A self-recovering cycle control system for ultra-low ambient temperature

Abstract

The application belongs to the technical field of heat exchangers, and discloses a self-recovery cycle control system for ultra-low ambient temperature, which comprises a temperature acquisition module, an environment state analysis module, a control target determination module, a control parameter acquisition module and a control execution module. The temperature acquisition module acquires ambient temperature at a preset frequency and forms a temperature sequence. The environment state analysis module calculates a corrected environment index according to the temperature sequence and determines the environment state. The control target determination module determines a control target based on the environment state. The control parameter acquisition module determines a control item according to the control target and acquires control parameters by using a corresponding optimization method. The control execution module executes control according to the control parameters. The intelligent control strategy is used to dynamically adjust the operation mode, and different control algorithms are used for different operation states, so that efficient and stable operation under various environmental conditions is realized. The system can intelligently adapt to various environmental conditions, adopt the optimal control strategy under different working conditions, and thus realize efficient and stable operation under ultra-low ambient temperature.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heat exchangers, and more particularly to a self-recovery cascade cycle control system for ultra-low ambient temperature. BACKGROUND

[0002] Currently, in the field of ultra-low temperature heating, the main technologies used include single-stage compression, quasi-two-stage compression, two-stage compression and multi-stage cascade cycle, etc. However, these technologies have certain limitations in actual application. Single-stage compression and quasi-two-stage compression systems are difficult to meet the heating demand under large cycle temperature span, which may cause the compressor to shut down. Although two-stage compression and multi-stage cascade cycle can solve some problems, they are high in cost and complex in structure, which is not conducive to large-scale application.

[0003] In recent years, the self-recovery cascade system has gradually attracted attention due to its unique advantages. This system only uses one compressor and realizes structure simplification and cost reduction by reasonably matching high and low boiling point mixed refrigerants. However, in the deep cold environment, the self-recovery cascade system still faces many challenges. Mainly including how to improve the product performance of the unit under harsh working conditions of large cycle temperature span and ensure the economic efficiency of operation; how to effectively reduce the exhaust temperature of the unit and prolong the service life of the compressor; how to maximize the operating temperature range of the unit and ensure that the suction pressure of the unit is higher than the low-pressure shutdown pressure under ultra-low ambient temperature; and how to broaden the use of the unit and the application scenarios. Therefore, the conventional self-recovery cascade cycle is difficult to ensure energy efficiency under large temperature span, and may even have problems such as low-pressure shutdown and high exhaust temperature alarm. SUMMARY

[0004] In order to overcome the problems of the prior art that it is difficult to ensure energy efficiency under large temperature span, and may even have problems such as low-pressure shutdown and high exhaust temperature alarm, the present application provides a self-recovery cascade cycle control system for ultra-low ambient temperature, which solves the above problems.

[0005] The present application provides the following technical solutions:

[0006] A self-recovery cascade cycle control system for ultra-low ambient temperature, comprising:

[0007] A temperature acquisition module for acquiring ambient temperature at a predetermined frequency and sorting the acquired ambient temperature data in chronological order to obtain an ambient temperature sequence;

[0008] An environment state analysis module for obtaining a basic environment index and an environment correction parameter from the ambient temperature sequence, correcting the basic environment index using the environment correction parameter to obtain a corrected environment index, and determining the environment state according to the corrected environment index;

[0009] A control target determination module for determining a control target according to a preset control target rule and the environment state;

[0010] The control parameter acquisition module is configured to determine a control item according to a preset control item rule and a regulation and control target, and acquire the control parameter according to the control item using a corresponding optimization method;

[0011] The control execution module is configured to execute the control according to the acquired control parameter.

[0012] Preferably, the basic environment index is the latest collected environment temperature value in the environment temperature sequence; the environment correction parameter includes a fluctuation parameter and a trend parameter; and the environment state includes a normal state, a low-temperature state and an ultra-low-temperature state.

[0013] Preferably, the corrected environment index is calculated by the following formula:

[0014] XZ=DZ+tanh(qs)×ln(1+bd),

[0015] wherein, XZ represents the corrected environment index, DZ represents the basic environment index, qs represents the trend parameter, and bd represents the fluctuation parameter.

[0016] Preferably, the acquisition step of the environment correction parameter includes:

[0017] acquiring an environment temperature sequence, removing outliers and performing smoothing processing to obtain a smoothed environment temperature sequence;

[0018] acquiring a standard deviation of the smoothed environment temperature sequence as the fluctuation parameter;

[0019] extracting the latest n data from the smoothed environment temperature sequence according to a preset interval to form an analysis sequence, wherein n≥10;

[0020] setting a sliding window with a size of m on the analysis sequence, wherein 1<m<n, extracting the content through the sliding window to obtain n-m+1 sub-sequences with a size of m;

[0021] performing linear regression on each sub-sequence to obtain a slope of the sub-sequence, denoted as k i ; wherein, i represents an index, i=1, 2,..., n-m+1, and k i represents the slope of the i-th sub-sequence;

[0022] calculating the trend parameter using the following formula:

[0023]

[0024] wherein, ω i represents the weight of the i-th sub-sequence, and the calculation formula of ω i is:

[0025]

[0026] The fluctuation parameter and the trend parameter are used to form an environment correction parameter.

[0027] Preferably, the step of determining the environment state according to the correction environment index comprises:

[0028] An index threshold is set, the index threshold comprising a low temperature index and an ultra-low temperature index, wherein the low temperature index is greater than the ultra-low temperature index;

[0029] An array Q of state records with a length of l and a current state record Q_c are initialized;

[0030] The correction environment index is compared with the index threshold, and the current state record Q_c is updated:

[0031] When the correction environment index is greater than the low temperature index, the Q_c is set as a normal state;

[0032] When the correction environment index is less than or equal to the low temperature index and greater than the ultra-low temperature index, the Q_c is set as a low temperature state;

[0033] When the correction environment index is less than or equal to the ultra-low temperature index, the Q_c is set as an ultra-low temperature state;

[0034] The Q_c is added to the end of the array Q of state records, while the earliest state record in the array Q of state records is removed, so that the length of the array is maintained as l;

[0035] The state distribution in the array Q of state records is counted:

[0036] If all elements in the Q are the same state, the state is determined as the current environment state;

[0037] If there are different states in the Q, the environment state is maintained unchanged.

[0038] Preferably, the regulation target comprises maintaining the operation of the main circulation loop, starting the air supplementing loop and starting the hot gas bypass loop; and the preset regulation target rule comprises:

[0039] When the environment state is the normal state, it is determined that the regulation target is to maintain the operation of the main circulation loop;

[0040] When the environment state is the low temperature state, it is determined that the regulation target is to start the air supplementing loop;

[0041] When the environment state is the ultra-low temperature state, it is determined that the regulation target is to start the hot gas bypass loop.

[0042] Preferably, the preset control item rule comprises:

[0043] When the regulation target is to maintain the operation of the main circulation loop, it is determined that the control item is the opening degree of the main circulation loop;

[0044] When the regulation target is the starting air supplement path, the control item is determined to be the main circulation path opening degree and the air supplement path opening degree;

[0045] When the regulation target is the starting hot gas bypass path, the control item is determined to be the main circulation path opening degree, the air supplement path opening degree and the hot gas bypass path opening degree.

[0046] Preferably, the step of obtaining the control parameter according to the control item using the corresponding optimization method comprises:

[0047] When the control item is the main circulation path opening degree, a PID control algorithm is used to calculate the main circulation path opening degree adjustment amount, and the main circulation path opening degree adjustment amount is used as the control parameter;

[0048] When the control item is the main circulation path opening degree and the air supplement path opening degree, the main circulation path opening degree is set to the maximum value, a fuzzy control algorithm is used to calculate the air supplement path opening degree according to the ambient temperature and the system pressure ratio, and the main circulation path opening degree and the air supplement path opening degree are used as the control parameters;

[0049] When the control item is the main circulation path opening degree, the air supplement path opening degree and the hot gas bypass path opening degree, the main circulation path opening degree and the air supplement path opening degree are set to the maximum value, an adaptive control algorithm is used to calculate the hot gas bypass path opening degree, and the main circulation path opening degree, the air supplement path opening degree and the hot gas bypass path opening degree are used as the control parameters.

[0050] Preferably, the step of using the PID control algorithm to calculate the main circulation path opening degree comprises: setting a target suction temperature; monitoring the actual suction temperature in real time; calculating the deviation between the target temperature and the actual temperature, and calculating the cumulative value and the change rate of the temperature deviation over time; according to the preset proportional, integral and differential parameters, combining the temperature deviation, the cumulative value of the deviation and the change rate of the deviation, calculating the main circulation path opening degree adjustment amount;

[0051] The step of using the fuzzy control algorithm to calculate the air supplement path opening degree according to the ambient temperature and the system pressure ratio comprises: dividing the ambient temperature and the system pressure ratio into low, medium and high, and small, medium and large, respectively; establishing a fuzzy rule base, defining the air supplement path opening degree adjustment strategy under different combinations of ambient temperature and system pressure ratio; determining the membership degree of the current ambient temperature and system pressure ratio in their respective grades; based on the fuzzy rule base and the membership degree, deriving the fuzzy output of the air supplement path opening degree; defuzzifying the fuzzy output to convert it into a specific air supplement path opening degree value;

[0052] The step of using the adaptive control algorithm to calculate the hot gas bypass path opening degree comprises: setting the upper and lower limits of the target suction pressure; monitoring the system suction pressure in real time; calculating the deviation of the actual suction pressure from the median value of the target pressure range; adjusting the adaptive coefficient according to the pressure deviation and the current system state; using the adjusted adaptive coefficient to calculate the opening degree of the hot gas bypass path.

[0053] The application provides a self-recovery cycle control system for ultra-low ambient temperature, which has the following beneficial effects:

[0054] Firstly, the system adopts an intelligent control strategy, which can dynamically adjust the operation mode according to the change of the ambient temperature. The system collects and analyzes the ambient temperature sequence, calculates the corrected ambient index, and thus accurately judges the current environmental state. This method not only considers the instantaneous temperature, but also fully considers the temperature change trend and fluctuation, making the environmental state judgment more accurate and stable. According to the judged environmental state, the system can automatically select the most suitable operation mode, such as maintaining the main circulation path, starting the air supplement path or starting the hot gas bypass path. This adaptive control method enables the system to always maintain high-efficiency and stable operation under various environmental conditions, greatly expanding the use region and application scenarios of the unit.

[0055] Secondly, the system adopts differentiated control algorithms for different operation states, realizing more fine and efficient control. The PID control algorithm is used in normal environmental state, the fuzzy control algorithm is used in low-temperature state, and the adaptive control algorithm is used in ultra-low-temperature state. This diversified control strategy fully considers the characteristics and needs of the system under different working conditions, ensuring that the system can achieve optimal performance and efficiency under various environmental conditions, greatly improving the adaptability and reliability of the system.

[0056] In summary, the system of the application can intelligently adapt to various environmental conditions and adopt the optimal control strategy under different working conditions, thereby realizing high-efficiency and stable operation under ultra-low ambient temperature conditions. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a module schematic diagram of the self-recovery cycle control system for ultra-low ambient temperature. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the application.

[0059] Embodiment 1

[0060] Please refer to Figure 1 In this embodiment, the self-recovery cycle control system for ultra-low ambient temperature includes:

[0061] The temperature collection module is configured to collect the ambient temperature at a preset frequency and sort the collected ambient temperature data in chronological order to obtain an ambient temperature sequence.

[0062] In this embodiment, the temperature sensor collects the ambient temperature at a preset frequency (e.g., 5 minutes). The temperature data is sorted in chronological order of collection time to form an ambient temperature sequence. The sequence contains the change of the ambient temperature over a period of time, providing basic data for subsequent analysis.

[0063] The environment state analysis module is configured to obtain a basic environment index and an environment correction parameter based on the ambient temperature sequence, correct the basic environment index using the environment correction parameter to obtain a corrected environment index, and determine the environment state based on the corrected environment index.

[0064] The basic environment index is the latest collected ambient temperature value in the ambient temperature sequence. The environment correction parameter includes a fluctuation parameter and a trend parameter. The environment state includes a normal state, a low-temperature state, and an ultra-low-temperature state.

[0065] The corrected environment index is calculated by the following formula:

[0066] XZ=DZ+tanh(qs)×ln(1+bd),

[0067] In the formula, XZ represents the corrected environment index, DZ represents the basic environment index, qs represents the trend parameter, and bd represents the fluctuation parameter.

[0068] The obtaining step of the environment correction parameter includes:

[0069] Obtain the ambient temperature sequence, remove outliers, and perform smoothing to obtain a smoothed ambient temperature sequence.

[0070] Obtain the standard deviation of the smoothed ambient temperature sequence as the fluctuation parameter.

[0071] According to a preset interval, extract the latest n data from the smoothed ambient temperature sequence to form an analysis sequence, where n≥10.

[0072] Set a sliding window with a size of m on the analysis sequence, where 1<m<n. Extract the content through the sliding window to obtain n-m+1 sub-sequences with a size of m.

[0073] Perform linear regression on each sub-sequence to obtain the slope of the sub-sequence, denoted as k i , where i represents the index, i=1,2,...,n-m+1, and k i represents the slope of the i-th sub-sequence.

[0074] The trend parameter is calculated using the following formula:

[0075]

[0076] ωi i ωi i The calculation formula is as follows:

[0077]

[0078] The volatility parameter and the trend parameter are used to form the environment correction parameter.

[0079] The step of determining the environment state according to the correction environment index comprises:

[0080] The index threshold comprises a low temperature index and an ultra-low temperature index, wherein the low temperature index is greater than the ultra-low temperature index;

[0081] An initialized state record array Q with a length of l and a current state record Q_c are set;

[0082] The correction environment index is compared with the index threshold, and the current state record Q_c is updated:

[0083] When the correction environment index is greater than the low temperature index, the Q_c is set as a normal state;

[0084] When the correction environment index is less than or equal to the low temperature index and greater than the ultra-low temperature index, the Q_c is set as a low temperature state;

[0085] When the correction environment index is less than or equal to the ultra-low temperature index, the Q_c is set as an ultra-low temperature state;

[0086] The Q_c is added to the end of the state record array Q, and the earliest state record in the state record array Q is removed, so that the length of the array is kept as l;

[0087] The state distribution in the state record array Q is counted:

[0088] If all elements in Q are the same state, the state is determined as the current environment state;

[0089] If there are different states in Q, the environment state is kept unchanged.

[0090] In this embodiment, firstly, the latest collected temperature value in the environment temperature sequence is taken as a basic environment index. This index reflects the current instant temperature condition. Next, the environment correction parameter is calculated, which comprises the following steps:

[0091] First, the ambient temperature sequence is subjected to outlier removal and smoothing processing. Outlier removal can use methods such as median filtering, while smoothing processing can use moving average method. The temperature sequence after such processing can better reflect the true trend of temperature change, reducing the influence of accidental factors. Then, the standard deviation of the smoothed sequence is calculated as the volatility parameter. This parameter reflects the intensity of temperature change. Next, a certain number of recent data is extracted from the smoothed temperature sequence according to a predetermined interval (such as every 5 takes 1 data, a total of 20) to form an analysis sequence. On this analysis sequence, a sliding window is set, and multiple sub-sequences are extracted through this window. For each sub-sequence, linear regression analysis is performed to obtain the slope of each sub-sequence. These slopes reflect the trend of temperature change in different time periods. Using a predefined weight formula, a comprehensive trend parameter is calculated based on these slopes. With the base environment index, volatility parameter and trend parameter, the corrected environment index can be calculated. The calculation of the corrected environment index takes into account the current temperature, temperature change trend and temperature change amplitude, so it can more comprehensively reflect the environmental state.

[0092] Finally, the corrected environment index is compared with a predetermined threshold to determine the current environmental state. In order to avoid frequent changes in environmental state, a fixed-length state record array is maintained. Each time a new state is determined, it is added to the end of the array, while the earliest record is removed. Only when all state records in the array are consistent, the environmental state will be updated; otherwise, the environmental state remains unchanged.

[0093] Through this method, the instantaneous value, change trend and volatility of temperature can be considered comprehensively to accurately determine the current environmental state. This provides a reliable basis for subsequent control strategy adjustment.

[0094] The control target determination module is configured to determine a control target according to a preset control target rule and an environmental state.

[0095] The control target includes maintaining the main circulation path running, starting the air supplement path, and starting the hot gas bypass path; and the preset control target rule includes:

[0096] When the environmental state is normal, the control target is determined to be maintaining the main circulation path running;

[0097] When the environmental state is low temperature, the control target is determined to be starting the air supplement path;

[0098] When the environmental state is ultra-low temperature, the control target is determined to be starting the hot gas bypass path.

[0099] In this embodiment, the regulation target determination module determines the regulation target according to the preset regulation target rule and the environment state. The regulation target includes three types: maintaining the main circulation loop operation, starting the supplementary gas loop, and starting the hot gas bypass loop. The determination process of the regulation target is as follows: first, the current environment state is checked. If the environment state is a normal state, the regulation target is set to maintain the main circulation loop operation. In this case, the heating demand can be met only through the main circulation loop, and there is no need to start the additional auxiliary loop. If the environment state is a low-temperature state, the regulation target is set to start the supplementary gas loop. At this time, the main circulation loop and the supplementary gas loop operate simultaneously to cope with the lower environment temperature. The start of the supplementary gas loop can significantly improve the heating capacity and energy efficiency ratio in the low-temperature environment. When the environment state is determined to be an ultralow-temperature state, the regulation target is set to start the hot gas bypass loop. In this extreme case, the main circulation loop, the supplementary gas loop, and the hot gas bypass loop operate simultaneously to ensure that they can continue to work normally in the ultralow-temperature environment and prevent shutdown due to low pressure.

[0100] Through this regulation target determination mechanism based on the environment state, the operation mode can be flexibly adjusted according to different environment temperature conditions, which not only ensures the heating effect but also improves the adaptability and reliability. This method enables the self-recovery cascade cycle to operate efficiently and stably in a wider temperature range, and is particularly suitable for application in regions with large changes in environment temperature and severe cold in winter.

[0101] It should be noted that the main circulation loop is the basic operation path of the self-recovery cascade cycle, and is usually used in normal environment temperature. It includes basic components such as a compressor, a condenser, an expansion valve, and an evaporator, and can meet the heating demand in normal working conditions. When the environment state is determined to be a normal state, the regulation target is set to maintain the main circulation loop operation to maintain the basic heating function. The supplementary gas loop is an auxiliary loop added on the basis of the main circulation loop, and is usually started when the environment temperature is low. Its main role is to improve the heating capacity and efficiency by injecting gaseous refrigerant into the middle stage of the compressor. When the environment state is determined to be a low-temperature state, the regulation target is set to start the supplementary gas loop. In this way, the heating performance can be improved in the low-temperature environment, and the exhaust temperature of the compressor can be reduced to prolong the service life of the equipment. The hot gas bypass loop is a protective measure taken in an extremely low-temperature environment. It increases the temperature and pressure of the evaporator by directly introducing part of the high-temperature and high-pressure refrigerant into the evaporator. When the environment state is determined to be an ultralow-temperature state, the regulation target is set to start the hot gas bypass loop. This operation can prevent shutdown due to low evaporation pressure in an extremely low temperature, thereby expanding the temperature range of the equipment.

[0102] The control parameter acquisition module is configured to determine a control item according to a preset control item rule and the regulation target, and acquire the control parameter by using a corresponding optimization method according to the control item.

[0103] The preset control item rule comprises:

[0104] When the regulation target is to maintain the main circulation path operation, the control item is determined as the main circulation path opening degree;

[0105] When the regulation target is to start the air supplement path, the control item is determined as the main circulation path opening degree and the air supplement path opening degree;

[0106] When the regulation target is to start the hot gas bypass path, the control item is determined as the main circulation path opening degree, the air supplement path opening degree and the hot gas bypass path opening degree.

[0107] The step of obtaining the control parameter according to the control item using the corresponding optimization method comprises:

[0108] When the control item is the main circulation path opening degree, a PID control algorithm is used to calculate the main circulation path opening degree adjustment amount, and the main circulation path opening degree adjustment amount is taken as the control parameter;

[0109] When the control item is the main circulation path opening degree and the air supplement path opening degree, the main circulation path opening degree is set as the maximum value, a fuzzy control algorithm is used to calculate the air supplement path opening degree according to the ambient temperature and the system pressure ratio, and the main circulation path opening degree and the air supplement path opening degree are taken as the control parameters;

[0110] When the control item is the main circulation path opening degree, the air supplement path opening degree and the hot gas bypass path opening degree, the main circulation path opening degree and the air supplement path opening degree are set as the maximum values, an adaptive control algorithm is used to calculate the hot gas bypass path opening degree, and the main circulation path opening degree, the air supplement path opening degree and the hot gas bypass path opening degree are taken as the control parameters.

[0111] The step of calculating the main circulation path opening degree using the PID control algorithm comprises: setting a target suction temperature; monitoring the actual suction temperature in real time; calculating the deviation between the target temperature and the actual temperature, and calculating the cumulative value and the change rate of the temperature deviation with time; according to preset proportional, integral and differential parameters, combining the temperature deviation, the cumulative value of the deviation and the change rate of the deviation, calculating the main circulation path opening degree adjustment amount;

[0112] The step of calculating the air supplement path opening degree according to the ambient temperature and the system pressure ratio using the fuzzy control algorithm comprises: dividing the ambient temperature and the system pressure ratio into low, medium and high, and small, medium and large three levels respectively; establishing a fuzzy rule base, defining the air supplement path opening degree adjustment strategy under different combinations of ambient temperature and system pressure ratio; determining the membership degree of the current ambient temperature and the system pressure ratio in their respective levels; based on the fuzzy rule base and the membership degree, deducing the fuzzy output of the air supplement path opening degree; defuzzifying the fuzzy output to convert it into a specific air supplement path opening degree value;

[0113] The calculating the opening of the hot gas bypass passage using the adaptive control algorithm comprises: setting upper and lower limits of a target suction pressure; monitoring a system suction pressure in real time; calculating a deviation of an actual suction pressure from a median value in the target pressure range; adjusting an adaptive coefficient according to the pressure deviation and a current system state; and calculating the opening of the hot gas bypass passage using the adjusted adaptive coefficient.

[0114] In the embodiment, the control parameter acquisition module selects a control item according to a preset control item rule and the determined regulation and control target, and acquires the control parameter using a corresponding optimization method. It should be noted that the PID control algorithm, the fuzzy control algorithm and the adaptive control algorithm are all existing mature technologies and have been widely applied in the industrial control field. In the embodiment, different control algorithms are selected for different operating states. This process is divided into three cases according to the operating state:

[0115] When the regulation and control target is to maintain the operation of the main circulation path, only the opening of the main circulation path needs to be controlled. At this time, the PID control algorithm is used to calculate the adjustment amount of the opening of the main circulation path. The PID control algorithm is selected because the main circulation path is the basic operating path, and its control target is relatively simple and clear, mainly to maintain a stable suction temperature. The PID control algorithm has the characteristics of simple structure, high reliability and strong adaptability, and can effectively handle linear control problems. For example, in actual operation, a target suction temperature is first set, for example 5℃. Then, the actual suction temperature is monitored in real time through a temperature sensor. The deviation between the target temperature and the actual temperature is calculated, and the cumulative value and the rate of change of the deviation over time are calculated. Assuming that at a certain time, the actual suction temperature is 3℃, then the temperature deviation is 2℃. According to the pre-set proportional, integral and differential parameters, the adjustment amount of the opening of the main circulation path is calculated in combination with the current temperature deviation, the cumulative value of the deviation and the rate of change of the deviation. For example, if the calculated adjustment amount is +5%, it means that the opening of the main circulation path needs to be increased by 5%.

[0116] When the target of regulation is the start-up of the supplemental air path, both the main circulation path opening and the supplemental air path opening need to be controlled. In this case, the main circulation path opening is set to the maximum value to ensure the heating capacity of the main circulation. For the control of the supplemental air path opening, a fuzzy control algorithm is used. The fuzzy control algorithm is chosen because the control of the supplemental air path involves multiple input variables (such as ambient temperature and pressure ratio), and the relationship between these variables and the optimal supplemental air amount is complex and difficult to describe with an accurate mathematical model. The fuzzy control algorithm can handle this multi-input, nonlinear control problem well. For example, in practical applications, the ambient temperature and pressure ratio are first divided into low, medium, and high, and small, medium, and large, respectively. For example, the ambient temperature can be divided into: low temperature (less than -10°C), medium temperature (-10°C to 0°C), and high temperature (greater than 0°C); the pressure ratio can be divided into: small (less than 2.5), medium (2.5 to 3.5), and large (greater than 3.5). Then, a fuzzy rule base is established to define the supplemental air path opening adjustment strategy under different combinations of ambient temperature and pressure ratio. For example, when the ambient temperature is low and the pressure ratio is large, a larger supplemental air path opening may be needed. Based on the current ambient temperature and pressure ratio, the membership degree of each in its respective grade is determined. Based on the fuzzy rule base and these membership degrees, the fuzzy output of the supplemental air path opening is derived. Finally, this fuzzy output is defuzzified to convert it into a specific supplemental air path opening value, such as 30%.

[0117] When the target of regulation is the start-up of the hot gas bypass path, the main circulation path opening, the supplemental air path opening, and the hot gas bypass path opening need to be controlled. In this case, both the main circulation path opening and the supplemental air path opening are set to the maximum value to provide the maximum heating capacity. For the control of the hot gas bypass path opening, an adaptive control algorithm is used. The adaptive control algorithm is chosen because the hot gas bypass path is mainly used in extremely low temperature environments, and the operating characteristics in this case can change significantly and are difficult to model accurately in advance. The adaptive control algorithm can automatically adjust the control parameters according to the actual operating state, making it particularly suitable for handling situations with large changes in characteristics or high uncertainty. For example, in the actual control process, the upper and lower limits of the target suction pressure are first set, such as 0.3 MPa to 0.4 MPa. Then, the suction pressure is monitored in real time by a pressure sensor. The deviation of the actual suction pressure from the median value of the target pressure range is calculated. For example, if the actual suction pressure is 0.25 MPa and the median value of the target pressure range is 0.35 MPa, the pressure deviation is 0.1 MPa. Based on this pressure deviation and the current state, the adaptive coefficient is adjusted. Finally, the adjusted adaptive coefficient is used to calculate the opening of the hot gas bypass path, such as 15%.

[0118] In this way, the opening degree of each circuit can be flexibly adjusted according to different operating states and environmental conditions to achieve the best heating effect and efficiency. This control strategy enables the self-lift cycle to maintain stable and efficient operation at various environmental temperatures, especially ensuring reliable heating performance under ultra-low ambient temperature conditions. By selecting different control algorithms, the embodiment realizes accurate control of the self-lift cycle under different operating states, significantly improving adaptability and reliability.

[0119] The control execution module is configured to execute the control according to the obtained control parameters.

[0120] In the embodiment, the control execution module is responsible for executing the actual system control according to the control parameters calculated by the control parameter acquisition module. The precise control of the system is realized by adjusting the corresponding valves on each circuit. The control execution module converts the calculated opening degree value into a corresponding control signal, such as a voltage or current signal, and sends it to the driving device of the valve, thereby realizing precise adjustment of the valve opening degree.

[0121] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only one, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0122] The above description is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

[0123] Finally: the above description is only the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A self-recovering cycle control system for ultra-low ambient temperature, characterized by, The method comprises the following steps: a temperature acquisition module is used to acquire the ambient temperature according to a preset frequency and sort the acquired ambient temperature data in time sequence to obtain an ambient temperature sequence; an environment state analysis module is used to obtain a basic environment index and an environment correction parameter according to the ambient temperature sequence, correct the basic environment index using the environment correction parameter to obtain a corrected environment index, and determine the environment state according to the corrected environment index; a control target determination module is used to determine a control target according to a preset control target rule and the environment state; a control parameter acquisition module is used to determine a control item according to a preset control item rule and the control target, and acquire a control parameter using a corresponding optimization method according to the control item; a control execution module is used to execute control according to the acquired control parameter; the basic environment index is the latest acquired ambient temperature value in the ambient temperature sequence; the environment correction parameter comprises a fluctuation parameter and a trend parameter; the environment state comprises a normal state, a low temperature state and an ultra-low temperature state; the steps of obtaining the environment correction parameter comprise: obtaining the ambient temperature sequence, removing abnormal values and performing smoothing processing to obtain a smoothed ambient temperature sequence; obtaining the standard deviation of the smoothed ambient temperature sequence as the fluctuation parameter; The most recent data from the smoothed ambient temperature sequence is extracted at preset intervals to form an analysis sequence, wherein ; and ; A sliding window of size is set on the analysis sequence, where , the content is extracted by the sliding window, obtaining subsequences of size ; Linear regression is performed on each sub-sequence to obtain the slope of the sub-sequence, denoted as ; wherein, denotes the index, , denotes the slope of the th sub-sequence; calculating the trend parameter using the following formula: ， In the formula, denotes the weight of the th sub-sequence, The calculation formula is: ； using the fluctuation parameter and the trend parameter to form the environment correction parameter; the steps of determining the environment state according to the corrected environment index comprise: setting an index threshold, wherein the index threshold comprises a low temperature index and an ultra-low temperature index, and the low temperature index is greater than the ultra-low temperature index; an array of state records of length is initialized and the current state record ; comparing the modified environment index to the index threshold and updating the current state record : When the modified ambient index is greater than the low temperature index, set Normal; When the modified ambient index is less than or equal to the low temperature index and greater than the ultra-low temperature index, set a low temperature state; When the modified ambient index is less than or equal to the ultra-low temperature index, set to an ultra-low temperature state; Will Add to the status record array At the end of the state record array, remove The earliest state record in the array is kept as ; Statistical state record array State distribution in If all elements in the middle are in the same state, then the state is determined as the current environment state; If If there are different states, the environment state is kept unchanged.

2. A self-cascade cycle control system for ultra-low ambient temperature according to claim 1, characterized in that, the corrected environment index is calculated by the following formula: ， In the formula, represents a revised environmental index, represents a basic environmental index, represents a trend parameter, represents a fluctuation parameter.

3. A self-cascade cycle control system for ultra-low ambient temperature according to claim 2, characterized in that, the control target comprises maintaining the operation of the main circulation loop, starting the air supplementing loop and starting the hot gas bypass loop; the preset control target rule comprises: when the environment state is the normal state, determining the control target as maintaining the operation of the main circulation loop; when the environment state is the low temperature state, determining the control target as starting the air supplementing loop; when the environment state is the ultra-low temperature state, determining the control target as starting the hot gas bypass loop.

4. A self-cascade cycle control system for ultra-low ambient temperature according to claim 3, characterized in that, the preset control item rule comprises: when the control target is maintaining the operation of the main circulation loop, determining the control item as the main circulation loop opening degree; when the control target is starting the air supplementing loop, determining the control item as the main circulation loop opening degree and the air supplementing loop opening degree; when the control target is starting the hot gas bypass loop, determining the control item as the main circulation loop opening degree, the air supplementing loop opening degree and the hot gas bypass loop opening degree.

5. A self-cascade cycle control system for ultra-low ambient temperature according to claim 4, characterized in that, the steps of acquiring the control parameter using the corresponding optimization method according to the control item comprise: when the control item is the main circulation loop opening degree, using a PID control algorithm to calculate the main circulation loop opening degree adjustment amount, and taking the main circulation loop opening degree adjustment amount as the control parameter; when the control item is the main circulation loop opening degree and the air supplementing loop opening degree, setting the main circulation loop opening degree as the maximum value, using a fuzzy control algorithm to calculate the air supplementing loop opening degree according to the ambient temperature and the system pressure ratio, and using the main circulation loop opening degree and the air supplementing loop opening degree as the control parameter; when the control item is the main circulation loop opening degree, the air supplementing loop opening degree and the hot gas bypass loop opening degree, setting the main circulation loop opening degree and the air supplementing loop opening degree as the maximum value, using an adaptive control algorithm to calculate the hot gas bypass loop opening degree, and using the main circulation loop opening degree, the air supplementing loop opening degree and the hot gas bypass loop opening degree as the control parameter.

6. A self-cascade cycle control system for ultra-low ambient temperatures according to claim 5, characterized in that, The PID control algorithm comprises: setting a target suction temperature; monitoring the actual suction temperature in real time; calculating the deviation between the target temperature and the actual temperature, and calculating the accumulated value and the change rate of the temperature deviation over time; calculating the adjustment amount of the main cycle path opening degree according to the preset proportional, integral and differential parameters, and combining the temperature deviation, the accumulated value and the change rate of the deviation; The fuzzy control algorithm comprises: dividing the ambient temperature and the system pressure ratio into low, medium and high levels, and small, medium and large levels, respectively; establishing a fuzzy rule base to define the adjustment strategy of the air supplement path opening degree under different combinations of ambient temperature and system pressure ratio; determining the membership degree of the current ambient temperature and system pressure ratio in their respective levels; deriving the fuzzy output of the air supplement path opening degree based on the fuzzy rule base and the membership degree; and de-fuzzifying the fuzzy output to convert it into a specific air supplement path opening degree value; The adaptive control algorithm comprises: setting the upper and lower limits of the target suction pressure; monitoring the system suction pressure in real time; calculating the deviation of the actual suction pressure from the median value of the target pressure range; adjusting the adaptive coefficient according to the pressure deviation and the current system state; and calculating the opening degree of the hot gas bypass path using the adjusted adaptive coefficient.

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