Medical refrigerator compressor refrigeration temperature control system and method based on big data

Abstract

The present invention relates to the field of refrigeration control technology, specifically to a medical refrigerator compressor refrigeration temperature control system and method based on big data, wherein the system includes a storage item identification module, a heat capacity calculation module, a compressor load adjustment module, a refrigerant flow control module, and a compressor performance correction module. The present invention accurately identifies the types and characteristics of stored items, extracts detailed parameters, and uses high-precision temperature sensors and environmental parameter sets to dynamically calculate refrigeration requirements, thereby achieving accurate adjustment of compressor output power, ensuring temperature stability and drug quality in medical refrigerators, and achieving more accurate temperature control strategies by refining the storage requirements of each drug and optimizing the refrigerant supply. By correcting the performance parameters of the compressor, the overall energy efficiency and reliability are improved, which can more effectively reduce energy waste, ensure the optimization of storage conditions, and significantly improve the overall efficiency and response speed of the refrigeration system.

Description

Technical Field

[0001] The present invention relates to the field of refrigeration control technology, and in particular to a medical refrigerator compressor refrigeration temperature control system and method based on big data. Background Art

[0002] The field of refrigeration control technology includes core technologies such as temperature regulation, compressor drive control, and refrigerant cycle optimization. Among them, temperature regulation involves temperature detection, temperature setting, and feedback regulation. Temperature data is usually collected through thermistors, thermocouples, or semiconductor temperature sensors, and the operating status of the refrigeration system is adjusted in combination with algorithms such as PID control and fuzzy control. Compressor drive control covers variable frequency control, start-stop strategies, and energy efficiency optimization. Variable frequency drive circuits are usually used to control the compressor speed to achieve precise temperature control and reduce energy consumption. Refrigerant cycle optimization involves the regulation of condensation, evaporation, and expansion processes. The refrigerant flow is often controlled through an electronic expansion valve, and the system operation is adjusted in combination with the heat exchange parameters of the condenser and evaporator to ensure stable operation of the refrigeration system.

[0003] Among them, the medical refrigerator compressor refrigeration temperature control system refers to a system used to control the operating state of the medical refrigerator compressor to maintain a stable storage environment temperature. The system covers technical matters such as compressor start and stop management, temperature detection and feedback adjustment, and refrigerant flow control. Specifically, the compressor variable frequency adjustment circuit is used to control the compressor speed to match the load demand and reduce temperature fluctuations; a high-precision temperature sensor is used to detect the storage cavity temperature, and the temperature deviation is calculated by combining fuzzy control or PID control algorithms, and a control signal is generated by a microprocessor to adjust the compressor operation; an electronic expansion valve is used to dynamically adjust the refrigerant supply according to the refrigerant flow demand to optimize the evaporator heat exchange efficiency and improve the temperature control accuracy.

[0004] Traditional control systems cannot accurately respond to the specific needs of different storage items. For example, for medical refrigerators, traditional control systems rely only on basic temperature feedback mechanisms and fail to take into account the specific thermal characteristics and storage needs of stored items, resulting in inaccurate temperature control, which can cause drugs to lose effectiveness due to inappropriate temperatures. In addition, due to the lack of the ability to correct compressor performance in real time, traditional systems do not respond quickly or accurately when faced with environmental fluctuations or load changes, which affects the refrigeration effect and energy efficiency, resulting in unnecessary energy consumption. This limits the application effect of refrigeration equipment in specific medical protection environments and has a negative impact on drug safety and the quality of medical services.

[0005] The Chinese patent with the authorization announcement number CN112524836B provides a three-pipe multi-split system and its control method, including an outdoor unit, a multi-split indoor unit and a hydraulic module. Although it optimizes the refrigerant system to avoid throttling of the refrigerant before flowing through the refrigerant heat dissipation module or overcooling through the plate heat exchanger, so that the temperature of the refrigerant entering the refrigerant heat dissipation module is relatively low, causing condensation in the refrigerant heat dissipation module, and then the condensation water damages the compressor frequency conversion module, and more refrigerant flows through the refrigerant heat dissipation module to reduce the module temperature, and at the same time cooperates with the system to increase the control logic to avoid the module temperature being too high, effectively reducing the compressor module temperature, but it lacks the ability to correct the compressor performance in real time. When facing environmental fluctuations or load changes, it cannot accurately control the refrigerated storage temperature. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a medical refrigerator compressor refrigeration temperature control system and method based on big data.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A medical refrigerator compressor refrigeration temperature control system based on big data, the system comprising:

[0009] The storage item recognition module extracts the specific heat capacity, density, thermal conductivity and storage temperature requirements of the items stored in the medical drug database based on the medical items in the storage cavity of the medical refrigerator, and calculates the temperature difference between the inside and outside of the storage cavity to obtain a storage environment parameter set;

[0010] The heat capacity calculation module calls the storage environment parameter set, calculates the heat capacity of the air in the storage cavity, calculates the refrigeration power requirement value per unit time in combination with the heat exchange rate per unit mass of the stored items, and obtains the refrigeration power requirement information of the medical refrigerator;

[0011] A compressor load adjustment module, which calls the refrigeration power demand information of the medical refrigerator, calculates the deviation between the current output power of the compressor and the refrigeration demand, adjusts the power output of the compressor, and obtains the compressor load adjustment parameter;

[0012] The refrigerant flow control module calls the compressor load adjustment parameter, obtains the electronic expansion valve opening, the refrigerant flow rate and the evaporator heat exchange rate, calculates the refrigerant supply and demand difference, adjusts the electronic expansion valve opening, and obtains the refrigerant flow adjustment parameter;

[0013] The compressor performance correction module calls the refrigerant flow adjustment parameter, calculates the compressor operation efficiency offset, corrects the compressor target load parameter, and obtains the compressor operation correction information.

[0014] Furthermore, the storage environment parameter set includes storage cavity temperature, storage cavity humidity, storage cavity air pressure, storage item category, storage item thermal parameters and temperature difference between the inside and outside of the cavity; the medical refrigerator refrigeration power demand information includes storage cavity air unit heat capacity, storage cavity total heat capacity, medical refrigerator internal and external temperature change rate, total heat capacity fluctuation and refrigeration load matching; the compressor load adjustment parameters include compressor current output power, refrigeration demand deviation and compressor target operating frequency; the refrigerant flow adjustment parameters include electronic expansion valve opening, refrigerant flow rate, evaporator heat exchange rate and refrigerant supply and demand difference; the compressor operation correction information includes compressor vibration frequency, condensing pressure fluctuation, refrigeration power change rate, compressor operation offset, set operation threshold and compressor target load parameters.

[0015] Further, the stored item identification module includes:

[0016] The item information parsing submodule, based on the medical items in the storage cavity of the medical refrigerator, calls the RFID tag information and barcode data, extracts the identification code corresponding to the item category, compares it with the database record, screens the matching medical item category code, and obtains the item category code set;

[0017] A storage parameter extraction submodule retrieves the specific heat capacity, density, thermal conductivity and storage temperature requirements of the corresponding items from a large database of medical drugs based on the item category code set to obtain storage parameter information;

[0018] The storage environment calculation submodule, based on the storage parameter information, calls the cold storage area sensor network to collect temperature data inside and outside the storage cavity, using the formula:

[0019] ;

[0020] The average temperature difference within the time period is calculated and obtained to obtain a storage environment parameter set;

[0021] in, Represents the average temperature difference between the inside and outside of the storage chamber, Representing time point The external temperature of the storage chamber is Representing time point The internal temperature of the storage chamber is Represents the total number of samples in the time interval.

[0022] Further, the heat capacity calculation module includes:

[0023] The storage cavity parameter acquisition submodule calls the storage environment parameter set to obtain the temperature, humidity and heat exchange rate of the air in the storage cavity of the medical refrigerator, and screens the effective heat exchange parameters in combination with the heat exchange boundary conditions inside the storage cavity to obtain the storage cavity heat exchange parameters;

[0024] The storage object heat capacity calculation submodule calculates the heat capacity of the air in the storage cavity based on the heat exchange parameters of the storage cavity, and calculates and obtains the total heat capacity of the stored objects in combination with the heat exchange rate per unit mass of the stored objects;

[0025] The cooling power demand calculation submodule calls the total heat capacity of the stored items and uses the formula:

[0026] ;

[0027] Calculate and obtain the refrigeration power demand per unit time, and obtain the refrigeration power demand information of the medical refrigerator;

[0028] in, Represents the cooling power demand per unit time, represents the calculation time interval, Representative The quality of the stored items, Representative The specific heat capacity of the storage items, Representative The class stores the current temperature of an item. Representative Storage temperature of the stored items, represents the heat transfer coefficient between the cavity and outside, represents the heat transfer area of ​​the cavity, Represents the average temperature difference between the inside and outside of the storage chamber, Represents the number of types of stored items.

[0029] Further, the compressor load adjustment module includes:

[0030] The compressor operation status acquisition submodule calls the refrigeration power demand information of the medical refrigerator, obtains the compressor operation current, start-stop frequency and exhaust temperature, and screens each parameter to obtain the compressor operation status parameter;

[0031] The compressor power deviation calculation submodule calculates the current output power of the compressor based on the compressor operating state parameters and compares it with the refrigeration power demand information of the medical refrigerator using the formula:

[0032] ;

[0033] Calculate and obtain the power deviation value to obtain the compressor power deviation information;

[0034] in, Represents the power deviation value, Represents the number of compressor operations. Representative The running current of the first operation, Representative The voltage of the second operation, Representative The compressor operating efficiency of each operation, Indicates Duration of the run, Represents the cooling power demand value per unit time;

[0035] The compressor power adjustment submodule calculates the adjusted power correction amount based on the compressor power deviation information, adjusts the power output of the compressor, and obtains the compressor load adjustment parameter.

[0036] Furthermore, the refrigerant flow control module includes:

[0037] The refrigerant supply and demand parameter acquisition submodule calls the compressor load adjustment parameter, obtains the electronic expansion valve opening, the refrigerant flow rate and the evaporator heat exchange rate, and performs data normalization processing to obtain the refrigerant supply and demand parameters;

[0038] The refrigerant supply and demand deviation calculation submodule adopts the formula based on the refrigerant supply and demand parameters:

[0039] ;

[0040] Calculate and obtain the refrigerant supply and demand deviation, and obtain the refrigerant supply and demand deviation information;

[0041] in, Represents the deviation between refrigerant supply and demand, Represents the number of measurement points on the refrigerant flow path, Representative The refrigerant mass flow rate at each measuring point, Representative Refrigerant inlet enthalpy at each measuring point, Representative The refrigerant outlet enthalpy at each measuring point, represents the evaporator heat exchange rate;

[0042] The electronic expansion valve adjustment submodule calculates the adjusted valve control amount based on the refrigerant supply and demand deviation information, adjusts the opening of the electronic expansion valve, and obtains the refrigerant flow adjustment parameter.

[0043] Further, the compressor performance correction module includes:

[0044] The compressor operation status acquisition submodule calls the refrigerant flow adjustment parameter to obtain the compressor vibration frequency, condensing pressure fluctuation and refrigeration power change rate, and performs data preprocessing to obtain the compressor operation status parameters;

[0045] The operating efficiency offset calculation submodule calculates the current operating efficiency of the compressor based on the compressor operating state parameters and compares it with the target operating efficiency using the formula:

[0046] ;

[0047] Calculate and obtain the operating efficiency offset, and obtain the compressor operating efficiency offset information;

[0048] in, Represents the operating efficiency offset, Represents the number of measurement points in the operating state, Representative The compressor output power at each measuring point is Representative The compressor input power at each measuring point, Represents the set target operating efficiency;

[0049] The target load correction submodule determines whether the offset exceeds a set operating threshold based on the compressor operating efficiency offset information. If the offset exceeds the set operating threshold, the compressor target load parameter is adjusted to obtain the compressor operation correction information.

[0050] The medical refrigerator compressor refrigeration temperature control method based on big data is executed according to the above-mentioned medical refrigerator compressor refrigeration temperature control system based on big data, and includes the following steps:

[0051] S1: Based on the medical items in the storage cavity of the medical refrigerator, the specific heat capacity, density, thermal conductivity and storage temperature requirements of the items stored in the medical drug database are extracted, and the temperature difference inside and outside the storage cavity is calculated to obtain the storage environment parameter set;

[0052] S2: calling the storage environment parameter set, calculating the heat capacity of the air in the storage cavity, combining the heat exchange rate per unit mass of the stored items, calculating the refrigeration power requirement value per unit time, and obtaining the refrigeration power requirement information of the medical refrigerator;

[0053] S3: calling the refrigeration power demand information of the medical refrigerator, calculating the deviation between the current output power of the compressor and the refrigeration demand, adjusting the power output of the compressor, and obtaining the compressor load adjustment parameter;

[0054] S4: calling the compressor load adjustment parameter, obtaining the electronic expansion valve opening, the refrigerant flow rate and the evaporator heat exchange rate, calculating the refrigerant supply and demand difference, adjusting the electronic expansion valve opening, and obtaining the refrigerant flow adjustment parameter;

[0055] S5: calling the refrigerant flow adjustment parameter, calculating the compressor operation efficiency offset, correcting the compressor target load parameter, and obtaining the compressor operation correction information.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are:

[0057] In the present invention, by accurately identifying the types and characteristics of stored items, extracting detailed parameters, and using high-precision temperature sensors and environmental parameter sets, the refrigeration demand is dynamically calculated, thereby achieving precise adjustment of the compressor output power, ensuring the temperature stability and drug quality in the medical refrigerator, and achieving a more accurate temperature control strategy by refining the storage requirements of each drug and optimizing the refrigerant supply. By correcting the performance parameters of the compressor, the overall energy efficiency and reliability are improved, which can more effectively reduce energy waste, ensure the optimization of storage conditions, and significantly improve the overall efficiency and response speed of the refrigeration system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a system flow chart of the present invention;

[0059] Figure 2 A flowchart of a storage object identification module of the present invention;

[0060] Figure 3 This is a flow chart of the heat capacity calculation module of the present invention;

[0061] Figure 4 A flow chart of a compressor load adjustment module of the present invention;

[0062] Figure 5 It is a flow chart of the refrigerant flow control module of the present invention;

[0063] Figure 6 It is a flow chart of the compressor performance correction module of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] See also Figure 1 The present invention provides a technical solution: a medical refrigerator compressor refrigeration temperature control system based on big data, the system includes:

[0066] The storage item identification module, based on the medical items in the storage cavity of the medical refrigerator, calls the RFID tag information and barcode data, analyzes the storage item category, extracts the specific heat capacity, density, thermal conductivity and storage temperature requirements of the items stored in the medical drug database, calls the cold storage area sensor network, collects the temperature inside and outside the storage cavity, calculates the temperature difference, and obtains the storage environment parameter set;

[0067] The heat capacity calculation module calls the storage environment parameter set, obtains the air temperature, humidity and heat exchange rate of the medical refrigerator storage cavity, calculates the heat capacity of the air in the storage cavity, calculates the total heat capacity of the stored items in combination with the heat exchange rate per unit mass of the stored items, calls the cavity cooling rate, calculates the refrigeration power demand value per unit time, and obtains the refrigeration power demand information of the medical refrigerator;

[0068] The compressor load adjustment module calls the refrigeration power demand information of the medical refrigerator, obtains the compressor operating current, start-stop frequency and exhaust temperature, calculates the deviation between the current output power of the compressor and the refrigeration demand, adjusts the power output of the compressor, and obtains the compressor load adjustment parameters;

[0069] The refrigerant flow control module calls the compressor load adjustment parameters, obtains the electronic expansion valve opening, the refrigerant flow rate and the evaporator heat exchange rate, calculates the difference between the refrigerant supply and demand, adjusts the electronic expansion valve opening, and obtains the refrigerant flow adjustment parameters;

[0070] The compressor performance correction module calls the refrigerant flow adjustment parameters, obtains the compressor vibration frequency, condensing pressure fluctuation, and refrigeration power change rate, calculates the compressor operation efficiency offset, and if the offset exceeds the set operation threshold, corrects the compressor target load parameters to obtain the compressor operation correction information;

[0071] The storage environment parameter set includes the storage cavity temperature, storage cavity humidity, storage cavity air pressure, storage item category, storage item thermal parameters and the temperature difference between the inside and outside of the cavity. The refrigeration power demand information of the medical refrigerator includes the unit heat capacity of the storage cavity air, the total heat capacity of the storage cavity, the temperature change rate inside and outside the medical refrigerator, the total heat capacity fluctuation and the refrigeration load matching. The compressor load adjustment parameters include the current output power of the compressor, the refrigeration demand deviation and the target operating frequency of the compressor. The refrigerant flow adjustment parameters include the opening of the electronic expansion valve, the refrigerant flow rate, the evaporator heat exchange rate and the refrigerant supply and demand difference. The compressor operation correction information includes the compressor vibration frequency, condensing pressure fluctuation, refrigeration power change rate, compressor operation offset, set operation threshold and compressor target load parameters.

[0072] See also Figure 2 , the storage item identification module includes:

[0073] The item information parsing submodule, based on the medical items in the storage cavity of the medical refrigerator, calls the RFID tag information and barcode data, extracts the identification code corresponding to the item category, compares it with the database record, screens the matching medical item category code, and obtains the item category code set;

[0074] First, the RFID tag information and barcode data are called, and the EPC code and barcode data in the RFID tag are read for each stored medical item to obtain its unique identification mark, and the identification mark is parsed in data format, and the EPC code is converted to hexadecimal format. At the same time, the product identification code in the barcode is extracted and converted into a database searchable format, and then the database index query function is called to match the data according to the converted EPC code and product identification code. In the case where multiple EPC codes may correspond to the same product, the product identification code is used as an auxiliary screening condition, and the logical matching algorithm is used to compare the EPC code and barcode combination one by one to see if there is a unique corresponding relationship. If the unique match is successful, the category code of the item is determined. If there are multiple matches Items are combined with the item classification rules in the database, and are screened according to factors such as the applicable category of the commodity storage environment, batch number, and manufacturer information. The best matching item is selected as the item category code, and finally an item category code set is formed. This set is used for subsequent storage parameter extraction and storage environment calculation. For example, two RFID tags are detected in the storage cavity of a medical refrigerator, corresponding to EPC codes "0xABC1234" and "0xDEF5678", and barcode identification codes are "6930001234567" and "6930007654321" respectively. After calling the database for retrieval, it is matched that "0xABC1234" corresponds to drug A, and "0xDEF5678" corresponds to drug B, so the item category code set is {A, B}.

[0075] The storage parameter extraction submodule retrieves the specific heat capacity, density, thermal conductivity and storage temperature requirements of the corresponding items from the medical drug database based on the item category code set, traverses the item category code set, and for each code in the set, locates the physical property parameters of the drug of this category through the database index, calls the database query interface to obtain the specific values ​​of specific heat capacity, density and thermal conductivity, and extracts the minimum storage temperature and maximum storage temperature of the drug from the storage requirement parameter table to form complete storage parameter information. If the item category code set stored in a medical refrigerator is {A, B}, the specific heat capacity of Class A drugs is 3.8 J / (g·K) and the density is 1.1 g / cm 3 , thermal conductivity is 0.25 W / (m·K), storage temperature requirement is 2°C to 8°C, specific heat capacity of Class B drugs is 2.7 J / (g·K), density is 0.95 g / cm 3, thermal conductivity is 0.18 W / (m·K), and storage temperature requirement is 4°C to 10°C. The storage parameter information is shown in Table 1 below.

[0076] Table 1: Drug storage parameters

[0077]

[0078] As shown in Table 1, the storage parameter information will be used as an input variable in the subsequent calculation of the storage environment.

[0079] The storage environment calculation submodule, based on the storage parameter information, calls the cold storage area sensor network to collect temperature data inside and outside the storage cavity, using the formula:

[0080] ;

[0081] The average temperature difference within the time period is calculated and obtained to obtain a storage environment parameter set;

[0082] in, Represents the average temperature difference between the inside and outside of the storage chamber, Representing time point The external temperature of the storage chamber is Representing time point The internal temperature of the storage chamber is Represents the total number of samples in the time interval;

[0083] The temperature sensor in the storage cavity adopts a distributed measurement method. Temperature sensors are installed at multiple locations in the cold storage area. Each sensor collects data at an interval of 1 second and records the temperature value within the set time interval. The external temperature of the storage cavity is obtained by the external environment monitoring sensor and transmitted to the central processing unit via wireless transmission. The external temperature and internal temperature data at each time point are stored in the time series database respectively. When calculating the average temperature difference, all temperature data within the set time interval are traversed according to the formula:

[0084] ;

[0085] Sum the data at all time points and divide it by the number of sampling times N to obtain the temperature change trend. If in a certain period of time, the temperature data outside the storage cavity is {5.0, 5.2, 5.1, 5.3, 5.0, 4.8}°C, and the corresponding temperature data inside the storage cavity is {3.1, 3.2, 3.0, 3.1, 3.2, 3.3}°C, then the calculation is as follows:

[0086] ;

[0087] ;

[0088] The average temperature difference of the storage environment during this period is 1.92°C, and the temperature difference threshold is set to 3.0°C. The value is determined based on the long-term storage environment evaluation experiment data. The experiment statistically analyzes the temperature difference fluctuation range under different storage conditions, and takes the 95% quantile data as the upper limit to ensure that the temperature fluctuation is controlled within a reasonable range. The calculation result is used to evaluate the temperature stability of the cold storage area. If the value continues to exceed the set threshold of 3.0°C in multiple time periods, it is necessary to adjust the operating parameters of the refrigerator cooling system to reduce the impact of temperature fluctuations and ensure a stable storage environment.

[0089] See also Figure 3 , the heat capacity calculation module includes:

[0090] The storage cavity parameter acquisition submodule calls the storage environment parameter set to obtain the temperature, humidity and heat exchange rate of the air in the storage cavity of the medical refrigerator, and screens the effective heat exchange parameters in combination with the heat exchange boundary conditions inside the storage cavity to obtain the heat exchange parameters of the storage cavity;

[0091] First, the temperature data of multiple time points are extracted from the air temperature data set inside the storage cavity, and the average temperature within the time interval is calculated. Then, the humidity sensor data is called to sample the air humidity inside the storage cavity. The time series average calculation is used to obtain the mean air humidity of the storage cavity. Based on the heat exchange inside the storage cavity, the heat exchange rate data set of the inner and outer surfaces of the storage cavity is obtained. The abnormal data points are removed by screening to obtain effective heat exchange parameters. For the temperature and humidity fluctuations that may occur during the data collection process, the abnormal data threshold is set. If the temperature fluctuation exceeds the set temperature error range (for example, ±0.5°C), the time is eliminated. The temperature error range of ±0.5°C is set based on the stability requirements of the temperature control in the refrigeration area of ​​medical refrigerators. The threshold setting refers to the standard drug storage regulations, which stipulate that the storage temperature fluctuation should be controlled within ±0.5°C to ensure that the stored items will not be affected by abnormal short-term temperature changes. This value fluctuates with the ambient temperature. When the external temperature deviates from the target set temperature by more than 2°C for a long time, the error range may need to be appropriately reduced to ±0.3°C to avoid the refrigeration system from being unable to respond to large temperature changes in time. For example, the storage cavity temperature data set of a medical refrigerator is {2.1, 2.2, 2.0, 2.3, 2.1}°C, the humidity data set is {65.1, 66.0, 64.8, 65.4, 66.2}%RH, and the heat exchange rate data set is {5.2, 5.1,5.3, 5.4, 5.2} W / m 2 The heat exchange parameters of the storage cavity obtained after calculation are shown in Table 2 below.

[0092] Table 2: Storage cavity heat exchange parameters

[0093]

[0094] As shown in Table 2, the heat exchange parameters of the storage cavity are used for subsequent calculation of the heat capacity of the stored items.

[0095] The heat capacity calculation submodule of the stored items calculates the heat capacity of the air in the storage cavity based on the heat exchange parameters of the storage cavity, and calculates the total heat capacity of the stored items in combination with the heat exchange rate per unit mass of the stored items;

[0096] First, the total heat capacity of the air is calculated based on the mass, specific heat capacity and temperature change of the air in the storage cavity. The air mass is calculated from the air density inside the storage cavity and the volume of the storage cavity. The specific heat capacity of the air in the storage parameter information is called and calculated in combination with the air mass and temperature change. Then, the total heat capacity of the stored items is calculated in combination with the heat exchange rate per unit mass of the stored items. For example, if the storage cavity volume is 5m 3 , air density 1.225kg / m 3 , the specific heat capacity of air is 1005 J / (kg·K), and the temperature of the air in the storage chamber changes by 0.5°C. The heat capacity of the air in the storage chamber is calculated as follows:

[0097] ;

[0098] For the total heat capacity of the stored items, assuming that the stored items include Class A drugs with a mass of 3 kg and a specific heat capacity of 3.8 J / (g·K), and Class B drugs with a mass of 2 kg and a specific heat capacity of 2.7 J / (g·K), and the temperature changes of the two types of drugs are 0.4°C and 0.6°C respectively, the heat capacity of the stored items is calculated as follows:

[0099] ;

[0100] ;

[0101] ;

[0102] Finally, the heat capacity of the air in the storage cavity is 3070.125J, and the total heat capacity of the stored items is 7800J.

[0103] The cooling power demand calculation submodule calls the total heat capacity of the stored items and uses the formula:

[0104] ;

[0105] Calculate and obtain the refrigeration power demand per unit time, and obtain the refrigeration power demand information of the medical refrigerator;

[0106] in, Represents the cooling power demand per unit time, represents the calculation time interval, Representative The quality of the stored items, Representative The specific heat capacity of the storage items, Representative The class stores the current temperature of an item. Representative Storage temperature of the stored items, represents the heat transfer coefficient between the cavity and outside, represents the heat transfer area of ​​the cavity, Represents the average temperature difference between the inside and outside of the storage chamber, Represents the number of types of stored items;

[0107] First, determine the calculation time interval Δt, using the heat capacity data of the stored items and the heat exchange parameters of the air in the cavity, according to the formula:

[0108] ;

[0109] The calculation time interval Δt is set to 600 seconds, and the heat transfer coefficient h inside and outside the cavity is set to 5.5 W / (m 2 K), the cavity heat exchange area A is 2.5m 2 , the average temperature difference ΔT_avg is 1.92°C, and the total heat capacity of the stored items is 7800J for calculation:

[0110] ;

[0111] The refrigeration power requirement of the medical refrigerator is 39.4W, where the heat transfer coefficient h=5.5W / (m 2 The setting of K) is based on the material of the storage cavity and the convection heat transfer characteristics of the refrigerated environment. This value depends on the thermal conductivity of the inner wall material, the surface heat transfer characteristics and the air flow state of the cavity. When using high thermal conductivity materials (such as aluminum alloy), this value is usually 5.0W / (m 2 K) to 7.0W / (m 2 If the storage cavity is made of low thermal conductivity materials (such as ABS plastic), the value is usually reduced to 3.5W / (m 2 K) to 4.5W / (m 2 K), and is affected by the air flow rate inside the cavity. In the case of fan-assisted convection, this value can be increased by 10%-15%. The heat exchange area A=2.5m 2The setting basis is the internal structure of the storage cavity, and the calculation method is the effective convection heat exchange area inside the cavity, including the contact surface area of ​​the top, bottom and side walls. If there are multiple partition layers inside the cavity, this value needs to be adjusted according to the air convection conditions between the layers.

[0112] See also Figure 4 , the compressor load adjustment module includes:

[0113] The compressor operation status acquisition submodule calls the refrigeration power demand information of the medical refrigerator to obtain the compressor operation current, start and stop frequency and exhaust temperature, and screens each parameter to obtain the compressor operation status parameters;

[0114] First, the real-time operating current data is obtained from the current detection module of the compressor, and the time series data set is recorded. After the data recording is completed, the average operating current in the specified time interval is calculated. Then, the start-stop frequency data is obtained from the compressor controller, the number of starts and stops of the compressor per unit time is recorded, and the average start-stop interval of the compressor is calculated. The data interface of the exhaust temperature sensor is further called to read the compressor exhaust temperature, and abnormal data points are screened, and data mutation points are eliminated. The current screening threshold is set to ±10% of the rated working current. If the current exceeds this range, it is judged to be abnormal. The setting is based on the rated current working range of the compressor, and its range changes with the load and is affected by the ambient temperature. It is usually within 10% of the rated current. The value fluctuates within %; if it exceeds, it may be due to excessive load or abnormal operation. For the start-stop frequency, the maximum allowable value is set to 5 times / hour. According to the compressor working mode, the upper limit of this value depends on the start-stop loss of the compressor. If it is higher than this value, the compressor enters a frequent start-stop state, which may lead to a decrease in operating efficiency. This value is determined by the refrigeration cycle test data. The exhaust temperature threshold setting range is 70°C to 90°C. This range is determined by the compressor refrigerant characteristics and condensing temperature. If it is lower than 70°C, the refrigeration efficiency may decrease, and if it is higher than 90°C, it may cause carbonization of the lubricating oil. The data points that meet the requirements are screened, and finally the compressor operating state parameters are obtained. For example, the operating current data set of a medical refrigerator compressor is {2.1, 2.3, 2.2, 2.0, 2.4}A, the start-stop frequency is 4 times / hour, and the exhaust temperature data set is {75.2, 76.0, 75.5, 74.8, 76.3}°C. The compressor operating state parameters obtained after screening are shown in Table 3 below.

[0115] Table 3: Compressor operating status parameter table

[0116]

[0117] As shown in Table 3, the compressor operating state parameters are used for subsequent compressor power deviation calculation.

[0118] The compressor power deviation calculation submodule calculates the current output power of the compressor based on the compressor operating status parameters and compares it with the refrigeration power demand information of the medical refrigerator using the formula:

[0119] ;

[0120] Calculate and obtain the power deviation value to obtain the compressor power deviation information;

[0121] in, Represents the power deviation value, Represents the number of compressor operations. Representative The running current of the first operation, Representative The voltage of the second operation, Representative The compressor operating efficiency of each operation, Indicates Duration of the run, Represents the cooling power demand value per unit time;

[0122] First determine the number of times the compressor runs , then extract the current for each run ,Voltage and compressor operating efficiency , and the corresponding running time , calculate the actual output power per unit time and compare it with the cooling power demand value For comparison, for example, the refrigeration power requirements of medical refrigerators , compressor operation times , operating current data {2.1, 2.3, 2.2, 2.0, 2.4}A, operating voltage , the running efficiency data is {0.85, 0.86, 0.84, 0.83, 0.87}, the running time is {30, 32, 28, 31, 29} seconds, and the calculation is as follows:

[0123] ;

[0124] Calculate the power deviation:

[0125] ;

[0126] Power deviation calculation involves operating efficiency The setting of this value comes from the compressor performance test data, which is generally in the range of 0.8-0.9. The specific value is affected by the compressor load, condensing temperature and running time. The efficiency curve under different working conditions is selected through experimental testing. If the compressor runs at low load for a long time, the operating efficiency is low. The compressor power deviation information is 25.87 W.

[0127] The compressor power adjustment submodule calculates the adjusted power correction amount based on the compressor power deviation information, adjusts the power output of the compressor, and obtains the compressor load adjustment parameter;

[0128] First, according to the power deviation value The size of the compressor is used to determine whether the working state of the compressor needs to be adjusted. If the deviation value exceeds the set adjustment threshold (such as ±5W), the adjustment strategy is executed. The adjustment methods include modifying the compressor operating current, adjusting the start-stop time interval, or optimizing the compressor operating frequency. , then increase the running time or increase the running current. , then reduce the running time or lower the running current. If the calculated power deviation is 25.87W, which exceeds the set 5W adjustment threshold, then increase the running current and set the adjustment amount to , the adjusted operating current is , and recalculate the expected output power to ensure that the refrigeration demand is met, and finally obtain the compressor load adjustment parameters. The setting basis of the compressor adjustment threshold of 5W is the dynamic balance of the refrigeration system of the medical refrigerator. If the adjustment value is too small, it may cause the system adjustment frequency to be too high and the compressor to start and stop frequently. If the adjustment value is too large, the power deviation may not be corrected in time. This value is determined based on the actual operation data of the medical refrigerator and is usually set to within 10%-15% of the refrigeration power demand.

[0129] See also Figure 5 , the refrigerant flow control module includes:

[0130] The refrigerant supply and demand parameter acquisition submodule calls the compressor load adjustment parameters, obtains the electronic expansion valve opening, refrigerant flow rate and evaporator heat exchange rate, and performs data normalization to obtain the refrigerant supply and demand parameters;

[0131] First, the current opening data is obtained from the opening control module of the electronic expansion valve, and the data is stored in the time series database. Then, the refrigerant flow rate data is extracted from the refrigerant flow sensor, and the average refrigerant flow rate within the set time interval is calculated. For the evaporator heat exchange rate, the evaporator heat exchange detection module is called to obtain the heat exchange power per unit time and record the data. For all the acquired data, the electronic expansion valve opening, refrigerant flow rate and evaporator heat exchange rate are normalized to the range of [0,1] by normalization processing, so as to calculate the refrigerant supply and demand deviation later. For example, the electronic expansion valve opening data of a medical refrigerator refrigerant system is {45, 47, 46, 44, 48}%, the refrigerant flow rate data is {0.18, 0.19, 0.17, 0.18,0.20}kg / s, and the evaporator heat exchange rate data is {800, 820, 790, 810, 830}W, then the normalization calculation is adopted:

[0132] ;

[0133] For the electronic expansion valve opening:

[0134] ;

[0135] Calculate the normalized value:

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] ;

[0141] For refrigerant flow rate:

[0142] ;

[0143] Calculate the normalized value:

[0144] ;

[0145] ;

[0146] ;

[0147] ;

[0148] ;

[0149] For the evaporator heat transfer rate:

[0150] ;

[0151] Calculate the normalized value:

[0152] ;

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] The obtained refrigerant supply and demand parameters are shown in Table 4 below.

[0158] Table 4: Normalized parameters of refrigerant supply and demand

[0159]

[0160] The minimum value used for normalization and maximum value The selection basis is the historical operation data of the equipment. Under the condition of stable system operation, the opening range of the electronic expansion valve is generally between 44% and 48%, the refrigerant flow rate is between 0.17kg / s and 0.20kg / s, and the evaporator heat exchange rate range is generally between 790W and 830W. Therefore, the corresponding maximum and minimum values ​​are selected as normalization standards to ensure that the calculated normalized value can be within the range of [0,1].

[0161] As shown in Table 4, the normalized refrigerant supply and demand parameters are used for subsequent refrigerant supply and demand deviation calculations.

[0162] The refrigerant supply and demand deviation calculation submodule is based on the refrigerant supply and demand parameters and uses the formula:

[0163] ;

[0164] Calculate and obtain the refrigerant supply and demand deviation, and obtain the refrigerant supply and demand deviation information;

[0165] in, Represents the deviation between refrigerant supply and demand, Represents the number of measurement points on the refrigerant flow path, Representative The refrigerant mass flow rate at each measuring point, Representative Refrigerant inlet enthalpy at each measuring point, Representative The refrigerant outlet enthalpy at each measuring point, represents the evaporator heat exchange rate;

[0166] First, obtain the number of measurement points on the refrigerant flow path , extract the refrigerant mass flow rate at each measuring point , entrance enthalpy and outlet enthalpy , and calculate the energy change value of the refrigerant flowing through each measuring point, and finally compare it with the evaporator heat exchange rate For comparison, for example, the number of measurement points in a medical refrigerator refrigerant system , refrigerant mass flow data {0.18, 0.19, 0.17} kg / s, refrigerant inlet enthalpy data {250, 255, 248} kJ / kg, refrigerant outlet enthalpy data {210,215, 208} kJ / kg, evaporator heat exchange rate , calculated as follows:

[0167] ;

[0168] Calculate the refrigerant supply and demand deviation:

[0169] ;

[0170] The refrigerant supply and demand deviation information obtained is 788.4 W.

[0171] When calculating the refrigerant supply and demand deviation, the adjustment threshold is set to ±50W. The selection basis of this threshold is based on the refrigeration capacity of the system. Under normal circumstances, the fluctuation range of refrigerant supply and demand should not exceed 6% of the evaporator heat exchange rate (810W×6% ≈50W). When the deviation exceeds this range, it is necessary to adjust the electronic expansion valve to balance the refrigerant supply and demand.

[0172] The electronic expansion valve adjustment submodule calculates the adjusted valve control amount based on the refrigerant supply and demand deviation information, adjusts the opening of the electronic expansion valve, and obtains the refrigerant flow adjustment parameters;

[0173] First determine the supply and demand deviation Whether it exceeds the set adjustment threshold (±50W), if it exceeds, adjust the opening of the electronic expansion valve, and make adjustments by calculating the correction amount. For example, if the calculated refrigerant supply and demand deviation is 788.4W, which exceeds the set threshold, the opening of the electronic expansion valve needs to be adjusted, and the adjustment ratio is set to , adjust the opening of the electronic expansion valve ;

[0174] ;

[0175] Such as the original electronic expansion valve opening , then after adjustment:

[0176] ;

[0177] The refrigerant flow adjustment parameter obtained is that the opening of the electronic expansion valve is adjusted to 90.8%.

[0178] See also Figure 6 , the compressor performance correction module includes:

[0179] The compressor operation status acquisition submodule calls the refrigerant flow adjustment parameters, obtains the compressor vibration frequency, condensing pressure fluctuation and refrigeration power change rate, and performs data preprocessing to obtain the compressor operation status parameters;

[0180] First, the vibration frequency data is obtained from the compressor vibration monitoring system, and the vibration amplitude signal within a period of time is collected. The vibration frequency data at multiple time points are stored in time series, and the average vibration frequency within the time period is calculated. Then, the condensing pressure data is obtained from the condensing pressure sensor, and the pressure fluctuation range is recorded. The standard deviation of the condensing pressure is calculated to measure the degree of fluctuation of the condensing pressure. Then, the refrigeration power measurement module is called to obtain the instantaneous power change rate of the compressor, and the average refrigeration power change rate within the set time interval is calculated. For all the acquired data, data preprocessing is performed, including removing abnormal data points, normalization processing and filtering processing to eliminate data noise and ensure data accuracy. For example, the vibration frequency data of a medical refrigerator compressor is {40.5, 42.0, 41.3, 39.8, 42.5}Hz, the condensing pressure data is {1.25, 1.30,1.27, 1.22, 1.31}MPa, and the refrigeration power change rate data is {5.2, 5.1, 5.3, 5.0, 5.4}W / s, then the calculation is as follows:

[0181] ;

[0182] ;

[0183] ;

[0184] For the calculation of the standard deviation of the condensing pressure fluctuation, the threshold value is set according to the pressure adjustment range of the condensing system. In general, the standard deviation of the condensing pressure fluctuation needs to be controlled within ±0.05MPa. If it exceeds this range, it indicates that the condensing system is unstable. The setting of this value refers to the refrigerant pressure-temperature characteristic curve. For R134a refrigerant, under normal operating conditions (evaporation temperature -10°C, condensation temperature 40°C), the condensing pressure fluctuation should be controlled within the range of ±5% to ensure stable operation of the system. Therefore, 0.05MPa is selected as the threshold here. The compressor operating status parameters obtained are shown in Table 5 below.

[0185] Table 5: Compressor operating status parameter table

[0186]

[0187] As shown in Table 5, the compressor operating state parameters are used for subsequent operating efficiency offset calculation.

[0188] The operating efficiency offset calculation submodule calculates the current operating efficiency of the compressor based on the compressor operating status parameters and compares it with the target operating efficiency using the formula:

[0189] ;

[0190] Calculate and obtain the operating efficiency offset, and obtain the compressor operating efficiency offset information;

[0191] in, Represents the operating efficiency offset, Represents the number of measurement points in the operating state, Representative The compressor output power at each measuring point is Representative The compressor input power at each measuring point, Represents the set target operating efficiency;

[0192] First, get the number of measurement points in the running state , and then extract the compressor input power at each measurement point and output power , calculate the actual operating efficiency and compare it with the target operating efficiency For comparison, for example, the number of measurement points for a medical refrigerator compressor , compressor input power data {300, 310, 305, 295}W, output power data {250, 260, 255, 245}W, target operating efficiency , calculated as follows:

[0193] ;

[0194] Calculate the operating efficiency offset:

[0195] ;

[0196] The target operating efficiency threshold of 0.85 is set based on the energy efficiency ratio of the compressor under rated working conditions. Under normal circumstances, the operating efficiency of the compressor of a commercial medical refrigerator should not be less than 0.85 under rated refrigeration capacity conditions (such as ambient temperature 25°C, evaporation temperature -5°C), otherwise it means that the energy utilization rate of the compressor is reduced. The setting of this value refers to the standard GB 12021.2-2015 "Limited Values ​​of Electric Refrigerator Power Consumption and Energy Efficiency Grades" to ensure that it meets the energy efficiency requirements of refrigeration equipment. The compressor operating efficiency offset information obtained is 0.0153.

[0197] The target load correction submodule determines whether the offset exceeds a set operating threshold based on the compressor operating efficiency offset information, and if the offset exceeds the set operating threshold, adjusts the compressor target load parameter to obtain compressor operation correction information;

[0198] If the offset exceeds the set operating threshold (such as ±0.01), the compressor target load parameters are adjusted. For example, if the calculated operating efficiency offset is 0.0153, which exceeds the set threshold, the compressor target load parameters need to be adjusted. The adjustment methods include increasing the compressor input power or optimizing the refrigerant flow rate, and setting the adjustment ratio. , adjust the compressor input power ;

[0199] ;

[0200] If the original compressor input power is W, then after adjustment:

[0201] ;

[0202] The set operating efficiency offset adjustment threshold is ±0.01. The setting of this value is based on the compressor load adjustment range. Under normal circumstances, the operating efficiency fluctuation range of the compressor is controlled within ±1%. If the offset exceeds 1%, it means that the system may be overloaded or energy efficiency is lost. The compressor operation correction information obtained is that the input power is adjusted to 310.49W.

[0203] A medical refrigerator compressor refrigeration temperature control method based on big data includes the following steps:

[0204] S1: Based on the medical items in the storage cavity of the medical refrigerator, the specific heat capacity, density, thermal conductivity and storage temperature requirements of the items stored in the medical drug database are extracted, and the temperature difference inside and outside the storage cavity is calculated to obtain the storage environment parameter set;

[0205] S2: calling the storage environment parameter set, calculating the heat capacity of the air in the storage cavity, combining the heat exchange rate per unit mass of the stored items, calculating the refrigeration power requirement value per unit time, and obtaining the refrigeration power requirement information of the medical refrigerator;

[0206] S3: calling the refrigeration power demand information of the medical refrigerator, calculating the deviation between the current output power of the compressor and the refrigeration demand, adjusting the power output of the compressor, and obtaining the compressor load adjustment parameter;

[0207] S4: calling the compressor load adjustment parameter, obtaining the electronic expansion valve opening, the refrigerant flow rate and the evaporator heat exchange rate, calculating the refrigerant supply and demand difference, adjusting the electronic expansion valve opening, and obtaining the refrigerant flow adjustment parameter;

[0208] S5: calling the refrigerant flow adjustment parameter, calculating the compressor operation efficiency offset, correcting the compressor target load parameter, and obtaining the compressor operation correction information.

[0209] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A medical refrigerator compressor refrigeration temperature control system based on big data, characterized in that: The system comprises: The storage item recognition module extracts the specific heat capacity, density, thermal conductivity and storage temperature requirements of the items stored in the medical drug database based on the medical items in the storage cavity of the medical refrigerator, and calculates the temperature difference between the inside and outside of the storage cavity to obtain a storage environment parameter set; The heat capacity calculation module calls the storage environment parameter set, calculates the heat capacity of the air in the storage cavity, calculates the refrigeration power requirement value per unit time in combination with the heat exchange rate per unit mass of the stored items, and obtains the refrigeration power requirement information of the medical refrigerator; A compressor load adjustment module, which calls the refrigeration power demand information of the medical refrigerator, calculates the deviation between the current output power of the compressor and the refrigeration demand, adjusts the power output of the compressor, and obtains the compressor load adjustment parameter; The refrigerant flow control module calls the compressor load adjustment parameter, obtains the electronic expansion valve opening, the refrigerant flow rate and the evaporator heat exchange rate, calculates the refrigerant supply and demand difference, adjusts the electronic expansion valve opening, and obtains the refrigerant flow adjustment parameter; The compressor performance correction module calls the refrigerant flow adjustment parameter, calculates the compressor operation efficiency offset, corrects the compressor target load parameter, and obtains the compressor operation correction information.

2. The big data-based medical refrigerator compressor refrigeration temperature control system according to claim 1 is characterized in that: The storage environment parameter set includes storage cavity temperature, storage cavity humidity, storage cavity air pressure, storage item category, storage item thermal parameters and temperature difference between the inside and outside of the cavity; the medical refrigerator refrigeration power demand information includes storage cavity air unit heat capacity, storage cavity total heat capacity, medical refrigerator internal and external temperature change rate, total heat capacity fluctuation and refrigeration load matching; the compressor load adjustment parameters include compressor current output power, refrigeration demand deviation and compressor target operating frequency; the refrigerant flow adjustment parameters include electronic expansion valve opening, refrigerant flow rate, evaporator heat exchange rate and refrigerant supply and demand difference; the compressor operation correction information includes compressor vibration frequency, condensing pressure fluctuation, refrigeration power change rate, compressor operation offset, set operation threshold and compressor target load parameters.

3. The big data-based medical refrigerator compressor refrigeration temperature control system according to claim 1 is characterized in that: The stored item identification module comprises: The item information parsing submodule, based on the medical items in the storage cavity of the medical refrigerator, calls the RFID tag information and barcode data, extracts the identification code corresponding to the item category, compares it with the database record, screens the matching medical item category code, and obtains the item category code set; A storage parameter extraction submodule retrieves the specific heat capacity, density, thermal conductivity and storage temperature requirements of the corresponding items from a large database of medical drugs based on the item category code set to obtain storage parameter information; The storage environment calculation submodule, based on the storage parameter information, calls the cold storage area sensor network to collect temperature data inside and outside the storage cavity, using the formula: ； The average temperature difference within the time period is calculated and obtained to obtain a storage environment parameter set; in, Represents the average temperature difference between the inside and outside of the storage chamber, Representing time point The external temperature of the storage chamber is Representing time point The internal temperature of the storage chamber is Represents the total number of samples in the time interval.

4. The big data-based medical refrigerator compressor refrigeration temperature control system according to claim 1, characterized in that: The heat capacity calculation module includes: The storage cavity parameter acquisition submodule calls the storage environment parameter set to obtain the temperature, humidity and heat exchange rate of the air in the storage cavity of the medical refrigerator, and screens the effective heat exchange parameters in combination with the heat exchange boundary conditions inside the storage cavity to obtain the storage cavity heat exchange parameters; The storage object heat capacity calculation submodule calculates the heat capacity of the air in the storage cavity based on the heat exchange parameters of the storage cavity, and calculates and obtains the total heat capacity of the stored objects in combination with the heat exchange rate per unit mass of the stored objects; The cooling power demand calculation submodule calls the total heat capacity of the stored items and uses the formula: ； Calculate and obtain the refrigeration power demand per unit time, and obtain the refrigeration power demand information of the medical refrigerator; in, Represents the cooling power demand per unit time, represents the calculation time interval, Representative The quality of the stored items, Representative The specific heat capacity of the storage items, Representative The class stores the current temperature of an item. Representative Storage temperature of the stored items, represents the heat transfer coefficient between the cavity and outside, represents the heat transfer area of ​​the cavity, Represents the average temperature difference between the inside and outside of the storage chamber, Represents the number of types of stored items.

5. The medical refrigerator compressor refrigeration temperature control system based on big data according to claim 1 is characterized in that: The compressor load adjustment module comprises: The compressor operation status acquisition submodule calls the refrigeration power demand information of the medical refrigerator, obtains the compressor operation current, start-stop frequency and exhaust temperature, and screens each parameter to obtain the compressor operation status parameter; The compressor power deviation calculation submodule calculates the current output power of the compressor based on the compressor operating state parameters and compares it with the refrigeration power demand information of the medical refrigerator using the formula: ； Calculate and obtain the power deviation value to obtain the compressor power deviation information; in, Represents the power deviation value, Represents the number of compressor operations. Representative The running current of the first operation, Representative The voltage of the second operation, Representative The compressor operating efficiency of each operation, Indicates Duration of the run, Represents the cooling power demand value per unit time; The compressor power adjustment submodule calculates the adjusted power correction amount based on the compressor power deviation information, adjusts the power output of the compressor, and obtains the compressor load adjustment parameter.

6. The big data-based medical refrigerator compressor refrigeration temperature control system according to claim 1, characterized in that: The refrigerant flow control module comprises: The refrigerant supply and demand parameter acquisition submodule calls the compressor load adjustment parameter, obtains the electronic expansion valve opening, the refrigerant flow rate and the evaporator heat exchange rate, and performs data normalization processing to obtain the refrigerant supply and demand parameters; The refrigerant supply and demand deviation calculation submodule adopts the formula based on the refrigerant supply and demand parameters: ； Calculate and obtain the refrigerant supply and demand deviation, and obtain the refrigerant supply and demand deviation information; in, Represents the deviation between refrigerant supply and demand, Represents the number of measurement points on the refrigerant flow path, Representative The refrigerant mass flow rate at each measuring point, Representative Refrigerant inlet enthalpy at each measuring point, Representative The refrigerant outlet enthalpy at each measuring point, represents the evaporator heat exchange rate; The electronic expansion valve adjustment submodule calculates the adjusted valve control amount based on the refrigerant supply and demand deviation information, adjusts the opening of the electronic expansion valve, and obtains the refrigerant flow adjustment parameter.

7. The medical refrigerator compressor refrigeration temperature control system based on big data according to claim 1 is characterized in that: The compressor performance correction module comprises: The compressor operation status acquisition submodule calls the refrigerant flow adjustment parameter to obtain the compressor vibration frequency, condensing pressure fluctuation and refrigeration power change rate, and performs data preprocessing to obtain the compressor operation status parameters; The operating efficiency offset calculation submodule calculates the current operating efficiency of the compressor based on the compressor operating state parameters and compares it with the target operating efficiency using the formula: ； Calculate and obtain the operating efficiency offset, and obtain the compressor operating efficiency offset information; in, Represents the operating efficiency offset, Represents the number of measurement points in the operating state, Representative The compressor output power at each measuring point is Representative The compressor input power at each measuring point, Represents the set target operating efficiency; The target load correction submodule determines whether the offset exceeds a set operating threshold based on the compressor operating efficiency offset information. If the offset exceeds the set operating threshold, the compressor target load parameter is adjusted to obtain the compressor operation correction information.

8. A method for controlling the refrigeration temperature of a medical refrigerator compressor based on big data, characterized in that: The big data-based medical refrigerator compressor refrigeration temperature control system according to any one of claims 1 to 7 comprises the following steps: S1: Based on the medical items in the storage cavity of the medical refrigerator, the specific heat capacity, density, thermal conductivity and storage temperature requirements of the items stored in the medical drug database are extracted, and the temperature difference inside and outside the storage cavity is calculated to obtain the storage environment parameter set; S2: calling the storage environment parameter set, calculating the heat capacity of the air in the storage cavity, combining the heat exchange rate per unit mass of the stored items, calculating the refrigeration power requirement value per unit time, and obtaining the refrigeration power requirement information of the medical refrigerator; S3: calling the refrigeration power demand information of the medical refrigerator, calculating the deviation between the current output power of the compressor and the refrigeration demand, adjusting the power output of the compressor, and obtaining the compressor load adjustment parameter; S4: calling the compressor load adjustment parameter, obtaining the electronic expansion valve opening, the refrigerant flow rate and the evaporator heat exchange rate, calculating the refrigerant supply and demand difference, adjusting the electronic expansion valve opening, and obtaining the refrigerant flow adjustment parameter; S5: calling the refrigerant flow adjustment parameter, calculating the compressor operation efficiency offset, correcting the compressor target load parameter, and obtaining the compressor operation correction information.

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