Method and system for optimizing intermediate pressure in a magnetic levitation multi-stage compression heat pump

Abstract

The application relates to the technical field of magnetic suspension compressor control, in particular to a magnetic suspension multistage compression heat pump intermediate pressure optimization method and system. The method comprises the following steps: acquiring an intermediate pressure time series data set output by a compressor pressure sensor and performing dynamic characteristic decomposition to extract three characteristic quantities, namely, an instantaneous change rate, a change trend acceleration and a deviation potential energy; combining a dynamic adjustment factor calculated in real time based on a current compressor load rate to calculate a direction speed correction amount; and superimposing the direction speed correction amount on an original speed instruction to generate a dynamic optimization speed instruction, so that virtual damping control of intermediate pressure fluctuation is realized. The application can quickly and stably suppress intermediate pressure fluctuation and improve system operation efficiency and reliability without increasing additional hardware on the premise of utilizing the speed regulation capacity of the magnetic suspension compressor.

Description

Technical Field

[0001] This application relates to the field of magnetic levitation compressor control technology, and in particular to a method and system for optimizing intermediate pressure of a magnetic levitation multi-stage compression heat pump. Background Technology

[0002] In the field of industrial thermal energy drive and high-efficiency refrigeration, magnetic levitation multi-stage compression heat pump systems have been widely used in key scenarios such as district heating, industrial waste heat recovery and high-temperature process cooling. Their energy efficiency and operational stability are directly related to the achievement of carbon emission targets, the extension of equipment life and the guarantee of system safety. They are the core components of modern green and low-carbon energy systems and intelligent thermal management equipment.

[0003] However, existing intermediate pressure control methods lack a mechanism for real-time, multi-dimensional fusion and active damping suppression of pressure dynamic characteristics when facing complex pressure fluctuations caused by sudden load changes, uneven refrigerant flow, and multi-stage coupling conditions. This not only fails to effectively support the forward-looking adaptive adjustment of compressor speed, but may also cause inter-stage surge or instability under extreme conditions, posing serious safety hazards. Summary of the Invention

[0004] This application provides a method and system for optimizing the intermediate pressure of a magnetic levitation multi-stage compression heat pump to solve the above-mentioned problems.

[0005] In a first aspect, this application provides a method for optimizing the intermediate pressure of a magnetically levitated multi-stage compression heat pump, the method comprising: The intermediate pressure time series dataset output by the compressor pressure sensor over several consecutive control cycles is obtained, and the intermediate pressure time series dataset is subjected to dynamic feature decomposition to extract instantaneous change rate feature, change trend acceleration feature, and deviation potential energy feature. The directional speed correction is calculated based on the instantaneous rate of change characteristic, the trend acceleration characteristic, the deviation potential energy characteristic, and the dynamic adjustment factor calculated in real time based on the current compressor load rate. The directional speed correction amount is adjusted to the original speed command of the magnetic levitation compressor to generate a dynamically optimized speed command, thereby realizing virtual damping control of intermediate pressure fluctuations.

[0006] Optionally, the extraction of instantaneous rate of change feature, trend acceleration feature, and deviation potential energy feature includes: The intermediate pressure time series dataset includes the intermediate pressure value and control cycle duration for each control cycle. The intermediate pressure value of the current control cycle is differentially calculated with the intermediate pressure value of the previous control cycle, divided by the control cycle duration, and combined with a preset noise suppression factor and the pressure sliding average value calculated from the historical pressure sampling values ​​in the intermediate pressure time series dataset to generate the instantaneous change rate feature. The instantaneous rate of change characteristic of the current control cycle is differentially calculated with the instantaneous rate of change characteristic of the previous control cycle, divided by the control cycle duration, and combined with the acceleration amplification factor dynamically output according to the real-time compressor load rate to generate the trend acceleration characteristic. Extract all N historical pressure sampling values ​​within a preset historical time window before the current control cycle from the intermediate pressure time series dataset to obtain the average pressure value. Subtract the current pressure value from the average pressure value and combine it with the potential energy correction factor determined in real time based on the compressor load rate and intermediate pressure fluctuation amplitude to generate the deviation potential energy characteristic quantity.

[0007] Optionally, the instantaneous rate of change feature is generated by calculating the following formula: ; in, The instantaneous rate of change characteristic quantity, The intermediate pressure value within the current control cycle. This refers to the intermediate pressure value within the previous control cycle. The duration of the control cycle, The preset noise suppression factor, The pressure sliding average value within a preset historical time window prior to the current control cycle.

[0008] Optionally, the acceleration characteristic of the changing trend is calculated using the following formula: ; in, The acceleration characteristic of the changing trend, This refers to the instantaneous rate of change characteristic quantity corresponding to the current control cycle. This refers to the instantaneous rate of change characteristic quantity corresponding to the previous control cycle. The duration of the control cycle, is the acceleration amplification factor.

[0009] Optionally, the deviation potential energy characteristic quantity is generated by calculating the following formula: ; in, This refers to the deviation potential energy characteristic quantity. The intermediate pressure value within the current control cycle. This is the preset number of data points used to calculate historical average pressure values. Indicates to , … The average of N historical pressure values ​​is calculated. is the potential energy correction factor.

[0010] Optionally, the dynamic adjustment factor calculated in real time based on the current compressor load rate includes: Obtain the ratio between the compressor's current actual speed and its rated speed, and use this ratio as the current load rate; Based on the current load rate, the initial adjustment factor is calculated through a monotonically increasing nonlinear mapping relationship, which is determined by the compressor's rotor dynamics characteristics and the maximum allowable damping strength. The initial adjustment factor is added to the basic damping coefficient determined by the compressor's own structural parameters to obtain the dynamic adjustment factor; The value of the dynamic adjustment factor is constrained by the compressor bearing support stiffness, rotor mass, and maximum permissible vibration amplitude, and is limited to the minimum and maximum damping strength allowed by the physical system.

[0011] Optionally, the calculation of the directional rotational speed correction is performed using the following formula: ; in, This is the rotational speed correction amount in the stated direction. The dynamic adjustment factor, The instantaneous rate of change characteristic quantity, To pre-determine the weighting coefficients for the influence of trends, The acceleration characteristic of the changing trend, To preset the balancing influence weighting coefficients, This refers to the deviation potential energy characteristic quantity. This is the preset damping response limiting factor.

[0012] Optionally, generating the dynamically optimized speed command includes: The directional speed correction amount is superimposed with the original speed command to obtain the unlimited intermediate speed command; The unlimited intermediate speed command is constrained within the physical safety range formed by the compressor's minimum and maximum operating speeds, thereby generating the dynamically optimized speed command. The dynamically optimized speed command is sent to the compressor driver, so that the actual output speed of the compressor tracks the dynamically optimized speed command in real time, thereby realizing virtual damping control of intermediate pressure fluctuations.

[0013] Optionally, constraining the unlimited intermediate speed command within the physical safety range formed by the compressor's minimum and maximum permissible operating speeds includes: The value of the unlimited intermediate speed command is compared with the maximum allowable operating speed of the compressor. If the unlimited intermediate speed command is greater than the maximum operating speed, the value of the dynamically optimized speed command is set to the maximum operating speed. If the unlimited intermediate speed command is less than the minimum operating speed, then the dynamic optimized speed command is set to the minimum operating speed. If the unlimited intermediate speed command is between the minimum operating speed and the maximum operating speed, its value remains unchanged.

[0014] This application provides a method for optimizing the intermediate pressure of a magnetically levitated multi-stage compression heat pump. This method acquires a time-series dataset of intermediate pressure output from the compressor pressure sensor over several consecutive control cycles. The dataset undergoes dynamic feature decomposition to extract instantaneous rate of change, acceleration trend, and deviation potential energy features. These features are then combined with a dynamic adjustment factor calculated in real-time based on the current compressor load rate to calculate a directional speed correction. Finally, this correction is adjusted to the original speed command of the magnetically levitated compressor to generate a dynamically optimized speed command. Through three-dimensional feature decomposition of the intermediate pressure time-series data, the control system can simultaneously perceive the instantaneous rate of pressure change, the acceleration trend, and the degree of deviation from the historical average level. Based on these three dimensions of features and an adaptive dynamic adjustment factor, a weighted combination is performed to generate a directional speed correction that simulates physical damping characteristics. This virtual damping mechanism allows the compressor speed to automatically adjust in the opposite direction based on the characteristics of pressure fluctuations, effectively consuming the energy of pressure fluctuations and thus achieving rapid and stable suppression of intermediate pressure fluctuations. Therefore, this solution effectively solves the serious challenge of rapid and stable control of intermediate pressure in the long-term operation of multi-stage compression heat pump systems. Due to factors such as dynamic load changes, non-uniformity of refrigerant flow, and external disturbances, intermediate pressure is prone to high-frequency fluctuations or large oscillations, which leads to increased system energy consumption, may induce unstable modes in the rotor system, and accelerate component wear. Thus, it avoids the situation of decreased efficiency, deteriorated operational stability, and shortened equipment life caused by pressure fluctuations, and improves the system's operational reliability and control accuracy under a wide range of operating conditions.

[0015] Secondly, this application provides a magnetic levitation multi-stage compression heat pump intermediate pressure optimization system, the system comprising: The dynamic feature decomposition module is used to acquire the intermediate pressure time series dataset output by the compressor pressure sensor in several consecutive control cycles, and to perform dynamic feature decomposition on the intermediate pressure time series dataset to extract instantaneous change rate feature, change trend acceleration feature, and deviation potential energy feature. The correction calculation module is used to calculate the directional speed correction amount based on the instantaneous rate of change characteristic, the trend acceleration characteristic, the deviation potential energy characteristic, and the dynamic adjustment factor calculated in real time based on the current compressor load rate. The optimization command generation module is used to adjust the directional speed correction amount to the original speed command of the magnetic levitation compressor, generate a dynamically optimized speed command, and realize virtual damping control of intermediate pressure fluctuations. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of an application scenario provided in an embodiment of this application; Figure 2 A flowchart illustrating an embodiment of this application provides a method for optimizing the intermediate pressure of a magnetically levitated multi-stage compression heat pump; Figure 3 This is a schematic diagram of the intermediate pressure optimization system of a magnetic levitation multi-stage compression heat pump provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0020] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0021] Figure 1 This is a black-and-white top-down schematic diagram of a heat pump, divided into two main areas: the heat pump fluid structure and the optimization control system. The upper section shows the top-down layout of a two-stage magnetic levitation multi-stage compression heat pump. The refrigerant piping sequentially connects to the first-stage compressor, the interstage intermediate cavity, and the second-stage compressor. The intermediate cavity piping is equipped with an intermediate pressure sensor measuring point. The lower section is the control logic module. The dashed signal links connect the sensor to three core modules: dynamic feature decomposition, correction calculation, and optimization command generation. These are simultaneously connected to the compressor load rate calculation unit. Each module sequentially outputs three-dimensional pressure characteristics, dynamic adjustment factors, and speed correction values. These are superimposed on the original speed command and, after speed limiting, sent to the compressor driver, forming a closed loop of speed feedback. In the diagram, thick solid lines represent fluid piping, and thin dashed lines represent signal transmission lines, fully presenting the physical structure of the heat pump and the entire process of the multi-feature virtual damping pressure optimization control of this patent.

[0022] The specific implementation method can be referred to in the following embodiments, wherein the data mentioned in the embodiments are only for reference and examples, so that relevant personnel can better understand them.

[0023] Figure 2 A flowchart illustrating an intermediate pressure optimization method for a magnetically levitated multi-stage compression heat pump, as provided in an embodiment of this application, is shown below. Figure 2 The process, specifically the implementation steps, are as follows: Example 1: In one embodiment, this application provides a method for optimizing the intermediate pressure of a magnetically levitated multi-stage compression heat pump, the method comprising: Step 1: Obtain the intermediate pressure time series dataset output by the compressor pressure sensor over several consecutive control cycles, and perform dynamic feature decomposition on the intermediate pressure time series dataset to extract instantaneous change rate feature, change trend acceleration feature, and deviation potential energy feature.

[0024] The compressor pressure sensor is installed in the fluid channel between the outlet of the first-stage compressor impeller and the inlet of the second-stage compressor impeller to monitor the interstage piping pressure status of the multi-stage compression heat pump system. The controller reads the sensor signal through an analog-to-digital converter interface at a preset sampling frequency and converts it into pressure values. These values ​​are then stored in a circular buffer in chronological order, forming an intermediate pressure time-series dataset containing current and historical pressure records. This dataset comprehensively describes the dynamic changes in intermediate pressure.

[0025] Dynamic feature decomposition is performed on intermediate pressure time series datasets to separate signal components representing different physical properties from a single pressure time series. Through algorithms such as differential operations, moving averages, and weighted calculations, the original data is mapped into three independent and complementary feature quantities: the instantaneous rate of change feature quantity represents the speed of pressure change per unit time, reflecting the immediate intensity of pressure fluctuations; the trend acceleration feature quantity represents the change in the rate of pressure change, reflecting whether the fluctuation is accelerating or decelerating; and the deviation potential energy feature quantity represents the degree of deviation of the current pressure value from the historical average level, reflecting the magnitude of the potential energy accumulated by the system deviating from its equilibrium state. The resulting feature vector comprehensively covers the velocity, direction, and acceleration state of pressure fluctuations, providing a high-dimensional state information foundation for subsequently generating targeted speed correction strategies.

[0026] Step 2: Calculate the directional speed correction based on the instantaneous rate of change characteristic, the trend acceleration characteristic, the deviation potential energy characteristic, and the dynamic adjustment factor calculated in real time based on the current compressor load rate.

[0027] The directional speed correction is a signed speed adjustment value used to indicate the direction and magnitude of compressor speed adjustment. Its physical meaning lies in counteracting pressure fluctuations by changing the refrigerant's work rate. A positive value indicates that the speed needs to be increased to suppress pressure drops or cope with increased load, while a negative value indicates that the speed needs to be decreased to alleviate excessive pressure or overload.

[0028] The dynamic adjustment factor, as the core parameter determining the virtual damping strength, is a real-valued coefficient calculated in real time based on the compressor's current load rate. First, the ratio of the compressor's current actual speed to its rated speed is calculated as the current load rate. Then, based on the monotonically increasing nonlinear mapping relationship between the rotor's dynamic characteristics and the maximum permissible damping strength, an initial adjustment factor is calculated from the current load rate. Finally, the initial adjustment factor is added to the basic damping coefficient determined by the compressor's structural parameters to obtain the dynamic adjustment factor. The value of this factor is constrained between the minimum and maximum damping strengths defined by the bearing support stiffness, rotor mass, and maximum permissible vibration amplitude to ensure that the adjustment action does not trigger system instability.

[0029] The controller uses a dynamic adjustment factor as the base gain and performs weighted summations on the instantaneous rate of change characteristic, the acceleration characteristic of the change trend, and the deviation potential energy characteristic. The deviation potential energy characteristic is typically given a negative sign to reflect the trend towards equilibrium. Through the above weighted summation operation, the directional speed correction amount, which integrates multi-dimensional feature information and adaptive adjustment parameters, is calculated. This command can adaptively output the optimal correction force based on the severity of pressure fluctuations and the current load state of the system.

[0030] Step 3: Adjust the directional speed correction amount to the original speed command of the magnetic levitation compressor to generate a dynamically optimized speed command, thereby achieving virtual damping control of intermediate pressure fluctuations.

[0031] The original speed command is the target speed setpoint calculated by the upper-level control system based on the current heat load demand. The dynamically optimized speed command is the final speed control target after real-time correction using the method described in this application. It retains the responsiveness to macroscopic loads while incorporating fine-tuning components for intermediate pressure fluctuations.

[0032] The controller reads the current raw speed command and algebraically adds the calculated directional speed correction to it to obtain an unlimited intermediate speed command. To ensure equipment safety, this unlimited intermediate speed command is constrained within the physical safety range formed by the compressor's minimum and maximum operating speeds: if it exceeds the maximum operating speed, it is limited to the maximum value; if it is below the minimum operating speed, it is limited to the minimum value; if it falls between the two, the added value remains unchanged, thus generating a dynamically optimized speed command.

[0033] The controller modifies the speed setpoint sent to the variable frequency drive, enabling the compressor's actual output speed to dynamically optimize the speed command in real time. When the intermediate pressure shows an upward trend, the compressor generates resistance to suppress the pressure rise through slight speed adjustments, and vice versa. This active reverse regulation is physically equivalent to adding a damper to the system, effectively dissipating fluctuation energy and achieving virtual damping control of intermediate pressure fluctuations, thus maintaining stable system pressure.

[0034] In the aforementioned technical process, the intermediate pressure time-series dataset provides real-time observation of the system state. Dynamic feature decomposition transforms it into feature quantities with clear physical meaning. The dynamic adjustment factor adaptively determines the control strength based on the operating conditions. The direction and speed correction amount fuses the state information with the control strength to generate specific adjustment actions. Finally, the compressor is driven to perform physical adjustments through dynamically optimized speed commands. The synergistic effect of each component enables the magnetic levitation compressor to actively absorb pressure fluctuation energy like a damper. Without increasing additional hardware costs, it achieves rapid and stable suppression of intermediate pressure fluctuations, improving the operational stability and energy efficiency of the multi-stage compression heat pump system.

[0035] Example 2: In one implementation, this application provides a method for extracting instantaneous rate of change, trend acceleration, and deviation potential energy characteristic quantities. The method includes: Step 1: The intermediate pressure time series dataset includes the intermediate pressure value and control cycle duration for each control cycle.

[0036] Pressure sensors deployed on the interstage piping of a multi-stage compression heat pump collect data at a fixed sampling frequency, constructing an intermediate pressure time-series dataset that forms the basis for feature extraction. This dataset characterizes a structured time series of pressure evolution over time, where each data unit strictly corresponds to a control cycle, including the intermediate pressure value collected within that control cycle and the duration of that control cycle. The control cycle duration serves as a preset fixed time interval for the system, used to unify the time reference and ensure that the rate features obtained from subsequent differential operations have clear physical meaning; for example, it can be set to 10 milliseconds, 50 milliseconds, or 100 milliseconds. The aforementioned time-series data is maintained and updated in real-time in the controller's memory in the form of a first-in-first-out (FIFO) queue, thereby ensuring the real-time performance of the feature extraction process.

[0037] Step 2: Perform a difference operation between the intermediate pressure value of the current control cycle and the intermediate pressure value of the previous control cycle, divide by the control cycle duration, and combine with the preset noise suppression factor and the pressure moving average value calculated from the historical pressure sampling values ​​in the intermediate pressure time series dataset to generate the instantaneous change rate feature.

[0038] The difference between the intermediate pressure value of the current control cycle and the intermediate pressure value of the previous control cycle is calculated to reflect the pressure change within one cycle. This difference is divided by the control cycle duration to obtain a preliminary rate value. To eliminate misjudgments caused by high-frequency noise from the sensor or data jumps, a preset noise suppression factor and a pressure moving average are introduced to correct the preliminary rate value. The pressure moving average is calculated based on historical pressure sampling values ​​within the intermediate pressure time-series dataset, reflecting the recent background pressure level. By calculating the deviation of the current pressure value from the pressure moving average, and using the preset noise suppression factor to suppress gains in cases of large deviations, a more robust instantaneous rate of change characteristic is generated. This characteristic not only reflects the magnitude of pressure change but also filters out non-physical abrupt noise through nonlinear suppression, providing a reliable first-order derivative basis for subsequent speed correction.

[0039] Step 3: Perform a difference operation between the instantaneous rate of change characteristic of the current control cycle and the instantaneous rate of change characteristic of the previous control cycle, divide by the control cycle duration, and combine with the acceleration amplification factor dynamically output according to the real-time compressor load rate to generate the trend acceleration characteristic.

[0040] The instantaneous rate of change characteristic is subjected to a second-order differential processing to describe the acceleration or deceleration trend of pressure changes, i.e., the rate of change, reflecting the inertial characteristics of pressure fluctuations. The currently calculated instantaneous rate of change characteristic is subtracted from the instantaneous rate of change characteristic of the previous control cycle to obtain the rate change, which is then divided by the control cycle duration to obtain the unadjusted acceleration value. To adapt to the dynamic response characteristics of the compressor under different operating conditions, an acceleration amplification factor is introduced to weight and adjust the above acceleration value. The acceleration amplification factor is dynamically output based on the real-time compressor load rate: when the load rate is high, the system inertia is large, requiring stronger control sensitivity, and the acceleration amplification factor outputs a larger value; conversely, when the load rate is low, a smaller value is output. By combining this factor to generate the final trend acceleration characteristic, the control algorithm can predict the trend direction and intensity of pressure changes, thereby applying adjustments in advance before drastic pressure fluctuations.

[0041] Step 4: Extract all N historical pressure sampling values ​​within the preset historical time window before the current control cycle from the intermediate pressure time series dataset to obtain the average pressure value. Subtract the current pressure value from the average pressure value and combine it with the potential energy correction factor determined in real time based on the compressor load rate and intermediate pressure fluctuation amplitude to generate the deviation potential energy characteristic quantity.

[0042] Define a preset historical time window covering the time span preceding the current control cycle, and extract all N historical pressure samples (e.g., 50, 100, or 200 samples) within the window. Calculate the arithmetic mean of all N historical pressure samples to obtain the average pressure value representing the system's static equilibrium position within the historical window. Calculate the difference between the current pressure value and this average pressure value to visually reflect the degree of deviation. Finally, combine the potential energy correction factor to generate a deviation potential energy characteristic quantity. The potential energy correction factor is determined in real-time based on the compressor load rate and the intermediate pressure fluctuation amplitude, and is used to adjust the weight of the deviation term in the control strategy. For example, when the pressure fluctuation amplitude is large, the potential energy correction factor is larger to strengthen the driving force for returning to equilibrium; when the fluctuation is small, the value is smaller to avoid over-adjustment. Through this step, the system establishes a quantitative evaluation index for accumulated deviations, ensuring that the compressor speed correction not only focuses on instantaneous changes but also gradually eliminates long-term steady-state deviations.

[0043] The above steps, through a hierarchical feature extraction approach, transform the original intermediate pressure signal into a high-dimensional feature space containing velocity, acceleration, and deviation potential energy. This multi-dimensional feature quantization not only captures the dynamic details of pressure changes but also enhances the algorithm's robustness and adaptability under complex working conditions through noise suppression and dynamic factor correction mechanisms, laying a data foundation for calculating accurate directional speed corrections.

[0044] Example 3: In one implementation, the method provided in this application embodiment further defines a specific calculation formula for the instantaneous rate of change characteristic quantity, which is calculated using the following formula: ; in, The instantaneous rate of change characteristic quantity, The intermediate pressure value within the current control cycle. This refers to the intermediate pressure value within the previous control cycle. The duration of the control cycle, The preset noise suppression factor, The pressure sliding average value within a preset historical time window prior to the current control cycle.

[0045] The above formula constructs a first-order forward difference model with an adaptive gain adjustment mechanism. From a physical perspective, this model analogizes the evolution of intermediate pressure to the concept of velocity in kinematics. (Molecular part) Characterized in small control cycles The absolute change in internal pressure directly reflects the instantaneous change in the system's pressure state; the denominator... As a time scale, the absolute change is normalized to the rate of change per unit time, thereby eliminating the influence of different sampling frequencies on the dimensions of the feature quantity and ensuring the consistency of the calculation results in time.

[0046] The product term on the right-hand side of the formula This constitutes a nonlinear dynamic gain suppression stage. Among them, This indicates the degree of deviation of the current intermediate pressure value from the historical average level, i.e., the pressure moving average norm. This indicator is used to quantify whether the pressure signal contains abnormal high-frequency noise. When the intermediate pressure is in a stable fluctuation range, the current pressure value is very close to the moving average, causing the norm term to approach zero, and consequently, the gain term to approach its maximum value of 1. In this state, the formula is equivalent to standard difference calculation, which can preserve the algorithm's sensitivity to changes in the actual pressure trend to the greatest extent.

[0047] When the pressure signal is mixed with sudden noise interference or transient spikes occur, the difference between the current pressure value and the moving average value increases significantly, causing the norm term value to rise sharply. Given... The preset noise suppression factor is positive, and the denominator is... The value of this value increases accordingly, forcing the overall gain coefficient to decrease. This adaptive attenuation mechanism can automatically reduce the weight of the instantaneous rate of change characteristic when a sudden change that is not physically significant is detected, effectively filtering out high-frequency noise interference with the control logic and preventing subsequent speed correction stages from malfunctioning due to noise misleading.

[0048] parameter The specific value depends on the trade-off between the system's noise tolerance and response speed. To enhance noise suppression, the value can be... Set to a larger value; if the focus is on capturing minute pressure fluctuations for a rapid response, decrease the value accordingly. The numerical value of the value is obtained through the above-described explicit definition. This ensures that different control systems or computing platforms can generate consistent and stable instantaneous rate-of-change characteristic quantities when processing the same intermediate pressure time-series data, providing a data foundation for the subsequent generation of dynamically optimized speed commands.

[0049] Example 4: In another embodiment, this application also provides a specific method for calculating the changing trend acceleration feature, which is calculated using the following formula: ; in, The acceleration characteristic of the changing trend, This refers to the instantaneous rate of change characteristic quantity corresponding to the current control cycle. This refers to the instantaneous rate of change characteristic quantity corresponding to the previous control cycle. The duration of the control cycle, is the acceleration amplification factor.

[0050] In the above formula, Characterizes the differential operation performed on the instantaneous rate of change. The second derivative is calculated based on the rate difference between adjacent control cycles, accurately quantifying the rate of change in intermediate pressure; divided by... The calculation results are time-normalized, giving the acceleration characteristic a clear physical meaning, namely the change in the rate of pressure change per unit time.

[0051] As an acceleration amplification factor, it is used to dynamically adjust the response intensity of acceleration characteristic quantities according to real-time operating conditions. When the compressor is under high load conditions, the system response is relatively slow due to the influence of the rotor's rotational inertia. At this time, it can be... Configure it to a larger value (e.g., 1.2) to enhance sensitivity to acceleration trends; while under low load conditions, to avoid over-adjustment caused by small fluctuations, it can be... Configure it to a smaller value (e.g., 0.8).

[0052] Calculated using this formula It reflects the acceleration characteristics of the intermediate pressure fluctuation trend, which can provide a forward-looking decision basis for the calculation of the subsequent directional speed correction, enabling the system to intervene and adjust in the early stage when the pressure change trend is established, thereby improving the timeliness and stability of control.

[0053] Example 5: In one implementation, this application also provides a specific calculation process for generating the deviation potential energy characteristic quantity, which is calculated using the following formula: ; in, This refers to the deviation potential energy characteristic quantity. The intermediate pressure value within the current control cycle. This is the preset number of data points used to calculate historical average pressure values. Indicates to , … The average of N historical pressure values ​​is calculated. is the potential energy correction factor.

[0054] The terms in the above formula This represents the historical average pressure obtained by applying a moving average to the intermediate pressure time-series dataset. This historical average pressure constructs a baseline pressure line reflecting the recent center of pressure fluctuations, used to filter out high-frequency noise and identify the overall pressure shift trend. Parameters The time span of the sliding window is determined, and its value needs to be balanced between the system's response speed and the filtering effect. For example, it can be set to 50 or 100, etc. This is just an example.

[0055] Intermediate pressure value of the current control cycle The difference between the current pressure and the historical average pressure characterizes the degree to which the current pressure state deviates from the steady-state center, analogous to the potential energy displacement in a physical system. Potential Energy Correction Factor This weight is used to adjust the degree of deviation in real time based on the compressor load rate and the fluctuation range of the intermediate pressure. When the fluctuation range of the intermediate pressure is large, the weight is increased. The value of is chosen to enhance the control strategy's ability to correct deviations; when fluctuations are small, the value is reduced. The value of is chosen to avoid over-adjustment that could cause system oscillations.

[0056] The deviation potential energy characteristic quantity obtained from the above calculation It not only includes information on the magnitude of pressure deviation, but also... The introduction of operating condition adaptability enables the control commands generated based on this characteristic to more accurately suppress long-term deviations in intermediate pressure and improve the steady-state accuracy of the system.

[0057] Example 6: In another embodiment, this application provides a method for calculating a dynamic adjustment factor based on compressor load rate, the method comprising: Step 1: Obtain the ratio between the compressor's current actual speed and its rated speed, which will be used as the current load rate.

[0058] The current actual compressor speed, measured in revolutions per minute (RPM), is read from the compressor's inverter drive or controller register. Simultaneously, the compressor's rated speed, stored in the system parameter table, is retrieved. This rated speed represents the compressor's maximum continuous operating speed baseline during design. The current actual speed is divided by the compressor's rated speed to calculate a dimensionless ratio, i.e., the current load factor. This ratio directly reflects the compressor rotor's kinetic energy level and centrifugal load state, physically representing the utilization rate of the current speed relative to rated capacity, and serves as the input for subsequent dynamic adjustment of damping strength. For example, when the compressor is operating at half its rated speed, the current load factor is 0.5.

[0059] Step 2: Based on the current load rate, the initial adjustment factor is calculated through a monotonically increasing nonlinear mapping relationship. The nonlinear mapping relationship is determined by the compressor's rotor dynamic characteristics and the maximum allowable damping strength.

[0060] The initial adjustment factor is a dynamic gain used in virtual damping control to adapt the system response characteristics under different loads. A monotonically increasing nonlinear mapping relationship is constructed, causing the initial adjustment factor to increase curvilinearly with increasing load rate, although its rate of change is not constant. This mapping relationship is constructed based on the compressor's rotor dynamics characteristics and maximum allowable damping strength: in the low load rate range, the rotor's moment of inertia has a smaller impact, and to avoid control response hysteresis, the slope of the mapping curve is set to be relatively gentle; in the high load rate range, the airflow excitation force and rotor imbalance force increase significantly, and to effectively suppress vibration, the slope of the mapping curve becomes correspondingly steeper.

[0061] In practice, this nonlinear mapping relationship can take the form of a piecewise linear function or an S-curve. For example, when the current load factor is less than or equal to 0.5, the initial adjustment factor increases linearly according to the first slope; when the current load factor is greater than 0.5, the initial adjustment factor increases according to a larger second slope. Through the above nonlinear mapping, the physically optimal damping intervention strength is matched under different operating conditions, overcoming the shortcomings of traditional fixed parameter control in terms of insufficient or excessive damping under certain operating conditions.

[0062] Step 3: Add the initial adjustment factor to the basic damping coefficient determined by the compressor's own structural parameters to obtain the dynamic adjustment factor.

[0063] The dynamic adjustment factor is a comprehensive parameter ultimately involved in the speed correction calculation, formed by the superposition of the initial adjustment factor and the basic damping coefficient. The basic damping coefficient characterizes the inherent damping capability of the compressor's physical structure and is a constant determined by physical properties such as bearing support stiffness and rotor mass. Based on the bearing's geometric parameters and material properties, the bearing support stiffness is extracted. Combined with the rotor's mass distribution and moment of inertia, the damping value corresponding to the system's natural attenuation characteristics without active control intervention is calculated, thus forming the basic damping coefficient.

[0064] The initial adjustment factor calculated in the preceding steps is arithmetically added to the basic damping coefficient to generate the dynamic adjustment factor. This calculation process embodies the principle of superposition of inherent damping and active damping: the basic damping coefficient provides a stable physical baseline, and the initial adjustment factor is dynamically supplemented based on the operating conditions. Through this combination, the dynamic adjustment factor can accurately reflect the total damping capacity of the system at the current moment.

[0065] Step 4: The value of the dynamic adjustment factor is constrained by the compressor bearing support stiffness, rotor mass and maximum permissible vibration amplitude, and is limited to the minimum and maximum damping strength allowed by the physical system.

[0066] To ensure the physical feasibility of control commands and system safety, strict physical boundary constraints are imposed on the range of values ​​for the dynamic adjustment factor. This constraint mechanism is jointly determined based on the compressor bearing support stiffness, rotor mass, and maximum permissible vibration amplitude. Specifically, the bearing support stiffness and rotor mass determine the system's natural frequencies and modal characteristics, thus defining the effective range of the damping correction; the maximum permissible vibration amplitude specifies the vibration limit for safe system operation.

[0067] The minimum and maximum damping strengths allowed by the physical system are set as threshold boundaries. The minimum damping strength is usually set to a positive value slightly greater than the basic damping coefficient to ensure that normal operation is not excessively disturbed under low load or steady state. The maximum damping strength is set based on the rotor instability threshold to prevent control saturation or reverse excitation of unstable modes due to excessive adjustment. The generated dynamic adjustment factor is clamped: if the calculated result exceeds the upper limit, it is forced to be the maximum damping strength; if it is below the lower limit, it is forced to be the minimum damping strength. Through these boundary constraints, it is ensured that the dynamic adjustment factor always remains within the bearing capacity of the mechanical structure, achieving a balance between safety margin and control performance.

[0068] Through the above steps, a closed-loop adaptive adjustment mechanism is established: using real-time load rate to drive nonlinear mapping, combined with the inherent basic damping coefficient of the structure, a dynamic adjustment factor strictly constrained by physical boundaries is generated. This dynamic adjustment factor serves as a core parameter, providing a gain benchmark that conforms to the current physical operating conditions for subsequent accurate calculation of directional speed correction, enabling virtual damping control to adjust the suppression intensity in real time and adaptively as the compressor's operating state changes.

[0069] Example 7: In another embodiment, the method further includes the step of calculating the directional speed correction amount using the following formula: ; in, This is the rotational speed correction amount in the stated direction. The dynamic adjustment factor, The instantaneous rate of change characteristic quantity, To pre-determine the weighting coefficients for the influence of trends, The acceleration characteristic of the changing trend, To preset the balancing influence weighting coefficient, This refers to the deviation potential energy characteristic quantity. This is the preset damping response limiting factor.

[0070] The above formulas constitute the core operational logic of the virtual damping control strategy, aiming to transform multi-dimensional pressure fluctuation characteristics into specific speed regulation commands. This model is not a simple linear superposition, but rather constructs a composite control mechanism that includes dynamic intensity regulation, trend prediction feedback, equilibrium negative feedback, and safety limiting. The calculation result is a directional real value, with its positive or negative sign indicating the direction of acceleration or deceleration, and the absolute value corresponding to the magnitude of the speed adjustment. Physically, this formula simulates a damper with a variable damping coefficient, where the combinations of terms within parentheses constitute the system's force input. It then acts as a variable damping coefficient, thereby enabling fine-grained control of the system's dynamic response.

[0071] Preset trend influence weighting coefficient The proportion of the changing trend acceleration characteristic quantity in the correction calculation is used to adjust for this. Acceleration term. This reflects the rate of pressure change, that is, it characterizes the inertial direction of pressure fluctuations. Introducing this term and multiplying it by a weight... This gives the controller the ability to anticipate pressure change trends, enabling it to apply reverse regulation before pressure extremes occur. The magnitude of this value determines the intensity of this proactive intervention; for example, it can be set to 0.3 or 0.5, and the specific value is usually calibrated based on the rotational inertia of the compressor rotor system and the engineering requirements for dynamic response speed. If Setting the value too high may cause the speed adjustment to be too sensitive, resulting in unnecessary oscillations; setting it too low will prevent the effective use of the predictive value of acceleration information.

[0072] Preset balance influence weighting coefficient Used to adjust the proportion of the deviation potential energy characteristic in the correction calculation. Deviation potential energy characteristic This represents the cumulative offset energy of the current intermediate pressure relative to the historical average level. The negative sign in the formula... This constitutes a key negative feedback mechanism, ensuring that when the pressure is above average, a negative correction is generated to perform a deceleration operation, and when the pressure is below average, a positive correction is generated to perform an acceleration operation. The specific value of this coefficient determines the magnitude of the driving force for the system to return to its steady-state equilibrium point; for example, it can be set to 0.2 or 0.4. The setting of this coefficient needs to comprehensively consider the system's ability to eliminate steady-state error and its anti-interference capability, and can be achieved through adjustment. It can achieve the best balance between eliminating steady-state errors and suppressing dynamic fluctuations.

[0073] Preset damping response limiting factor As a nonlinear constraint term to ensure the safe operation of the compressor, it is used to limit the theoretically calculated correction amount to the safe range allowed by the physical system. It is typically designed as a function that monotonically decreases as the absolute value of the correction increases. For example, The calculation logic can be to first calculate the original correction amount without limiting. Then, based on the maximum permissible speed change in a single step. Normalization is performed to form, such as A formal mapping relationship. When an excessively large speed correction requirement is calculated. The value will decrease accordingly, thus automatically compressing the correction range of the final output during multiplication operations. This mechanism effectively prevents sudden and large jumps in compressor speed commands, avoiding impact on the motor drive and mechanical bearings, and ensuring the stability and safety of system operation.

[0074] The directional speed correction calculated by the above formula integrates the instantaneous rate of change of pressure, trend acceleration, and potential energy information of deviation from the equilibrium position. Under the dual constraints of dynamic adjustment factor and amplitude limiting factor, it generates a precise adjustment amount that has both fast response characteristics and meets safety constraints, providing a direct data basis for the subsequent generation of dynamically optimized speed commands.

[0075] Example 8: In one implementation, this application provides a method for generating dynamically optimized speed commands and implementing virtual damping control, the method comprising: Step 1: Add the directional speed correction amount to the original speed command to obtain the unlimited intermediate speed command.

[0076] Based on the directional speed correction calculated in the preceding steps, it is algebraically added to the current original speed command to generate an unlimited intermediate speed command. The original speed command is typically determined by the upper-level control system based on heat load requirements, reflecting the basic control intent; while the directional speed correction includes strategies to suppress intermediate pressure fluctuations. Through this superposition operation, the virtual damping control effect is seamlessly embedded into the conventional speed control logic, enabling proactive intervention in pressure fluctuations. At this point, the unlimited intermediate speed command is the algebraic sum of the original speed command and the directional speed correction.

[0077] Step 2: Constrain the unlimited intermediate speed command within the physical safety range formed by the compressor's minimum and maximum operating speeds, thereby generating a dynamically optimized speed command.

[0078] Given that the unlimited intermediate speed command only undergoes feature calculation and superposition processing, it may potentially exceed the physical operating limits of the compressor. Therefore, boundary constraints are necessary to ensure equipment safety. The physical safety range is defined by the compressor's minimum and maximum permissible operating speeds. These two parameters depend on hardware conditions such as motor characteristics, bearing capacity, and impeller strength, and are inherent attributes ensuring stable compressor operation. In the specific constraint process, if the unlimited intermediate speed command is higher than the maximum operating speed, it is limited to the maximum operating speed; if it is lower than the minimum operating speed, it is limited to the minimum operating speed; if it falls between the two, the value remains unchanged. The dynamically optimized speed command generated after this clamping process retains the ability to regulate pressure fluctuations while being strictly limited to the equipment's physical tolerance range, effectively preventing unit overload, instability, or hardware damage caused by exceeding control output limits.

[0079] Step 3: Send the dynamic optimization speed command to the compressor driver so that the actual output speed of the compressor tracks the dynamic optimization speed command in real time, thereby realizing virtual damping control of intermediate pressure fluctuations.

[0080] The generated dynamically optimized speed command is transmitted to the compressor driver in real time. As the core hardware unit for speed control, the compressor driver receives this command and converts it into an electrical signal (such as voltage, current, or frequency) to drive the motor, thereby adjusting the rotational speed of the magnetically levitated compressor rotor. The closed-loop control within the driver ensures that the actual output speed of the compressor closely tracks the input dynamically optimized speed command. When the command dynamically adjusts with pressure fluctuations, the compressor speed undergoes a corresponding slight change, altering the intake or exhaust characteristics and creating a reverse regulating effect on the interstage pressure. This process is equivalent to injecting damping energy into the system, consuming the oscillating component of the pressure fluctuations, and causing the intermediate pressure to quickly return to a stable state.

[0081] By strictly constraining the superimposed speed command within the physical safety range defined by the minimum and maximum operating speeds, and driving the compressor to precisely track this command, the above steps ensure that the system's safety is not compromised while introducing a virtual damping control mechanism. This dual mechanism of front-end intelligent dynamic correction and end-point safety limiting allows the magnetic levitation multi-stage compression heat pump system to operate within the equipment's permissible physical limits while actively controlling and smoothing intermediate pressure fluctuations, thus achieving a balance between improving control performance and ensuring operational safety.

[0082] Example 9: In one implementation, the method includes the step of constraining the unlimited intermediate speed command within the physical safety range formed by the compressor's minimum and maximum permissible operating speeds, as detailed below: Step 1: Compare the value of the unlimited intermediate speed command with the maximum allowable operating speed of the compressor. If the unlimited intermediate speed command is greater than the maximum operating speed, then set the value of the dynamically optimized speed command to the maximum operating speed.

[0083] The directional speed correction is superimposed on the original speed command to obtain the unlimited intermediate speed command. This unlimited intermediate speed command is then used as input to execute threshold-based speed comparison logic. The system obtains the pre-set maximum operating speed of the compressor, which is typically determined by physical parameters such as the compressor's mechanical strength limit, the motor's maximum speed, and the driver capacity. Through numerical comparison, it is determined whether the unlimited intermediate speed command exceeds this physical upper limit. When the value of the unlimited intermediate speed command is greater than the maximum operating speed, it means that although the correction direction generated by the aforementioned virtual damping control is correct, the magnitude is too large. Direct execution would cause the compressor to enter a dangerous operating condition of mechanical overspeed or motor overload. In this case, the output value of the dynamically optimized speed command is forcibly set to the maximum operating speed, thereby truncating the command portion exceeding the safe range and ensuring that the compressor always operates within the physically safe range.

[0084] Step 2: If the unlimited intermediate speed command is less than the minimum operating speed, then the dynamic optimization speed command will be set to the minimum operating speed.

[0085] After completing the upper limit comparison, the lower limit protection logic continues to be executed. The minimum operating speed is usually determined by safety constraints such as the levitation stability requirements of the magnetic levitation bearing, lubrication and cooling needs, and surge margin. If the value of the unlimited intermediate speed command is detected to be lower than the minimum operating speed, it indicates that the correction amount has excessively suppressed the speed, which may lead to risks such as rotor instability or airflow backflow. In this case, the value of the dynamically optimized speed command is forcibly locked to the minimum operating speed. Through this lower limit cutoff mechanism, the compressor is prevented from leaving the stable operating range due to excessively low speed, ensuring the bottom-line safety of system operation.

[0086] Step 3: If the unlimited intermediate speed command is between the minimum operating speed and the maximum operating speed, then keep its value unchanged.

[0087] When the value of the unlimited intermediate speed command falls within the closed interval formed by the minimum and maximum operating speeds, it indicates that the speed correction calculated in the preceding steps fully conforms to the physical operating constraints of the compressor, requiring no trimming or modification. In this case, the value hold operation is directly executed, meaning the unlimited intermediate speed command is directly output as the final dynamically optimized speed command. Through this conditional branching logic, while ensuring safety, the original adjustment accuracy of the virtual damping algorithm is preserved to the greatest extent possible, allowing for the most subtle and precise suppression of intermediate pressure fluctuations.

[0088] The above steps, through the synergistic effect of logical judgment and numerical assignment, constitute a complete software safety limiting mechanism. This mechanism is equivalent to connecting a nonlinear element with saturation characteristics in series in the control loop, which can automatically filter out all speed commands exceeding the physical load capacity, allowing only control quantities within the safe range to pass. This method of constraining unlimited intermediate speed commands within the physical safety range inherits the rapid response advantage of virtual damping control in the above embodiments, and eliminates safety hazards under extreme conditions through hard constraints of boundary conditions. This allows the entire optimization method to achieve absolute safety and reliability at the engineering application level while pursuing ultimate performance.

[0089] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0090] Figure 3 A schematic diagram of an intermediate pressure optimization system for a magnetic levitation multi-stage compression heat pump provided in an embodiment of this application is shown below. Figure 3 As shown, the intermediate pressure optimization system 300 of a magnetic levitation multi-stage compression heat pump in this embodiment includes: a dynamic feature decomposition module 301, a correction amount calculation module 302, and an optimization instruction generation module 303.

[0091] The dynamic feature decomposition module 301 is used to acquire the intermediate pressure time series dataset output by the compressor pressure sensor in several consecutive control cycles, and to perform dynamic feature decomposition on the intermediate pressure time series dataset to extract instantaneous change rate feature, change trend acceleration feature, and deviation potential energy feature. The correction calculation module 302 is used to calculate the directional speed correction amount based on the instantaneous rate of change characteristic, the trend acceleration characteristic, the deviation potential energy characteristic, and the dynamic adjustment factor calculated in real time based on the current compressor load rate. The optimization instruction generation module 303 is used to adjust the directional speed correction amount to the original speed instruction of the magnetic levitation compressor, generate a dynamically optimized speed instruction, and realize virtual damping control of intermediate pressure fluctuations.

[0092] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A method for optimizing the intermediate pressure of a magnetically levitated multi-stage compression heat pump, characterized in that, include: The intermediate pressure time series dataset output by the compressor pressure sensor over several consecutive control cycles is obtained, and the intermediate pressure time series dataset is subjected to dynamic feature decomposition to extract instantaneous change rate feature, change trend acceleration feature, and deviation potential energy feature. The directional speed correction is calculated based on the instantaneous rate of change characteristic, the trend acceleration characteristic, the deviation potential energy characteristic, and the dynamic adjustment factor calculated in real time based on the current compressor load rate. The directional speed correction amount is adjusted to the original speed command of the magnetic levitation compressor to generate a dynamically optimized speed command, thereby realizing virtual damping control of intermediate pressure fluctuations.

2. The method according to claim 1, characterized in that, The extracted instantaneous rate of change feature, trend acceleration feature, and deviation potential energy feature include: The intermediate pressure time series dataset includes the intermediate pressure value and control cycle duration for each control cycle. The intermediate pressure value of the current control cycle is differentially calculated with the intermediate pressure value of the previous control cycle, divided by the control cycle duration, and combined with a preset noise suppression factor and the pressure sliding average value calculated from the historical pressure sampling values ​​in the intermediate pressure time series dataset to generate the instantaneous change rate feature. The instantaneous rate of change characteristic of the current control cycle is differentially calculated with the instantaneous rate of change characteristic of the previous control cycle, divided by the control cycle duration, and combined with the acceleration amplification factor dynamically output according to the real-time compressor load rate to generate the trend acceleration characteristic. Extract all N historical pressure sampling values ​​within a preset historical time window before the current control cycle from the intermediate pressure time series dataset to obtain the average pressure value. Subtract the current pressure value from the average pressure value and combine it with the potential energy correction factor determined in real time based on the compressor load rate and intermediate pressure fluctuation amplitude to generate the deviation potential energy characteristic quantity.

3. The method according to claim 2, characterized in that, The instantaneous rate of change characteristic quantity is generated by calculating the following formula: ； in, The instantaneous rate of change characteristic quantity, The intermediate pressure value within the current control cycle. This refers to the intermediate pressure value within the previous control cycle. The duration of the control cycle, The preset noise suppression factor, The pressure sliding average value within a preset historical time window prior to the current control cycle.

4. The method according to claim 2, characterized in that, The acceleration characteristic of the changing trend is calculated using the following formula: ； in, The acceleration characteristic of the changing trend, This refers to the instantaneous rate of change characteristic quantity corresponding to the current control cycle. This refers to the instantaneous rate of change characteristic quantity corresponding to the previous control cycle. The duration of the control cycle, is the acceleration amplification factor.

5. The method according to claim 2, characterized in that, The deviation potential energy characteristic quantity is generated by calculating the following formula: ； in, This refers to the deviation potential energy characteristic quantity. The intermediate pressure value within the current control cycle. This is the preset number of data points used to calculate historical average pressure values. Indicates to , … The average of N historical pressure values ​​is calculated. is the potential energy correction factor.

6. The method according to claim 2, characterized in that, The dynamic adjustment factor calculated in real time based on the current compressor load rate includes: Obtain the ratio between the compressor's current actual speed and its rated speed, and use this ratio as the current load rate; Based on the current load rate, the initial adjustment factor is calculated through a monotonically increasing nonlinear mapping relationship, which is determined by the compressor's rotor dynamics characteristics and the maximum allowable damping strength. The initial adjustment factor is added to the basic damping coefficient determined by the compressor's own structural parameters to obtain the dynamic adjustment factor; The value of the dynamic adjustment factor is constrained by the compressor bearing support stiffness, rotor mass, and maximum permissible vibration amplitude, and is limited to the minimum and maximum damping strength allowed by the physical system.

7. The method according to claim 6, characterized in that, The calculation of the directional rotational speed correction is performed using the following formula: ； in, This is the rotational speed correction amount in the stated direction. The dynamic adjustment factor, The instantaneous rate of change characteristic quantity, To pre-determine the weighting coefficients for the influence of trends, The acceleration characteristic of the changing trend, To preset the balancing influence weighting coefficient, This refers to the deviation potential energy characteristic quantity. This is the preset damping response limiting factor.

8. The method according to claim 6, characterized in that, The generation of dynamically optimized speed commands includes: The directional speed correction amount is superimposed with the original speed command to obtain the unlimited intermediate speed command; The unlimited intermediate speed command is constrained within the physical safety range formed by the compressor's minimum and maximum operating speeds, thereby generating the dynamically optimized speed command. The dynamically optimized speed command is sent to the compressor driver, so that the actual output speed of the compressor tracks the dynamically optimized speed command in real time, thereby realizing virtual damping control of intermediate pressure fluctuations.

9. The method according to claim 8, characterized in that, The constraint of the unlimited intermediate speed command within the physical safety range formed by the compressor's minimum and maximum permissible operating speeds includes: The value of the unlimited intermediate speed command is compared with the maximum allowable operating speed of the compressor. If the unlimited intermediate speed command is greater than the maximum operating speed, the value of the dynamically optimized speed command is set to the maximum operating speed. If the unlimited intermediate speed command is less than the minimum operating speed, then the dynamic optimized speed command is set to the minimum operating speed. If the unlimited intermediate speed command is between the minimum operating speed and the maximum operating speed, its value remains unchanged.

10. A magnetic levitation multi-stage compression heat pump intermediate pressure optimization system, characterized in that, The method applied to any one of claims 1-9 includes: The dynamic feature decomposition module is used to acquire the intermediate pressure time series dataset output by the compressor pressure sensor in several consecutive control cycles, and to perform dynamic feature decomposition on the intermediate pressure time series dataset to extract instantaneous change rate feature, change trend acceleration feature, and deviation potential energy feature. The correction calculation module is used to calculate the directional speed correction amount based on the instantaneous rate of change characteristic, the trend acceleration characteristic, the deviation potential energy characteristic, and the dynamic adjustment factor calculated in real time based on the current compressor load rate. The optimization command generation module is used to adjust the directional speed correction amount to the original speed command of the magnetic levitation compressor, generate a dynamically optimized speed command, and realize virtual damping control of intermediate pressure fluctuations.

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