Energy-saving and environment-friendly carbon dioxide refrigerating system and method

Abstract

The invention relates to the technical field of energy-saving refrigeration, in particular to an energy-saving and environment-friendly carbon dioxide refrigeration system and method, and the system comprises a state analysis module, an energy efficiency analysis module, a compression ratio calculation module, a risk judgment module and an adjustment optimization module. According to the method, the system fluctuation trend is captured by analyzing the outlet pressure of the evaporator and the outlet pressure of the condenser and combining the operation state vectors generated by the suction flow and the exhaust flow of the compressor, the fluctuation trend is associated with the energy efficiency ratio change of the compressor, and dynamic energy efficiency fluctuation is provided; the compression ratio is continuously monitored, the air flow and the opening degree of an expansion valve are adjusted, stable operation of the system is ensured, the optimal energy efficiency is achieved, compression ratio adjustment is optimized in real time by analyzing fluctuation of the compression ratio and deviation between the fluctuation and an adjusting interval, the system is adjusted according to real-time data through a dynamic adjusting mechanism, energy efficiency fluctuation is restrained, and flexibility and stability are improved. Energy waste is reduced, and environmental changes are responded, and automatic adjustment is performed to maximize energy utilization.

Description

Technical Field

[0001] This invention relates to the field of energy-saving refrigeration technology, and in particular to an energy-saving and environmentally friendly carbon dioxide refrigeration system and method. Background Technology

[0002] The field of energy-saving refrigeration technology encompasses research and application of refrigeration technologies aimed at reducing energy consumption, improving energy efficiency, and minimizing environmental pollution. Its core content involves optimizing the use of refrigerants, improving heat exchange efficiency, and refining refrigeration cycle structures and control strategies to achieve efficient energy utilization and rational allocation. Energy-saving refrigeration systems often involve vapor compression refrigeration, absorption refrigeration, and gas compression refrigeration cycles, and are widely used in food refrigeration, industrial production, and building environmental regulation. Driven by the trend of energy conservation and environmental protection, the use of refrigerants with low global warming potential and natural refrigerants such as ammonia and carbon dioxide has become an important research direction in this field. The key technical approach lies in the efficient integration of system structure optimization and refrigerant property matching.

[0003] Among them, the energy-saving and environmentally friendly carbon dioxide refrigeration system and method refers to a refrigeration system that uses carbon dioxide as the working fluid and performs thermodynamic processes such as compression, condensation, expansion, and evaporation within a closed-loop structure to achieve the transfer and regulation of heat and cold. The technical aspects covered include the construction of the refrigeration cycle, the setting of carbon dioxide compression and expansion control methods, and the design optimization of the heat exchanger structure. Specifically, it is based on establishing a transcritical carbon dioxide refrigeration cycle, and achieves energy conversion of the working fluid under multiple temperature and pressure conditions by setting up a combination of gas cooler, expansion device, and evaporator. Furthermore, the system employs a highly efficient heat exchange method to control the phase change of the working fluid, and uses a pressure regulating device to achieve system stability and energy saving. The overall system adopts a continuous closed-loop structure to achieve the refrigeration function.

[0004] While current technologies in energy-saving refrigeration systems emphasize heat exchange efficiency and refrigerant optimization, their energy efficiency often fails to reach optimal levels under various load conditions due to the reliance on static compression ratio settings and the lack of real-time adjustment and dynamic feedback. For example, under significant load fluctuations, the refrigeration system may exhibit unstable energy efficiency, leading to increased energy consumption. Traditional technologies typically use a fixed compression ratio, which is ill-suited to rapid changes in environment and load. With load fluctuations, the system may experience over-compression or under-compression, impacting overall system performance. Furthermore, although existing refrigerants are optimized using low global warming potential refrigerants, their energy efficiency management remains lagging, unable to adjust compressor operation in real-time to cope with rapid changes in external conditions. Summary of the Invention

[0005] To address the technical problems of energy-saving refrigeration in existing technologies, embodiments of the present invention provide an energy-saving and environmentally friendly carbon dioxide refrigeration system and method. The technical solution is as follows:

[0006] On the one hand, an energy-saving and environmentally friendly carbon dioxide refrigeration system is provided, which includes:

[0007] The state analysis module collects the outlet pressure of the evaporator and condenser and analyzes the pressure gradient. It constructs an operating state vector by combining the suction and discharge flow of the bipolar compressor, calculates the fluctuation correlation intensity through the Pearson correlation coefficient, generates a set of fluctuation parameters, and transmits them to the energy efficiency analysis module.

[0008] The energy efficiency analysis module collects compressor temperature and power based on the fluctuation parameter group and calculates the energy efficiency ratio. It analyzes the trend of energy efficiency ratio change and judges energy efficiency fluctuation in combination with pressure gradient. It generates energy efficiency fluctuation judgment result and transmits it to the compression ratio calculation module.

[0009] The compression ratio calculation module calls the energy efficiency fluctuation judgment result to obtain the compressor's compression ratio, combines the compressor's energy efficiency ratio to construct a trend line to analyze the trend direction, calculates the compression ratio adjustment range, and transmits it to the risk judgment module.

[0010] The risk assessment module obtains the current cycle compression ratio, calculates the deviation from the compression ratio adjustment range, determines the status of the carbon dioxide refrigeration system, generates a compression ratio assessment result, and transmits it to the adjustment and optimization module.

[0011] The adjustment and optimization module calls the compression ratio determination result, adjusts the compressor return gas flow and expansion valve opening, records the energy efficiency impact of the adjustment on the carbon dioxide refrigeration system, and determines the matching compression ratio adjustment result.

[0012] As a further embodiment of the present invention, the fluctuation parameter group includes pressure gradient change value, intake and exhaust flow difference, and state vector correlation coefficient; the energy efficiency fluctuation judgment result includes energy efficiency ratio change amplitude, pressure gradient corresponding interval, and energy efficiency trend direction; the compression ratio adjustment interval specifically includes upper limit of compression ratio, lower limit of compression ratio, and trend change slope; the compression ratio judgment result includes compression ratio deviation, refrigerant condition label, and state identification mark; and the compression ratio adjustment result includes return gas flow adjustment amplitude, expansion valve opening adjustment amplitude, and energy consumption change rate.

[0013] As a further aspect of the present invention, the state analysis module includes:

[0014] The pressure monitoring submodule collects the outlet pressure of the evaporator and condenser and performs synchronous calibration. It calculates the difference between the evaporator and condenser pressure values ​​at each time point, calculates the pressure difference change sequence over a continuous time period, obtains the pressure difference change rate in multiple window intervals, and generates pressure gradient values.

[0015] The state vector construction submodule calls the pressure gradient value, collects the intake and exhaust flow rates of the bipolar compressor, matches and integrates the three-dimensional operating state data group with the pressure gradient value, and arranges it into a vector set in chronological order to generate an operating state vector group.

[0016] The fluctuation parameter generation submodule, based on the running state vector group, analyzes the vector groups between adjacent time periods through Pearson correlation coefficient, calculates the fluctuation correlation strength, performs peak and valley identification and amplitude analysis, extracts the time periods with the best fluctuation degree and labels the corresponding features, and generates the fluctuation parameter group.

[0017] As a further aspect of the present invention, the energy efficiency analysis module includes:

[0018] The energy efficiency calculation submodule, based on the fluctuation parameter group, collects the temperature and power of the bipolar compressor in multiple time periods and performs synchronous pairing. It calculates the refrigeration temperature difference value under the compressor power in each time period and performs ratio processing to establish the operating energy efficiency ratio sequence in each cycle and generate an energy efficiency ratio value group.

[0019] The trend recognition submodule calls the energy efficiency ratio value group, calculates the difference of energy efficiency ratio in continuous intervals according to time sequence, detects the magnitude and direction of change of energy efficiency ratio in continuous time period, extracts and labels the trend type of multiple change segments according to the change rate change points, and generates the energy efficiency ratio change trend.

[0020] The fluctuation determination submodule pairs the energy efficiency ratio change trend with the pressure gradient, calculates the directional comparison parameters, filters the intervals where the energy efficiency change and pressure fluctuation are consistent and classifies and labels them, and generates the energy efficiency fluctuation determination result.

[0021] As a further aspect of the present invention, the compression ratio calculation module includes:

[0022] The compression ratio extraction submodule, based on the energy efficiency fluctuation determination result, obtains the intake pressure value and exhaust pressure value of the two-stage compressor within the corresponding time period, performs synchronous calibration on the pressure data within each time period and calculates the compression ratio value, arranges them in chronological order to form a compression ratio sequence, and generates compression ratio change results.

[0023] The trend line fitting submodule calls the compression ratio change results, combines them with the energy efficiency ratio numerical group under the same time period, uses linear fitting to construct a trend line of compression ratio and energy efficiency ratio change for the paired data, calculates the slope of the trend line and generates the trend direction coefficient.

[0024] The adjustment interval calculation submodule selects the time point when the slope direction of the compression ratio changes according to the trend direction coefficient, divides the compression ratio change value in adjacent change segments into intervals and marks the start and end boundaries of the adjustment, calculates the range of compression ratio variation in each segment, and obtains the compression ratio adjustment interval.

[0025] As a further aspect of the present invention, the trend direction coefficient is set by judging the trend and direction of change of the two over multiple time periods, based on the slope of the trend line between the compression ratio and the energy efficiency ratio fitted by time series data.

[0026] As a further aspect of the present invention, the risk assessment module includes:

[0027] The compression ratio acquisition submodule acquires the intake and exhaust pressure values ​​of the bipolar compressor within the current monitoring period, calculates the pressure ratio for each set of pressure data and converts it into time series data, filters out invalid readings and arranges them according to time points to generate the periodic compression ratio.

[0028] The deviation calculation submodule calls the period compression ratio, combines it with the compression ratio adjustment range, calculates the difference between each compression ratio data point and the upper and lower boundaries of the corresponding adjustment range, identifies all time periods that exceed the range and analyzes the average amount of the offset amplitude to obtain the compression ratio deviation.

[0029] The status judgment submodule, based on the compression ratio deviation, combines the currently collected carbon dioxide refrigerant evaporation pressure and refrigerant outlet temperature, performs a joint partition comparison of the three values ​​according to the carbon dioxide system operating baseline value, sets status labels, and then matches the degree of deviation with the status labels to obtain the compression ratio judgment result.

[0030] As a further aspect of the present invention, the operating reference value of the carbon dioxide system is set by referring to the standard refrigeration cycle model to set typical operating point parameters, and combined with the current operating condition range of the system, the evaporation pressure value, exhaust temperature value and compression ratio conventional range are selected as the reference setting.

[0031] As a further aspect of the present invention, the adjustment and optimization module includes:

[0032] The adjustment parameter selection submodule calls the compression ratio determination result, sets the corresponding compression state level according to the determination label, determines the adjustable compressor return gas flow range and expansion valve opening range within the current operating cycle, and obtains the adjustment control parameters.

[0033] The control quantity adjustment submodule adjusts the set value of the return gas flow rate of the bipolar compressor and the opening value of the expansion valve based on the adjustment control parameters, collects and summarizes the refrigerant evaporation pressure, condensation temperature and compressor operating frequency, calculates the total energy consumption after adjustment and the difference between it and the original energy consumption data, and obtains the energy consumption change rate.

[0034] The performance impact assessment submodule, based on the energy consumption change rate and combined with the adjustment range of return air flow and expansion valve opening in the regulation control parameters, maps the adjustment variables to energy consumption changes, filters parameter combinations with negative energy consumption change rates and their corresponding compression ratio response spaces, and obtains the compression ratio adjustment results.

[0035] On the other hand, an energy-saving and environmentally friendly carbon dioxide refrigeration method, which is based on the aforementioned energy-saving and environmentally friendly carbon dioxide refrigeration system, includes the following steps:

[0036] S1: Collect the outlet pressure of the evaporator and condenser and analyze the pressure gradient. Combine the suction and exhaust flow of the bipolar compressor to construct the operating state vector. Calculate the correlation of the fluctuation trend through the Pearson correlation coefficient and generate a set of fluctuation parameters.

[0037] S2: Based on the fluctuation parameter group, the compressor temperature and power are collected and the energy efficiency ratio is calculated. The energy efficiency ratio change trend is analyzed and the energy efficiency fluctuation is judged in combination with the pressure gradient. The energy efficiency fluctuation judgment result is generated.

[0038] S3: Call the energy efficiency fluctuation judgment result to obtain the compressor compression ratio, combine it with the compressor energy efficiency ratio to construct a trend line to analyze the trend direction, and calculate the compression ratio adjustment range;

[0039] S4: Obtain the current cycle compression ratio, calculate the deviation from the compression ratio adjustment range, determine the status of the carbon dioxide refrigeration system, and generate a compression ratio determination result;

[0040] S5: Call the compression ratio determination result, adjust the compressor return gas flow and expansion valve opening, record the energy efficiency impact of the adjustment on the carbon dioxide refrigeration system, and determine the matching compression ratio adjustment result.

[0041] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0042] By analyzing the evaporator and condenser outlet pressures and combining them with the operating state vector generated from the compressor's suction and discharge flow rates, the system's fluctuation trends can be accurately captured. This fluctuation trend is correlated with changes in the compressor's energy efficiency ratio (EER), effectively providing information on the system's energy efficiency fluctuations during dynamic changes. Continuous monitoring of the compression ratio and adjustment of the gas flow rate and expansion valve opening ensures that the system operates stably while achieving optimal energy efficiency. Furthermore, by analyzing the fluctuations in the compression ratio and its deviation from the adjustment range, the operating status of the refrigeration system can be determined in real time, and the compression ratio adjustment can be optimized. This dynamic adjustment and optimization mechanism allows the system to self-adjust based on real-time data, avoiding the limitations of fixed compression ratio settings in traditional technologies. Energy efficiency fluctuations are effectively suppressed, while simultaneously improving the system's operational flexibility and stability. In this process, refined adjustments and real-time risk assessment make the entire energy-saving process more efficient, significantly improving the EER, reducing energy waste, and ensuring system stability under multiple loads. The innovation of this mechanism lies in its ability to respond to environmental changes in real time, automatically adjusting to maximize energy utilization and reduce environmental pollution. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of an energy-saving and environmentally friendly carbon dioxide refrigeration system provided in an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0046] Figure 3 This is a flowchart of the state analysis module in this invention;

[0047] Figure 4 This is a flowchart of the energy efficiency analysis module in this invention;

[0048] Figure 5 This is a flowchart of the compression ratio calculation module in this invention;

[0049] Figure 6 This is a flowchart of the risk assessment module in this invention;

[0050] Figure 7 This is a flowchart of the adjustment and optimization module in this invention;

[0051] Figure 8This is a flowchart of an energy-saving and environmentally friendly carbon dioxide refrigeration method provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0053] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0054] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0055] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0056] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0057] This invention provides an energy-saving and environmentally friendly carbon dioxide refrigeration system, such as... Figure 1-2 The diagram shows an energy-saving and environmentally friendly carbon dioxide refrigeration system, which includes:

[0058] The state analysis module collects the outlet pressure of the evaporator and condenser and analyzes the pressure gradient. It constructs an operating state vector by combining the suction and discharge flow of the bipolar compressor, calculates the fluctuation correlation intensity through the Pearson correlation coefficient, generates a set of fluctuation parameters, and transmits them to the energy efficiency analysis module.

[0059] The energy efficiency analysis module collects compressor temperature and power based on the fluctuation parameter group and calculates the energy efficiency ratio. It analyzes the trend of energy efficiency ratio change and judges energy efficiency fluctuation by combining pressure gradient. It generates energy efficiency fluctuation judgment results and transmits them to the compression ratio calculation module.

[0060] The compression ratio calculation module calls the energy efficiency fluctuation judgment result to obtain the compressor's compression ratio, combines the compressor's energy efficiency ratio to construct a trend line to analyze the trend direction, calculates the compression ratio adjustment range, and transmits it to the risk judgment module.

[0061] The risk assessment module obtains the current cycle compression ratio, calculates the deviation from the compression ratio adjustment range, determines the status of the carbon dioxide refrigeration system, generates the compression ratio assessment result, and transmits it to the adjustment and optimization module.

[0062] The adjustment and optimization module calls the compression ratio determination result, adjusts the compressor return gas flow and expansion valve opening, records the energy efficiency impact of the adjustment on the carbon dioxide refrigeration system, and determines the matching compression ratio adjustment result.

[0063] The fluctuation parameter group includes pressure gradient change value, intake and exhaust flow difference, and state vector correlation coefficient. The energy efficiency fluctuation judgment results include energy efficiency ratio change amplitude, pressure gradient corresponding interval, and energy efficiency trend direction. The compression ratio adjustment interval specifically includes compression ratio upper limit, compression ratio lower limit, and trend change slope. The compression ratio judgment results include compression ratio deviation, refrigerant condition label, and status identification mark. The compression ratio adjustment results include return gas flow adjustment amplitude, expansion valve opening adjustment amplitude, and energy consumption change rate.

[0064] Specifically, such as Figure 2 , 3 As shown, the state analysis module includes:

[0065] The pressure monitoring submodule collects the outlet pressure of the evaporator and condenser and performs synchronous calibration. It calculates the difference between the evaporator and condenser pressure values ​​at each time point, calculates the pressure difference change sequence over a continuous time period, obtains the pressure difference change rate in multiple window intervals, and generates pressure gradient values.

[0066] The outlet pressures of the evaporator and condenser are collected and synchronously calibrated. Using pressure sensors, the evaporator pressure is collected at time t = 0s, reaching 2.0 MPa, and the condenser pressure is collected at 2.5 MPa. Synchronous calibration ensures data consistency through timestamp alignment (system clock synchronization error less than 0.01s). The difference is calculated at each time point: condenser pressure minus evaporator pressure (at t = 0s, ΔP = 2.5 - 2.0 = 0.5 MPa). ΔP represents the pressure difference between the condenser and evaporator pressures. This process is repeated continuously. Five time points were selected for the time period (t = 0 to 4 s, Δt = 1 s), where t represents a time point and Δt represents a time interval. The pressure difference sequence was [0.50, 0.52, 0.51, 0.53, 0.54] MPa. The pressure difference change sequence was calculated using first-order difference (change value sequence: [0.02, -0.01, 0.02, 0.01] MPa). The rate of change of pressure difference was obtained over a multi-window interval (window size set to 3 points). Window 1 covered t = 0-2 s, and the rate was calculated as the average of the change values. Window 2 covers t = 1 - 3 seconds, with a speed of Window 3 covers t = 2-4s, with a speed of The pressure gradient value is generated by taking the maximum value of the multi-window velocity, which is 0.015 MPa / s.

[0067] The state vector construction submodule calls the pressure gradient value, collects the intake and exhaust flow rates of the bipolar compressor, matches and integrates the three-dimensional operating state data group with the pressure gradient value, and arranges it into a vector set in chronological order to generate the operating state vector group.

[0068] The pressure gradient value of 0.015 MPa / s is used to collect the suction flow rate (0.5 kg / s measured by the flow meter at t = 0 s) and discharge flow rate (0.52 kg / s at t = 0 s) of the bipolar compressor. The three-dimensional operating status data group (the combination of pressure gradient value, suction flow rate value, and discharge flow rate value is [0.015, 0.5, 0.52]) is matched and integrated. The vector set of the continuous time period t = 0, 1, 2 s is arranged in time order as [[0.015, 0.50, 0.52], [0.016, 0.51, 0.53], [0.014, 0.49, 0.50]].

[0069] The fluctuation parameter generation submodule, based on the running state vector group, analyzes the vector groups between adjacent time periods through Pearson correlation coefficient, calculates the fluctuation correlation strength, performs peak and valley identification and amplitude analysis, extracts the time periods with the best fluctuation degree and labels the corresponding features, and generates the fluctuation parameter group.

[0070] Based on the operating state vector set, ρ represents the Pearson correlation coefficient between adjacent time periods k and l, calculated through the linear correlation of three feature vectors: pressure gradient, inhalation flow rate, and exhaust flow rate. The value range is [-1, 1], with negative values ​​indicating negative correlation and positive values ​​indicating positive correlation. N represents the total number of feature points in the operating state vector (here, N = 3), corresponding to pressure gradient (feature 1), inhalation flow rate (feature 2), and exhaust flow rate (feature 3). i represents the feature index number (values ​​1, 2, 3), used to traverse all feature points. and The normalized value of the i-th feature point in time periods k and l is represented by the minmax normalization method to eliminate the influence of dimensions. Pressure gradient: Inspiratory flow rate: Exhaust flow rate: Simultaneously calculate covariance and standard deviation: Feature 1: Covariance Standard deviation Feature 2: Correlation coefficient: ρ1 = 0,

[0071] Similarly, ρ3 is calculated to be -0.01414, overall. Normalized value calculation (min-max method), pressure gradient: x min =0.014MPa / s,x max =0.016MPa / s, Inspiratory flow rate: x min =0.49kg / s,x max = 0.51 kg / s, exhaust flow rate: x min =0.50kg / s,x max =0.53kg / s, normalized value (mean) of time period k (t=0-1s): Feature 1: Feature 2: Feature 3: Normalized value (mean) for time period l (t = 1-2s): Feature 1: Feature 2: Feature 3: Substitute into the formula to calculate H = 0.0971 + 0.2777 = 0.3748. The threshold for optimal fluctuation is set at 0.7. Based on statistical data (in 100 sets of air conditioning system operation data, the H value distribution range is [0.4, 0.8], the H fluctuation correlation strength is mean 0.6, standard deviation 0.1), mean + 1 × standard deviation = 0.6 + 0.1 = 0.7, that is, H ≥ 0.7 is defined as abnormal fluctuation. The calculated H = 0.3748 is less than the threshold of 0.7, so it is not selected into the optimal set.

[0072] Specifically, such as Figure 2 , 4 As shown, the energy efficiency analysis module includes:

[0073] The energy efficiency calculation submodule, based on the fluctuation parameter group, collects the temperature and power of the bipolar compressor in multiple time periods and performs synchronous pairing. It calculates the cooling temperature difference value under the compressor power in each time period and performs ratio processing to establish the operating energy efficiency ratio sequence in each cycle and generate an energy efficiency ratio value group.

[0074] Based on the fluctuation parameter set, [H, amplitude] = [0.3748, 0.002], H is the fluctuation correlation strength, and the evaporator inlet temperature (measured by thermocouple) of the bipolar compressor is collected in three time periods (t = 0, 1, 2 s). evap =10.0,10.2,10.1]℃) and condenser outlet temperature (T cond = [45.0, 45.3, 45.1]℃), synchronous pairing ensures timestamp alignment (error < 0.01s), and the input power P is collected. in (Power meter measured P) in=12.0,12.2,12.1]kW), m represents the time point, calculate the cooling temperature difference (ΔT). m =T cond,m -T evap,m (e.g., when m=0, ΔT0=45.0-10.0=35.0℃), ratio processing is performed, and the inhalation flow rate is collected. Specific heat capacity of water c p = 4.18 kJ / (kg·K), including when m = 0 η represents the energy efficiency ratio. Similarly, calculate η1≈6.126, η2≈5.942, establish the operating energy efficiency ratio sequence [6.099,6.126,5.942], and generate the energy efficiency ratio numerical group.

[0075] The trend recognition submodule calls the energy efficiency ratio numerical group, calculates the difference of the energy efficiency ratio in continuous intervals according to the time sequence, detects the magnitude and direction of the change of the energy efficiency ratio in continuous time period, extracts and labels the trend type of multiple change segments according to the change rate, and generates the energy efficiency ratio change trend.

[0076] The energy efficiency ratio (EER) value set [6.099, 6.126, 5.942] is called, and the difference (Δη) is calculated for consecutive intervals (adjacent time points) according to the time sequence. m =η m+1 -η m This includes the following parameters: at time point m = 0, Δη0 = 6.126 - 6.099 = 0.027; at time point m = 1, Δη1 = 5.942 - 6.126 = -0.184. The parameters include the magnitude of change (absolute values ​​|Δη0| = 0.027, |Δη1| = 0.184) and direction (Δη0 > 0 is positive, Δη1 < 0 is negative), and the rate of change. Δt = 1s, r0 = 0.027s -1 r1 = -0.184s -1 ), Δt represents the time interval, Δη m : The rate of change of energy efficiency ratio at time point m, where r0 represents the rate of change of energy efficiency ratio, r m It is η m Extract the time differential change point (r sign change point: from positive to negative when m=1), label the trend type (e.g., upward trend when m=0, downward trend when m=1), and generate the energy efficiency ratio change trend (result: [increase, decrease]).

[0077] The fluctuation determination submodule pairs the energy efficiency ratio change trend with the pressure gradient, calculates the directional comparison parameters, filters the intervals where the energy efficiency change and pressure fluctuation are consistent and classifies and labels them, and generates the energy efficiency fluctuation determination result.

[0078] Based on the trend of energy efficiency ratio change ([increasing, decreasing]) and pressure gradient (previous paragraph 1 sequence P) m =0.015,0.016,0.014]MPa / s) for time point pairing (m=0,1), D m : Directional comparison parameter (dimensionless), subscript m indicates time point index, used to quantify the directional consistency between energy efficiency change and pressure fluctuation at time point m, α: directional comparison intensity factor (dimensionless), amplifies the same-direction fluctuation signal, β: energy efficiency change rate-pressure conversion coefficient (unit MPa·s) 2 To balance the dimensional differences between the rate of change in energy efficiency and the pressure gradient, P m : The pressure gradient measurement value (unit: MPa / s) at time point m, directly referenced from the previous text, Δη m : Energy efficiency ratio change rate (dimensionless) at time point m, derived from the difference calculation result of the trend identification submodule, P m Directly call the pressure gradient sequence (m=0: P0=0.015MPa / s, m=1: P1=0.016MPa / s), Δη m : Call the energy efficiency ratio change rate (m=0: Δη0=η1-η0=6.126-6.099=0.027, m=1: Δη1=η2-η1=5.942-6.126=-0.184), α=1.0: Regress and calibrate using 100 sets of data (linear fitting slope of the same-direction fluctuation dataset), β=0.1: Dimensional balance calculation (making β 2 (Δη m ) 2 and (of similar order of magnitude), example: →Take 0.1 and substitute it into the formula. m=0 and m=1: Example for m=0: Numerator = 1.0 × 0.015 × |0.015| = 0.000225, Denominator = (0.1) 2 ×(0.027) 2 +(0.015) 2 =0.00023229, Example with m=1: Numerator = 1.0 × 0.016 × |0.016| = 0.000256, Denominator = (0.1) 2 ×(-0.184) 2 +(0.016) 2 =0.00059456, Directional consistency threshold D th =0.8, set based on: 100 sets of data statistics (D when fluctuating in the same direction) mDistribution interval [0.75, 0.95], mean 0.85, standard deviation 0.05): D th =Mean - 1 × Standard Deviation = 0.85 - 0.05 = 0.80, D0 = 0.9687 > 0.8: This indicates that the pressure increase (P0 > 0) and energy efficiency increase (Δη0 > 0) at time point m = 0 are in the same direction, and are marked as positively correlated. D1 = 0.4306 < 0.8: This indicates that the pressure increase (P1 > 0) and energy efficiency decrease (Δη1 < 0) at time point m = 1 are in opposite directions, and are marked as "no correlation". The energy efficiency fluctuation judgment result ([positive correlation, no correlation]) is generated, and the same-direction fluctuation interval is selected.

[0079] Specifically, such as Figure 2 , 5 As shown, the compression ratio calculation module includes:

[0080] The compression ratio extraction submodule, based on the energy efficiency fluctuation judgment result, obtains the intake pressure value and exhaust pressure value of the two-stage compressor within the corresponding time period, performs synchronous calibration on the pressure data within each time period and calculates the compression ratio value, arranges them in chronological order to form a compression ratio sequence, and generates the compression ratio change result.

[0081] Based on the energy efficiency fluctuation determination results (positive correlation, no correlation), the suction pressure value of the two-stage compressor for the corresponding time period (t=0,1,2s) is obtained (pressure sensor P is collected). 吸 = [0.40, 0.41, 0.39] MPa) and exhaust pressure value (P 排 = [1.60, 1.62, 1.58] MPa), synchronous calibration ensures timestamp alignment (clock error < 0.01s), calculate compression ratio, CR m This represents the compression ratio, and m represents the time point, where m=0. When m=1 When m=2 Arrange the values ​​in chronological order to form a compression ratio sequence [4.0, 3.951, 4.051], and generate the compression ratio change results.

[0082] The trend line fitting submodule calls the compression ratio change results, combines them with the energy efficiency ratio numerical group under the same time period, uses linear fitting to construct the trend line of compression ratio and energy efficiency ratio change for the paired data, calculates the slope of the trend line and generates the trend direction coefficient.

[0083] The compression ratio change results (sequence [4.0, 3.951, 4.051]) are retrieved and combined with the energy efficiency ratio values ​​for the same period (η from paragraph 1 above). m =[6.099,6.126,5.942]), η mThe rate of change of energy efficiency ratio at time point m is represented by a least squares linear fit (constructing a paired dataset {(CR0,η0),(CR1,η1),(CR2,η2)}), and the slope k is calculated (formula). n represents the number of data points. Represents the sum of squares of compression ratios, (∑CR) m ) 2 CR represents the square of the sum of compression ratios. m Represents the compression ratio, η m Represents the energy efficiency ratio, m represents the time point, where the number of data points n = 3, ∑CR m =4.0 + 3.951 + 4.051 = 12.002, ∑η m =6.099+6.126+5.942=18.167,∑(CR m η m )=(4.0×6.099)+(3.951×6.126)+(4.051×5.942)=24.396+24.207+24.071=72.674, Substitution ), analyze the slope direction (k≈-2.429<0 is judged as negative), and generate the trend direction coefficient (marked as -1).

[0084] The adjustment interval calculation submodule selects the time point when the slope direction of the compression ratio changes based on the trend direction coefficient, divides the compression ratio change value in adjacent change segments into intervals and marks the start and end boundaries of adjustment, calculates the range of compression ratio variation in each segment, and obtains the compression ratio adjustment interval.

[0085] Based on the trend direction coefficient (-1), select the time point where the slope of the compression ratio changes (since the slope is always negative, there is no point of change, and the entire period from t=0 to t=2s is regarded as a single interval), mark the adjustment boundary (starting point t=0, ending point t=2s), calculate the range of compression ratio variation (maximum value max(CR)=4.051, minimum value min(CR)=3.951, range of variation ΔCR=4.051-3.951=0.100), where CR represents the compression ratio, and obtain the compression ratio adjustment interval (result: [0s, 2s], range of variation 0.100).

[0086] Table 1: Fitting Data of Compression Ratio and Energy Efficiency Ratio

[0087] Time point m Compression ratio CR Energy efficiency ratio η 0 4.000 6.099 1 3.951 6.126 2 4.051 5.942

[0088] Specifically, such as Figure 2 , 6 As shown, the risk assessment module includes:

[0089] The compression ratio acquisition submodule acquires the intake and exhaust pressure values ​​of the bipolar compressor within the current monitoring period, calculates the pressure ratio for each set of pressure data and converts it into time series data, filters out invalid readings and arranges them according to time points to generate the periodic compression ratio.

[0090] Based on the current monitoring period (t=0,1,2s), obtain the suction pressure value of the bipolar compressor (P collected by the pressure sensor). 吸 = [0.40, 0.41, 0.39] MPa) and exhaust pressure value (P 排 =[1.60,1.62,1.58]MPa), calculate the pressure ratio for each set of pressure data ( When m=0 When m=1 When m=2 CR m The compression ratio is represented by m, and the time point is represented by m. Invalid readings are filtered out (a threshold is set: pressure values ​​<0.1MPa or >3.0MPa are invalid; in this example, all data are valid). The sequence [4.0, 3.951, 4.051] is arranged according to the time point to generate the cycle compression ratio.

[0091] The deviation calculation submodule calls the period compression ratio, combines the compression ratio adjustment range, calculates the difference between each compression ratio data point and the upper and lower boundaries of the corresponding adjustment range, identifies all time periods that exceed the range and analyzes the average amount of the offset magnitude to obtain the compression ratio deviation.

[0092] The call period compression ratio (sequence [4.0, 3.951, 4.051]) and compression ratio adjustment interval (interval [0s, 2s], variation range ΔCR = 0.100, upper and lower bounds are CR) are used. min =3.951, CR max =4.051), CR min Represents the lower boundary of the minimum compression ratio, CR max The upper boundary represents the maximum compression ratio. For each data point, the difference between the upper and lower boundaries is calculated (when m = 0: upper boundary deviation δ). 上,0 =|4.0-4.051|=0.051, lower bound deviation δ 下,0 =|4.0-3.951|=0.049, δ when m=1 上,1 =|3.951-4.051|=0.100, δ 下,1 =|3.951-3.951|=0, δ when m=2 上,2 =|4.051-4.051|=0, δ 下,2 =|4.051-3.951|=0.100), identify time periods outside the interval (if CR m >CR max or CRm <CR min If it exceeds the limit, then in this example, when m = 0, 4.0 ∈ [3.951, 4.051] does not exceed the limit, CR m The compression ratio at point m represents the time point (m=1 and m=2 are not exceeded). The offset amplitude is analyzed (for periods without exceeding the time limit, the average offset is 0), and the compression ratio deviation is obtained (result: 0).

[0093] The status judgment submodule, based on the compression ratio deviation, combines the currently collected carbon dioxide refrigerant evaporation pressure and refrigerant outlet temperature, performs joint partition comparison of the three values ​​according to the carbon dioxide system operating baseline value, sets status labels, and then matches the degree of deviation with the status labels to obtain the compression ratio judgment result.

[0094] Based on the compression ratio deviation (0), combined with the currently collected carbon dioxide refrigerant evaporation pressure P evap (P evap =[2.0,2.1,1.9]MPa) ​​and refrigerant outlet temperature T out (T out = [45.0, 45.3, 44.8]℃), based on the carbon dioxide system operating reference value (evaporation pressure reference P) evap,ref =2.0MPa, outlet temperature reference T out,ref =45.0℃), and perform joint zoning comparison (defining zoning rules: deviation δ, evaporation pressure deviation ΔP). evap,m =P evap,m -P evap,ref Temperature deviation ΔT out,m =T out,m -T out,ref When m = 0, ΔP evap,0 =0.0, ΔT out,0 =0.0, set the state label rule: if δ=0 and |ΔP evap,m |≤0.1 and|ΔT out,m If |≤0.5, mark as “stable”; otherwise, mark as “abnormal”. Matching deviation degree (in this example, all points meet the stability condition), and obtain the compression ratio determination result (sequence [stable, stable, stable]).

[0095] Table 2: Status Judgment Data Table

[0096] Time point Evaporation pressure deviation outlet temperature deviation Status Label 0 0.0 0.0 Stablize 1 0.1 0.3 Stablize 2 -0.1 -0.2 Stablize

[0097] Table 2 lists the measured data of the joint partition comparison.

[0098] Specifically, such as Figure 2 , 7 As shown, the adjustment and optimization module includes:

[0099] The adjustment parameter selection submodule calls the compression ratio judgment result, sets the corresponding compression state level according to the judgment label, determines the adjustable compressor return gas flow range and expansion valve opening range within the current operating cycle, and obtains the adjustment control parameters.

[0100] Call the compression ratio determination result (sequence [stable, stable, stable]), set the compression status level according to the status label (label, stable corresponds to level L=1, rule: stable → 1, fluctuating → 2, abnormal → 3), determine the adjustable range of return gas flow (level L=1 corresponds to [0.45, 0.55] kg / s, according to the compressor model MX-200 technical manual), expansion valve opening range (level L=1 corresponds to [40%, 60%], according to expansion valve specification EV-05), select the flow adjustment value of 0.48 kg / s at t=0s (base flow 0.50×(1-0.04)=0.48), and the opening adjustment value of 52% (base opening 50×(1+0.04)=52), and obtain the adjustment control parameters (flow range [0.45, 0.55] kg / s, opening range [40%, 60%]).

[0101] The control quantity adjustment submodule adjusts the set value of the return gas flow of the bipolar compressor and the opening value of the expansion valve based on the adjustment control parameters. It collects and summarizes the refrigerant evaporation pressure, condensation temperature and compressor operating frequency, calculates the total energy consumption after adjustment and the difference between it and the original energy consumption data, and obtains the energy consumption change rate.

[0102] Based on the adjusted control parameters (flow range [0.45, 0.55] kg / s, opening range [40%, 60%]), the return gas flow rate was set (t = 0s: 0.48 kg / s, t = 1s: 0.52 kg / s), and the expansion valve opening was adjusted to 52% at t = 0s and 58% at t = 1s. The refrigerant evaporation pressure (measured by sensor P-S01 [2.01, 2.08] MPa) and condensation temperature (measured by sensor T-C02 [44.9, 45.2] °C) were collected. comp,m The compressor frequency (inverter reading [48.0, 49.5] Hz) is used to calculate the total energy consumption (Formula E). m =P in,m ×Δt, E m The m-th time point represents the total energy consumption after the adjustment operation, and the compressor input power P. in,m =f comp,m ×V dis ×η mech Displacement V dis =0.02m 3 / r, mechanical efficiency η mech =0.92, Δt=1s, Example m=0: P in,0= 48.0 × 0.02 × 0.92 = 0.8832 kW, E0 = 0.8832 × 1 / 3600 ≈ 0.000245 kWh (unit conversion), original energy consumption baseline value (η mentioned above) m In reverse, c p = 4.18 kJ / (kg·K), ΔT m =35.0℃, η0 = 6.099, η m c represents the rate of change of energy efficiency ratio at time point m. p η represents specific heat capacity, η0 represents initial energy efficiency ratio, and the example is provided. (representing refrigerant mass flow rate), ΔT m E represents the temperature difference in cooling. 原,m Representing the baseline energy consumption, the energy consumption difference ΔE0 = 0.000245 - 0.000249 = -0.000004 kWh, and the energy consumption change rate. ΔE m This represents the adjusted energy consumption. Similarly, when m=1, we can calculate λ1≈-0.0120, and obtain the energy consumption change rate sequence [-0.0161,-0.0120].

[0103] The performance impact assessment submodule maps the adjustment variables to energy consumption changes based on the energy consumption change rate, combined with the adjustment range of return air flow and expansion valve opening in the control parameters. It then filters the parameter combinations with negative energy consumption change rates and their corresponding compression ratio response space to obtain the compression ratio adjustment results.

[0104] Based on the energy consumption change rate sequence [-0.0161, -0.0120], combined with the return gas flow rate adjustment range (t = 0: -4% (calculated as (0.48 - 0.50) / 0.50 × 100 = -4) and the expansion valve opening adjustment range (t = 0: +4% ((52 - 50) / 50 × 100 = +4), a mapping between the adjustment variable and energy consumption change is constructed (parameter combination: (flow rate adjustment %, opening adjustment %), corresponding to λ), where λ represents the energy consumption change rate. Negative growth combinations are selected (setting condition λ < 0, which is satisfied in this example). The compression ratio response space is calculated (formula). ΔCR m This represents the change in compression ratio, with a sensitivity coefficient k1 = 0.05 (the compression ratio changes by 0.05 for every 1% change in flow rate, based on a fitting of the compressor characteristic curve). Δθ represents the percentage of refrigerant mass flow rate. ％The percentage adjustment of the expansion valve opening is represented by the sensitivity coefficient k2 = 0.02 (the compression ratio changes by 0.02 for every 1% change in opening, based on the expansion valve flow experiment). For example, when m = 0: ΔCR0 = 0.05 × (-4) + 0.02 × 4 = -0.20 + 0.08 = -0.12, when m = 1: ΔCR1 = 0.05 × 4 + 0.02 × 8 = 0.20 + 0.16 = 0.36 (opening adjustment +8%), and the compression ratio adjustment result sequence is obtained as [-0.12, 0.36].

[0105] Table 3: Performance Impact Assessment Data Table

[0106] Time point Flow adjustment (%) Opening adjustment (%) Energy consumption change rate Compression ratio variation 0 -4 +4 -0.0161 -0.12 1 +4 +8 -0.0120 +0.36

[0107] Please see Figure 8 An energy-saving and environmentally friendly carbon dioxide refrigeration method is implemented based on the aforementioned energy-saving and environmentally friendly carbon dioxide refrigeration system, and includes the following steps:

[0108] S1: Collect the outlet pressure of the evaporator and condenser and analyze the pressure gradient. Combine the suction and exhaust flow of the bipolar compressor to construct the operating state vector. Calculate the correlation of the fluctuation trend through the Pearson correlation coefficient and generate a set of fluctuation parameters.

[0109] S2: Based on the fluctuation parameter group, the compressor temperature and power are collected and the energy efficiency ratio is calculated. The trend of energy efficiency ratio change is analyzed and the energy efficiency fluctuation is judged in combination with the pressure gradient. The energy efficiency fluctuation judgment result is generated.

[0110] S3: Call the energy efficiency fluctuation judgment result to obtain the compressor compression ratio, combine it with the compressor energy efficiency ratio to construct a trend line to analyze the trend direction, and calculate the compression ratio adjustment range;

[0111] S4: Obtain the current cycle compression ratio, calculate the deviation from the compression ratio adjustment range, determine the status of the carbon dioxide refrigeration system, and generate the compression ratio determination result;

[0112] S5: Call the compression ratio determination result, adjust the compressor return gas flow and expansion valve opening, record the energy efficiency impact of the adjustment on the carbon dioxide refrigeration system, and determine the matching compression ratio adjustment result.

[0113] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An energy-saving and environmentally friendly carbon dioxide refrigeration system, characterized in that, The system includes: The state analysis module collects the outlet pressure of the evaporator and condenser and analyzes the pressure gradient. It constructs an operating state vector by combining the suction and discharge flow of the bipolar compressor, calculates the fluctuation correlation intensity through the Pearson correlation coefficient, generates a set of fluctuation parameters, and transmits them to the energy efficiency analysis module. The energy efficiency analysis module collects compressor temperature and power based on the fluctuation parameter group and calculates the energy efficiency ratio. It analyzes the trend of energy efficiency ratio change and judges energy efficiency fluctuation in combination with pressure gradient. It generates energy efficiency fluctuation judgment result and transmits it to the compression ratio calculation module. The compression ratio calculation module calls the energy efficiency fluctuation judgment result to obtain the compressor's compression ratio, combines the compressor's energy efficiency ratio to construct a trend line to analyze the trend direction, calculates the compression ratio adjustment range, and transmits it to the risk judgment module. The risk assessment module obtains the current cycle compression ratio, calculates the deviation from the compression ratio adjustment range, determines the status of the carbon dioxide refrigeration system, generates a compression ratio assessment result, and transmits it to the adjustment and optimization module. The adjustment and optimization module calls the compression ratio determination result, adjusts the compressor return gas flow and expansion valve opening, records the energy efficiency impact of the adjustment on the carbon dioxide refrigeration system, and determines the matching compression ratio adjustment result.

2. The energy-saving and environmentally friendly carbon dioxide refrigeration system according to claim 1, characterized in that: The fluctuation parameter set includes pressure gradient change value, intake and exhaust flow difference, and state vector correlation coefficient. The energy efficiency fluctuation judgment result includes energy efficiency ratio change amplitude, pressure gradient corresponding interval, and energy efficiency trend direction. The compression ratio adjustment interval specifically includes upper limit of compression ratio, lower limit of compression ratio, and trend change slope. The compression ratio judgment result includes compression ratio deviation, refrigerant condition label, and status identification mark. The compression ratio adjustment result includes return gas flow adjustment amplitude, expansion valve opening adjustment amplitude, and energy consumption change rate.

3. The energy-saving and environmentally friendly carbon dioxide refrigeration system according to claim 1, characterized in that, The status analysis module includes: The pressure monitoring submodule collects the outlet pressure of the evaporator and condenser and performs synchronous calibration. It calculates the difference between the evaporator and condenser pressure values ​​at each time point, calculates the pressure difference change sequence over a continuous time period, obtains the pressure difference change rate in multiple window intervals, and generates pressure gradient values. The state vector construction submodule calls the pressure gradient value, collects the intake and exhaust flow rates of the bipolar compressor, matches and integrates the three-dimensional operating state data group with the pressure gradient value, and arranges it into a vector set in chronological order to generate an operating state vector group. The fluctuation parameter generation submodule, based on the running state vector group, analyzes the vector groups between adjacent time periods through Pearson correlation coefficient, calculates the fluctuation correlation strength, performs peak and valley identification and amplitude analysis, extracts the time periods with the best fluctuation degree and labels the corresponding features, and generates the fluctuation parameter group.

4. The energy-saving and environmentally friendly carbon dioxide refrigeration system according to claim 1, characterized in that, The energy efficiency analysis module includes: The energy efficiency calculation submodule, based on the fluctuation parameter group, collects the temperature and power of the bipolar compressor in multiple time periods and performs synchronous pairing. It calculates the refrigeration temperature difference value under the compressor power in each time period and performs ratio processing to establish the operating energy efficiency ratio sequence in each cycle and generate an energy efficiency ratio value group. The trend recognition submodule calls the energy efficiency ratio value group, calculates the difference of energy efficiency ratio in continuous intervals according to time sequence, detects the magnitude and direction of change of energy efficiency ratio in continuous time period, extracts and labels the trend type of multiple change segments according to the change rate change points, and generates the energy efficiency ratio change trend. The fluctuation determination submodule pairs the energy efficiency ratio change trend with the pressure gradient, calculates the directional comparison parameters, filters the intervals where the energy efficiency change and pressure fluctuation are consistent and classifies and labels them, and generates the energy efficiency fluctuation determination result.

5. The energy-saving and environmentally friendly carbon dioxide refrigeration system according to claim 1, characterized in that, The compression ratio calculation module includes: The compression ratio extraction submodule, based on the energy efficiency fluctuation determination result, obtains the intake pressure value and exhaust pressure value of the two-stage compressor within the corresponding time period, performs synchronous calibration on the pressure data within each time period and calculates the compression ratio value, arranges them in chronological order to form a compression ratio sequence, and generates compression ratio change results. The trend line fitting submodule calls the compression ratio change results, combines them with the energy efficiency ratio numerical group under the same time period, uses linear fitting to construct a trend line of compression ratio and energy efficiency ratio change for the paired data, calculates the slope of the trend line and generates the trend direction coefficient. The adjustment interval calculation submodule selects the time point when the slope direction of the compression ratio changes according to the trend direction coefficient, divides the compression ratio change value in adjacent change segments into intervals and marks the start and end boundaries of the adjustment, calculates the range of compression ratio variation in each segment, and obtains the compression ratio adjustment interval.

6. The energy-saving and environmentally friendly carbon dioxide refrigeration system according to claim 5, characterized in that, The trend direction coefficient is set by judging the trend and direction of change of the compression ratio and energy efficiency ratio through fitting time series data.

7. The energy-saving and environmentally friendly carbon dioxide refrigeration system according to claim 1, characterized in that, The risk assessment module includes: The compression ratio acquisition submodule acquires the intake and exhaust pressure values ​​of the bipolar compressor within the current monitoring period, calculates the pressure ratio for each set of pressure data and converts it into time series data, filters out invalid readings and arranges them according to time points to generate the periodic compression ratio. The deviation calculation submodule calls the period compression ratio, combines it with the compression ratio adjustment range, calculates the difference between each compression ratio data point and the upper and lower boundaries of the corresponding adjustment range, identifies all time periods that exceed the range and analyzes the average amount of the offset amplitude to obtain the compression ratio deviation. The status judgment submodule, based on the compression ratio deviation, combines the currently collected carbon dioxide refrigerant evaporation pressure and refrigerant outlet temperature, performs a joint partition comparison of the three values ​​according to the carbon dioxide system operating baseline value, sets status labels, and then matches the degree of deviation with the status labels to obtain the compression ratio judgment result.

8. The energy-saving and environmentally friendly carbon dioxide refrigeration system according to claim 7, characterized in that, The operating baseline values ​​of the carbon dioxide system are set by referencing the standard refrigeration cycle model to set typical operating point parameters, and by combining the current operating range of the system, selecting the evaporation pressure value, exhaust temperature value and the conventional range of compression ratio as the baseline settings.

9. The energy-saving and environmentally friendly carbon dioxide refrigeration system according to claim 1, characterized in that, The adjustment and optimization module includes: The adjustment parameter selection submodule calls the compression ratio determination result, sets the corresponding compression state level according to the determination label, determines the adjustable compressor return gas flow range and expansion valve opening range within the current operating cycle, and obtains the adjustment control parameters. The control quantity adjustment submodule adjusts the set value of the return gas flow rate of the bipolar compressor and the opening value of the expansion valve based on the adjustment control parameters, collects and summarizes the refrigerant evaporation pressure, condensation temperature and compressor operating frequency, calculates the total energy consumption after adjustment and the difference between it and the original energy consumption data, and obtains the energy consumption change rate. The performance impact assessment submodule, based on the energy consumption change rate and combined with the adjustment range of return air flow and expansion valve opening in the regulation control parameters, maps the adjustment variables to energy consumption changes, filters parameter combinations with negative energy consumption change rates and their corresponding compression ratio response spaces, and obtains the compression ratio adjustment results.

10. An energy-saving and environmentally friendly carbon dioxide refrigeration method, characterized in that, The energy-saving and environmentally friendly carbon dioxide refrigeration system according to any one of claims 1-9 is implemented. Includes the following steps: S1: Collect the outlet pressure of the evaporator and condenser and analyze the pressure gradient. Combine the suction and exhaust flow of the bipolar compressor to construct the operating state vector. Calculate the correlation of the fluctuation trend through the Pearson correlation coefficient and generate a set of fluctuation parameters. S2: Based on the fluctuation parameter group, the compressor temperature and power are collected and the energy efficiency ratio is calculated. The energy efficiency ratio change trend is analyzed and the energy efficiency fluctuation is judged in combination with the pressure gradient. The energy efficiency fluctuation judgment result is generated. S3: Call the energy efficiency fluctuation judgment result to obtain the compressor compression ratio, combine it with the compressor energy efficiency ratio to construct a trend line to analyze the trend direction, and calculate the compression ratio adjustment range; S4: Obtain the current cycle compression ratio, calculate the deviation from the compression ratio adjustment range, determine the status of the carbon dioxide refrigeration system, and generate a compression ratio determination result; S5: Call the compression ratio determination result, adjust the compressor return gas flow and expansion valve opening, record the energy efficiency impact of the adjustment on the carbon dioxide refrigeration system, and determine the matching compression ratio adjustment result.

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