Red blood cell cold storage and its optimal design method

Abstract

The application discloses a red blood cell cold storage optimal design method, and relates to the technical field of refrigeration equipment, comprising: setting constraint conditions of various factors of the red blood cell cold storage, wherein the various factors include the specific surface area, the variable cross-section height, the position of the air cooler, the position of the internal heat source, the air supply temperature of the air cooler and the air supply speed; acquiring a factor level table based on the number of levels of the various factors, designing orthogonal experiment conditions; calculating thermal performance evaluation indexes under different conditions, and acquiring orthogonal simulation results under different conditions; based on the orthogonal simulation results, performing range analysis on the thermal performance evaluation indexes under the orthogonal experiment conditions, and acquiring orthogonal experiment calculation results; analyzing the orthogonal experiment calculation results, and acquiring optimal combined conditions of the various factors, so that unnecessary energy loss is reduced, temperature uniformity and speed field distribution are optimized, and the stability of the storage environment is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of refrigeration equipment, in particular to a red blood cell freezer and an optimal design method thereof. BACKGROUND

[0002] The thermal performance optimization of the red blood cell freezer is crucial for the quality assurance and storage safety of red blood cells. The optimal storage temperature of red blood cells should be maintained at 2℃-6℃ to inhibit cell metabolism and prolong the shelf life. Any fluctuation within this temperature range may cause red blood cells to be damaged, induce metabolic abnormalities, or increase the risk of hemolysis. Therefore, ensuring the uniformity and stability of the internal temperature of the freezer is the key to improving the efficiency and safety of red blood cell storage.

[0003] Currently, the design of traditional red blood cell freezers focuses mainly on air flow conditions, storage location layout, and air curtain research, with less attention paid to the optimization of the specific surface area and internal variable cross-section of the freezer. Reasonable design of the specific surface area value can effectively reduce the heat exchange between the interior and exterior of the freezer, and the internal variable cross-section design can effectively guide the uniform distribution of air flow, reducing local air flow stagnation and improving air flow efficiency. In addition, with the introduction of intelligent and automated technologies, freezers are gradually equipped with sensors, mechanical arms, and remote monitoring systems. Although these technologies can improve operational efficiency through real-time monitoring and automatic adjustment, these devices act as internal heat sources when in operation, affecting the uniformity and stability of the temperature field within the freezer and increasing energy consumption. SUMMARY

[0004] In order to overcome the defects of high energy consumption, non-uniformity and instability of the internal temperature field of the red blood cell freezer in the prior art, the present application proposes a red blood cell freezer and an optimal design method thereof.

[0005] To achieve the above purpose, the present application adopts the following technical solution, a red blood cell freezer optimal design method, comprising:

[0006] S1: setting the constraint conditions of each factor of the red blood cell freezer; wherein each factor includes the specific surface area, variable cross-section height, cold air fan position, internal heat source position, cold air fan supply air temperature, and supply air speed of the red blood cell freezer;

[0007] S2: obtaining a factor level table based on the number of levels of each factor, and designing an orthogonal experimental condition;

[0008] S3: calculating the thermal performance evaluation index under different conditions, and obtaining the orthogonal simulation results under different conditions;

[0009] S4: based on the orthogonal simulation results, performing range analysis on the thermal performance evaluation index under the orthogonal experimental condition, and obtaining the orthogonal experimental calculation results;

[0010] S5: analyzing the calculation results of the orthogonal experiment to obtain the optimal combination of each factor.

[0011] Preferably, in step S1, the constraint conditions of each factor of the red blood cell cold storage are set, including:

[0012] S11: setting the specific surface area S of the red blood cell cold storage A , obtaining the dimensions of the external length L n and width W n of the red blood cell cold storage based on the calculation formula of the specific surface area:

[0013]

[0014] Wherein, H f is the height of the red blood cell cold storage, A is the outer surface area of the red blood cell cold storage, and V is the volume of the red blood cell cold storage;

[0015] S12: setting the variable cross-section height H1 of the red blood cell cold storage to satisfy i is an integer, and i∈0,3];

[0016] S13: setting the position L1 of the cold air fan in the red blood cell cold storage to satisfy j is an integer, and j∈[0,2];

[0017] S14: setting the position L2 of the internal heat source in the red blood cell cold storage to satisfy m is an integer, and m∈1,4];

[0018] S15: setting the supply air temperature Tf of the red blood cell cold storage to satisfy Ta≤Tf≤Tb; wherein, the supply air temperature refers to the air temperature at the air outlet of the cold air fan; Ta and Tb are the set minimum and maximum supply air temperatures, respectively;

[0019] S16: setting the supply air speed Vf of the red blood cell cold storage to satisfy Va≤Vf≤Vb; wherein, the supply air speed refers to the air speed at the air outlet of the cold air fan; Va and Vb are the set minimum and maximum supply air speeds, respectively.

[0020] Preferably, in step S2, the number of levels of each factor refers to the sum of the number of values that can be selected for each factor.

[0021] Preferably, in step S3, the thermal performance evaluation index includes the average temperature temperature range R, temperature non-uniformity coefficient σ t , and speed non-uniformity coefficient σ v , and the calculation formula is as follows:

[0022]

[0023] Wherein, x is the red blood cell cold storage internal X-Y cross section cloud number, n is the cross section number, Tx is the average temperature of the xth cross section, T max is the maximum value of the cold storage temperature, T min is the minimum value of the cold storage temperature, v x is the average wind speed of the xth cross section, is the average wind speed of the whole cold storage.

[0024] Preferably, in step S4, based on the orthogonal simulation result, the range analysis is carried out on the thermal performance evaluation index under the orthogonal experimental condition, and the orthogonal experimental calculation result is obtained, wherein, Kp represents the sum of the corresponding thermal performance evaluation index under all conditions when the factor is at the pth level, m is the total level number corresponding to each factor; the range S is the difference between the maximum value and the minimum value in k p , which represents the influence of each factor on the experimental result; the importance degree is sorted according to the size of S, and the optimal level is k p , which is the factor level corresponding to the minimum value.

[0025] Preferably, in step S5, when the orthogonal experimental calculation result is analyzed to obtain the optimal combination condition of each factor, the selection priority of the optimal level of each factor should first consider the average temperature, which must meet the blood red cell storage condition of 2-6℃, and then consider the temperature non-uniformity coefficient and the temperature range, and finally consider the speed non-uniformity coefficient.

[0026] A red blood cell cold storage, as a design basis of a red blood cell cold storage optimization design method, comprises a storage body 1 and a door 2, a plurality of shelves 3 are arranged on the two sides in the storage body, an adjustable-position cold air fan is arranged in the storage body along the length direction of the storage body, air supply ports 4 and air return ports 5 are arranged on the two sides of the cold air fan, a variable cross section 6 with adjustable height is arranged on one side in the storage body, a lighting lamp 7 is arranged at the center of the top of the storage body, an internal heat source with adjustable position is further arranged in the storage body along the length direction of the storage body, the internal heat source comprises a sensor 8, a mechanical arm and a full-automatic device 9 and a monitor 10, the mechanical arm and the full-automatic device 9 are fixed in the matrix measurement range of the sensor 8, and the sensor 8, the mechanical arm, the full-automatic device 9 and the monitor 10 are in the same side plane.

[0027] A readable storage medium, which has a computer program stored thereon, the computer program is executed to realize a red blood cell cold storage optimization design method.

[0028] A computer program product, comprising a computer program / instruction, which is executed by a processor to realize a red blood cell cold storage optimization design method.

[0029] The advantages of the present application are that:

[0030] (1) The application comprehensively considers six key factors and their constraint conditions, such as specific surface area, variable cross-section height, cold air fan position, internal heat source position, air supply temperature and speed, when optimizing the design of the red blood cell cold storage, and the multi-factor optimization can cover the main variables affecting the performance of the red blood cell cold storage, thereby ensuring the comprehensiveness and scientificity of the cold storage performance from the design stage.

[0031] (2) The application optimizes multiple factors by using orthogonal experimental design, reduces the number of actual experiments by reasonably selecting the factor level table, ensures the representativeness of the experimental results, and can effectively reduce the research cost and improve the experimental efficiency and optimization accuracy of the red blood cell cold storage design.

[0032] (3) The application introduces the average temperature temperature range R, temperature non-uniformity coefficient σ t and velocity non-uniformity coefficient σ v as the thermal performance evaluation index, comprehensively quantifies and evaluates the temperature and airflow distribution characteristics in the cold storage, preferentially meets the strict temperature requirements (2-6℃) of red blood cell storage, and secondarily optimizes the temperature uniformity and velocity field distribution to ensure the stability of the storage environment.

[0033] (4) The application quantifies the influence of each factor on the thermal performance index by using range analysis, and clearly defines the importance ranking and priority of each factor; the method is intuitive and scientific, and ensures that the optimized combination of the final design scheme is based on reliable statistical analysis.

[0034] (5) The application significantly improves the thermal performance utilization efficiency, reduces unnecessary energy loss, by optimizing the specific surface area, internal variable cross-section height, and the position layout of the cold air fan and internal heat source of the red blood cell cold storage; at the same time, by scientifically controlling the air supply temperature, avoiding overloading operation of the equipment, further reducing the system energy consumption, prolonging the service life of the equipment, and improving the economy and reliability of the cold storage operation. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 is a model diagram of the red blood cell cold storage;

[0036] Figure 2 is a flow chart of the red blood cell cold storage optimization design method. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0038] Example 1

[0039] As Figure 1 shown, the application proposes a red blood cell cold storage, comprising: a storage body 1 and a door 2, a plurality of shelves 3 are arranged on both sides of the inside of the storage body, a position-adjustable air cooler is arranged in the inside of the storage body, the position of the air cooler is adjusted along the length direction of the storage body, air supply ports 4 and return air ports 5 are arranged on both sides of the air cooler, a variable cross-section 6 with adjustable height is arranged on one side of the inside of the storage body, which can effectively guide the uniform distribution of the airflow inside the red blood cell cold storage, a lighting lamp 7 is arranged at the center of the top of the storage body, which can provide light and facilitate operation, an internal heat source with adjustable position is further arranged in the inside of the storage body along the length direction of the storage body, the internal heat source comprises a sensor 8, a mechanical arm and a full-automatic device 9, and a monitor 10, the sensor 8 is responsible for monitoring the environment, early warning failure and recording data, assisting the precise action of the mechanical arm, the mechanical arm and the full-automatic device 9 are used for realizing efficient handling and reducing labor cost; the monitor 10 is used for real-time monitoring of the cold storage situation to protect storage, the mechanical arm and the full-automatic device 9 are fixed in the matrix measurement range of the sensor 8, and the sensor 8, the mechanical arm, the full-automatic device 9 and the monitor 10 are in the same side plane.

[0040] From the principle of fluid dynamics, the variable cross-section design is adopted inside the red blood cell cold storage, when the airflow passes through the variable cross-section area, according to the continuity equation and Bernoulli principle, the airflow velocity and pressure will produce adaptive changes, thereby effectively guiding the uniform distribution of the airflow, stabilizing the temperature in the storage, and protecting the good preservation environment of the red blood cells.

[0041] Example 2

[0042] As Figure 2 shown, the application proposes a red blood cell cold storage optimization design method, comprising:

[0043] Step 1: Set the specific surface area S of the red blood cell cold storage A , based on the calculation formula of the specific surface area, the size of the external length L n and width W n of the red blood cell cold storage is obtained:

[0044]

[0045] Wherein, H f is the height of the red blood cell cold storage, A is the external surface area of the red blood cell cold storage, and V is the volume of the red blood cell cold storage.

[0046] The specific surface area of the red blood cell cold storage is the ratio of the external structure surface area of the red blood cell cold storage to the volume, and the influence of the variable cross-section design on the volume is ignored here.

[0047] In this embodiment, the height H f of the red blood cell cold storage is 2m; the volume V of the red blood cell cold storage is 25m 3; In the case of fixed height and fixed volume, the dimensions of the external length L of the red blood cell cold storage are obtained n and the width W n , n is the sample point number, n = 1, 2, … N; In this example, N is 4, a total of 4 sample points, that is, 4 specific surface area values. The smaller specific surface area can reduce heat exchange and energy loss, thereby improving the insulation performance, helping to reduce the energy consumption of the cold storage and improve the overall operating efficiency. Therefore, the specific surface area is as small as possible, and in this example, 2.1 m -1 , 2.2 m -1 , 2.3 m -1 (initial value), and 2.4 m -1 .

[0048] Step 2: Set the variable cross-section height H1 of the red blood cell cold storage to satisfy i is an integer, i ∈ [0, 3]; The variable cross-section height of the red blood cell cold storage is the distance from the top of the red blood cell cold storage along the vertical direction downward.

[0049] In this example, the variable cross-section height has a total of 4 values, i = 0, H1 = 0; i = 1, (initial value); i = 2, i = 3,

[0050] Step 3: Set the position L1 of the cold air fan in the red blood cell cold storage to satisfy j is an integer, j ∈ [0, 2]; The position of the cold air fan in the red blood cell cold storage refers to the distance between the side far from the variable cross-section in the red blood cell cold storage and the cold air fan.

[0051] Step 4: Set the position L2 of the internal heat source in the red blood cell cold storage to satisfy m is an integer, m ∈ 1, 4]. In this example, the internal heat source includes sensors, mechanical arms, full-automatic devices and monitors. The position of the internal heat source in the red blood cell cold storage refers to the distance between the side far from the variable cross-section in the red blood cell cold storage and the internal heat source.

[0052] Step 5: Set the supply air temperature T f of the red blood cell cold storage to satisfy 0℃ ≤ T f ≤ 1.5℃; wherein the supply air temperature refers to the air temperature of the air supply outlet 4 of the cold air fan.

[0053] Step 6: Set the supply air speed Vf of the red blood cell cold storage to satisfy 2.6 m / s ≤ Vf ≤ 3.2 m / s; wherein the supply air speed refers to the air speed of the air supply outlet 4 of the cold air fan.

[0054] Step 7: Based on the number of levels for each factor in the red blood cell cold storage, obtain a factor level table and design an orthogonal experimental setup. A common method for designing orthogonal experimental setups is to perform permutation analysis of representative level combinations for each factor to represent the overall level experiment. Using L... n (p m ), where L represents an orthogonal array, n represents the number of trials and the number of rows in the orthogonal array, p represents the number of levels for each factor, and m represents the number of factors, i.e., the number of columns in the orthogonal array.

[0055] The factors include specific surface area (A), variable cross-sectional height (B), location of the air cooler (C), location of the internal heat source (D), supply air temperature (E), and supply air velocity (F). These factors all affect the uniformity of the temperature and velocity fields in the red blood cell cold storage. The level number of each factor refers to the sum of the number of selectable values ​​for each factor. The factor level table is shown in Table 1. Based on the factor level table, a mixed-level orthogonal L-shaped model with 5 factors and 4 levels, and 1 factor and 3 levels was constructed. n (4 5 ·3 1 The quasi-level orthogonal method is adopted, which transforms low-level factors into virtual levels to meet the requirements of the standard orthogonal array. A relatively close standard orthogonal array L is selected. 25 (4 6 Simulate orthogonality.

[0056] Table 1 Factor Level Table

[0057]

[0058] Step 8: Calculate the thermal performance evaluation indexes under different operating conditions and obtain the orthogonal simulation results under different operating conditions. The thermal performance evaluation indexes include the average temperature. Temperature range R, temperature non-uniformity coefficient σ t Velocity non-uniformity coefficient σ v The formula is as follows:

[0059]

[0060] Where x is Figure 1 The XY cross-sectional cloud diagram inside the red blood cell cold storage (divided by simulation software Ansys) is numbered, where n is the number of cross-sections, Tx is the average temperature of the x-th XY cross-section, and T... max T represents the maximum temperature of the cold storage. min v represents the minimum temperature of the cold storage. x Let x be the average wind speed at the x-th cross-section. The average wind speed of the entire cold storage, the average temperature of the cross section, the maximum and minimum temperatures of the cold storage, the average wind speed of all cross sections, and the average wind speed of the entire cold storage are all automatically generated by the simulation software Ansys.

[0061] A three-dimensional model of the red blood cell cold storage was established under various combinations of factors and operating conditions. Then, a numerical model was built using computational fluid dynamics (CFD) simulation software. After the CFD calculations converged, the data was exported. Using temperature contour maps of different cross-sections within the red blood cell cold storage as a reference, the arithmetic mean of temperature, temperature range, temperature non-uniformity coefficient, and velocity non-uniformity coefficient were calculated according to formulas. The orthogonal simulation results are shown in Table 2.

[0062] Table 2. Results of Orthogonal Simulation

[0063]

[0064] Step 9: Based on the orthogonal simulation results, perform range analysis on the thermal performance evaluation indices under the orthogonal experimental conditions to obtain the orthogonal experimental calculation results. The orthogonal experimental calculation results are shown in Table 3, where Kp represents the sum of the corresponding thermal performance evaluation indices under all conditions at the p-th level of the factor. m is the total number of levels for each factor. The range S is k. p The difference between the maximum and minimum values ​​of S represents the magnitude of each factor's influence on the experimental results. A larger S value indicates a more significant influence of that factor. Importance is ranked according to the magnitude of S, with the optimal level being k. p The minimum value corresponds to the factor level. Table 3 shows the differences in different thermal properties.

[0065] Table 3 Calculation results of orthogonal experiments

[0066]

[0067] Step 10: Analyze the orthogonal experimental calculation results to obtain the optimal combination of factors.

[0068] Based on the storage characteristics of red blood cells, the values ​​of k1, k2, k3, and k4 for each factor are compared to analyze the optimal combination of factors. The priority for selecting the optimal level for each factor should first consider the average temperature, which must meet the red blood cell storage condition of 2–6℃. Next, the temperature non-uniformity coefficient and temperature range should be considered; the smaller these are, the better, to ensure a stable temperature field. Finally, the velocity non-uniformity coefficient should be considered. For example, when selecting the level of factor A, first consider whether the average temperature meets the 2–6℃ requirement. Since only k1 does not meet this condition according to Table 3, level A can be selected from 2 to 4. Next, consider selecting a smaller temperature non-uniformity coefficient and temperature range. Table 3 shows that for the temperature non-uniformity coefficient, k2 has the smallest value of 0.28, while for the temperature range, the values ​​of k1 and k3 are roughly the same. Therefore, level 2 is selected for A. The selection of other factors follows the same principle. Furthermore, when the selection of the temperature non-uniformity coefficient and temperature range is uncertain, a smaller velocity non-uniformity coefficient should be considered.

[0069] Based on the above thermal performance evaluation priorities, the optimal combination of the six factors for red blood cell cold storage was found to be A2B3C1D4E3F4, corresponding to a specific surface area of ​​2.2 m². -1 The height of the variable cross section is 2 / 6H f (2 / 6 of the height of the red blood cell cold storage), the air cooler is at position 0 (the leftmost position along the length of the red blood cell cold storage), and the internal heat source is at position 4 / 6. Ln (4 / 6 of the way along the length of the red blood cell cold storage), the air supply temperature is 1℃. The air supply velocity is 3.2m / s.

[0070] Based on the above design scheme, the thermal performance and energy consumption of the red blood cell cold storage before and after optimization were analyzed and compared.

[0071] Table 4 shows the comparison of thermal performance evaluation indicators and energy consumption of the red blood cell cold storage before and after optimization. It can be seen that after optimization, the overall average temperature of the red blood cell cold storage decreased by 1.01℃, the temperature range decreased by 1.39℃, the temperature non-uniformity coefficient decreased by 57.14%, the velocity non-uniformity coefficient decreased by 16.22%, and the energy consumption E decreased by 2.31%. This result indicates that the optimized geometry, equipment layout, and air supply conditions have achieved significant results in improving the temperature uniformity and airflow efficiency of the cold storage.

[0072] Table 4 Comparison of thermal performance evaluation indicators and energy consumption before and after optimization.

[0073]

[0074] For those skilled in the art, the present invention is not limited to the details of the exemplary embodiments described above, but also includes the same or similar structures that can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0075] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0076] The technologies, shapes, and structures not described in detail in this invention are all known technologies.

Claims

1. A method for optimizing the design of a red blood cell cold storage facility, characterized in that, include: S1: Set constraints for various factors of the red blood cell cold storage; these factors include the specific surface area of ​​the red blood cell cold storage, variable cross-sectional height, location of the air cooler, location of the internal heat source, air cooler supply temperature, and air supply velocity; step S1 includes: S11: Set the specific surface area S of the red blood cell cold storage. A Based on the formula for calculating specific surface area, the external length L of the red blood cell cold storage is obtained. n and width W n Size: Among them, H f Let A be the height of the red blood cell cold storage, A be the outer surface area of ​​the red blood cell cold storage, and V be the volume of the red blood cell cold storage. S12: Set the variable cross-sectional height H1 of the red blood cell cold storage to meet the following requirements. i is an integer, and i∈0,3]; S13: Set the location of the air cooler in the red blood cell cold storage L1 to meet the requirements. j is an integer, and j∈[0,2]; S14: Set the location of the internal heat source in the red blood cell cold storage L2 to satisfy... m is an integer, and m∈1,4]; S15: Set the air supply temperature Tf of the red blood cell cold storage to satisfy Ta≤Tf≤Tb; where, the air supply temperature refers to the air temperature at the air outlet of the air cooler; Ta and Tb are the set minimum and maximum air supply temperatures, respectively. S16: Set the air supply velocity Vf of the red blood cell cold storage to satisfy Va≤Vf≤Vb; where air supply velocity refers to the air velocity at the air outlet of the air cooler; Va and Vb are the set minimum and maximum air supply velocities, respectively. S2: Based on the number of levels for each factor, obtain the factor level table and design orthogonal experimental conditions; S3: Calculate the thermal performance evaluation indicators under different operating conditions and obtain orthogonal simulation results under different operating conditions; the thermal performance evaluation indicators include average temperature. Temperature range R, temperature non-uniformity coefficient σ t Velocity non-uniformity coefficient σ v ; S4: Based on the orthogonal simulation results, range analysis is performed on the thermal performance evaluation index under orthogonal experimental conditions to obtain the orthogonal experimental calculation results; S5: Analyze the results of the orthogonal experiment to obtain the optimal combination of factors.

2. The red blood cell cold storage optimization design method as described in claim 1, characterized in that, In step S2, the level number of each factor refers to the sum of the number of selectable values ​​for each factor.

3. The red blood cell cold storage optimization design method as described in claim 1, characterized in that, In step S3, the average temperature Temperature range R, temperature non-uniformity coefficient σ t and the velocity non-uniformity coefficient σ v The calculation formula is as follows: Where x is the XY cross-sectional cloud map number inside the red blood cell cold storage, n is the number of cross-sections, Tx is the average temperature of the x-th cross-section, and T max T represents the maximum temperature of the cold storage. min v represents the minimum temperature of the cold storage. x Let x be the average wind speed at the x-th cross-section. This represents the average wind speed of the entire cold storage facility.

4. The red blood cell cold storage optimization design method as described in claim 1, characterized in that, In step S4, based on the orthogonal simulation results, range analysis is performed on the thermal performance evaluation indices under the orthogonal experimental conditions to obtain the orthogonal experimental calculation results. Here, Kp represents the sum of the corresponding thermal performance evaluation indices for all conditions at the p-th level of the factor. m is the total number of levels for each factor; the range S is k. p The difference between the maximum and minimum values ​​represents the magnitude of each factor's influence on the experimental results; importance is ranked according to the size of S, with the optimal level being k. p The factor level corresponding to the minimum value.

5. The red blood cell cold storage optimization design method as described in claim 1, characterized in that, In step S5, when analyzing the results of the orthogonal experiment and obtaining the optimal combination of each factor, the priority of selecting the optimal level of each factor should first consider the average temperature, which must meet the blood red blood cell storage conditions of 2-6℃, followed by the temperature non-uniformity coefficient and temperature range, and finally the velocity non-uniformity coefficient.

6. A red blood cell cold storage facility, serving as the design basis for the red blood cell cold storage optimization design method as described in any one of claims 1-5, characterized in that, include: The warehouse consists of a warehouse body (1) and a door (2). Multiple shelves (3) are installed on both sides inside the warehouse body. An adjustable air cooler is installed inside the warehouse body along the length of the warehouse body. An air supply vent (4) and an air return vent (5) are installed on both sides of the air cooler. An adjustable cross-section (6) is installed on one side inside the warehouse body. A lighting lamp (7) is installed in the center of the top of the warehouse body. An adjustable internal heat source is also installed inside the warehouse body along the length of the warehouse body. The internal heat source includes a sensor (8), a robotic arm and a fully automatic device (9), and a monitor (10). The robotic arm and the fully automatic device (9) are fixed within the measurement range of the sensor (8) matrix, and the sensor (8), the robotic arm and the fully automatic device (9), and the monitor (10) are all in the same plane.

7. A readable storage medium, characterized in that, It stores a computer program, which, when executed, implements the red blood cell cold storage optimization design method according to any one of claims 1 to 5.

8. A computer program product, characterized in that, It includes a computer program / instruction that, when executed by a processor, implements the red blood cell cold storage optimization design method as described in any one of claims 1 to 5.

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