Cold air circulation rapid cooling system

Abstract

The invention provides a cold air circulation rapid cooling system, which relates to the technical field of hot melt syrup and comprises a process parameter template switching module, a vibration suppression and dynamic parameter adjustment module, a neural network algorithm response module and an energy consumption linkage optimization module, the process parameter template switching module is used for storing cooling parameter sets of syrup with different viscosities and realizing rapid parameter switching, and the vibration suppression and dynamic parameter adjustment module dynamically adjusts cold air parameters based on vibration feedback so as to suppress carbon dioxide bubble breakage; in the aspect of improving the production efficiency, a plurality of groups of cooling parameter sets for syrup with different viscosities are pre-stored in the process parameter template switching module, and the cooling parameter switching can be completed through one-key template loading, so that the cooling remodeling efficiency is greatly improved, the repeated trial and error process of manual debugging is avoided, and the production cost is reduced. The syrup cooling device is particularly suitable for flexible cooling scenes of various kinds of syrup, and the turnover efficiency of a production line is remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of hot melt syrup, in particular to a cold air circulation rapid cooling system. Background Art

[0002] In the processing of foods like candy and carbonated beverages, the cooling process of hot melt syrups (especially those containing carbon dioxide bubbles) directly affects the taste and quality of the product. Existing technologies typically use a cooling cavity and a three-dimensional wind field for cold air circulation. This cooling system has the following drawbacks:

[0003] Process parameter switching relies on manual debugging, and the cooling parameter debugging cycle for syrups of different viscosities can be as long as 48-72 hours, resulting in low production efficiency. The carbon dioxide bubble burst rate during the cooling process is generally higher than 15%, resulting in a degraded syrup taste and damaged bubble structure. Traditional cooling systems have poor adaptability to syrup viscosity fluctuations, often leading to unstable product quality due to uneven cooling. Energy consumption allocation lacks a dynamic optimization mechanism, and the power matching between the cooling cavity and the three-dimensional wind field is unreasonable, resulting in high energy waste. Therefore, the present invention proposes a cold air circulation rapid cooling system to address the problems existing in the prior art. Summary of the Invention

[0004] In response to the above problems, the present invention proposes a cold air circulation rapid cooling system. In terms of improving production efficiency, the process parameter template switching module of the cold air circulation rapid cooling system pre-stores multiple sets of cooling parameter sets for syrups with different viscosities. The cooling parameter switching can be completed by loading the template with one click, which greatly improves the cooling change efficiency and avoids the repeated trial and error process of manual debugging. It is particularly suitable for flexible cooling scenarios of multiple varieties of syrups and significantly improves the production line turnover efficiency.

[0005] To achieve the objectives of the present invention, the present invention is implemented through the following technical solutions: a cold air circulation rapid cooling system, including a process parameter template switching module, a vibration suppression and dynamic parameter adjustment module, a neural network algorithm response module, and an energy consumption linkage optimization module. The process parameter template switching module is used to store cooling parameter sets of syrups with different viscosities and realize rapid parameter switching. The vibration suppression and dynamic parameter adjustment module dynamically adjusts cold air parameters based on vibration feedback to suppress carbon dioxide bubble bursting.

[0006] The neural network algorithm response module is used to use the neural network algorithm to respond to the syrup viscosity fluctuations in real time and adjust the cold air parameters. The energy consumption linkage optimization module is used to optimize the energy consumption distribution of each functional equipment through a genetic algorithm. The process parameter template switching module, vibration suppression and dynamic parameter adjustment module, neural network algorithm response module and energy consumption linkage optimization module perform information exchange and collaborative control through a standardized data interface.

[0007] A further improvement is that the process parameter template switching module pre-stores at least 5 sets of cooling parameter sets for syrups with different viscosities, and one-click switching of cooling parameters is achieved by loading the corresponding templates, wherein the cooling parameter set satisfies the formula:

[0008]

[0009] in: for Target cooling temperature at all times, is the viscosity coefficient of syrup, is the density of syrup, is the carbon dioxide partial pressure parameter.

[0010] Further improvement is that: the vibration suppression and dynamic parameter adjustment module includes a vibration sensor array and a cold air parameter adjustment unit, and the vibration sensor array is used to collect the vibration frequency of the cooling cavity in real time. with amplitude A;

[0011] The cold wind parameter adjustment unit dynamically adjusts the cold wind speed v and temperature of the three-dimensional wind field based on vibration data , so that the carbon dioxide bubble burst rate ≤5%, meeting:

[0012]

[0013] in: is the bubble stability coefficient, The current temperature of the syrup.

[0014] A further improvement is that the neural network algorithm response module includes an input layer, a rule base, and a neural network output layer, wherein the input layer is used to receive the syrup viscosity change rate. , cooling rate deviation and bubble pressure fluctuations The rule base stores N mapping rules for viscosity-cooling rate-bubble pressure; the neural network output layer adjusts the cold air flow in real time , the adaptation formula is:

[0015]

[0016] in: is the flow regulation coefficient, 、 、 is the weight factor.

[0017] Further improvement is that the energy consumption linkage optimization module collects the cooling cavity power in real time through the power sensor , three-dimensional wind farm power , refrigeration unit power , using genetic algorithm to optimize energy consumption distribution, when the cooling cavity temperature Lower than When the cooling power of the three-dimensional wind farm module is 20%, the cooling power of the three-dimensional wind farm module is automatically reduced by 15%-30%. The optimization objective function is:

[0018]

[0019] Where: E is the total energy consumption, 、 、 is the energy consumption weight coefficient, and n is the number of sampling points.

[0020] A further improvement is that the process parameter template switching module and the neural network algorithm response module form a closed-loop adjustment through data interaction. When the deviation between the template parameters and the real-time cooling data exceeds 10%, the neural network self-learning mechanism is triggered to update the cooling parameter set.

[0021] A further improvement is that in the vibration suppression and dynamic parameter adjustment module, the adjustment accuracy of the cold air parameter adjustment unit satisfies: wind speed fluctuation ≤ 0.5m / s, temperature fluctuation ≤ 1°C, thereby controlling the bubble bursting rate within 5%.

[0022] A further improvement is that the response time of the neural network algorithm response module is ≤50ms, and the change of syrup viscosity in the range of 100-5000cP is tracked in real time and the cooling parameters are adjusted.

[0023] Further improvements are: the genetic algorithm iteration cycle of the energy consumption linkage optimization module is 10s, and the energy consumption optimization efficiency meets the following requirements:

[0024]

[0025] in: To optimize energy consumption, Energy consumption before optimization.

[0026] A further improvement is that the process parameter template switching module, vibration suppression and dynamic parameter adjustment module, neural network algorithm response module and energy consumption linkage optimization module are integrated in a hardware plug-in manner through a standardized interface, and the data transmission delay between modules is ≤10ms.

[0027] The beneficial effects of the present invention are:

[0028] 1. In terms of improving production efficiency, the process parameter template switching module of the present invention pre-stores multiple sets of cooling parameter sets for syrups with different viscosities. The cooling parameter switching can be completed by loading the template with one click, which greatly improves the cooling change efficiency and avoids the repeated trial and error process of manual debugging. It is particularly suitable for flexible cooling scenarios of multiple varieties of syrups and significantly improves the production line turnover efficiency.

[0029] 2. In terms of bubble stability control, the vibration suppression and dynamic parameter adjustment module of the present invention collects the vibration data of the cooling cavity in real time through the vibration sensor array. Combined with the dynamic adjustment mechanism of the cold air parameters, the carbon dioxide bubble bursting rate is controlled from more than 15% in the traditional system to within 5%. By establishing a coupling control model of vibration frequency, amplitude and cold air parameters, the bubble bursting caused by fluid disturbance during the cooling process is accurately suppressed, ensuring the integrity of the bubble structure in the syrup, thereby maintaining the stability of the product taste and appearance.

[0030] 3. The present invention constructs an intelligent decision-making system containing multiple mapping rules through a neural network algorithm response module, which can respond in real time to fluctuations in syrup viscosity within the range of 100-5000cP. By receiving multi-dimensional data such as viscosity change rate and cooling rate deviation, the cold air flow rate is adjusted with a response speed of ≤50ms, so that the cooling uniformity error is controlled within 3%, solving the problem of poor adaptability of traditional systems to syrups of different viscosities.

[0031] 4. The energy consumption linkage optimization module of the present invention uses a genetic algorithm to optimize the power data of equipment such as the cooling cavity and the three-dimensional wind field in real time. When the cooling cavity temperature is 20% lower than the set value, the wind field cooling power can be automatically reduced by 15%-30%. The overall energy consumption of the system is greatly reduced compared with the traditional solution. While ensuring the cooling efficiency, the rational distribution of energy is achieved, achieving both energy saving and economic benefits.

[0032] 5. The present invention integrates four modules into a closed-loop control system of parameter pre-storage, dynamic adjustment and intelligent optimization through a standardized data interface. The data interaction between the process parameter module and the neural network module can trigger the self-learning update of the parameter library. The coordinated control of the vibration suppression module and the energy consumption module realizes the dual optimization of quality and energy saving, forming an integrated advantage in rapid cooling, bubble protection, energy consumption management, etc., and providing a complete cooling solution for the cooling of hot melt syrup containing carbon dioxide bubbles with both efficiency, quality and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a composition diagram of the present invention;

[0034] Figure 2 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0035] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to the examples. The examples are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0036] Example 1

[0037] according to Figure 1 、2 The embodiment shown proposes a cold air circulation rapid cooling system, which comprises a process parameter template switching module, a vibration suppression and dynamic parameter adjustment module, a neural network algorithm response module and an energy consumption linkage optimization module. The process parameter template switching module is used to store different viscosity syrup cooling parameter sets and realize rapid parameter switching. The vibration suppression and dynamic parameter adjustment module dynamically adjusts the cold air parameters based on vibration feedback to suppress carbon dioxide bubble rupture.

[0038] The neural network algorithm response module is used to adjust the cold air parameters in real time in response to syrup viscosity fluctuations using a neural network algorithm. The energy consumption linkage optimization module is used to optimize the energy consumption distribution of each functional device through a genetic algorithm. The process parameter template switching module, the vibration suppression and dynamic parameter adjustment module, the neural network algorithm response module and the energy consumption linkage optimization module interact and cooperatively control information through a standardized data interface.

[0039] The process parameter template switching module pre-stores at least 5 sets of cooling parameter sets for different viscosity syrups, and realizes one-key switching of cooling parameters by loading the corresponding template. The cooling parameter set satisfies the formula:

[0040]

[0041] Wherein: is the target cooling temperature at the moment, is the syrup viscosity coefficient, is the syrup density, is the carbon dioxide partial pressure parameter. It can be understood that the process parameter template switching module pre-stores at least 5 sets of cooling parameter sets for different viscosity syrups, and realizes rapid switching of cooling parameters by one-key loading of the corresponding template, which greatly shortens the traditional debugging period from 48-72 hours to 4-8 hours. The formula satisfied by the cooling parameter set is

[0042] It is shown that: the target cooling temperature at the moment is a function relationship of the syrup viscosity coefficient , the syrup density and the carbon dioxide partial pressure parameter The system automatically calculates and matches the corresponding cooling temperature curve by real-time acquisition of these three key physical parameters, without the need for repeated manual debugging, thereby realizing the "plug and play" of different characteristic syrup cooling parameters and significantly improving the production change efficiency.

[0043] ​In a preferred solution of this embodiment, the vibration suppression and dynamic parameter adjustment module includes a vibration sensor array and a cold air parameter adjustment unit. The vibration sensor array is used to collect the vibration frequency of the cooling cavity in real time. with amplitude A;

[0044] The cold wind parameter adjustment unit dynamically adjusts the cold wind speed v and temperature of the three-dimensional wind field based on vibration data , so that the carbon dioxide bubble burst rate ≤5%, meeting:

[0045]

[0046] in: is the bubble stability coefficient, The current temperature of the syrup.

[0047] In this embodiment, in the vibration suppression and dynamic parameter adjustment module, the adjustment accuracy of the cold air parameter adjustment unit satisfies: wind speed fluctuation ≤ 0.5 m / s, temperature fluctuation ≤ 1°C, thereby controlling the bubble bursting rate within 5%.

[0048] It is understandable that the vibration suppression and dynamic parameter adjustment module collects the vibration frequency of the cooling cavity in real time through the vibration sensor array. and amplitude A, and use the cold wind parameter adjustment unit to dynamically adjust the cold wind speed v and temperature based on these vibration data , thereby reducing the carbon dioxide bubble burst rate Controlled below 5%. The core formula shows that: bubble burst rate It is proportional to the square of the vibration frequency and amplitude, as well as the cold air temperature and the current syrup temperature. The temperature difference is related to The module calculates the coupled effects of vibration data and temperature differences in real time, dynamically optimizing cooling air parameters to precisely suppress bubble bursting caused by fluid vibration during the cooling process and ensure the stability of the bubble structure within the syrup.

[0049] In a preferred embodiment of the present invention, the neural network algorithm response module comprises an input layer, a rule base, and a neural network output layer. The input layer is used to receive the syrup viscosity change rate. , cooling rate deviation and bubble pressure fluctuations The rule base stores N mapping rules for viscosity-cooling rate-bubble pressure; the neural network output layer adjusts the cold air flow in real time , the adaptation formula is:

[0050]

[0051] in: is the flow regulation coefficient, 、 、 is the weight factor.

[0052] Specifically, the neural network algorithm response module has a response time of ≤50ms, and tracks changes in syrup viscosity within a range of 100-5000 cP in real time and adjusts cooling parameters.

[0053] The process parameter template switching module and the neural network algorithm response module form a closed-loop adjustment through data interaction. When the template parameters deviate from the real-time cooling data by more than 10%, the neural network self-learning mechanism is triggered to update the cooling parameter set.

[0054] It is understandable that the neural network algorithm response module realizes intelligent adjustment of cooling parameters through a three-layer architecture: the input layer collects the syrup viscosity change rate in real time , cooling rate deviation and bubble pressure fluctuations These three key parameters; the fuzzy rule library has 27 built-in mapping rules based on "viscosity-cooling rate-bubble pressure" (composed of three inputs, each divided into three levels of fuzzy sets), which convert the input parameters into fuzzy control signals; the neural network output layer calculates the cold air flow rate through the formula ,in is the flow regulation coefficient, 、 、 The module dynamically allocates the influence weights of different parameters through weight factors, combined with the adjustment coefficients It can achieve precise flow scaling and respond to syrup viscosity fluctuations within the range of 100-5000cP within 50ms, so that the cold air flow rate is matched with the syrup characteristics in real time, solving the problem of poor adaptability of traditional systems to viscosity changes.

[0055] In this embodiment, the genetic algorithm iteration cycle of the energy consumption linkage optimization module is 10s, and the energy consumption optimization efficiency satisfies:

[0056]

[0057] in: To optimize energy consumption, Energy consumption before optimization.

[0058] In a preferred solution of this embodiment, the energy consumption linkage optimization module collects the cooling cavity power in real time through the power sensor. , three-dimensional wind farm power , refrigeration unit power , using genetic algorithm to optimize energy consumption distribution, when the cooling cavity temperature Lower than When the cooling power of the three-dimensional wind farm module is 20%, the cooling power of the three-dimensional wind farm module is automatically reduced by 15%-30%. The optimization objective function is:

[0059]

[0060] Where: E is the total energy consumption, 、 、 is the energy consumption weight coefficient, and n is the number of sampling points.

[0061] It is understandable that the energy consumption linkage optimization module collects the cooling cavity power in real time through the power sensor , three-dimensional wind farm power and refrigeration unit power , and use genetic algorithms to dynamically optimize energy consumption distribution. Lower than When the cooling power of the three-dimensional wind farm module is reduced by 20%, the system will automatically reduce the cooling power of the three-dimensional wind farm module by 15%-30%. In its core optimization objective function, E represents the total energy consumption. 、 、 The weight coefficient is set based on the energy importance of each module, where n is the number of sampling points. This module uses a genetic algorithm to iteratively adjust the weight coefficient to minimize total energy consumption and achieve optimal energy allocation. For example, when the cooling chamber temperature falls below the set value, the system determines that excessive cooling power is not required and automatically reduces it. This effectively reduces energy consumption while maintaining cooling effectiveness, resulting in a 25%-40% improvement in energy optimization efficiency compared to traditional systems.

[0062] Example 2

[0063] according to Figure 1 、 2 As shown, this embodiment proposes a cold air circulation rapid cooling system, including a process parameter template switching module, a vibration suppression and dynamic parameter adjustment module, a neural network algorithm response module and an energy consumption linkage optimization module. The process parameter template switching module is used to store cooling parameter sets of syrups with different viscosities and realize rapid parameter switching. The vibration suppression and dynamic parameter adjustment module dynamically adjusts the cold air parameters based on vibration feedback to suppress the bursting of carbon dioxide bubbles.

[0064] The neural network algorithm response module is used to use the neural network algorithm to respond to the syrup viscosity fluctuations in real time and adjust the cold air parameters. The energy consumption linkage optimization module is used to optimize the energy consumption distribution of each functional equipment through a genetic algorithm. The process parameter template switching module, vibration suppression and dynamic parameter adjustment module, neural network algorithm response module and energy consumption linkage optimization module perform information exchange and collaborative control through a standardized data interface.

[0065] The process parameter template switching module, vibration suppression and dynamic parameter adjustment module, neural network algorithm response module and energy consumption linkage optimization module are integrated in hardware through standardized interfaces, and the data transmission delay between modules is ≤10ms. Each module is integrated in hardware through a standardized interface, just like a "plug-and-play" modular component, which can be quickly disassembled and replaced according to production needs. This design supports flexible expansion of the cooling cavity volume within the range of 50-500L, and can be adapted from small experimental equipment to medium-sized production lines. At the same time, the data transmission delay between modules is controlled within 10 milliseconds, ensuring that real-time data interaction such as process parameter template switching and vibration adjustment is smooth, which not only meets the hardware scalability of production of different scales, but also ensures the real-time performance of collaborative control of each module through low-latency communication, realizing the flexible integration capability of the system "changing on demand".

[0066] This cold air circulation rapid cooling system improves production efficiency through a process parameter template switching module that pre-stores multiple sets of cooling parameter sets for syrups of different viscosities. Cooling parameter switching can be completed by loading the template with one click, greatly improving cooling changeover efficiency and avoiding the trial-and-error process of manual debugging. It is particularly suitable for flexible cooling scenarios for multiple varieties of syrups, significantly improving production line turnover efficiency. Furthermore, in terms of bubble stability control, the vibration suppression and dynamic parameter adjustment module uses a vibration sensor array to collect real-time vibration data from the cooling chamber. Combined with the dynamic adjustment mechanism of cold air parameters, it controls the carbon dioxide bubble rupture rate from over 15% in traditional systems to less than 5%. By establishing a coupled control model for vibration frequency, amplitude, and cold air parameters, it accurately suppresses bubble rupture caused by fluid disturbances during the cooling process, ensuring the integrity of the bubble structure within the syrup, thereby maintaining the stability of the product's taste and appearance. The present invention constructs an intelligent decision-making system containing multiple mapping rules through a neural network algorithm response module. It can respond in real time to fluctuations in syrup viscosity within the range of 100-5000 cP. By receiving multi-dimensional data such as viscosity change rate and cooling rate deviation, it adjusts the cold air flow rate with a response speed of ≤50ms, keeping the cooling uniformity error within 3%, and solving the problem of poor adaptability of traditional systems to syrups of different viscosities. The energy consumption linkage optimization module of the present invention uses a genetic algorithm to optimize the power data of equipment such as the cooling cavity and the three-dimensional wind field in real time. When the cooling cavity temperature is 20% lower than the set value, it can automatically reduce the wind field cooling power by 15%-30%. The overall energy consumption of the system is greatly reduced compared to traditional solutions. It achieves rational energy distribution while ensuring cooling efficiency, and has both energy saving and economic benefits. The four modules are integrated into a closed-loop control system of parameter pre-storage, dynamic adjustment and intelligent optimization through a standardized data interface. The data interaction between the process parameter module and the neural network module can trigger self-learning and updating of the parameter library. The coordinated control of the vibration suppression module and the energy consumption module realizes the dual optimization of quality and energy saving, forming integrated advantages in rapid cooling, bubble protection, energy consumption management, etc., and providing a complete cooling solution for the cooling of hot-melt syrup containing carbon dioxide bubbles that combines efficiency, quality and economy.

[0067] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A cold air circulation rapid cooling system, comprising a process parameter template switching module, a vibration suppression and dynamic parameter adjustment module, a neural network algorithm response module, and an energy consumption linkage optimization module, characterized in that: The process parameter template switching module is used to store cooling parameter sets of syrups with different viscosities and realize rapid parameter switching. The vibration suppression and dynamic parameter adjustment module dynamically adjusts the cold air parameters based on vibration feedback to suppress the bursting of carbon dioxide bubbles. The neural network algorithm response module is used to use the neural network algorithm to respond to the syrup viscosity fluctuations in real time and adjust the cold air parameters. The energy consumption linkage optimization module is used to optimize the energy consumption distribution of each functional equipment through a genetic algorithm. The process parameter template switching module, vibration suppression and dynamic parameter adjustment module, neural network algorithm response module and energy consumption linkage optimization module perform information exchange and collaborative control through a standardized data interface.

2. The cold air circulation rapid cooling system according to claim 1, characterized in that: The process parameter template switching module pre-stores at least 5 sets of cooling parameter sets for syrups with different viscosities, and realizes one-key switching of cooling parameters by loading corresponding templates, wherein the cooling parameter set satisfies the formula: ； in: for Target cooling temperature at all times, is the viscosity coefficient of syrup, is the density of syrup, is the carbon dioxide partial pressure parameter.

3. The cold air circulation rapid cooling system according to claim 2, characterized in that: The vibration suppression and dynamic parameter adjustment module includes a vibration sensor array and a cold air parameter adjustment unit. The vibration sensor array is used to collect the vibration frequency of the cooling cavity in real time. with amplitude A; The cold wind parameter adjustment unit dynamically adjusts the cold wind speed v and temperature of the three-dimensional wind field based on vibration data , so that the carbon dioxide bubble burst rate ≤5%, meeting: ； in: is the bubble stability coefficient, The current temperature of the syrup.

4. The cold air circulation rapid cooling system according to claim 3, characterized in that: The neural network algorithm response module includes an input layer, a rule base, and a neural network output layer. The input layer is used to receive the syrup viscosity change rate. , cooling rate deviation and bubble pressure fluctuations The rule base stores N mapping rules for viscosity-cooling rate-bubble pressure; the neural network output layer adjusts the cold air flow in real time , the adaptation formula is: ； in: is the flow regulation coefficient, 、 、 is the weight factor.

5. The cold air circulation rapid cooling system according to claim 4, characterized in that: The energy consumption linkage optimization module collects the cooling cavity power in real time through the power sensor , three-dimensional wind farm power , refrigeration unit power , using genetic algorithm to optimize energy consumption distribution, when the cooling cavity temperature Lower than When the cooling power of the three-dimensional wind farm module is 20%, the cooling power of the three-dimensional wind farm module is automatically reduced by 15%-30%. The optimization objective function is: ； Where: E is the total energy consumption, 、 、 is the energy consumption weight coefficient, and n is the number of sampling points.

6. The cold air circulation rapid cooling system according to claim 4, characterized in that: The process parameter template switching module and the neural network algorithm response module form a closed-loop adjustment through data interaction. When the template parameters deviate from the real-time cooling data by more than 10%, the neural network self-learning mechanism is triggered to update the cooling parameter set.

7. The cold air circulation rapid cooling system according to claim 3, characterized in that: In the vibration suppression and dynamic parameter adjustment module, the adjustment accuracy of the cold air parameter adjustment unit meets the following requirements: wind speed fluctuation ≤ 0.5 m / s, temperature fluctuation ≤ 1°C, thereby controlling the bubble bursting rate within 5%.

8. The cold air circulation rapid cooling system according to claim 4, characterized in that: The neural network algorithm response module has a response time of ≤50ms, and tracks changes in syrup viscosity within the range of 100-5000cP in real time and adjusts cooling parameters.

9. The cold air circulation rapid cooling system according to claim 5, characterized in that: The genetic algorithm iteration cycle of the energy consumption linkage optimization module is 10s, and the energy consumption optimization efficiency satisfies: ； in: To optimize energy consumption, Energy consumption before optimization.

10. A cold air circulation rapid cooling system according to any one of claims 1 to 9, characterized in that: The process parameter template switching module, vibration suppression and dynamic parameter adjustment module, neural network algorithm response module and energy consumption linkage optimization module are integrated in a hardware plug-in manner through a standardized interface, and the data transmission delay between modules is ≤10ms.

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