Electronic Cooling Control Method and Device Based on Large Language Model, and Electronic Equipment

Abstract

The present application provides an electronic cooling control method, device, and electronic device based on a large language model, which relates to the technical field of electronic cooling control. Through the collaborative architecture of the main control model and the sub-control models of each electronic cooling device, the main control model performs semantic parsing on the natural language instructions of electronic cooling input by the user; the main control model constructs a physical model for controlling the target electronic cooling device according to the semantic parsing result, and determines the preliminary control parameters for controlling the target electronic cooling device according to the physical model and the current actual operating state of the target electronic cooling device; further, when the target electronic cooling device receives the preliminary control parameters, the sub-control model of the target electronic cooling device analyzes the preliminary control parameters, and determines whether to adjust the preliminary control parameters according to the analysis result, so as to realize the intelligent control, fault prediction, and natural language interaction of the electronic cooling device.

Description

Technical Field

[0001] This application relates to the field of electronic cooling control technology, and particularly to an electronic cooling control method and device, and an electronic device based on a large language model. Background Art

[0002] In an accelerator, an electronic cooling control system is a core technology for improving the quality of ion beams. Its core function is to reduce the temperature and momentum dispersion of ion beams through the interaction between low-temperature electron beams and high-temperature ion beams, thereby achieving the stability and focusing of the beam.

[0003] Traditional electronic cooling control systems rely on preset rules and manual experience, and have the following problems:

[0004] 1) Insufficient adaptability: Unable to dynamically respond to complex working condition changes and difficult to optimize beam parameters;

[0005] 2) Delayed fault diagnosis: Relying on manual analysis of logs and difficult to predict equipment anomalies in real time;

[0006] 3) High interaction complexity: Control instructions require professional programming, with high maintenance costs and easy to make mistakes.

[0007] Existing technologies have tried to introduce traditional machine learning methods, but the model generalization ability is limited and natural language interaction cannot be achieved. Therefore, it is urgent to solve this technical problem. Summary of the Invention

[0008] In view of the above problems, this application is proposed to provide an electronic cooling control method and device, an electronic device, a storage medium, and a computer program product based on a large language model that overcome the above problems or at least partially solve the above problems. The technical solutions are as follows:

[0009] In a first aspect, an electronic cooling control method based on a large language model is provided, including:

[0010] Pre-deploy a large language model with the first parameter on the master control server, obtain the electronic cooling operation log and electronic cooling theoretical literature as the first sample data set, and perform pre-training, instruction fine-tuning, and reinforcement learning on the large language model with the first parameter to obtain a master control model that can understand the physical laws of electronic cooling;

[0011] Pre-deploy a large language model with the second parameter on each electronic cooling device. For the large language model with the second parameter on each electronic cooling device, obtain the operation log and theoretical literature of this electronic cooling device as the second sample data set, and train the large language model with the second parameter on this electronic cooling device to obtain a sub-control model of this electronic cooling device, thereby obtaining sub-control models of each electronic cooling device;

[0012] In response to an electronic cooling natural language instruction input by a user, a main control model performs semantic parsing on the electronic cooling natural language instruction to obtain a semantic parsing result including controlling a target electronic cooling device; the main control model constructs a physical model for controlling the target electronic cooling device according to the semantic parsing result, and determines preliminary control parameters for controlling the target electronic cooling device according to the physical model and the current actual operating state of the target electronic cooling device;

[0013] The target electronic cooling device receives the preliminary control parameters, and a sub-control model of the target electronic cooling device analyzes the preliminary control parameters and determines whether to adjust the preliminary control parameters according to the analysis result.

[0014] In a possible implementation manner, the method further includes:

[0015] An electronic cooling knowledge base including a theory library and an operation library is pre-constructed, where the theory library includes electronic cooling physical theories, electronic cooling technical literature, and historical experimental data; the operation library includes operation parameters of each electronic cooling device collected in real time.

[0016] In a possible implementation manner, obtaining an electronic cooling operation log and electronic cooling theory literature as a first sample data set includes:

[0017] Obtaining an electronic cooling operation log and electronic cooling theory literature from the pre-constructed electronic cooling knowledge base as the first sample data set;

[0018] Obtaining the operation log and theory literature of the electronic cooling device as a second sample data set includes:

[0019] Obtaining the operation log and theory literature of the electronic cooling device from the pre-constructed electronic cooling knowledge base as the second sample data set.

[0020] In a possible implementation manner, the main control model performs semantic parsing on the electronic cooling natural language instruction to obtain a semantic parsing result including controlling a target electronic cooling device, including:

[0021] The main control model combines the electronic cooling knowledge base to perform semantic parsing on the electronic cooling natural language instruction to obtain a semantic parsing result including controlling a target electronic cooling device.

[0022] In a possible implementation manner, pre-training, instruction fine-tuning, and reinforcement learning are performed on a large language model of a first parameter to obtain a main control model capable of understanding electronic cooling physical laws, including:

[0023] Taking beam stability, energy consumption efficiency, and fault prediction accuracy as optimization objectives, pre-train, instruction-tune, and reinforce a large language model of the first parameter to obtain a main control model that can understand the physical laws of electron cooling.

[0024] In a possible implementation, determining whether to adjust the preliminary control parameters according to the analysis results includes:

[0025] If it is determined according to the analysis results that the preliminary control parameters need to be adjusted, generate a multi-objective optimization solution. If the adjusted control parameters are obtained through the solution, the target electron cooling device executes according to the adjusted control parameters; if the adjusted control parameters are not obtained through the solution, generate a reporting message indicating that the adjusted control parameters are not obtained through the solution and report it to the main control model.

[0026] In a possible implementation, determining whether to adjust the preliminary control parameters according to the analysis results includes:

[0027] If it is determined according to the analysis results that the preliminary control parameters do not need to be adjusted, the target electron cooling device executes according to the preliminary control parameters. During the execution of the target electron cooling device, the sub-control model analyzes and identifies the execution information of the target electron cooling device. If a fault of the target electron cooling device is identified, execute a preset resolution strategy and generate an alarm message indicating that the target electron cooling device has a fault and report it to the main control model.

[0028] In a second aspect, an electron cooling control device based on a large language model is provided for implementing the above-mentioned electron cooling control method based on a large language model. The device includes:

[0029] A first deployment unit for pre-deploying a large language model of the first parameter on a main control server, obtaining an electron cooling operation log and electron cooling theoretical literature as a first sample data set, and pre-training, instruction-tuning, and reinforcing the large language model of the first parameter to obtain a main control model that can understand the physical laws of electron cooling;

[0030] A second deployment unit for pre-deploying a large language model of the second parameter on each electron cooling device. For the large language model of the second parameter on each electron cooling device, obtain the operation log and theoretical literature of the electron cooling device as a second sample data set, and train the large language model of the second parameter on the electron cooling device to obtain a sub-control model of the electron cooling device, thereby obtaining sub-control models of each electron cooling device;

[0031] The first control unit is configured to respond to the natural language instruction for electronic cooling input by the user. The main control model performs semantic parsing on the natural language instruction for electronic cooling to obtain a semantic parsing result including controlling the target electronic cooling device. The main control model constructs a physical model for controlling the target electronic cooling device according to the semantic parsing result, and determines preliminary control parameters for controlling the target electronic cooling device based on the physical model and the current actual operating state of the target electronic cooling device.

[0032] The second control unit is configured to, when the target electronic cooling device receives the preliminary control parameters, analyze the preliminary control parameters by the sub-control model of the target electronic cooling device, and determine whether to adjust the preliminary control parameters according to the analysis result.

[0033] In a third aspect, an electronic device is provided. The electronic device includes a processor and a memory. Among them, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the electronic cooling control method based on a large language model described in any one of the above.

[0034] In a fourth aspect, a storage medium is provided. The storage medium stores a computer program, and the computer program is configured to execute the electronic cooling control method based on a large language model described in any one of the above when running.

[0035] In a fifth aspect, a computer program product is provided, including a computer program, and the computer program is configured to execute the electronic cooling control method based on a large language model described in any one of the above when running.

[0036] By means of the above technical solutions, the electronic cooling control method and device, electronic device, storage medium, and computer program product based on a large language model provided by the embodiments of the present application. The electronic cooling control method based on a large language model realizes intelligent control, fault prediction, and natural language interaction of the electronic cooling device through the collaborative architecture of the main control model and the sub-control models of each electronic cooling device, and is particularly suitable for optimizing the beam quality and improving the system stability of electronic cooling. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for describing the embodiments of the present application will be briefly introduced below.

[0038] Figure 1 Shows a schematic diagram of the electronic cooling principle provided by the embodiments of the present application;

[0039] Figure 2 Shows a flowchart of the electronic cooling control method based on a large language model provided by the embodiments of the present application;

[0040] Figure 3 It shows the main - sub - control collaborative architecture diagram provided by the embodiments of the present application;

[0041] Figure 4 It shows the control flow chart of the main - control model provided by the embodiments of the present application;

[0042] Figure 5 It shows the schematic diagram of the design of the reinforcement learning reward mechanism provided by the embodiments of the present application;

[0043] Figure 6 It shows the structure diagram of the electronic cooling control device based on the large - language model provided by the embodiments of the present application;

[0044] Figure 7 It shows the structure diagram of the electronic cooling control device based on the large - language model provided by another embodiment of the present application;

[0045] Figure 8 It shows the structure diagram of an electronic device provided by the embodiments of the present application. Detailed implementation manners

[0046] Hereinafter, the exemplary embodiments of the present application will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be completely conveyed to those skilled in the art.

[0047] It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above - mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such use can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "comprising" and its variants should be interpreted as open - ended terms meaning "including but not limited to".

[0048] As introduced above, in an accelerator, the electron cooling control system reduces the temperature and momentum spread of the ion beam through the interaction between the cryogenic electron beam and the high-temperature ion beam, thereby achieving the stability and focusing of the beam. The electron cooling control system may include a high-voltage platform device for generating a high-voltage electric field, an electron gun for emitting an electron beam, a grid for controlling the turn-off of the electron beam, an anode for extracting the electron beam, a filament for generating electrons, a collector for collecting the electron beam, a deflection plate for deflecting the direction of the electron beam, one or more horizontal correction power supplies for correcting the horizontal error of the electron beam, one or more vertical correction power supplies for correcting the vertical error of the electron beam, a resonant power supply for supplying 20 kHz (kilohertz) AC power to the high-voltage platform device, an accelerating tube, a decelerating tube, a beam position detector, and other electron cooling devices.

[0049] See Figure 1 , for illustration, 1 is the electron gun, 2 is the accelerating tube, 3 is the vacuum chamber, 4 is the cooling section, 5 is the collector, and 6 is the high-voltage platform device. The electron gun 1 can generate an electron beam of 0 - 3 A. After being accelerated by the accelerating tube 2, the electron beam enters the cooling section 4 through the vacuum chamber 3 and deflection by the deflection plate. In the cooling section 4, it interacts with the ion beam. After cooling the ion beam, it enters the collector 5 through the deflection plate. After the electrons are collected by the collector 5, they are emitted again by the electron gun 1; the high-voltage platform device 6 can extract and accelerate the electron beam to a pre-set energy through the negative high voltage generated by it. Here, the collector 5 is used to improve the utilization efficiency of the electron beam and achieve the secondary utilization of electrons.

[0050] The embodiment of the present application provides an electron cooling control method based on a large language model. Here, the large language model (Large Language Model, LLM), first, as the name implies, is large-scale, with the number of network parameters reaching tens of billions, hundreds of billions or even more; second, generality, which means not limited to specific problems or fields; third, emergence, that is, the generation of unexpected new capabilities.

[0051] As Figure 2 shown, the electron cooling control method based on the large language model may include the following steps S201 to S204:

[0052] Step S201, deploy a large language model with the first parameters on the master server in advance, obtain the electron cooling operation log and electron cooling theoretical literature as the first sample data set, and perform pre-training, instruction tuning, and reinforcement learning on the large language model with the first parameters to obtain a master model that can understand the physical laws of electron cooling.

[0053] In this step, the large language model can be an open-source large language model. As the model base, there can be different parameter versions, such as parameters of 1.5 billion, 7 billion, 14 billion, 32 billion, 671 billion, etc. The selection of the parameter scale can be combined with computing power resources and performance requirements, etc., and this embodiment does not limit this.

[0054] Step S202: Pre-deploy the large language model with the second parameter on each electronic cooling device. For the large language model with the second parameter on each electronic cooling device, obtain the operation log and theoretical literature of this electronic cooling device as the second sample dataset, and train the large language model with the second parameter on this electronic cooling device to obtain the sub-control model of this electronic cooling device, thereby obtaining the sub-control models of each electronic cooling device.

[0055] In this step, when deploying the large language model with the second parameter on each electronic cooling device, considering the hardware resources and actual requirements, the second parameter can be smaller than the first parameter.

[0056] In addition, the same large language model with the second parameter can be deployed on different electronic cooling devices. For example, open-source large language models with 1.5 billion parameters are deployed on different electronic cooling devices. Different electronic cooling devices can also deploy different large language models with the second parameter. For example, an open-source large language model with 1.5 billion parameters is deployed on the electron gun, an open-source large language model with 7 billion parameters is deployed on the accelerating tube, and an open-source large language model with 14 billion parameters is deployed on the high-voltage platform device, etc. It should be noted that the examples listed here are only illustrative and do not limit this embodiment.

[0057] See Figure 3 , as an illustration, an open-source large language model with 671 billion parameters is deployed on the main control server, an open-source large language model with 1.5 billion parameters is deployed on the FPGA (Field Programmable Gate Array) power controller of the high-voltage platform device, an open-source large language model with 1.5 billion parameters is deployed on the FPGA power controller of the electron gun, an open-source large language model with 1.5 billion parameters is deployed on the FPGA power controller of the accelerating tube, an open-source large language model with 1.5 billion parameters is deployed on the FPGA power controller of the collector, etc. This embodiment does not limit this.

[0058] In addition, an open-source large language model with 1.5 billion parameters can also be deployed on the Figure 3 first beam position detector shown as needed, and an open-source large language model with 1.5 billion parameters is deployed on the second beam position detector.

[0059] Step S203: In response to the user input of an electronic cooling natural language instruction, the main control model performs semantic parsing on the electronic cooling natural language instruction to obtain a semantic parsing result including controlling the target electronic cooling device; the main control model constructs a physical model for controlling the target electronic cooling device according to the semantic parsing result, and determines preliminary control parameters for controlling the target electronic cooling device according to the physical model and the current actual operating state of the target electronic cooling device.

[0060] Figure 4 This is the control process of the main control model. First, in response to the user input of an electronic cooling natural language instruction, the main control model performs semantic parsing on the electronic cooling natural language instruction to obtain a semantic parsing result including controlling the target electronic cooling device; the main control model constructs a physical model for controlling the target electronic cooling device according to the semantic parsing result; the main control model determines preliminary control parameters for controlling the target electronic cooling device according to the physical model and the current actual operating state of the target electronic cooling device, that is, control parameter generation.

[0061] For example, the user input of the electronic cooling natural language instruction is "when the electron beam energy is E k , adjust the deflector plate energy so that the electron beam can be normally recycled". At this time, the main control model performs semantic parsing on this electronic cooling natural language instruction to obtain a semantic parsing result including controlling the target electronic cooling device (here it is the deflector plate); the main control model constructs a physical model for controlling the deflector plate according to the semantic parsing result as follows:

[0062]

[0063] Among them, V is the theoretical parameter of the deflector plate energy, m 0 is the rest mass of the electron beam, e is the elementary charge, R is the turning radius, c is the speed of light, E 0 is the rest energy of the electron beam, d is the distance between the plates.

[0064] Then, according to the physical model and the current actual operating state of the deflector plate, substituting R = 1 m (meter), d = 0.07 m, E 0 = 511 keV (kilo-electron volts) and other physical parameters and operating parameters of the deflector plate, we can get:

[0065]

[0066] Meanwhile, combining with the historical operation data of the deflection plate, the cumulative error of the deflection plate is obtained ΔV and finally the preliminary control parameters for controlling the deflection plate are obtained V r =V + ΔV .

[0067] It should be noted that the above examples are only illustrative and do not limit this embodiment

[0068] Step S204: The target electronic cooling device receives the preliminary control parameters, and the sub-control model of the target electronic cooling device analyzes the preliminary control parameters and determines whether to adjust the preliminary control parameters according to the analysis results

[0069] In this embodiment, through the collaborative architecture of the main control model and the sub-control models of each electronic cooling device, it can directly respond to the electronic cooling natural language instruction input by the user. The main control model performs semantic parsing on the electronic cooling natural language instruction to obtain a semantic parsing result including controlling the target electronic cooling device; the main control model constructs a physical model for controlling the target electronic cooling device according to the semantic parsing result, and determines the preliminary control parameters for controlling the target electronic cooling device according to the physical model and the current actual operating state of the target electronic cooling device; furthermore, the target electronic cooling device receives the preliminary control parameters, and the sub-control model of the target electronic cooling device analyzes the preliminary control parameters and determines whether to adjust the preliminary control parameters according to the analysis results, realizing the intelligent control, fault prediction and natural language interaction of the electronic cooling device, especially suitable for optimizing the beam quality and improving the system stability of electronic cooling. The fault prediction here can be that the sub-control model of the target electronic cooling device predicts whether there is a fault in the target electronic cooling device when analyzing the preliminary control parameters, or the sub-control model of the target electronic cooling device analyzes the operation data of the target electronic cooling device to predict whether there is a fault in the target electronic cooling device

[0070] In an embodiment of the present application, a possible implementation manner is provided. An electronic cooling knowledge base including a theory library and an operation library can also be pre-constructed. Among them, the theory library includes electronic cooling physical theories, electronic cooling technical literature and historical experimental data; the operation library includes the operation parameters of each electronic cooling device collected in real time. Pre-constructing the electronic cooling knowledge base facilitates providing rich data for the training of the main control model and the sub-control models of each electronic cooling device, and can also provide data support in the inference stage

[0071] In an embodiment of the present application, a possible implementation is provided. In step S201 above, the electronic cooling operation log and the electronic cooling theoretical literature are obtained as the first sample data set. Specifically, it may include obtaining the electronic cooling operation log and the electronic cooling theoretical literature as the first sample data set from a pre-constructed electronic cooling knowledge base, thus providing rich data for the training of the main control model.

[0072] In the electronic cooling operation log, the operation data of all electronic cooling devices at the same moment can be formed into a 1×n array, where n is the number of electronic cooling devices. The data per minute forms a 60×n matrix and is sent to a large language model with the first parameter for training. This training method solves the problems of large-scale device training data organization and time series. Here, the 1×n array, such as filament 5.1A, grid 500V, anode 500V, high-voltage platform device voltage 139000V, high-voltage platform leakage current 0.22mA, collector 2.3A, positive deflection plate 13900V, negative deflection plate 13700V, etc. It should be noted that the enumeration here is only illustrative and does not limit this embodiment.

[0073] In an embodiment of the present application, a possible implementation is provided. In step S202 above, the operation log and the theoretical literature of the electronic cooling device are obtained as the second sample data set. Specifically, it may include obtaining the operation log and the theoretical literature of the electronic cooling device as the second sample data set from a pre-constructed electronic cooling knowledge base, thus providing rich data for the training of the sub-control models of each electronic cooling device.

[0074] In an embodiment of the present application, a possible implementation is provided. In step S203 above, the main control model performs semantic parsing on the electronic cooling natural language instruction to obtain a semantic parsing result including controlling the target electronic cooling device. Specifically, it may be that the main control model combines the electronic cooling knowledge base to perform semantic parsing on the electronic cooling natural language instruction to obtain a semantic parsing result including controlling the target electronic cooling device. Here, the main control model combines the electronic cooling knowledge base to perform semantic parsing on the electronic cooling natural language instruction, which can improve the accuracy of semantic parsing.

[0075] In an embodiment of the present application, a possible implementation is provided. In step S201 above, the large language model with the first parameter is pre-trained, instruction fine-tuned, and reinforcement learned to obtain a main control model that can understand the physical laws of electronic cooling. Specifically, with beam stability, energy consumption efficiency, and fault prediction accuracy as the optimization objectives, the large language model with the first parameter is pre-trained, instruction fine-tuned, and reinforcement learned to obtain a main control model that can understand the physical laws of electronic cooling.

[0076] See Figure 5, reward function design can be carried out, including control precision reward, response speed reward, and efficiency reward. Here, the control precision reward aims to optimize beam current stability and fault prediction accuracy, and the efficiency reward aims to optimize energy consumption efficiency. In addition, the control precision reward adopts PPO (Proximal Policy Optimization), which is a reinforcement learning algorithm designed to improve training stability by restricting the magnitude of policy updates and enhance the stability of the model through online iterative processes.

[0077] For example, ; where V t is t the total reward at time R t is t the immediate reward at time V t+1 is t+1 the total reward at time γ is the discount factor. It determines to what extent "future rewards" are considered in "current rewards".

[0078] The reward function is designed as R t =R precsion +R response +R effencient , where R precsion is the control precision, R response is the response speed, R effencient is the energy consumption efficiency. It should be noted that the example here is only illustrative and does not limit this embodiment.

[0079] A possible implementation is provided in the embodiment of the present application. In step S204 above, it is determined whether to adjust the preliminary control parameters according to the analysis results. Specifically, it can be the following step A1:

[0080] Step A1, if it is determined according to the analysis results that the preliminary control parameters need to be adjusted, then multi-objective optimization solution is generated. If the adjusted control parameters are obtained through the solution, the target electronic cooling device executes according to the adjusted control parameters; if the adjusted control parameters are not obtained through the solution, an information report indicating that the adjusted control parameters are not obtained through the solution is generated and reported to the main control model.

[0081] A possible implementation is provided in the embodiments of the present application. In step S204 above, it is determined whether to adjust the preliminary control parameters according to the analysis result. Specifically, it may also be the following step A2:

[0082] Step A2, if it is determined according to the analysis result that the preliminary control parameters do not need to be adjusted, then the target electronic cooling device executes according to the preliminary control parameters. During the execution of the target electronic cooling device, the sub-control model analyzes and identifies the execution information of the target electronic cooling device. If it is identified that the target electronic cooling device fails, a preset resolution strategy is executed, and an alarm message indicating that the target electronic cooling device fails is generated and reported to the main control model.

[0083] In this embodiment, the problem of inconsistent instructions between the main control model and the sub-control model can be solved through the above steps A1 and A2.

[0084] Continuing with the previous example, after obtaining the preliminary control parameters of the deflection plate, the set data is sent to the deflection plate controller. Normally, the analysis result of the sub-control model of the deflection plate is that the preliminary control parameters do not need to be adjusted. Then the deflection plate controller operates according to the preliminary control parameters, but there may be two abnormal situations that require conflict resolution:

[0085] 1) After adding the cumulative error to the theoretical value, the issued parameter exceeds the maximum parameter that the device itself can withstand, and the device rejects the parameter. The sub-control model of the deflection plate generates a multi-objective optimization solution and attempts to adjust the parameter within the device's capabilities. If the task cannot be completed, relevant information is fed back to the upper-layer main control model.

[0086] 2) After the issued parameter is run, the leakage current of the deflection plate is too large, indicating that the parameter calculation is incorrect. The preset resolution strategy is executed, and the underlying controller cancels the instruction and attempts a small-range adjustment, and feeds back the adjustment result and the alarm message of the device failure to the upper-layer main control model.

[0087] The above introduces Figure 2 Multiple implementation manners of each link of the illustrated embodiment. Next, the electronic cooling control method based on the large language model in the embodiments of the present application will be further described through specific embodiments.

[0088] In this specific embodiment, it may include hardware deployment, model training, and conflict resolution implementation, specifically as follows:

[0089] 1) Hardware deployment

[0090] Select an FPGA chip and configure a DDR4 (Double Data Rate Fourth, fourth-generation double data rate)-3200 memory (72-bit width).

[0091] The master control model runs on the master control server, and the slave control models are deployed on FPGA embedded devices, namely, each electronic cooling device.

[0092] 2) Model training

[0093] Pre-training stage: Use electronic cooling theory literature and historical operation logs.

[0094] Instruction fine-tuning stage: Annotate natural language control instructions for electronic cooling (such as "Adjust the magnetic field to xx T (Tesla)"), fault codes, and natural language descriptions.

[0095] Reinforcement learning stage: Use control accuracy, response speed, and energy consumption efficiency as the reward function to iteratively optimize the strategy.

[0096] 3) Conflict resolution implementation

[0097] When the slave control model detects an execution anomaly (such as power overlimit), it preferentially triggers the local protection mechanism and reports to the master control model simultaneously;

[0098] The master control model regenerates instructions based on the comprehensive global state and resolves conflicts through dynamic weight adjustment.

[0099] This embodiment realizes the intelligent control and fault prediction of electronic cooling through the master-slave collaborative architecture, FPGA hardware, and automated training system; the master control model optimizes the global beam parameters, the slave control models perform high-precision closed-loop control, and combined with the conflict resolution mechanism and natural language interaction, it significantly improves the system stability and maintainability.

[0100] It should be noted that the sequence numbers of the steps in the above embodiments do 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 to the implementation process of the embodiments of the present application. In practical applications, all the above possible implementation manners can be combined in any combination to form possible embodiments of the present application, which will not be elaborated here one by one.

[0101] Based on the electronic cooling control method based on the large language model provided in the above embodiments, based on the same inventive concept, the embodiments of the present application also provide an electronic cooling control device based on the large language model.

[0102] Figure 6 It is the structural diagram of the electronic cooling control device based on the large language model provided by the embodiments of the present application. As Figure 6 shown, the electronic cooling control device based on the large language model may specifically include a first deployment unit 610, a second deployment unit 620, a first control unit 630, and a second control unit 640.

[0103] The first deployment unit 610 is used to pre - deploy a large - language model of the first parameter on the master server, obtain the electronic cooling operation log and electronic cooling theoretical literature as the first sample data set, and perform pre - training, instruction fine - tuning and reinforcement learning on the large - language model of the first parameter to obtain a master model that can understand the physical laws of electronic cooling;

[0104] The second deployment unit 620 is used to pre - deploy a large - language model of the second parameter on each electronic cooling device. For the large - language model of the second parameter on each electronic cooling device, obtain the operation log and theoretical literature of this electronic cooling device as the second sample data set, and train the large - language model of the second parameter on this electronic cooling device to obtain a sub - control model of this electronic cooling device, so as to obtain the sub - control models of each electronic cooling device;

[0105] The first control unit 630 is used to respond to the electronic cooling natural - language instruction input by the user. The master model performs semantic parsing on the electronic cooling natural - language instruction to obtain a semantic parsing result containing the control of the target electronic cooling device; the master model constructs a physical model for controlling the target electronic cooling device according to the semantic parsing result, and determines the preliminary control parameters for controlling the target electronic cooling device according to the physical model and the current actual operating state of the target electronic cooling device;

[0106] The second control unit 640 is used for the target electronic cooling device to receive the preliminary control parameters. The sub - control model of this target electronic cooling device analyzes the preliminary control parameters and determines whether to adjust the preliminary control parameters according to the analysis result.

[0107] In an embodiment of the present application, a possible implementation manner is provided, as Figure 7 shown. The device shown above Figure 6 may further include a construction unit 710, which is used to pre - construct an electronic cooling knowledge base including a theory library and an operation library. Among them, the theory library includes electronic cooling physical theories, electronic cooling technical literature and historical experimental data; the operation library includes the operation parameters of each electronic cooling device collected in real - time.

[0108] In an embodiment of the present application, a possible implementation manner is provided. The first deployment unit 610 is further used for: obtaining the electronic cooling operation log and electronic cooling theoretical literature from the pre - constructed electronic cooling knowledge base as the first sample data set;

[0109] The second deployment unit 620 is further used for: obtaining the operation log and theoretical literature of this electronic cooling device from the pre - constructed electronic cooling knowledge base as the second sample data set.

[0110] In an embodiment of the present application, a possible implementation manner is provided. The first control unit 630 is further used for:

[0111] The master control model combines with the electronic cooling knowledge base to perform semantic parsing on the natural language instructions for electronic cooling, and obtains a semantic parsing result including controlling the target electronic cooling device.

[0112] In a possible implementation provided in the embodiments of the present application, the first deployment unit 610 is further configured to:

[0113] Taking beam stability, energy consumption efficiency, and fault prediction accuracy as optimization objectives, pre-train, perform instruction fine-tuning, and reinforcement learning on the large language model of the first parameter, and obtain a master control model that can understand the physical laws of electronic cooling.

[0114] In a possible implementation provided in the embodiments of the present application, the second control unit 640 is further configured to:

[0115] If it is determined according to the analysis result that the preliminary control parameters need to be adjusted, generate a multi-objective optimization solution. If the adjusted control parameters are obtained through the solution, the target electronic cooling device executes according to the adjusted control parameters; if the adjusted control parameters are not obtained through the solution, generate a reporting message indicating that the adjusted control parameters are not obtained through the solution and report it to the master control model.

[0116] In a possible implementation provided in the embodiments of the present application, the second control unit 640 is further configured to:

[0117] If it is determined according to the analysis result that the preliminary control parameters do not need to be adjusted, the target electronic cooling device executes according to the preliminary control parameters. During the execution of the target electronic cooling device, the sub-control model analyzes and identifies the execution information of the target electronic cooling device. If it is identified that the target electronic cooling device has a fault, execute a preset resolution strategy and generate an alarm message indicating that the target electronic cooling device has a fault and report it to the master control model.

[0118] Based on the same inventive concept, the embodiments of the present application further provide an electronic device, including a processor and a memory. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the electronic cooling control method based on the large language model in any one of the above embodiments.

[0119] In an exemplary embodiment, an electronic device is provided, as Figure 8 shown Figure 8The electronic device 800 shown includes: a processor 801 and a memory 803. Among them, the processor 801 and the memory 803 are connected, such as through a bus 802. Optionally, the electronic device 800 may further include a transceiver 804. It should be noted that in practical applications, the transceiver 804 is not limited to one, and the structure of the electronic device 800 does not constitute a limitation to the embodiments of the present application.

[0120] The processor 801 can be a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in connection with the disclosure of the present application. The processor 801 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0121] The bus 802 may include a path for transmitting information between the above components. The bus 802 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 802 can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 8 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0122] The memory 803 can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0123] The memory 803 is used to store the computer program code for executing the solution of this application and is controlled by the processor 801 for execution. The processor 801 is used to execute the computer program code stored in the memory 803 to implement the content shown in the foregoing method embodiments.

[0124] Among them, the electronic device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The shown electronic device is only an example and should not impose any limitations on the functions and usage scope of the embodiments of this application.

[0125] Based on the same inventive concept, the embodiments of this application also provide a storage medium, in which a computer program is stored, and the computer program is set to execute the electronic cooling control method based on the large language model in any one of the foregoing embodiments when running.

[0126] Based on the same inventive concept, the embodiments of this application also provide a computer program product, including a computer program, and the computer program is configured to execute the electronic cooling control method based on the large language model in any one of the foregoing embodiments when running.

[0127] Those skilled in the art can clearly understand the specific working processes of the above-described systems, devices, and modules. The corresponding processes in the foregoing method embodiments can be referred to. For the sake of brevity, they will not be described in detail here.

[0128] Those of ordinary skill in the art can understand that the technical solution of this application can essentially be embodied in the form of a software product, and the computer software product is stored in a storage medium, which includes a number of program instructions for causing an electronic device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of this application when the program instructions are run. The foregoing storage medium includes various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0129] Alternatively, all or part of the steps of implementing the foregoing method embodiments can be completed by hardware related to program instructions (such as an electronic device such as a personal computer, a server, or a network device), and the program instructions can be stored in a computer-readable storage medium. When the program instructions are executed by the processor of the electronic device, the electronic device executes all or part of the steps of the methods described in the embodiments of this application.

[0130] The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that within the spirit and principles of this application, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features therein. These modifications or replacements do not cause the corresponding technical solutions to deviate from the protection scope of this application.

Claims

1. An electronic cooling control method based on a large language model, characterized in that, Including: Pre - deploy a large - language model with the first parameter on the master control server, obtain the electronic cooling operation log and electronic cooling theoretical literature as the first sample data set, and perform pre - training, instruction fine - tuning, and reinforcement learning on the large - language model with the first parameter to obtain a master control model that can understand the physical laws of electronic cooling; Pre - deploy a large - language model with the second parameter on each electronic cooling device. For the large - language model with the second parameter on each electronic cooling device, obtain the operation log and theoretical literature of this electronic cooling device as the second sample data set, and train the large - language model with the second parameter on this electronic cooling device to obtain a sub - control model for this electronic cooling device, thus obtaining sub - control models for each electronic cooling device; In response to the electronic cooling natural - language instruction input by the user, the master control model performs semantic parsing on the electronic cooling natural - language instruction to obtain a semantic parsing result that includes controlling the target electronic cooling device; the master control model constructs a physical model for controlling the target electronic cooling device according to the semantic parsing result, and determines the preliminary control parameters for controlling the target electronic cooling device according to the physical model and the current actual operating state of the target electronic cooling device; When the target electronic cooling device receives the preliminary control parameters, the sub - control model of this target electronic cooling device analyzes the preliminary control parameters and determines whether to adjust the preliminary control parameters according to the analysis result.

2. The electronic cooling control method based on a large language model according to claim 1, wherein It also includes: Pre - construct an electronic cooling knowledge base including a theory library and an operation library. Among them, the theory library includes electronic cooling physical theories, electronic cooling technical literature, and historical experimental data; the operation library includes the operation parameters of each electronic cooling device collected in real - time.

3. The electronic cooling control method based on a large language model according to claim 2, wherein, Obtaining the electronic cooling operation log and electronic cooling theoretical literature as the first sample data set includes: Obtain the electronic cooling operation log and electronic cooling theoretical literature from the pre - constructed electronic cooling knowledge base as the first sample data set; Obtaining the operation log and theoretical literature of this electronic cooling device as the second sample data set includes: Obtain the operation log and theoretical literature of this electronic cooling device from the pre - constructed electronic cooling knowledge base as the second sample data set.

4. The electronic cooling control method based on a large language model according to claim 2, wherein The master control model performs semantic parsing on the electronic cooling natural - language instruction to obtain a semantic parsing result that includes controlling the target electronic cooling device, including: The master control model combines the electronic cooling knowledge base to perform semantic parsing on the electronic cooling natural - language instruction to obtain a semantic parsing result that includes controlling the target electronic cooling device.

5. The electronic cooling control method based on a large language model according to claim 1, wherein, Performing pre - training, instruction fine - tuning, and reinforcement learning on the large - language model with the first parameter to obtain a master control model that can understand the physical laws of electronic cooling, including: Taking beam stability, energy consumption efficiency, and fault prediction accuracy as optimization objectives, perform pre - training, instruction fine - tuning, and reinforcement learning on the large - language model with the first parameter to obtain a master control model that can understand the physical laws of electronic cooling.

6. The electronic cooling control method based on a large language model according to claim 1, characterized in that Determining whether to adjust the preliminary control parameters according to the analysis result includes: If it is determined according to the analysis result that the preliminary control parameters need to be adjusted, a multi-objective optimization solution is generated. If the adjusted control parameters are obtained through the solution, the target electronic cooling device executes according to the adjusted control parameters; if the adjusted control parameters are not obtained through the solution, a reporting message indicating that the adjusted control parameters are not obtained through the solution is generated and reported to the main control model.

7. The electronic cooling control method based on a large language model according to claim 1, wherein Determining whether to adjust the preliminary control parameters according to the analysis result includes: If it is determined according to the analysis result that the preliminary control parameters do not need to be adjusted, the target electronic cooling device executes according to the preliminary control parameters. During the execution of the target electronic cooling device, the sub-control model analyzes and identifies the execution information of the target electronic cooling device. If it is identified that the target electronic cooling device has a fault, a preset resolution strategy is executed, and an alarm message indicating that the target electronic cooling device has a fault is generated and reported to the main control model.

8. An electronic cooling control device based on a large language model, characterized in that, A device for implementing the electronic cooling control method based on a large language model according to claim 1, the device includes: A first deployment unit, configured to pre-deploy a large language model with a first parameter on the main control server, obtain an electronic cooling operation log and electronic cooling theoretical literature as a first sample data set, and perform pre-training, instruction fine-tuning, and reinforcement learning on the large language model with the first parameter to obtain a main control model capable of understanding the physical laws of electronic cooling; A second deployment unit, configured to pre-deploy a large language model with a second parameter on each electronic cooling device. For the large language model with the second parameter on each electronic cooling device, obtain the operation log and theoretical literature of the electronic cooling device as a second sample data set, and perform training on the large language model with the second parameter on the electronic cooling device to obtain a sub-control model of the electronic cooling device, thereby obtaining sub-control models of each electronic cooling device; A first control unit, configured to respond to an electronic cooling natural language instruction input by a user, perform semantic parsing on the electronic cooling natural language instruction by the main control model to obtain a semantic parsing result including controlling the target electronic cooling device; the main control model constructs a physical model for controlling the target electronic cooling device according to the semantic parsing result, and determines preliminary control parameters for controlling the target electronic cooling device according to the physical model and the current actual operating state of the target electronic cooling device; A second control unit, configured to when the target electronic cooling device receives the preliminary control parameters, analyze the preliminary control parameters by the sub-control model of the target electronic cooling device, and determine whether to adjust the preliminary control parameters according to the analysis result.

9. An electronic device, characterized in that, It includes a processor and a memory. Among them, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the electronic cooling control method based on a large language model according to any one of claims 1 to 7.

10. A storage medium, characterized in that, A computer program is stored in the storage medium, and the computer program is configured to execute the electronic cooling control method based on a large language model according to any one of claims 1 to 7 when running.

11. A computer program product, comprising a computer program, characterized in that, The computer program is configured to execute the large language model-based electronic cooling control method according to any one of claims 1 to 7 when running.

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