A method, apparatus and equipment for multivariable coordinated control of a cooling system

Abstract

This application discloses a multivariable coordinated control method, apparatus, and device for a cooling system. The method includes: establishing a target controlled autoregressive integral moving average model based on collected real-time operating state parameters of the cooling system; constructing a one-step predictive control model for target parameters based on the target controlled autoregressive integral moving average model; setting a comprehensive control performance optimization function for the one-step predictive control model of the target parameters; and coordinating control of multiple target parameters of the cooling system according to the comprehensive control performance optimization function. The target controlled autoregressive integral moving average model established by this invention can effectively describe the input-output relationship of the cooling system, the one-step predictive control model predicts the target parameters, and the comprehensive control performance optimization function takes into account the multiple target parameters and high maneuverability of the cooling system, thereby achieving coordinated control of multiple parameter variables in the cooling system and keeping the cooling system in an optimal operating state.

Description

Technical Field

[0001] This application relates to the field of cooling system control technology, and more specifically, to a multivariable coordinated control method, apparatus and equipment for a cooling system. Background Technology

[0002] Currently, the cooling system is a crucial component of the ship's propulsion system, supporting its normal operation. A ship's cooling system is a sea-passing system with a large seawater flow rate and large pipe diameter. During operation, the seawater pumps generate significant noise and consume substantial energy, resulting in poor economic efficiency. Reducing the noise of the cooling water system and improving the economic efficiency of the propulsion system are important directions for the development of ship cooling systems.

[0003] In the existing technology, the operation control of ship cooling systems mainly relies on heat balance calculations. The reference values ​​of the control variables of the ship cooling system are determined based on the calculation results. Then, the control variables are adjusted by means of engineering trial and error tuning around the reference values. The control variables are gradually adjusted by means of engineering trial and error tuning so that the actual values ​​gradually approach the reference values.

[0004] However, the existing control methods for ship cooling systems only consider the reasonable matching and stable control of parameters such as temperature and pressure under steady-state conditions. They cannot fully utilize the coordination and optimization capabilities of multiple control variables in the redundant mechanisms of pumps and valves in the cooling system, and it is difficult to take into account the coordinated control of multiple variables in the cooling system so that the system is in the optimal working state. Summary of the Invention

[0005] In response to at least one defect or improvement requirement of the prior art, the present invention provides a multivariable coordinated control method, apparatus and equipment for a cooling system, which solves the problem that the prior art cannot fully utilize the coordinated optimization capability of multiple control variables in different components within the cooling system, and is difficult to take into account the coordinated control of multiple parameter variables in the cooling system.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a multivariable coordinated control method for a cooling system is provided, comprising:

[0007] A target controlled autoregressive integral moving average model is established based on the collected real-time operating status parameters of the cooling system.

[0008] A one-step predictive control model for target parameters is constructed based on a target-controlled autoregressive integral moving average model.

[0009] Set up a comprehensive control performance optimization function for the one-step predictive control model of the target parameters;

[0010] The cooling system's multiple target parameters are coordinated and controlled based on the comprehensive control performance optimization function.

[0011] In one possible implementation, a target controlled autoregressive integral moving average model is established based on the collected real-time operating state parameters of the cooling system, including:

[0012] The vacuum control target, subcooling control target, input variables, and intermediate controlled variables are determined from the real-time operating status parameters.

[0013] Establish an initial controlled autoregressive integral moving average model of vacuum degree control target, subcooling degree control target, input variables, and intermediate controlled variables;

[0014] The transfer function of the initial controlled autoregressive integral moving average model is discretized to obtain the target controlled autoregressive integral moving average model.

[0015] In one possible implementation, a one-step predictive control model for the target parameters is constructed based on a target-controlled autoregressive integral moving average model, including:

[0016] A prediction step size equation is introduced based on the target-controlled autoregressive integral moving average model.

[0017] Set the prediction step size and construct a one-step predictive control model for the target parameters based on the target controlled autoregressive integral moving average model and the prediction step size equation.

[0018] In one possible implementation, multiple target parameters of the cooling system are coordinated and controlled according to a comprehensive control performance optimization function, including:

[0019] By setting the partial derivative of the flow rate change of the nozzle regulating valve group in the comprehensive control performance optimization function to zero, the flow rate change of the nozzle regulating valve group at each time step is calculated.

[0020] In one possible implementation, the coordinated control of multiple target parameters of the cooling system based on a comprehensive control performance optimization function further includes:

[0021] The relationship between the condensate flow rate of the nozzle regulating valve group and the flow coefficient of the nozzle regulating valve group is calculated based on the flow rate change of the nozzle regulating valve group at various times.

[0022] The opening values ​​of multiple valves in the nozzle regulating valve group are determined based on the correspondence.

[0023] In one possible implementation, the coordinated control of multiple target parameters of the cooling system based on a comprehensive control performance optimization function further includes:

[0024] The pressure difference across the nozzle regulating valve assembly is determined based on the opening values ​​of multiple valves in the nozzle regulating valve assembly.

[0025] Adjust the speed of the condensate circulation pump so that the pressure difference across the nozzle regulating valve assembly is the preset pressure difference value.

[0026] In one possible implementation, the coordinated control of multiple target parameters of the cooling system based on a comprehensive control performance optimization function further includes:

[0027] Calculate the condensate flow rate through the nozzle regulating valve assembly based on the correspondence;

[0028] Adjust the condensate flow rate through the nozzle regulating valve assembly to be greater than or equal to the preset condensate flow rate.

[0029] According to a second aspect of the present invention, a multivariable coordinated control device for a cooling system is also provided, comprising:

[0030] The first modeling module is configured to establish a target controlled autoregressive integral moving average model based on the collected real-time operating status parameters of the cooling system.

[0031] The second modeling module is configured to construct a one-step predictive control model of the target parameters based on the target controlled autoregressive integral moving average model.

[0032] The optimization function module is configured to set up a comprehensive control performance optimization function for a one-step predictive control model with target parameters.

[0033] The coordination control module is configured to coordinate the control of multiple target parameters of the cooling system according to the comprehensive control performance optimization function.

[0034] According to a third aspect of the present invention, a multivariable coordinated control device for a cooling system is also provided, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of any of the above-described multivariable coordinated control methods for a cooling system.

[0035] According to a fourth aspect of the invention, a storage medium is also provided, which stores a computer program executable by an access authentication device, which, when run on the access authentication device, causes the access authentication device to perform the steps of any of the above-described multivariable coordinated control methods for a cooling system.

[0036] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0037] This invention provides a multivariable coordinated control method for a cooling system. It establishes a target controlled autoregressive integral moving average model using real-time operating state parameters of the cooling system. This model effectively describes the relationships between various inputs and outputs in the cooling system, facilitating coordinated control of multiple target parameters. Furthermore, a one-step predictive control model for the target parameters is constructed based on this model to predict these parameters. By setting a comprehensive control performance optimization function for the one-step predictive control model, the method can balance the multiple target parameters and high maneuverability of the cooling system, achieving coordinated control of various parameter variables and ensuring the cooling system operates in its optimal state. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating an embodiment of the multivariable coordinated control method for a cooling system provided by the present invention.

[0040] Figure 2 Provided by the present invention Figure 1 A schematic flowchart of one embodiment of step S100;

[0041] Figure 3 This is a schematic diagram of an embodiment of the multivariable coordinated control device for a cooling system provided by the present invention.

[0042] Figure 4 This is a schematic diagram of the structure of a multivariable coordinated control device for a cooling system provided in an embodiment of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0044] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0045] This invention provides a multivariable coordinated control method, apparatus, and device for a cooling system, which are described below.

[0046] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the multivariable coordinated control method for a cooling system provided by the present invention. In a specific embodiment of the present invention, a multivariable coordinated control method for a cooling system is disclosed, comprising:

[0047] S100. Establish a target controlled autoregressive integral moving average model based on the collected real-time operating status parameters of the cooling system.

[0048] S200. A one-step predictive control model for target parameters is constructed based on a target-controlled autoregressive integral moving average model.

[0049] S300, Set the comprehensive control performance optimization function for the one-step predictive control model of the target parameters;

[0050] S400: Coordinate and control multiple target parameters of the cooling system according to the comprehensive control performance optimization function.

[0051] In the above embodiment, the ship cooling system mainly consists of a mixing condenser, a seawater heat exchanger, a condensate circulation pump, a return flow regulating valve, a nozzle regulating valve assembly, connecting pipes, and other accessories. The turbine exhaust steam entering the mixing condenser comes into direct contact with the cooling condensate sprayed from the nozzle regulating valve assembly. After thorough mixing and cooling, the condensate is drawn out by the variable frequency condensate circulation pump. The pressurized condensate is divided into two streams: one stream returns directly to the mixing condenser via the return flow regulating valve without participating in heat exchange; the other stream passes through the seawater heat exchanger, where it undergoes flow heat exchange with seawater before entering the nozzle regulating valve assembly. The flow rate is regulated by the nozzle regulating valve assembly before being sprayed into the mixing condenser. Seawater, through the inlet / outlet pipes and flow deflectors on the ship's side, converts the dynamic pressure head of the oncoming water flow during ship navigation into the internal static pressure of the cooling system, driving the cooling seawater to flow through the seawater heat exchanger, cooling the condensate flowing through the seawater heat exchanger, and meeting the cooling requirements of the ship's propulsion system.

[0052] The condensate circulation pump, return flow regulating valve, and nozzle regulating valve assembly in the cooling system are all adjustable, constituting a redundant actuator multivariable control system. Under the premise of ensuring a constant condensate flow rate through the nozzle regulating valve assembly, various coordinated control modes are possible. For example, the condensate circulation pump operates at a constant speed, maintaining a constant total condensate flow rate. In this mode, the return flow regulating valve activates, coordinating with the nozzle regulating valve assembly to adjust the amount of condensate participating in the heat exchange within the seawater heat exchanger. Excess condensate is returned directly to the mixing condenser via the return flow regulating valve. Alternatively, the return flow regulating valve can be closed, and the condensate circulation pump operates in variable frequency mode, with the condensate flow rate altered by the condensate circulation pump and the nozzle regulating valve assembly. However, these two coordinated control modes do not fully utilize the synchronous coordination and optimization capabilities of multiple control variables such as the condensate circulation pump, return flow regulating valve, and nozzle regulating valve assembly. Under varying operating conditions, it is difficult to simultaneously achieve precise control and high maneuverability of the cooling system's vacuum (pressure) and subcooling (temperature).

[0053] In this embodiment, the rotational speed of the condensate circulation pump is adjusted to maintain the pressure difference across the nozzle regulating valve assembly within a certain range. This ensures that the cooling condensate sprayed from the nozzle regulating valve assembly forms a complete film, making full contact with the turbine exhaust steam and improving the heat exchange effect of direct steam-water mixing. Simultaneously, the opening of the return regulating valve is adjusted to ensure that the condensate circulation pump is above the minimum stable flow rate. Excess flow is directly returned to the mixing condenser via the return regulating valve, avoiding unstable operation of the condensate circulation pump and equipment damage. Furthermore, the opening of the nozzle regulating valve assembly is synchronously adjusted to ensure that the condensate flow through the nozzle regulating valve assembly meets the precise control requirements of vacuum (pressure) and subcooling (temperature), and to ensure that the opening action of the nozzle regulating valve assembly is consistent. During full-condition operation, there is no interlocking start-up or switching of multiple variable redundant actuators such as the condensate circulation pump, return regulating valve, and nozzle regulating valve assembly. The objectives of each control variable are clear, the control logic is clear, and it is easy to coordinate and optimize comprehensively. This can improve high mobility performance while meeting the precise control of vacuum (pressure) and subcooling (temperature).

[0054] Compared with existing technologies, this embodiment provides a multivariable coordinated control method for a cooling system. It establishes a target controlled autoregressive integral moving average model using real-time operating state parameters of the cooling system. This model effectively describes the relationships between various inputs and outputs in the cooling system, enabling coordinated control of multiple target parameters. Based on this model, a one-step predictive control model for the target parameters is further constructed to predict these parameters. By setting a comprehensive control performance optimization function for the one-step predictive control model, the method can balance the multiple target parameters and high maneuverability of the cooling system, achieving coordinated control of various parameter variables and ensuring the cooling system operates in its optimal state.

[0055] Please see Figure 2 , Figure 2Provided by the present invention Figure 1 A flowchart illustrating an embodiment of step S100. In some embodiments of the present invention, a target controlled autoregressive integral moving average model is established based on the collected real-time operating status parameters of the cooling system, including:

[0056] S110. Determine the vacuum control target, subcooling control target, input variables, and intermediate controlled variables from the real-time operating status parameters;

[0057] S120. Establish an initial controlled autoregressive integral moving average model of the vacuum degree control target, the supercooling degree control target, the input variables, and the intermediate controlled variables.

[0058] S130. Discretize the transfer function of the initial controlled autoregressive integral moving average model to obtain the target controlled autoregressive integral moving average model.

[0059] In the above embodiments, the operating status parameters of the cooling system monitored and collected in real time include turbine exhaust flow rate, condensate circulation pump flow rate, nozzle regulating valve group flow rate, mixing condenser throat pressure, pressure difference across the nozzle regulating valve group, mixing condenser condensate temperature, condensate circulation pump speed, reflux regulating valve opening, and nozzle regulating valve opening. Among these, the difference between the mixing condenser throat pressure and atmospheric pressure is used as the vacuum control target, the difference between the mixing condensate temperature and the saturation temperature corresponding to the mixing condenser throat pressure is used as the subcooling control target, the turbine exhaust flow rate is the input variable, the nozzle regulating valve group flow rate is the intermediate controlled variable, and the condensate circulation pump speed, reflux regulating valve opening, and nozzle regulating valve opening are multiple control variables.

[0060] This embodiment uses an initial controlled autoregressive integral moving average (CARIMA) model to represent the vacuum degree control objective, the subcooling degree control objective, and the input variables and intermediate controlled variables of the cooling system as follows:

[0061] ;

[0062] ;

[0063] In the formula, This indicates the change in vacuum caused by the flow rate of the nozzle regulating valve assembly and the turbine exhaust volume; This indicates the change in subcooling caused by the flow rate of the nozzle regulating valve assembly and the turbine exhaust volume; This refers to the change in flow rate of the nozzle regulating valve assembly; This represents the change in turbine exhaust volume; The transfer function between flow rate and vacuum level in the nozzle regulating valve assembly; This is the transfer function between the turbine exhaust volume and the vacuum level. The transfer function between the flow rate and subcooling of the nozzle regulating valve assembly; This is the transfer function between the turbine exhaust volume and the subcooling.

[0064] right , , and The transfer function is discretized, transforming the transfer function of a continuous-time system (if the original model is continuous) into a discrete-time form. This is generally achieved using the Z-transform, converting the transfer function in the s-domain (Laplace domain) into the z-domain (discrete-time domain). Discretization yields:

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] The target-controlled autoregressive integral moving average model for both vacuum control and supercooling control can be obtained as follows:

[0070] ;

[0071] In the formula, , , , , , , , It is a polynomial.

[0072] In some embodiments of the present invention, a one-step predictive control model for target parameters is constructed based on a target-controlled autoregressive integral moving average model, including:

[0073] A prediction step size equation is introduced based on the target-controlled autoregressive integral moving average model.

[0074] Set the prediction step size and construct a one-step predictive control model for the target parameters based on the target controlled autoregressive integral moving average model and the prediction step size equation.

[0075] In the above embodiments, a one-step predictive control model for the vacuum degree control target and the supercooling degree control target is established based on the established target-controlled autoregressive integral moving average model, and the following equations are introduced:

[0076] ;

[0077] ;

[0078] Where j represents the prediction step size, , , , , and These are the parameters to be designed.

[0079] In a preferred embodiment, the prediction step size is set in this invention. j=1 After sorting, we can obtain:

[0080] ;

[0081] Similarly, we can conclude that

[0082] ;

[0083] In the formula, , , , For the parameters to be designed, The target for vacuum control at the current moment, The current supercooling control target is... The target vacuum level for the next moment is predicted. The supercooling control target is predicted for the next moment.

[0084] The two equations above are the one-step predictive control model for the vacuum control target and the subcooling control target of the cooling system. Based on the vacuum control target and the subcooling control target at the current moment, the change in the flow rate of the nozzle regulating valve group and the change in the turbine exhaust volume, the vacuum control target and the subcooling control target at the next moment can be predicted.

[0085] In some embodiments of the present invention, coordinated control of multiple target parameters of the cooling system is performed according to a comprehensive control performance optimization function, including:

[0086] By setting the partial derivative of the flow rate change of the nozzle regulating valve group in the comprehensive control performance optimization function to zero, the flow rate change of the nozzle regulating valve group at each time step is calculated.

[0087] In the above embodiments, to balance the vacuum control target, the subcooling control target, and high maneuverability of the cooling system, the comprehensive control performance optimization function is designed as follows:

[0088] ;

[0089] Among them, the first item This represents the square of the error in vacuum control. This indicates the target setpoint for vacuum control. This represents the corresponding weighting coefficient; the second term This represents the square of the target error for subcooling control. This indicates the target setpoint for subcooling control. This represents the corresponding weighting coefficient; The square value representing the change in flow rate of the nozzle regulating valve assembly is closely related to its maneuverability. A larger change in flow rate requires a longer adjustment time, thus impacting high maneuverability. This represents the corresponding weighting coefficient. It changes according to operational control requirements. , , It allows for the adjustment of the relative importance of vacuum control targets, supercooling control targets, and high maneuverability.

[0090] Set the partial derivative of the flow rate change of the nozzle regulating valve assembly in the integrated control performance optimization function to zero, that is... We can obtain:

[0091] ;

[0092] At this point, the flow rate change of the nozzle regulating valve group at various times can be determined. This flow rate value, as an intermediate controlled variable, is closely related to multiple control variables such as the condensate circulation pump speed, the opening of the return regulating valve, and the opening of the nozzle regulating valve.

[0093] In some embodiments of the present invention, coordinating control of multiple target parameters of the cooling system according to a comprehensive control performance optimization function further includes:

[0094] The relationship between the condensate flow rate of the nozzle regulating valve group and the flow coefficient of the nozzle regulating valve group is calculated based on the flow rate change of the nozzle regulating valve group at various times.

[0095] The opening values ​​of multiple valves in the nozzle regulating valve group are determined based on the correspondence.

[0096] In the above embodiments, by combining the multi-variable control logic of the cooling system condensate circulation pump speed, the opening degree of the return flow regulating valve, and the opening degree of the nozzle regulating valve, the opening value of the nozzle regulating valve group is synchronously adjusted to ensure that the condensate flow rate through the nozzle regulating valve group meets the requirements given in the above steps. The calculated values ​​also require consistent opening action of the nozzle regulating valve assembly to ensure uniform distribution of condensate flow among the nozzle regulating valve assemblies.

[0097] The relationship between the condensate flow rate through the nozzle regulating valve assembly and the flow coefficient of the nozzle regulating valve assembly is as follows:

[0098] ;

[0099] ;

[0100] In the formula, This represents the current flow rate of condensate passing through the nozzle regulating valve assembly. The condensate flow rate through the nozzle regulating valve assembly at the previous moment. This refers to the number of valves in the nozzle regulating valve assembly. The flow coefficient for each nozzle regulating valve, For the fluid density through the valve, For reference density, This refers to the pressure difference across the nozzle regulating valve assembly.

[0101] Based on the obtained flow coefficient of the nozzle regulating valve assembly, by consulting the characteristic curve of the flow coefficient versus opening of the nozzle regulating valve assembly, the position of the nozzle regulating valve assembly in the flow regulating valve assembly can be determined. n The opening value of each valve.

[0102] In some embodiments of the present invention, coordinating control of multiple target parameters of the cooling system according to a comprehensive control performance optimization function further includes:

[0103] The pressure difference across the nozzle regulating valve assembly is determined based on the opening values ​​of multiple valves in the nozzle regulating valve assembly.

[0104] Adjust the speed of the condensate circulation pump so that the pressure difference across the nozzle regulating valve assembly is the preset pressure difference value.

[0105] In the above embodiments, the nozzle regulating valve assembly is defined as follows: n The minimum pressure difference across each valve is taken as the pressure difference across the nozzle regulating valve assembly. Adjust the speed of the condensate circulation pump to achieve the pressure difference across the nozzle regulating valve assembly. The preset differential pressure value (in a preferred embodiment, the preset differential pressure value used in this invention is 25 kPa) ensures that the cooling condensate flowing through the nozzle regulating valve group has a certain pressure, can form a complete film, and can fully contact the turbine exhaust steam.

[0106] According to the characteristic curve of the condensate circulation pump, when the condensate flow rate through the nozzle regulating valve group is kept constant, if the pressure difference across the nozzle regulating valve group is small, the speed of the condensate circulation pump should be increased; if the pressure difference across the nozzle regulating valve group is large, the speed of the condensate circulation pump should be decreased.

[0107] In some embodiments of the present invention, coordinating control of multiple target parameters of the cooling system according to a comprehensive control performance optimization function further includes:

[0108] Calculate the condensate flow rate through the nozzle regulating valve assembly based on the correspondence;

[0109] Adjust the condensate flow rate through the nozzle regulating valve assembly to be greater than or equal to the preset condensate flow rate.

[0110] In the above embodiments, in order to ensure that the condensate circulation pump is above the minimum stable flow rate and to avoid unstable operation and equipment damage, it is necessary to design the minimum flow rate of the condensate circulation pump in advance. The minimum flow rate of the condensate circulation pump can be designed based on the pump's performance characteristics and process requirements. The minimum stable continuous flow rate and the minimum stable thermal control flow rate of the pump can be calculated. Taking into account factors such as pump performance, process requirements and equipment safety, the minimum flow rate of the condensate circulation pump is determined. The rationality of the minimum flow rate is verified in actual operation and adjusted according to the actual situation.

[0111] If the condensate flow rate through the nozzle regulating valve assembly is greater than or equal to the designed minimum flow rate of the condensate circulating pump, the condensate circulating pump is considered to be in a stable operating state. The pump speed is maintained constant, and the return regulating valve is closed. If the condensate flow rate through the nozzle regulating valve assembly is less than the designed minimum flow rate of the condensate circulating pump, the pump is considered to be in an unstable operating state. In this case, the pump speed needs to be adjusted to increase the condensate flow rate above the minimum stable flow rate. Simultaneously, the opening of the return regulating valve is adjusted to return the difference between the condensate circulating pump flow rate and the condensate flow rate through the nozzle regulating valve assembly to the mixing condenser. In other words, by adjusting the opening of the return regulating valve, the condensate circulating pump flow rate is ensured to be above the minimum stable flow rate, and the condensate flow rate through the nozzle regulating valve assembly is [value missing]. .

[0112] To better implement the multivariable coordinated control method for cooling systems in this invention embodiment, based on the multivariable coordinated control method for cooling systems, please refer to the corresponding documentation. Figure 3 , Figure 3 This is a schematic diagram of an embodiment of the multivariable coordinated control device for a cooling system provided by the present invention. The embodiment of the present invention provides a multivariable coordinated control device 300 for a cooling system, comprising:

[0113] The first modeling module 310 is configured to establish a target controlled autoregressive integral moving average model based on the collected real-time operating status parameters of the cooling system.

[0114] The second modeling module 320 is configured to construct a one-step predictive control model of the target parameters based on the target controlled autoregressive integral moving average model.

[0115] The optimization function module 330 is configured to set a comprehensive control performance optimization function for a one-step predictive control model of the target parameters;

[0116] The coordination control module 330 is configured to coordinate the control of multiple target parameters of the cooling system according to the comprehensive control performance optimization function.

[0117] It should be noted that the device 300 provided in the above embodiments can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding content in the above method embodiments, and will not be repeated here.

[0118] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a multivariable coordinated control device for a cooling system provided in an embodiment of the present invention. Based on the above-described multivariable coordinated control method for a cooling system, the present invention also provides a multivariable coordinated control device for a cooling system. This multivariable coordinated control device for a cooling system can be a computing device such as a mobile terminal, desktop computer, laptop, handheld computer, or server. The multivariable coordinated control device 400 for a cooling system includes a processor 410, a memory 420, and a display 430. Figure 4 Only a portion of the components of the synchronous tracking flight welding equipment for real-time battery altitude measurement are shown; however, it should be understood that implementation of all shown components is not required, and more or fewer components may be implemented instead.

[0119] In some embodiments, memory 420 may be an internal storage unit of the multivariable coordinated control device 400 for the cooling system, such as a hard disk or memory of the multivariable coordinated control device 400 for the cooling system. In other embodiments, memory 420 may be an external storage device of the multivariable coordinated control device 400 for the cooling system, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the multivariable coordinated control device 400 for the cooling system. Furthermore, memory 420 may include both internal storage units and external storage devices of the multivariable coordinated control device 400 for the cooling system. Memory 420 is used to store application software and various types of data installed on the multivariable coordinated control device 400 for the cooling system, such as program code installed on the multivariable coordinated control device 400 for the cooling system. Memory 420 may also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 420 stores a multivariate coordination control program 440 for a cooling system, which can be executed by the processor 410 to implement the multivariate coordination control method for a cooling system according to the embodiments of this application.

[0120] In some embodiments, processor 410 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in memory 420 or process data, such as executing a multivariable coordinated control method for a cooling system.

[0121] In some embodiments, display 430 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 430 is used to display information from the multivariable coordination control device 400 for the cooling system and to display a visual user interface. Components 410-430 of the multivariable coordination control device 400 for the cooling system communicate with each other via a system bus.

[0122] In one embodiment, when the processor 410 executes the multivariable coordinated control program 440 for the cooling system in the memory 420, the steps in the multivariable coordinated control method for the cooling system described above are implemented.

[0123] This embodiment also provides a computer-readable storage medium storing a multivariable coordination control program for a cooling system, which, when executed by a processor, performs the following steps:

[0124] A target controlled autoregressive integral moving average model is established based on the collected real-time operating status parameters of the cooling system.

[0125] A one-step predictive control model for target parameters is constructed based on a target-controlled autoregressive integral moving average model.

[0126] Set up a comprehensive control performance optimization function for the one-step predictive control model of the target parameters;

[0127] The cooling system's multiple target parameters are coordinated and controlled based on the comprehensive control performance optimization function.

[0128] In summary, the present invention provides a multivariable coordinated control method for a cooling system. It establishes a target controlled autoregressive integral moving average model using real-time operating state parameters of the cooling system. This model effectively describes the relationships between various inputs and outputs in the cooling system, facilitating coordinated control of multiple target parameters. Furthermore, based on this model, a one-step predictive control model for the target parameters is constructed to predict these parameters. By setting a comprehensive control performance optimization function for the one-step predictive control model, the method can balance the multiple target parameters and high maneuverability of the cooling system, achieving coordinated control of various parameter variables and ensuring the cooling system operates in its optimal state.

[0129] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0130] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0131] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0133] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0134] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0135] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0136] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0137] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multivariable coordinated control method for a cooling system, characterized by, The method comprises the following steps: establishing a target controlled autoregressive integrated moving average model according to the collected real-time operating state parameters of the cooling system; constructing a one-step predictive control model of the target parameter based on the target controlled autoregressive integrated moving average model; setting an integrated control performance optimization function for the one-step predictive control model of the target parameter; coordinately controlling multiple target parameters of the cooling system according to the integrated control performance optimization function; wherein the step of establishing a target controlled autoregressive integrated moving average model according to the collected real-time operating state parameters of the cooling system comprises the following steps: determining a vacuum degree control target, a supercooling degree control target, an input variable and an intermediate controlled variable from the real-time operating state parameters; establishing an initial controlled autoregressive integrated moving average model of the vacuum degree control target, the supercooling degree control target, the input variable and the intermediate controlled variable; discretizing the transfer function of the initial controlled autoregressive integrated moving average model to obtain a target controlled autoregressive integrated moving average model; the initial controlled autoregressive integrated moving average model (CARIMA) of the vacuum degree control target, the supercooling degree control target, the input variable and the intermediate controlled variable of the cooling system is expressed as follows: ； ； In the formula, represents the vacuum degree variation amount due to the nozzle regulating valve group flow rate and the turbine exhaust amount; represents the supercooling degree variation amount due to the nozzle regulating valve group flow rate and the turbine exhaust amount; is the nozzle regulating valve group flow rate variation amount; is the turbine exhaust amount variation amount; is the transfer function between the nozzle regulating valve group flow rate and the vacuum degree; is the transfer function between the turbine exhaust amount and the vacuum degree; is the transfer function between the nozzle regulating valve group flow rate and the supercooling degree; is the transfer function between the turbine exhaust amount and the supercooling degree; The transfer function represented by , , and is discretized to convert the transfer function of the continuous-time system into a discrete-time form, and is converted from the s domain to the z domain by using the Z transform, and the discretization gives: ； ； ； ； the target controlled autoregressive integrated moving average model for the vacuum degree control target and the supercooling degree control target is as follows: ； wherein , , , , , , , is a polynomial.

2. The multivariable coordinated control method for a cooling system according to claim 1, wherein, the step of constructing a one-step predictive control model of the target parameter based on the target controlled autoregressive integrated moving average model comprises the following steps: introducing a prediction step equation based on the target controlled autoregressive integrated moving average model; setting a prediction step and constructing a one-step predictive control model of the target parameter according to the target controlled autoregressive integrated moving average model and the prediction step equation.

3. The multivariable coordinated control method for a cooling system according to claim 1, wherein, the step of coordinately controlling multiple target parameters of the cooling system according to the integrated control performance optimization function comprises the following steps: calculating the flow variation of the nozzle adjusting valve group at each time by setting the partial derivative of the flow variation of the nozzle adjusting valve group in the integrated control performance optimization function to zero.

4. The multivariable coordinated control method for a cooling system according to claim 3, wherein the step of coordinately controlling multiple target parameters of the cooling system according to the integrated control performance optimization function further comprises the following steps: calculating the corresponding relationship between the condensate flow of the nozzle adjusting valve group and the flow coefficient of the nozzle adjusting valve group according to the flow variation of the nozzle adjusting valve group at each time; determining the opening values of multiple valves in the nozzle adjusting valve group based on the corresponding relationship.

5. The multivariable coordinated control method for a cooling system according to claim 4, wherein, the step of coordinately controlling multiple target parameters of the cooling system according to the integrated control performance optimization function further comprises the following steps: determining the front and back pressure difference of the nozzle adjusting valve group according to the opening values of multiple valves in the nozzle adjusting valve group; adjusting the rotating speed of the condensate circulating pump so that the front and back pressure difference of the nozzle adjusting valve group is a preset pressure difference value.

6. The multivariable coordinated control method for a cooling system according to claim 4, wherein the step of coordinately controlling multiple target parameters of the cooling system according to the integrated control performance optimization function further comprises the following steps: calculating the condensate flow through the nozzle adjusting valve group based on the corresponding relationship; adjusting the condensate flow through the nozzle adjusting valve group to be greater than or equal to a preset condensate flow.

7. A multivariable coordinated control device for a cooling system, characterized by, The method comprises the following steps: a first modeling module configured to establish a target controlled autoregressive integrated moving average model according to the collected real-time operating state parameters of the cooling system; a second modeling module configured to construct a one-step predictive control model of the target parameter based on the target controlled autoregressive integrated moving average model; an optimization function module configured to set an integrated control performance optimization function for the one-step predictive control model of the target parameter; a coordinated control module configured to perform coordinated control on the multiple target parameters of the cooling system according to the integrated control performance optimization function; the initial controlled autoregressive integrated moving average model (CARIMA) of the vacuum degree control target, the supercooling degree control target, the input variable, and the intermediate controlled variable of the cooling system is expressed as follows: ； ； In the formula, represents the vacuum degree variation amount due to the nozzle regulating valve group flow rate and the turbine exhaust amount; represents the supercooling degree variation amount due to the nozzle regulating valve group flow rate and the turbine exhaust amount; is the nozzle regulating valve group flow rate variation amount; is the turbine exhaust amount variation amount; is the transfer function between the nozzle regulating valve group flow rate and the vacuum degree; is the transfer function between the turbine exhaust amount and the vacuum degree; is the transfer function between the nozzle regulating valve group flow rate and the supercooling degree; is the transfer function between the turbine exhaust amount and the supercooling degree; The transfer function represented by , , and is discretized to convert the transfer function of the continuous-time system into a discrete-time form, and is converted from the s domain to the z domain by using the Z transform, and the discretization gives: ； ； ； ； the target controlled autoregressive integrated moving average model for the vacuum degree control target and the supercooling degree control target is as follows: ； wherein , , , , , , , is a polynomial.

8. A multivariable coordinated control device for a cooling system, characterized by, a computer program product, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program which, when executed by the processing unit, causes the processing unit to perform the steps of the multivariable coordinated control method for a cooling system according to any one of claims 1-6.

9. A storage medium, characterized by a computer program product, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program which, when executed by the processing unit, causes the processing unit to perform the steps of the multivariable coordinated control method for a cooling system according to any one of claims 1-6.

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