An AI cluster training method using distributed energy and related devices

Abstract

The application provides an AI cluster training method using distributed energy, belonging to the field of artificial intelligence technology, which can effectively utilize distributed energy and improve the efficiency and accuracy of AI cluster training. By reasonably allocating computing tasks among local devices, edge devices and cloud devices, the computing resources of each device can be fully utilized, the data transmission time can be reduced, and the delay can be reduced. In addition, by using image segmentation algorithms and deep learning model training, the accuracy and speed of data processing can be further improved. Finally, by integrating the results processed by different devices, more comprehensive and accurate inference results can be obtained to meet the application requirements in complex scenarios.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to an AI cluster training method and related devices using distributed energy. Background Art

[0002] In today's rapidly advancing technological landscape, artificial intelligence (AI) technology has been widely adopted in various fields, from self-driving cars to intelligent voice assistants to medical diagnostic systems. However, the rapid development of AI technology requires large-scale computing resources and efficient data processing capabilities. Traditional centralized data centers are no longer able to meet the high computing demands of AI training. Therefore, AI cluster training methods that utilize distributed energy resources have emerged.

[0003] Furthermore, distributed energy systems can improve the flexibility and scalability of AI clusters. While traditional centralized data centers typically require significant infrastructure investment, distributed energy systems offer flexible scaling of computing resources through modular design. This allows for the rapid addition or removal of computing nodes based on actual needs, improving resource utilization and reducing operating costs.

[0004] During AI cluster training, distributed energy systems can also achieve the optimal match between computing resources and energy through intelligent scheduling algorithms. For example, they can dynamically adjust the energy supply of computing nodes based on the real-time demands of training tasks, ensuring efficient utilization of computing resources. Furthermore, real-time monitoring and management of energy consumption can further optimize energy efficiency and reduce energy waste. This is typically done in a centralized training room, where models are trained on manually labeled samples. This process is cumbersome and inefficient, as edge devices are only responsible for performing inference applications based on the model and cannot participate in the model training process. Summary of the Invention

[0005] The embodiments of the present application provide an AI cluster training method and related devices using distributed energy to improve the above-mentioned problems.

[0006] To achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, embodiments of the present application provide an AI cluster training method using distributed energy, applicable to an AI cluster training system using distributed energy. The system includes a control terminal, an edge device, a local device, and a cloud device. The method includes:

[0008] The control terminal obtains the target inference sample, performs preliminary sampling on the target inference sample, and determines the predicted total computing power requirement of the target inference sample based on the result of the preliminary sampling;

[0009] The control terminal allocates the target inference sample based on the predicted total computing power requirement, and meets:

[0010] When the predicted total computing power is greater than the rated computing power of the edge-side device and the local device, the control terminal splits the target inference sample to form a first sub-sample and a second sub-sample;

[0011] The control terminal controls the local device and the edge-side device to process the first sub-sample, and uploads the second sub-sample to the cloud device for processing;

[0012] The control terminal obtains the processing result of the cloud device, and integrates the processing result with the processing result of the local device and the edge-side device.

[0013] In combination with the first aspect, in some embodiments, the control terminal obtains the target inference sample, performs preliminary sampling on the target inference sample, and determines the predicted total computing power requirement of the target inference sample based on the result of the preliminary sampling, including:

[0014] The control terminal obtains the target inference sample, and obtains a prediction sample from the target inference sample based on a random sampling method, and obtains a corresponding relationship between the prediction sample and the target inference sample;

[0015] The control terminal imports the prediction sample into a training device, and obtains the unit computing power requirement corresponding to the prediction sample based on the training result and the training time fed back by the training device;

[0016] The control terminal determines the predicted total computing power requirement of the target inference sample based on the corresponding relationship between the prediction sample and the target inference sample, and the unit computing power requirement.

[0017] In combination with the first aspect, in some embodiments, the control terminal controls the local device and the edge-side device to process the first sub-sample, and uploads the second sub-sample to the cloud device for processing, including:

[0018] The control terminal stores the first sub-sample on the edge-side device, and splits and identifies the first sub-sample using an image segmentation algorithm;

[0019] The control terminal controls the local device to perform a preprocessing operation according to the result of the splitting and identifying of the edge-side device, and controls the local device to perform preliminary analysis on the first sub-sample according to a preset algorithm, and sends the analysis result to the edge-side device;

[0020] After receiving the preliminary analysis result, the edge-side device further refines the processing, extracts key features, and shares the key features with the cloud device.

[0021] In conjunction with the first aspect, in some embodiments, the control terminal controls the local device and the edge device to process the first subsample and upload the second subsample to the cloud device for processing, further comprising:

[0022] After the cloud device completes processing, it returns the final processing results to the control terminal, which then integrates these results with the processing results of the local device and edge device to form a complete inference result.

[0023] In conjunction with the first aspect, in some embodiments, the system further includes: a data integration terminal, wherein the control terminal controls the local device and the edge device to process the first subsample and upload the second subsample to the cloud device for processing, including:

[0024] After receiving the preliminary analysis results, the edge device further refines the results, extracts key features, and uploads the key features to the data integration terminal;

[0025] The local device extracts the key features corresponding to the second sub-sample and uploads the key features to the data integration terminal;

[0026] The data integration terminal integrates the key features provided by the local device and the edge-side device provided by the edge-side device, and sends the integration results to the cloud device.

[0027] In conjunction with the first aspect, in some embodiments, the data integration terminal integrates the key features provided by the local device and the edge device provided by the edge device, and sends the integration results to the cloud device, including:

[0028] The data integration terminal performs weighted averaging on key features through a preset algorithm, and trains a deep learning model based on the integration results to optimize model parameters.

[0029] In conjunction with the first aspect, in some embodiments, the method further includes:

[0030] The control terminal adjusts the data processing strategies of local devices and edge devices based on the model performance after training on the cloud device, and the local devices and edge devices process data with maximum computing power.

[0031] In a second aspect, an embodiment of the present application proposes an AI cluster training system using distributed energy. The system includes a control terminal, an edge device, a local device, and a cloud device. The method includes:

[0032] The control terminal obtains the target inference sample, performs preliminary sampling on the target inference sample, and determines the predicted total computing power requirement of the target inference sample based on the result of the preliminary sampling;

[0033] The control terminal allocates target inference samples based on the predicted total computing power requirements and satisfies:

[0034] When the predicted total computing power is greater than the rated computing power of the edge device and the local device, the control terminal splits the target inference sample so that the target inference sample forms a first sub-sample and a second sub-sample;

[0035] The control terminal controls the local device and the edge device to process the first subsample, and uploads the second subsample to the cloud device for processing;

[0036] The control terminal obtains the processing results of the cloud device and integrates the processing results with the processing results of the local device and the edge device.

[0037] In conjunction with the second aspect, in some implementations, the control terminal obtains target inference samples, performs preliminary sampling on the target inference samples, and determines the predicted total computing power requirement of the target inference samples based on the results of the preliminary sampling, including:

[0038] The control terminal obtains a target inference sample, obtains a prediction sample from the target inference sample based on a random sampling method, and obtains a corresponding relationship between the prediction sample and the target inference sample;

[0039] The control terminal imports the prediction sample into the training device and obtains the unit computing power requirement corresponding to the prediction sample based on the training results and training time fed back by the training device;

[0040] The control terminal determines the predicted total computing power requirement of the target inference sample based on the correspondence between the predicted sample and the target inference sample, as well as the unit computing power requirement.

[0041] In conjunction with the second aspect, in some embodiments, the control terminal controls the local device and the edge device to process the first subsample and upload the second subsample to the cloud device for processing, including:

[0042] The control terminal stores the first subsample on the edge device, and segments and identifies the first subsample using an image segmentation algorithm;

[0043] The control terminal controls the terminal to perform a preprocessing operation on the local device according to the segmentation and recognition results of the edge side device, controls the local device to perform a preliminary analysis on the first subsample according to a preset algorithm, and sends the analysis result to the edge side device;

[0044] After receiving the preliminary analysis results, the edge device further refines the processing, extracts key features, and shares the key features with the cloud device.

[0045] In conjunction with the second aspect, in some embodiments, the control terminal controls the local device and the edge device to process the first subsample and upload the second subsample to the cloud device for processing, further comprising:

[0046] After the cloud device completes processing, it returns the final processing results to the control terminal, which then integrates these results with the processing results of the local device and edge device to form a complete inference result.

[0047] In conjunction with the first aspect, in some embodiments, the system further includes: a data integration terminal, wherein the control terminal controls the local device and the edge device to process the first subsample and upload the second subsample to the cloud device for processing, including:

[0048] After receiving the preliminary analysis results, the edge device further refines the results, extracts key features, and uploads the key features to the data integration terminal;

[0049] The local device extracts the key features corresponding to the second sub-sample and uploads the key features to the data integration terminal;

[0050] The data integration terminal integrates the key features provided by the local device and the edge-side device provided by the edge-side device, and sends the integration results to the cloud device.

[0051] In conjunction with the second aspect, in some embodiments, the data integration terminal integrates the key features provided by the local device and the edge device provided by the edge device, and sends the integration results to the cloud device, including:

[0052] The data integration terminal performs weighted averaging on key features through a preset algorithm, and trains a deep learning model based on the integration results to optimize model parameters.

[0053] In conjunction with the second aspect, in some embodiments, the method further includes:

[0054] The control terminal adjusts the data processing strategies of local devices and edge devices based on the model performance after training on the cloud device, and the local devices and edge devices process data with maximum computing power.

[0055] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including:

[0056] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method proposed in the first aspect of the embodiment of the present invention.

[0057] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the embodiment of the present invention.

[0058] In summary, the above method and system have the following technical effects:

[0059] The method proposed in the embodiment of the present application is as follows: first, the control terminal obtains the target reasoning sample, performs preliminary sampling on the target reasoning sample, and determines the predicted total computing power requirement of the target reasoning sample based on the result of the preliminary sampling. Then, the control terminal allocates the target reasoning sample based on the predicted total computing power requirement and satisfies: when the predicted total computing power is greater than the rated computing power of the edge device and the local device, the control terminal divides the target reasoning sample so that the target reasoning sample forms a first sub-sample and a second sub-sample. Then, the control terminal controls the local device and the edge device to process the first sub-sample and uploads the second sub-sample to the cloud device for processing. Then, the control terminal obtains the processing result of the cloud device and integrates the processing result with the processing result of the local device and the edge device. An AI cluster training method using distributed energy proposed in the embodiment of the present application can effectively utilize distributed energy and improve the efficiency and accuracy of AI cluster training. By reasonably allocating computing tasks among local devices, edge devices and cloud devices, the computing resources of each device can be fully utilized, data transmission time can be reduced, and latency can be reduced. In addition, by training with image segmentation algorithms and deep learning models, the accuracy and speed of data processing can be further improved. Ultimately, by integrating the results processed by different devices, more comprehensive and accurate inference results can be obtained to meet application requirements in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flowchart of an AI cluster training method using distributed energy proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0061] 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 them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0062] The embodiment of the present application proposes an AI cluster training method using distributed energy, which is applicable to an AI cluster training system using distributed energy. The system includes a control terminal, edge devices, local devices, and cloud devices. Figure 1 , the method comprises the following steps:

[0063] S101: The control terminal obtains the target reasoning sample, performs preliminary sampling on the target reasoning sample, and determines the predicted total computing power requirement of the target reasoning sample based on the results of the preliminary sampling.

[0064] As will be appreciated, in the present invention, the control terminal is responsible for acquiring target inference samples. First, the control terminal performs a series of operations to ensure successful acquisition of the target inference samples. These operations may include communicating with a data storage device to retrieve the desired samples or receiving sample data from an external data source. Once the control terminal successfully acquires the target inference samples, the next step is to perform preliminary sampling on these samples.

[0065] Initial sampling refers to the process of selecting a representative sample from the target inference sample. This step is crucial because it directly impacts the accuracy and efficiency of subsequent processing. During the initial sampling process, the control terminal may employ various sampling techniques, such as random sampling, stratified sampling, or cluster sampling. These techniques ensure that the selected samples closely reflect the characteristics and distribution of the entire target inference sample.

[0066] Specifically, as an implementation method, step S101 may include the following steps:

[0067] S1011: The control terminal obtains a target inference sample, obtains a prediction sample from the target inference sample based on a random sampling method, and obtains a corresponding relationship between the prediction sample and the target inference sample.

[0068] In this system, the control terminal is responsible for executing a series of operations to ensure the efficient processing of target inference samples. First, the control terminal retrieves target inference samples from a storage device or data source. These samples are predefined datasets used for subsequent inference and analysis. After obtaining the target inference samples, the control terminal uses random sampling to select a subset of them as prediction samples.

[0069] S1012: The control terminal imports the predicted sample into the training device, and obtains the unit computing power requirement corresponding to the predicted sample based on the training results and training time fed back by the training device.

[0070] As you can understand, the control terminal is responsible for importing the prediction samples into the training device for processing. Specifically, the control terminal transmits the prediction samples to the training device. Upon receiving these samples, the training device performs calculations and analysis based on the preset training model and algorithm. During the training process, the training device provides real-time feedback on the training results, including but not limited to key metrics such as model accuracy and loss. The control terminal also monitors the training device's training time, which is the time it takes from the start of training to its completion.

[0071] S1013: The control terminal determines the predicted total computing power requirement of the target inference sample based on the correspondence between the predicted sample and the target inference sample, as well as the unit computing power requirement.

[0072] As you can understand, when calculating the predicted total computing power requirements for a target inference sample, the control terminal comprehensively considers the correspondence between the predicted sample and the target inference sample. This correspondence refers to the similarity or correlation between the predicted sample's characteristics, structure, or behavior and the target inference sample. By analyzing this correspondence, the control terminal can better understand the characteristics of the target inference sample and thus more accurately predict its total computing power requirements.

[0073] S102: The control terminal allocates target inference samples based on the predicted total computing power demand, and satisfies:

[0074] When the predicted total computing power is greater than the rated computing power of the edge device and the local device, the control terminal splits the target inference sample so that the target inference sample forms a first sub-sample and a second sub-sample.

[0075] It can be understood that when the total computing power required for the prediction exceeds the rated computing power that the edge-side device and the local device can provide, the control terminal will take measures to split the target inference sample. Specifically, the control terminal will split the target inference sample into two parts, forming a first sub-sample and a second sub-sample. This splitting operation is to ensure that the computing requirements of each sub-sample are within the processing capabilities of the edge-side device and the local device, so that the inference process can proceed smoothly on these devices. In this way, the control terminal effectively solves the problem of insufficient computing power and ensures the smooth execution of the inference task.

[0076] S103: The control terminal controls the local device and the edge side device to process the first subsample, and uploads the second subsample to the cloud device for processing.

[0077] It can be understood that the control terminal is responsible for commanding and managing local devices and edge-side devices to perform corresponding processing operations on the first sub-sample. During this process, the control terminal will ensure that the local devices and edge-side devices work together to efficiently complete the processing task of the first sub-sample. After the processing is completed, the control terminal will also upload the second sub-sample to the cloud device so that the cloud device can receive the data that needs further processing. This upload process will ensure that the second sub-sample can be received by the cloud device and perform subsequent processing, thereby achieving efficient collaboration of the entire system and smooth data processing.

[0078] Specifically, in this embodiment, the control terminal stores the first sub-sample on the edge side device, and uses the image segmentation algorithm to segment and identify the first sub-sample. Then, the control terminal performs preprocessing operations on the local device based on the segmentation and identification results of the edge side device, and controls the local device to perform a preliminary analysis of the first sub-sample according to a preset algorithm, and sends the analysis results to the edge side device. Then, after receiving the preliminary analysis results, the edge side device further refines the processing, extracts key features, and shares the key features with the cloud device.

[0079] Optionally, after the cloud device completes processing, it returns the final processing results to the control terminal, which then integrates these results with the processing results of the local device and edge device to form a complete inference result.

[0080] In some embodiments, the system further includes a data integration terminal for expediting data exchange. In this embodiment, after receiving the preliminary analysis results, the edge device further refines the analysis, extracts key features, and uploads the key features to the data integration terminal. The data integration terminal then integrates the key features provided by the local device and the edge device, and sends the integration results to the cloud device.

[0081] It is understandable that after receiving the preliminary analysis results, the edge-side device will perform further refinement. Specifically, the edge-side device will extract key features from the preliminary analysis results. These key features are important elements that can represent the core information of the data. After the extraction is completed, the edge-side device will upload these key features to the data integration terminal. After receiving these key features, the data integration terminal will proceed to the next step. It will not only integrate the key features provided by the local device, but also integrate other relevant data provided by the edge-side device. Through this integration, the data integration terminal can fuse data from different sources to obtain more comprehensive and accurate information. After the integration is completed, the data integration terminal will send the integration results to the cloud device. After receiving the integration results, the cloud device can perform further analysis and processing to better utilize these data for decision support or other applications.

[0082] Optionally, the data integration terminal performs weighted averaging processing on key features through a preset algorithm, and trains a deep learning model based on the integration results to optimize model parameters.

[0083] S104: The control terminal obtains the processing results of the cloud device and integrates the processing results with the processing results of the local device and the edge device.

[0084] In this system, the control terminal is responsible for receiving processing results from cloud devices. These results are derived from analysis and calculations performed by the cloud devices based on the received data. After obtaining these results, the control terminal integrates them with the processing results from local and edge devices. During this integration process, the control terminal considers various factors, such as data real-time availability, accuracy, and processing power, to ensure that the final integrated results meet system requirements. In this way, the control terminal can fully utilize the computing resources of the cloud, local, and edge devices, improving the processing efficiency and performance of the entire system.

[0085] Optionally, in some implementations, the control terminal adjusts the data processing strategies of the local device and the edge device based on the model performance after training of the cloud device, and the local device and the edge device process data with maximum computing power.

[0086] The method proposed in the embodiment of the present application is as follows: first, the control terminal obtains the target reasoning sample, performs preliminary sampling on the target reasoning sample, and determines the predicted total computing power requirement of the target reasoning sample based on the result of the preliminary sampling. Then, the control terminal allocates the target reasoning sample based on the predicted total computing power requirement and satisfies: when the predicted total computing power is greater than the rated computing power of the edge device and the local device, the control terminal divides the target reasoning sample so that the target reasoning sample forms a first sub-sample and a second sub-sample. Then, the control terminal controls the local device and the edge device to process the first sub-sample and uploads the second sub-sample to the cloud device for processing. Then, the control terminal obtains the processing result of the cloud device and integrates the processing result with the processing result of the local device and the edge device. An AI cluster training method using distributed energy proposed in the embodiment of the present application can effectively utilize distributed energy and improve the efficiency and accuracy of AI cluster training. By reasonably allocating computing tasks among local devices, edge devices and cloud devices, the computing resources of each device can be fully utilized, data transmission time can be reduced, and latency can be reduced. In addition, by training with image segmentation algorithms and deep learning models, the accuracy and speed of data processing can be further improved. Ultimately, by integrating the results processed by different devices, more comprehensive and accurate inference results can be obtained to meet application requirements in complex scenarios.

[0087] Based on the same inventive concept, the embodiment of the present application proposes an AI cluster training system using distributed energy. The system includes a control terminal, edge devices, local devices, and cloud devices. The method includes:

[0088] The control terminal obtains the target inference sample, performs preliminary sampling on the target inference sample, and determines the predicted total computing power requirement of the target inference sample based on the result of the preliminary sampling;

[0089] The control terminal allocates target inference samples based on the predicted total computing power requirements and satisfies:

[0090] When the predicted total computing power is greater than the rated computing power of the edge device and the local device, the control terminal splits the target inference sample so that the target inference sample forms a first sub-sample and a second sub-sample;

[0091] The control terminal controls the local device and the edge device to process the first subsample, and uploads the second subsample to the cloud device for processing;

[0092] The control terminal obtains the processing results of the cloud device and integrates the processing results with the processing results of the local device and the edge device.

[0093] In conjunction with the second aspect, in some implementations, the control terminal obtains target inference samples, performs preliminary sampling on the target inference samples, and determines the predicted total computing power requirement of the target inference samples based on the results of the preliminary sampling, including:

[0094] The control terminal obtains a target inference sample, obtains a prediction sample from the target inference sample based on a random sampling method, and obtains a corresponding relationship between the prediction sample and the target inference sample;

[0095] The control terminal imports the prediction sample into the training device and obtains the unit computing power requirement corresponding to the prediction sample based on the training results and training time fed back by the training device;

[0096] The control terminal determines the predicted total computing power requirement of the target inference sample based on the correspondence between the predicted sample and the target inference sample, as well as the unit computing power requirement.

[0097] In conjunction with the second aspect, in some embodiments, the control terminal controls the local device and the edge device to process the first subsample and upload the second subsample to the cloud device for processing, including:

[0098] The control terminal stores the first subsample on the edge device, and segments and identifies the first subsample using an image segmentation algorithm;

[0099] The control terminal controls the terminal to perform a preprocessing operation on the local device according to the segmentation and recognition results of the edge side device, controls the local device to perform a preliminary analysis on the first subsample according to a preset algorithm, and sends the analysis result to the edge side device;

[0100] After receiving the preliminary analysis results, the edge device further refines the processing, extracts key features, and shares the key features with the cloud device.

[0101] In conjunction with the second aspect, in some embodiments, the control terminal controls the local device and the edge device to process the first subsample and upload the second subsample to the cloud device for processing, further comprising:

[0102] After the cloud device completes processing, it returns the final processing results to the control terminal, which then integrates these results with the processing results of the local device and edge device to form a complete inference result.

[0103] In conjunction with the first aspect, in some embodiments, the system further includes: a data integration terminal, wherein the control terminal controls the local device and the edge device to process the first subsample and upload the second subsample to the cloud device for processing, including:

[0104] After receiving the preliminary analysis results, the edge device further refines the results, extracts key features, and uploads the key features to the data integration terminal;

[0105] The local device extracts the key features corresponding to the second sub-sample and uploads the key features to the data integration terminal;

[0106] The data integration terminal integrates the key features provided by the local device and the edge-side device provided by the edge-side device, and sends the integration results to the cloud device.

[0107] In conjunction with the second aspect, in some embodiments, the data integration terminal integrates the key features provided by the local device and the edge device provided by the edge device, and sends the integration results to the cloud device, including:

[0108] The data integration terminal performs weighted averaging on key features through a preset algorithm, and trains a deep learning model based on the integration results to optimize model parameters.

[0109] In conjunction with the second aspect, in some embodiments, the method further includes:

[0110] The control terminal adjusts the data processing strategies of local devices and edge devices based on the model performance after training on the cloud device, and the local devices and edge devices process data with maximum computing power.

[0111] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including:

[0112] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method proposed in the first aspect of the embodiment of the present invention.

[0113] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the embodiment of the present invention.

[0114] The system proposed in the embodiment of the present application first controls the terminal to obtain target reasoning samples, performs preliminary sampling on the target reasoning samples, and determines the predicted total computing power requirement of the target reasoning samples based on the results of the preliminary sampling. Then, the control terminal allocates the target reasoning samples based on the predicted total computing power requirement and satisfies the following conditions: when the predicted total computing power is greater than the rated computing power of the edge device and the local device, the control terminal divides the target reasoning samples so that the target reasoning samples form a first subsample and a second subsample. Then, the control terminal controls the local device and the edge device to process the first subsample and upload the second subsample to the cloud device for processing. Then, the control terminal obtains the processing result of the cloud device and integrates the processing result with the processing result of the local device and the edge device. The embodiment of the present application proposes an AI cluster training method using distributed energy, which can effectively utilize distributed energy and improve the efficiency and accuracy of AI cluster training. By reasonably allocating computing tasks among local devices, edge devices, and cloud devices, the computing resources of each device can be fully utilized, data transmission time can be reduced, and latency can be reduced. In addition, by training with image segmentation algorithms and deep learning models, the accuracy and speed of data processing can be further improved. Ultimately, by integrating the results processed by different devices, more comprehensive and accurate inference results can be obtained to meet application requirements in complex scenarios.

[0115] Based on the same inventive concept, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0116] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the automatic overheating protection method for a universal testing machine based on an embodiment of the present application.

[0117] In addition, to achieve the above-mentioned purpose, an embodiment of the present application further proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the automatic overheating protection method based on a universal testing machine according to an embodiment of the present application.

[0118] The following is a detailed introduction to the various components of electronic equipment:

[0119] The term "processor" refers to the control center of an electronic device and can be a single processor or a collective term for multiple processing elements. For example, the processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0120] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0121] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0122] Alternatively, the memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processor via an interface circuit of the electronic device, and this is not specifically limited in the embodiments of the present invention.

[0123] A transceiver is used to communicate with network devices or terminal devices.

[0124] Optionally, the transceiver may include a receiver and a transmitter, wherein the receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0125] Optionally, the transceiver may be integrated with the processor, or may exist independently and be coupled to the processor via an interface circuit of the router, which is not specifically limited in the embodiment of the present invention.

[0126] In addition, the technical effects of the electronic device can refer to the technical effects of the data transmission method in the above method embodiment, and will not be repeated here.

[0127] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), but may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0128] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0129] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0130] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0131] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0132] It should be understood that in various embodiments of the present invention, 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 invention.

[0133] 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. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

Claims

1. An AI cluster training method using distributed energy, characterized in that: Applicable to an AI cluster training system using distributed energy, the system includes a control terminal, edge devices, a data integration terminal, local devices, and cloud devices, and the method includes: The control terminal obtains a target inference sample, performs preliminary sampling on the target inference sample, and determines a predicted total computing power requirement of the target inference sample based on a result of the preliminary sampling, wherein the control terminal obtains the target inference sample, obtains a predicted sample from the target inference sample based on a random sampling method, and obtains a corresponding relationship between the predicted sample and the target inference sample; The control terminal imports the predicted sample into the training device, and obtains the unit computing power requirement corresponding to the predicted sample based on the training result and training time fed back by the training device; The control terminal determines the predicted total computing power requirement of the target inference sample based on the correspondence between the predicted sample and the target inference sample, and the unit computing power requirement; The control terminal allocates the target inference samples based on the predicted total computing power requirement and satisfies: When the predicted total computing power is greater than the rated computing power of the edge device and the local device, the control terminal splits the target inference sample so that the target inference sample forms a first sub-sample and a second sub-sample; The control terminal controls the local device and the edge-side device to process the first subsample, and uploads the second subsample to the cloud device for processing, wherein the control terminal stores the first subsample on the edge-side device and segments and identifies the first subsample using an image segmentation algorithm; The control terminal performs a preprocessing operation on the local device according to the segmentation and recognition results of the edge side device, controls the local device to perform a preliminary analysis on the first subsample according to a preset algorithm, and sends the analysis result to the edge side device; After receiving the preliminary analysis results, the edge device further refines the results, extracts key features, and shares the key features with the cloud device; After receiving the preliminary analysis results, the edge device further refines the results, extracts key features, and uploads the key features to the data integration terminal; The local device extracts the key feature corresponding to the second sub-sample, and uploads the key feature to the data integration terminal; The data integration terminal integrates the key features provided by the local device and the edge-side features provided by the edge-side device, and sends the integration result to the cloud device; The control terminal obtains the processing result of the cloud device and integrates the processing result with the processing results of the local device and the edge side device.

2. The AI ​​cluster training method using distributed energy according to claim 1, characterized in that: The control terminal controls the local device and the edge device to process the first subsample, and uploads the second subsample to the cloud device for processing, further comprising: After the cloud device completes the processing, it returns the final processing results to the control terminal, and the control terminal then integrates these results with the processing results of the local device and the edge device to form a complete inference result.

3. The AI ​​cluster training method using distributed energy according to claim 2, characterized in that: The data integration terminal integrates the key features provided by the local device and the edge-side features provided by the edge-side device, and sends the integration result to the cloud device, including: The data integration terminal performs weighted averaging processing on the key features through a preset algorithm, and trains a deep learning model based on the integration results to optimize model parameters.

4. The AI ​​cluster training method using distributed energy according to claim 3 is characterized in that: The method further comprises: The control terminal adjusts the data processing strategies of the local device and the edge device according to the model performance after training of the cloud device, and the local device and the edge device perform data processing with maximum computing power.

5. An AI cluster training system using distributed energy, characterized in that: For executing the AI ​​cluster training method using distributed energy according to claim 1, the system includes a control terminal, an edge device, a data integration terminal, a local device, and a cloud device, and the system is configured as follows: The control terminal obtains a target reasoning sample, performs preliminary sampling on the target reasoning sample, and determines a predicted total computing power requirement of the target reasoning sample based on a result of the preliminary sampling; The control terminal allocates the target inference samples based on the predicted total computing power requirement and satisfies: When the predicted total computing power is greater than the rated computing power of the edge device and the local device, the control terminal splits the target inference sample so that the target inference sample forms a first sub-sample and a second sub-sample; The control terminal controls the local device and the edge device to process the first subsample, and uploads the second subsample to the cloud device for processing; The control terminal obtains the processing result of the cloud device and integrates the processing result with the processing results of the local device and the edge side device.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; And, a memory communicatively connected to at least one of the processors; wherein the memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the method as set forth in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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