A FID intelligent variable frequency control method for suction pressure of refrigeration compressor

Abstract

The present application relates to the technical field of refrigeration system, and aims to provide a kind of FID intelligent frequency conversion control method of refrigeration compressor suction pressure, which is a kind of feedforward-feedback control model, including learning and control stage.Learning stage adopts function mining technology to realize the learning of historical data, establishes the feedforward control model of suction pressure-the function law between the change of suction pressure and other influence parameters;On the basis of feedforward control model, control stage monitors the operating parameters of refrigeration compressor in real time, accurately predicts the suction pressure at the next control time, and determines the working frequency in the next control cycle based on the difference between the predicted value of suction pressure and the ideal set value.The present application realizes the accurate control of refrigeration compressor suction pressure, and can significantly reduce the operating energy consumption of refrigeration compressor.

Description

Technical Field

[0001] This invention belongs to the field of refrigeration system technology, and in particular to an intelligent control method for compressor suction pressure in a refrigeration system, specifically an FID intelligent variable frequency control method for refrigeration compressor suction pressure. Background Technology

[0002] Refrigeration systems are a major consumer of energy in my country, accounting for more than 15% of the country's total electricity consumption. For example, in 2021, the national electricity consumption of refrigeration systems exceeded 1.2 trillion kilowatt-hours. Therefore, reducing the energy consumption of refrigeration systems can save energy and reduce the operating costs of enterprises.

[0003] The refrigeration compressor is the core and heart of the refrigeration system, typically accounting for over 50% of the system's total energy consumption. Therefore, reducing the compressor's energy consumption will significantly reduce the overall system energy consumption. In a refrigeration system, the compressor transforms the refrigerant from a low-pressure gaseous state to a high-pressure liquid state, ensuring its continuous circulation within the system. Simultaneously, to improve refrigeration efficiency, the compressor's suction pressure needs to be controlled at a specific ideal setpoint. Therefore, precisely controlling the suction pressure to maintain this ideal setpoint is crucial for the refrigeration system. This is because: if the suction pressure exceeds the ideal setpoint, the refrigerant's evaporation temperature increases, preventing it from achieving its refrigeration effect; if the suction pressure is below the ideal setpoint, the increased ratio of discharge pressure to suction pressure leads to increased compressor energy consumption.

[0004] Currently, feedback control methods, such as PID (Proportional, Integral, Derivative), are commonly used to adjust the operating frequency of the refrigeration compressor and control the suction pressure. However, these control methods have low precision, and the suction pressure often fluctuates significantly around the ideal setpoint, resulting in low operating efficiency of the refrigeration system. Therefore, there is an urgent need for a more efficient method for controlling the suction pressure of the refrigeration compressor. Summary of the Invention

[0005] To address the technical problems of low control accuracy and high energy consumption associated with current suction pressure feedback control methods, this invention proposes a FID (Function, Integral, Derivative) intelligent variable frequency control method for refrigeration compressor suction pressure. This method is a feedforward-feedback control model, specifically including the following steps:

[0006] Learning phase: Establish a feedforward control model for inhalation pressure, that is, the functional relationship between the change in inhalation pressure and other influencing parameters;

[0007] Control phase: Based on the variation pattern of the suction pressure of the refrigeration compressor in the previous control cycle, the suction pressure at the next control moment is predicted. Based on the difference between the predicted suction pressure and the ideal set value, the operating frequency of the refrigeration compressor in the next control cycle is determined, so as to achieve precise control of the suction pressure.

[0008] Furthermore, in the learning phase, the functional relationship between the change in inspiratory pressure and other influencing parameters is established. The specific process is as follows:

[0009] Step 1.1: Data Acquisition: Set freq min freq max These are the minimum and maximum operating frequencies of the refrigeration compressor, respectively. Within the range [freq] min freq max Within the range, multiple different operating frequencies (freq) are selected. During the process of the inhalation pressure changing from high to low, parameters affecting the change in inhalation pressure are collected at a fixed sampling period (S): inhalation pressure p1, inhalation temperature t1, exhaust pressure p2, exhaust temperature t2, and operating frequency (freq).

[0010] Step 1.2: Establish a feedforward control model for inhalation pressure, that is, establish the functional relationship between the change in inhalation pressure and other influencing parameters, i.e.:

[0011]

[0012] In equation (1), This represents the change in inspiratory pressure p1 over one sampling period. It is equal to the difference between the initial inspiratory pressure and the final inspiratory pressure of the sampling period. The function represents F(Function) in the FID intelligent variable frequency control method. Based on the historical data collected in step 1.1, Functions can be analyzed using function mining techniques.

[0013] Further: In the control phase, based on the variation pattern of the suction pressure in the previous control cycle, the suction pressure at the next control moment is predicted; based on the difference between the predicted suction pressure and the ideal setpoint, the operating frequency of the refrigeration compressor in the next control cycle is determined. The specific process is as follows:

[0014] Step 2.1: Set the current time This is a single data sampling point, and the refrigeration compressor is in operation. The data collected at any given time is recorded as: inspiratory pressure Intake temperature Exhaust pressure Exhaust temperature Operating frequency

[0015] Step 2.2: Let C be the control period, which is typically 1 to 2 times the sampling period S. If at the current time... Compared to the previous control time When the time interval is control period C, then at the current moment... It is necessary to calculate the operating frequency of the refrigeration compressor in the next control cycle, and record... The data collected at each moment is: inspiratory pressure Intake temperature Exhaust pressure Exhaust temperature Operating frequency

[0016] Step 2.3: In At any given time, calculate the increase in inspiratory pressure during the previous control cycle. This increment is due to refrigerant injection and evaporation, and is defined by the following integral formula:

[0017]

[0018] In equation (2), This indicates that within the previous control cycle, according to the function... The calculated theoretical reduction in inspiratory pressure, I (Integral) represents the FID intelligent variable frequency control method;

[0019] Step 2.4: In At any given moment, predict the inspiratory pressure for the next control moment. The calculation formula is:

[0020]

[0021] In equation (3), It describes the variation pattern of inspiratory pressure during the previous control cycle; The trend of inspiratory pressure increment over two consecutive control cycles is defined by the following differential formula:

[0022]

[0023] In equation (4), Indicates the previous control time. The increase in inhalation pressure at that time. D (Derivative) represents the FID intelligent variable frequency control method;

[0024] Step 2.5: In At any given moment, calculate the operating frequency of the refrigeration compressor for the next control cycle.

[0025] Step 2.6: In At any given time, the calculated operating frequency will be... The signal is sent to the refrigeration compressor and executed at that operating frequency, thereby controlling the refrigeration compressor.

[0026] Furthermore, in The process of calculating the operating frequency of the refrigeration compressor for the next control cycle is as follows: First, calculate the suction pressure at the next control point. The ideal setpoint p for inhalation pressure 1target The difference between

[0027]

[0028] (1) If The operating frequency of the refrigeration compressor in the next control cycle is:

[0029]

[0030] (2) If The operating frequency of the refrigeration compressor in the next control cycle is determined according to the formula. To calculate, that is: to search It satisfies the following formula:

[0031]

[0032] like but like but

[0033] (3) If the shutdown conditions of the refrigeration compressor are met, then:

[0034] Furthermore, the FID intelligent frequency conversion control method is applicable to integrated or split refrigeration systems.

[0035] A FID intelligent variable frequency control device for the suction pressure of a refrigeration compressor includes:

[0036] Learning module: used to establish a feedforward control model for inhalation pressure, that is, the functional relationship between the change in inhalation pressure and other influencing parameters;

[0037] Control module: Used for precise control of the suction pressure of the refrigeration compressor, that is: based on the change pattern of the suction pressure of the refrigeration compressor in the previous control cycle, predict the suction pressure at the next control moment, and determine the operating frequency of the refrigeration compressor in the next control cycle based on the difference between the predicted value of the suction pressure and the ideal set value.

[0038] A computer-readable storage medium stores a computer program, wherein, when the computer program is executed, it performs a FID intelligent variable frequency control method for suction pressure of a refrigeration compressor. The FID intelligent variable frequency control method for suction pressure of a refrigeration compressor provided by this invention can adjust the operating frequency of the refrigeration compressor in a timely and accurate manner according to changes in suction pressure, precisely controlling the suction pressure to maintain it at an ideal set value, thereby ensuring that the refrigeration compressor operates in optimal condition, reducing the energy consumption of the refrigeration compressor, and achieving energy-saving effects. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of a refrigeration system;

[0041] Figure 2 A comparison chart of intake pressure curves for FID intelligent frequency converter control and PID frequency converter control (one start-up cycle);

[0042] Figure 3 This is a comparison chart of the intake pressure curves over multiple consecutive operating cycles. Detailed Implementation

[0043] It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0046] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0047] In the description of this invention, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is generally based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this invention. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0048] For ease of description, spatial relative terms such as "above," "over," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation besides the orientation of the device as described in the figures. For example, if the device in the figures is inverted, a device described as "above" or "above" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0049] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0050] like Figure 1 As shown, the refrigeration system of this invention consists of one outdoor unit, multiple indoor units, and interconnected piping. The outdoor unit comprises a refrigeration compressor, a condenser, a liquid receiver, and a gas-liquid separator, while the indoor units consist of an evaporator and a pressure reducing valve. A frequency converter is installed on the refrigeration compressor to adjust the operating frequency; pressure and temperature sensors are installed on the inlet and outlet pipes of the refrigeration compressor to detect the suction pressure, discharge pressure, suction temperature, and discharge temperature; and the built-in electronic control system on the refrigeration compressor detects the operating frequency.

[0051] A FID (Feedforward-Feedback) intelligent variable frequency control method for the suction pressure of a refrigeration compressor is disclosed. This method employs a feedforward-feedback control model to adjust the operating frequency (freq) of the refrigeration compressor, thereby maintaining the suction pressure as close as possible to the ideal setpoint. The FID intelligent variable frequency control method requires the installation of pressure and temperature sensors on the refrigeration system to detect parameters such as suction pressure p1, suction temperature t1, discharge pressure p2, discharge temperature t2, and the compressor operating frequency (freq).

[0052] The FID intelligent variable frequency control method for suction pressure of a refrigeration compressor includes two stages: a learning stage and a control stage. The purpose of the learning stage is to obtain a feedforward control model for the suction pressure, that is, the functional relationship between the change in suction pressure and other parameters. The control stage is to adjust the compressor's operating frequency freq in a timely and accurate manner based on real-time data, such as suction pressure p1, suction temperature t1, discharge pressure p2, discharge temperature t2, and compressor operating frequency freq, and to maintain the stability of the suction pressure.

[0053] The workflow for the two stages is as follows:

[0054] (1) Learning stage

[0055] The learning phase is used to establish the functional relationship between changes in inhalation pressure and other influencing parameters, preparing for feedforward control. The specific steps are as follows:

[0056] Step 1.1: Given a refrigeration system (integrated or split type), which generally includes a refrigeration compressor, condenser, and evaporator (may be multiple units);

[0057] Step 1.2: Data Acquisition: Within the operating frequency range of the refrigeration compressor [freq] min freq max Within the range, select multiple different operating frequencies freq, and record data such as inhalation pressure p1, inhalation temperature t1, exhaust pressure p2, exhaust temperature t2, and operating frequency freq during the process of inhalation pressure changing from high to low, according to a fixed sampling period S (e.g., 500ms or 1000ms).

[0058] Step 1.3: Establish a feedforward control model for inhalation pressure, that is, the functional relationship between the change in inhalation pressure and other influencing parameters:

[0059]

[0060] In equation (1), This represents the change in inspiratory pressure p1 over one sampling period. It is equal to the difference between the initial inspiratory pressure and the final inspiratory pressure of the sampling cycle. The function represents F(Function) in the FID intelligent variable frequency control method. Based on the historical data collected in step 1.2, Functions can be analyzed using function mining techniques.

[0061] (2) Control phase

[0062] The control phase is used to calculate the operating frequency of the refrigeration compressor in a timely manner. The control phase is closely related to the control cycle C. In a refrigeration system, the control cycle C is generally set to 1 to 2 times the sampling period S. The purpose of setting the control cycle is to recalculate the compressor's operating frequency for the next control cycle every control cycle C when the refrigeration compressor is running (its operating frequency is always set to 0 when it is stopped). The specific steps are as follows:

[0063] Step 2.1: Set the current time This is a single data sampling point, and the compressor is in operation. The data collected at any given time is recorded as: inspiratory pressure Intake temperature Exhaust pressure Exhaust temperature Operating frequency

[0064] Step 2.2: If the current time Compared to the previous control time When the time interval is control period C, then at the current moment... It is necessary to calculate the operating frequency of the refrigeration compressor in the next control cycle, and record... The data collected at each moment is: inspiratory pressure Intake temperature Exhaust pressure Exhaust temperature Operating frequency

[0065] Step 2.3: In At any given time, calculate the increase in inspiratory pressure during the previous control cycle. This increment is due to refrigerant injection and evaporation, and is defined by the following integral formula:

[0066]

[0067] In the above formula, This indicates that within the previous control cycle, according to the function... The calculated theoretical reduction in inhalation pressure. I (Integral) represents the FID intelligent variable frequency control method;

[0068] Step 2.4: In At any given moment, predict the inspiratory pressure at the next control point. The calculation formula is:

[0069]

[0070] In equation (3), It describes the variation pattern of inspiratory pressure during the previous control cycle; It represents the trend of the increase in inspiratory pressure over two consecutive control cycles, and is defined by the following differential formula:

[0071]

[0072] In equation (4), Indicates the previous control time. The increase in inhalation pressure at that time. D (Derivative) represents the FID intelligent variable frequency control method;

[0073] Step 2.5: In At any given time, calculate the operating frequency of the refrigeration compressor. First, calculate the suction pressure at the next control point. The ideal setpoint p for inhalation pressure 1target The difference between

[0074]

[0075] (1) If The operating frequency of the refrigeration compressor in the next control cycle is then:

[0076]

[0077] (2) If The operating frequency of the refrigeration compressor in the next control cycle is determined according to the formula. To calculate, that is: to search It satisfies the following formula:

[0078]

[0079] In particular, if but like but

[0080] (3) If the shutdown conditions of the refrigeration compressor are met, then:

[0081] Step 2.6: In At any given time, the calculated operating frequency will be... The signal is sent to the refrigeration compressor and it operates at that frequency.

[0082] A refrigeration compressor suction pressure FID intelligent variable frequency control device, including

[0083] Learning module: Used to establish the functional relationship between changes in inspiratory pressure and other influencing parameters;

[0084] Control module: Used for precise control of the suction pressure of the refrigeration compressor, that is: based on the change pattern of the suction pressure of the refrigeration compressor in the previous control cycle, predict the suction pressure of the refrigeration compressor at the next control moment, and determine the operating frequency of the refrigeration compressor in the next control cycle based on the difference between the suction pressure of the refrigeration compressor at the next control moment and the ideal set value of the suction pressure.

[0085] A computer-readable storage medium comprising a computer program for learning and control, wherein, when the computer program is executed, it performs an FID intelligent variable frequency control method for the suction pressure of a refrigeration compressor, as described in any one of the preceding claims.

[0086] This invention has completed the development of the FID intelligent variable frequency control algorithm and verified it in a laboratory environment. The specific experimental conditions are as follows: For a certain type of refrigeration system (e.g., Figure 1 As shown, FID intelligent frequency conversion control and PID frequency conversion control are used to control the intake pressure respectively. The operating parameters of the two control methods are the same, namely: start-up pressure 7.5 Bar, stop pressure 3.0 Bar, data sampling period of 500ms, and control period of 1000ms.

[0087] Figure 2 Table 1 shows the statistical data for comparing the intake pressure curves of the two variable frequency control methods within one start-up cycle. The statistical time range is the first time the intake pressure curves of the two variable frequency control methods are compared with the ideal setpoint p within one start-up cycle. 1target (in this example, p) 1target =4.75Bar) The time corresponding to the point where the pressure intersects with the ideal inspiratory pressure setpoint p is the last time. 1target The time corresponding to the point of intersection.

[0088] Table 1: Comparison of Intake Pressure Data between FID Intelligent Variable Frequency Control and PID Variable Frequency Control

[0089]

[0090] Figure 3 This is a comparison of the intake pressure curves over multiple consecutive operating cycles.

[0091] Statistical data shows that FID intelligent frequency conversion control can control intake pressure more accurately and stably. Table 2 shows the energy-saving performance of FID intelligent frequency conversion control compared to PID frequency conversion control. Table 2 indicates that while the operating environment parameters of FID intelligent frequency conversion control are generally inferior to those of PID frequency conversion control, the energy consumption under FID intelligent frequency conversion control is 5.36% lower than that under PID frequency conversion control.

[0092] Table 2: Comparison of energy-saving data between FID intelligent frequency converter control and PID frequency converter control (all day)

[0093]

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for FID intelligent variable frequency control of suction pressure of a refrigeration compressor, characterized in that: Includes the following steps: Learning phase: Establish a feedforward control model for inhalation pressure, that is, the functional relationship between the change in inhalation pressure and other influencing parameters; Control phase: Based on the variation pattern of the suction pressure of the refrigeration compressor in the previous control cycle, the suction pressure at the next control moment is predicted; based on the difference between the predicted suction pressure and the ideal set value, the operating frequency of the refrigeration compressor in the next control cycle is determined, so as to achieve precise control of the suction pressure. The learning phase involves establishing the functional relationship between changes in inspiratory pressure and other influencing parameters. The specific process is as follows: Step 1.1: Data Acquisition: Let's assume... , These are the minimum and maximum operating frequencies of the refrigeration compressor, respectively, within the operating frequency range [ Within, select multiple different operating frequencies. During the change of inspiratory pressure from high to low, sampling is performed at a fixed period. Collect parameters that affect the change in inspiratory pressure: inspiratory pressure Intake temperature Exhaust pressure Exhaust temperature Operating frequency ; Step 1.2: Establish the functional relationship between the change in inhalation pressure and other influencing parameters, that is: (1) In equation (1), Inspiratory pressure within one sampling period The change It is equal to the difference between the initial inspiratory pressure and the final inspiratory pressure of the sampling period. The function represents the Function in the FID intelligent variable frequency control method; The control phase involves predicting the suction pressure at the next control moment based on the variation pattern of the suction pressure in the previous control cycle, and determining the operating frequency of the refrigeration compressor in the next control cycle based on the difference between the predicted suction pressure and the ideal setpoint. The specific process is as follows: Step 2.1: Set the current time This is a single data sampling point, and the refrigeration compressor is in operation. The data collected at any given time is recorded as: inspiratory pressure Intake temperature Exhaust pressure Exhaust temperature Operating frequency ; Step 2.2: Let To control the cycle, Typically the sampling period 1 to 2 times, if at the current moment Compared to the previous control time The time interval is the control cycle. When, then the current moment It is necessary to calculate the operating frequency of the refrigeration compressor in the next control cycle, and record... The data collected at any given time is: inspiratory pressure Intake temperature Exhaust pressure Exhaust temperature Operating frequency ; Step 2.3: In At time C, calculate the increase in inspiratory pressure within the previous control cycle C. This increment is due to refrigerant injection and evaporation, and is defined by the following integral formula: (2) In equation (2), This indicates that within the previous control cycle, according to the function... The calculated theoretical reduction in inhalation pressure, The Integral integral represents the FID intelligent variable frequency control method. Step 2.4: In At any given moment, predict the inspiratory pressure for the next control moment. , The calculation formula is: (3) In equation (3), + It describes the variation pattern of inspiratory pressure during the previous control cycle; The trend of the increase in inspiratory pressure over two consecutive control cycles is represented by the following differential formula: (4) In equation (4), Indicates the previous control time. Inspiratory pressure increment at time Derivative represents the derivative in the FID intelligent variable frequency control method; Step 2.5: In At any given time, calculate the operating frequency of the refrigeration compressor; Step 2.6: In At any given time, the calculated operating frequency will be... The signal is sent to the refrigeration compressor and executed at that operating frequency, thereby controlling the refrigeration compressor.

2. The FID intelligent variable frequency control method for suction pressure of a refrigeration compressor according to claim 1, characterized in that: The above The process of calculating the operating frequency of the refrigeration compressor at any given time is as follows: First, calculate the inhalation pressure at the next control point. Compared with the ideal setting value The difference between : (5) (1) If Then the operating frequency of the refrigeration compressor in the next control cycle is: (6) (2) If The operating frequency of the refrigeration compressor in the next control cycle is determined according to the formula. To calculate, that is: to search It satisfies the following formula: (7) like ,but ;like ,but ; (3) If the shutdown conditions of the refrigeration compressor are met, then: .

3. The FID intelligent variable frequency control method for suction pressure of a refrigeration compressor according to claim 1, characterized in that: This control method is applicable to integrated or split-type refrigeration systems.

4. The control device for the FID intelligent variable frequency control method of refrigeration compressor suction pressure according to claim 1, characterized in that: include: Learning module: used to establish a feedforward control model for inhalation pressure, that is, the functional relationship between the change in inhalation pressure and other influencing parameters; Control module: Used for precise control of the refrigeration compressor, that is: based on the change pattern of the suction pressure of the refrigeration compressor in the previous control cycle, predict the suction pressure at the next moment, and determine the operating frequency of the refrigeration compressor in the next control cycle based on the difference between the predicted value of the suction pressure and the ideal set value.

5. A computer-readable storage medium storing a computer program, wherein, When the computer program is executed, it performs the FID intelligent variable frequency control method for the suction pressure of a refrigeration compressor as described in any one of claims 1-3.

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