A liver image data storage system and a storage method

Abstract

The application is suitable for the technical field of storage, and provides a liver image data storage system and a storage method. The liver image data storage system comprises an image acquisition device, a scheduling server connected to the image acquisition device, and multiple storage servers connected to the scheduling server. The scheduling server comprises a data receiving module, a data processing module, and a storage management module. The data receiving module is configured to receive liver image data uploaded by the image acquisition device through a preset network. The data processing module is configured to call a large model, input the received liver image data into the large model, perform noise reduction processing on the received liver image data through the large model, and generate processed liver image data. The storage management module is configured to select an optimal storage server with the maximum comprehensive evaluation value as an optimal storage server, and save the processed liver image data to the optimal storage server. The application can improve the storage security of the processed liver image data.

Description

Technical Field

[0001] This application belongs to the field of storage technology, and in particular relates to a storage system and method for liver imaging data. Background Technology

[0002] Liver imaging data possesses remarkable structural clarity and a high degree of standardization, characteristics that give it a natural advantage in storage and management. Systematic preservation of liver imaging data can provide rich and traceable research materials for scientific research, technological experiments, and data analysis.

[0003] However, in the process of storing liver image data, existing technologies cannot identify the optimal storage server among multiple storage servers. This results in the optimal storage server being idle, which not only wastes storage resources but also prevents the optimal server from leveraging its high-efficiency processing advantages in data storage, thereby reducing the resource utilization rate of the entire storage system. Therefore, how to save liver image data to the optimal storage server is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a storage system for liver imaging data, aiming to solve the technical problem of how to save liver imaging data to an optimal storage server.

[0005] In a first aspect, embodiments of this application provide a liver imaging data storage system, which includes an image acquisition device, a scheduling server connected to the image acquisition device, and multiple storage servers connected to the scheduling server; the scheduling server includes a data receiving module, a data processing module, and a storage management module; The data receiving module is used to receive liver image data uploaded by the image acquisition device through a preset network; The data processing module is used to call the large model, input the received liver image data into the large model, and perform noise reduction processing on the received liver image data through the large model to generate processed liver image data. The storage management module is used to obtain the amount of processed liver image data, the remaining storage capacity and access latency of each storage server, select the storage server with a remaining storage capacity higher than the amount of data and an access latency lower than a preset latency as the preferred storage server, obtain the total carbon emissions and total electricity consumption of the region where each preferred storage server is located, divide the total carbon emissions of the region where each preferred storage server is located by the total electricity consumption of the region to generate a carbon emission factor for each preferred storage server, generate the carbon emission amount of each preferred storage server within a preset time period based on the carbon emission factor, the power consumption and carbon emission model of each preferred storage server within a preset time period, generate the comprehensive evaluation value of each preferred storage server based on the carbon emission amount of each preferred storage server within a preset time period, the number of input / output operations per second of each preferred storage server, the throughput of each preferred storage server and a comprehensive evaluation model, and select the preferred storage server with the highest comprehensive evaluation value as the optimal storage server, and save the processed liver image data to the optimal storage server.

[0006] In one possible implementation of the first aspect, the data receiving module is specifically used to send an upload command to the image acquisition device via a preset network and receive liver image data uploaded by the image acquisition device according to the upload command.

[0007] In one possible implementation of the first aspect, the data processing module is specifically used for: Send a call request to the large model, and receive a response message from the large model based on the call request; When the response message is a success message, the large model is invoked, the received liver image data is input into the large model, and the large model performs noise reduction processing on the received liver image data to generate processed liver image data. In one possible implementation of the first aspect, the storage management module is specifically used for: The data volume of the processed liver imaging data is obtained, the monitoring data of each storage server is obtained, the remaining storage capacity and access latency of each storage server are obtained from the monitoring data of each storage server, and the storage server with the remaining storage capacity higher than the data volume and the access latency lower than the preset latency is selected as the preferred storage server. The carbon emissions of all power plants in the region where each preferred storage server is located are added together to generate the total carbon emissions of the region where each preferred storage server is located in the power generation process. The carbon emissions of all power plants in the region where each preferred storage server is located are added together to generate the total carbon emissions of the region where each preferred storage server is located in the power generation process. The total carbon emissions of the region where each preferred storage server is located in the power generation process are divided by the total electricity consumption of the region where each preferred storage server is located to generate the carbon emission factor corresponding to each preferred storage server. Based on the carbon emission factor corresponding to each preferred storage server, the power consumption and carbon emission model of each preferred storage server within a preset time period, the carbon emission amount of each preferred storage server within the preset time period is generated. Based on the carbon emission amount of each preferred storage server within the preset time period, the number of input / output operations per second of each preferred storage server, the throughput of each preferred storage server, and the comprehensive evaluation model, a comprehensive evaluation value of each preferred storage server is generated. The preferred storage server with the largest comprehensive evaluation value is selected as the optimal storage server, and the processed liver image data is saved to the optimal storage server.

[0008] In one possible implementation of the first aspect, the carbon emission model is defined as follows: ; This represents the carbon emissions of the k-th preferred storage server within a preset time period; This represents the power consumption of the k-th preferred storage server within a preset time period; This represents the carbon emission factor corresponding to the k-th preferred storage server. The higher the carbon emission factor of the k-th preferred storage server, the higher the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity; the lower the carbon emission factor of the k-th preferred storage server, the lower the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity.

[0009] In one possible implementation of the first aspect, the comprehensive evaluation model is defined as follows: ; This represents the overall evaluation value of the k-th preferred storage server. The higher the overall evaluation value of the k-th preferred storage server, the stronger its overall performance in terms of increasing the number of input / output operations per second, increasing throughput, and reducing carbon emissions. The lower the overall evaluation value of the k-th preferred storage server, the weaker its overall performance in these three aspects. This represents the number of input / output operations per second for the k-th preferred storage server; This represents the maximum number of input / output operations per second provided by all preferred storage servers; This represents the throughput of the k-th preferred storage server; This represents the maximum throughput provided by all preferred storage servers; This represents the power consumption of the k-th preferred storage server within a preset time period; This represents the carbon emission factor corresponding to the k-th preferred storage server. The higher the carbon emission factor of the k-th preferred storage server, the higher the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity; the lower the carbon emission factor of the k-th preferred storage server, the lower the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity. This represents the carbon emissions of the k-th preferred storage server within a preset time period; This represents the maximum carbon emissions among all preferred storage servers over a preset time period.

[0010] In one possible implementation of the first aspect, the image acquisition device includes a magnetic resonance imaging device and an ultrasound imaging device.

[0011] In one possible implementation of the first aspect, the preset network includes Ethernet and wireless LAN.

[0012] In one possible implementation of the first aspect, the preset duration includes 1 hour and 2 hours.

[0013] Secondly, embodiments of this application provide a storage method based on the above-mentioned liver imaging data storage system, including: Obtain the data identifier, storage time, and storage path of the processed liver imaging data; Using JSON format, the data identifier, storage time, and storage path of the processed liver imaging data are packaged to generate an information package of the processed liver imaging data. The information package is then saved to the database through the database storage engine.

[0014] The beneficial effects of the embodiments of this application are as follows: Firstly, the total carbon emissions of the region where each preferred storage server is located in the power generation process are divided by the total electricity consumption of the region where each preferred storage server is located to generate a carbon emission factor corresponding to each preferred storage server. Based on the carbon emission factor corresponding to each preferred storage server, the power consumption and carbon emission model of each preferred storage server within a preset time period, the carbon emission amount of each preferred storage server within a preset time period is generated. Based on the carbon emission amount of each preferred storage server within a preset time period, the number of input / output operations per second of each preferred storage server, the throughput of each preferred storage server, and the comprehensive evaluation model, a comprehensive evaluation value of each preferred storage server is generated. The preferred storage server with the largest comprehensive evaluation value is selected as the optimal storage server, and the processed liver image data is saved to the optimal storage server. Since the optimal storage server has stable performance, high read / write efficiency, and lower power consumption, saving the processed liver image data to the optimal storage server can make the saving process of the processed liver image data faster and smoother, reduce the situation of saving failure, data damage or loss, and help improve the storage security of the processed liver image data. Secondly, the higher the comprehensive evaluation value of the preferred storage server, the stronger its overall performance in terms of increasing the number of input / output operations per second, increasing throughput, and reducing carbon emissions. Selecting the preferred storage server with the highest comprehensive evaluation value as the optimal storage server and saving the processed liver image data to the optimal storage server can improve the transmission efficiency of the processed liver image data, while reducing the carbon emissions generated by the operation of the storage system. This achieves a balance between data storage performance and green and low-carbon goals, improving the utilization rate of storage resources and promoting the development of storage systems towards high efficiency and energy saving. Attached Figure Description

[0015] Figure 1 This is a first structural block diagram of the storage system provided in an embodiment of this application; Figure 2 This is a second structural block diagram of the storage system provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the implementation of the storage method provided in this application embodiment. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0017] Example 1 Referring to Figure 1, which is a first structural block diagram of the storage system provided in an embodiment of this application, detailed below: A liver imaging data storage system includes an image acquisition device, a scheduling server connected to the image acquisition device, and multiple storage servers connected to the scheduling server; the scheduling server includes a data receiving module, a data processing module, and a storage management module. The data receiving module is used to receive liver image data uploaded by the image acquisition device through a preset network; The data processing module is used to call the large model, input the received liver image data into the large model, and perform noise reduction processing on the received liver image data through the large model to generate processed liver image data. The storage management module is used to obtain the amount of processed liver image data, the remaining storage capacity and access latency of each storage server, select the storage server with a remaining storage capacity higher than the amount of data and an access latency lower than a preset latency as the preferred storage server, obtain the total carbon emissions and total electricity consumption of the region where each preferred storage server is located, divide the total carbon emissions of the region where each preferred storage server is located by the total electricity consumption of the region to generate a carbon emission factor for each preferred storage server, generate the carbon emission amount of each preferred storage server within a preset time period based on the carbon emission factor, the power consumption and carbon emission model of each preferred storage server within a preset time period, generate the comprehensive evaluation value of each preferred storage server based on the carbon emission amount of each preferred storage server within a preset time period, the number of input / output operations per second of each preferred storage server, the throughput of each preferred storage server and a comprehensive evaluation model, and select the preferred storage server with the highest comprehensive evaluation value as the optimal storage server, and save the processed liver image data to the optimal storage server.

[0018] Specifically, the data receiving module is used to send upload instructions to the image acquisition device through a preset network and to receive liver image data uploaded by the image acquisition device according to the upload instructions.

[0019] The data processing module is specifically used for: Send a call request to the large model, and receive a response message from the large model based on the call request; When the response message is a success message, the large model is invoked, the received liver image data is input into the large model, and the large model performs noise reduction processing on the received liver image data to generate processed liver image data. The carbon emission model is defined as follows: ; This represents the carbon emissions of the k-th preferred storage server within a preset time period; This represents the power consumption of the k-th preferred storage server within a preset time period; This represents the carbon emission factor corresponding to the k-th preferred storage server. The higher the carbon emission factor of the k-th preferred storage server, the higher the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity; the lower the carbon emission factor of the k-th preferred storage server, the lower the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity.

[0020] The comprehensive evaluation model is defined as follows: ; This represents the overall evaluation value of the k-th preferred storage server. The higher the overall evaluation value of the k-th preferred storage server, the stronger its overall performance in terms of increasing the number of input / output operations per second, increasing throughput, and reducing carbon emissions. The lower the overall evaluation value of the k-th preferred storage server, the weaker its overall performance in these three aspects. This represents the number of input / output operations per second for the k-th preferred storage server; This represents the maximum number of input / output operations per second provided by all preferred storage servers; This represents the throughput of the k-th preferred storage server; This represents the maximum throughput provided by all preferred storage servers; This represents the power consumption of the k-th preferred storage server within a preset time period; This represents the carbon emission factor corresponding to the k-th preferred storage server. The higher the carbon emission factor of the k-th preferred storage server, the higher the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity; the lower the carbon emission factor of the k-th preferred storage server, the lower the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity. This represents the carbon emissions of the k-th preferred storage server within a preset time period; This represents the maximum carbon emissions among all preferred storage servers over a preset time period.

[0021] For ease of explanation, the following example is provided: For example, there are multiple preferred storage servers, namely, the first preferred storage server, the second preferred storage server, and the third preferred storage server; Among them, the number of input / output operations per second provided by the first preferred storage server, the number of input / output operations per second provided by the second preferred storage server, and the number of input / output operations per second provided by the third preferred storage server are A1, A2, and A3, respectively. At this point, in the comprehensive evaluation model It is the maximum value among A1, A2, and A3.

[0022] Among them, the throughput of the first preferred storage server, the throughput of the second preferred storage server, and the throughput of the third preferred storage server are B1, B2, and B3, respectively. At this point, in the comprehensive evaluation model It is the maximum value among B1, B2, and B3.

[0023] Among them, the carbon emissions of the first preferred storage server, the second preferred storage server, and the third preferred storage server within the preset time period are C1, C2, and C3, respectively. At this point, in the comprehensive evaluation model It is the maximum value among C1, C2, and C3.

[0024] The image acquisition equipment includes magnetic resonance imaging equipment and ultrasound imaging equipment.

[0025] The default networks include Ethernet and wireless LAN.

[0026] The preset durations include 1 hour and 2 hours.

[0027] Among them, the number of input / output operations per second is an indicator that measures the total number of data read and write requests that the preferred storage server can process per unit of time. The number of input / output operations per second directly reflects the concurrent processing capability and response efficiency of the storage system. The higher the number of input / output operations per second of the preferred storage server, the more data access requests the preferred storage server can support at the same time and the faster the processing speed. Among them, the lower the number of input / output operations per second of the preferred storage server, the fewer data access requests the preferred storage server can support at the same time and the slower the processing speed. Among them, the throughput of the preferred storage server refers to the total amount of data that the preferred storage server can stably transmit per unit time. The throughput of the preferred storage server directly reflects the data transfer capability of the preferred storage server.

[0028] The higher the throughput of the preferred storage server, the faster the preferred storage server can process large files and read and write continuous data. It can more smoothly complete the storage and reading of large files such as liver imaging data, and reduce the transmission time of liver imaging data.

[0029] The lower the throughput of the preferred storage server, the slower the preferred storage server is in processing large files and continuous data read and write. It cannot smoothly complete the storage and reading of large files such as liver imaging data, which will increase the transmission time of liver imaging data.

[0030] Among them, the preferred storage server with the highest comprehensive evaluation value is selected as the optimal storage server. Since no manual identification is required, the identification time of the optimal storage server is reduced, which helps to improve the identification efficiency of the optimal storage server.

[0031] Among them, the optimal server has a higher number of input / output operations per second, greater throughput and lower carbon emissions, which can significantly shorten the storage time of liver imaging data and improve the storage efficiency of liver imaging data.

[0032] For ease of explanation, the following example is provided: For example, a data center needs to archive a year's worth of liver imaging data, with a single liver imaging dataset reaching several gigabytes and a huge amount of new data added each month. Storing liver imaging data on the optimal storage server leverages the server's higher I / O operations per second, greater throughput, and lower carbon emissions to quickly complete the storage and archiving of liver imaging data. This avoids problems such as upload interruptions and excessively long processing times caused by insufficient storage performance, significantly improving the efficiency of liver imaging data collection and archiving.

[0033] In addition, compared with ordinary storage servers, optimal storage servers can significantly reduce power consumption and carbon emissions, achieving a balance between data storage performance and green and low-carbon goals. This not only meets the storage needs of liver imaging data but also achieves the low-carbon requirements of the storage system.

[0034] GB stands for gigabyte, a standard unit for measuring data storage capacity. One gigabyte equals 1024 megabytes.

[0035] The beneficial effects of the embodiments of this application are as follows: Firstly, the total carbon emissions of the region where each preferred storage server is located in the power generation process are divided by the total electricity consumption of the region where each preferred storage server is located to generate a carbon emission factor corresponding to each preferred storage server. Based on the carbon emission factor corresponding to each preferred storage server, the power consumption and carbon emission model of each preferred storage server within a preset time period, the carbon emission amount of each preferred storage server within a preset time period is generated. Based on the carbon emission amount of each preferred storage server within a preset time period, the number of input / output operations per second of each preferred storage server, the throughput of each preferred storage server, and the comprehensive evaluation model, a comprehensive evaluation value of each preferred storage server is generated. The preferred storage server with the largest comprehensive evaluation value is selected as the optimal storage server, and the processed liver image data is saved to the optimal storage server. Since the optimal storage server has stable performance, high read / write efficiency, and lower power consumption, saving the processed liver image data to the optimal storage server can make the saving process of the processed liver image data faster and smoother, reduce the situation of saving failure, data damage or loss, and help improve the storage security of the processed liver image data. Secondly, the higher the comprehensive evaluation value of the preferred storage server, the stronger its overall performance in terms of increasing the number of input / output operations per second, increasing throughput, and reducing carbon emissions. Selecting the preferred storage server with the highest comprehensive evaluation value as the optimal storage server and saving the processed liver image data to the optimal storage server can improve the transmission efficiency of the processed liver image data, while reducing the carbon emissions generated by the operation of the storage system. This achieves a balance between data storage performance and green and low-carbon goals, improving the utilization rate of storage resources and promoting the development of storage systems towards high efficiency and energy saving.

[0036] Example 2 refer to Figure 3 , Figure 3 This is a second structural block diagram of the storage system provided in the embodiments of this application, which is described in detail below: The liver imaging data storage system includes an imaging acquisition device, a scheduling server connected to the imaging acquisition device, and multiple storage servers connected to the scheduling server; the scheduling server includes a data receiving module, a data processing module connected to the data receiving module, and a storage management module connected to the data processing module.

[0037] The storage management module is specifically used for: The data volume of the processed liver imaging data is obtained, the monitoring data of each storage server is obtained, the remaining storage capacity and access latency of each storage server are obtained from the monitoring data of each storage server, and the storage server with the remaining storage capacity higher than the data volume and the access latency lower than the preset latency is selected as the preferred storage server. The carbon emissions of all power plants in the region where each preferred storage server is located are added together to generate the total carbon emissions of the region where each preferred storage server is located in the power generation process. The carbon emissions of all power plants in the region where each preferred storage server is located are added together to generate the total carbon emissions of the region where each preferred storage server is located in the power generation process. The total carbon emissions of the region where each preferred storage server is located in the power generation process are divided by the total electricity consumption of the region where each preferred storage server is located to generate the carbon emission factor corresponding to each preferred storage server. Based on the carbon emission factor corresponding to each preferred storage server, the power consumption and carbon emission model of each preferred storage server within a preset time period, the carbon emission amount of each preferred storage server within the preset time period is generated. Based on the carbon emission amount of each preferred storage server within the preset time period, the number of input / output operations per second of each preferred storage server, the throughput of each preferred storage server, and the comprehensive evaluation model, a comprehensive evaluation value of each preferred storage server is generated. The preferred storage server with the largest comprehensive evaluation value is selected as the optimal storage server, and the processed liver image data is saved to the optimal storage server.

[0038] For ease of explanation, the following example is provided: For example, there are multiple preferred storage servers, namely, the first preferred storage server, the second preferred storage server, and the third preferred storage server; The carbon emissions of all power plants in the region where the first preferred storage server is located are added together to generate the total carbon emissions of the region where the first preferred storage server is located in the power generation process. The carbon emissions of all power plants in the region where the first preferred storage server is located are added together to generate the total carbon emissions of the region where the first preferred storage server is located in the power generation process. The total carbon emissions of the region where the first preferred storage server is located in the power generation process are divided by the total electricity consumption of the region where the first preferred storage server is located to generate the carbon emission factor corresponding to the first preferred storage server. The carbon emissions of all power plants in the area where the second preferred storage server is located are added together to generate the total carbon emissions of the area where the second preferred storage server is located in the power generation process. The carbon emissions of all power plants in the area where the second preferred storage server is located are added together to generate the total carbon emissions of the area where the second preferred storage server is located in the power generation process. The total carbon emissions of the area where the second preferred storage server is located in the power generation process are divided by the total electricity consumption of the area where the second preferred storage server is located to generate the carbon emission factor corresponding to the second preferred storage server. The carbon emissions of all power plants in the region where the third preferred storage server is located are added together to generate the total carbon emissions of the region in the power generation process. The carbon emissions of all power plants in the region where the third preferred storage server is located are added together to generate the total carbon emissions of the region in the power generation process. The total carbon emissions of the region in the power generation process are divided by the total electricity consumption of the region where the third preferred storage server is located to generate the carbon emission factor corresponding to the third preferred storage server.

[0039] In this embodiment, the preferred storage server with the highest comprehensive evaluation value is selected as the optimal storage server. The processed liver image data is saved to the optimal storage server, which can effectively balance the load of the storage system, avoid excessive resource consumption of a single node, and improve the stability and reliability of the storage system. At the same time, it can give full play to the advantages of the optimal storage server in terms of throughput and response speed, optimize the data read and write process, and reduce the carbon emissions of the storage system.

[0040] Example 3 refer to Figure 3 , Figure 3 The following is a flowchart illustrating the implementation of the storage method provided in this application embodiment, detailed below: In step S301, the data identifier, storage time, and storage path of the processed liver imaging data are obtained; In step S302, the data identifier, storage time and storage path of the processed liver imaging data are packaged in JSON format to generate an information package of the processed liver imaging data. The information package is then saved to the database through the database storage engine.

[0041] JSON (JavaScript Object Notation) is a lightweight data exchange format that is easy for humans to read and write, and also easy for machines to parse and generate.

[0042] In this embodiment, the data identifier, storage time, and storage path of the processed liver imaging data are packaged using JSON format to generate an information package of the processed liver imaging data. The information package is then saved to the database through the database storage engine. Subsequently, the processed liver imaging data can be retrieved based on the information package in the database, eliminating the need for disk-by-disk retrieval and repeated traversal of storage devices, reducing a large amount of invalid read / write operations and system overhead, and improving the retrieval speed of the processed liver imaging data.

[0043] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and sub-samples of some embodiments may be included in or replace parts and sub-samples of other embodiments.

[0044] In this document, each embodiment focuses on describing the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0045] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. Furthermore, any software tools or components not belonging to this company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

Claims

1. A storage system for liver imaging data, characterized in that, The liver imaging data storage system includes an imaging acquisition device, a scheduling server connected to the imaging acquisition device, and multiple storage servers connected to the scheduling server; the scheduling server includes a data receiving module, a data processing module, and a storage management module; The data receiving module is used to receive liver image data uploaded by the image acquisition device through a preset network; The data processing module is used to call the large model, input the received liver image data into the large model, and perform noise reduction processing on the received liver image data through the large model to generate processed liver image data. The storage management module is used to obtain the amount of processed liver image data, the remaining storage capacity and access latency of each storage server, select the storage server with a remaining storage capacity higher than the amount of data and an access latency lower than a preset latency as the preferred storage server, obtain the total carbon emissions and total electricity consumption of the region where each preferred storage server is located, divide the total carbon emissions of the region where each preferred storage server is located by the total electricity consumption of the region to generate a carbon emission factor for each preferred storage server, generate the carbon emission amount of each preferred storage server within a preset time period based on the carbon emission factor, the power consumption and carbon emission model of each preferred storage server within a preset time period, generate the comprehensive evaluation value of each preferred storage server based on the carbon emission amount of each preferred storage server within a preset time period, the number of input / output operations per second of each preferred storage server, the throughput of each preferred storage server and a comprehensive evaluation model, and select the preferred storage server with the highest comprehensive evaluation value as the optimal storage server, and save the processed liver image data to the optimal storage server.

2. The liver imaging data storage system as described in claim 1, characterized in that, The data receiving module is specifically used to send upload commands to the image acquisition device via a preset network and to receive liver image data uploaded by the image acquisition device according to the upload commands.

3. The liver imaging data storage system as described in claim 1, characterized in that, The data processing module is specifically used for: Send a call request to the large model, and receive a response message from the large model based on the call request; When the response message is a success message, the large model is invoked, the received liver image data is input into the large model, and the large model performs noise reduction processing on the received liver image data to generate processed liver image data.

4. The liver imaging data storage system as described in claim 1, characterized in that, The storage management module is specifically used for: The data volume of the processed liver imaging data is obtained, the monitoring data of each storage server is obtained, the remaining storage capacity and access latency of each storage server are obtained from the monitoring data of each storage server, and the storage server with the remaining storage capacity higher than the data volume and the access latency lower than the preset latency is selected as the preferred storage server. The carbon emissions of all power plants in the region where each preferred storage server is located are added together to generate the total carbon emissions of the region where each preferred storage server is located in the power generation process. The carbon emissions of all power plants in the region where each preferred storage server is located are added together to generate the total carbon emissions of the region where each preferred storage server is located in the power generation process. The total carbon emissions of the region where each preferred storage server is located in the power generation process are divided by the total electricity consumption of the region where each preferred storage server is located to generate the carbon emission factor corresponding to each preferred storage server. Based on the carbon emission factor corresponding to each preferred storage server, the power consumption and carbon emission model of each preferred storage server within a preset time period, the carbon emission amount of each preferred storage server within the preset time period is generated. Based on the carbon emission amount of each preferred storage server within the preset time period, the number of input / output operations per second of each preferred storage server, the throughput of each preferred storage server, and the comprehensive evaluation model, a comprehensive evaluation value of each preferred storage server is generated. The preferred storage server with the largest comprehensive evaluation value is selected as the optimal storage server, and the processed liver image data is saved to the optimal storage server.

5. The liver imaging data storage system as described in claim 1, characterized in that, The carbon emission model is defined as follows: ； This represents the carbon emissions of the k-th preferred storage server within a preset time period; This represents the power consumption of the k-th preferred storage server within a preset time period; This represents the carbon emission factor corresponding to the k-th preferred storage server. The higher the carbon emission factor of the k-th preferred storage server, the higher the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity; the lower the carbon emission factor of the k-th preferred storage server, the lower the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity.

6. The liver imaging data storage system as described in claim 1, characterized in that, The comprehensive evaluation model is defined as follows: ； This represents the overall evaluation value of the k-th preferred storage server. The higher the overall evaluation value of the k-th preferred storage server, the stronger its overall performance in terms of increasing the number of input / output operations per second, increasing throughput, and reducing carbon emissions. The lower the overall evaluation value of the k-th preferred storage server, the weaker its overall performance in these three aspects. This represents the number of input / output operations per second for the k-th preferred storage server; This represents the maximum number of input / output operations per second provided by all preferred storage servers; This represents the throughput of the k-th preferred storage server; This represents the maximum throughput provided by all preferred storage servers; This represents the power consumption of the k-th preferred storage server within a preset time period; This represents the carbon emission factor corresponding to the k-th preferred storage server. The higher the carbon emission factor of the k-th preferred storage server, the higher the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity; the lower the carbon emission factor of the k-th preferred storage server, the lower the carbon emissions generated by the k-th preferred storage server using one kilowatt-hour of electricity. This represents the carbon emissions of the k-th preferred storage server within a preset time period; This represents the maximum carbon emissions among all preferred storage servers over a preset time period.

7. The liver imaging data storage system as described in claim 1, characterized in that, Image acquisition equipment includes magnetic resonance imaging equipment and ultrasound imaging equipment.

8. The liver imaging data storage system as described in claim 1, characterized in that, The default networks include Ethernet and wireless LAN.

9. The liver imaging data storage system as described in claim 1, characterized in that, The preset durations include 1 hour and 2 hours.

10. A storage method for liver imaging data based on a storage system according to any one of claims 1-9, characterized in that, include: Obtain the data identifier, storage time, and storage path of the processed liver imaging data; Using JSON format, the data identifier, storage time, and storage path of the processed liver imaging data are packaged to generate an information package of the processed liver imaging data. The information package is then saved to the database through the database storage engine.

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