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

Abstract

The invention discloses an intelligent pressurization and decompression oil return control method and system for an air conditioner system, and relates to the technical field of air conditioner lubricating oil control. The method comprises the steps that grids are divided through oil pipe layout data, single-grid oil resistance is calculated, a resistance thermodynamic diagram is generated, and pressure partitions are dynamically adjusted; a valve distribution strategy is set according to the pressure partitions, valve circulation scores are obtained, and three valves with the highest valve circulation scores are selected as main oil return paths; setting an oil pressure dynamic adjusting algorithm, compensating oil pressure deviation by combining plateau pressure, and adjusting the rotating speed of a variable-frequency oil pump and the opening of an electric adjusting valve; and dynamically adjusting the subarea heating power of the trunk road according to the real-time irradiance and the pressure subarea. The method solves the problem that in the plateau or extreme low-temperature environment, the temperature fluctuates violently, and the oil viscosity rises, so that the decision of the oil return route of the air conditioner is wrong.

Description

Technical Field

[0001] The present invention relates to the technical field of air-conditioning lubricating oil control, and in particular to an intelligent pressurization and decompression oil return control method and system for an air-conditioning system. Background Art

[0002] Inverter air conditioners achieve dynamic adjustment of cooling capacity and optimization of energy consumption by precisely controlling the compressor speed and refrigerant flow. However, in air conditioning systems, the compressor, as a core component, cannot operate normally without effective lubrication and heat dissipation, and lubricant recovery (oil return) is one of the key technologies to ensure long-term stable operation of the compressor.

[0003] In plateau areas above 4,000 meters above sea level, due to the low atmospheric pressure and extremely low temperature, the lubricating oil of the air-conditioning system becomes very viscous at low temperatures, and oil reflux (oil return) becomes 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 both day and night temperature fluctuations and extreme low temperature conditions.

[0004] In the actual oil return control of the air-conditioning system, the compressor in the air-conditioning system will bring out some of the lubricating oil during operation. If it cannot be effectively recovered, it may lead to insufficient lubrication, increased energy consumption and even equipment failure. In traditional air-conditioning systems, oil recovery is mostly achieved by fixed valves and simple mechanical structures, but this type of method has shortcomings such as poor adaptability, imprecise adjustment, high energy consumption and slow response to environmental changes. Especially under extreme working conditions such as high load, low temperature or plateau, the viscosity and fluidity of the oil change greatly. Traditional fixed control methods are difficult to ensure that there is sufficient oil in the compressor, which can easily lead to insufficient lubrication, friction and wear, and even liquid impact accidents, seriously affecting system reliability and equipment life.

[0005] To this end, the present invention provides an intelligent pressurization and decompression oil return control method and system for an air conditioning system. Summary of the invention

[0006] The object of the present invention is to provide an intelligent pressurization and decompression oil return control method and system for an air conditioning system to solve the existing problems raised in the above background technology.

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

[0008] S1. Divide the grid by the oil pipeline layout data, calculate the single grid oil resistance, generate the resistance heat map, and dynamically adjust the pressure partition;

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

[0010] S3. Set up an oil pressure dynamic adjustment algorithm to adjust the rotational speed of the variable-frequency oil pump and the opening degree of the electric control valve by combining the plateau pressure compensation for the oil pressure deviation.

[0011] S4. Dynamically adjust the heating power of the main road section according to the real-time irradiance and pressure zoning.

[0012] A further improvement of the present invention lies in that the specific steps of step S1 include:

[0013] S11. Import the oil pipe layout data through CAD drawings, including pipe diameter, length, and the number of elbows.

[0014] S12. Divide the oil pipe layout data along the oil flow direction with a pressure drop of every 0.2 MPa as a grid into an N×M grid, and mark the key nodes of the oil circuit, including compressors, oil storage tanks, and valves.

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

[0016] S14. Obtain the oil fluid resistance difference value by calculating the ratio of the difference value to the average value of the single-grid oil fluid resistances of adjacent grids.

[0017] S15. Merge the grids with an oil fluid resistance difference value less than 15% to obtain a new oil pressure zoning.

[0018] A further improvement of the present invention lies in that the single-grid oil fluid resistance model installs a flow velocity sensor and a temperature sensor in each grid to monitor the oil fluid flow velocity vol and temperature tem of all grids in real time, and extracts the number of valves and elbows eln in the grid. The viscosity-temperature characteristic lubrication equation is designed through the Vogel-Fulcher equation to obtain the oil fluid viscosity μ tem , and the single-grid resistance is calculated by combining the oil return pressure difference and the oil fluid flow velocity, and considering the dynamic roughness inside the pipe wall, respectively calculate the oil fluid resistances at the straight pipe, valve, and elbow to obtain the single-grid oil fluid resistance. The calculation formula is where vol i represents the oil fluid flow velocity of the i-th valve or elbow, L represents the length of the straight pipe, tem crit represents the critical temperature of oil fluid icing, ρ represents the oil fluid density, d represents the length of the straight pipe, k0 represents the set elbow resistance coefficient, and β represents the set temperature sensitivity coefficient.

[0019] A further improvement of the present invention lies in that the valve distribution strategy includes extracting the areas where the average oil resistance of all grids in the newly extracted oil pressure partition is greater than the set resistance threshold as high-resistance areas, calculating the Euclidean distance between each valve and the center of the high-resistance area, and multiplying it by the flow capacity ratio to obtain the valve flow score. The flow capacity ratio is obtained by the ratio of the rated flux of the current valve to the current actual flow of the system. The main oil return path from the compressor to the oil storage tank is set as the main road. The valve distribution strategy also incorporates an anti-blocking sub-strategy. If the allocated valve is less than 50 cm away from the main road, the valve closest to it is selected as the standby valve to replace it.

[0020] A further improvement of the present invention lies in that the specific process of the oil pressure dynamic regulation algorithm includes:

[0021] S31. Calculate the real-time pressure difference error e pt (t)=(ΔPtar - ΔPact)·(1 + 0.05(tem min -tem)), tem min represents the set low-temperature critical value, ΔPtar represents the target pressure difference, ΔPact represents the actual pressure difference at both ends of the oil circuit, and the K p 、K i 、K d of the real-time pressure difference error are calculated through PID as the optimization parameters;

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

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

[0024] S34. Calculate the root mean square error Δe pt between the pressure difference after actual temperature compensation of all control target particles and the pressure difference after target temperature compensation, as well as the weighted average of the variable-frequency oil pump power and valve energy consumption at this time;

[0025] S35. Construct the fitness function where α1 and α2 represent the set weights;

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

[0027] S37. Perform particle crossover on all the extracted control target particles to generate new control target particles;

[0028] S38. Repeat steps S34 - S37, extract the response time corresponding to all particles based on historical data. When the response time corresponding to 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 the present invention lies in 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 according to the oil return pressure difference of the fluid in the DC pipeline and the plateau pressure compensation.

[0030] A further improvement of the present invention lies in that the specific steps of S4 include: collecting the current real - time irradiance and the oil fluid resistance of all partitions of the main pipeline, and normalizing them. Calculate the total available power Ptol of the system through linear mapping according to the irradiance I. Multiply the proportion of the oil fluid resistance of each partition in the sum of the oil fluid resistances of all partitions as the power distribution weight of this partition by the available total power to obtain the heating power of all partitions. Calculate the average value of the single - grid temperature in each partition as the partition oil temperature. When the partition oil temperature is greater than the set target heating temperature of this partition, terminate the heating process of this partition and re - distribute the heating power of all partitions.

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

[0032] An oil pipeline partition module, which divides grids through oil pipeline layout data, calculates the oil fluid resistance of a single grid, generates a resistance - heat map, and dynamically adjusts the pressure partition;

[0033] A valve distribution module, which sets a valve distribution strategy according to the pressure partition, obtains a valve flow score, and selects the 3 valves with the highest valve flow score as the main oil return paths;

[0034] An oil pressure dynamic adjustment module, which combines the plateau pressure compensation for the oil pressure deviation and adjusts the rotational speed of the variable - frequency oil pump and the opening degree of the electric control valve;

[0035] A heating power distribution module, which dynamically adjusts the heating power of the main pipeline partitions according to the real - time irradiance and the pressure partition.

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

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

[0038] 1. First, the present invention finely divides the oil pipe grid and establishes a single-grid oil fluid resistance model to monitor the changes in oil fluid flow rate, temperature, and viscosity in real time. By using the oil fluid resistance heat map and dynamic zoning, high-resistance areas can be accurately identified, providing a basis for subsequent heating, valve distribution, and oil pressure regulation, thereby improving the oil return efficiency of the entire oil circuit 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 oil return valve is selected through comprehensive scoring, and a spare valve is reserved through an anti-blocking sub-strategy, effectively avoiding the problem of flow counterflow caused by the too-close valve position or oil circuit interference and ensuring the smoothness of the oil fluid flow path;

[0040] 3. An oil pressure dynamic regulation algorithm is introduced. Combining with high-altitude pressure compensation, the PID algorithm is used to adjust the pressure difference at both ends of the real-time oil circuit, thereby eliminating the pressure difference fluctuation caused by the change in oil fluid viscosity in a low-temperature environment by adjusting the rotation speed of the variable-frequency oil pump and the opening degree of the electric control valve, and improving the adaptability and stability of the system under high-altitude and low-temperature conditions. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0043] Figure 3 is a framework diagram of an intelligent pressurization and decompression oil return control system for an air-conditioning system according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The technical solution of the present invention will be described in detail below through the 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 on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0045] The term "and / or" merely describes the associated relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " generally represents an "or" relationship between the associated objects before and after.

[0046] Embodiment 1

[0047] Figure 1 shows a flowchart of an intelligent pressurization and decompression oil return control method for an air-conditioning system disclosed in this embodiment. The steps are as follows:

[0048] S1. Divide the grid according to the tubing layout data, calculate the oil fluid resistance of a single grid, generate a resistance heat map, and dynamically adjust the pressure zone; the specific steps include:

[0049] S11. Import the tubing layout data through CAD drawings, including pipe diameter, length, and number of elbows;

[0050] S12. Along the oil flow direction of the tubing, divide the tubing layout data into N×M grids with a pressure drop of 0.2 MPa per grid, and mark the key nodes of the oil circuit, including compressors, storage tanks, and valves;

[0051] S13. Establish a single-grid oil fluid resistance model to obtain the oil fluid resistance of a single grid; the single-grid oil fluid resistance model installs flow velocity sensors and temperature sensors in each grid to monitor the oil fluid flow velocity vol and temperature tem of all grids in real time, and extracts the number of valves and elbows eln in the grid. Design a viscosity-temperature characteristic lubrication equation through the Vogel-Fulcher equation to obtain the oil fluid viscosity μ tem , and the specific calculation formula is expressed as where μ0 represents the reference viscosity, tem0 represents the set standard temperature, B represents the material constant. For example, for L-AN32 oil, B = 1200, tem0 = -60 °C. Then, the single-grid resistance calculation combines the oil return pressure difference and the oil fluid flow velocity, and considering the dynamic roughness inside the pipe wall, calculates the oil fluid resistance at the straight pipe, valve, and elbow respectively to obtain the oil fluid resistance of a single grid. The calculation formula is where vol i represents the oil fluid flow velocity of the i-th valve or elbow, L represents the length of the straight pipe, tem crit represents the critical temperature of oil fluid icing, ρ represents the oil fluid density, r represents the radius of the straight pipe, k0 represents the set elbow resistance coefficient, β represents the set temperature sensitivity coefficient, which is calibrated by experiments;

[0052] k0(1 + β(tem crit - tem)) represents the coupling of temperature and surface roughness at the valve or elbow to quantify the influence of temperature on the elbow resistance, which changes with temperature and icing degree, and reflects the influence of icing or sediment on the inner wall of the pipe caused by low temperature;

[0053] Describe the oil return pressure difference of the fluid in the straight pipe under laminar flow state through the Hagen-Poiseuille formula The volume flow rate Q is expressed as the velocity multiplied by the cross-sectional area;

[0054] S14. Obtain the oil fluid resistance difference value by calculating the ratio of the difference value to the mean value of the single-grid oil fluid resistance of adjacent grids;

[0055] S15. Merge the meshes with the oil fluid resistance difference value less than 15% to obtain a new oil fluid pressure partition.

[0056] In an air-conditioning system, especially in a high-altitude or low-temperature environment, the viscosity of the oil fluid will increase due to the decrease in temperature. The present invention corrects the viscosity using the real-time oil temperature, and realizes the intelligent regulation of the oil fluid temperature by dynamically adjusting the partition boundary and the heating power, so as to ensure the oil return efficiency and the high efficiency of the system operation.

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

[0058] The valve distribution strategy includes extracting the area where the average value of the oil fluid resistance of all meshes in the new oil fluid pressure partition is greater than the set resistance threshold value and recording it as the high-resistance area. The selection principle is to select the valves that are close to the key area and have sufficient flow capacity. Calculate the Euclidean distance between each valve and the center of the high-resistance area, and multiply it by the flow capacity ratio to obtain the valve flow score. The flow capacity ratio is obtained by the ratio of the rated flux Q of the current valve max to the current actual flow rate Q of the system ref . Set the main oil return path from the compressor to the oil storage tank as the main road; then the valve flow score formula is expressed as: where Dis represents the Euclidean distance between the valve and the center of the high-resistance area; if the rated flux Q of the current valve max is greater than or equal to the current actual flow rate Q ref , then the ratio is greater than or equal to 1, indicating that the valve flow capacity is sufficient and the score is amplified positively. If the rated flux Q of the current valve max is less than the current actual flow rate Q ref , then the ratio is less than 1, indicating that the valve capacity is insufficient and the score is suppressed.

[0059] The valve distribution strategy also includes an anti-blocking sub-strategy. If the allocated valve is less than 50 cm away from the main road, select the valve closest to this valve as the standby valve to replace this valve, so as to avoid air flow counteracting or oil circuit interference caused by valve distribution.

[0060] The present invention uses the oil pipe mesh division, the resistance heat map and the dynamic partition to monitor the oil fluid flow state in real time, and selects the best oil return path through the valve distribution strategy, so as to ensure that the oil fluid can quickly flow back to the compressor and ensure its good lubrication and stable operation.

[0061] S3. Set the oil pressure dynamic adjustment algorithm, and combine the high-altitude pressure compensation to correct the oil pressure deviation, and adjust the rotation speed of the variable-frequency oil pump and the opening degree of the electric control valve;

[0062] S4. Dynamically adjust the heating power of the main road partition according to the real-time irradiance and the pressure partition.

[0063] Embodiment 2

[0064] Based on the inventive concept of Embodiment 1, this embodiment provides the 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 an intelligent pressurization, decompression and oil return control method for an air conditioning system according to the present invention is shown. The specific process includes:

[0065] S31. Calculate the real-time pressure difference error. Since the temperature difference between day and night on the plateau is relatively large, the influence of the drastic temperature fluctuation on the pressure difference change is compensated by (1 + 0.05(tem0 - tem)). It is manifested that the error influence is amplified at low temperatures. Then the real-time pressure difference error calculation formula is e pt (t) = (ΔPtar - ΔPact)·(1 + 0.05(tem min -tem)), tem min represents the set low-temperature critical value. In this embodiment, tem min = -20°C, ΔPtar represents the target pressure difference, ΔPact represents the actual pressure difference at both ends of the oil circuit. The K p 、K i 、K d of the real-time pressure difference error are calculated through PID and used 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 flow due to too high viscosity or too large resistance. Therefore, in each generation of particles, the target pressure difference is updated with the change of oil temperature. At low temperatures, the viscosity increases and a higher target pressure difference is required. In a high-altitude and low-pressure environment, an additional pressure difference is needed to compensate for the change in gas solubility. Then the target pressure difference is corrected according to the oil return pressure difference of the fluid in the DC pipeline and the plateau pressure compensation. The specific formula is: ΔPtar = smp' + ΔPalt. Then the plateau pressure compensation ΔPalt = alt·(H / 1000), where alt represents the increase in air pressure per 1000 meters of altitude. In this embodiment, alt can be equal to 0.12 MPa.

[0067] By introducing temperature compensation in the real-time pressure difference error calculation, the algorithm can amplify the error influence in a low-temperature environment, ensure accurate measurement and compensation of the oil circuit pressure difference under the plateau conditions with drastic temperature difference between day and night, so as to ensure the stable operation of the oil return system in a low-temperature environment and avoid poor oil return caused by a sharp increase in oil viscosity.

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

[0069] S33. Randomly generate N groups of control target particles and their corresponding real-time pressure difference errors and control target output combinations;

[0070] S34. Calculate the root mean square error Δe of the pressure difference ΔPtar·(1 + 0.05(tem min -tem)) after actual temperature compensation of all control target particles and the pressure difference ΔPact·(1 + 0.05(tem min -tem)) after target temperature compensation, pt and the weighted sum of the normalized variable-frequency oil pump power and valve energy consumption at this time, expressed as where, represents the weight of the variable-frequency oil pump power, represents the valve energy consumption, n represents the variable-frequency oil pump speed, and sum(θ) represents the total valve opening;

[0071] S35. Construct a fitness function where α1 and α2 represent the set weights; achieve the maximization of Fac, that is, minimize the deviation and energy consumption.

[0072] S36. Set a fitness threshold TFac, and extract the control target particles with Fac greater than TFac;

[0073] S37. Perform particle crossover on all the extracted control target particles to generate new control target particles;

[0074] S38. Repeat steps S34 - S37, and extract the response time corresponding to all particles according to historical data. When the response time corresponding to 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] The present 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 opening of the electric control valve, according to the current oil temperature, oil pressure and environmental conditions, effectively coping with the changing working conditions in the plateau environment and enhancing the robustness and reliability of the system.

[0076] By performing weighted processing on the oil pump power and valve energy consumption in the fitness function and combining historical response time data, the algorithm can take into account both control accuracy and system response speed, achieve fast, energy-saving and stable dynamic oil pressure regulation, and take into account the response time while considering the pressure difference deviation and energy consumption, which helps to reduce the overall energy consumption of the air-conditioning system.

[0077] The present invention combines real-time irradiance to adjust the heating power of the main road partition, which can not only ensure the improvement of oil fluidity, but also optimize the system energy consumption and improve the energy-saving effect of the overall air-conditioning system. The dynamic oil pressure regulation algorithm and PID optimization strategy further ensure the rapid response and precise control of the system.

[0078] The specific steps of S4 include: collecting the current real-time irradiance and the oil fluid resistance of all partitions of the main road, and normalizing them. According to the irradiance I, calculate the total available power of the system Ptol = min(Ptol_max, λ×I) through linear mapping. Ptol_max represents the maximum heating power that each partition can withstand, which is set by those skilled in the art. Multiply the proportion of the oil fluid resistance of each partition in the sum of the oil fluid resistances of all partitions as the power distribution weight of this partition by the available total power to obtain the heating power of all partitions; calculate the average value of the single-grid temperature in each partition as the partition oil temperature. When the partition oil temperature is greater than the set target heating temperature of this partition, terminate the heating process of this partition, and re-distribute the heating power of all partitions. The target heating temperature of the high-resistance area partition is -15 °C, and other partitions are ordinary areas. The target heating temperature of the ordinary area partition is -25 °C, so as to ensure that the partitions with large resistance obtain more heating resources and quickly improve the flow condition.

[0079] The setting of the threshold, weight, target value, etc. can be based on the default settings of the present invention or can be set by the operator himself.

[0080] Embodiment 3

[0081] Figure 3 It shows a framework diagram of an intelligent pressurization, decompression and oil return control system for an air-conditioning system of the present invention. Based on the same inventive concept as Embodiment 1 and Embodiment 2, the present invention provides an intelligent pressurization, decompression and oil return control system for an air-conditioning system, including:

[0082] The oil pipe partition module divides the grid through the oil pipe layout data, calculates the single-grid oil fluid resistance, generates a resistance thermal map, and dynamically adjusts the pressure partition; it includes a single-grid oil fluid resistance model establishment unit and an oil fluid grid merging unit; the single-grid oil fluid resistance model establishment unit is used to calculate the single-grid oil fluid resistance; the oil fluid grid merging unit is used to obtain a new oil fluid pressure partition according to the output of the single-grid oil fluid resistance model establishment unit.

[0083] The valve distribution module sets the valve distribution strategy according to the pressure partition, obtains the valve flow score, and selects the 3 valves with the highest valve flow score as the main oil return path;

[0084] The oil pressure dynamic adjustment module combines the plateau pressure compensation oil pressure deviation to adjust the rotation speed of the variable-frequency oil pump and the opening degree of the electric control valve;

[0085] The heating power distribution module dynamically adjusts the heating power of the main road partition according to the real-time irradiance and the pressure partition.

[0086] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0087] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0088] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0090] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present invention and without departing from the spirit and scope protected by the present invention's claims, can also make many forms, and all of these fall within the protection scope of the present invention.

Claims

1. An intelligent pressurization, decompression and oil return control method for an air conditioning system, characterized in that: It includes the following steps: S1. Divide the grid according to the oil pipe layout data, calculate the oil fluid resistance of a single grid, generate a resistance heat map, and dynamically adjust the pressure partition; S2. Set the valve distribution strategy according to the pressure partition, obtain the valve flow score, and select the 3 valves with the highest valve flow score as the main oil return path; S3. Set the oil pressure dynamic adjustment algorithm, combine the plateau pressure compensation to correct the oil pressure deviation, and adjust the speed of the variable frequency oil pump and the opening of the electric control valve; S4. Dynamically adjust the heating power of the main road partition according to the real-time irradiance and pressure partition.

2. The intelligent pressurization, decompression and oil return control method for an air conditioning system according to claim 1, characterized in that: The specific steps of step S1 include: S11. Import the oil pipe layout data through CAD drawings, including pipe diameter, length, and the number of elbows; S12. Along the oil flow direction of the oil pipe layout data, divide it into N×M grids with a pressure drop of 0.2MPa per grid, and mark the key nodes of the oil path, including compressors, oil storage tanks, and valves; S13. Establish a single-grid oil fluid resistance model to obtain the oil fluid resistance of a single grid; S14. Obtain the oil fluid resistance difference value by calculating the ratio of the difference value of the oil fluid resistance of adjacent grids to the mean value; S15. Merge the grids with an oil fluid resistance difference value less than 15% to obtain a new oil pressure partition.

3. An intelligent pressurization, decompression and oil return control method for an air conditioning system according to claim 2, characterized in that: The single-grid oil fluid resistance model installs flow velocity sensors and temperature sensors in each grid to monitor the oil fluid flow velocity vol and temperature tem of all grids in real time, extracts the number of valves and elbows eln in the grid, designs a viscosity-temperature characteristic lubrication equation through the Vogel-Fulcher equation, and obtains the oil fluid viscosity μ tem , then the single-grid resistance calculation combines the oil return pressure difference and the oil fluid flow velocity, and considering the dynamic roughness inside the pipe wall, calculates the oil fluid resistance at the straight pipe, valve and elbow respectively to obtain the single-grid oil fluid resistance. The calculation formula is where, vol i represents the oil fluid flow velocity of the i-th valve or elbow, L represents the length of the straight pipe, tem crit represents the critical temperature of oil fluid icing, ρ represents the oil fluid density, r represents the radius of the straight pipe, k0 represents the set elbow resistance coefficient, and β represents the set temperature sensitivity coefficient.

4. An intelligent pressurization, decompression and oil return control method for an air conditioning system according to claim 1, characterized in that: The valve distribution strategy includes extracting the areas where the mean value of the oil fluid resistance of all grids in the new oil pressure partition is greater than the set resistance threshold and recording them as high-resistance areas, calculating the Euclidean distance between each valve and the center of the high-resistance area, and multiplying it by the flow capacity ratio to obtain the valve flow score. The flow capacity ratio is obtained by the ratio of the rated flux of the current valve to the current actual flow of the system. Set the main oil return path from the compressor to the oil storage tank as the main road; the valve distribution strategy also includes an anti-blocking sub-strategy. If the allocated valve is less than 50cm away from the main road, select the valve closest to this valve as the standby valve to replace this valve.

5. An intelligent pressurization, decompression 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 pressure difference error e pt (t) = (ΔPtar - ΔPact)·(1 + 0.05(tem min -tem)), tem min represents the set low-temperature critical value, ΔPtar represents the target pressure difference, ΔPact represents the actual pressure difference across the oil circuit, and the K p 、K i 、K d obtained by calculating the real-time pressure difference error through PID are used as optimization parameters; S32. Define the control target particles, including the speed of the variable frequency oil pump and the opening of the electric control valve; S33. Randomly generate N groups of control target particles and their corresponding real-time pressure difference errors and control target output combinations; S34. Calculate the root mean square error Δe between the pressure difference after actual temperature compensation of all controlled target particles and the pressure difference after target temperature compensation, pt and the weighted average of the variable frequency oil pump power and valve energy consumption at this time; S35. Construct the fitness function where α1 and α2 represent the set weights; S36. Set the fitness threshold TFac, and extract the control target particles with Fac greater than TFac; S37. Perform particle crossover on all the extracted control target particles to generate new control target particles; S38. Repeat steps S34 - S37, and extract the response time corresponding to all particles according to historical data. When the response time corresponding to the new control target particles is less than the set response time threshold, stop the iteration and output the control target output combination at this time.

6. An intelligent pressurization, decompression and oil return control method for an air conditioning system according to claim 5, characterized in that: The target pressure difference represents the minimum driving pressure difference required for oil return. In each generation of particles, correct the target pressure difference according to the oil return pressure difference of the fluid in the straight pipeline and the plateau pressure compensation.

7. The intelligent pressurization, decompression 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 fluid resistance of all partitions of the main road, and normalizing them. Calculate the total available power Ptol of the system through linear mapping according to the irradiance I. Multiply the proportion of the oil fluid resistance of each partition in the sum of the oil fluid resistances of all partitions as the power distribution weight of this partition by the available total power to obtain the heating power of all partitions; calculate the average value of the single-grid temperature in each partition as the partition oil temperature. When the partition oil temperature is greater than the set target heating temperature of this partition, terminate the heating process of this partition and re-distribute the heating power of all partitions.

8. An intelligent pressure boosting, pressure reducing and oil return control system for an air conditioning system, which is used to execute an intelligent pressure boosting, pressure reducing and oil return control method for an air conditioning system according to any one of claims 1-7, and is characterized in that: including: A tubing partition module that divides grids through tubing layout data, calculates the oil fluid resistance of a single grid, generates a resistance heat map, and dynamically adjusts the pressure partition; A valve distribution module that sets a valve distribution strategy according to the pressure partition, obtains a valve flow score, and selects the 3 valves with the highest valve flow score as the main oil return path; An oil pressure dynamic adjustment module that combines the plateau pressure compensation to correct the oil pressure deviation and adjusts the rotational speed of the variable-frequency oil pump and the opening of the electric control valve; A heating power distribution module that dynamically adjusts the heating power of the main road partitions according to the real-time irradiance and the pressure partition.

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

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