Freezer temperature equalizing method and system based on dynamic wind speed control

Abstract

The invention discloses a refrigerator temperature equalization method and system based on dynamic wind speed control, and relates to the technical field of refrigerator temperature equalization, and the method comprises the following steps: monitoring the standard temperature of each region under the optimal operation state of a refrigerator, and obtaining the temperature equalization data of the refrigerator; acquiring load temperature influence data and fan processing influence data which influence the temperature equalization of the refrigerator; carrying out machine learning on the load temperature influence data and the fan processing influence data to obtain a temperature change suitable model; and controlling the output wind speed and output stop of the fan according to the output of the temperature change suitable model in combination with the temperature uniformity data. According to the invention, the wind speed is accurately controlled, so that the problem that the temperature nonuniformity is influenced by over-high or over-low regulation of the wind speed is solved.

Description

Technical Field

[0001] The present invention relates to the field of refrigerator temperature equalization technology, and more specifically, the present invention relates to the field of refrigerator temperature equalization method and system technology based on dynamic wind speed control. Background Art

[0002] Due to factors such as spatial structure, frequent door opening and closing, and uneven storage of items, the temperature inside the freezer is prone to unevenness. In order to solve this problem, a technology that achieves uniform temperature distribution inside the freezer is used through a variety of means such as adjusting the flow of cold air, temperature monitoring, and fan control. It not only ensures the quality and freshness of the items in the freezer, but also improves the energy efficiency and service life of the freezer.

[0003] The method for maintaining uniform temperature in the refrigerator space generally uses a fan to keep the temperature inside the refrigerator uniform, but adjusting the wind speed too high or too low or not stopping it in time will affect the temperature unevenness, and the change in fan speed may cause temperature instability in a short period of time, and even have an adverse effect on the items in the refrigerator.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a refrigerator temperature uniformity method and system based on dynamic wind speed control, which solves the problem of temperature unevenness caused by excessively high or low wind speed adjustment through precise control of wind speed.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for uniform temperature of a refrigerator based on dynamic wind speed control includes the following steps: obtaining temperature uniformity data of the refrigerator by monitoring the standard temperature of each area under the optimal operating state of the refrigerator; obtaining load temperature influence data and fan processing influence data that affect the uniform temperature of the refrigerator; obtaining a temperature change suitability model through machine learning for the load temperature influence data and the fan processing influence data; and controlling the output wind speed and output stop of the fan according to the output of the temperature change suitability model combined with the temperature uniformity data.

[0008] In a preferred embodiment, the temperature uniformity data of the refrigerator is obtained by monitoring the standard temperature of each area under the optimal operating state of the refrigerator, and the specific steps are as follows: according to the gear set of the refrigerator, the standard temperature of all areas of the refrigerator under the gear is obtained; the standard temperature is based on the preset optimal reasonable temperature to remove the abnormal highest and abnormal lowest temperatures to obtain the remaining standard temperature data set; the remaining standard temperature data set is summed and calculated based on the averaging method to obtain the temperature uniformity data of the refrigerator.

[0009] In a preferred embodiment, the load temperature influence data includes a load item influence coefficient and a refrigerator equipment influence coefficient. The load item influence coefficient is specifically obtained by the following method: obtaining the initial temperature and layout of the load items placed in the refrigerator and the flow rate of cold air; obtaining the cold air influence factor according to the degree to which the initial temperature of the load items is reduced by the flow rate of cold air within the K time; analyzing the layout of the load items based on fluid dynamics, simulating using the flow rate of cold air, and obtaining the rate of change of the flow rate of cold air after passing through the layout of the load items; obtaining the degree of influence of the layout of the load items on the flow rate of cold air according to the rate of change of the flow rate of cold air after passing through the layout of the load items, and obtaining the influence coefficient of the layout of the load items on the cooling effect of cold air according to the degree of influence; calculating the load item influence coefficient based on the initial temperature of the load items combined with the cold air influence factor and the influence coefficient of the cooling effect of cold air based on a preset load item influence formula.

[0010] In a preferred embodiment, the refrigerator equipment influence coefficient is specifically obtained by the following method: obtaining the load of the refrigerator and the power of the refrigeration equipment; obtaining the heat coefficient generated by the refrigerator refrigeration equipment by analyzing the influence of the load of the refrigerator on the load of the refrigerator refrigeration equipment; obtaining the thermal insulation of the material of the refrigerator equipment and the ambient temperature outside the refrigerator; obtaining the external temperature penetration heat based on the thermal insulation of the material of the refrigerator equipment and the ambient temperature processing capacity outside the refrigerator; and calculating the refrigerator equipment influence coefficient based on the heat coefficient generated by the refrigerator refrigeration equipment and the external temperature penetration heat based on a preset refrigerator equipment influence formula.

[0011] In a preferred embodiment, the fan processing influence data is specifically obtained by the following method: obtaining the first influence data of the fan, the first influence data including the number of fan blades, the area of ​​a single fan blade and the fit of the bearing; analyzing the power applied by the motor on the fan during the G time to obtain the force exerted by the motor on the fan; obtaining the air volume and wind speed of the fan based on the force exerted by the motor on the fan combined with the number of fan blades and the area of ​​a single fan blade; obtaining the effect factor of the fan's air volume and wind speed on the cold air circulation of the refrigerator and combining it with the first influence data of the fan to obtain the fan processing influence data based on the preset fan processing influence formula.

[0012] In a preferred embodiment, the output wind speed and output stop of the fan are controlled according to the output of the temperature change suitability model combined with the temperature uniformity data, and the specific steps are as follows: if the output of the temperature change suitability model is greater than the temperature uniformity data, the fan is controlled to continue to provide the wind speed, and when the output of the temperature change suitability model drops to half of the original, the wind speed of the fan is controlled to drop to half of the original wind speed; if the output of the temperature change suitability model is equal to the temperature uniformity data, the output of the fan is controlled to stop, and the temperature in the refrigerator reaches the temperature uniformity data.

[0013] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0014] 1. More accurate temperature control management can be achieved by accurately monitoring the temperature of each area of ​​the freezer, the impact of load items, and the processing capacity of the fan. This method optimizes the temperature uniformity in the freezer, improves energy efficiency, and improves the operating efficiency of the equipment through data analysis and machine learning. It can not only dynamically adjust the working status of the fan and refrigeration system, but also reduce unnecessary energy consumption and ensure that the temperature in the freezer remains stable under different loads, ultimately improving the temperature control accuracy and intelligence level and reducing manual intervention.

[0015] 2. By using machine learning to build a temperature change suitability model, it is possible to accurately predict temperature changes under different conditions by automatically learning complex patterns in the data, thereby improving the accuracy and adaptability of the model and helping to optimize decision-making and resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the structure of a refrigerator temperature equalization method based on dynamic wind speed control provided in an embodiment of the present application.

[0017] Figure 2 A schematic diagram of the structure of a refrigerator temperature equalization system based on dynamic wind speed control provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1, Figure 1 The schematic diagram of the structure of the refrigerator temperature equalization method based on dynamic wind speed control provided in the embodiment of the present application includes the following steps:

[0020] S1, by monitoring the standard temperature of each area under the optimal operating state of the refrigerator, the temperature uniformity data of the refrigerator is obtained.

[0021] In this example, by monitoring the standard temperature of each area in the refrigerator under the optimal operating state, it can be understood that the temperature with the best preservation effect in all areas of the refrigerator is obtained, and the temperature is set as the standard temperature. Data analysis is performed based on the standard temperature to obtain the temperature uniformity data of the refrigerator. Using the temperature uniformity data of the refrigerator as a standard, it is possible to infer areas where the temperature does not meet the standard or is uneven, and by obtaining this data, it can be used as a reference standard for controlling the fan output wind speed and output stop in the following text.

[0022] The specific method for obtaining the temperature uniformity data is as follows:

[0023] According to the gear setting of the refrigerator, obtain the standard temperature of all areas of the refrigerator at the gear setting;

[0024] The standard temperature is based on the preset optimal reasonable temperature, and the abnormally highest and the abnormally lowest temperatures are removed to obtain a remaining standard temperature data set;

[0025] The remaining standard temperature data sets are summed and calculated based on the average method to obtain the temperature uniformity data of the refrigerator.

[0026] S2, obtaining load temperature impact data and fan processing impact data that affect the average temperature of the refrigerator.

[0027] In this example, the load temperature impact data includes the load item impact coefficient and the refrigerator equipment impact coefficient. The load item impact coefficient refers to the fact that the high temperature of the items placed in the refrigerator will affect the change of the surrounding temperature, resulting in uneven temperature distribution. If the items are placed too closely, it will affect the flow of cold air, resulting in the high temperature items not being able to cool down in a short time, resulting in the temperature cooling too slowly and unable to reach a uniform temperature.

[0028] Obtaining the load item influence coefficient has the following advantages for the refrigerator temperature control:

[0029] Improve the temperature control accuracy of the freezer: The load item influence coefficient reflects the degree of influence of different items on the temperature change in the freezer. By accurately understanding these coefficients, the freezer can adjust the cold air flow and cooling power according to the characteristics of different load items (such as heat capacity, heat preservation, etc.), so that the freezer can maintain a more uniform and accurate temperature. This helps to avoid overcooling or overheating and ensure the quality and safety of refrigerated items.

[0030] Optimize energy efficiency: The refrigeration system of the freezer can adjust its operation strategy under the guidance of the load item influence coefficient. By properly controlling the working time and refrigeration intensity of the compressor, ineffective overcooling can be avoided and energy consumption can be reduced. Understanding the impact of item load on temperature changes can help design smarter control strategies to improve the energy efficiency of the freezer.

[0031] Improve the temperature uniformity of items in the freezer: The load item influence factor helps to understand the relationship between the layout of items in the freezer and the flow of cold air. By dynamically adjusting the temperature control strategy, the temperature of different areas inside the freezer can be kept uniform, thereby avoiding areas with too high or too low temperatures, especially in the refrigeration environment of large volumes or diverse items.

[0032] Enhanced intelligent control capabilities: In modern intelligent refrigerators, the temperature control system can be adjusted in real time based on the load of items. By collecting and analyzing the influence coefficient of the load items and combining it with the Internet of Things technology, more accurate temperature control and intelligent adjustment can be achieved, improving the intelligence level of the refrigerator. This allows the device to not only automatically adapt to various loads, but also optimize and predict based on historical data.

[0033] Improve logistics and inventory management efficiency: By understanding the impact of load items on temperature, the control system of the freezer can adjust the temperature control according to the different needs of the inventory. This not only ensures that the refrigeration needs of the items are met, but also helps warehouse managers better master cold chain management, achieve accurate inventory temperature control, and reduce inventory loss.

[0034] The load item influence coefficient is obtained in the following specific method:

[0035] Obtain the initial temperature and layout of the load items placed in the refrigerator and the flow rate of the cold air;

[0036] The cold air impact factor is obtained according to the degree to which the initial temperature of the load items is reduced by the flow rate of the cold air within the K time;

[0037] The layout of the load items is analyzed based on fluid dynamics, and the flow rate of cold air is used for simulation to obtain the change rate of the flow rate of cold air after passing through the layout of the load items;

[0038] Obtaining the degree of influence of the layout of the load items on the flow rate of the cold air according to the change rate of the flow rate of the cold air after passing through the layout of the load items, and obtaining the influence coefficient of the layout of the load items on the cooling effect of the cold air according to the degree of influence;

[0039] The load item influence coefficient is calculated based on a preset load item influence formula according to the initial temperature of the load item combined with the cold air influence factor and the influence coefficient of the cold air refrigeration effect.

[0040] The specific calculation formula of the cold air impact factor is as follows:

[0041]

[0042] The specific calculation formula of the load item influence coefficient is as follows:

[0043]

[0044] In the formula, η C is the load item influence coefficient, ε is the cold air influence factor, v s ′ is the change rate of the flow rate of cold air after passing through the layout of the load items, v s is the flow rate of cold air, δ is the influence coefficient of cold air refrigeration effect, T0 is the initial temperature of the load, T K is the temperature of the load object after K time, m is the mass of the load object, C W is the specific heat capacity of the load.

[0045] It should be noted that the cold air impact factor is obtained by the degree to which the initial temperature of the load items is reduced by the flow rate of cold air within the K time. It can be understood that the flow rate of cold air will gradually reduce the temperature of the surface of the items, and the cold air impact factor is obtained by the degree to which the surface temperature of the items is reduced due to the influence of the cold air flow within the K time.

[0046] The influence coefficient of the refrigerator equipment refers to the heat generated by the refrigerator's refrigeration system due to changes in load. This heat will also cause uneven temperatures in the refrigerator area. Obtaining the influence coefficient of the refrigerator equipment has the following advantages for analyzing the additional heat generated by the refrigerator:

[0047] Accurately analyze the source of heat generation: In addition to the refrigeration mechanism, the freezer may also generate additional heat during operation. By obtaining the influence coefficient of the freezer equipment, the contribution of each link to heat generation can be accurately analyzed. The influence coefficient of each component can quantify the heat released under different equipment working conditions, allowing designers and engineers to identify the main sources of heat generation and take targeted optimization measures.

[0048] Improve energy efficiency and save energy: The heat generated by refrigerator equipment is directly related to the energy efficiency. By quantifying the influence coefficients of various components, engineers can accurately control energy consumption at different stages of refrigerator operation to avoid energy waste caused by over-cooling or other ineffective operations. For example, when running at low load, the refrigerator's refrigeration system can adjust operating parameters to reduce unnecessary heat generation, thereby achieving energy conservation.

[0049] Improve environmental temperature control management: Refrigerator equipment not only needs to meet refrigeration requirements, but also needs to avoid adverse effects on the ambient temperature due to excessive heat emission. Through the analysis of the influence coefficient, a refrigeration system that better meets environmental requirements can be designed to avoid excessive heat release affecting the temperature control of the surrounding environment, especially in temperature-sensitive places (such as food storage, pharmaceutical cold chain, etc.).

[0050] The specific method for obtaining the influence coefficient of the refrigerator equipment is as follows:

[0051] Get the load of the freezer and the power of the refrigeration equipment;

[0052] The heat coefficient generated by the refrigerator refrigeration equipment is obtained by analyzing the influence of the load of the refrigerator on the load of the refrigerator refrigeration equipment;

[0053] Obtain the thermal insulation of the material of the freezer equipment and the ambient temperature outside the freezer;

[0054] The external temperature infiltration heat is obtained according to the heat insulation property of the material of the refrigerator equipment and the ability of the refrigerator equipment to handle the ambient temperature outside the refrigerator;

[0055] The influence coefficient of the refrigerator equipment is calculated based on the heat coefficient generated by the refrigerator refrigeration equipment and the heat infiltrated by the external temperature based on the preset refrigerator equipment influence formula.

[0056] The specific calculation method of the influence coefficient of the refrigerator equipment is as follows:

[0057]

[0058] In the formula, α S is the influence coefficient of refrigerator equipment, μ R is the heat transfer coefficient, A is the surface area of ​​the refrigerator, T env is the external ambient temperature, T int is the internal temperature of the refrigerator, d is the thickness of the insulation material, Q coll is the heat generated by the refrigeration equipment, p is the power of the equipment, Q L is the thermal coefficient and I is the thermal insulation of the material.

[0059] It should be noted that the influence of the load of the freezer on the load of the freezer refrigeration equipment can be understood as follows: when the load in the freezer increases, the refrigeration system requires greater power to cool the load. At this time, the refrigeration system will generate more heat due to work. The heat penetrating from the external temperature is obtained based on the thermal insulation of the material of the freezer equipment and the ability to handle the ambient temperature outside the freezer. It can be understood that changes in external temperature will penetrate into the freezer, causing additional temperature changes, but the thermal insulation of the freezer material will block part of the temperature from being absorbed, but there will still be unprocessed heat that penetrates into the freezer, causing uneven temperature changes.

[0060] The fan processing impact data refers to using the wind output by the fan to accelerate the circulation of cold air in the refrigerator and diffuse it reasonably, so that each area in the refrigerator can be affected by the flow of cold air and quickly bring about a cooling effect, so that the temperature of each area in the refrigerator reaches the optimal cooling temperature and remains balanced.

[0061] Obtaining fan processing impact data has the following advantages for refrigerator temperature control:

[0062] Improve temperature control accuracy: The operating status of the fan directly affects the airflow distribution and temperature uniformity inside the freezer. By monitoring the fan data, you can understand the fan's working conditions in real time, so as to more accurately control the temperature distribution inside the freezer. After obtaining the fan processing impact data, the control system can dynamically adjust the fan operation mode based on this data to avoid the phenomenon of excessively high or low temperatures in local areas.

[0063] Reduce energy consumption: The fan is one of the components that consumes the most energy in the freezer. By monitoring the fan's impact data, the fan's operating mode can be adjusted as needed to avoid the fan being in a high-load state for a long time. The optimized operation of the fan can not only improve the temperature control accuracy, but also effectively reduce unnecessary energy consumption, thereby achieving energy saving goals.

[0064] Optimize the overall operating efficiency of the freezer: The temperature control accuracy of the freezer is closely related to the operating status of the fan. By obtaining the fan processing impact data, the fan operation time, wind speed and mode can be adjusted more intelligently, so that the freezer can reduce energy consumption and maintain good working efficiency while maintaining uniform temperature. This not only helps to improve the overall performance of the freezer, but also improves the stability of the freezer under various load conditions.

[0065] Reduced human intervention: If there is no fan affecting the data, the temperature control system of the freezer needs to manually adjust the fan or other parameters to achieve the desired effect. Through real-time monitoring and data analysis, the freezer system can respond automatically without human intervention, thereby reducing human operational errors and ensuring that the freezer remains in optimal operating condition for a long time.

[0066] The fan processing impact data is specifically obtained in the following manner:

[0067] Acquire first influencing data of the fan, where the first influencing data includes the number of blades of the fan, the area of ​​a single blade, and the fit of a bearing;

[0068] The power exerted by the motor on the fan during the G time is analyzed to obtain the force exerted by the motor on the fan;

[0069] The air volume and wind speed of the fan are obtained according to the force exerted by the motor on the fan combined with the number of fan blades and the area of ​​a single fan blade;

[0070] The effect factor of the air volume and wind speed of the fan on the cold air circulation of the refrigerator is obtained and the fan processing influence data is calculated based on a preset fan processing influence formula in combination with the first influence data of the fan.

[0071] The fan processing influences the data, and the specific calculation formula is as follows:

[0072]

[0073] In the formula, P is the impact data of fan processing, Y is the force exerted by the motor on the fan, B C is the fit of the bearing, N Y is the number of fan blades, A Y is the area of ​​a single fan blade, V f is the wind speed of the fan, V Q is the air volume of the fan, It is the effect factor of cold air circulation in refrigerator.

[0074] It should be noted that the effect factor of the fan's air volume and wind speed on the cold air circulation in the refrigerator can be understood as the fan's air volume and wind speed promoting the cold air in the refrigerator, so that the cold air can reach a constant temperature quickly at a faster speed and distribution area. This rapid achievement of a constant temperature is the effect factor.

[0075] S3, using machine learning to obtain a temperature change suitability model for the load temperature impact data and the fan processing impact data. The specific steps are as follows:

[0076] A temperature change suitability model is constructed through machine learning based on the load temperature impact data and the fan processing impact data, and the temperature change suitability coefficient is output based on the temperature change suitability model.

[0077] The specific calculation formula of the temperature change suitability coefficient is as follows:

[0078]

[0079] In the formula, γ is the temperature change suitability coefficient, is the wind turbine processing impact data, α S is the influence coefficient of refrigerator equipment, η C is the load item influence coefficient, is the weight coefficient of the fan processing influencing data, ξ η is the weight coefficient of the load item impact coefficient, ξ α is the weight coefficient of the influence coefficient of the refrigerator equipment.

[0080] S4, controlling the output wind speed and output stop of the fan according to the output of the temperature change suitability model combined with the temperature uniformity data, the specific steps are as follows;

[0081] If the output of the temperature change suitability model is greater than the temperature uniformity data, the fan is controlled to continue to provide wind speed. When the output of the temperature change suitability model drops to half of the original, the wind speed of the fan is controlled to drop to half of the original wind speed.

[0082] If the output of the temperature variation suitability model is equal to the temperature uniformity data, the output of the control fan is stopped and the temperature inside the refrigerator reaches the temperature uniformity data.

[0083] Embodiment 2, Figure 2 The schematic diagram of the structure of the refrigerator temperature uniformity system based on dynamic wind speed control provided in the embodiment of the present application includes a temperature uniformity data acquisition module, a refrigerator temperature uniformity data acquisition module, a model generation module and a fan control module, and there are connections between the modules:

[0084] The temperature uniformity data acquisition module is used to obtain the temperature uniformity data of the refrigerator by monitoring the standard temperature of each area under the optimal operating state of the refrigerator;

[0085] A module for acquiring data affecting the average temperature of the refrigerator, used to acquire load temperature impact data and fan processing impact data affecting the average temperature of the refrigerator;

[0086] A model generation module is used to obtain a suitable temperature change model by machine learning based on the load temperature impact data and the fan processing impact data;

[0087] The fan control module is used to control the output wind speed and output stop of the fan according to the output of the temperature change suitability model combined with the temperature uniformity data.

[0088] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0089] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0090] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0091] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0092] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0093] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evenly controlling the temperature of a refrigerator based on dynamic wind speed control, characterized in that: The steps include: By monitoring the standard temperature of each area under the optimal operating condition of the refrigerator, the temperature uniformity data of the refrigerator can be obtained; Obtain the load temperature impact data and fan processing impact data that affect the average temperature of the refrigerator; The load temperature impact data and fan processing impact data are used for machine learning to obtain a suitable model for temperature change; The output wind speed and output stop of the fan are controlled according to the output of the temperature variation suitability model combined with the temperature uniformity data.

2. The method for temperature equalization of a refrigerator based on dynamic wind speed control according to claim 1, characterized in that: The temperature uniformity data of the refrigerator is obtained by monitoring the standard temperature of each area under the optimal operating state of the refrigerator. The specific steps are as follows: According to the gear setting of the refrigerator, obtain the standard temperature of all areas of the refrigerator at the gear setting; The standard temperature is based on the preset optimal reasonable temperature, and the abnormally highest and the abnormally lowest temperatures are removed to obtain a remaining standard temperature data set; The remaining standard temperature data sets are summed and calculated based on the average method to obtain the temperature uniformity data of the refrigerator.

3. The method for temperature equalization of a refrigerator based on dynamic wind speed control according to claim 1, characterized in that: The load temperature impact data includes the load item impact coefficient and the refrigerator equipment impact coefficient. The load item impact coefficient is specifically obtained as follows: Obtain the initial temperature and layout of the load items placed in the refrigerator and the flow rate of the cold air; The cold air impact factor is obtained according to the degree to which the initial temperature of the load items is reduced by the flow rate of the cold air within the K time; The layout of the load items is analyzed based on fluid dynamics, and the flow rate of cold air is used for simulation to obtain the change rate of the flow rate of cold air after passing through the layout of the load items; Obtaining the degree of influence of the layout of the load items on the flow rate of the cold air according to the change rate of the flow rate of the cold air after passing through the layout of the load items, and obtaining the influence coefficient of the layout of the load items on the cooling effect of the cold air according to the degree of influence; The load item influence coefficient is calculated based on a preset load item influence formula according to the initial temperature of the load item combined with the cold air influence factor and the influence coefficient of the cold air refrigeration effect.

4. The method for temperature equalization of a refrigerator based on dynamic wind speed control according to claim 3, characterized in that: The specific method for obtaining the influence coefficient of the refrigerator equipment is as follows: Get the load of the freezer and the power of the refrigeration equipment; The heat coefficient generated by the refrigerator refrigeration equipment is obtained by analyzing the influence of the load of the refrigerator on the load of the refrigerator refrigeration equipment; Obtain the thermal insulation of the freezer equipment and the ambient temperature outside the freezer; The external temperature infiltration heat is obtained according to the heat insulation property of the material of the refrigerator equipment and the ability of the refrigerator equipment to handle the ambient temperature outside the refrigerator; The influence coefficient of the refrigerator equipment is calculated based on the heat coefficient generated by the refrigerator refrigeration equipment and the heat infiltrated by the external temperature based on the preset refrigerator equipment influence formula.

5. The method for temperature equalization of a refrigerator based on dynamic wind speed control according to claim 1, characterized in that: The fan processing impact data is specifically obtained in the following manner: Acquire first influencing data of the fan, where the first influencing data includes the number of blades of the fan, the area of ​​a single blade, and the fit of a bearing; The power exerted by the motor on the fan during the G time is analyzed to obtain the force exerted by the motor on the fan; The air volume and wind speed of the fan are obtained according to the force exerted by the motor on the fan combined with the number of fan blades and the area of ​​a single fan blade; The effect factor of the air volume and wind speed of the fan on the cold air circulation of the refrigerator is obtained and the fan processing influence data is calculated based on a preset fan processing influence formula in combination with the first influence data of the fan.

6. The method for temperature equalization of a refrigerator based on dynamic wind speed control according to claim 1, characterized in that: The output of the temperature change suitability model is combined with the temperature uniformity data to control the output wind speed and output stop of the fan, and the specific steps are as follows; If the output of the temperature change suitability model is greater than the temperature uniformity data, the fan is controlled to continue to provide wind speed. When the output of the temperature change suitability model drops to half of the original, the wind speed of the fan is controlled to drop to half of the original wind speed. If the output of the temperature variation suitability model is equal to the temperature uniformity data, the output of the control fan is stopped and the temperature inside the refrigerator reaches the temperature uniformity data.

7. The method for temperature equalization of a refrigerator based on dynamic wind speed control according to claim 3, characterized in that: The specific calculation formula of the cold air impact factor is as follows: The specific calculation formula of the load item influence coefficient is as follows: In the formula, η C is the load item influence coefficient, ε is the cold air influence factor, v s ' is the rate of change of the flow rate of cold air after passing through the layout of the load items, v s is the flow rate of cold air, δ is the influence coefficient of cold air refrigeration effect, T0 is the initial temperature of the load, T K is the temperature of the load object after K time, m is the mass of the load object, C W is the specific heat capacity of the load.

8. The method for temperature equalization of a refrigerator based on dynamic wind speed control according to claim 4, characterized in that: The specific calculation method of the influence coefficient of the refrigerator equipment is as follows: In the formula, α S is the influence coefficient of refrigerator equipment, μ R is the heat transfer coefficient, A is the surface area of ​​the refrigerator, T env is the external ambient temperature, T int is the internal temperature of the refrigerator, d is the thickness of the insulation material, Q coll is the heat generated by the refrigeration equipment, p is the power of the equipment, Q L is the thermal coefficient and I is the thermal insulation of the material.

9. The method for maintaining a uniform temperature in a refrigerator based on dynamic wind speed control according to claim 5, characterized in that: The fan processing influences the data, and the specific calculation formula is as follows: In the formula, P is the impact data of fan processing, Y is the force exerted by the motor on the fan, B C is the fit of the bearing, N Y is the number of fan blades, A Y is the area of ​​a single fan blade, V f is the wind speed of the fan, V Q is the air volume of the fan, It is the effect factor of cold air circulation in refrigerator.

10. A system using the refrigerator temperature equalization method based on dynamic wind speed control as described in any one of claims 1 to 9, characterized in that: It includes temperature uniformity data acquisition module, refrigerator uniform temperature data acquisition module, model generation module and fan control module. There are connections between the modules: The temperature uniformity data acquisition module is used to obtain the temperature uniformity data of the refrigerator by monitoring the standard temperature of each area under the optimal operating state of the refrigerator; A module for acquiring data affecting the average temperature of the refrigerator, used to acquire load temperature impact data and fan processing impact data affecting the average temperature of the refrigerator; A model generation module is used to obtain a suitable model for temperature change by machine learning based on load temperature impact data and fan processing impact data; The fan control module is used to control the output wind speed and output stop of the fan according to the output of the temperature change suitability model combined with the temperature uniformity data.

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