Multi-system energy-saving control method and system for airport ground air conditioning unit

Abstract

The invention discloses an airport ground air conditioning unit multi-system energy-saving control method and system, and the method comprises the steps: collecting refrigerant flow data, temperature monitoring data and load demand data of an airport air conditioning system, recognizing a phase change boundary, extracting a gas-liquid conversion point, and carrying out enthalpy value coupling in combination with superheat degree distribution to generate a phase change energy field; identifying heat transfer bottleneck points based on the phase change energy field, generating an equivalent thermal resistance network, and extracting heat capacity characteristic parameters to construct a thermal response path diagram; refrigerant shunting analysis is carried out to extract primary and secondary loops, and an optimal distribution point is identified to generate a load balance matrix; reconstructing a compressor adjusting curve, and identifying a high-efficiency operation interval to generate a frequency conversion adjusting sequence; determining an energy storage window period, and obtaining a dynamic buffer threshold to construct a peak clipping and valley filling instruction set; and an execution deviation track is monitored, the optimal convergence path is recognized, self-adaptive correction is conducted, an energy-saving control instruction is generated, and intelligent multi-system coordination efficient energy-saving control over the airport ground air conditioning unit is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air conditioning energy-saving control, in particular to an airport ground air conditioning unit multi-system energy-saving control method and system. BACKGROUND

[0002] The airport ground air conditioning unit, as an important infrastructure of the airport, undertakes the temperature and humidity regulation tasks of large space such as the terminal building, office area and equipment room. With the rapid development of civil aviation industry and the continuous expansion of the airport scale, the energy consumption problem of the air conditioning system is increasingly prominent, which has become an important part of the airport operation cost. The existing airport air conditioning system generally has problems such as redundant equipment configuration, single operation strategy and inaccurate load matching, resulting in low overall energy efficiency ratio of the system.

[0003] The traditional air conditioning control method mainly relies on simple temperature feedback control and fixed frequency operation mode, and lacks deep analysis of the phase change characteristics of the refrigerant and comprehensive consideration of the thermodynamic characteristics of the system. The existing technology cannot realize the coordinated optimization of the core components such as compressor, condenser, expansion valve and evaporator, and cannot effectively handle the peak-valley difference of energy consumption caused by load fluctuation. In addition, the existing control system lacks accurate control ability of refrigerant flow distribution, heat transfer bottleneck identification and dynamic energy storage regulation, resulting in significant decrease of system operation efficiency under partial load conditions and serious energy waste. Therefore, a method is needed to solve at least one of the above problems. SUMMARY

[0004] The present application discloses an airport ground air conditioning unit multi-system energy-saving control method and system, which realizes multi-component coordinated optimization and intelligent energy-saving control by deeply mining the phase change law of refrigerant and the thermodynamic characteristics of the system. The method identifies the heat transfer bottleneck based on phase change energy field analysis, constructs equivalent thermal resistance network and thermal response path diagram, optimizes refrigerant distribution and load balance control, combines compressor variable frequency regulation and energy storage window period management, and finally realizes peak clipping and valley filling and adaptive energy-saving control, improving the operation efficiency and energy-saving level of the airport ground air conditioning unit.

[0005] The present application discloses an airport ground air conditioning unit multi-system energy-saving control method and system, which realizes multi-component coordinated optimization and intelligent energy-saving control by deeply mining the phase change law of refrigerant and the thermodynamic characteristics of the system. The method identifies the heat transfer bottleneck based on phase change energy field analysis, constructs equivalent thermal resistance network and thermal response path diagram, optimizes refrigerant distribution and load balance control, combines compressor variable frequency regulation and energy storage window period management, and finally realizes peak clipping and valley filling and adaptive energy-saving control, improving the operation efficiency and energy-saving level of the airport ground air conditioning unit. The method comprises the following steps: identify a heat transfer bottleneck point based on the phase change energy field, perform thermal resistance analysis on the heat transfer bottleneck point to generate an equivalent thermal resistance network, extract a heat capacity characteristic parameter from the equivalent thermal resistance network, and construct a thermodynamic response path graph using the equivalent thermal resistance network and the heat capacity characteristic parameter; perform refrigerant flow analysis on the thermodynamic response path graph to extract a primary and secondary circuit, analyze enthalpy difference distribution of the primary and secondary circuit to identify an optimal distribution point, perform dynamic weight distribution on the optimal distribution point to generate a load balance matrix; reconstruct a compressor adjustment curve based on the load balance matrix, perform efficiency mapping of the compressor adjustment curve and the superheat distribution to identify a high-efficiency operation interval, and generate a variable frequency adjustment sequence using the high-efficiency operation interval; determine an energy storage window period by performing time sequence correlation analysis on the variable frequency adjustment sequence and the load demand data, obtain a dynamic buffer threshold value based on matching of the energy storage window period and the heat capacity characteristic parameter, and construct a peak clipping and valley filling instruction set according to the dynamic buffer threshold value; monitor an execution deviation trajectory of the peak clipping and valley filling instruction set, identify an optimal convergence path based on convergence analysis of the deviation trajectory, and generate an energy-saving control instruction by adaptively modifying the variable frequency adjustment sequence along the optimal convergence path.

[0006] The second aspect of the present application proposes an airport ground air conditioning unit multi-system energy-saving control system, comprising: A data acquisition module is configured to acquire refrigerant flow data, temperature monitoring data and load demand data of an airport air conditioning unit, identify a gas-liquid conversion point based on phase change boundaries of the refrigerant flow data, extract a superheat distribution from the temperature monitoring data, and generate a phase change energy field by coupling enthalpy values of the gas-liquid conversion point and the superheat distribution. A thermal resistance analysis module is configured to identify a heat transfer bottleneck point based on the phase change energy field, perform thermal resistance analysis on the heat transfer bottleneck point to generate an equivalent thermal resistance network, extract a heat capacity characteristic parameter from the equivalent thermal resistance network, and construct a thermodynamic response path graph using the equivalent thermal resistance network and the heat capacity characteristic parameter. A load balance module is configured to perform refrigerant flow analysis on the thermodynamic response path graph to extract a primary and secondary circuit, analyze enthalpy difference distribution of the primary and secondary circuit to identify an optimal distribution point, perform dynamic weight distribution on the optimal distribution point to generate a load balance matrix. A variable frequency control module is configured to reconstruct a compressor adjustment curve based on the load balance matrix, perform efficiency mapping of the compressor adjustment curve and the superheat distribution to identify a high-efficiency operation interval, and generate a variable frequency adjustment sequence using the high-efficiency operation interval. An energy management module is configured to determine a storage energy window period by time sequence correlation analysis of the variable frequency regulation sequence and the load demand data, obtain a dynamic buffer threshold based on matching of the storage energy window period and the thermal capacity characteristic parameter, and construct a peak load shifting instruction set according to the dynamic buffer threshold. An adaptive optimization module is configured to monitor an execution deviation trajectory of the peak load shifting instruction set, identify an optimal convergence path based on convergence analysis of the deviation trajectory, and generate an energy saving control instruction by adaptive correction of the variable frequency regulation sequence along the optimal convergence path.

[0007] The beneficial effects of the present application are embodied in the following aspects: first, by means of refrigerant phase change boundary identification and gas-liquid conversion point extraction technology, combined with superheat degree distribution analysis to construct a phase change energy field, the thermodynamic state of core components such as compressors, condensers, expansion valves and evaporators is accurately described, and through heat bottleneck point identification and equivalent thermal resistance network construction, a system-level thermal response path diagram is established, improving the stability and reliability of system operation. Secondly, by using refrigerant shunt analysis and load balance matrix technology, through primary and secondary circuit identification and optimal distribution point determination, intelligent distribution and dynamic balance of refrigerant among multiple parallel circuits are realized, combined with compressor regulation curve reconstruction and high efficiency operation interval identification, an adaptive variable frequency regulation sequence is generated, effectively improving the operation efficiency of the system under different load conditions and reducing the overall energy consumption level. Finally, by using storage energy window period identification and dynamic buffer threshold matching technology, a peak load shifting instruction set is constructed, through execution deviation trajectory monitoring and convergence analysis, the optimal convergence path is identified and adaptive correction of the variable frequency regulation sequence is realized, forming a closed-loop intelligent energy saving control system, which can dynamically adjust the control strategy according to the actual operating conditions, realizing accurate energy consumption management and efficient energy saving operation of the airport ground air conditioning unit.

[0008] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0009] The drawings herein show specific examples of the technical solutions described in the present application, and constitute part of the specification together with the specific embodiments, for explaining the technical solutions, principles and effects of the present application.

[0010] Unless specifically stated or otherwise, the same reference signs in different drawings represent the same or similar technical features, and different reference signs may also be used to represent the same or similar technical features.

[0011] Figure 1 is a flowchart of an airport ground air conditioning unit multi-system energy saving control method of the present application.

[0012] Figure 2 is a structural block diagram of a multi-system energy-saving control system of an airport ground air conditioning unit. DETAILED DESCRIPTION

[0013] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0014] It is to be understood that the terminology“including,”“comprising,”“consisting of,” and more specifically“consisting essentially of’ used in the specification and in the following claims indicates the presence of the stated features, integers, steps, operations, elements, and / or components but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0015] Reference throughout this specification to“one embodiment” or“an embodiment” or“some embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, the appearances of the phrases“in one embodiment” or“in some embodiments” or“in other embodiments” or“in additional embodiments” or the like in various places throughout this specification are not necessarily referring to the same embodiment, unless otherwise specified. The terms“including,”“comprising,”“consisting of,” and variations thereof, as used in this specification, mean“including but not limited to,” unless otherwise specified.

[0016] The technical solutions of the embodiments of the present application are introduced as follows.

[0017] As shown in Figure 1 The embodiment of the present application provides a multi-system energy-saving control method of an airport ground air conditioning unit, which comprises the following steps S110-S160. Step S110, collecting refrigerant flow data, temperature monitoring data and load demand data of the airport air conditioning unit, performing phase change boundary identification on the refrigerant flow data to extract a gas-liquid conversion point, extracting a superheat degree distribution from the temperature monitoring data, and performing enthalpy coupling on the gas-liquid conversion point and the superheat degree distribution to generate a phase change energy field.

[0018] Specifically, the refrigerant flow data is obtained by arranging mass flow meters at the key nodes of the four core components of the compressor, condenser, expansion valve, and evaporator. The mass flow meters use Coriolis mass flow meters with a measurement accuracy of ±0.1% and a sampling frequency of 5 Hz to ensure the capture of transient change characteristics of the refrigerant during its flow between components. The refrigerant flow data includes core measurement points such as compressor suction and discharge flow, condenser inlet and outlet flow, expansion valve before and after flow, and evaporator inlet and outlet flow, as well as monitoring the gas-liquid separator outlet flow and the flow difference before and after the drying filter. Each measurement point is equipped with an independent data acquisition channel. Temperature monitoring data of each key component of the airport ground air conditioning unit is collected synchronously using platinum resistance temperature sensors PT1000 with a temperature measurement range of -40°C to 150°C, an accuracy of ±0.1°C, and a response time of not more than 2 seconds. The temperature monitoring data covers compressor suction and discharge temperature, condenser inlet and outlet temperature, expansion valve before and after temperature, evaporator inlet and outlet temperature, as well as condensing fan and evaporating fan inlet and outlet temperature. The sensors use four-wire connection to eliminate the influence of lead resistance. Load demand data of the airport ground air conditioning unit is collected, system high and low pressure parameters are obtained through pressure controllers, the operating status of the condensing fan and evaporating fan is monitored, and the refrigeration load demand, supply air temperature set value, and indoor temperature and humidity parameters of each air conditioning area are recorded.

[0019] The phase change boundary is identified and the gas-liquid conversion point is extracted from the refrigerant flow data. The fluid phase state is identified by calculating the density of the refrigerant flow data in the evaporator and condenser. When the refrigerant density changes abruptly, it is marked as the phase change location. The positions with a density change rate exceeding 5% / second are determined as the key phase change regions. The influence of the expansion valve throttling process and the gas-liquid separator separation effect on the phase change is analyzed using the continuity equation of the refrigerant flow data between components: ρ1A1v1=ρ2A2v2, where ρ is the density, A is the cross-sectional area, and v is the flow rate. The characteristic positions of flow pulsation and pressure drop change are identified in the refrigerant flow data at the inlet and outlet of the evaporator and condenser. These positions usually correspond to the boundary regions of gas-liquid phase change, and the positions with a pulsation amplitude greater than 20% of the average value are highlighted. The characteristic frequency of the phase change process is extracted from the frequency domain analysis of the refrigerant flow data before and after the drying filter. The gas-liquid conversion process is accompanied by specific flow oscillation patterns, mainly concentrated in the 0.1-2 Hz frequency band. A multi-component coordinated phase change boundary identification program is established to determine the accurate position of the gas-liquid conversion point based on the operating parameters such as compressor operating conditions, condensing fan speed, and evaporating fan speed. The identification accuracy reaches ±1% of the pipe length. The gas-liquid conversion points extracted from the refrigerant flow data contain key parameter information such as spatial coordinates, conversion time, and phase change intensity in each core component. Each conversion point record contains 15 characteristic parameters.

[0020] The superheat degree distribution is extracted from the temperature monitoring data. The superheat degree values of the refrigerant at the key positions such as the compressor suction, the evaporator outlet, and the condenser outlet are calculated by using the collected temperature monitoring data of each core component. The superheat degree is defined as the difference between the actual temperature and the saturation temperature under the corresponding pressure. The temperature field distribution of the refrigerant is established based on the temperature monitoring data of the evaporator, the condenser interior, and the connecting pipeline. The continuous temperature distribution is obtained through spatial interpolation technology. The spatial range and temperature gradient distribution of the evaporator outlet superheat region and the condenser inlet superheat region are identified from the temperature monitoring data. The region with a superheat degree exceeding 5°C is classified as a strong superheat zone. The dynamic change law of the superheat degree is analyzed by using the temperature monitoring data corresponding to the compressor operating conditions and the evaporator fan speed. The stable region and the change region of the superheat degree distribution are identified. The superheat degree calculation relationship ΔT_sh = T_actual - T_sat(P) is established, where ΔT_sh is the superheat degree, T_actual is the actual temperature of the temperature monitoring data, and T_sat(P) is the saturation temperature under the corresponding pressure. The boundary position of the superheat degree distribution is determined by gradient analysis of the temperature monitoring data of the connecting pipeline between each core component. The superheat degree distribution map covering the entire airport ground air conditioning unit multi-system is formed.

[0021] In some embodiments, the enthalpy coupling of the gas-liquid conversion point and the superheat degree distribution generates a phase change energy field, including: identifying a dryness gradient distribution at the gas-liquid conversion point; matching the dryness gradient distribution with the superheat degree distribution to locate a phase change completion boundary; extracting an enthalpy difference driving potential along the phase change completion boundary; and constructing a phase change energy field based on the spatial distribution of the enthalpy difference driving potential.

[0022] The dryness gradient distribution is identified at the gas-liquid conversion point. The dryness x = (h - h_f) / (h_g - h_f) is calculated using the flow rate and density information at the gas-liquid conversion point, where h is the enthalpy, h_f is the saturated liquid enthalpy, and h_g is the saturated gas enthalpy. The calculation process considers the simultaneous influence of temperature and pressure. The spatial description of the dryness distribution is established in the surrounding area of the gas-liquid conversion point. The dryness gradient ∇x is calculated through finite difference technology, with a grid size of 1 mm x 1 mm to ensure calculation accuracy. The phase change intensity at the gas-liquid conversion point is analyzed. The dryness gradient value is larger in strong phase change regions and relatively smaller in weak phase change regions. The regions with a gradient value greater than 0.1 / mm are classified as strong phase change zones. The time evolution law of the dryness gradient is analyzed through time series data of the gas-liquid conversion point. The dominant direction and change rate of the dryness are identified. The calculation period of the change rate is 10 seconds. The identified dryness gradient distribution is subjected to spatial filtering processing to eliminate the influence of measurement noise on gradient calculation. A 5x5 Gaussian filter kernel is used for processing. The dryness gradient distribution at the gas-liquid conversion point reflects the intensity and spatial non-uniformity of the phase change process. The maximum gradient value usually appears in the initial stage of the phase change.

[0023] The phase change completion boundary is located by matching the dryness gradient distribution and the superheat distribution. The boundary position where the phase change process changes from gas-liquid coexistence to complete vaporization is identified by spatial superposition analysis of the dryness gradient distribution and the superheat distribution. The superposition analysis uses a weighted average method to process the data overlap area. The phase change completion boundary is determined at the position where the dryness gradient distribution is zero and the superheat distribution begins to appear. This boundary marks the completion of the refrigerant vaporization process, and the boundary width is usually in the range of 2-5 mm. The key control points of the phase change completion boundary are located by the intersection of the dryness gradient distribution contour and the superheat distribution contour. The intersection calculation uses bilinear interpolation technology to improve accuracy. A matching processing program is established to achieve accurate matching by minimizing the spatial misalignment between the dryness gradient distribution and the superheat distribution, with an error controlled within 1 mm. The determination of the phase change completion boundary combines the phase change information of the dryness gradient distribution and the temperature information of the superheat distribution, improving the accuracy and reliability of boundary identification.

[0024] For example, the enthalpy difference driving potential is extracted along the phase change completion boundary, including: arranging key monitoring positions on both sides of the phase change completion boundary; extracting local temperature gradient and pressure gradient from the key monitoring positions; generating an enthalpy distribution curve along the boundary based on the temperature gradient and the pressure gradient; and converting the change trend of the enthalpy distribution curve into an enthalpy difference driving potential.

[0025] Key monitoring positions are arranged on both sides of the phase change completion boundary. Based on the positioning of the phase change completion boundary, high-precision monitoring points are set at specific positions on both sides of the boundary to capture detailed thermodynamic parameter changes during the phase change process. Non-contact sensing technology is used to avoid interference with the flow field. Along the normal direction of the phase change completion boundary, monitoring positions are set at a distance of 0.5 mm and 1.0 mm from the boundary, respectively, to ensure that parameter comparisons before and after phase change can be measured. The positioning accuracy of the monitoring positions is ±0.1 mm. The selection of key monitoring positions takes into account the geometric characteristics of the phase change completion boundary. In areas with large boundary curvature, the density of monitoring points is increased. In areas with a curvature radius of less than 5 mm, the density of monitoring points is doubled. Each key monitoring position is equipped with a miniature temperature sensor and a pressure sensor. The response time of the sensors is less than 10 ms, allowing them to capture rapid phase change processes. The outer diameter of the sensors is not more than 0.3 mm to minimize the impact on the flow field. Liquid phase monitoring positions are arranged on the upstream side of the phase change completion boundary to monitor the thermodynamic state of the liquid refrigerant before it changes to a gaseous state. The liquid phase monitoring points focus on parameter changes near the saturation temperature. Gas phase monitoring positions are set on the downstream side of the phase change completion boundary to focus on the superheated gas state after vaporization. The spacing between gas phase monitoring points is adjusted according to the superheat gradient. A three-dimensional coordinate database of key monitoring positions is established to record the precise position relationship of each monitoring point relative to the phase change completion boundary. The coordinate accuracy is on the order of microns. Through the rational layout of key monitoring positions, it is ensured that the thermodynamic parameter changes near the phase change completion boundary can be fully captured, and the monitoring coverage rate reaches more than 95% of the total length of the boundary.

[0026] Local temperature gradients and pressure gradients are extracted from key monitoring positions. Temperature and pressure data from each monitoring point are collected in real time using the arranged key monitoring positions to calculate local temperature gradients and pressure gradients. The data acquisition frequency is set to 100 Hz to ensure the capture of transient changes. The temperature gradient dT / dx = (T2-T1) / (x2-x1) is calculated using the temperature difference and spatial distance between adjacent key monitoring positions, where T is the temperature and x is the spatial coordinate. The central difference format is used to improve the accuracy of the gradient calculation. The pressure gradient dP / dx = (P2-P1) / (x2-x1) is calculated in the same way from the key monitoring positions, where P is the pressure. The pressure measurement accuracy is controlled within ±0.1% of the full scale range. The extracted local temperature gradients and pressure gradients are time-averaged to eliminate the influence of instantaneous fluctuations on gradient calculation. The average time window is set to 1 second. The correlation between temperature gradients and pressure gradients at key monitoring positions is analyzed. The coupling relationship between the two reflects the thermodynamic characteristics of the phase change process. The correlation coefficient is usually between 0.7 and 0.9.

[0027] Based on the temperature gradient and pressure gradient along the boundary, an enthalpy distribution curve is generated. The enthalpy calculation formula is where h is the enthalpy, cp is the specific heat at constant pressure, and V is the specific volume. The enthalpy change is calculated by combining the temperature gradient and pressure gradient information. Along each location point of the phase transition completion boundary, the corresponding enthalpy is calculated using the local temperature gradient and pressure gradient data, forming a discrete enthalpy distribution data with a data point spacing of 0.5% of the total length of the boundary. By using spline interpolation technology to connect each enthalpy data point, a smooth and continuous enthalpy distribution curve is generated, with an interpolation order of 3 to ensure the smoothness and continuity of the curve. The enthalpy distribution curve reflects the energy state change of the refrigerant along the phase transition completion boundary, and the slope of the curve represents the degree of energy change, with the maximum slope usually occurring in the most active area of the phase transition. Geometric analysis is performed on the generated enthalpy distribution curve to identify characteristic positions such as extreme points, inflection points, and linear segments of the curve, which correspond to key state transition points of the phase transition process. Based on the spatial distribution characteristics of the temperature gradient and pressure gradient, the enthalpy distribution curve presents a change pattern corresponding to the phase transition process, and the curve shape reflects the completion degree and efficiency of the phase transition.

[0028] The trend of the enthalpy distribution curve is converted into the enthalpy difference driving potential. The first derivative dh / ds of the enthalpy distribution curve is calculated, where s is the arc length coordinate along the phase transition completion boundary. The derivative represents the rate of change of enthalpy along the boundary, and the sign of the derivative indicates the direction of energy flow. The curvature change of the enthalpy distribution curve is analyzed by using the second derivative d²h / ds², and the acceleration characteristics of the driving potential change are identified. The curvature change reflects the stability of the phase transition process. The enthalpy difference driving potential is defined as the difference Δh = h_downstream - h_upstream between the enthalpy values on both sides of the phase transition completion boundary, which drives the refrigerant to complete the phase transition process. The size of the driving potential determines the driving ability of the phase transition. The integral property of the enthalpy distribution curve is used to calculate the total enthalpy difference driving potential ∫Δh·ds. The integral result represents the total driving ability of the entire phase transition completion boundary, and the integral is numerically calculated using the trapezoidal rule. A spatial distribution function of the enthalpy difference driving potential is established to map the local trend of the curve into the intensity distribution of the driving potential, and the distribution function uses piecewise linear interpolation technology. The converted enthalpy difference driving potential is normalized to facilitate comparison and analysis with other driving potentials, and the normalization reference value is selected as the maximum driving potential value on the boundary.

[0029] The phase change energy field is constructed based on the spatial distribution of enthalpy potential. The enthalpy potential is extended to three-dimensional space by potential function theory, and the scalar distribution of the energy field Φ(x, y, z) is constructed. The potential function satisfies the boundary conditions and continuity requirements. The Laplace equation ∇²Φ = -ρ(x, y, z) is solved using finite element technology, where ρ is the spatial density distribution of enthalpy potential. The solution is discretized using a triangular grid. In the phase change energy field, areas with high enthalpy potential correspond to high-intensity areas of the energy field, and areas with low enthalpy potential correspond to low-intensity areas. The intensity ratio can be greater than 10:1. A vector representation of the phase change energy field is established, and the direction and intensity distribution of energy flow are obtained by gradient operation ∇Φ. The vector field shows the main channel of energy transfer. The constructed phase change energy field is visualized, and the spatial structure of the energy field is displayed using three-dimensional isopotential surfaces and streamlines. The isopotential surface spacing is set to 5% increments of the total energy. The construction of the phase change energy field integrates the distribution information of the enthalpy potential, forming a complete field description of the energy conversion during the phase change process. The maximum field strength usually occurs in the main phase change region.

[0030] In step S120, the heat transfer bottleneck points are identified based on the phase change energy field, the heat resistance analysis is performed on the heat transfer bottleneck points to generate an equivalent heat resistance network, the heat capacity characteristic parameters are extracted from the equivalent heat resistance network, and the thermodynamic response path diagram is constructed based on the equivalent heat resistance network and the heat capacity characteristic parameters.

[0031] Specifically, the heat transfer bottleneck points are identified based on the phase change energy field. In the phase change energy field, the regions with abnormal energy density gradient are searched, and the positions with gradient mutation exceeding 30% of the average value are marked as potential heat transfer bottleneck points. The regions with blocked energy transfer are identified through streamline analysis of the phase change energy field, and the positions with sparse streamlines or backflow usually correspond to heat transfer bottleneck points. Combining the geometric characteristics of the compressor, condenser, expansion valve, and evaporator, the heat transfer bottleneck points at the connection positions of the components and the internal heat exchange surfaces are located in the phase change energy field. The influence of the working state of the condenser fan and the evaporator fan on the distribution of the phase change energy field is analyzed, and the heat transfer bottleneck points caused by insufficient fan performance are identified. In the positions of auxiliary components such as dry filters and gas-liquid separators, the heat transfer bottleneck points are determined by the local minimum points of the phase change energy field. A hierarchical system of heat transfer bottleneck points is established, and the bottleneck points are divided into three levels of severe, moderate, and slight according to the degree of energy loss in the phase change energy field. The specific coordinate positions, bottleneck strength, and influence range of the identified heat transfer bottleneck points in the phase change energy field are recorded, and a bottleneck point database is established.

[0032] The heat resistance analysis of the heat transfer bottleneck points generates an equivalent heat resistance network. The heat resistance calculation system of the heat transfer path at the heat transfer bottleneck points is established, including three basic heat resistance forms of conduction heat resistance, convection heat resistance and radiation heat resistance. The conduction heat resistance at the heat transfer bottleneck points is calculated, and the conduction heat resistance formula is R_cond = L / (λA), where L is the length of the heat transfer path, λ is the thermal conductivity, and A is the heat transfer area. The convection heat transfer at the heat transfer bottleneck points is analyzed, and the convection heat resistance R_conv = 1 / (hA) is calculated, where h is the convection heat transfer coefficient, and the influence of the rotating speed of the condensation and evaporation fans on the heat transfer coefficient is considered. The heat resistance connection relationship between the heat transfer bottleneck points is established, and the heat resistance network topology structure is constructed according to the series and parallel characteristics of the heat transfer path. The local heat resistance of each heat transfer bottleneck point is combined into the equivalent heat resistance network of the system through the network connection mode, the network node represents the temperature measuring point, and the network edge represents the heat resistance connection. The influence of the compressor operating condition and the pressure controller set value on the heat resistance value of the heat transfer bottleneck point is considered, and the dynamic relationship of the heat resistance with the operating condition is established. The generated equivalent heat resistance network is topologically optimized, the redundant heat resistance branches are simplified, and the key heat resistance paths with a dominant influence on the heat transfer process are retained. The equivalent heat resistance network covers all important heat transfer bottleneck points and heat resistance elements in the complete cycle process from the compressor to the evaporator.

[0033] In some embodiments, the extracting the heat capacity characteristic parameter from the equivalent heat resistance network comprises: analyzing the topology structure of the equivalent heat resistance network to identify series and parallel relationships; calculating an equivalent heat resistance ratio distribution based on the series and parallel relationships; deriving a time constant spectrum from the equivalent heat resistance ratio distribution; and extracting a dominant component from the time constant spectrum as the heat capacity characteristic parameter.

[0034] The topology structure of the equivalent heat resistance network is analyzed to identify series and parallel relationships. The connection mode of the heat resistance elements is identified through the adjacency matrix analysis of the equivalent heat resistance network, and the matrix elements are 1 for direct connection and 0 for no connection. The specific distribution positions of the series branches and the parallel branches in the equivalent heat resistance network are identified using the connectivity analysis method in graph theory. The series relationship identification is realized by finding the nodes with a degree of 2 in the equivalent heat resistance network, and the heat resistance elements connected to these nodes form a series relationship. The parallel relationship identification is determined by analyzing multiple paths with the same starting point and ending point in the equivalent heat resistance network, and the heat resistance elements on the multiple paths form a parallel relationship. A hierarchical decomposition system of the equivalent heat resistance network is established to decompose the complex network structure into multiple simple series and parallel sub-networks. The identified series and parallel relationships are numbered and classified, and a topology database of the equivalent heat resistance network is established to record the connection type and position information of each heat resistance element. Through the series and parallel relationship analysis of the equivalent heat resistance network, the key heat transfer paths and the secondary heat transfer paths in the heat resistance network are determined.

[0035] The equivalent thermal resistance ratio distribution is calculated based on the series-parallel relationship. The series thermal resistance in the equivalent thermal resistance network is calculated by merging, and the series equivalent thermal resistance R_series = ΣR_i, where R_i is the resistance value of the i-th thermal resistance element in the series branch. The parallel thermal resistance in the equivalent thermal resistance network is calculated by merging, and the parallel equivalent thermal resistance 1 / R_parallel = Σ(1 / R_i) is obtained by summing the inverse. The calculation system of the equivalent thermal resistance ratio is established, and the ratio distribution R_ratio = R_i / R_total of each thermal resistance element relative to the total thermal resistance of the network is calculated. The statistical characteristics of the equivalent thermal resistance ratio distribution are analyzed, including the average value, standard deviation, maximum value and minimum value of the ratio. The dominant thermal resistance and the secondary thermal resistance in the network are identified by the equivalent thermal resistance ratio distribution. The thermal resistance element with a ratio greater than 0.1 is classified as a dominant thermal resistance. The spatial mapping relationship of the equivalent thermal resistance ratio distribution is established, and the ratio data is mapped to the actual component position of the airport ground air conditioning unit. The rationality of the thermal resistance distribution in the network is analyzed by using the equivalent thermal resistance ratio distribution, and the abnormal area with excessive or insufficient thermal resistance is identified.

[0036] The time constant spectrum is derived by the equivalent thermal resistance ratio distribution. The calculation relationship of thermal resistance-thermal capacity time constant τ = RxC is established, where τ is the time constant, R is the thermal resistance value in the equivalent thermal resistance ratio distribution, and C is the thermal capacity value at the corresponding position. The time constant is calculated by using the weight information of the equivalent thermal resistance ratio distribution. The time constant corresponding to the thermal resistance with large weight dominates in the spectrum. The equivalent thermal resistance ratio distribution is converted into the frequency spectrum distribution of the time constant by the frequency domain analysis technology. The frequency spectrum reflects the intensity of the response at different time scales. The mathematical expression of the time constant spectrum S(τ) = Σw_iδ(τ-τ_i) is established, where S(τ) is the time constant spectrum, w_i is the weight coefficient, δ is the Dirac function, and τ_i is the i-th time constant. The peak position and peak intensity in the time constant spectrum are analyzed. The peak position corresponds to the characteristic response time of the system, and the peak intensity reflects the importance of the response. The derived time constant spectrum is smoothed to eliminate the influence of the calculation error of the equivalent thermal resistance ratio distribution on the spectrum shape. The corresponding relationship between the time constant spectrum and the dynamic response characteristics of the system is established. The short time constant corresponds to the fast response process, and the long time constant corresponds to the slow response process.

[0037] The dominant component is extracted from the time constant spectrum as the heat capacity characteristic parameter. The main peak position in the spectrum is identified by the peak value detection technology of the time constant spectrum, and the peak with a peak value greater than 50% of the maximum value of the spectrum is determined as the dominant component. The energy contribution of each component is calculated by the energy analysis method of the time constant spectrum, and the component with an energy contribution greater than 10% of the total energy is selected as the dominant component. The screening criteria of the dominant component are established, and factors such as the peak value intensity, energy contribution and frequency position of the component in the time constant spectrum are considered comprehensively. The time constant value of the extracted dominant component is accurately calculated, the dominant time constant τ_dominant is obtained by peak positioning, and the dominant heat capacity characteristic parameter C_dominant is calculated based on the relationship τ=R·C, wherein τ is the time constant, R is the thermal resistance value, C is the heat capacity, and R_node is the corresponding node thermal resistance. The distribution rule of the dominant component in the time constant spectrum is analyzed, and the fast response dominant component (τ<10s), the medium speed response dominant component (10s<τ<100s) and the slow speed response dominant component (τ>100s) are identified. The corresponding relationship between the heat capacity characteristic parameter and the actual components of the airport ground air conditioning unit is established, and the dominant component is mapped to the specific heat capacity element. The numerical value, time scale and physical meaning of the extracted heat capacity characteristic parameter are recorded to form a system heat capacity characteristic parameter database.

[0038] The thermal response path diagram is constructed by using the equivalent thermal resistance network and the heat capacity characteristic parameter. The thermal resistance elements in the equivalent thermal resistance network are paired and combined with the heat capacity characteristic parameter to form a thermal response unit with time dynamic characteristics. The main transfer path of the thermal response path diagram is determined by using the topological structure of the equivalent thermal resistance network, and the starting point of the path is the heat source input point and the ending point is the heat dissipation output point. The response delay time of each path segment in the thermal response path diagram is calculated by using the time constant information of the heat capacity characteristic parameter, and the delay time reflects the propagation speed of the thermal disturbance in the system. The key thermal response nodes in the thermal response path diagram are identified, including the compressor exhaust point, the middle part of the condenser, the front of the expansion valve, the middle part of the evaporator and other important positions. Combined with the impedance distribution characteristics of the equivalent thermal resistance network, the thermal resistance weight is set in the thermal response path diagram, and the path segment with large weight corresponds to the area with slow response speed. The mathematical expression of the thermal response path diagram is established, and the dynamic relationship between the input thermal disturbance and the output temperature response is described by using the transfer function. The thermal response path diagram integrates the spatial characteristics of the equivalent thermal resistance network and the time characteristics of the heat capacity characteristic parameter, forming a complete description of the dynamic response of the thermal system.

[0039] In step S130, the refrigerant distribution analysis of the thermal response path diagram is performed to extract the primary and secondary circuits, analyze the enthalpy difference distribution of the primary and secondary circuits, identify the optimal distribution point, and perform dynamic weight distribution on the optimal distribution point to generate a load balance matrix.

[0040] Specifically, the refrigerant flow distribution analysis of the thermodynamic response path diagram extracts the primary and secondary circuits. The main circulation path of the refrigerant is identified through the flow weight information in the thermodynamic response path diagram, which includes the basic circulation loop of the compressor-condenser-expansion valve-evaporator. The branch structure of the thermodynamic response path diagram is used to analyze the refrigerant flow distribution in parallel evaporators, multi-path condensers, and split gas-liquid separators. The secondary circuits, including oil circulation loops, bypass circuits, and auxiliary cooling circuits, are extracted from the thermodynamic response path diagram. The influence of condensing fan and evaporating fan on the air flow distribution in the thermodynamic response path diagram is analyzed to determine the coupling relationship between the air flow circuit and the refrigerant circuit. The key nodes of refrigerant flow distribution are identified through the node analysis of the thermodynamic response path diagram, including the positions of multi-way valves, distributors, and collectors. The flow distribution ratio calculation of the primary and secondary circuits is established, with the primary circuit accounting for 70-90% of the total flow and the secondary circuit accounting for the remaining flow. The flow resistance and pressure drop distribution of each circuit are determined using the impedance distribution characteristics of the thermodynamic response path diagram, with the circuit with high resistance corresponding to the secondary circuit with small flow. The specific path information, flow distribution, and thermodynamic parameters of the extracted primary and secondary circuits in the thermodynamic response path diagram are recorded to establish a circuit classification database.

[0041] The enthalpy difference distribution of the primary and secondary circuits is analyzed to identify the optimal distribution point. The temperature T and pressure P of the refrigerant are measured at the key positions of the primary and secondary circuits, and the corresponding enthalpy h=f(T,P) is calculated to establish the enthalpy distribution curve along the circuit. The enthalpy difference distribution difference between the primary and secondary circuits is analyzed, and the primary circuit usually has a larger enthalpy difference change range, while the enthalpy difference change of the secondary circuit is relatively flat. The area of uneven refrigerant distribution is identified through the comparative analysis of the enthalpy difference distribution of the primary and secondary circuits, and the position with a larger enthalpy difference gradient indicates insufficient energy utilization. Detailed measurements of the enthalpy difference distribution are performed at the intersection and distribution points of the primary and secondary circuits, and the enthalpy difference characteristics of these positions determine the distribution effect. The optimal distribution point is identified using the optimization criteria of the enthalpy difference distribution, which should maximize the enthalpy difference utilization rate of each circuit. The identification conditions of the optimal distribution point are established: where η_i is the enthalpy difference utilization rate of the i-th circuit, and x is the distribution parameter. The specific position coordinates of the optimal distribution point are determined through the energy balance analysis of the enthalpy difference distribution of the primary and secondary circuits, and the optimal point is usually located between the compressor outlet and the evaporator inlet. The position information, enthalpy difference characteristics, and distribution potential of the identified optimal distribution point in the primary and secondary circuits are recorded to provide parameter basis for dynamic weight distribution.

[0042] In some embodiments, the dynamic weight distribution of the optimal distribution points generates a load balancing matrix, including: identifying high-density channels and low-density channels based on the optimal distribution points for energy flow density analysis; using the high-density channels to compensate for the flow of the low-density channels to form balanced channels; spatially arranging and combining the balanced channels to generate a distribution topology; and matrix encoding the distribution topology to generate a load balancing matrix.

[0043] Based on the optimal distribution points, high-density channels and low-density channels are identified through energy flow density analysis. Using the identified optimal distribution points as the starting point for analysis, the refrigerant energy flow density and transfer intensity through each distribution point are calculated. An energy flow density calculation system is established at the optimal distribution points, with energy flow density defined as the energy passing through per unit time per unit cross-sectional area ρ_e=Q / (A·v), where Q is the heat flow, A is the channel cross-sectional area, and v is the flow rate. The energy flow density values of each channel are calculated through pressure and flow monitoring data at the optimal distribution points, with the density calculation taking into account the phase change and physical property parameters of the refrigerant. The energy flow density distribution characteristics of each channel connected to the optimal distribution points are analyzed, and channel combinations with large differences in energy flow density are identified. Channels with energy flow density greater than 1.5 times the average value are defined as high-density channels, which carry the main energy transfer task. Channels with energy flow density less than 0.6 times the average value are defined as low-density channels, which have relatively insufficient energy transfer capacity. An identification database of high-density channels and low-density channels is established, recording the energy flow density values, channel characteristics, and connection relationships of each channel at the optimal distribution points. The influence of condensing and evaporating fan operating states on the energy flow density distribution at the optimal distribution points is analyzed, and changes in fan speed will change the density level of the channels.

[0044] For example, the use of high-density channels to compensate for the flow of low-density channels to form balanced channels includes: using the high-density channels as energy supply sources to inject compensation flow into the low-density channels; closed-loop control of the compensation flow to form a dynamic equilibrium state; using the dynamic equilibrium state to continuously optimize the flow distribution accuracy; and when the distribution accuracy meets the balance requirements, maintaining channel flow distribution to form balanced channels.

[0045] A high-density channel is used as an energy supply source to inject compensation flow into a low-density channel. Based on the identified high-density channel and low-density channel, a flow compensation mechanism is established with the high-density channel as the supply source. A flow extraction device is installed on the high-density channel to extract part of the refrigerant flow from the high-density channel as a compensation flow source. The extraction ratio of the compensation flow is calculated as α = (m_high - m_target) / m_high, where α is the extraction ratio, m_high is the high-density channel flow, and m_target is the target flow of the high-density channel, to ensure that the high-density channel can still maintain normal operation after extracting the compensation flow. The extracted refrigerant is delivered to the low-density channel through a compensation flow pipeline, which is designed according to the principle of minimum pressure drop and shortest path. A flow regulating valve is installed at the compensation flow injection point, and the valve opening is adjusted according to the flow demand of the low-density channel. A pressure balance condition is established for the compensation flow injection to ensure that the injection process does not adversely affect the pressure distribution of the high-density channel and the low-density channel. The running state of the high-density channel after flow extraction is monitored, including pressure changes, temperature response, and flow stability. The flow distribution changes of each channel during the compensation flow injection process are recorded to establish an injection effect database. By injecting compensation flow from the high-density channel to the low-density channel, energy redistribution and balance between channels are achieved.

[0046] A closed-loop control of the compensation flow is established to form a dynamic equilibrium state. A closed-loop control system for the compensation flow is established to monitor and adjust the flow distribution in real time to maintain the dynamic balance between channels. Flow sensors are installed to monitor the real-time flow of the high-density channel and the low-density channel, and the sensor signals are used as feedback signals for the control system. A control target set value is established, and the target flow is the average flow of each channel. The task of the control system is to make the flow of each channel approach the set value. A PID controller is used to adjust the compensation flow, and the controller parameters are set according to the system response characteristics. The compensation flow regulating valve is driven by the control system to achieve accurate control and real-time adjustment of the compensation flow. Stability criteria for the control system are established, and the system is considered to have reached a dynamic equilibrium state when the fluctuation amplitude of the flow of each channel is less than 5% of the set value. The maintenance time and stability of the dynamic equilibrium state are monitored, and the control performance of the system under different operating conditions is recorded. The anti-interference ability of the closed-loop control system is tested to ensure that the control system can quickly recover to balance when the load changes or the equipment starts and stops.

[0047] The dynamic balance state is used to continuously optimize the accuracy of flow distribution. Based on the established dynamic balance state, the accuracy and stability of flow distribution are further improved through refined control strategies. An adaptive control strategy is introduced based on the dynamic balance state, and the control parameters are automatically adjusted according to the system operating state and external disturbances. A quantitative index of flow distribution accuracy σ = √(Σ(m_i-m_avg)² / n) is established, where σ is the distribution accuracy, m_i is the flow of the i-th channel, m_avg is the average flow, and n is the number of channels. The main factors affecting the distribution accuracy are identified through analysis of the operating data of the dynamic balance state, including compressor operating condition changes, environmental temperature fluctuations, and load demand changes. A precision optimization control strategy is designed, and corresponding compensation measures are taken for different disturbance factors. The stability characteristics of the dynamic balance state are used to predict the response trend of the system, and the control parameters are adjusted in advance to maintain high-precision distribution. An iterative mechanism for precision optimization is established, and the precision level of flow distribution is gradually improved through multiple fine-tuning. The changes in control parameters and the improvement in effects during the precision optimization process are recorded, and an optimization strategy database is established.

[0048] When the distribution accuracy meets the balance requirements, the channel flow distribution is maintained to form a balanced channel. The balance determination criteria for distribution accuracy are established, and the balanced channel is confirmed when the flow distribution accuracy reaches the preset target value. The specific value of the balance requirement is set, and the flow deviation between channels is less than 3% of the average flow, which is considered to meet the balance requirement. After meeting the balance requirement, the control system enters the maintenance mode, and the current flow distribution state and control parameter setting are maintained. A state monitoring mechanism for balanced channels is established to continuously track the flow distribution of each channel and the system stability. A protection strategy for balanced channels is designed, and when external disturbances cause the distribution accuracy to decrease, the control system automatically starts the rebalancing program. The system parameters, control settings, and operating state when the balanced channel is formed are recorded, and a state file of the balanced channel is established. By maintaining the channel flow distribution, the balanced channel becomes a stable state for the efficient operation of the airport ground air conditioning unit, providing good conditions for subsequent energy-saving control.

[0049] The space arrangement of the equalization channels generates the distribution topology. The three-dimensional topology structure is established by using the space coordinate information of the equalization channels, which reflects the actual connection relationship and spatial distribution among the channels. Different distribution topology schemes are generated by the parallel and series combination of the equalization channels, and each scheme corresponds to a different flow distribution mode. The constraint conditions of the space arrangement of the equalization channels by the four core components, i.e., the compressor, the condenser, the expansion valve and the evaporator, are analyzed to ensure that the topology structure meets the equipment layout requirements. The optimization criteria of the distribution topology are established, and the topology structure with the minimum flow resistance and the most uniform pressure drop distribution is selected as the optimal scheme. The space arrangement of the equalization channels is mathematically described by using the graph theory method, in which the nodes of the topology graph represent the connection points, and the edges represent the channel connections. The influence of the installation positions of the condenser cooling fan and the evaporator fan on the arrangement of the equalization channels is considered, and the channel layout is optimized to reduce the airflow resistance. The generated distribution topologies are numbered and classified, and a topology scheme library is established, each topology containing the channel connection matrix and the space coordinate information.

[0050] The distribution topology is matrix-encoded to generate the load balance matrix. Based on the generated distribution topology, the connection relationship and weight information of the topology structure are converted into a mathematical matrix expression form. The topology-matrix conversion rule is established, in which the nodes in the topology graph correspond to the rows or columns of the matrix, and the weight of the edge corresponds to the numerical value of the matrix element. The connection matrix C is generated by using the adjacency relationship of the distribution topology, and the matrix element C_ij represents whether there is a direct connection between node i and node j. The weight matrix W is calculated by the flow distribution ratio of each channel in the distribution topology, and the matrix element W_ij represents the flow distribution weight from node i to node j. The connection matrix and the weight matrix are combined to generate the load balance matrix B=C⊙W, where ⊙ represents the Hadamard product operation of the matrix. The generated load balance matrix is sparsified by setting the matrix elements with a value less than a threshold to zero, thereby simplifying the matrix structure. The storage format of the load balance matrix is established, and the compressed sparse row format is used to reduce the memory occupation and the calculation complexity. The eigenvalue analysis is performed on the load balance matrix, and the eigenvalue reflects the stability and convergence characteristics of the distribution topology. The dimension, sparsity and characteristic parameters of the generated load balance matrix are recorded, and the matrix parameter database is established.

[0051] In step S140, the compressor adjustment curve is reconstructed based on the load balance matrix, the compressor adjustment curve is efficiency-mapped with the superheat degree distribution to identify the high-efficiency operation interval, and the variable frequency adjustment sequence is generated by using the high-efficiency operation interval.

[0052] Specifically, the compressor adjustment curve is reconstructed based on the load balance matrix. The optimal operating parameters of the compressor under different load conditions, including the speed, suction pressure and discharge pressure settings, are determined by the weight distribution information in the load balance matrix. The key control points of the compressor adjustment curve are calculated based on the matrix elements of the load balance matrix, and the positions with larger matrix element values correspond to the working points where the compressor needs to provide higher power. A two-dimensional coordinate system of the compressor adjustment curve is established using the row and column information of the load balance matrix, with the horizontal axis representing the integrated load demand of the condenser and evaporator and the vertical axis representing the adjustment response of the compressor. The main trend of the compressor adjustment curve is determined by eigenvalue analysis of the load balance matrix, and the eigenvector indicates the dominant direction of the adjustment curve. The response sensitivity and adjustment range of the compressor adjustment curve are adjusted in combination with the changes in the opening of the expansion valve and the set value of the pressure controller reflected by the load balance matrix. The influence of the operating state of the condensing fan and the evaporating fan on the load balance matrix is considered, and a fan coupling correction term is introduced into the compressor adjustment curve. The mathematical expression of the reconstructed compressor adjustment curve is f_comp = M·L + C, where f_comp is the compressor adjustment output, M is the load balance matrix, L is the load vector, and C is a correction constant. The reconstructed compressor adjustment curve is smoothed to ensure the continuity and operability of the curve between different working points.

[0053] In some embodiments, the efficiency mapping of the compressor adjustment curve and the superheat distribution identifies a high-efficiency operating interval, including: dividing the compressor adjustment curve by the equivalent efficiency line to identify the efficiency ridge line; finding a matching area in the superheat distribution based on the efficiency ridge line; performing stability evaluation on the matching area to form a stable operating band; and performing boundary optimization on the stable operating band to determine a high-efficiency operating interval.

[0054] The compressor adjustment curve is divided by the iso-efficiency line to identify the efficiency ridge. The iso-efficiency lines are drawn in the two-dimensional space of the compressor adjustment curve, each connecting the operating points with the same efficiency level. The efficiency value is calculated using the power input and cooling capacity output data of the compressor adjustment curve, η = Q_cooling / P_input, where Q_cooling is the cooling capacity and P_input is the input power. The efficiency variation gradient in the compressor adjustment curve is identified by the density distribution of the iso-efficiency lines, and the area with dense iso-efficiency lines represents a sharp change in efficiency. The connection path of the efficiency peak is found in the iso-efficiency line graph, which constitutes the efficiency ridge of the compressor adjustment curve. The efficiency ridge represents the highest efficiency operating trajectory that the compressor can achieve under different load conditions, and is the ideal path for system energy-saving operation. The distribution characteristics of the efficiency ridge along the compressor adjustment curve are analyzed, including the length, curvature and efficiency level variation of the ridge. The efficiency ridge is extracted and refined using mathematical morphology methods to ensure the geometric accuracy and continuity of the ridge. The parametric expression of the efficiency ridge is established, and the polynomial or spline function is used to describe the geometric shape and efficiency distribution of the ridge.

[0055] The matching area is found in the superheat distribution based on the efficiency ridge. The coordinate information of the efficiency ridge is mapped to the spatial coordinate system of the superheat distribution, establishing the spatial correspondence between the ridge and the superheat distribution. The area with relatively flat superheat variation is searched along the projection path of the efficiency ridge in the superheat distribution, which represents stable thermodynamic state. The area with matching temperature gradient is found in the superheat distribution using the efficiency gradient information of the efficiency ridge, and the ridge segment with high efficiency corresponds to the distribution area with moderate superheat. The quantification index of matching degree is established where is the efficiency gradient, is the superheat gradient, and α is the matching coefficient. The optimal matching area of the efficiency ridge and the superheat distribution is determined by optimizing the matching coefficient α to minimize the matching index M. The spatial range and boundary characteristics of the matching area in the superheat distribution are analyzed, and the matching area usually presents a strip or elliptical distribution. The influence of the evaporator outlet superheat and the condenser inlet superheat on the matching area is considered, and the boundary range of the matching area is adjusted. The coordinate position, matching degree and stability characteristics of the found matching area in the superheat distribution are recorded.

[0056] The stability evaluation of the matching region forms a stable operation band. Based on the matching region found in the superheat distribution, the thermodynamic stability and operation reliability of the region are comprehensively evaluated. The time fluctuation characteristics of the superheat in the matching region are analyzed, and the standard deviation σ T and the coefficient of variation CV T = σ T / μ T of the superheat are calculated, where μ T is the average superheat. The spatial stability of the region is evaluated by using the spatial distribution characteristics of the temperature gradient in the matching region, and the region with a gentle gradient change has better stability. A stability evaluation index is established wherein is the temperature Laplacian length, ΔP is the pressure fluctuation amplitude, w1, w2, w3 are weight coefficients. The stable part of the matching region is screened by the stability evaluation index, and the region with a stability index less than a threshold value is retained as a candidate operation band. Considering the influence of compressor operating condition changes and condenser and evaporator fan speed regulation on the stability of the matching region, a dynamic stability evaluation mechanism is established. The stability performance of the matching region under different load conditions is analyzed, and the region that remains stable under various operating conditions is identified. The stable regions after evaluation are geometrically connected and merged to form a continuous stable operation band. The stable operation band has good thermodynamic stability and operation reliability, and provides a stable operating condition environment for efficient operation.

[0057] The boundary optimization is performed on the stable operation band to determine the high-efficiency operation interval. Based on the formed stable operation band, the final high-efficiency operation interval is determined by using the boundary optimization technique to achieve the best balance between efficiency and stability. The boundary of the stable operation band is geometrically analyzed to identify the concave-convex characteristics and geometric singular points of the boundary, and the singular points may cause unstable operation. The irregular boundary of the stable operation band is smoothed by using the boundary optimization strategy to eliminate sharp mutations and local depressions. The objective function F of the boundary optimization is established as F = w1 · Area + w2 · Efficiency + w3 · Stability, where Area is the interval area, Efficiency is the average efficiency, and Stability is the stability index. The optimal boundary of the stable operation band is determined by solving the multi-objective optimization, and the optimization result considers the interval size, efficiency level and stability degree. The influence of the operating condition constraints in the actual operation on the boundary optimization is considered, including the compressor speed regulation range, the expansion valve regulation limit and the pressure controller setting range. The optimized boundary is checked for engineering realizability to ensure that the determined high-efficiency operation interval can be stably operated in the actual system. The mathematical description of the high-efficiency operation interval is established, and the precise boundary of the interval is defined by using a parameter equation or an inequality group. The geometric parameters, efficiency characteristics and stability index of the determined high-efficiency operation interval are recorded to provide accurate operating condition boundaries for the generation of the variable frequency regulation sequence.

[0058] The high-efficiency operation interval is used to generate the variable frequency regulation sequence. The discrete control points of the variable frequency regulation are established in the high-efficiency operation interval, and the interval between the control points is set according to the efficiency gradient of the interval. The frequency range of the variable frequency regulation sequence is determined by using the boundary information of the high-efficiency operation interval, and the upper and lower limits of the frequency correspond to the boundary values of the interval. The step strategy of the variable frequency regulation is designed by using the efficiency distribution characteristics in the high-efficiency operation interval, and small steps are used in the area with large efficiency gradient, and large steps are used in the area with gentle efficiency. The timing control logic of the variable frequency regulation sequence is established, and the sequence is dynamically adjusted according to the load change and system response. In combination with the coordinated operation requirements of the compressor, condenser, expansion valve and evaporator, the variable frequency regulation sequence of multi-parameter linkage is designed. Considering the coordination relationship between the variable frequency control of the condensing fan and the evaporating fan and the variable frequency of the compressor, the variable frequency regulation sequence of multi-device joint is established. The protection mechanism of the variable frequency regulation sequence is designed by using the stability characteristics of the high-efficiency operation interval, and the regulation strategy is automatically adjusted when the system operation deviates from the high-efficiency interval. The execution priority of the variable frequency regulation sequence is established, and the priority order of the regulation is determined according to the efficiency level in different positions in the high-efficiency operation interval. The generated variable frequency regulation sequence contains frequency set value, regulation timing and linkage parameter, which provides accurate instructions for intelligent control of the compressor.

[0059] In step S150, the variable frequency regulation sequence is time-correlation analyzed with the load demand data to determine the energy storage window period, the dynamic buffer threshold is obtained based on the matching of the energy storage window period and the thermal capacity characteristic parameter, and the peak load shifting and valley filling instruction set is constructed according to the dynamic buffer threshold.

[0060] Specifically, the variable frequency regulation sequence is time-correlation analyzed with the load demand data to determine the energy storage window period. By comparing and analyzing the frequency change characteristics of the variable frequency regulation sequence and the load fluctuation of the load demand data, the time period in which the frequency regulation margin and the load demand do not match is found. The identification of the low frequency operation period of the compressor in the variable frequency regulation sequence is used in combination with the low valley period of the load demand data to determine the time window in which the system has energy storage conditions. The phase relationship between the variable frequency regulation sequence and the load demand data is analyzed, and when the sequence response lags behind the demand change, the lag period can be used as a candidate interval of the energy storage window period. By comparing the regulation margin calculation of the variable frequency regulation sequence and the peak-valley difference of the load demand data, the period with excess regulation capacity is identified as the energy storage window period. Considering the influence of the operation cycle of the condensing fan and the evaporating fan on the variable frequency regulation sequence, the system has large thermal capacity during the low speed operation of the fan, which is suitable for being the energy storage window period. The identification conditions of the energy storage window period are established: the frequency utilization rate of the variable frequency regulation sequence is less than 70% and the load demand data is in the low valley section of the daily load curve. The cross-correlation coefficient of the variable frequency regulation sequence and the load demand data is calculated by using the time-correlation analysis technology, and the period with low correlation coefficient indicates that the matching degree of the two is low, which has the potential for energy storage regulation. The time range, duration and energy storage potential of the determined energy storage window period are recorded, and the energy storage window period database is established.

[0061] In some embodiments, the obtaining the dynamic buffer threshold based on the matching of the energy storage window period and the thermal capacity characteristic parameter comprises: constructing a time period energy distribution based on the energy storage window period; identifying energy peaks and valleys in the time period energy distribution; performing peak clipping and valley filling potential evaluation on the energy peaks and valleys to obtain an adjustment margin; and obtaining the dynamic buffer threshold based on the fusion of the adjustment margin and the thermal capacity characteristic parameter.

[0062] The time period energy distribution is constructed based on the energy storage window period. The energy storage window period is divided into multiple sub-periods in chronological order, and the length of each sub-period is determined according to the total duration of the energy storage window period and the system response characteristics. Through real-time monitoring of the compressor power consumption, condenser heat dissipation, evaporator refrigeration capacity, and expansion valve throttling loss during the energy storage window period, an energy balance account for each time period is established. The energy density distribution E_density = Q_total / (V_system·Δt) of each sub-period in the energy storage window period is calculated, where Q_total is the total energy of the time period, V_system is the system volume, and Δt is the time period length. The energy consumption distribution of the condenser fan and the evaporator fan during the energy storage window period is analyzed, and the fan energy consumption is an important component of the total system energy. The energy contribution of auxiliary components such as the drying filter and the gas-liquid separator during the energy storage window period is utilized to improve the calculation accuracy of the time period energy distribution. A visual representation of the time period energy distribution is established, and a column chart or a curve chart is used to display the energy change trend over time during the energy storage window period. Through energy distribution analysis of the energy storage window period, the dominant direction and transfer efficiency of energy transfer are identified, and the energy distribution is reasonably configured for high-efficiency periods.

[0063] Energy peaks and valleys are identified in the time period energy distribution. The first derivative of the time period energy distribution is used to identify the extreme points of energy change, and the position where the derivative is zero corresponds to the peak or valley of energy. The nature of the extreme points is determined by the second derivative test of the time period energy distribution, and the extreme points with negative second derivatives are energy peaks, and the extreme points with positive second derivatives are energy valleys. An amplitude calculation system for energy peaks and valleys is established, and the peak amplitude is the average difference between the peak value and the adjacent valley value, and the valley depth is the average difference between the valley value and the adjacent peak value. The periodic characteristics of energy peaks and valleys in the time period energy distribution are analyzed to identify the dominant period and the secondary period of energy change in the energy storage window period. Frequency domain analysis techniques are used to extract the frequency spectrum characteristics of the time period energy distribution, and the peaks in the frequency spectrum correspond to the characteristic frequencies of energy fluctuations. The influence of compressor start-stop cycles and fan speed regulation on the formation of peaks and valleys in the time period energy distribution is considered, and the fluctuations caused by device operation and the fluctuations caused by load changes are distinguished. A hierarchical system of energy peaks and valleys is established, and the peaks and valleys are divided into three levels of strong fluctuations, medium fluctuations, and weak fluctuations according to the amplitude. The time position, amplitude, and duration of the identified energy peaks and valleys in the energy storage window period are recorded, and a peak and valley characteristic database is established.

[0064] The peak and valley of energy wave is evaluated to produce the adjustment margin. The peak clipping potential of energy wave is calculated, which is equal to the smaller value of the peak amplitude and the system adjustment capacity, indicating the peak energy that can be eliminated by adjustment. The valley filling potential of energy wave is analyzed, which is the smaller value of the valley depth and the system energy storage capacity, reflecting the valley energy that can be filled by pre-storage. The comprehensive evaluation index P_potential of peak and valley clipping potential is established, P_potential=α·P_peak+β·P_valley, where P_peak is the peak clipping potential, P_valley is the valley filling potential, and α and β are weight coefficients. The constraint effects of compressor frequency conversion range, condenser heat storage capacity, evaporator cold storage capacity and expansion valve adjustment range on peak and valley clipping potential are considered. The support capacity of condenser and evaporator fan speed regulation range on energy wave peak and valley adjustment is analyzed, and the fan adjustment capacity affects the implementation effect of peak and valley clipping. The actual achievable adjustment margin is determined by matching the peak and valley clipping potential and the length of the energy storage window period. The calculation relationship of adjustment margin M_adjust is established, M_adjust=P_potential·T_window·η_efficiency, where T_window is the length of the energy storage window period, and η_efficiency is the adjustment efficiency. The generated adjustment margin value, time distribution and technical constraint conditions are recorded to provide quantitative indicators for the determination of dynamic buffer threshold.

[0065] The dynamic buffer threshold is obtained by fusing the adjustment margin and the thermal capacity characteristic parameter. The optimal dynamic buffer threshold is determined by fusing the generated adjustment margin and the thermal capacity characteristic parameter extracted from the equivalent thermal resistance network through parameter fusion technology. The fusion weight distribution of the adjustment margin and the thermal capacity characteristic parameter is established, and the weight distribution considers the contribution degree of the two types of parameters to the buffering effect. The time scale of the adjustment margin and the time constant of the thermal capacity characteristic parameter are matched and analyzed, and the combination with high matching degree corresponds to more effective buffer threshold setting. The comprehensive buffer capacity C_buffer is calculated as C_buffer = w1·M_adjust + w2·C_thermal, where C_buffer is the comprehensive buffer capacity, w1 and w2 are the fusion weights, and C_thermal is the thermal capacity characteristic parameter. The upper and lower limits of the dynamic buffer threshold are determined by the comprehensive buffer capacity, the upper limit threshold Th_upper = C_buffer + σ_safety, and the lower limit threshold Th_lower = C_buffer - σ_safety, where σ_safety is the safety margin. Considering the dynamic influence of environmental temperature change and load fluctuation in the energy storage window on the fused parameters, a real-time correction mechanism for the threshold is established. The influence of the thermal capacity distribution of the compressor, condenser, expansion valve, and evaporator on the spatial distribution of the dynamic buffer threshold is analyzed. A stability test mechanism for the dynamic buffer threshold is established to ensure that the threshold setting can maintain system stability under various operating conditions. The numerical range, applicable conditions, and adjustment accuracy of the obtained dynamic buffer threshold are recorded to provide accurate control benchmarks for the construction of peak shaving and valley filling instruction sets.

[0066] The peak shaving and valley filling instruction set is constructed according to the dynamic buffer threshold. The triggering condition of the peak shaving instruction is established using the dynamic buffer threshold, and the peak shaving control instruction is started when the system load exceeds the upper limit of the threshold. The specific content of the peak shaving control instruction is designed, including compressor frequency reduction, condenser heat dissipation fan speed increase, expansion valve opening adjustment, and evaporator fan speed control. The activation mechanism of the valley filling instruction is established, and the valley filling operation instruction is executed when the system load is lower than the lower limit of the dynamic buffer threshold. The parameter setting of the valley filling control instruction is formulated, and energy storage is achieved by starting the compressor in advance, pre-cooling the evaporator, and increasing the condenser storage, etc. The timing control logic of the peak shaving and valley filling instruction set is constructed, and the instructions are scheduled according to the real-time monitoring results of the dynamic buffer threshold and the preset execution priority. The influence of the pressure controller set value and the gas-liquid separator operating state on the execution effect of the peak shaving and valley filling instructions is considered, and the instruction parameters are adjusted. The coordinated execution mechanism of the instruction set is established to ensure that the peak shaving and valley filling instructions do not conflict with each other and are consistent with the frequency regulation sequence. The protection function of the peak shaving and valley filling instruction set is designed, and the instruction execution is automatically stopped and the normal operation is restored when the system operating parameters exceed the safety range. The generated peak shaving and valley filling instruction set contains instruction types, execution parameters, triggering conditions, and coordination logic, providing a complete instruction library for the intelligent energy-saving control of airport ground air conditioning units.

[0067] Step S160, monitor the execution deviation trajectory of the peak clipping instruction set, perform convergence analysis based on the deviation trajectory to identify the optimal convergence path, and adaptively modify the variable frequency adjustment sequence along the optimal convergence path to generate energy-saving control instructions.

[0068] Specifically, the execution deviation trajectory of the peak clipping instruction set is monitored. By comparing the execution parameters of the peak clipping instruction set with the actual response of the system, the execution deviation δ(t)=y_target(t)-y_actual(t) at each time is calculated, where y_target is the target value of the instruction and y_actual is the actual response of the system. By monitoring the compressor frequency response, condenser cooling fan speed regulation effect, evaporator fan operating state and expansion valve opening adjustment in real time, the response deviation of each component to the peak clipping instruction is recorded. The execution deviation characteristics of the peak clipping instruction set under different load conditions are analyzed, and the main causes and influencing factors of the deviation are identified. A data acquisition system for deviation trajectory is established, with a frequency setting of 10 Hz to ensure capturing the details of deviation changes in the system dynamic response process. Through the monitoring of the running state of auxiliary components such as dry filter and gas-liquid separator, the influence of auxiliary systems on the execution deviation of the peak clipping instruction is analyzed. Considering the influence of environmental temperature change, load fluctuation and equipment aging on the execution deviation of the peak clipping instruction set, a multi-factor deviation analysis system is established. A visual display of the deviation trajectory is established, and a time series chart is used to show the evolution process of the deviation with time, to identify the trend and periodic characteristics of the deviation change. The monitored deviation trajectory data, including deviation amplitude, change frequency and duration, are recorded, and a deviation trajectory database is established.

[0069] In some embodiments, the convergence analysis based on the deviation trajectory to identify the optimal convergence path includes: extracting oscillation characteristics and decay characteristics from the deviation trajectory; establishing a trajectory family in phase space based on the oscillation characteristics; using the decay characteristics to perform stability screening on the trajectory family to produce a candidate path set; and determining the optimal convergence path based on the candidate path set for energy minimization selection.

[0070] The oscillation characteristics and the decay characteristics are extracted from the deviation trajectory. The oscillation characteristics are extracted by time-frequency analysis of the deviation trajectory, including the oscillation frequency, the oscillation amplitude, and the oscillation phase. The decay characteristics are extracted by envelope analysis of the deviation trajectory, reflecting the recovery ability of the system from the perturbed state to the stable state. The oscillation frequency ω = 2π / T of the deviation trajectory is calculated, where T is the oscillation period, and the accurate value of the oscillation period is determined by peak detection technology. The variation of the oscillation amplitude with time is analyzed, and the decay rate of the oscillation amplitude reflects the stability performance of the system. The decay characteristics are calculated using the logarithmic decay rate δ = ln(A_n / A_{n+1}), where A_n is the nth oscillation peak, and the larger the logarithmic decay rate, the faster the system decays. The symmetry and regularity of the oscillation are determined by zero-point analysis of the deviation trajectory, and symmetric oscillation indicates that the system has good stability characteristics. The influence of compressor start-stop and fan speed regulation on the oscillation characteristics of the deviation trajectory is considered, and the inherent oscillation of the system and the oscillation caused by external excitation are distinguished. The parameter database of oscillation characteristics and decay characteristics is established to record the characteristic parameter distribution of the deviation trajectory under different working conditions.

[0071] The trajectory family in the phase space is established based on the oscillation characteristics. The trajectory family distribution of the deviation trajectory in the phase space is established by phase space reconstruction technology using the extracted oscillation characteristics. The deviation and its first and second derivatives are selected as the coordinate axes of the phase space to construct a three-dimensional phase space representing the dynamic behavior of the system. The basic shape and distribution range of the trajectory in the phase space are determined using the frequency and amplitude information of the oscillation characteristics. The relative position and rotation direction of the trajectory family in the phase space are determined by the phase information of the oscillation characteristics. The shapes of the phase trajectories corresponding to different oscillation characteristics are analyzed, including typical forms such as elliptical trajectories, spiral trajectories, and limit cycle trajectories. The trajectory family is constructed in the phase space using the diversity of the oscillation characteristics, and each combination of oscillation characteristics corresponds to a specific trajectory in the trajectory family. The mathematical expression of the trajectory family is established, and the geometric characteristics and dynamic properties of the trajectory family in the phase space are described using parametric equations. The distribution density and coverage range of the trajectory family are determined by statistical analysis of the oscillation characteristics, and the high-density area represents the preferred operating state of the system.

[0072] The trajectory family is screened for stability using the decay characteristic to generate a candidate path set. The stability of each trajectory in the trajectory family is evaluated using the logarithmic decay rate of the decay characteristic, and trajectories with a larger logarithmic decay rate have better stability. The trajectory family is screened using the decay time constant of the decay characteristic, where ζ is the damping ratio and ω_n is the natural frequency, and trajectories with a smaller time constant converge faster. A comprehensive index S = a · δ + β · (1 / τ) is established for stability screening, where δ is the logarithmic decay rate, a and β are weight coefficients, and τ is the time constant, and trajectories with a higher comprehensive index have better stability. The convergence accuracy of each trajectory in the trajectory family is analyzed, and the convergence accuracy is defined as the deviation range of the trajectory converging to the equilibrium point. The trajectory family is screened using the monotonicity criterion of the decay characteristic, and trajectories that decay monotonically have better control characteristics than trajectories that oscillate and decay. The influence of the actual operation constraints of the airport ground air conditioning unit on the stability of the trajectory is considered, and trajectories that exceed the performance range of the equipment are excluded. The trajectories that meet the stability requirements are selected from the trajectory family through the stability screening mechanism to form the candidate path set. An evaluation system for the candidate path set is established, and each candidate path contains attribute information such as stability index, convergence time, and control difficulty.

[0073] The optimal convergence path is determined based on the candidate path set for energy minimization selection. The energy consumption of each path in the candidate path set is calculated, including compressor power consumption, condenser cooling fan power consumption, evaporator fan power consumption, and control system power consumption. The calculation relationship of path energy consumption is established, and the total energy consumption of the path is calculated by accumulating the power consumption of each device during the convergence path, including the time accumulation values of compressor power consumption, condenser fan power consumption, evaporator fan power consumption, and control system power consumption, where each power consumption is obtained through real-time power monitoring and time accumulation. The path with the smallest energy-time product is found by weighing the convergence time and energy consumption of the candidate path set. The Pareto optimal solution is searched in the candidate path set using multi-objective optimization technology to balance convergence speed, energy consumption level, and stability requirements. The influence of the expansion valve adjustment energy consumption and the pressure controller action energy consumption on path selection is considered, and a full-system energy consumption evaluation system is established. The energy consumption performance of each path in the candidate path set under different load conditions is analyzed, and the path with strong adaptability is selected as the optimal selection. The selection criteria for the optimal convergence path are established, considering the three objectives of minimum energy consumption, fastest convergence, and best stability. The robustness of each path to parameter changes is evaluated through sensitivity analysis of the candidate path set, and the path with strong robustness is selected. The trajectory parameters, energy consumption characteristics, and applicable conditions of the determined optimal convergence path are recorded to provide optimal path guidance for the generation of energy-saving control instructions.

[0074] The variable frequency adjustment sequence is adaptively corrected along the optimal convergence path to generate the energy-saving control instruction. The correction amount of the variable frequency adjustment sequence is calculated using the trajectory information of the optimal convergence path, and the correction amount is determined according to the actual performance of the deviation trajectory. The adjustment step and adjustment frequency of the variable frequency adjustment sequence are adjusted through the convergence characteristics of the optimal convergence path, and different adjustment strategies are adopted at different stages of the convergence process. A feedback control mechanism of adaptive correction is established, and the sequence correction program is automatically started when the system deviates from the optimal convergence path. The generation logic of the energy-saving control instruction is designed, and the instruction content includes the coordinated setting of the compressor variable frequency parameters, the condenser heat dissipation fan speed, the evaporative fan speed and the expansion valve opening degree. The influence of the pressure controller set value and the gas-liquid separator operating parameters on the execution effect of the energy-saving control instruction is considered, and the parameter configuration of the instruction is adjusted. A priority mechanism of the energy-saving control instruction is established, and the execution order of the instruction is determined according to the importance and urgency of the optimal convergence path. The energy-saving control instruction is generated in advance through the prediction function of the optimal convergence path, realizing the combination of feedforward control and feedback control. The generated energy-saving control instruction integrates the convergence characteristics of the optimal convergence path and the adjustment capacity of the variable frequency adjustment sequence, realizing the intelligent energy-saving control of the airport ground air conditioning unit.

[0075] In order to perform the airport ground air conditioning unit multi-system energy-saving control method corresponding to the above-mentioned method embodiment, the corresponding functions and technical effects are realized. Referring to Figure 2 , Figure 2 The structure block diagram of the airport ground air conditioning unit multi-system energy-saving control system 200 provided by the embodiment of the application is shown. For ease of illustration, only the parts related to the embodiment are shown. The airport ground air conditioning unit multi-system energy-saving control system 200 provided by the embodiment of the application comprises: The data acquisition module 201 is configured to acquire refrigerant flow data, temperature monitoring data and load demand data of the airport air conditioning unit, identify a phase change boundary from the refrigerant flow data to extract a gas-liquid conversion point, extract a superheat degree distribution from the temperature monitoring data, and generate a phase change energy field by enthalpy coupling the gas-liquid conversion point and the superheat degree distribution. The thermal resistance analysis module 202 is configured to identify a heat transfer bottleneck point based on the phase change energy field, perform thermal resistance analysis on the heat transfer bottleneck point to generate an equivalent thermal resistance network, extract a thermal capacity characteristic parameter from the equivalent thermal resistance network, and construct a thermal response path graph using the equivalent thermal resistance network and the thermal capacity characteristic parameter. The load balance module 203 is configured to perform refrigerant shunt analysis on the thermal response path graph to extract a main circuit and a secondary circuit, analyze the enthalpy difference distribution of the main circuit and the secondary circuit to identify an optimal distribution point, and perform dynamic weight distribution on the optimal distribution point to generate a load balance matrix. The variable frequency control module 204 is configured to reconstruct a compressor adjustment curve based on the load balance matrix, perform efficiency mapping on the compressor adjustment curve and the superheat degree distribution to identify a high-efficiency operation interval, and generate a variable frequency adjustment sequence using the high-efficiency operation interval. The energy management module 205 is configured to perform time sequence correlation analysis on the variable frequency adjustment sequence and the load demand data to determine a storage energy window period, obtain a dynamic buffer threshold based on matching of the storage energy window period and the thermal capacity characteristic parameter, and construct a peak load shifting and valley load filling instruction set according to the dynamic buffer threshold. The adaptive optimization module 206 is configured to monitor an execution deviation trajectory of the peak load shifting and valley load filling instruction set, perform convergence analysis based on the deviation trajectory to identify an optimal convergence path, and perform adaptive correction on the variable frequency adjustment sequence along the optimal convergence path to generate an energy-saving control instruction.

[0076] The airport ground air conditioning unit multi-system energy-saving control system 200 described above can implement an airport ground air conditioning unit multi-system energy-saving control method according to the method embodiment described above. The optional items in the method embodiment described above are also applicable to this embodiment, and will not be described in detail here. The remaining content of the embodiment of the present application can refer to the content of the method embodiment described above, and will not be described in detail in this embodiment.

[0077] The purpose of the above embodiments is to exemplarily reproduce and deduce the technical solutions of the present application, and to completely describe the technical solutions, purposes and effects of the present application. The purpose is to make the public understand the disclosure of the present application more thoroughly and comprehensively, and does not limit the protection scope of the present application.

[0078] The above embodiments are also not an exhaustive enumeration based on the present application, and there can be many other unlisted embodiments. Any replacement and improvement made without violating the concept of the present application is within the protection scope of the present application.

Claims

1. An airport ground air conditioning unit multi-system energy saving control method, characterized in that, The method comprises the following steps: Collecting refrigerant flow data, temperature monitoring data and load demand data of an airport air conditioning unit, identifying a phase change boundary from the refrigerant flow data to extract a gas-liquid conversion point, extracting a superheat degree distribution from the temperature monitoring data, coupling the enthalpy value of the gas-liquid conversion point and the superheat degree distribution to generate a phase change energy field; Identifying a heat transfer bottleneck point based on the phase change energy field, analyzing the thermal resistance of the heat transfer bottleneck point to generate an equivalent thermal resistance network, extracting thermal capacity characteristic parameters from the equivalent thermal resistance network, and constructing a thermodynamic response path diagram using the equivalent thermal resistance network and the thermal capacity characteristic parameters; Performing refrigerant flow analysis on the thermodynamic response path diagram to extract primary and secondary circuits, analyzing the enthalpy difference distribution of the primary and secondary circuits to identify an optimal distribution point, and performing dynamic weight distribution on the optimal distribution point to generate a load balance matrix; Reconstructing a compressor adjustment curve based on the load balance matrix, performing efficiency mapping on the compressor adjustment curve and the superheat degree distribution to identify a high-efficiency operation interval, and generating a variable frequency adjustment sequence using the high-efficiency operation interval; Performing time sequence correlation analysis on the variable frequency adjustment sequence and the load demand data to determine an energy storage window period, matching the energy storage window period with the thermal capacity characteristic parameters to obtain a dynamic buffer threshold, and constructing a peak clipping and valley filling instruction set according to the dynamic buffer threshold; Monitoring the execution deviation trajectory of the peak clipping and valley filling instruction set, performing convergence analysis based on the deviation trajectory to identify an optimal convergence path, and performing adaptive correction on the variable frequency adjustment sequence along the optimal convergence path to generate an energy-saving control instruction.

2. The method of claim 1, wherein, The method comprises the following steps: Identifying a dryness gradient distribution at the gas-liquid conversion point; Matching and positioning the dryness gradient distribution and the superheat degree distribution to determine a phase change completion boundary; Extracting an enthalpy difference driving potential along the phase change completion boundary; Constructing a phase change energy field based on the spatial distribution of the enthalpy difference driving potential.

3. The method of claim 1, wherein, The method comprises the following steps: Analyzing the topological structure of the equivalent thermal resistance network to identify series and parallel connection relationships; Calculating an equivalent thermal resistance ratio distribution based on the series and parallel connection relationships; Deriving a time constant spectrum from the equivalent thermal resistance ratio distribution; Extracting a dominant component from the time constant spectrum as a thermal capacity characteristic parameter.

4. The method of claim 1, wherein, The method comprises the following steps: Performing energy flow density analysis based on the optimal distribution point to identify high-density channels and low-density channels; Compensating the flow of the low-density channels using the high-density channels to form balanced channels; Spatially arranging and combining the balanced channels to generate a distribution topology; Matrix encoding the distribution topology to generate a load balance matrix.

5. The method of claim 1, wherein, The method comprises the following steps: Dividing the compressor adjustment curve by an equivalent efficiency line to identify an efficiency ridge line; Finding a matching area in the superheat degree distribution based on the efficiency ridge line; Stability evaluating the matching area to form a stable operation band; The boundary optimization is performed on the stable operation zone to determine an efficient operation interval.

6. The method of claim 1, wherein, The dynamic buffer threshold is obtained based on matching of the energy storage window period and the heat capacity characteristic parameter. The time period energy distribution is constructed based on the energy storage window period. Energy wave crests and troughs are identified in the time period energy distribution. Peak clipping and valley filling potential of the energy wave crests and troughs is evaluated to generate an adjustment margin. The dynamic buffer threshold is obtained based on fusion of the adjustment margin and the heat capacity characteristic parameter.

7. The method of claim 1, wherein, The optimal convergence path is identified based on the convergence analysis of the deviation trajectory, including: Oscillation characteristics and decay characteristics are extracted from the deviation trajectory. A trajectory family is established in a phase space based on the oscillation characteristics. A candidate path set is generated by performing stability screening on the trajectory family using the decay characteristics. The optimal convergence path is determined by performing energy consumption minimization selection based on the candidate path set.

8. The method of claim 2, wherein, The enthalpy difference driving potential is extracted along the phase change completion boundary, including: Key monitoring positions are arranged on both sides of the phase change completion boundary. Local temperature gradients and pressure gradients are extracted from the key monitoring positions. An enthalpy value distribution curve is generated along the boundary based on the temperature gradients and the pressure gradients. The change trend of the enthalpy value distribution curve is converted into an enthalpy difference driving potential.

9. The method of claim 4, wherein, The flow compensation processing of the low-density channel is performed by the high-density channel to form an equilibrium channel, including: The high-density channel is used as an energy supply source to inject compensation flow into the low-density channel. A dynamic equilibrium state is formed by closed-loop control of the compensation flow. The dynamic equilibrium state is used to continuously optimize the flow distribution accuracy. When the distribution accuracy meets the equilibrium requirement, the channel flow distribution is maintained to form an equilibrium channel.

10. An airport ground air conditioning unit multi-system energy saving control system, characterized in that, The data acquisition module is configured to acquire refrigerant flow data, temperature monitoring data, and load demand data of the airport air conditioning unit, identify and extract a gas-liquid conversion point from the phase change boundary of the refrigerant flow data, extract a superheat distribution from the temperature monitoring data, and generate a phase change energy field by enthalpy coupling of the gas-liquid conversion point and the superheat distribution. The thermal resistance analysis module is configured to identify a heat transfer bottleneck point based on the phase change energy field, generate an equivalent thermal resistance network by thermal resistance analysis of the heat transfer bottleneck point, extract a heat capacity characteristic parameter from the equivalent thermal resistance network, and construct a thermodynamic response path graph using the equivalent thermal resistance network and the heat capacity characteristic parameter. The load balance module is configured to extract a primary and secondary circuit by refrigerant shunt analysis of the thermodynamic response path graph, identify an optimal distribution point by analyzing the enthalpy difference distribution of the primary and secondary circuit, and generate a load balance matrix by dynamic weight distribution of the optimal distribution point. The frequency conversion control module is configured to reconstruct a compressor adjustment curve based on the load balance matrix, identify an efficient operation interval by efficiency mapping of the compressor adjustment curve and the superheat distribution, and generate a frequency conversion adjustment sequence using the efficient operation interval. The energy management module is configured to determine an energy storage window period by time sequence correlation analysis of the frequency conversion adjustment sequence and the load demand data, obtain a dynamic buffer threshold based on matching of the energy storage window period and the heat capacity characteristic parameter, and construct a peak clipping and valley filling instruction set according to the dynamic buffer threshold. ​ An adaptive optimization module is configured to monitor an execution deviation trajectory of the peak clipping instruction set, perform a convergence analysis based on the deviation trajectory to identify an optimal convergence path, and perform adaptive correction on the variable frequency regulation sequence along the optimal convergence path to generate an energy-saving control instruction.

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