Intelligent pressurization and decompression oil return control method and system for air conditioning system

Abstract

The application discloses an intelligent pressurization and pressure reduction oil return control method and system of an air conditioner system, relates to the technical field of air conditioner lubricating oil control, and comprises the following steps: dividing a grid through oil pipe layout data, calculating single-grid oil liquid resistance, generating a resistance thermodynamic diagram, and dynamically adjusting a pressure partition; setting a valve distribution strategy according to the pressure partition, obtaining a valve flow circulation score, and selecting three valves with the highest valve flow circulation scores as main oil return paths; setting an oil pressure dynamic adjustment algorithm, combining plateau pressure compensation oil pressure deviation, adjusting the rotating speed of a frequency conversion oil pump and the opening degree of an electric regulating valve; and dynamically adjusting the trunk road partition heating power according to real-time irradiance and the pressure partition. The application solves the problem of decision-making errors of air conditioner oil return routes caused by temperature fluctuations and increased oil viscosity in a plateau or extremely low-temperature environment.

Description

Technical Field

[0001] This invention relates to the field of air conditioning lubricating oil control technology, specifically to an intelligent pressurization, depressurization and oil return control method and system for air conditioning systems. Background Technology

[0002] Variable frequency air conditioners achieve dynamic adjustment of cooling capacity and optimization of energy consumption by precisely controlling compressor speed and refrigerant flow. However, in air conditioning systems, the compressor, as a core component, relies on effective lubrication and heat dissipation for normal operation, and the recovery of lubricating oil (oil return) is one of the key technologies to ensure the long-term stable operation of the compressor.

[0003] In high-altitude areas above 4,000 meters, due to low atmospheric pressure and extremely low temperatures, the lubricating oil in the air conditioning system becomes very viscous at low temperatures, making oil return (oil return) extremely difficult. In addition, solar energy resources are abundant, but the length of the sunshine cycle varies greatly. The base needs an air conditioning system that can meet the requirements of diurnal temperature fluctuations and adapt to extreme low temperature conditions.

[0004] In actual air conditioning system oil return control, the compressor carries away some lubricating oil during operation. If this oil cannot be effectively recovered, it can lead to insufficient lubrication, increased energy consumption, or even equipment failure. Traditional air conditioning systems often use fixed valves and simple mechanical structures for oil recovery. However, these methods suffer from poor adaptability, inaccurate adjustment, high energy consumption, and slow response to environmental changes. Especially under extreme conditions such as high load, low temperature, or high altitude, the viscosity and fluidity of the oil change significantly. Traditional fixed control methods struggle to ensure sufficient oil levels inside the compressor, easily leading to insufficient lubrication, friction and wear, or even liquid slugging, severely impacting system reliability and equipment lifespan.

[0005] Therefore, the present invention provides an intelligent pressurization, depressurization and oil return control method and system for air conditioning systems. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent pressurization, depressurization and oil return control method and system for air conditioning systems, so as to solve the existing problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent pressurization, depressurization, and oil return control of an air conditioning system, comprising the following steps:

[0008] S1. Divide the oil pipe layout data into grids, calculate the oil resistance of a single grid, generate a resistance heat map, and dynamically adjust the pressure zones.

[0009] S2. Set the valve allocation strategy according to the pressure zone, obtain the valve flow score, and select the three valves with the highest valve flow score as the main return oil path.

[0010] S3. Set up a dynamic oil pressure adjustment algorithm, and combine it with high-altitude pressure to compensate for oil pressure deviation, and adjust the speed of the variable frequency oil pump and the opening of the electric regulating valve.

[0011] S4. Dynamically adjust the heating power of the main road zones based on real-time irradiance and pressure zones.

[0012] A further improvement of the present invention is that step S1 specifically includes the following steps:

[0013] S11. Import pipeline layout data from CAD drawings, including pipe diameter, length, and number of bends;

[0014] S12. Divide the oil pipeline layout data into N×M grids along the oil flow direction, with each 0.2MPa pressure drop divided into a grid. Label the key nodes of the oil pipeline, including the compressor, oil tank and valves.

[0015] S13. Establish a single-grid oil resistance model to obtain the single-grid oil resistance;

[0016] S14. The oil resistance difference value is obtained by calculating the ratio of the oil resistance difference between adjacent grids to the mean value.

[0017] S15. Merge grids with oil resistance differences of less than 15% to obtain a new oil pressure zone.

[0018] A further improvement of this invention lies in that the single-grid oil resistance model monitors the oil flow velocity (vol) and temperature (tem) of all grids in real time by installing flow velocity and temperature sensors in each grid, extracts the number of valves and elbows (eln) within the grid, and designs a viscosity-temperature characteristic lubrication equation using the Vogel-Fulcher equation to obtain the oil viscosity. The single-grid resistance calculation combines the return oil pressure difference and oil flow velocity, and considers the dynamic roughness inside the pipe wall. It calculates the oil resistance at the direct-flow pipe, valve, and elbow respectively, obtaining the single-grid oil resistance. The calculation formula is as follows: ,in, Let L represent the oil flow rate at the i-th valve or elbow, and L represent the length of the direct-flow pipe. This indicates the critical temperature at which the oil freezes. The value represents the oil density, and d represents the length of the DC pipe. This indicates the set elbow resistance coefficient. This indicates the set temperature sensitivity coefficient.

[0019] A further improvement of this invention is that the valve allocation strategy includes extracting regions in the new oil pressure zone where the average oil resistance of all grids is greater than a set resistance threshold and labeling them as high-resistance regions. The Euclidean distance between each valve and the center of the high-resistance region is calculated and multiplied by the flow capacity ratio to obtain a valve flow score. The flow capacity ratio is obtained by the ratio of the rated throughput of the current valve to the current actual flow of the system. The main return oil path from the compressor to the oil storage tank is set as the main channel. The valve allocation strategy also includes an anti-blocking sub-strategy. If the allocated valve is located less than 50cm away from the main channel, the valve closest to that valve is selected as the backup valve to replace it.

[0020] A further improvement of this invention is that the specific process of the oil pressure dynamic adjustment algorithm includes:

[0021] S31. Calculate the real-time differential pressure error. , This indicates the set low-temperature critical value. Indicates the target pressure difference. This represents the actual pressure difference between the two ends of the oil circuit, and K is the real-time pressure difference error calculated using PID control. p K i K d , as optimization parameters;

[0022] S32. Define the target particles for control, including the speed of the variable frequency oil pump and the opening degree of the electric regulating valve;

[0023] S33. Randomly generate N sets of control target particles and corresponding real-time differential pressure error and control target output combinations;

[0024] S34. Calculate the root mean square error between the actual temperature-compensated pressure difference and the target temperature-compensated pressure difference for all controlled target particles. And the weighted sum of the variable frequency oil pump power and valve energy consumption at this time, denoted as Een;

[0025] S35. Constructing the fitness function ,in, and Indicates the set weight;

[0026] S36. Set the fitness threshold TFac and extract control target particles whose Fac is greater than TFac;

[0027] S37. Perform particle cross-linking on all extracted control target particles to generate new control target particles;

[0028] S38. Repeat steps S34-S37 and extract the response time of all particles based on historical data. When the response time of the new control target particle is less than the set response time threshold, stop the iteration and output the control target output combination at this time.

[0029] A further improvement of this invention is that the target pressure difference represents the minimum driving pressure difference required for oil return. In each generation of particles, the target pressure difference is corrected based on the oil return pressure difference of the fluid in the DC pipeline and the plateau pressure compensation.

[0030] A further improvement of this invention is that step S4 specifically includes: collecting the current real-time irradiance and the oil resistance of all zones in the main channel, and normalizing them; calculating the total available power Ptol of the system based on the irradiance I through linear mapping; multiplying the proportion of the oil resistance of each zone to the sum of the oil resistance of all zones as the power allocation weight of that zone with the total available power to obtain the heating power of all zones; calculating the average temperature of a single grid in each zone as the zone oil temperature; when the zone oil temperature is greater than the set target heating temperature of that zone, terminating the heating process of that zone, and redistributing the heating power of all zones.

[0031] On the other hand, the present invention provides an intelligent pressurization, depressurization and oil return control system for an air conditioning system, comprising:

[0032] The tubing partitioning module divides the tubing layout data into grids, calculates the oil resistance of each grid, generates a resistance heat map, and dynamically adjusts the pressure partitions.

[0033] The valve allocation module sets a valve allocation strategy based on pressure zones, obtains valve flow scores, and selects the three valves with the highest valve flow scores as the main return oil path.

[0034] The oil pressure dynamic adjustment module, combined with high-altitude pressure compensation for oil pressure deviation, adjusts the variable frequency oil pump speed and the opening of the electric regulating valve;

[0035] The heating power distribution module dynamically adjusts the heating power of the main channel zones based on real-time irradiance and pressure zones.

[0036] A further improvement of the present invention is that the oil pipe partitioning module includes a single-grid oil resistance model establishment unit and an oil grid merging unit; the single-grid oil resistance model establishment unit is used to calculate the single-grid oil resistance; the oil grid merging unit is used to obtain a new oil pressure partition based on the output of the single-grid oil resistance model establishment unit.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. This invention first refines the oil pipe mesh and establishes a single-mesh oil resistance model, enabling real-time monitoring of oil flow rate, temperature, and viscosity changes. Utilizing an oil resistance thermogram and dynamic zoning, high-resistance areas can be accurately identified, providing a basis for subsequent heating, valve allocation, and oil pressure regulation, thereby improving the overall oil return efficiency and ensuring sufficient lubricating oil inside the compressor.

[0039] 2. Combining the distance weight between the valve and the center of the high resistance area and the flow capacity weight, the main return valve is selected by comprehensive scoring, and a backup valve is reserved through the anti-blocking sub-strategy to effectively avoid flow interference caused by valves being too close or oil circuit interference, and ensure that the oil flow path is unobstructed.

[0040] 3. Introduce a dynamic oil pressure regulation algorithm, combined with high-altitude pressure compensation, and use the PID algorithm to adjust the pressure difference between the two ends of the oil circuit in real time. This eliminates pressure fluctuations caused by changes in oil viscosity under low-temperature conditions by adjusting the speed of the variable frequency oil pump and the opening of the electric regulating valve, thereby improving the adaptability and stability of the system under high-altitude and low-temperature conditions. Attached Figure Description

[0041] Figure 1 This is a flowchart of an intelligent pressurization, depressurization and oil return control method for an air conditioning system according to the present invention;

[0042] Figure 2 This is a flowchart of the oil pressure dynamic adjustment algorithm for an intelligent pressurization, depressurization and oil return control method for an air conditioning system according to the present invention;

[0043] Figure 3 This is a framework diagram of an intelligent pressurization, depressurization and oil return control system for an air conditioning system according to the present invention. Detailed Implementation

[0044] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0045] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0046] Example 1

[0047] Figure 1 This embodiment presents a flowchart of an intelligent pressurization, depressurization, and oil return control method for an air conditioning system, with the following steps:

[0048] S1. Divide the tubing layout data into grids, calculate the oil resistance of each grid, generate a resistance heatmap, and dynamically adjust the pressure zones; specific steps include:

[0049] S11. Import pipeline layout data from CAD drawings, including pipe diameter, length, and number of bends;

[0050] S12. Divide the oil pipeline layout data into N×M grids along the oil flow direction, with each 0.2MPa pressure drop divided into a grid. Label the key nodes of the oil pipeline, including the compressor, oil tank and valves.

[0051] S13. Establish a single-grid oil resistance model to obtain the single-grid oil resistance. This model monitors the oil flow velocity (vol) and temperature (tem) in real time across all grids by installing flow rate and temperature sensors in each grid. It also extracts the number of valves and elbows (eln) within each grid and designs a viscosity-temperature characteristic lubrication equation using the Vogel-Fulcher equation to obtain the oil viscosity. The specific calculation formula is expressed as follows: ,in, Indicates the reference viscosity. This indicates the set standard temperature, and B represents the material constant, such as B=1200 for L-AN32 oil. The single-grid resistance calculation combines the return oil pressure difference and oil flow velocity, and considers the dynamic roughness inside the pipe wall. It calculates the oil resistance at the direct-flow pipe, valve, and elbow respectively, obtaining the single-grid oil resistance. The calculation formula is as follows: ,in, Let L represent the oil flow rate at the i-th valve or elbow, and L represent the length of the direct-flow pipe. This indicates the critical temperature at which the oil freezes. The value represents the oil density, and r represents the radius of the DC pipe. This indicates the set elbow resistance coefficient. This indicates the set temperature sensitivity coefficient, which is calibrated experimentally.

[0052] This indicates the coupling of temperature with the surface roughness of valves or elbows to quantify the effect of temperature on elbow resistance. It varies with temperature and the degree of icing, reflecting the effect of icing or deposits on the inner wall of the pipe caused by low temperature.

[0053] The Hagen-Poiseuille equation describes the return pressure difference of a fluid under laminar flow conditions in a direct-flow pipe. The volumetric flow rate Q is expressed as velocity multiplied by the cross-sectional area;

[0054] S14. The oil resistance difference value is obtained by calculating the ratio of the oil resistance difference between adjacent grids to the mean value.

[0055] S15. Merge grids with oil resistance differences of less than 15% to obtain a new oil pressure zone.

[0056] In air conditioning systems, especially in high-altitude or low-temperature environments, the viscosity of the oil will increase as the temperature decreases. This invention uses real-time oil temperature to correct viscosity and achieves intelligent control of oil temperature by dynamically adjusting the zone boundaries and heating power, thereby ensuring oil return efficiency and high system operating efficiency.

[0057] S2. Set the valve allocation strategy according to the pressure zone, obtain the valve flow score, and select the 3 valves with the highest valve flow score as the main return oil path.

[0058] The valve allocation strategy includes extracting regions from the new oil pressure zones where the average oil resistance of all grids exceeds a set resistance threshold, designating these regions as high-resistance areas. The selection principle is to choose valves that are close to the critical areas and have sufficient flow capacity. The Euclidean distance between each valve and the center of the high-resistance area is calculated, and multiplied by the flow capacity ratio to obtain a valve flow score. The flow capacity ratio is the rated throughput of the current valve. Compared with the current actual traffic of the system The ratio is obtained, and the main return oil path from the compressor to the oil tank is set as the main channel; then the valve flow scoring formula is expressed as: Where Dis represents the Euclidean distance between the valve and the center of the high-resistance region; if the current valve's rated flow rate... Greater than or equal to the current actual traffic If the ratio is greater than or equal to 1, it indicates that the valve's flow capacity is sufficient, and the score is positively amplified. If the current valve's rated flow rate... Less than the current actual traffic If the ratio is less than 1, it indicates that the valve's capacity is insufficient and the score is suppressed.

[0059] The valve allocation strategy also includes an anti-blocking sub-strategy. If the allocation valve is located less than 50cm away from the main road, the nearest valve will be selected as the backup valve to replace it, thus avoiding airflow collision or oil circuit interference caused by valve allocation.

[0060] This invention utilizes oil pipe mesh division, resistance heat map and dynamic zoning to monitor the oil flow status in real time, and selects the optimal oil return path through valve distribution strategy, thereby ensuring that the oil can quickly return to the compressor, ensuring its good lubrication and stable operation.

[0061] S3. Set up a dynamic oil pressure adjustment algorithm, and combine it with high-altitude pressure to compensate for oil pressure deviation, and adjust the speed of the variable frequency oil pump and the opening of the electric regulating valve.

[0062] S4. Dynamically adjust the heating power of the main road zones based on real-time irradiance and pressure zones.

[0063] Example 2

[0064] Based on the inventive concept of Embodiment 1, this embodiment provides a specific implementation process of the oil pressure dynamic adjustment algorithm in step 3. Figure 2 The flowchart of the oil pressure dynamic adjustment algorithm of the intelligent pressurization, depressurization and return oil control method for an air conditioning system of the present invention is shown. The specific process includes:

[0065] S31. Calculate the real-time differential pressure error. Due to the large diurnal temperature range on the plateau, therefore... The effect of drastic temperature fluctuations on differential pressure changes is compensated for by the amplified error at low temperatures. Therefore, the formula for calculating the real-time differential pressure error is as follows: , This represents the set low-temperature critical value, in this embodiment... , Indicates the target pressure difference. This represents the actual pressure difference between the two ends of the oil circuit, and K is the real-time pressure difference error calculated using PID control. p K i K d , as optimization parameters;

[0066] The target pressure difference represents the minimum driving pressure difference required for oil return. If the actual pressure difference is less than the target pressure difference, the oil may not be able to flow due to excessive viscosity or excessive resistance. Therefore, in each generation of particles, the target pressure difference is updated according to the oil temperature. At low temperatures, the viscosity increases, requiring a higher target pressure difference. In high-altitude, low-pressure environments, additional pressure difference is needed to compensate for changes in gas solubility. The target pressure difference is then corrected based on the return oil pressure difference in the direct-flow pipeline and the high-altitude pressure compensation. The specific formula is as follows: Then, high-altitude pressure compensation , This represents the increase in air pressure for every 1000 meters increase in altitude. In this embodiment... It can be equal to 0.12 MPa.

[0067] By introducing temperature compensation into the real-time differential pressure error calculation, the algorithm can amplify the error impact under low temperature conditions, ensuring that the oil circuit differential pressure is accurately measured and compensated under the plateau conditions with drastic day-night temperature differences. This ensures that the oil return system can still operate stably under low temperature conditions and avoids poor oil return caused by a sharp increase in oil viscosity.

[0068] S32. Define the target particles for control, including the speed of the variable frequency oil pump and the opening degree of the electric regulating valve;

[0069] S33. Randomly generate N sets of control target particles and corresponding real-time differential pressure error and control target output combinations;

[0070] S34. Calculate the pressure difference after compensation for the actual temperature of all controlled target particles. Pressure difference after compensation with target temperature Root mean square error And the weighted sum of the normalized power of the variable frequency oil pump and the energy consumption of the valve at this time, expressed as: ,in, Indicates the power weight of the variable frequency oil pump. This indicates the valve's energy consumption, and n represents the variable frequency oil pump speed. This represents the total valve opening degree;

[0071] S35. Constructing the fitness function ,in, and This represents the set weights; the goal is to maximize Fac, i.e. minimize bias and energy consumption.

[0072] S36. Set the fitness threshold TFac and extract control target particles whose Fac is greater than TFac;

[0073] S37. Perform particle cross-linking on all extracted control target particles to generate new control target particles;

[0074] S38. Repeat steps S34-S37 and extract the response time of all particles based on historical data. When the response time of the new control target particle is less than the set response time threshold, stop the iteration and output the control target output combination at this time.

[0075] This invention dynamically generates control target particles and performs real-time iterative optimization, enabling the system to adaptively adjust the control output, namely the variable frequency oil pump speed and the electric regulating valve opening, based on the current oil temperature, oil pressure, and environmental conditions. This effectively copes with the changing working conditions in high-altitude environments and improves the robustness and reliability of the system.

[0076] By weighting the oil pump power and valve energy consumption in the fitness function and combining historical response time data, the algorithm can balance control accuracy and system response speed, achieving fast, energy-saving, and stable dynamic oil pressure regulation. While considering pressure difference deviation and energy consumption, it also takes into account response time, which helps to reduce the overall energy consumption of the air conditioning system.

[0077] This invention combines real-time irradiance adjustment to regulate the heating power of the main air conditioning zone, which not only improves oil flow but also optimizes system energy consumption and enhances the overall energy efficiency of the air conditioning system. The dynamic oil pressure regulation algorithm and PID optimization strategy further ensure rapid system response and precise control.

[0078] The specific steps of S4 include: collecting the current real-time irradiance and the oil resistance of all zones in the main channel, normalizing them, and calculating the total available power of the system based on the irradiance I through linear mapping. , The maximum heating power that each zone can withstand is set by those skilled in the art. The power allocation weight for each zone is calculated by multiplying the ratio of its oil resistance to the sum of the oil resistances of all zones by the total available power. The average temperature of each grid cell within each zone is calculated as the zone's oil temperature. When the zone's oil temperature exceeds the set target heating temperature for that zone, the heating process for that zone is terminated, and the heating power for all zones is redistributed. The target heating temperature for high-resistance zones is -15 degrees Celsius, while other zones are considered normal zones with a target heating temperature of -25 degrees Celsius. This ensures that zones with high resistance receive more heating resources and quickly improve flow conditions.

[0079] The threshold, weight, and target value settings can be configured using the default settings according to the present invention, or they can be set by the operator.

[0080] Example 3

[0081] Figure 3 This invention illustrates a framework diagram of an intelligent pressurization, depressurization, and oil return control system for an air conditioning system. Based on the same inventive concept as Embodiments 1 and 2, this invention provides an intelligent pressurization, depressurization, and oil return control system for an air conditioning system, comprising:

[0082] The tubing partitioning module divides the tubing layout data into grids, calculates the oil resistance of each grid cell, generates a resistance heatmap, and dynamically adjusts the pressure partitions. It includes a single-grid oil resistance model establishment unit and an oil grid merging unit. The single-grid oil resistance model establishment unit is used to calculate the oil resistance of each grid cell. The oil grid merging unit is used to obtain new oil pressure partitions based on the output of the single-grid oil resistance model establishment unit.

[0083] The valve allocation module sets a valve allocation strategy based on pressure zones, obtains valve flow scores, and selects the three valves with the highest valve flow scores as the main return oil path.

[0084] The oil pressure dynamic adjustment module, combined with high-altitude pressure compensation for oil pressure deviation, adjusts the variable frequency oil pump speed and the opening of the electric regulating valve;

[0085] The heating power distribution module dynamically adjusts the heating power of the main channel zones based on real-time irradiance and pressure zones.

[0086] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0090] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for intelligent pressurization, depressurization, and oil return control of an air conditioning system, characterized in that: Includes the following steps: S1. Divide the oil pipe layout data into grids, calculate the oil resistance of a single grid, generate a resistance heat map, and dynamically adjust the pressure zones. S2. Set the valve allocation strategy according to the pressure zone, obtain the valve flow score, and select the three valves with the highest valve flow score as the main return oil path. S3. Set up a dynamic oil pressure adjustment algorithm, and combine it with high-altitude pressure to compensate for oil pressure deviation, and adjust the speed of the variable frequency oil pump and the opening of the electric regulating valve. S4. Dynamically adjust the heating power of the main road zones based on real-time irradiance and pressure zones.

2. The intelligent pressurization, depressurization and oil return control method for an air conditioning system according to claim 1, characterized in that: Step S1 includes the following specific steps: S11. Import pipeline layout data from CAD drawings, including pipe diameter, length, and number of bends; S12. Divide the oil pipeline layout data into N×M grids along the oil flow direction, with each 0.2MPa pressure drop divided into a grid. Label the key nodes of the oil pipeline, including the compressor, oil tank and valves. S13. Establish a single-grid oil resistance model to obtain the single-grid oil resistance; S14. The oil resistance difference value is obtained by calculating the ratio of the oil resistance difference between adjacent grids to the mean value. S15. Merge grids with oil resistance differences of less than 15% to obtain a new oil pressure zone.

3. The intelligent pressurization, depressurization and oil return control method for an air conditioning system according to claim 2, characterized in that: The single-grid oil resistance model monitors the oil flow velocity (vol) and temperature (tem) in real time across all grids by installing flow velocity and temperature sensors in each grid. It also extracts the number of valves and elbows (eln) within each grid and designs a viscosity-temperature characteristic lubrication equation using the Vogel-Fulcher equation to obtain the oil viscosity. The single-grid resistance calculation combines the return oil pressure difference and oil flow velocity, and considers the dynamic roughness inside the pipe wall. It calculates the oil resistance at the direct-flow pipe, valve, and elbow respectively, obtaining the single-grid oil resistance. The calculation formula is as follows: ,in, Let L represent the oil flow rate at the i-th valve or elbow, and L represent the length of the direct-flow pipe. This indicates the critical temperature at which the oil freezes. The value represents the oil density, and r represents the radius of the DC pipe. This indicates the set elbow resistance coefficient. This indicates the set temperature sensitivity coefficient.

4. The intelligent pressurization, depressurization and oil return control method for an air conditioning system according to claim 1, characterized in that: The valve allocation strategy includes extracting regions in the new oil pressure zone where the average oil resistance of all grids is greater than a set resistance threshold and labeling them as high-resistance regions. The Euclidean distance between each valve and the center of the high-resistance region is calculated and multiplied by the flow capacity ratio to obtain a valve flow score. The flow capacity ratio is obtained by comparing the rated throughput of the current valve with the current actual flow rate of the system. The main return oil path from the compressor to the oil storage tank is designated as the main channel. The valve allocation strategy also includes an anti-blocking sub-strategy: if the allocated valve is located less than 50cm from the main channel, the nearest valve is selected as the backup valve to replace it.

5. The intelligent pressurization, depressurization, and oil return control method for an air conditioning system according to claim 1, characterized in that: The specific process of the oil pressure dynamic adjustment algorithm includes: S31. Calculate the real-time differential pressure error. , This indicates the set low-temperature critical value. Indicates the target pressure difference. This represents the actual pressure difference between the two ends of the oil circuit, and K is the real-time pressure difference error calculated using PID control. p K i K d , as optimization parameters; S32. Define the target particles for control, including the speed of the variable frequency oil pump and the opening degree of the electric regulating valve; S33. Randomly generate N sets of control target particles and corresponding real-time differential pressure error and control target output combinations; S34. Calculate the root mean square error between the actual temperature-compensated pressure difference and the target temperature-compensated pressure difference for all controlled target particles. And the weighted sum of the variable frequency oil pump power and valve energy consumption at this time, denoted as Een; S35. Constructing the fitness function ,in, and Indicates the set weight; S36. Set the fitness threshold TFac and extract control target particles whose Fac is greater than TFac; S37. Perform particle cross-linking on all extracted control target particles to generate new control target particles; S38. Repeat steps S34-S37 and extract the response time of all particles based on historical data. When the response time of the new control target particle is less than the set response time threshold, stop the iteration and output the control target output combination at this time.

6. The intelligent pressurization, depressurization and oil return control method for an air conditioning system according to claim 5, characterized in that: The target differential pressure represents the minimum driving differential pressure required for oil return. In each generation of particles, the target differential pressure is corrected based on the oil return differential pressure of the fluid in the DC pipeline and the plateau pressure compensation.

7. The intelligent pressurization, depressurization and oil return control method for an air conditioning system according to claim 1, characterized in that: The specific steps of S4 include: collecting the current real-time irradiance and the oil resistance of all zones in the main channel, and normalizing them; calculating the total available power Ptol of the system based on the irradiance I through linear mapping; multiplying the ratio of the oil resistance of each zone to the sum of the oil resistance of all zones as the power allocation weight of that zone with the total available power to obtain the heating power of all zones; calculating the average temperature of a single grid in each zone as the zone oil temperature; when the zone oil temperature is greater than the set target heating temperature of that zone, terminating the heating process of that zone, and redistributing the heating power of all zones.

8. An intelligent pressurization, depressurization, and oil return control system for an air conditioning system, used to execute the intelligent pressurization, depressurization, and oil return control method for an air conditioning system as described in any one of claims 1-7, characterized in that: include: The tubing partitioning module divides the tubing layout data into grids, calculates the oil resistance of each grid, generates a resistance heat map, and dynamically adjusts the pressure partitions. The valve allocation module sets a valve allocation strategy based on pressure zones, obtains valve flow scores, and selects the three valves with the highest valve flow scores as the main return oil path. The oil pressure dynamic adjustment module, combined with high-altitude pressure compensation for oil pressure deviation, adjusts the variable frequency oil pump speed and the opening of the electric regulating valve; The heating power distribution module dynamically adjusts the heating power of the main channel zones based on real-time irradiance and pressure zones.

9. The intelligent pressurization, depressurization, and oil return control system for an air conditioning system according to claim 8, characterized in that: The oil pipe partitioning module includes a single-grid oil resistance model establishment unit and an oil grid merging unit; the single-grid oil resistance model establishment unit is used to calculate the single-grid oil resistance; the oil grid merging unit is used to obtain a new oil pressure partition based on the output of the single-grid oil resistance model establishment unit.

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