the corresponding relationship between the outdoor temperature and the water supply temperature, and then uses the second model to analyze the second type of data source, and then optimizes the parameters of the preliminary control on the basis of the preliminary control. Through the step-by-step control method, the present invention can ensure the efficient operation of the air source heat pump unit while avoiding large fluctuations in the system. Specifically: The present invention first uses the first model to perform prediction, and can obtain the basic load required for unit refrigeration at different outdoor temperatures, and then obtain the basic working parameters of the air source heat pump unit according to the basic load. Since the first model can directly reflect the corresponding relationship between the water supply temperature and the outdoor temperature, the water supply temperature can be quickly obtained according to the outdoor temperature, and then the basic working parameters of the air source heat pump unit can be obtained according to the water supply temperature; The present invention then uses the second model to predict the cooling load at the next moment and calculates the load difference between the predicted load and the basic load. The second model comprehensively considers the target indoor temperature of the building, the indoor temperature at the historical moment, the water supply temperature, the return water temperature and other parameters, as well as the influence of outdoor humidity, outdoor wind direction and outdoor radiation on the building. After real-time prediction by the PSO-LSTM algorithm model, the load difference can be quickly obtained. Finally, the present invention superimposes the basic load and the load difference to obtain the cooling capacity that the air source heat pump unit needs to provide; the water supply temperature corresponding to the cooling capacity is calculated according to the relationship between the load and the water supply temperature, the return water temperature, and the flow rate, and the water supply temperature is regulated using the regulation conditions. According to the regulation results, the parameters of the air source heat pump unit are adjusted. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Figure 1 A system block diagram of a preferred embodiment of the present invention is shown; Figure 2 A flow chart showing a preferred embodiment of the present invention is shown; Figure 3 The curve constructed according to Table 1 in the preferred embodiment of the present invention is shown; Figure 4 shows an architecture diagram of the second model in a preferred embodiment of the present invention; Figure 5 A comparison diagram of the unit performance coefficient before and after using the technical solution of the present invention in a preferred embodiment of the present invention is shown; Figure 6 The figure shows the indoor temperature change diagram before and after using the technical solution of the present invention in the preferred embodiment of the present invention; Figure 7 The point cloud diagram of energy consumption changes before and after the present invention is applied to a sports venue is shown; Figure 8 The energy consumption curve diagram before and after the present invention is applied to a sports venue is shown; Fig. 9 The point cloud diagram and histogram of room temperature changes before and after the present invention is applied to a sports venue are shown; Fig.10 The point cloud diagram of energy consumption changes before and after the present invention is applied to a hospital is shown; Fig.11 The energy consumption curve diagram before and after the present invention is applied to a hospital is shown; Fig.12 The figure shows the room temperature variation curve before and after the present invention is applied to a certain hospital; Fig.13 A point cloud diagram of room temperature changes before and after the present invention is applied to a certain hospital is shown. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 ; The first embodiment, a system for intelligently adjusting parameters of an air source heat pump unit in a refrigeration system, mainly comprises: The data source acquisition module acquires a first-class data source and a second-class data source. The first-class data source includes the operating status, water supply temperature, return water temperature, instantaneous flow rate, and outdoor temperature of the air source heat pump unit at different times; the second-class data source includes a first-class data source at different times, indoor measured temperature, indoor target temperature, outdoor humidity, outdoor wind direction, outdoor wind speed, outdoor radiation, and the basic load output by the first model; the indoor measured temperature refers to the temperature actually measured indoors by a temperature sensor; the indoor target temperature refers to the ideal indoor temperature in the building (the unit refrigeration is to make the room temperature suitable, which is the indoor target temperature to be achieved, such as 20°C); it should be noted that: the data source includes a historical data source and a predicted data source at the current moment, wherein the historical data source is used to establish the first model and the second model; the predicted data source at the current moment is used to input the first model and the second model to obtain the prediction result; The first model is used to predict the base load and perform preliminary control of the air source heat pump unit; the first model is designed to capture the cooling demand characteristics of the building under different outdoor temperature conditions. The first model reflects the thermal response characteristics of the building through a curve. For example, the horizontal axis of the curve represents the outdoor temperature, and the vertical axis represents the water supply temperature (such as the unit water supply set temperature or the unit actual water supply temperature). The construction of this curve is based on a large number of measured historical data sources, which is used to depict the water supply temperature required to meet indoor comfort requirements under various outdoor temperature conditions. The first model not only reflects the physical properties of the building itself, but also takes into account the user's comfort preferences. It can serve as an important basis for formulating water supply temperature strategies. Through this basis, it can be ensured that the final water supply temperature not only meets the actual needs of the building, but also avoids excessive deviation from the ideal value; The first model mainly includes: Unit 1 uses the first type of data source to construct a corresponding relationship between the water supply temperature and the outdoor temperature; the corresponding relationship can be constructed in the following manner: First, obtain the required historical data sources, including the operating status of the air source heat pump unit at different times, water supply temperature, return water temperature, flow rate, outdoor temperature, etc.

[0022] Then, the historical data source is preprocessed using the first preprocessing module, including removing the data within a preset time (e.g., 30 minutes) after the air source heat pump unit is turned on (e.g., if it is turned on at 2 p.m., the data before 2:30 p.m. is removed), deleting the data when the air source heat pump unit is not turned on, filtering out abnormal data (e.g., data when the supply water temperature is less than the return water temperature), and filtering outliers for the supply water temperature, return water temperature, flow rate, and outdoor temperature; it should be noted that preprocessing is not necessary, and in order to improve the accuracy of the first model, preprocessing is preferably performed; Finally, the corresponding relationship between the water supply temperature and the outdoor temperature is constructed by using the first type of data source; specifically: At each outdoor temperature, the average supply and return water temperature difference is calculated based on the corresponding supply water temperature, return water temperature, and instantaneous flow rate; Average supply and return water temperature difference = return water temperature - supply water temperature = cooling capacity / [(specific heat capacity of water) • instantaneous flow rate]; Based on the water supply temperature and outdoor temperature data, the corresponding relationship between the water supply temperature and the outdoor temperature is established: Using the method of mean aggregation and quadratic polynomial fitting, the mean of the corresponding water supply temperature is calculated according to the outdoor temperature range. The calculation formula is: T _供水温度 = a • (T _室外温度 )^2 + b•T _室外温度 + c; Among them, T _室外温度 is the outdoor temperature, T _供水温度 is the water supply temperature, a, b, c are constant terms in the curve equation, Output outdoor temperature and water supply temperature reference curves.

[0023] See also Figure 3 ,The following is a detailed description of the construction process of the corresponding relationship through a specific case; First, a set of data is obtained, as shown in Table 1: Table 1 shows the actual water supply temperature and set water supply temperature of the unit at different outdoor temperatures

[0024] The data in Table 1 are all historical data; then, based on the data in Table 1, we construct Figure 3 The curve shown, where a = 0.01691, b = -1.276, c = 35.42; The curve is updated daily when it is actually applied, incorporating historical data from the previous day into the model.

[0025] Unit No. 2 uses the corresponding relationship to calculate the basic load at different times, and performs preliminary control of the air source heat pump unit according to the basic load; specifically: Firstly, different outdoor temperatures are introduced into the corresponding relationship (the first model curve after integration) to obtain the water supply temperature under different outdoor temperatures; Then calculate the basic load at the corresponding time, such as cooling capacity demand, power consumption, etc., where: The calculation formula of the cooling capacity is: cooling capacity = specific heat capacity of water • instantaneous flow rate • average supply and return water temperature difference; The calculation formula for the power consumption is: power consumption = power consumption of the air source unit + power consumption of the water pump.

[0026] Finally, the data of the first model is stored in the database, which may include information such as time, outdoor temperature, supply water temperature, return water temperature, load, etc., to facilitate subsequent query and analysis.

[0027] Table 2 shows the first model data stored in the database.

[0028] The first model mainly reflects the characteristic curves of outdoor temperature and water supply temperature in the building, so it does not consider the influence of other key environmental factors such as humidity, wind speed and solar radiation. Therefore, the water supply temperature calculated by the first model alone cannot fully meet the actual precise control needs, so the second model is introduced to obtain accurate prediction of water supply temperature and load.

[0029] In order to improve the accuracy of the first model, it is necessary to perform model update training regularly. For example, the data of the previous day can be imported into the first model for training at 0:00 every day, thereby updating the first model.

[0030] Using the first model, the correspondence between the outdoor temperature and the water supply temperature can be obtained. In actual use, when the outdoor temperature change is detected, the water supply temperature corresponding to each outdoor temperature can be quickly obtained, and then the working state of the unit can be controlled and the water supply temperature provided according to the corresponding demand can be provided based on the water supply temperature.

[0031] The second model uses the PSO-LSTM algorithm model and two types of data sources to predict the load difference; In the second model of the air source heat pump system, outdoor temperature, outdoor humidity, outdoor wind speed, outdoor wind direction, outdoor radiation, etc. are selected as features based on the complex mechanism of system thermodynamics and energy transfer. The intensity of solar radiation directly affects the heat exchange process of the building, and affects the operating load of the heat pump system by changing the temperature of the building's outer surface. The outdoor wind direction and wind speed significantly affect the energy efficiency and heat transfer of the heat pump system by adjusting the convective heat transfer coefficient. These multidimensional features are not just simple independent indicators, but systematic elements that are interrelated and work together. By introducing these features and using the PSO-LSTM algorithm, the inherent laws of system operation can be captured more comprehensively and accurately, significantly improving the accuracy and adaptability of the load forecasting model, and providing more accurate decision support for the intelligent regulation of the air source heat pump system.

[0032] The second model includes: The second preprocessing module and the second model mainly focus on missing values ​​and outliers. Because the data are all minute-level time series data with abundant data volume, the data with missing features are filtered. For outliers in the data after filtering missing values, the following processing is mainly performed: a. Filter abnormal values ​​of water supply temperature, return water temperature, flow rate and outdoor temperature; b. Filter and remove data where the supply water temperature is greater than or equal to the return water temperature; c. Filter and remove data in the unpowered state; d. Filter and remove samples within 30 minutes after startup (there is an abnormal increase in water supply temperature, which adds noise interference to the second model training); e. Exclude features with identical columns.

[0033] Actual data examples (mainly showing some abnormal data); Table 3 is an example table of missing values ​​and outliers that the second model focuses on

[0034] Training objectives and feature selection modules, One of the key steps in modeling the second model is to determine the load difference △Q learned by the regression model. First, the average load (average cooling capacity) of each outdoor temperature range under the target room temperature is used, and then the correspondence between the outdoor temperature and the average load is obtained through polynomial regression. This correspondence is applied to all samples to obtain the standard load (standardQ, each sample corresponds to an average load) of all samples, and finally the load difference △Q of all samples is obtained by subtracting the current load of the unit (calculated by Q=cmΔt) from the standard load. In terms of feature selection and processing, the second model does not require screening in the case of a small number of total features. Only the indoor temperature sensor (some projects also include indoor humidity sensors) is processed by the mean, and some features such as whether it is a working day, X days, and X hours are derived.

[0035] In this embodiment, the training target refers to the difference between the standard load and the current load, that is, the target load difference.

[0036] The results of feature selection are outdoor temperature, unit water supply temperature, unit return water temperature, instantaneous flow, room temperature sensor data (after mean processing), outdoor meteorological parameters (outdoor temperature, outdoor humidity, outdoor wind speed, outdoor wind direction, outdoor radiation, etc.), whether it is a working day, and other time-related features.

[0037] Model selection, see Figure 4 In terms of the second model selection, first, then by comparing the effects of each model, it is found that PSO-LSTM is superior to other alternative models in terms of effect indicators. The PSO-LSTM algorithm, which combines Particle Swarm Optimization (PSO) with Long Short-Term Memory (LSTM), has significant advantages in time series prediction tasks. The PSO-LSTM algorithm significantly improves the performance of time series prediction tasks by combining the global optimization capability of PSO and the time series processing capability of LSTM. Its advantages are mainly concentrated in hyperparameter optimization, adaptive learning rate adjustment, avoiding overfitting and parallel computing, and the advantages include improving prediction accuracy, accelerating convergence speed, enhancing robustness and reducing manual intervention.

[0038] In the model training and evaluation module, the sampling time of the training samples is first set. For example, the training samples of the second model are 10 minutes (the specific time length can be adjusted as needed), and then time series aggregation is performed. Finally, the data set is split into training and testing at a ratio of 4:1. After deployment, all data (excluding the current moment) is used for training, and the current moment data is used for prediction.

[0039] Model evaluation is a key step to ensure the reliability and generalization ability of machine learning models. This model adopts a multi-dimensional evaluation method. Through the k-fold cross-validation technique, the data set is randomly divided into k subsets, and k-1 subsets are selected for training each time, and the remaining 1 subset is used for verification to reduce accidental errors and improve the stability of model evaluation.

[0040] In terms of performance indicator selection, in addition to the R² coefficient, the root mean square error (RMSE) and mean absolute error (MAE) are also introduced as supplementary evaluation indicators. R² reflects the model's ability to explain variance, while RMSE and MAE directly reflect the absolute error size of the prediction results. Preliminary experimental results show that the R² of the model on the test set is above 0.97, the RMSE is controlled at ±0.3 degrees, and the MAE is about 0.2 degrees, indicating that the model has a high prediction accuracy.

[0041] The parameter tuning module first superimposes the basic load and the load difference to obtain the cooling capacity that the air source heat pump unit needs to provide; then calculates the standard water supply temperature corresponding to the cooling capacity based on the relationship between the cooling capacity and the supply water temperature, return water temperature, and flow rate; finally, the water supply temperature is regulated using the regulation conditions, and the preliminary control parameters are adjusted according to the regulation results.

[0042] In the parameter tuning module, the basic load and the load difference must first be superimposed to calculate the cooling capacity that the air source heat pump unit needs to provide. Next, based on the relationship between the cooling capacity and the supply water temperature, return water temperature, and flow rate, the water supply temperature corresponding to the cooling capacity is further calculated. After obtaining this water supply temperature, it is regulated using the established regulation conditions. Based on the results of the regulation processing, we make corresponding adjustments to the initially set control parameters to ensure that the air source heat pump unit can operate efficiently and accurately.

[0043] The second model is used for prediction and post-processing of the prediction results. The prediction result of the second model is the load difference △Q. The predicted cooling capacity required by the unit is equal to the basic load + load difference △Q. Then, through the load and supply water temperature, return water temperature, and flow relationship Q=cmΔt, T 供 =T 回 -(Q / cm), calculate the unit water supply temperature corresponding to the cooling capacity, T 供 is the water supply temperature, T 回 is the return water temperature, c is the specific heat capacity of water, m is the instantaneous flow rate, Δt is the difference between the supply water temperature and the return water temperature, Δt=T 回 -T 供 . According to the specific project operation situation, the calculated water supply temperature is regulated and restricted. For example, the upper and lower limits of the water supply temperature have certain constraints, such as (9, 14); The absolute value of the difference between the water supply temperature predicted by the second model and the water supply temperature predicted by the first model does not exceed 2 degrees; Time-sharing water supply temperature range limitation; In the present invention, the first model outputs a correlation curve between the outdoor temperature and the water supply temperature, which reflects the characteristics of the building itself. The curve is obtained through regression fitting and smoothing of the sample distribution, and can provide a corresponding relationship between each outdoor temperature range and the water supply temperature. However, under the various outdoor meteorological parameter conditions and indoor temperature and humidity conditions in actual business scenarios, the water supply temperature it provides cannot reflect the real-time changes of various parameters (such as outdoor humidity, outdoor wind speed, outdoor wind direction, outdoor radiation and other key environmental factors), and the accuracy needs to be calibrated. The second model is used to compensate or reduce the insufficient or excess cooling capacity predicted by the first model. Through the combination of the first model and the second model, the basic load prediction provided by the first model and the load difference predicted by the second model based on real-time meteorological parameters and room temperature parameters are superimposed, and it is expected that a more accurate predicted load under various characteristic parameters at that moment can be obtained.

[0044] The second model can compensate for the insufficient or excessive supply of the cooling capacity predicted by the first model for the current outdoor meteorological conditions.

[0045] For the second embodiment, please refer to Figure 2 , a method for intelligently optimizing parameters of an air source heat pump unit in a refrigeration system, using the system of the first embodiment, performing the following steps: S1. Obtain a first-class data source and a second-class data source, wherein the first-class data source includes the operating status, water supply temperature, return water temperature, instantaneous flow rate, and outdoor temperature of the air source heat pump unit at different times; the second-class data source includes the first-class data source, outdoor humidity, outdoor wind direction, outdoor wind speed, outdoor radiation, and the basic load output by the first model at different times; In this embodiment, we are committed to acquiring and analyzing two important data sources for further data processing and analysis. One type of data source mainly involves the operating status of the air source heat pump unit at different time points, including key parameters such as the unit's operating status, water supply temperature, return water temperature, instantaneous flow rate, and outdoor temperature. These data are crucial for understanding the real-time performance and efficiency of the unit, and can help us monitor and optimize the operation of the heat pump system.

[0046] The second type of data source contains more comprehensive information. It not only includes all the data from the first type of data source, but also covers other important parameters related to the outdoor environment. These parameters include outdoor humidity, outdoor wind direction, outdoor wind speed, outdoor radiation intensity, and basic load data calculated by the first model. These environmental parameters are crucial for evaluating the performance of heat pump units under different environmental conditions. They can help the system more accurately predict and adjust the operation strategy of the heat pump system to adapt to the changing external environmental conditions.

[0047] S2. Importing a type of data source into a first model to predict a base load; the first model is used to predict the base load and perform preliminary control on the air source heat pump unit; the first model first uses the type of data source to construct a corresponding relationship between the water supply temperature and the outdoor temperature; then uses the corresponding relationship to calculate the base load at different times, and performs preliminary control on the air source heat pump unit according to the base load; S3, importing the two types of data sources into a second model to predict the load difference; the second model includes a PSO-LSTM algorithm model; S4. Optimize the parameters of the air source heat pump unit. First, superimpose the basic load and the load difference to obtain the cooling capacity that the air source heat pump unit needs to provide. Then, calculate the standard water supply temperature corresponding to the cooling capacity based on the relationship between the cooling capacity and the water supply temperature, the return water temperature, and the flow rate. Finally, use the regulation conditions to regulate the water supply temperature. According to the regulation results, adjust the parameters of the preliminary control.

[0048] A third embodiment is a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for intelligently adjusting parameters of an air source heat pump unit in a refrigeration system.

[0049] A fourth embodiment is a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned method for intelligently adjusting parameters of an air source heat pump unit in a refrigeration system.

[0050] See also Figure 5After using the technical solution of the present application, the coefficient of performance (COP) of the air source heat pump unit is significantly improved; the present invention first provides a benchmark water supply temperature (the water supply temperature obtained by the basic load of the first model), which can accurately depict the water supply temperature corresponding to each outdoor temperature range. This provides a scientific basis for the operation of the air source heat pump unit, and then dynamically adjusts the model (the second model) according to the basic water supply temperature provided by the first model, comprehensively considering environmental factors such as outdoor humidity, outdoor wind direction, outdoor wind speed and solar radiation, and further improves the adaptability and response speed of the system. Through this load matching method, the system can maintain the best cooling effect under different meteorological conditions, avoiding the problem of insufficient or excessive cooling capacity due to environmental changes, thereby significantly reducing power consumption and improving the unit's coefficient of performance; See also Figure 6 After using the technical solution of this application, the comfort of indoor personnel can be improved. The combination of the two models enables users to feel higher comfort while enjoying a stable supply of cold air, significantly reducing the indoor temperature during the cooling season. By real-time monitoring and dynamic adjustment of the water supply temperature, the system can better adapt to changes in user needs and ensure the comfort of the indoor environment. This flexibility not only improves user satisfaction, but also improves the intelligence level of the system. During use, users can experience more precise temperature control effects, avoiding temperature fluctuations and discomfort common in traditional systems, thereby improving the comfort of living and working environments.

[0051] The system reduces energy consumption and carbon emissions by improving the energy efficiency of the air source heat pump unit, and promotes sustainable development. The system can maintain efficient operation under complex meteorological conditions, reducing energy waste caused by over-refrigeration or insufficient refrigeration. The present invention can effectively reduce operating costs. By optimizing the supply of cooling capacity and improving the energy efficiency ratio, the overall operating cost of the system can be reduced. While users enjoy a comfortable environment, the economic burden is also reduced accordingly. During the operation of the refrigeration system, the operation strategy can be intelligently adjusted according to real-time meteorological data and indoor environmental changes, avoiding unnecessary energy consumption and waste of resources. This economy makes the method highly competitive in the market, and can attract more users' attention and application, especially in commercial buildings and large public facilities, which can significantly improve the return on investment. The present invention can adapt to application needs in different regions and under different climatic conditions. It can be adjusted according to real-time data to ensure that the best cooling effect is always provided. This flexibility makes the technology not only suitable for new buildings, but also can effectively transform the refrigeration system of existing buildings to improve their energy efficiency and comfort.

[0052] See also Fig.10 , Fig.10The energy consumption point cloud changes of a hospital before and after the algorithm was used are shown. The blue point cloud is the energy consumption point cloud after the algorithm was used under different temperature conditions, and the red point cloud is the energy consumption point cloud before the algorithm was used under different temperature conditions. See also Fig.11 , Fig.11 The diagram shows the energy consumption changes of a hospital before and after the algorithm was used. The blue curve is the energy consumption curve after the algorithm was used at different times, and the red curve is the energy consumption curve before the algorithm was used at different times. See also Fig.12 , Fig.12 The figure shows the room temperature changes in a hospital before and after the algorithm was used. The blue curve is the room temperature curve after the algorithm was used at different times, and the red curve is the room temperature curve before the algorithm was used at different times. See also Fig.13 , Fig.13 The figure shows the changes in room temperature before and after the algorithm was used in a hospital. The blue point cloud is the indoor temperature point cloud after the algorithm was used under different outdoor temperature conditions, and the red point cloud is the indoor temperature point cloud before the algorithm was used under different temperature conditions. Experimental environment The example selected by the present invention is a hospital project with an actual energy consumption area of ​​42,000 square meters. The energy consumption area includes the inpatient building, outpatient building and ancillary building. The air source units used in the project are 18 Midea units, model: DNL-E1550 / NSN1-H2. The air source units are arranged in the open space on the ground of the hospital, and 2 circulating water pumps and auxiliary equipment are arranged in the machine room on the first underground floor. The ends of the energy supply area are all fan coils.

[0053] Experimental setup In this example, the key parameters of the algorithm application are the unit cooling water supply temperature and indoor temperature. The target range of cooling water supply temperature set in this example is 7℃~13℃. The average terminal room temperature is guaranteed to be between 25℃~27℃. The number of circulating water pumps is regulated according to the supply and return water temperature difference and the predicted load value, and the unit start-stop temperature difference is set to 2℃.

[0054] Experimental Procedure To monitor the indoor temperature and humidity at the terminal, 30 indoor temperature and humidity sensors are installed in representative typical areas. The cooling period in this example in 2024 is from May 15 to October 13. To compare the effect of algorithm regulation, manual regulation is used from May 15 to July 31, and the algorithm application period is from August 1 to October 13. The main strategies in the algorithm regulation process are: Temperature setting: after taking over, the water supply temperature is adjusted in real time according to the outdoor temperature and indoor temperature; Adjust the number of units and gradually change the number of units turned on according to the outdoor temperature; The number of water pumps is adjusted, and different numbers of water pumps and water pump frequencies are turned on according to the energy consumption characteristics during the day and at night.

[0055] Result analysis: This example compares the effects of algorithm control and manual control from several aspects, such as energy consumption data, indoor temperature comfort, and unit performance coefficient. Data analysis and comparison prove that the algorithm is superior to manual control in these aspects.

[0056] Compared with manual experience, the algorithm control can set the water outlet temperature higher under the same outdoor temperature while ensuring that the indoor temperature is within a comfortable range; Compared with manual experience, the total power consumption of algorithm control is relatively lower under the same outdoor temperature. Compared with manual experience, the algorithm control can slightly improve the unit COP under the same outdoor temperature conditions.

[0057] See also Figure 7 , Figure 7 The energy consumption point cloud changes of a sports venue before and after the algorithm is used are shown. The blue point cloud is the energy consumption point cloud after the algorithm is used under different temperature conditions, and the red point cloud is the energy consumption point cloud before the algorithm is used under different temperature conditions. See also Figure 8 , Figure 8 The energy consumption changes of a sports venue before and after the algorithm is used are shown. The blue curve is the energy consumption curve after the algorithm is used, and the red curve is the energy consumption curve before the algorithm is used. See also Fig. 9 , Fig. 9 It shows the changes in room temperature under different outdoor temperatures in a sports venue before and after the algorithm is used. In the left figure, the blue point cloud is the room temperature point cloud under different outdoor temperatures after the algorithm is used, and the red point cloud is the room temperature point cloud under different outdoor temperatures before the algorithm is used; the right figure is the histogram corresponding to the left figure, showing the concentration of room temperature distribution after the algorithm is used. It can be seen that after the algorithm is used, the room temperature is mostly distributed in a more comfortable range.

[0058] Experimental environment: The present invention was experimented in a large sports stadium. The air source heat pump unit model selected was the DNL-E1550 / NSN1-H2 series. The unit has high efficiency and good adaptability, and is suitable for the cooling and heating needs of large-area stadiums. The experimental conditions were set as follows: the outdoor temperature range was 18℃~37℃, the indoor set temperature was 26℃, and the humidity range was 30%~70%. During the experiment, the usage in the stadium simulated the peak audience flow to ensure the representativeness of the experimental results.

[0059] Experimental setup Parameter setting: During the algorithm application process, the key parameters and their value ranges are as follows: Outdoor temperature: 18℃~37℃; Indoor set temperature: 26℃; Water supply temperature: calculated based on the load matching initial model, ranging from 8°C to 14°C; Humidity (H): 30% to 70%; Wind speed (V): 0.5 m / s to 5 m / s; Solar radiation (SR): 0 W / m² to 800 W / m²; Model baseline water supply temperature: obtained through polynomial regression analysis based on historical data; Load matching compensation: Dynamically adjust according to real-time environmental parameters, use LSTM algorithm to predict the load required at the next moment, comprehensively consider multi-dimensional factors such as room temperature, and infer the corresponding water supply temperature.

[0060] Experimental process: 1. Experimental preparation: Sensors are installed inside and outside the sports venues to monitor real-time environmental parameters such as indoor and outdoor temperature, humidity, wind speed, and solar radiation. These sensors can provide high-precision data to ensure the accuracy of the experiment.

[0061] Configure the air source heat pump unit and ensure its normal operation, and carry out necessary debugging and testing to ensure the stability of the system during the experiment.

[0062] 2. Data Collection: Under different outdoor temperature conditions, record the system operation data such as water supply temperature, indoor temperature and humidity.

[0063] A two-month experiment was conducted to collect environmental data and system operation data at different time periods.

[0064] 3. Algorithm application: First, based on historical operating data such as outdoor temperature, water supply temperature, etc., the model is used to calculate the benchmark water supply temperature.

[0065] The load matching compensation mechanism is then used to dynamically adjust the water supply temperature of the baseline model according to the real-time monitored environmental parameters using a machine learning prediction algorithm.

[0066] 4. Comparative experiment: A comparative test was conducted using the traditional artificial experience-based strategy to adjust the water supply temperature, and the changes in indoor temperature and humidity under the same environmental conditions were recorded.

[0067] Result analysis: The experimental results show that after adopting the load matching algorithm model of the present invention, the cooling effect of the sports venue is significantly better than the traditional method.

[0068] Energy efficiency ratio (COP): The average COP of the air source heat pump unit using the method of the present invention during the entire experimental period was 3.98, while the average COP of the traditional method was 3.23, an increase of more than 23.2%. This significant improvement shows that the method of the present invention is effective in improving the energy efficiency of the system and can provide higher cooling capacity at the same energy consumption.

[0069] Indoor temperature stability: When the outdoor temperature fluctuates greatly, the indoor temperature fluctuation range of the method of the present invention is 21 o C~26 o C, kept within the set range, the indoor temperature fluctuation range of the traditional method is 20 o C~29 o C, the fluctuation range is large and the comfort level decreases. This result shows that the method of the present invention can better maintain the stability of the indoor environment and improve the user experience.

[0070] Energy consumption analysis: The method of the present invention performs well in energy consumption control, reducing energy consumption by 24.21% under the same outdoor temperature conditions, saving costs and bringing significant economic benefits. By optimizing the operation strategy, unnecessary energy consumption is reduced, and the economy and sustainability of the system are improved.

[0071] In summary, this experiment verified the universality and effectiveness of the load matching algorithm model in sports venue projects, significantly improved the energy efficiency, comfort and user satisfaction of the air source heat pump unit, and proved the wide applicability and superiority of this technology in practical applications.

[0072] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (e.g., infrared, wireless, microwave, etc.) mode) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidState Disk (SSD)), etc.

[0073] The above is only a preferred embodiment of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A system for intelligently adjusting parameters of an air source heat pump unit in a refrigeration system, characterized in that: include: A data source acquisition module is used to acquire a first-class data source and a second-class data source, wherein the first-class data source includes the operating status, water supply temperature, return water temperature, instantaneous flow rate, and outdoor temperature of the air source heat pump unit at different times; and the second-class data source includes the first-class data source, outdoor humidity, outdoor wind direction, outdoor wind speed, outdoor radiation, and the basic load output by the first model at different times; The first model is used to predict the base load and perform preliminary control of the air source heat pump unit; The first model includes: Unit 1, using the first type of data source, constructs a corresponding relationship between the water supply temperature and the outdoor temperature; Unit No. 2 calculates the base load at different times using the corresponding relationship, and performs preliminary control on the air source heat pump unit according to the base load; The second model uses the PSO-LSTM algorithm model and two types of data sources to predict the load difference; The parameter tuning module first superimposes the basic load and the load difference to obtain the cooling capacity that the air source heat pump unit needs to provide; then calculates the standard water supply temperature corresponding to the cooling capacity based on the relationship between the cooling capacity and the supply water temperature, return water temperature, and flow rate; finally, the water supply temperature is regulated using the regulation conditions, and the preliminary control parameters are adjusted according to the regulation results.

2. The system for intelligently adjusting parameters of air source heat pump units in a refrigeration system according to claim 1, characterized in that: The first model includes a first preprocessing module, and the preprocessing of the first preprocessing module includes: Remove the data within the preset time period after the air source heat pump unit is turned on; Delete the data of the air source heat pump unit not starting up; Filter out abnormal data and filter outliers for supply water temperature, return water temperature, flow rate and outdoor temperature.

3. The system for intelligently adjusting parameters of air source heat pump units in a refrigeration system according to claim 1, characterized in that: The use of the first type of data source to construct a corresponding relationship between the water supply temperature and the outdoor temperature includes: Using the method of mean aggregation and quadratic polynomial fitting, the mean of the corresponding water supply temperature is calculated according to the outdoor temperature range. The calculation formula is: T _供水温度 = a •(T _室外温度 )^2 + b•T _室外温度 + c; Among them, T _室外温度 is the outdoor temperature, T _供水温度 is the water supply temperature, a, b, c are constant terms in the curve equation, Output outdoor temperature and water supply temperature reference curves.

4. A method for intelligently optimizing parameters of an air source heat pump unit in a refrigeration system, characterized in that: include: S1. Obtaining a first data source and a second data source, wherein the first data source includes the operating status, water supply temperature, return water temperature, instantaneous flow rate, and outdoor temperature of the air source heat pump unit at different times; The second type of data source includes the first type of data source at different times, outdoor humidity, outdoor wind direction, outdoor wind speed, outdoor radiation and the basic load output by the first model; S2, importing a type of data source into the first model to predict the basic load; The first model is used to predict the base load and perform preliminary control of the air source heat pump unit; The first model first uses the first type of data source to construct a corresponding relationship between the water supply temperature and the outdoor temperature; then uses the corresponding relationship to calculate the basic load at different times, and performs preliminary control on the air source heat pump unit according to the basic load; S3, importing the two types of data sources into the second model to predict the load difference; the second model includes a PSO-LSTM algorithm model; S4. Optimize the parameters of the air source heat pump unit. First, superimpose the basic load and the load difference to obtain the cooling capacity that the air source heat pump unit needs to provide. Then, calculate the standard water supply temperature corresponding to the cooling capacity based on the relationship between the cooling capacity and the water supply temperature, the return water temperature, and the flow rate. Finally, use the regulation conditions to regulate the water supply temperature. According to the regulation results, adjust the parameters of the preliminary control.

5. The method for intelligently optimizing parameters of an air source heat pump unit in a refrigeration system according to claim 4, characterized in that: The first model includes a first preprocessing module, and the preprocessing of the first preprocessing module includes: Remove the data within the preset time period after the air source heat pump unit is turned on; Delete the data of the air source heat pump unit not starting up; Filter out abnormal data and filter outliers for supply water temperature, return water temperature, flow rate and outdoor temperature.

6. The method for intelligently optimizing parameters of an air source heat pump unit in a refrigeration system according to claim 4, characterized in that: The use of the first type of data source to construct a corresponding relationship between the water supply temperature and the outdoor temperature includes: Using the method of mean aggregation and quadratic polynomial fitting, the mean of the corresponding water supply temperature is calculated according to the outdoor temperature range. The calculation formula is: T _供水温度 = a •(T _室外温度 )^2 + b•T _室外温度 + c; Among them, T _室外温度 is the outdoor temperature, T _供水温度 is the water supply temperature, a, b, c are constant terms in the curve equation, Output outdoor temperature and water supply temperature reference curves.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for intelligently adjusting the parameters of an air source heat pump unit in a refrigeration system as described in any one of claims 4 to 6 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method for intelligently adjusting parameters of an air source heat pump unit in a refrigeration system as described in any one of claims 4 to 6 is implemented.

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