Multi-environment adaptive refrigeration control system based on artificial intelligence

Abstract

The present invention discloses an artificial intelligence-based multi-environment adaptive refrigeration control system, which relates to the field of intelligent control and refrigeration technology. It is used to solve the problem that accurate cooling scheduling and system-level energy efficiency coordination cannot be achieved under concurrent demands of multiple environments, thereby reducing the operating efficiency of the refrigeration system and the safety of user use. By introducing a distributed game model constructed based on cooling demand differences and environmental parameters, dynamic balance and intelligent distribution of cooling among multiple regions are achieved, and a truncated Gaussian mechanism is used to encrypt cooling demand data. Without affecting the scheduling accuracy, a system-level objective function is constructed and jointly optimized by integrating cooling scheduling strategies and operating status data of different regions. The mapping relationship between environmental characteristics and cooling characteristics is continuously updated, and the load models and control objectives under different scenarios can be dynamically adapted, thereby improving the versatility and flexibility of the system and enhancing the engineering practicality of the system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control and refrigeration technology, and more specifically, to a multi-environment adaptive refrigeration control system based on artificial intelligence. Background Art

[0002] Multi-zone cooling control technology coordinates cooling demands across multiple zones, optimizing system energy efficiency and meeting the needs of diverse scenarios. This technology holds broad application potential in data centers, large commercial buildings, and smart homes. However, existing technologies have limitations when addressing conflicting cooling demands across multiple zones, particularly in optimizing global energy efficiency in complex environments. This can lead to decreased system efficiency and increased energy consumption.

[0003] The existing technology has the following deficiencies:

[0004] At present, most multi-zone refrigeration systems rely on fixed threshold control strategies and lack a real-time response mechanism to dynamic changes in cooling demand. At the same time, there is a lack of effective privacy protection measures when processing user environment data. As a result, it is impossible to achieve accurate cooling scheduling and system-level energy efficiency coordination under the concurrent demands of multiple environments, which reduces the operating efficiency of the refrigeration system and the safety of user use. Therefore, a multi-environment adaptive refrigeration control system based on artificial intelligence is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-environment adaptive refrigeration control system based on artificial intelligence. By introducing a distributed game model driven by cooling difference and a privacy protection algorithm of the truncated Gaussian mechanism, it realizes intelligent cooling allocation and global optimization control of energy efficiency for multi-region environments, and solves the problems raised in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a multi-environment adaptive refrigeration control system based on artificial intelligence, comprising a cooling demand collection unit, an intelligent allocation decision unit, a privacy protection encryption unit, and a global energy efficiency optimization unit;

[0008] The cooling demand collection unit is used to collect cooling demand data and environmental parameter data, classify each area according to the cooling demand data, and determine whether to activate the privacy protection mechanism. If it is activated, the marking signal is transmitted to the privacy protection encryption unit. Otherwise, the cooling demand data and environmental parameter data are transmitted to the intelligent allocation decision unit.

[0009] The intelligent allocation decision unit is used to analyze the differences in cooling demand, integrate the differences in cooling demand and environmental parameter data to build a distributed game model, and use the distributed game model to perform preliminary calculations on the cooling allocation strategy. The calculation results are sent to the privacy protection encryption unit or the global energy efficiency optimization unit.

[0010] The privacy protection encryption unit is used to encrypt the cooling demand data, generate noisy data using a truncated Gaussian mechanism, and transmit the encrypted data and processed signals to the global energy efficiency optimization unit;

[0011] The global energy efficiency optimization unit is used to adjust the system operating parameters according to the processing signals transmitted by different units.

[0012] In a preferred embodiment, the cooling demand data includes a target temperature, a humidity range, and a heat load coefficient, and the environmental parameter data includes outdoor temperature, humidity, and wind speed, wherein the target temperature is the desired temperature value set in the area.

[0013] In a preferred embodiment, the cooling demand collection unit classifies each area according to the cooling demand data, that is, classifies the high temperature demand area into a first type of area and the low temperature demand area into a second type of area according to the target temperature.

[0014] In a preferred embodiment, the privacy protection mechanism interferes with the true distribution of cooling demand data by introducing random noise, and the noise intensity is dynamically adjusted by a truncated Gaussian mechanism.

[0015] In a preferred embodiment, the intelligent allocation decision unit compares the heat load coefficients of different areas to confirm the difference in cooling demand, and constructs a distributed game model by recording the cooling demand differences and environmental parameter changes over multiple time periods and merging them into a demand change data set and an environmental change data set respectively.

[0016] In a preferred embodiment, the specific steps of constructing a distributed game model by the intelligent allocation decision unit are as follows:

[0017] Data preparation: Combine the demand change dataset and the environment change dataset into the input matrix X, set the objective function to y, and set the ratio to divide the input matrix into a training dataset and a validation dataset;

[0018] Model construction: Define the objective function of the distributed game model and use a hybrid quantum-classical optimization algorithm to accelerate model convergence;

[0019] Model evaluation: Use the training dataset and validation dataset to calculate the objective function and adjust the weight coefficient of the objective function based on the calculation results;

[0020] Evaluate the rationality of cooling capacity allocation: Select the optimal solution in the calculated objective function and compare it with the preset allocation threshold to evaluate the rationality of cooling capacity allocation.

[0021] In a preferred embodiment, the privacy protection encryption unit sets a noise distribution interval when encrypting the cooling demand data, first standardizes the cooling demand data, and when the standardized data conforms to the normal distribution characteristics, uses a truncated Gaussian mechanism to generate noisy data.

[0022] In a preferred embodiment, the noisy data in the privacy protection encryption unit includes noise intensity and noise distribution range, the privacy protection degree of the cooling demand is analyzed based on the noisy data, the privacy protection error is analyzed based on the actual distribution of the cooling demand data, and the security of the cooling demand data is determined by comprehensively considering the privacy protection degree and the privacy protection error.

[0023] In a preferred embodiment, the global energy efficiency optimization unit activates the privacy protection mechanism when receiving the processing signal transmitted by the cooling demand collection unit;

[0024] Adjust the cooling capacity allocation strategy upon receiving the processing signal transmitted by the intelligent allocation decision unit;

[0025] When receiving the processing signal transmitted by the privacy protection encryption unit, the system operating parameters are corrected according to the security of the cooling demand data.

[0026] In a preferred embodiment, the global energy efficiency optimization unit adjusts the compressor frequency, fan speed and coolant flow of the refrigeration equipment according to the cooling capacity allocation strategy when adjusting the system operating parameters.

[0027] The technical effects and advantages of the present invention are as follows:

[0028] The present invention introduces a distributed game model based on the difference in cooling demand and environmental parameters to achieve dynamic balance and intelligent allocation of cooling capacity among multiple regions. It adopts a truncated Gaussian mechanism to encrypt the cooling demand data, and ensures the privacy and security of user environmental data without affecting the scheduling accuracy. By integrating the cooling scheduling strategies and operating status data of different regions, the system-level objective function is constructed and jointly optimized, and the mapping relationship between environmental characteristics and cooling characteristics is continuously updated. It can dynamically adapt to the load models and control objectives in different scenarios, improve the versatility and flexibility of the system, and enhance the engineering practicality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The figure is a schematic diagram of the construction and optimization process of the distributed game model in the multi-environment adaptive refrigeration control system based on artificial intelligence of the present invention.

[0030] Figure 2This is a structural block diagram of the multi-environment adaptive refrigeration control system based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention. Example

[0032] The present invention provides a multi-environment adaptive refrigeration control system based on artificial intelligence, the structure of which mainly includes a cooling demand acquisition unit, an intelligent allocation decision unit, a privacy protection encryption unit and a global energy efficiency optimization unit.

[0033] These units are connected by high-speed communication buses for signal connection and data transmission, such as Figure 1 The cooling demand collection unit is located at the front end of the system and is responsible for collecting cooling demand data and environmental parameter data for each area. It then classifies high-temperature demand areas into Class I areas and low-temperature demand areas into Class II areas based on the target temperature.

[0034] The intelligent allocation decision unit is connected to the cooling demand collection unit, receives the classified cooling demand data and environmental parameter data, analyzes the cooling demand differences and builds a distributed game model.

[0035] The privacy protection encryption unit is connected to the cooling demand collection unit and the intelligent allocation decision unit respectively, and encrypts the data to be protected and then transmits it to the global energy efficiency optimization unit.

[0036] As the back-end core component of the system, the global energy efficiency optimization unit receives processing signals from other units and adjusts the system operating parameters.

[0037] The specific implementation of the cooling demand collection unit is as follows: the unit contains multiple sensor nodes for real-time collection of target temperature, humidity range, heat load coefficient cooling demand data, and records outdoor temperature, humidity and wind speed environmental parameter data.

[0038] The sensor nodes upload data to the core processor of the cooling demand collection unit through a high-speed communication bus.

[0039] The core processor first classifies the cooling demand data and determines whether the privacy protection mechanism needs to be activated.

[0040] Specifically, the process of determining whether to activate the privacy protection mechanism is as follows:

[0041] The privacy sensitivity score of the current scene is obtained by collecting the difference between the target temperature and the current temperature, the current scene volume, and the collection frequency in the current scene, performing normalization processing, and substituting them into the weighted formula;

[0042] The privacy sensitivity score of the current scene is compared with the preset sensitivity threshold. If the privacy sensitivity score of the current scene is greater than or equal to the preset sensitivity threshold, it needs to be started. Conversely, if the privacy sensitivity score of the current scene is less than the preset sensitivity threshold, the cooling demand data and environmental parameter data are directly transmitted to the intelligent allocation decision unit.

[0043] Among them, the standardization processing method includes but is not limited to a standard linear transformation based on interval scaling, a Z-Score standardization method based on statistics, or a normalization method based on a nonlinear mapping function. The application method of the standardization processing is not described in detail here;

[0044] It should be noted that the preset sensitivity threshold was obtained by our researchers based on a comprehensive analysis of the statistical distribution of regional cooling demand characteristics in multiple typical application scenarios and the impact of user behavior patterns on privacy leakage risks. We will not elaborate on this here.

[0045] Furthermore, if activation is required, a flag signal is generated and sent to the privacy protection encryption unit via a high-speed communication bus;

[0046] In actual applications, cooling demand collection units are usually deployed in various areas of a building complex, such as data center rooms, office areas, and laboratories, to ensure that the cooling needs of different areas can be fully covered.

[0047] After receiving the cooling demand data and environmental parameter data, the intelligent allocation decision unit first compares the heat load coefficients of different areas to confirm the cooling demand differences, and then merges the cooling demand differences and environmental parameter changes in multiple time periods into a demand change data set and an environmental change data set, respectively.

[0048] Then, the intelligent allocation decision-making unit constructs a distributed game model. The specific steps are as follows: Figure 2 shown.

[0049] First, the demand change dataset and the environment change dataset are merged into the input matrix X, and the objective function y is set.

[0050] The input matrix X is divided into a training dataset and a validation dataset according to a certain ratio for subsequent model evaluation.

[0051] Secondly, in the model construction stage, the objective function of the distributed game model is defined, and a hybrid quantum-classical optimization algorithm is used to accelerate model convergence.

[0052] The hybrid quantum-classical optimization algorithm dynamically adjusts the optimization path through a quantum annealing process combined with an adaptive step-size strategy, significantly improving solution efficiency.

[0053] Finally, in the model evaluation stage, the objective function is calculated and the weight coefficient is adjusted using the training dataset and the validation dataset respectively. The optimal solution is selected and compared with the preset allocation threshold to evaluate the rationality of cooling capacity allocation.

[0054] After completing the above steps, the intelligent allocation decision unit sends the preliminary calculation results to the privacy protection encryption unit or the global energy efficiency optimization unit.

[0055] The core function of the privacy protection encryption unit is to encrypt the cooling demand data to prevent privacy risks caused by data leakage.

[0056] The specific implementation is as follows:

[0057] First, the privacy protection encryption unit normalizes the received cooling demand data to make it conform to the normal distribution characteristics.

[0058] Subsequently, a truncated Gaussian mechanism is used to generate noisy data, and the noise intensity is dynamically adjusted by the truncated Gaussian mechanism to ensure that the noise distribution range is reasonable and meets privacy protection requirements.

[0059] Noise-enhanced data includes noise intensity and noise distribution range. The privacy protection level of cooling demand data can be evaluated by analyzing the noisy data.

[0060] In addition, the privacy protection encryption unit also calculates the privacy protection error based on the true distribution of the cooling demand data, and determines the security of the cooling demand data by comprehensively considering the privacy protection degree and the privacy protection error.

[0061] After completing the encryption process, the privacy protection encryption unit transmits the noisy data and processed signals to the global energy efficiency optimization unit through a high-speed communication bus.

[0062] The global energy efficiency optimization unit is the core control module of the entire system, responsible for adjusting the system operating parameters according to the processing signals transmitted by different units.

[0063] When receiving the processing signal from the cooling demand collection unit, the global energy efficiency optimization unit activates the privacy protection mechanism;

[0064] When receiving the processing signal from the intelligent allocation decision unit, the cooling capacity allocation strategy is adjusted;

[0065] When receiving the processing signal transmitted by the privacy protection encryption unit, the system operating parameters are corrected according to the security of the cooling demand data.

[0066] In actual operation, the global energy efficiency optimization unit optimizes the cooling capacity distribution strategy by adjusting the compressor frequency, fan speed and coolant flow of the refrigeration equipment.

[0067] For example, for high-temperature demand areas, i.e., Class I areas, the compressor frequency and coolant flow rate are appropriately increased to increase the cooling capacity;

[0068] For low temperature demand areas, i.e. Class II areas, the fan speed is reduced to reduce energy consumption.

[0069] At the same time, the global energy efficiency optimization unit monitors the deviation between the actual temperature and the target temperature of each area in real time, and dynamically adjusts the optimization strategy according to the deviation value to ensure that temperature control fluctuations are minimized.

[0070] In actual application scenarios, this system can be deployed in large commercial complexes or industrial plants.

[0071] For example, in a complex of buildings including a data center, office areas, and laboratories, cooling demand collection units are distributed in various areas to collect cooling demand data and environmental parameter data in real time.

[0072] The intelligent allocation decision unit builds a distributed game model based on the collected data and preliminarily calculates the cooling capacity allocation strategy.

[0073] If sensitive data is involved, the privacy protection encryption unit will encrypt it and then pass it to the global energy efficiency optimization unit.

[0074] The global energy efficiency optimization unit adjusts the refrigeration equipment parameters according to the final cooling distribution strategy to ensure that the cooling demand of each area is met while reducing overall energy consumption.

[0075] Through the above technical solutions, the system realizes the intelligent coordination and efficient allocation of cooling demand in multiple regions, improves the overall energy efficiency and reduces energy consumption, while effectively preventing the risk of leakage of cooling demand data.

[0076] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is further supplemented below with reference to a specific application scenario.

[0077] In a large commercial complex, the building contains multiple functional areas such as data centers, office areas and laboratories.

[0078] The cooling demand in different areas varies significantly. For example, data centers require a continuous low-temperature environment to ensure equipment operation, while office areas need to dynamically adjust the cooling capacity according to the population density.

[0079] To meet these complex needs, the system uses a cooling demand collection unit to monitor the cooling demand data of each area, such as the target temperature, humidity range and heat load coefficient, in real time, and records environmental parameter data such as outdoor temperature, humidity and wind speed.

[0080] The sensor node uploads the collected data to the core processor of the cooling demand collection unit. The core processor classifies the high-temperature demand area as a Class I area and the low-temperature demand area as a Class II area based on the target temperature.

[0081] Subsequently, the classified cooling demand data and environmental parameter data are transmitted to the intelligent allocation decision unit for analysis and processing.

[0082] The intelligent allocation decision unit first compares the heat load coefficients of different areas to confirm the differences in cooling demand, and then merges the cooling demand differences and environmental parameter changes in multiple time periods into a demand change data set and an environmental change data set, respectively.

[0083] On this basis, the intelligent allocation decision-making unit constructs a distributed game model, and its specific implementation steps are as follows:

[0084] First, the demand change dataset and the environment change dataset are combined into the input matrix X, and the objective function y is set;

[0085] Secondly, the training dataset and validation dataset are divided into two parts according to a certain ratio for subsequent model evaluation. During the model construction phase, after defining the objective function, a hybrid quantum-classical optimization algorithm is used to accelerate model convergence.

[0086] The hybrid quantum-classical optimization algorithm dynamically adjusts the optimization path through a quantum annealing process combined with an adaptive step-size strategy, thereby significantly improving the solution efficiency.

[0087] In the model evaluation stage, the training dataset and validation dataset are used to calculate the objective function and adjust the weight coefficient respectively. The optimal solution is selected and compared with the preset allocation threshold to evaluate the rationality of cooling capacity allocation.

[0088] After completing the above steps, the intelligent allocation decision unit sends the preliminary calculation results to the privacy protection encryption unit or the global energy efficiency optimization unit.

[0089] If sensitive data is involved, the privacy protection encryption unit will encrypt the cooling demand data.

[0090] Specifically, the privacy-preserving encryption unit first standardizes the received cooling demand data to make it conform to the normal distribution characteristics.

[0091] Subsequently, a truncated Gaussian mechanism is used to generate noisy data, and the noise intensity is dynamically adjusted by the truncated Gaussian mechanism to ensure that the noise distribution range is reasonable and meets privacy protection requirements.

[0092] By analyzing the noisy data, the privacy protection level of the cooling demand data can be evaluated. Furthermore, the privacy-preserving encryption unit calculates the privacy protection error based on the true distribution of the cooling demand data. The security of the cooling demand data is determined by combining the privacy protection level and the privacy protection error.

[0093] After completing the encryption process, the privacy protection encryption unit transmits the noisy data and processed signals to the global energy efficiency optimization unit through a high-speed communication bus.

[0094] As the back-end core component of the system, the global energy efficiency optimization unit is responsible for adjusting the system operating parameters according to the processing signals transmitted by different units.

[0095] When receiving the processing signal from the cooling demand collection unit, the global energy efficiency optimization unit activates the privacy protection mechanism;

[0096] When receiving the processing signal from the intelligent allocation decision unit, the cooling capacity allocation strategy is adjusted;

[0097] When receiving the processing signal transmitted by the privacy protection encryption unit, the system operating parameters are corrected according to the security of the cooling demand data.

[0098] In actual operation, the global energy efficiency optimization unit optimizes the cooling capacity distribution strategy by adjusting the compressor frequency, fan speed and coolant flow of the refrigeration equipment.

[0099] For example, for high-temperature demand areas, i.e., Class I areas, the compressor frequency and coolant flow rate are appropriately increased to increase the cooling capacity;

[0100] For low temperature demand areas, i.e. Class II areas, the fan speed is reduced to reduce energy consumption.

[0101] At the same time, the global energy efficiency optimization unit monitors the deviation between the actual temperature and the target temperature of each area in real time, and dynamically adjusts the optimization strategy according to the deviation value to ensure that temperature control fluctuations are minimized.

[0102] In the above scenario, the system comprehensively covers the cooling demand of different areas through the cooling demand collection unit. The intelligent allocation decision unit realizes the intelligent coordination and efficient allocation of cooling demand based on the distributed game model. The privacy protection encryption unit effectively prevents the risk of leakage of cooling demand data. The global energy efficiency optimization unit ensures that the cooling demand of each area is met while reducing overall energy consumption by dynamically adjusting the parameters of the refrigeration equipment.

[0103] Through the above technical solutions, this system realizes the intelligent coordination and efficient distribution of cooling demand in multiple zones, improves overall energy efficiency and reduces energy consumption, while meeting the requirements of modern multi-zone refrigeration systems for efficiency, intelligence and safety.

[0104] The contents not described in detail in the specification belong to the existing technology known to those skilled in the art, and the model parameters of each electrical appliance are not specifically limited, and conventional equipment can be used.

[0105] In this technical solution, the electrical control components not mentioned belong to the prior art and are not shown in the figures and will not be described here.

[0106] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0107] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0108] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0109] Those skilled in the art will appreciate that the units 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 beyond the scope of this application.

[0110] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0113] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0114] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0115] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. Multi-environment adaptive refrigeration control system based on artificial intelligence, characterized by: It includes cooling demand collection unit, intelligent allocation decision unit, privacy protection encryption unit and global energy efficiency optimization unit; The cooling demand collection unit is used to collect cooling demand data and environmental parameter data, classify each area according to the cooling demand data, and determine whether to activate the privacy protection mechanism. If it is activated, the marking signal is transmitted to the privacy protection encryption unit. Otherwise, the cooling demand data and environmental parameter data are transmitted to the intelligent allocation decision unit. The intelligent allocation decision unit is used to analyze the differences in cooling demand, integrate the differences in cooling demand and environmental parameter data to build a distributed game model, and use the distributed game model to perform preliminary calculations on the cooling allocation strategy. The calculation results are sent to the privacy protection encryption unit or the global energy efficiency optimization unit. The privacy protection encryption unit is used to encrypt the cooling demand data, generate noisy data using a truncated Gaussian mechanism, and transmit the encrypted data and processed signals to the global energy efficiency optimization unit; The global energy efficiency optimization unit is used to adjust the system operating parameters according to the processing signals transmitted by different units.

2. The artificial intelligence-based multi-environment adaptive refrigeration control system according to claim 1, characterized in that: The cooling demand data includes the target temperature, humidity range and heat load coefficient, and the environmental parameter data includes the outdoor temperature, humidity and wind speed, where the target temperature is the expected temperature value set in the area.

3. The artificial intelligence-based multi-environment adaptive refrigeration control system according to claim 2, characterized in that: The cooling demand collection unit classifies each area according to the cooling demand data, that is, classifies the high temperature demand area into a first-class area and the low temperature demand area into a second-class area according to the target temperature.

4. The artificial intelligence-based multi-environment adaptive refrigeration control system according to claim 3, characterized in that: The privacy protection mechanism interferes with the true distribution of cooling demand data by introducing random noise, and the noise intensity is dynamically adjusted by the truncated Gaussian mechanism.

5. The artificial intelligence-based multi-environment adaptive refrigeration control system according to claim 2, characterized in that: The intelligent allocation decision-making unit compares the heat load coefficients of different areas to confirm the difference in cooling demand. By recording the cooling demand differences and environmental parameter changes over multiple time periods and merging them into a demand change dataset and an environmental change dataset, a distributed game model is constructed.

6. The artificial intelligence-based multi-environment adaptive refrigeration control system according to claim 5, characterized in that: The specific steps for the intelligent allocation decision-making unit to build a distributed game model are as follows: Data preparation: Combine the demand change dataset and the environment change dataset into the input matrix X, set the objective function to y, and set the ratio to divide the input matrix into a training dataset and a validation dataset; Model construction: Define the objective function of the distributed game model and use a hybrid quantum-classical optimization algorithm to accelerate model convergence; Model evaluation: Use the training dataset and validation dataset to calculate the objective function and adjust the weight coefficient of the objective function based on the calculation results; Evaluate the rationality of cooling capacity allocation: Select the optimal solution in the calculated objective function and compare it with the preset allocation threshold to evaluate the rationality of cooling capacity allocation.

7. The artificial intelligence-based multi-environment adaptive refrigeration control system according to claim 4, characterized in that: When the privacy protection encryption unit encrypts the cooling demand data, it sets the noise distribution interval and first standardizes the cooling demand data. When the standardized data conforms to the normal distribution characteristics, the truncated Gaussian mechanism is used to generate noisy data.

8. The artificial intelligence-based multi-environment adaptive refrigeration control system according to claim 7, characterized in that: The noisy data in the privacy-preserving encryption unit includes noise intensity and noise distribution range. The privacy protection degree of the cooling demand is analyzed based on the noisy data. The privacy protection error is analyzed based on the actual distribution of the cooling demand data. The security of the cooling demand data is determined by combining the privacy protection degree and privacy protection error.

9. The artificial intelligence-based multi-environment adaptive refrigeration control system according to claim 2, characterized in that: The global energy efficiency optimization unit activates the privacy protection mechanism when receiving the processing signal transmitted by the cooling demand collection unit; Adjust the cooling capacity allocation strategy upon receiving the processing signal transmitted by the intelligent allocation decision unit; When receiving the processing signal transmitted by the privacy protection encryption unit, the system operating parameters are corrected according to the security of the cooling demand data.

10. The artificial intelligence-based multi-environment adaptive refrigeration control system according to claim 9, characterized in that: When adjusting the system operating parameters, the global energy efficiency optimization unit adjusts the compressor frequency, fan speed and coolant flow of the refrigeration equipment according to the cooling capacity distribution strategy.

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