Guide vane angle and rotating speed collaborative control method and device based on optimal energy efficiency of heat pump system

Abstract

This application discloses a method and apparatus for coordinated control of guide vane angle and speed based on optimal energy efficiency of a heat pump system. The method includes: acquiring the operating parameters and operating conditions of a magnetic levitation compressor; establishing a performance model of the magnetic levitation compressor based on the operating parameters and operating conditions; optimizing the target performance parameters, and under the condition that the load of the magnetic levitation compressor meets the current load demand, jointly optimizing the operating speed and guide vane angle of the magnetic levitation compressor based on the performance model to generate candidate control parameter combinations; performing multi-constraint collaborative decision processing on the candidate control parameter combinations to output a target control parameter combination that satisfies all operating constraints; and adjusting and controlling the operating speed and guide vane angle of the magnetic levitation compressor according to the target control parameter combination. This solves the problems of lack of coordination in variable regulation and difficulty in balancing safety and energy efficiency when multiple constraints conflict in related technologies, achieving optimal energy efficiency operation of the magnetic levitation compressor.

Description

Technical Field

[0001] This application relates to the field of intelligent monitoring and control technology for magnetic levitation compressors, and in particular to a method, a control device, and a computer-readable storage medium for coordinated control of guide vane angle and rotation speed based on optimal energy efficiency of a heat pump system. Background Technology

[0002] In related technologies, the flow and load regulation of magnetic levitation compressors typically employs a single-variable control strategy, namely, an operating mode that uses individual variable speed regulation, individual inlet guide vane opening regulation, or a simple superposition of two regulation methods. However, this mode mainly relies on single-variable independent control logic and fixed boundary protection rules, without considering the coupling and coordination characteristics of speed and guide vane angle, and without taking optimal system energy efficiency as the control objective. It is difficult to achieve coordinated decision-making of control variables in multi-constraint linkage scenarios, which can easily lead to low operating energy efficiency of the magnetic levitation compressor and the problem of touching the safety boundary at some operating points. Summary of the Invention

[0003] This application provides a method, a control device, and a computer-readable storage medium for coordinated control of guide vane angle and rotation speed based on optimal energy efficiency of a heat pump system.

[0004] This application provides a method for coordinated control of guide vane angle and rotational speed based on optimal energy efficiency of a heat pump system, the method comprising: Obtain the operating parameters and operating conditions of the magnetic levitation compressor; Based on the operating parameters and operating conditions, a performance model of the magnetic levitation compressor is established. The performance model is used to characterize the target performance parameters of the magnetic levitation compressor and the mapping relationship between them and the operating parameters and operating conditions. The target performance parameters include the input power or energy efficiency ratio of the magnetic levitation compressor. With the goal of optimizing the target performance parameters, and under the condition that the load of the magnetic levitation compressor meets the current load requirements, based on the performance model, the operating speed and guide vane angle of the magnetic levitation compressor are jointly optimized and solved to generate candidate control parameter combinations, which include corresponding candidate operating speeds and candidate guide vane angles. The candidate control parameter combinations are subjected to multi-constraint collaborative decision-making processing to output the target control parameter combination that satisfies all operational constraints. The operating speed and guide vane angle of the magnetic levitation compressor are adjusted and controlled according to the target control parameter combination.

[0005] In this way, by collecting operating parameters and operating conditions in real time, constructing a performance model of the magnetic levitation compressor, carrying out joint optimization of operating speed and guide vane angle, and executing a closed-loop control architecture with multi-constraint collaborative decision-making, the target performance parameter optimization objective and operating constraints are collaboratively decided. Under the premise of meeting the dynamic load requirements of the system, it can effectively solve the problems of lack of coordination in variable adjustment and difficulty in balancing safety and energy efficiency when multiple constraints conflict in related technologies. This enables the magnetic levitation compressor to achieve optimal energy efficiency operation across the entire operating range and improves the stability and economy of system operation.

[0006] In some embodiments, the operating parameters include the operating speed and / or guide vane angle of the magnetic levitation compressor; the operating conditions include at least one of the following: intake pressure, exhaust pressure, inlet water temperature, outlet water temperature, and flow rate of the magnetic levitation compressor.

[0007] In this way, the internal operating status and external system operating conditions of the magnetic levitation compressor can be fully covered, providing complete and accurate input data for subsequent performance modeling, optimization solutions and multi-constraint verification, ensuring the accuracy of control decisions and the adaptability to operating conditions.

[0008] In some embodiments, establishing a performance model of the magnetic levitation compressor based on the operating parameters and the operating conditions includes: The performance test data of the magnetic levitation compressor under different operating speeds and different guide vane angle combinations are obtained. The performance test data includes input power data or energy efficiency ratio data. A preset fitting method is used to fit the performance test data to establish a mapping relationship between the operating speed, the guide vane angle, the operating conditions, and the target performance parameters, so as to establish the performance model. The preset fitting method includes experimental calibration data lookup table method and / or interpolation fitting model method.

[0009] In this way, by using measured multi-condition performance data, a precise quantitative mapping relationship between operating speed, guide vane angle, and operating conditions and target performance parameters is constructed, ensuring that the performance model fits the actual operating characteristics of the magnetic levitation compressor.

[0010] In some implementations, with the goal of optimizing the target performance parameters, and assuming the load of the magnetic levitation compressor meets the current load requirements, based on the performance model, a joint optimization solution is performed on the operating speed and guide vane angle of the magnetic levitation compressor to generate candidate control parameter combinations, including: Based on the premise of meeting the current load demand, determine the feasible range of values ​​for the operating speed and the guide vane angle; Within the feasible range of values, based on the performance model, the target performance parameters corresponding to different combinations of values ​​are calculated. The combinations of values ​​are used to characterize the working condition matching relationship between the operating speed and the guide vane angle. The optimal value combination of the target performance parameter is selected as the candidate control parameter combination.

[0011] In this way, the feasible solution space for operating speed and guide vane angle can be defined with the current load demand as a pre-constraint. Under the dual-variable collaborative dimension, the matching working condition combination can be traversed and the target performance parameter performance can be quantitatively evaluated. The synergistic gain potential of speed regulation and guide vane regulation can be fully utilized to determine the parameter combination with the optimal target performance under the current working condition. This provides the candidate control parameter combination with the optimal target performance parameters for the subsequent multi-constraint collaborative decision-making process, while ensuring the consistency of the target and the reliability of the results in the optimization solution process.

[0012] In some implementations, the operational constraints include safety constraints, equipment physical boundary constraints, and / or load demand constraints; The safety constraints include anti-surge constraints, which are used to indicate the safety margin requirement between the actual operating flow rate of the magnetic levitation compressor and the surge boundary flow rate. The physical boundary constraints of the equipment include speed operation constraints, which are used to indicate that the operating speed of the magnetic levitation compressor is within the speed operation range formed by the minimum operating speed and the maximum operating speed; The physical boundary constraints of the equipment include guide vane angle constraints, which are used to indicate that the guide vane angle of the magnetic levitation compressor is within the guide vane angle adjustment range formed by the minimum allowable angle and the maximum allowable angle. The load demand constraint is used to indicate that the absolute value of the deviation between the actual output load of the magnetic levitation compressor and the current load demand is less than or equal to a preset deviation threshold.

[0013] In this way, a complete operational constraint system can be constructed from three dimensions: safe operation, hardware capabilities, and system requirements. The judgment criteria and boundary range of each operational constraint are clearly defined. This system can ensure the safety baseline of the magnetic levitation compressor operation with anti-surge constraints, match the hardware design limits of the equipment with the physical boundary constraints of the equipment such as operating speed and guide vane angle, and ensure that the system output capacity matches the user-side requirements with load demand constraints. This provides a clear and comprehensive verification basis and judgment criteria for subsequent multi-constraint collaborative decision-making.

[0014] In some implementations, the step of performing multi-constraint collaborative decision processing on the candidate control parameter combinations to output a target control parameter combination that satisfies all operational constraints includes: The feasibility of each combination of candidate control parameters is verified by inputting them one by one into each of the operational constraints. When the candidate control parameter combination satisfies all constraints, the feasibility verification is determined to be successful, and the candidate control parameter combination is determined as the target control parameter combination.

[0015] In this way, the feasibility of candidate control parameter combinations can be fully verified through a mechanism of full constraint item-by-item verification, ensuring that the final output target control parameter combination meets all operating constraints. Under the premise of adhering to the bottom line of safety, not exceeding the physical boundaries of the equipment, and meeting the load requirements, the optimal target performance parameters obtained by joint optimization are retained, providing a compliant and reliable control basis for the stable and efficient operation of the magnetic levitation compressor.

[0016] In some implementations, the step of inputting the candidate control parameter combinations one by one into each of the operational constraints for feasibility verification includes: Based on the constraint type and preset security level of each of the aforementioned operational constraints, a priority is set for each of the aforementioned operational constraints; The feasibility of the candidate control parameter combinations is verified sequentially in descending order of priority.

[0017] Thus, by adopting a sequential verification mechanism from high to low priority, subsequent low-priority verification steps can be terminated directly when high-priority constraints are not met, reducing unnecessary computational overhead and improving the execution efficiency of multi-constraint decision-making.

[0018] In some embodiments, the method further includes: If the candidate control parameter combination has a constraint violation or multiple constraint conflict, the feasibility verification is determined to have failed. Based on a preset adjustment strategy, the candidate control parameter combinations are adjusted to generate a corrected control parameter combination. The modified control parameter combination is subjected to multi-constraint collaborative decision-making processing to output the target control parameter combination that satisfies all operational constraints.

[0019] Thus, when faced with abnormal scenarios where there are constraint violations or multiple constraint conflicts in the candidate control parameter combinations, the parameters are adaptively corrected through a preset adjustment strategy, and then multi-constraint decision-making is carried out again. This can achieve a balance between system operation safety and operation efficiency by getting as close as possible to the target performance parameters while complying with all operational constraints.

[0020] In some implementations, adjusting the candidate control parameter combinations based on a preset adjustment strategy to generate corrected control parameter combinations includes: The constraints violated by the candidate control parameter combination and / or the constraints with multiple constraint conflicts are taken as the target constraints. Based on the priority of the preset adjustment strategy and the target constraint, and according to the constraint boundary corresponding to the target constraint, the safety correction value of the candidate control parameter combination is derived. The operating speed and the guide vane angle are adjusted in conjunction with the safety correction value to generate the correction control parameter combination.

[0021] In this way, based on the target constraints that cause the verification failure, the direction and reasonable magnitude of the safety correction can be derived by combining the constraint priority and the corresponding constraint boundary. The operating speed and guide vane angle can be corrected in a linked manner to retain the optimized target performance parameter level as much as possible while satisfying all operating constraints, thereby achieving a balance between system operating safety and operating efficiency.

[0022] In some embodiments, adjusting and controlling the operating speed and guide vane angle of the magnetic levitation compressor according to the target control parameter combination includes: Calculate the difference between the target control parameter combination and the current operating parameters, and generate control commands; Based on the control command, the frequency converter is controlled to adjust the operating speed of the magnetic levitation compressor to gradually adjust it to the target operating speed in the target control parameter combination; Based on the control command, the servo actuator is controlled to adjust the guide vane angle of the magnetic levitation compressor to the target guide vane angle in the target control parameter combination.

[0023] In this way, control commands can be generated based on the deviation between the target control parameters and the current operating parameters. By gradually adjusting and smoothly transitioning, sudden changes in the control quantities of speed and guide vane angle can be avoided, preventing drastic fluctuations in system flow and pressure caused by control steps, and ensuring the stability of the magnetic levitation compressor operation.

[0024] This application provides a collaborative control device for executing the above-described collaborative control method for guide vane angle and rotational speed. The collaborative control device includes an acquisition module, a model building module, an optimization solution module, a multi-constraint decision module, and an execution control module. The acquisition module is communicatively connected to the sensor system of the magnetic levitation compressor and is configured to acquire the operating parameters and operating conditions of the magnetic levitation compressor. The model building module is communicatively connected to the acquisition module and is configured to establish a performance model of the magnetic levitation compressor based on the operating parameters and the operating conditions. The performance model is used to characterize the mapping relationship between the target performance parameters of the magnetic levitation compressor and the operating parameters. The target performance parameters include the input power or energy efficiency ratio of the magnetic levitation compressor. The optimization solution module is communicatively connected to the model building module and the acquisition module, respectively. It is configured to optimize the target performance parameters and, under the condition that the load of the magnetic levitation compressor meets the current load requirements, perform joint optimization solution on the operating speed and guide vane angle of the magnetic levitation compressor based on the performance model to generate candidate control parameter combinations. The multi-constraint decision module is communicatively connected to the optimization solution module and is configured to perform multi-constraint collaborative decision processing on the candidate control parameter combination and output the target control parameter combination that satisfies all operational constraints. The execution control module is communicatively connected to the multi-constraint decision module and the actuator of the magnetic levitation compressor, and is configured to adjust and control the operating speed and guide vane angle of the magnetic levitation compressor according to the target control parameter combination.

[0025] In this way, the collaborative control device can collect operating parameters and operating conditions in real time, construct a performance model of the magnetic levitation compressor, carry out joint optimization of operating speed and guide vane angle, and execute a closed-loop control architecture with multi-constraint collaborative decision-making. It can make collaborative decisions on the target performance parameter optimization target and operating constraints, and effectively solve the problems of lack of coordination in variable adjustment and difficulty in balancing safety and energy efficiency when multiple constraints conflict, while meeting the dynamic load requirements of the system. This enables the magnetic levitation compressor to achieve optimal energy efficiency operation across the entire operating range and improves the stability and economy of the system operation.

[0026] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described above.

[0027] Additional aspects and advantages of embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this application. Attached Figure Description

[0028] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein: Figure 1 This is one of the flowcharts illustrating the collaborative control method of certain embodiments of this application; Figure 2This is a schematic diagram of the structure of a cooperative control device according to certain embodiments of this application; Figure 3 This is a second schematic flowchart of a collaborative control method according to certain embodiments of this application; Figure 4 This is the third flowchart illustrating a collaborative control method according to certain embodiments of this application; Figure 5 This is the fourth flowchart illustrating a collaborative control method according to certain embodiments of this application; Figure 6 This is the fifth flowchart illustrating a collaborative control method according to certain embodiments of this application; Figure 7 This is a flowchart of a collaborative control method according to certain embodiments of this application, number six. Figure 8 This is the seventh flowchart illustrating a collaborative control method according to certain embodiments of this application; Figure 9 This is the eighth flowchart of a collaborative control method according to certain embodiments of this application. Detailed Implementation

[0029] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of this application, and should not be construed as limiting the embodiments of this application.

[0030] With the expansion of magnetic levitation compressor applications, their operating range has evolved from a single rated operating condition to complex scenarios involving wide loads and variable operating conditions. In practical engineering, influenced by factors such as ambient temperature fluctuations, user-side load changes, and dynamic drift of system operating boundaries, compressors frequently need to operate under off-design conditions, placing higher demands on the responsiveness and decision-making quality of the regulating mechanism. However, in related technologies, the flow and load regulation of magnetic levitation compressors still generally adopts a single-variable control strategy, namely, using separate variable speed regulation, separate inlet guide vane opening regulation, or a combination operation mode based on simple superposition of the two regulation methods and empirical rules. This type of strategy typically aims to maintain stable intake or exhaust pressure and prevent the compressor from exceeding its operating range, with the control logic based on setpoint tracking and boundary constraints.

[0031] However, this type of mode has significant limitations: First, the control logic of this mode is mostly based on independent adjustment of single variables, lacking modeling and quantification of the aerodynamic coupling characteristics between speed and guide vane angle. It cannot utilize the synergistic effect between the two to broaden the high-efficiency operating range, resulting in significant energy loss under partial load conditions. Second, the optimization target is usually set as intermediate variables such as pressure and flow rate, rather than direct economic indicators such as energy efficiency ratio or input power. This means that while the control action can maintain system stability, it deviates from the technical direction of improving overall operating efficiency. More importantly, the protection mechanism of this strategy often adopts hard constraint logic with fixed thresholds, lacking adaptive adjustment capability for complex operating conditions where multiple constraints (such as surge boundaries, speed limits, guide vane stroke limits, load deviation tolerances, etc.) coexist and are mutually coupled. When multiple constraints approach the boundary simultaneously, command conflicts or mismatches easily occur between control quantities, causing the magnetic levitation compressor's operating point to be forced back to the conservative region, or even touching the surge boundary or triggering a protective shutdown, thereby affecting the continuity of system power supply and equipment lifespan.

[0032] In summary, existing control methods are unable to achieve optimal coordinated decision-making between rotational speed and guide vane angle under varying operating conditions. They generally suffer from technical defects such as low operating efficiency, unreasonable allocation of safety margin, and rough handling of constraint boundaries, which can easily lead to low operating efficiency of magnetic levitation compressors and the problem of touching safety boundaries at some operating points.

[0033] Based on the above issues, please refer to Figure 1 and Figure 2 This application provides a method for coordinated control of guide vane angle and rotation speed based on optimal energy efficiency of a heat pump system. The method includes: 01: Obtain the operating parameters and operating conditions of the magnetic levitation compressor; 02: Establish a performance model for the magnetic levitation compressor based on operating parameters and operating conditions; 03: With the goal of optimizing the target performance parameters, under the condition that the load of the magnetic levitation compressor meets the current load requirements, based on the performance model, the operating speed and guide vane angle of the magnetic levitation compressor are jointly optimized and solved to generate candidate control parameter combinations; 04: Perform multi-constraint collaborative decision-making on candidate control parameter combinations and output the target control parameter combination that satisfies all operational constraints; 05: Adjust and control the operating speed and guide vane angle of the magnetic levitation compressor according to the target control parameter combination.

[0034] This application also provides an electronic device, including a memory and a processor. The method for coordinated control of guide vane angle and speed based on optimal energy efficiency of a heat pump system, as described in this application, can be implemented by the electronic device described in this application. Specifically, the memory stores a computer program, and the processor is used to acquire the operating parameters and operating conditions of the magnetic levitation compressor. Based on the operating parameters and operating conditions, a performance model of the magnetic levitation compressor is established. With the goal of optimizing target performance parameters, and assuming the load of the magnetic levitation compressor meets the current load demand, the operating speed and guide vane angle of the magnetic levitation compressor are jointly optimized based on the performance model to generate candidate control parameter combinations. The processor is also used to perform multi-constraint collaborative decision processing on the candidate control parameter combinations, outputting a target control parameter combination that satisfies all operating constraints. Finally, the operating speed and guide vane angle of the magnetic levitation compressor are adjusted and controlled according to the target control parameter combination.

[0035] This application also provides a collaborative control device 1000. The collaborative control method for guide vane angle and speed based on optimal energy efficiency of the heat pump system according to this application can be implemented by the collaborative control device 1000. Specifically, the collaborative control device 1000 includes a data acquisition module 100, a model building module 200, an optimization solution module 300, a multi-constraint decision module 400, and an execution control module 500. The data acquisition module 100 is used to acquire the operating parameters and operating conditions of the magnetic levitation compressor. The model building module is used to establish a performance model of the magnetic levitation compressor based on the operating parameters and operating conditions. The optimization solution module 300 is used to perform joint optimization of the operating speed and guide vane angle of the magnetic levitation compressor based on the performance model, with the target performance parameters as the objective, and under the condition that the load of the magnetic levitation compressor meets the current load demand, generating candidate control parameter combinations. The multi-constraint decision module 400 is used to perform multi-constraint collaborative decision processing on the candidate control parameter combinations and output the target control parameter combination that satisfies all operating constraints. The execution control module 500 is used to adjust and control the operating speed and guide vane angle of the magnetic levitation compressor according to the target control parameter combination.

[0036] Specifically, the magnetic levitation compressor refers to a centrifugal magnetic levitation compressor that uses magnetic levitation bearings to achieve contactless rotor support. It has the characteristics of low friction loss, wide speed range and high operating energy efficiency, and is the control object of the guide vane angle and speed coordinated control method provided in the embodiments of this application.

[0037] Operating parameters refer to the operating status parameters of the magnetic levitation compressor itself, including the real-time operating speed of the compressor and the current opening angle of the inlet guide vanes. These are the direct adjustment objects and real-time status feedback quantities of the control method. The operating speed is usually achieved by adjusting the motor power supply frequency through a frequency converter, directly determining the impeller's rotational speed and being one of the variables affecting the flow rate and pressure ratio (i.e., the ratio of exhaust pressure to intake pressure) of the magnetic levitation compressor. The guide vane angle is the opening angle of the inlet guide vanes. By adjusting the angle of the guide vane blades, the direction of the airflow entering the impeller and the effective flow area can be changed, thereby adjusting the intake flow rate and the degree of airflow pre-swirl of the magnetic levitation compressor.

[0038] Operating conditions refer to the system operating environment and load conditions of the magnetic levitation compressor, including parameters such as suction pressure, suction temperature, discharge pressure, discharge temperature, system inlet and outlet water temperatures, and medium flow rate. These parameters characterize the current load level and operating boundary and are the basic input conditions for performance modeling and optimization solutions.

[0039] A performance model is a mathematical model that quantitatively describes the relationship between the target performance parameters of a magnetic levitation compressor and its operating parameters and conditions. It serves as the computational basis for optimization solutions. Specifically, the performance model can express the input power or energy efficiency ratio of a magnetic levitation compressor as a mapping relationship between operating speed, guide vane angle, and operating conditions (such as intake pressure and exhaust pressure).

[0040] Target performance parameters refer to evaluation indicators used to assess the effectiveness of control during the optimization process. These include two types of optional optimization objectives: input power and energy efficiency ratio (EER). Input power refers to the electrical power consumed by the magnetic levitation compressor, and the optimization objective is to minimize it. Energy efficiency ratio (EER) refers to the ratio of the system's cooling or heating capacity to its input power, and the optimization objective is to maximize it.

[0041] Load demand constraint refers to the rigid preconditions that must be met during the optimization process, namely, the actual output load of the magnetic levitation compressor must match the current system load demand to ensure that the system's energy supply capacity meets the user's requirements.

[0042] Joint optimization refers to treating operating speed and guide vane angle as two coupled decision variables, performing optimization searches simultaneously rather than adjusting them independently. Specifically, there is a strong coupling relationship between operating speed and guide vane angle; changes in operating speed alter the flow-pressure ratio characteristic curve of the magnetic levitation compressor, while changes in guide vane angle change the pre-swirl of the airflow entering the impeller and the effective flow area. Both factors jointly determine the actual operating performance of the magnetic levitation compressor. Even if different combinations of operating speed and guide vane angle produce the same flow rate, the corresponding input power or energy efficiency ratio may differ.

[0043] A candidate control parameter combination refers to the set of control parameters that, through optimization, theoretically optimize the target performance parameters, including candidate operating speeds and candidate guide vane angles. This candidate control parameter combination is a theoretically optimal solution without extensive constraint verification and only represents the optimal value of the target performance parameters.

[0044] Multi-constraint collaborative decision processing refers to a processing mechanism that performs multi-dimensional constraint verification on candidate control parameters and makes coordinated decisions when constraints conflict, in order to balance the relationship between target performance parameters and constraints such as safety constraints, equipment physical boundary constraints, and load demand constraints.

[0045] Operational constraints refer to the limitations that a magnetic levitation compressor must adhere to during operation. They encompass three main categories: safety constraints, equipment physical boundary constraints, and load demand constraints. These constraints serve as boundary conditions to ensure the safe and reliable operation of the magnetic levitation compressor.

[0046] The target control parameter combination refers to the final control parameters that simultaneously satisfy all operational constraints after multiple constraint verifications and decision adjustments. It serves as the basis for generating control commands that are actually issued to the actuators.

[0047] First, by deploying various sensors on the magnetic levitation compressor itself and the pipelines of the system to which the magnetic levitation compressor belongs, the operating parameters and conditions of the magnetic levitation compressor are collected, providing a realistic operating condition benchmark for subsequent performance modeling and optimization. This ensures that the subsequently determined control logic closely matches the actual operating state of the magnetic levitation compressor, avoiding deviations between the performance model and the operating conditions.

[0048] Subsequently, based on the collected operating parameters and operating conditions, a mapping relationship between the target performance parameters and operating parameters and operating conditions is constructed, and a performance model of the magnetic levitation compressor is established.

[0049] Next, based on the performance model, with the optimization of target performance parameters as the direction and load demand as a rigid pre-constraint, the two control variables of operating speed and guide vane angle are simultaneously optimized to determine the candidate control parameter combination.

[0050] Then, using the candidate control parameter combinations as input, they are substituted into all operating constraints one by one to verify whether there are any parameter overruns or multiple constraint conflicts. If the candidate operating speed and candidate guide vane angle in the candidate control parameter combinations satisfy all constraints, they are directly determined as the final target control parameters.

[0051] Finally, using the target control parameter combination as input, the digital control parameters are converted into physical action commands for the actuator. The speed of the magnetic levitation compressor is adjusted by the frequency converter, and the guide vane angle is adjusted by the servo actuator, so that the magnetic levitation compressor can operate smoothly under the target working conditions, thus completing this control.

[0052] In summary, the guide vane angle and speed coordinated control method and coordinated control device 1000 based on the optimal energy efficiency of the heat pump system provided in this application, by collecting operating parameters and operating conditions in real time, constructing a performance model of the magnetic levitation compressor, carrying out joint optimization of operating speed and guide vane angle, and executing a closed-loop control architecture of multi-constraint coordinated decision-making, can coordinate the optimization target of the target performance parameters with the operating constraints. Under the premise of meeting the dynamic load demand of the system, it can effectively solve the problems of lack of coordination in variable adjustment and difficulty in balancing safety and energy efficiency when multiple constraints conflict in related technologies, achieve optimal energy efficiency operation of the magnetic levitation compressor in the entire operating range, and improve the stability and economy of system operation.

[0053] In some implementations, the operating parameters include the operating speed and / or guide vane angle of the magnetic levitation compressor; the operating conditions include at least one of the following: intake pressure, exhaust pressure, inlet water temperature, outlet water temperature, and flow rate of the magnetic levitation compressor.

[0054] Specifically, inhalation pressure This refers to the working gas pressure at the inlet of the magnetic levitation compressor. It is a boundary parameter characterizing the aerodynamic state at the inlet of the magnetic levitation compressor and is the basic input for calculating the mass flow rate, pressure ratio, and performance of the magnetic levitation compressor.

[0055] Exhaust pressure This refers to the working gas pressure at the exhaust port of the magnetic levitation compressor, which corresponds to the condenser side pressure of the system. It is the core outlet boundary for the operation of the magnetic levitation compressor and the basis for judging the surge boundary and calculating the pressure ratio.

[0056] Inlet water temperature Outlet water temperature This refers to the inlet and outlet temperatures of the circulating water that exchanges heat with the heat exchanger in a heat pump or refrigeration system. Combined with flow rate parameters, the actual cooling or heating capacity output of the system can be calculated. It is the core parameter for calculating the load of a magnetic levitation compressor.

[0057] flow It refers to the volumetric flow rate or mass flow rate of the circulating working fluid or circulating water in the system. It can be used to calculate the actual gas delivery capacity of the magnetic levitation compressor, and can also be combined with the water temperature difference to calculate the system output load. At the same time, it is a judgment parameter for anti-surge constraint verification.

[0058] In some implementations, operating parameters may also include motor winding temperature, etc. Motor winding temperature refers to the real-time operating temperature of the stator windings of the magnetic levitation compressor drive motor, used to monitor the motor's thermal operating status and prevent winding overheating and burnout. Operating conditions may also include intake temperature and exhaust temperature. Intake temperature refers to the temperature of the working gas at the magnetic levitation compressor inlet, which can be used to calculate the working gas density and accurately calculate the mass flow rate of the magnetic levitation compressor in conjunction with the intake pressure. Exhaust temperature refers to the temperature of the working gas at the magnetic levitation compressor exhaust port, which can be used to reflect the thermal state of the compression process and help determine whether the magnetic levitation compressor deviates from its normal operating range. The specific operating parameters and conditions collected can be determined according to actual needs.

[0059] In this way, the internal operating status and external system operating conditions of the magnetic levitation compressor can be fully covered, providing complete and accurate input data for subsequent performance modeling, optimization solutions and multi-constraint verification, ensuring the accuracy of control decisions and the adaptability to operating conditions.

[0060] Please see Figure 3 In some implementations, step 02 includes: 021: Obtain performance test data of the magnetic levitation compressor under different operating speeds and different guide vane angle combinations; 022: Using a pre-defined fitting method, the performance test data is fitted to establish a mapping relationship between the operating speed, guide vane angle, and operating conditions and the target performance parameters, so as to establish a performance model.

[0061] In some implementations, the model building module 200 is also used to acquire performance test data of the magnetic levitation compressor under different combinations of operating speeds and guide vane angles. A preset fitting method is then used to fit the performance test data, establishing a mapping relationship between the operating speed, guide vane angle, operating conditions, and target performance parameters to build a performance model.

[0062] In some implementations, the processor is also used to acquire performance test data of the magnetic levitation compressor under different combinations of operating speeds and guide vane angles. A preset fitting method is then used to fit the performance test data, establishing a mapping relationship between the operating speed, guide vane angle, operating conditions, and target performance parameters to build a performance model.

[0063] Specifically, performance test data refers to the actual performance dataset of the magnetic levitation compressor collected through experiments or field tests under standard calibration conditions. Using different combinations of operating speeds and guide vane angles as variables, it serves as the original data foundation for constructing the performance model, ensuring that the constructed performance model closely reflects the actual operating characteristics of the equipment. In some implementations, the process for acquiring performance test data can be as follows: On a magnetic levitation compressor performance test bench, the magnetic levitation compressor under test is installed in a standard test pipeline, equipped with a complete refrigeration cycle system. The compressor speed is changed by adjusting the frequency converter, and the guide vane angle is adjusted by the servo actuator. Performance test data for each data point is measured and recorded under steady-state conditions. It should be noted that the performance test data needs to cover as many combinations of operating speeds and guide vane angles as possible within the normal operating range of the magnetic levitation compressor. This ensures that the subsequently fitted model has sufficient original data to support it near any operating point the magnetic levitation compressor might operate under, avoiding significant deviations in uncalibrated areas.

[0064] Input power data refers to the measured electrical power consumed by the drive motor of the magnetic levitation compressor under different operating conditions, which is the core indicator for measuring the energy consumption of the system.

[0065] The Coefficient of Performance (COP) is the measured value of the ratio of the cooling or heating output of a magnetic levitation compressor to its input power. It is a core indicator for measuring the energy efficiency of a system.

[0066] Preset fitting methods refer to a general term for various data processing and modeling methods that construct performance mapping relationships that can be called in real time based on discrete performance test data. The specific implementation method can be flexibly selected according to the controller's computing power and control accuracy requirements.

[0067] The experimental calibration data lookup table method refers to a discretization modeling approach. It stores pre-calibrated performance data for each operating condition as a two-dimensional lookup table indexed by operating speed and guide vane angle. During runtime, the corresponding performance parameters are directly matched and retrieved using the current operating speed and guide vane angle as indexes, eliminating the need for complex calculations. In some implementations, when controlling a magnetic levitation compressor, if it is necessary to obtain the performance parameters under a specific combination of operating speed and guide vane angle, the lookup table can first search for several known data points closest to the current input. Then, algorithms such as linear interpolation or bilinear interpolation can be used to calculate the performance parameter value at that point.

[0068] The interpolation fitting model method refers to a continuous modeling method that uses discrete performance test data as sample points and generates a continuous performance mapping function through algorithms such as linear interpolation, spline interpolation, or polynomial fitting. This performance mapping function uses the operating speed and guide vane angle as independent variables and the target performance parameter as dependent variable, and can calculate the performance parameters under any combination of speed and guide vane angle.

[0069] In some implementations, during the factory calibration phase of the magnetic levitation compressor, fixed operating boundary conditions such as intake pressure and exhaust pressure are used to traverse multiple combinations of operating speed and guide vane angle within the allowable operating range. The corresponding input power or energy efficiency ratio is tested and recorded point by point to form discrete performance test data covering the entire operating range.

[0070] Subsequently, the modeling method can be flexibly selected based on the controller's computing power and control accuracy requirements. If a lookup table method is used, the discrete test data will be organized into a two-dimensional mapping table, with rotational speed and guide vane angle as two index dimensions, and the corresponding performance parameters will be stored as output values. During the operation of the magnetic levitation compressor, the corresponding performance values ​​can be directly read based on the current operating speed and guide vane angle index. If an interpolation fitting model method is used, a continuous mathematical mapping function will be generated using discrete test points as a reference, through interpolation or fitting algorithms. During operation, any combination of operating speed and guide vane angle can be input, and the corresponding performance parameters can be calculated through the function.

[0071] In this way, by using measured multi-condition performance data, a precise quantitative mapping relationship between operating speed, guide vane angle, and operating conditions and target performance parameters is constructed, ensuring that the performance model fits the actual operating characteristics of the magnetic levitation compressor.

[0072] Please see Figure 4 In some implementations, step 03 includes: 031: Determine the feasible range of values ​​for operating speed and guide vane angle, taking the current load demand as a prerequisite; 032: Within the feasible range of values, calculate the target performance parameters corresponding to different combinations of values ​​based on the performance model; 033: Select the combination of values ​​from which the target performance parameters reach their optimal values ​​as candidate control parameter combinations.

[0073] In some implementations, the optimization solution module 300 is further used to determine the feasible range of values ​​for the operating speed and guide vane angle, taking the current load demand as a prerequisite. Within the feasible range, based on the performance model, it calculates the target performance parameters corresponding to different combinations of values. Finally, it selects the combination of values ​​where the target performance parameters reach their optimal values ​​as candidate control parameter combinations.

[0074] In some implementations, the processor is further configured to determine a feasible range of values ​​for the operating speed and guide vane angle, with the current load demand as a prerequisite. Within this feasible range, based on a performance model, it calculates the target performance parameters corresponding to different combinations of values. Finally, it selects the combination of values ​​from which the target performance parameters reach their optimal values ​​as candidate control parameter combinations.

[0075] Specifically, the current load demand refers to the cooling or heating capacity that the magnetic levitation compressor needs to output under current operating conditions, and its value is determined by the actual cooling and heating load on the user side. In some implementations, the current load demand can be calculated using the following formula: ,in, The density of water; Specific heat; Water flow rate; This refers to the inlet water temperature. This refers to the outlet water temperature. Current load demand can also be obtained by directly reading load demand commands issued by the upper-level control system.

[0076] Meeting the current load demand is a prerequisite, meaning that load matching is the primary hard constraint for optimization. All optimization calculations for speed and guide vane angle are limited to the parameter range that can output no less than the current load demand. This ensures that the optimized control parameters can first guarantee the system's energy supply capacity meets the standard, and then pursue the goal of optimal energy efficiency.

[0077] The feasible range of values ​​refers to the set of parameter intervals consisting of all legal values ​​of operating speed and guide vane angle that can meet the current load requirements. It is the search boundary for optimization and is used to eliminate invalid parameter combinations that cannot meet the load requirements.

[0078] The combination of values ​​refers to a set of parameters formed by pairing operating speed with guide vane angle, representing a specific operating condition matching scheme. Different operating speeds and guide vane combinations can achieve the same output load, but correspond to different operating power and energy efficiency levels.

[0079] The optimal value refers to the extreme value of the target performance parameter, corresponding to the best state of the optimization objective. When the optimization objective is input power, the optimal value is the minimum power; when the optimization objective is energy efficiency ratio, the optimal value is the maximum energy efficiency ratio.

[0080] Using the current load demand as a rigid constraint, and combining the mapping relationship between load demand, operating speed, and guide vane angle, the target load speed range and guide vane angle range can be derived in reverse. Since the same load can be achieved by multiple speed-guide vane combinations, such as high speed with small guide vane opening or low speed with large guide vane opening, the derived value range is defined by the equal load curve.

[0081] Subsequently, within the determined feasible value range, the rotational speed and guide vane angle are discretized and sampled according to a preset search step size, generating multiple ordered combinations of values. Each combination of values, along with the current operating condition parameters, is then input into the constructed performance model to calculate the target performance parameter values ​​for the corresponding operating condition.

[0082] Next, the performance calculation results of all feasible combinations are sorted and compared. Based on the preset optimization objective, the parameter combination with the corresponding extreme value is selected: if the optimization objective is to minimize the input power, the combination with the lowest power value is selected; if the optimization objective is to maximize the energy efficiency ratio, the combination with the highest energy efficiency ratio is selected. The parameter pairs finally selected are the candidate control parameter combinations, which are output to the maximum constraint decision module 400 for subsequent safety and physical boundary verification.

[0083] In this way, the feasible solution space for operating speed and guide vane angle can be defined with the current load demand as a pre-constraint. Under the dual-variable collaborative dimension, the matching working condition combination can be traversed and the target performance parameter performance can be quantitatively evaluated. The synergistic gain potential of speed regulation and guide vane regulation can be fully utilized to determine the parameter combination with the optimal target performance under the current working condition. This provides the candidate control parameter combination with the optimal target performance parameters for the subsequent multi-constraint collaborative decision-making process, while ensuring the consistency of the target and the reliability of the results in the optimization solution process.

[0084] In some implementations, operational constraints include safety constraints, equipment physical boundary constraints, and / or load demand constraints; Among them, safety constraints include anti-surge constraints, which are used to indicate the safety margin requirements between the actual operating flow rate of the magnetic levitation compressor and the surge boundary flow rate; The physical boundary constraints of the equipment include speed operation constraints, which are used to indicate that the operating speed of the magnetic levitation compressor is within the speed operation range formed by the minimum operating speed and the maximum operating speed; The physical boundary constraints of the equipment include guide vane angle constraints, which are used to indicate that the guide vane angle of the magnetic levitation compressor is within the guide vane angle adjustment range consisting of the minimum allowable angle and the maximum allowable angle. The load demand constraint is used to indicate that the absolute value of the deviation between the actual output load of the magnetic levitation compressor and the current load demand is less than or equal to a preset deviation threshold.

[0085] Specifically, safety constraints refer to the constraints that ensure the safe operation of the magnetic levitation compressor and prevent equipment failures and operational accidents. They are the bottom-line constraints with the highest priority among all constraints, including anti-surge constraints.

[0086] The physical boundary constraints of the equipment refer to the inherent operating limits determined by the hardware structure, component performance and design ratings of the magnetic levitation compressor. These are insurmountable hardware boundaries used to ensure long-term reliable operation of the equipment and to prevent component overload damage.

[0087] Load demand constraints refer to functional constraints that ensure the output capacity of the magnetic levitation compressor matches the system load requirements. They are the basic functional objectives that the control method needs to meet, ensuring that the system's energy supply capacity matches the user's needs.

[0088] Anti-surge constraints are the core of safety constraints, used to limit the minimum operating flow rate of the magnetic levitation compressor and prevent it from entering surge conditions. Surge is a periodic airflow oscillation phenomenon that occurs in a magnetic levitation compressor at low flow rates, which can cause increased rotor vibration, damage to bearings and impellers, and in severe cases, direct equipment failure. In some implementations, anti-surge constraints can be represented by the following relationship: ;in, This represents the current operating flow rate of the magnetic levitation compressor. For the surge boundary flow rate of the magnetic levitation compressor under current operating conditions, The design point flow rate of the magnetic levitation compressor. This refers to the safety margin factor. The above relationship means that the current operating flow rate of the magnetic levitation compressor must be higher than the surge boundary flow rate, and the ratio of the excess to the design point flow rate must be greater than the preset safety margin factor δ. This ensures that the compressor's operating point is far from the surge boundary and avoids entering the unstable operating region. The value of the safety margin factor δ can be set according to actual engineering requirements, such as 5% to 15%.

[0089] Specifically, the physical mechanism of surge can be summarized as follows: When the operating flow rate of the magnetic levitation compressor is too low, the work done by the impeller on the gas cannot overcome the resistance in the diffuser and pipeline, causing boundary layer separation or even backflow in the airflow in the blade channel, resulting in a sharp drop in pressure ratio. Subsequently, the high-pressure gas in the pipeline flows back to the magnetic levitation compressor, the flow rate increases instantaneously, and the magnetic levitation compressor regains its exhaust capacity. When the flow rate drops below the critical value again, surge occurs again. This cycle repeats, forming periodic large-amplitude oscillations in flow rate and pressure, with frequencies as low as a few hertz, producing a strong low-pitched roar and mechanical vibration, which is extremely destructive to the equipment.

[0090] Surge boundary flow refers to the critical flow value at which a magnetic levitation compressor experiences surge under the current operating conditions. It is the dividing point between the stable operating region and the surge region, and it usually changes dynamically with the operating pressure ratio.

[0091] Safety margin refers to the safety allowance reserved between the actual operating flow rate and the surge boundary flow rate. It is usually expressed as a relative proportion and is used to avoid surge caused by a sudden drop in flow rate due to fluctuations in operating conditions, ensuring that the operation has a safety redundancy. In some implementations, the safety margin can be 5% to 15% of the surge boundary flow rate.

[0092] Speed ​​operation constraints refer to a type of physical boundary constraint for equipment, limiting the legal range of operating speed for a magnetic levitation compressor. These constraints can be determined jointly by the stable operating limits of the magnetic levitation bearings, the rated speed of the motor, and the aerodynamic design speed of the impeller. In some implementations, speed operation constraints can be expressed by the following formula: ;in, This represents the current operating speed of the magnetic levitation compressor. Minimum operating speed, This is the maximum operating speed.

[0093] The minimum operating speed refers to the lowest speed value at which the magnetic levitation compressor can maintain stable levitation and continuous and reliable operation. Below this speed, problems such as magnetic levitation instability and aerodynamic performance deterioration will occur, and it will be unable to operate stably continuously.

[0094] The maximum operating speed refers to the maximum operating speed allowed by the design of the magnetic levitation compressor. It is limited by the mechanical strength of the impeller, the limits of the motor, and the bearing capacity. Overspeed operation can lead to fatigue damage of components or even mechanical failure.

[0095] Guide vane angle constraint refers to a type of physical boundary constraint of the equipment, limiting the adjustable angle range of the inlet guide vane, which is jointly determined by the mechanical stroke of the guide vane actuator and the aerodynamic design boundary. In some implementations, ,in, This represents the current guide vane opening of the magnetic levitation compressor. For the minimum allowable angle, This is the maximum permissible angle.

[0096] The minimum allowable angle refers to the minimum opening angle that the guide vane can be adjusted to, which corresponds to the minimum intake flow area. Excessively closing the guide vane will lead to a sharp increase in intake resistance and severe flow distortion, which will reduce operating efficiency and reliability.

[0097] The maximum permissible angle refers to the maximum opening angle that the guide vane can be adjusted to, corresponding to the maximum intake flow area, which is usually the guide vane in the fully open state.

[0098] The load demand constraint requires that the absolute value of the deviation between the actual output load of the magnetic levitation compressor and the current load demand be less than or equal to a preset deviation threshold. In some implementations, the load demand constraint can be expressed by the following relationship: ;in, For system load demand, This represents the flow rate at the current operating speed and current guide vane angle.

[0099] A preset deviation threshold refers to the maximum allowable deviation between the actual output load and the current load demand. It is used to ensure load accuracy while preventing system oscillations caused by frequent adjustments to the control input. In some implementations, the preset deviation threshold may be a percentage of the current load demand or a fixed value.

[0100] In some implementations, safety constraints may also include exhaust temperature constraints, which limit the maximum permissible value of the exhaust temperature of the magnetic levitation compressor to prevent excessively high exhaust temperatures from causing thermal aging of components, failure of seals, or deterioration of lubricating oil.

[0101] Safety constraints may also include motor winding over-temperature constraints, which limit the maximum operating temperature of the motor stator windings to prevent overheating damage and ensure the safe operation of the electrical system.

[0102] Load demand constraints may also include maximum load limit constraints, whereby the maximum load limit constraint is used to limit the output load of the magnetic levitation compressor to not exceed the rated maximum output capacity of the equipment, so as to avoid long-term overload operation of the equipment and ensure the service life of the equipment.

[0103] In this way, a complete operational constraint system can be constructed from three dimensions: safe operation, hardware capabilities, and system requirements. The judgment criteria and boundary range of each operational constraint are clearly defined. This system can ensure the safety baseline of the magnetic levitation compressor operation with anti-surge constraints, match the hardware design limits of the equipment with the physical boundary constraints of the equipment such as operating speed and guide vane angle, and ensure that the system output capacity matches the user-side requirements with load demand constraints. This provides a clear and comprehensive verification basis and judgment criteria for subsequent multi-constraint collaborative decision-making.

[0104] Please see Figure 5 In some implementations, step 04 includes: 041: Input the candidate control parameter combinations one by one into each operating constraint to verify feasibility; 042: When the candidate control parameter combination meets all constraints, the feasibility verification is deemed successful, and the candidate control parameter combination is determined as the target control parameter combination.

[0105] In some implementations, the multi-constraint decision module 400 is further configured to input candidate control parameter combinations one by one into each operational constraint for feasibility verification. When a candidate control parameter combination satisfies all constraints, the feasibility verification is determined to be successful, and the candidate control parameter combination is identified as the target control parameter combination.

[0106] In some implementations, the processor is further configured to input candidate control parameter combinations one by one into each operational constraint for feasibility verification. When a candidate control parameter combination satisfies all constraints, the feasibility verification is determined to be successful, and the candidate control parameter combination is identified as the target control parameter combination.

[0107] Specifically, feasibility verification refers to the process of verifying whether candidate control parameters meet the requirements of various operating boundaries one by one according to the preset constraint judgment rules. It is the core judgment link of multi-constraint collaborative decision-making and its function is to screen out control parameters that can be executed safely and reliably.

[0108] In some implementations, the feasibility verification process can be as follows: After receiving the candidate control parameter combinations output by the optimization solution, the corresponding operating flow rate and output load are derived based on the candidate operating speed and candidate guide vane angle, combined with the current operating conditions. Subsequently, these parameters are sent one by one to the judgment logic of each operating constraint to complete the verification: the operating flow rate is compared with the surge boundary flow rate with a safety margin to verify the anti-surge constraint; the candidate operating speed is compared with the minimum and maximum operating speeds to verify the speed operation constraint; the candidate guide vane angle is compared with the minimum and maximum allowable angles to verify the guide vane angle constraint; and the absolute value of the deviation between the actual output load and the current load demand is less than or equal to a preset deviation threshold to verify the load demand constraint.

[0109] Next, the verification results of all operating constraints are summarized. If and only if all constraints are determined to be satisfied, the candidate control parameter combination is confirmed to fall within the legal operating range, which can achieve optimal energy efficiency, has no safety risks or equipment overload risks, and can meet the load requirements. At this time, the feasibility verification is determined to be passed, and the candidate combination is directly determined as the target control parameter combination and output to the execution control module 500.

[0110] Thus, by constructing a multi-dimensional constraint system covering supply and demand matching, equipment safety, and operational stability, reasonable boundary conditions can be provided for optimization solutions.

[0111] Please see Figure 6 In some embodiments, step 041 includes: 0411: Based on the constraint type and preset security level of each operational constraint, set the priority for each operational constraint; 0412: Perform feasibility verification on candidate control parameter combinations in descending order of priority.

[0112] In some implementations, the multi-constraint decision module 400 is also used to set priorities for each operational constraint based on its constraint type and preset safety level, and to perform feasibility verification on candidate control parameter combinations in descending order of priority.

[0113] In some implementations, the processor is also used to set priorities for each operational constraint based on the constraint type and preset security level, and to perform feasibility verification on candidate control parameter combinations in descending order of priority.

[0114] Specifically, constraint type refers to the category of operational constraints according to functional attributes, including safety type, equipment physical boundary type, load demand type, etc. Different types correspond to different operational assurance dimensions and are one of the core bases for priority classification.

[0115] The preset safety level refers to the risk level that is pre-classified according to the severity of the consequences of constraint failure. The more severe the consequences of failure and the greater the loss, the higher the safety level and the higher the priority in the verification system.

[0116] Priority refers to the order in which constraints are executed during the verification process. Higher priority constraints are verified first, and also represent a higher degree of inviolability of the constraint. It is the sorting rule of a multi-constraint decision-making system. In some implementations, the constraint priorities in multi-constraint collaborative decision-making, from highest to lowest, can be: safety constraints, equipment physical boundary constraints, and load demand constraints.

[0117] Sequential verification refers to an execution method in which verifications are performed sequentially according to priority. The verification of the next constraint only begins after the previous constraint has passed verification. If the previous verification fails, the entire verification process can be terminated directly.

[0118] First, based on the constraint type and preset safety level of each operational constraint, all operational constraints included in the decision-making process are classified and their risk levels are assessed: Anti-surge constraints belong to the safety category; failure of these constraints will cause periodic airflow oscillations, rotor axial impact, permanent damage to bearings and impellers, and even direct damage to the entire machine. Therefore, they have the highest safety level and are set as the first priority. Speed ​​operation constraints and guide vane angle constraints belong to the physical boundary constraints of the equipment; failure of these constraints will lead to motor overload, component overstress, and actuator overtravel, shortening equipment life or causing hardware damage. Therefore, they have the second highest safety level and are set as the second priority. Load demand constraints belong to the functional category; failure of these constraints only affects power supply accuracy and will not cause safety accidents or hardware damage. Therefore, they have the lowest safety level and are set as the third priority.

[0119] Subsequently, the operating flow corresponding to the candidate control parameter combinations is extracted to complete the highest priority anti-surge constraint verification. If the anti-surge constraint verification fails, the overall feasibility verification is directly deemed a failure, and the verification of all subsequent low-priority constraints is terminated to avoid invalid calculations. If the anti-surge constraint verification passes, the second priority speed operation constraints and guide vane angle constraints are verified one by one to confirm whether the parameters are within the rated operating range of the equipment. If all physical boundary constraint verifications pass, the third priority load demand constraints are finally verified to confirm whether the load matching accuracy meets the standard.

[0120] Thus, by adopting a sequential verification mechanism from high to low priority, subsequent low-priority verification steps can be terminated directly when high-priority constraints are not met, reducing unnecessary computational overhead and improving the execution efficiency of multi-constraint decision-making.

[0121] Please see Figure 7 In some implementations, the method further includes: 043: If there are constraint violations or multiple constraint conflicts in the candidate control parameter combination, the feasibility verification fails. 044: Based on the preset adjustment strategy, adjust the candidate control parameter combinations to generate corrected control parameter combinations; 045: Perform multi-constraint collaborative decision-making on the modified control parameter combination and output the target control parameter combination that satisfies all operating constraints.

[0122] In some implementations, the processor is further configured to determine that the feasibility verification has failed when there are constraint violations or multiple constraint conflicts in the candidate control parameter combinations; adjust the candidate control parameter combinations based on a preset adjustment strategy to generate modified control parameter combinations; and perform multi-constraint collaborative decision-making processing on the modified control parameter combinations to output a target control parameter combination that satisfies all operational constraints.

[0123] In some implementations, the multi-constraint decision module 400 is further configured to determine that the feasibility verification has failed when there is a constraint violation or multi-constraint conflict in the candidate control parameter combination; adjust the candidate control parameter combination based on a preset adjustment strategy to generate a modified control parameter combination; and perform multi-constraint collaborative decision processing on the modified control parameter combination to output a target control parameter combination that satisfies all operational constraints.

[0124] Specifically, constraint violation refers to the operating state corresponding to a candidate control parameter exceeding the boundary of one or more operating constraints. For example, exceeding the maximum operating speed or the operating flow rate falling below the surge boundary with a safety margin are single-dimensional compliance failure states.

[0125] Multi-constraint conflict refers to a situation where a candidate control parameter combination simultaneously violates multiple constraints, and the reasons for these constraints being violated are interrelated, while the directions of correction are contradictory. For example, a candidate combination might choose a smaller guide vane angle and a lower rotational speed to improve energy efficiency, resulting in an actual flow rate lower than the surge boundary flow rate (violating the anti-surge constraint). Simultaneously, the low rotational speed leads to insufficient output load (violating the load demand constraint). To meet the anti-surge constraint, the rotational speed needs to be increased or the guide vane opening increased to increase the flow rate; to meet the load demand constraint, the output load also needs to be increased. These two directions are consistent and can be reconciled. However, a more complex type of conflict arises when a candidate combination chooses a high rotational speed and a large guide vane opening to meet the load demand constraint. This results in the magnetic levitation compressor operating point exceeding the high-efficiency zone and the rotational speed approaching the upper limit (violating the rotational speed constraint). Conversely, reducing the rotational speed to meet the rotational speed constraint leads to insufficient load (violating the load demand constraint). In this case, the directions of correction for the two constraints are contradictory, requiring a trade-off based on priority.

[0126] Feasibility verification failure means that the candidate control parameters fail the verification of all operational constraints, including both single constraint violations and multiple constraint conflicts, indicating that the set of parameters does not meet the conditions for safe and reliable execution.

[0127] The preset adjustment strategy refers to a set of pre-defined parameter correction rules. Based on the set priority, it clarifies the direction, magnitude and linkage logic of parameter adjustment under different constraint violation scenarios, and serves as the basis for calculating the correction parameters.

[0128] The modified control parameter combination refers to the intermediate parameter set obtained after making targeted adjustments to the non-compliant candidate control parameter combinations. It is the object of re-verification and needs to go through the complete multi-constraint decision-making process again to verify its compliance.

[0129] After completing the priority-based item-by-item verification, if any constraint is determined to be unmet, or if a conflict relationship between multiple constraints that cannot be satisfied simultaneously is detected, the feasibility verification is deemed to have failed, the candidate control parameters do not meet the conditions for direct execution, and the subsequent correction process is triggered. First, locate the non-compliant constraint or conflicting constraint group, and based on the constraint priority, invoke the preset adjustment strategy to perform parameter correction: for high-priority anti-surge constraint violations, prioritize adjusting the guide vane angle or operating speed to increase the operating flow rate and bring the operating point back within the surge safety boundary; for physical boundary constraint violations, correct the non-compliant variable to the corresponding boundary value, and adjust another variable in conjunction to maintain the output load as much as possible; for multi-constraint conflict scenarios, satisfy the constraints in order of priority from high to low, adjust the parameters based on the boundary of the high-priority constraint, and at the same time minimize the impact on the low-priority energy efficiency target.

[0130] Finally, the revised control parameter combination is re-entered into the multi-constraint decision module 400, and the feasibility of all constraints is verified again in descending order of priority. If the revised control parameter combination satisfies all constraints, it is directly output as the final target control parameter combination. If the revised control parameter combination still has constraint violations or conflicts, the revision process can be triggered again until a compliant control parameter combination is obtained. In some implementations, when the number of linked revisions reaches a preset threshold, the iterative revision process is terminated. Based on the compliance boundary of the highest priority constraint, a feasible control parameter combination that satisfies both safety constraints and equipment physical boundary constraints is output. At the same time, the constraint conflict event is recorded for subsequent performance model revision and operational strategy optimization.

[0131] In this way, based on the target constraints that cause the verification failure, the direction and reasonable magnitude of the safety correction can be derived by combining the constraint priority and the corresponding constraint boundary. The operating speed and guide vane angle can be corrected in a linked manner to retain the optimized target performance parameter level as much as possible while satisfying all operating constraints, thereby achieving a balance between system operating safety and operating efficiency.

[0132] Please see Figure 8 In some embodiments, step 044 includes: 0441: Take the constraints violated by the candidate control parameter combination and / or the constraints with multiple constraint conflicts as the target constraints; 0442: Based on the priority of the preset adjustment strategy and target constraints, and according to the constraint boundaries corresponding to the target constraints, derive the safety correction value of the candidate control parameter combination; 0443: The operating speed and guide vane angle are adjusted in conjunction with the safety correction value to generate a combination of corrected control parameters.

[0133] In some implementations, the multi-constraint decision module 400 is further configured to use the constraints violated by the candidate control parameter combination and / or the constraints with multiple constraint conflicts as target constraints. Based on a preset adjustment strategy and the priority of the target constraints, and according to the constraint boundaries corresponding to the target constraints, it derives a safety correction value for the candidate control parameter combination. It then performs a linked correction on the operating speed and guide vane angle according to the safety correction value to generate a corrected control parameter combination.

[0134] In some implementations, the processor is used to take the constraints violated by the candidate control parameter combinations and / or the constraints with multiple constraint conflicts as target constraints. Based on a preset adjustment strategy and the priority of the target constraints, and according to the constraint boundaries corresponding to the target constraints, the processor derives a safety correction value for the candidate control parameter combinations. The processor then performs a linked correction on the operating speed and guide vane angle according to the safety correction value, generating a corrected control parameter combination.

[0135] Specifically, target constraints refer to the single constraint violated by the candidate control parameter combination, or multiple sets of constraints that conflict with each other. They are the target of parameter correction and determine the direction of correction and the baseline boundary.

[0136] Constraint boundaries refer to the compliance thresholds corresponding to each operational constraint, serving as quantitative dividing lines that distinguish between compliant and non-compliant states. For example, the boundary of anti-surge constraints is the minimum allowable flow rate with a safety margin, while the boundaries of speed constraints are the minimum and maximum operating speed thresholds.

[0137] The safety correction value refers to the quantitative value that the control variables need to be adjusted to restore the target constraint to a compliant state. This value is calculated based on the constraint boundary and a reasonable safety margin is reserved to ensure that the corrected operating speed and guide vane opening are stably within the compliant range.

[0138] Linkage correction refers to the coordinated adjustment of two control variables, operating speed and guide vane angle, at the same time. By utilizing the different influence characteristics of these two variables on flow rate, load, and power, the output load is kept relatively stable while satisfying constraints, thereby reducing control disturbances.

[0139] First, analyze the reasons for the failure of the feasibility verification and filter out all constraints that are determined to be violated, i.e., the target constraints. If there are multiple conflicting constraints, then the contradictory constraint groups are listed together as target constraints.

[0140] Subsequently, the priority order of the target constraints is determined. For scenarios with multiple target constraints, the boundary of the constraint with the highest priority is used as the primary benchmark. Next, based on the difference between the current candidate parameter and the constraint boundary in the candidate control parameter combination, the basic adjustment amount required to bring the parameter back to the compliant range is calculated. Then, a preset safety margin is added on this basis to obtain the final safety correction value. For example, when the anti-surge constraint is violated, the difference between the current flow rate and the surge safety boundary flow rate is first calculated. Then, the operating speed and guide vane adjustment amount required to compensate for this flow rate difference are derived, and an additional safety margin is reserved to ensure that the operating point is stably within the safe zone after correction, avoiding the violation caused by fluctuations in operating conditions.

[0141] Finally, based on the derived safety correction value and combined with the coupling characteristics of speed and guide vane angle, the two control variables are adjusted collaboratively to generate a modified control parameter combination. For example, when it is necessary to increase the flow rate to avoid surge, the guide vane opening is increased first to increase the intake flow rate, while the speed is slightly adjusted to maintain stable output load.

[0142] In this way, based on the target constraints that cause the verification failure, the direction and reasonable magnitude of the safety correction can be derived by combining the constraint priority and the corresponding constraint boundary. The operating speed and guide vane angle can be corrected in a linked manner to retain the optimized target performance parameter level as much as possible while satisfying all operating constraints, thereby achieving a balance between system operating safety and operating efficiency.

[0143] Please see Figure 9 In some implementations, step 05 includes: 051: Calculate the difference between the target control parameter combination and the current operating parameters, and generate control commands; 052: Based on control commands, the frequency converter is controlled to gradually adjust the operating speed of the magnetic levitation compressor to the target operating speed in the target control parameter combination; 053: Based on control commands, control the servo actuator to adjust the guide vane angle of the magnetic levitation compressor to gradually adjust to the target guide vane angle in the target control parameter combination.

[0144] In some implementations, the execution control module 500 is further configured to calculate the difference between the target control parameter combination and the current operating parameters, and generate control commands. Based on the control commands, it controls the frequency converter to gradually adjust the operating speed of the magnetic levitation compressor to the target operating speed in the target control parameter combination. Also based on the control commands, it controls the servo actuator to gradually adjust the guide vane angle of the magnetic levitation compressor to the target guide vane angle in the target control parameter combination.

[0145] In some implementations, the processor is also used to calculate the difference between the target control parameter combination and the current operating parameters, and generate control instructions. Based on the control instructions, the processor controls the frequency converter to gradually adjust the operating speed of the magnetic levitation compressor to the target operating speed in the target control parameter combination. Furthermore, based on the control instructions, the processor controls the servo actuator to gradually adjust the guide vane angle of the magnetic levitation compressor to the target guide vane angle in the target control parameter combination.

[0146] Specifically, the current operating parameters refer to the current operating speed and guide vane angle values ​​of the magnetic levitation compressor, which are acquired in real time by the operating condition acquisition module 100 and serve as the benchmark values ​​for calculating the adjustment difference.

[0147] Control commands refer to execution instructions generated based on the difference between the target value in the target control parameter combination and the current operating parameters. They include adjustment direction, adjustment range, and adjustment rate requirements, and are direct signals that drive the actuator to move.

[0148] A frequency converter is a power electronic device used to adjust the power supply frequency of the drive motor of a magnetic levitation compressor. It achieves continuous adjustment of the motor speed by changing the power supply frequency and is the actuator for speed control.

[0149] Servo actuators refer to precision actuators used to drive the rotation of inlet guide vane blades. They can accurately control the guide vane opening angle and are the actuators for adjusting the guide vane angle.

[0150] Gradual adjustment refers to a phased and smooth transition to the target value, rather than a one-time step-to-the-point adjustment. That is, the adjustment is carried out in accordance with the aforementioned determined adjustment direction, adjustment range and adjustment rate requirements, in order to limit the rate of parameter change and suppress shocks.

[0151] The target operating speed refers to the final speed value of the magnetic levitation compressor that the target control parameter combination needs to achieve in this adjustment. It is the optimal compliant speed after energy efficiency optimization and multi-constraint verification.

[0152] The target guide vane angle refers to the final inlet guide vane opening value that the target control parameter combination needs to achieve in this adjustment, which matches the target operating speed to jointly achieve optimal energy efficiency and compliant operation.

[0153] First, the target control parameter combination confirmed by multi-constraint decision is read. At the same time, the actual operating speed and guide vane angle of the magnetic levitation compressor at the current moment are obtained from the operating condition acquisition module 100. The difference between the target value and the current value of the two control variables is calculated, including the adjustment direction and adjustment range. Combined with the preset change rate, the difference is converted into a control command including the adjustment rate.

[0154] Subsequently, the speed regulation branch sends control commands to the frequency converter. The frequency converter, according to the required adjustment rate, gradually changes the output frequency, driving the magnetic levitation compressor motor to smoothly increase or decrease speed, ultimately stabilizing at the target operating speed. The guide vane adjustment branch sends control commands to the servo actuator. The servo actuator, according to a synchronized adjustment rhythm, gradually drives the guide vane blades to rotate, ultimately precisely positioning them to the target guide vane angle.

[0155] In this way, control commands can be generated based on the deviation between the target control parameters and the current operating parameters. By gradually adjusting and smoothly transitioning, sudden changes in the control quantities of speed and guide vane angle can be avoided, preventing drastic fluctuations in system flow and pressure caused by control steps, and ensuring the stability of the magnetic levitation compressor operation.

[0156] Please refer to the following: Figure 2 This application provides a collaborative control device 1000, which is used to execute the above-mentioned collaborative control method of guide vane angle and rotation speed. The collaborative control device 1000 includes a data acquisition module 100, a model building module 200, an optimization solution module 300, a multi-constraint decision module 400, and an execution control module 500. The data acquisition module 100 is connected in communication with the sensor system of the magnetic levitation compressor and is configured to acquire the operating parameters and operating conditions of the magnetic levitation compressor. The model building module 200 is communicatively connected to the acquisition module 100 and is configured to establish a performance model of the magnetic levitation compressor based on the operating parameters and operating conditions. The performance model is used to characterize the mapping relationship between the target performance parameters and the operating parameters of the magnetic levitation compressor. The target performance parameters include the input power or energy efficiency ratio of the magnetic levitation compressor. The optimization solution module 300 is communicatively connected to the model building module 200 and the acquisition module 100, respectively. It is configured to optimize the target performance parameters and, under the condition that the load of the magnetic levitation compressor meets the current load requirements, jointly optimize the operating speed and guide vane angle of the magnetic levitation compressor based on the performance model to generate candidate control parameter combinations. The multi-constraint decision module 400 is communicatively connected to the optimization solution module 300 and is configured to perform multi-constraint collaborative decision processing on candidate control parameter combinations and output the target control parameter combination that satisfies all operational constraints. The execution control module 500 is communicatively connected to the multi-constraint decision module 400 and the actuator of the magnetic levitation compressor, and is configured to adjust and control the operating speed and guide vane angle of the magnetic levitation compressor according to the target control parameter combination.

[0157] Specifically, the collaborative control device 1000 refers to the complete control device for the magnetic levitation compressor equipped with a magnetic levitation bearing, including sensors, drive actuators, acquisition module 100, model building module 200, optimization solution module 300, multi-constraint decision module 400, and execution control module 500, which can execute the above-mentioned collaborative control method of guide vane angle and rotation speed.

[0158] The data acquisition module 100 refers to the data input unit of the collaborative control device 1000. This data acquisition module 100 is connected to the sensor system of the magnetic levitation compressor via a communication connection, and is responsible for receiving information uploaded by the sensor system and completing signal acquisition and data uploading. In some embodiments, the data acquisition module 100 also includes signal preprocessing, such as filtering and noise reduction, range conversion, unit conversion, and outlier removal.

[0159] The sensor system refers to various detection devices arranged at the air inlet, exhaust outlet, water circuit, motor, and guide vanes of the magnetic levitation compressor, including pressure sensors, temperature sensors, flow sensors, speed encoders, and guide vane angle and position sensors, which are used to collect operating parameters and operating conditions.

[0160] The model building module 200 is communicatively connected to the acquisition module 100, receiving operating parameters and operating condition data output by the acquisition module 100, and is able to build a performance model of the magnetic levitation compressor based on this data. In some embodiments, the model building module 200 stores a preset fitting method, which can acquire performance test data of the magnetic levitation compressor under different speeds and different guide vane angle combinations, and then use the preset fitting method to fit these data to construct a performance model of the target performance parameters with respect to the speed and guide vane angle.

[0161] The optimization solution module 300 is communicatively connected to both the model building module 200 and the data acquisition module 100. The connection to the model building module 200 is used to call the performance model to calculate the objective function value. The connection to the data acquisition module 100 is used to acquire the current operating parameters and current operating conditions. The optimization solution module 300, with the target performance parameters as the objective, and under the premise that the magnetic levitation compressor load meets the current load demand constraint, performs joint optimization of the operating speed and guide vane angle based on the performance model to generate candidate control parameter combinations.

[0162] The multi-constraint decision module 400 is communicatively connected to the optimization solution module 300, receives the candidate control parameter combinations output by the optimization solution module 300, and can perform multi-constraint collaborative decision processing on the candidate combinations to output the target control parameter combination that satisfies all operating constraints.

[0163] The execution control module 500 is communicatively connected to both the multi-constraint decision module 400 and the actuator of the magnetic levitation compressor. The connection to the multi-constraint decision module 400 is used to receive combinations of target control parameters. The connection to the actuator is used to convert digital commands into physical execution signals.

[0164] In this way, the collaborative control device 1000 can collect operating parameters and operating conditions in real time, construct a performance model of the magnetic levitation compressor, carry out joint optimization of operating speed and guide vane angle, and execute a closed-loop control architecture with multi-constraint collaborative decision-making. It can make collaborative decisions on the target performance parameter optimization target and operating constraints, and effectively solve the problems of lack of coordination in variable adjustment and difficulty in balancing safety and energy efficiency when multiple constraints conflict, while meeting the dynamic load requirements of the system. It can achieve optimal energy efficiency operation of the magnetic levitation compressor in the entire operating range and improve the stability and economy of system operation.

[0165] This application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps of the guide vane angle and rotational speed coordinated control method described above.

[0166] It is understood that a computer program includes computer program code. Computer program code can be in the form of source code, object code, executable files, or some intermediate form. Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc.

[0167] In this specification, the terms "specifically," "furthermore," "particularly," "understandably," etc., refer to specific features, structures, materials, or characteristics described in connection with embodiments or examples that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0168] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of executable request code comprising one or more steps for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0169] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for coordinated control of guide vane angle and rotational speed based on optimal energy efficiency of a heat pump system, characterized in that, The method includes: Obtain the operating parameters and operating conditions of the magnetic levitation compressor; Based on the operating parameters and operating conditions, a performance model of the magnetic levitation compressor is established. The performance model is used to characterize the target performance parameters of the magnetic levitation compressor and the mapping relationship between them and the operating parameters and operating conditions. The target performance parameters include the input power or energy efficiency ratio of the magnetic levitation compressor. With the goal of optimizing the target performance parameters, and under the condition that the load of the magnetic levitation compressor meets the current load requirements, based on the performance model, the operating speed and guide vane angle of the magnetic levitation compressor are jointly optimized and solved to generate candidate control parameter combinations, which include corresponding candidate operating speeds and candidate guide vane angles. The candidate control parameter combinations are subjected to multi-constraint collaborative decision-making processing to output the target control parameter combination that satisfies all operational constraints. The operating speed and guide vane angle of the magnetic levitation compressor are adjusted and controlled according to the target control parameter combination.

2. The method of claim 1, wherein, The operating parameters include the operating speed and / or guide vane angle of the magnetic levitation compressor; the operating conditions include at least one of the following: intake pressure, exhaust pressure, inlet water temperature, outlet water temperature, and flow rate of the magnetic levitation compressor.

3. The method of claim 1, wherein, The step of establishing a performance model for the magnetic levitation compressor based on the operating parameters and operating conditions includes: The performance test data of the magnetic levitation compressor under different operating speeds and different guide vane angle combinations are obtained. The performance test data includes input power data or energy efficiency ratio data. A preset fitting method is used to fit the performance test data to establish a mapping relationship between the operating speed, the guide vane angle, the operating conditions, and the target performance parameters, so as to establish the performance model. The preset fitting method includes experimental calibration data lookup table method and / or interpolation fitting model method.

4. The method of claim 1, wherein, With the goal of optimizing the target performance parameters, and assuming the load of the magnetic levitation compressor meets the current load requirements, based on the performance model, the operating speed and guide vane angle of the magnetic levitation compressor are jointly optimized to generate candidate control parameter combinations, including: Based on the premise of meeting the current load demand, determine the feasible range of values ​​for the operating speed and the guide vane angle; Within the feasible range of values, based on the performance model, the target performance parameters corresponding to different combinations of values ​​are calculated. The combinations of values ​​are used to characterize the working condition matching relationship between the operating speed and the guide vane angle. The optimal value combination of the target performance parameter is selected as the candidate control parameter combination.

5. The method of claim 1, wherein, The operational constraints include safety constraints, equipment physical boundary constraints, and / or load demand constraints. The safety constraints include anti-surge constraints, which are used to indicate the safety margin requirement between the actual operating flow rate of the magnetic levitation compressor and the surge boundary flow rate. The physical boundary constraints of the equipment include speed operation constraints, which are used to indicate that the operating speed of the magnetic levitation compressor is within the speed operation range formed by the minimum operating speed and the maximum operating speed; The physical boundary constraints of the equipment include guide vane angle constraints, which are used to indicate that the guide vane angle of the magnetic levitation compressor is within the guide vane angle adjustment range formed by the minimum allowable angle and the maximum allowable angle. The load demand constraint is used to indicate that the absolute value of the deviation between the actual output load of the magnetic levitation compressor and the current load demand is less than or equal to a preset deviation threshold.

6. The method according to claim 5, characterized in that, The process of performing multi-constraint collaborative decision-making on the candidate control parameter combinations to output a target control parameter combination that satisfies all operational constraints includes: The feasibility of each combination of candidate control parameters is verified by inputting them one by one into each of the operational constraints. When the candidate control parameter combination satisfies all constraints, the feasibility verification is determined to be successful, and the candidate control parameter combination is determined as the target control parameter combination.

7. The method of claim 6, wherein, The step of inputting the candidate control parameter combinations one by one into each of the operational constraints for feasibility verification includes: Based on the constraint type and preset security level of each of the aforementioned operational constraints, a priority is set for each of the aforementioned operational constraints; The feasibility of the candidate control parameter combinations is verified sequentially in descending order of priority.

8. The method of claim 6, wherein, The method further includes: If the candidate control parameter combination has a constraint violation or multiple constraint conflict, the feasibility verification is determined to have failed. Based on a preset adjustment strategy, the candidate control parameter combinations are adjusted to generate a corrected control parameter combination. The modified control parameter combination is subjected to multi-constraint collaborative decision-making processing to output the target control parameter combination that satisfies all operational constraints.

9. The method of claim 8, wherein, The step of adjusting the candidate control parameter combinations based on a preset adjustment strategy to generate a corrected control parameter combination includes: The constraints violated by the candidate control parameter combination and / or the constraints with multiple constraint conflicts are taken as the target constraints. Based on the priority of the preset adjustment strategy and the target constraint, and according to the constraint boundary corresponding to the target constraint, the safety correction value of the candidate control parameter combination is derived. The operating speed and the guide vane angle are adjusted in conjunction with the safety correction value to generate the correction control parameter combination.

10. The method of claim 1, wherein, The step of adjusting and controlling the operating speed and guide vane angle of the magnetic levitation compressor according to the target control parameter combination includes: Calculate the difference between the target control parameter combination and the current operating parameters, and generate control commands; Based on the control command, the frequency converter is controlled to adjust the operating speed of the magnetic levitation compressor to gradually adjust it to the target operating speed in the target control parameter combination; Based on the control command, the servo actuator is controlled to adjust the guide vane angle of the magnetic levitation compressor to the target guide vane angle in the target control parameter combination.

11. A synergic control device, characterized by, The collaborative control device is used to execute the guide vane angle and rotational speed collaborative control method as described in any one of claims 1-10. The collaborative control device includes an acquisition module, a model building module, an optimization solution module, a multi-constraint decision module, and an execution control module. The acquisition module is communicatively connected to the sensor system of the magnetic levitation compressor and is configured to acquire the operating parameters and operating conditions of the magnetic levitation compressor. The model building module is communicatively connected to the acquisition module and is configured to establish a performance model of the magnetic levitation compressor based on the operating parameters and the operating conditions. The performance model is used to characterize the mapping relationship between the target performance parameters of the magnetic levitation compressor and the operating parameters. The target performance parameters include the input power or energy efficiency ratio of the magnetic levitation compressor. The optimization solution module is communicatively connected to the model building module and the acquisition module, respectively. It is configured to optimize the target performance parameters and, under the condition that the load of the magnetic levitation compressor meets the current load requirements, perform joint optimization solution on the operating speed and guide vane angle of the magnetic levitation compressor based on the performance model to generate candidate control parameter combinations. The multi-constraint decision module is communicatively connected to the optimization solution module and is configured to perform multi-constraint collaborative decision processing on the candidate control parameter combination and output the target control parameter combination that satisfies all operational constraints. The execution control module is communicatively connected to the multi-constraint decision module and the actuator of the magnetic levitation compressor, and is configured to adjust and control the operating speed and guide vane angle of the magnetic levitation compressor according to the target control parameter combination.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by one or more processors, implements the method as described in any one of claims 1-10.

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