Large model operation method and device, equipment and storage medium

Abstract

The invention relates to the technical field of large language models, and discloses a large model operation method, device and equipment and a storage medium, the method comprises the following steps: in response to a large model trigger instruction, extracting a to-be-called IP and a to-be-called port from the large model trigger instruction; calling the IP to be called and the port to be called so as to run a simulation running environment process; and loading a preset large model file through the simulation running environment process so as to run a large model service corresponding to the preset large model file. According to the method, the dependence on network connection can be effectively reduced, the large model service can be quickly started, the large model processing efficiency is effectively improved, and meanwhile, as the user data does not need to be uploaded to the cloud, the user data can be prevented from being leaked, and the data security is enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of large language models, and in particular to large model operation methods, devices, equipment and storage media. Background Art

[0002] With the rapid development of artificial intelligence technology, the performance of large models in natural language processing, image recognition, and multimodal tasks has reached unprecedented heights. However, traditional large model deployment methods usually rely on cloud computing. Although this architecture can utilize the powerful computing power and storage resources of the cloud, it also has many limitations: (1) User data needs to be uploaded to the cloud, which poses a risk of privacy leakage, especially when processing sensitive data; (2) Cloud computing relies on network connections, and for tasks with high real-time requirements (such as voice interaction or complex web page parsing), network delays may affect user experience; (3) Continuous cloud service calls increase operating costs and have a high dependence on a stable network environment.

[0003] Therefore, how to prevent user data leakage, reduce the network dependence of large models, and improve the efficiency of using large models is a problem that needs to be solved urgently.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a large model operation method, device, equipment and storage medium, aiming to solve the technical problems that traditional large model deployment methods usually rely on cloud computing, user data needs to be uploaded to the cloud, there is a risk of privacy leakage, and cloud computing relies on network connection, resulting in low efficiency of large model processing.

[0006] To achieve the above objectives, the present application proposes a large model operation method, the method comprising:

[0007] In response to the large model trigger instruction, extracting the IP to be called and the port to be called from the large model trigger instruction;

[0008] Calling the IP to be called and the port to be called to run the simulation running environment process;

[0009] The preset large model file is loaded through the simulation operation environment process to run the large model service corresponding to the preset large model file.

[0010] In one embodiment, before extracting the IP to be called and the port to be called from the large model trigger instruction in response to the large model trigger instruction, the method further includes:

[0011] Get the user's hardware configuration information;

[0012] Integrate a simulated operating environment process in the browser according to the hardware configuration information;

[0013] Add the preset IP and preset port to the environment variables, and bind the environment variables to the simulation running environment process.

[0014] In one embodiment, integrating the simulated operating environment process in the browser according to the hardware configuration information includes:

[0015] Matching the corresponding simulation running environment process compression package according to the hardware configuration information;

[0016] The simulated operating environment process is integrated in the browser based on the simulated operating environment process compression package.

[0017] In one embodiment, integrating the simulated operating environment process in the browser based on the simulated operating environment process compression package includes:

[0018] Downloading the simulated operating environment process compressed package to the download cache area of ​​the browser;

[0019] Decompressing the simulated operating environment process compressed package in the download cache area to obtain a simulated operating environment process file;

[0020] Integrate the simulation running environment process file into the browser's installation directory.

[0021] In one embodiment, matching the corresponding simulated operating environment process compression package according to the hardware configuration information includes:

[0022] Determine the operating system type and processor architecture according to the hardware configuration information;

[0023] A corresponding simulated operating environment process compression package is selected from a preset simulated operating environment process compression package library according to the operating system type and the processor architecture.

[0024] In one embodiment, after integrating the simulated operating environment process in the browser according to the hardware configuration information, the method further includes:

[0025] Matching corresponding multiple large model files according to the hardware configuration information;

[0026] A plurality of large model files are downloaded to a preset folder associated with the simulation running environment process.

[0027] In one embodiment, matching the corresponding multiple large model files according to the hardware configuration information includes:

[0028] Determine the computing requirements and storage requirements of the large model according to the hardware configuration information;

[0029] According to the computing requirements and the storage requirements, a corresponding plurality of large model files are selected from a preset large model file library.

[0030] In one embodiment, before loading the preset large model file through the simulation running environment process to run the large model service corresponding to the preset large model file, the process further includes:

[0031] Get the current network status;

[0032] Selecting a target large model file from a preset folder according to the network status;

[0033] The target large model file is used as a preset large model file.

[0034] In one embodiment, selecting a target large model file from a preset folder according to the network status includes:

[0035] Get the network usage of each model file in the preset folder;

[0036] A target large model file is selected from the preset folder according to the current network status and the network occupancy rate of each large model file.

[0037] In one embodiment, after loading the preset large model file through the simulation running environment process to run the large model service corresponding to the preset large model file, the method further includes:

[0038] In response to a dialogue instruction inputted in the browser, determining question information according to the dialogue instruction;

[0039] According to the question information, corresponding answer information is generated and displayed through the large model service.

[0040] In one embodiment, before calling the IP to be called and the port to be called to run the simulation running environment process, the process further includes:

[0041] Read the actual IP and actual port corresponding to the simulated running environment process in the environment variables;

[0042] Verify the to-be-called IP and the to-be-called port according to the actual IP and the actual port to obtain a verification result;

[0043] When the verification result shows that the IP to be called is consistent with the actual IP and the port to be called is consistent with the actual port, the step of calling the IP to be called and the port to be called to run the simulation running environment process is executed.

[0044] In addition, to achieve the above purpose, the present application also proposes a large model operation device, the large model operation device comprising:

[0045] An extraction module, configured to extract the IP to be called and the port to be called from the large model trigger instruction in response to the large model trigger instruction;

[0046] A calling module, used to call the IP to be called and the port to be called to run the simulation running environment process;

[0047] The loading module is used to load the preset large model file through the simulation running environment process to run the large model service corresponding to the preset large model file.

[0048] In one embodiment, the large model running device further includes an integration module, and the integration module is used to obtain the hardware configuration information of the user;

[0049] Integrate a simulated operating environment process in the browser according to the hardware configuration information;

[0050] Add the preset IP and preset port to the environment variables, and bind the environment variables to the simulation running environment process.

[0051] In one embodiment, the integration module is further used to match the corresponding simulation running environment process compression package according to the hardware configuration information;

[0052] The simulated operating environment process is integrated in the browser based on the simulated operating environment process compression package.

[0053] In one embodiment, the integration module is further used to download the simulated operating environment process compression package to the download cache area of ​​the browser;

[0054] Decompressing the simulated operating environment process compressed package in the download cache area to obtain a simulated operating environment process file;

[0055] Integrate the simulation running environment process file into the browser's installation directory.

[0056] In one embodiment, the integration module is further used to determine the operating system type and processor architecture according to the hardware configuration information;

[0057] A corresponding simulated operating environment process compression package is selected from a preset simulated operating environment process compression package library according to the operating system type and the processor architecture.

[0058] In one embodiment, the integration module is further used to match the corresponding multiple large model files according to the hardware configuration information;

[0059] A plurality of large model files are downloaded to a preset folder associated with the simulation running environment process.

[0060] In one embodiment, the integration module is further used to determine the computing requirements and storage requirements of the large model according to the hardware configuration information;

[0061] According to the computing requirements and the storage requirements, a corresponding plurality of large model files are selected from a preset large model file library.

[0062] In addition, to achieve the above-mentioned purpose, the present application also proposes a large model operation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the large model operation method described above.

[0063] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the large model operation method described above are implemented.

[0064] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the large model operation method described above.

[0065] The present application provides a large model operation method, which first responds to a large model trigger instruction, extracts the IP to be called and the port to be called from the large model trigger instruction; calls the IP to be called and the port to be called to run a simulation operation environment process; loads a preset large model file through the simulation operation environment process to run the large model service corresponding to the preset large model file, which can effectively reduce the dependence on network connection, can quickly start the large model service, and effectively improve the large model processing efficiency. At the same time, since user data does not need to be uploaded to the cloud, it can prevent user data leakage and enhance data security.

[0066] In summary, the present application can quickly run the simulation operation environment process by extracting the IP and port to be called from the large model trigger instruction, and then calling the IP and port to be called. Since the simulation operation environment process is integrated in the browser, user data does not need to be uploaded to the cloud, which can prevent user data leakage. Then, the preset large model file can be loaded through the simulation operation environment process to quickly run the large model service corresponding to the preset large model file without relying on network connection, which effectively improves the processing efficiency and stability of the large model, and overcomes the traditional large model deployment method that usually relies on cloud computing, user data needs to be uploaded to the cloud, there is a risk of privacy leakage, and cloud computing relies on network connection, resulting in low efficiency of large model processing. Technical defects, can effectively reduce dependence on network connection, can quickly start the large model service, effectively improve the processing efficiency of the large model, at the same time, since user data does not need to be uploaded to the cloud, it can prevent user data leakage and enhance data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0068] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0069] Figure 1 A schematic diagram of a process flow provided for the first embodiment of the large model operation method of the present application;

[0070] Figure 2 A detailed workflow diagram of a large model operation provided in an embodiment of a large model operation method of the present application;

[0071] Figure 3 A schematic diagram of the process flow provided for the second embodiment of the large model operation method of the present application;

[0072] Figure 4 This is a schematic diagram of the module structure of the large model operation device of the embodiment of the present application;

[0073] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the large model operating method of the embodiment of the present application.

[0074] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0075] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0076] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0077] The main solution of the embodiment of the present application is: in response to a large model trigger instruction, extract the IP to be called and the port to be called from the large model trigger instruction; call the IP to be called and the port to be called to run a simulation operating environment process; load the preset large model file through the simulation operating environment process to run the large model service corresponding to the preset large model file.

[0078] With the rapid development of artificial intelligence technology, the performance of large models in natural language processing, image recognition and multimodal tasks has reached unprecedented heights. However, the traditional deployment of large models usually relies on cloud computing. Although this architecture can utilize the powerful computing power and storage resources of the cloud, it also has many limitations: (1) User data needs to be uploaded to the cloud, which poses a risk of privacy leakage, especially when processing sensitive data; (2) Cloud computing relies on network connections, and for tasks with high real-time requirements (such as voice interaction or complex web page parsing), network delays may affect user experience; (3) Continuous cloud service calls increase operating costs and have a high dependence on a stable network environment. Therefore, how to prevent user data leakage, reduce the network dependence of large models, and improve the efficiency of large model use is a problem that needs to be solved urgently.

[0079] The present application can quickly run the simulation operation environment process by extracting the IP and port to be called from the large model trigger instruction, and then calling the IP and port to be called. Since the simulation operation environment process is integrated in the browser, user data does not need to be uploaded to the cloud, which can prevent user data leakage. Then, the preset large model file can be loaded through the simulation operation environment process to quickly run the large model service corresponding to the preset large model file without relying on network connection, which effectively improves the processing efficiency and stability of the large model, overcomes the technical defects of traditional large model deployment methods that usually rely on cloud computing, user data needs to be uploaded to the cloud, there is a risk of privacy leakage, and cloud computing relies on network connection, resulting in low efficiency of large model processing, can effectively reduce dependence on network connection, can quickly start the large model service, and effectively improve the processing efficiency of the large model. At the same time, since user data does not need to be uploaded to the cloud, it can prevent user data leakage and enhance data security.

[0080] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a large model running device, etc. The following takes a large model running device as an example to illustrate this embodiment and the following embodiments.

[0081] Based on this, the present application embodiment provides a large model operation method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the large model operation method of the present application.

[0082] In this embodiment, the large model operation method includes steps S10 to S30:

[0083] Step S10, in response to the large model trigger instruction, extracting the IP to be called and the port to be called from the large model trigger instruction.

[0084] It should be noted that the large model trigger instruction refers to an instruction used to trigger the operation of the large model. The instruction can be triggered by the user in a specific way (such as clicking a button, entering a command, etc.), and no specific restrictions are made at this time.

[0085] It is understandable that the large model trigger instruction carries information about the IP to be called and the port to be called. The IP to be called and the port to be called can be manually input by the user or automatically obtained by the system based on the user's selection or operation. This embodiment does not impose any specific restrictions on this.

[0086] It is worth noting that the purpose of extracting the IP and port to be called is to determine the specific location and communication port of the simulation operating environment process required to run the large model service for subsequent calling and running.

[0087] Step S20, calling the IP to be called and the port to be called to run the simulation running environment process.

[0088] It should be noted that the simulation running environment process refers to a specific environment integrated in the browser, which is used to load and run the preset large model file, for example, the Ollama process. The simulation running environment process can provide interfaces and dependencies that are compatible with the large model file to ensure that the large model file can be loaded and run correctly. The Ollama process is a simulation environment integrated in the browser for running large models. The simulation environment process can simulate the various conditions and resources required for the operation of the large model, thereby ensuring that the large model can run smoothly in the browser.

[0089] It can be understood that by calling the IP to be called and the port to be called, the simulation running environment process can be started and prepared to load and run the large model service.

[0090] In a feasible implementation, before step S20, it also includes: reading the actual IP and actual port corresponding to the simulated running environment process in the environment variable; verifying the IP to be called and the port to be called according to the actual IP and the actual port to obtain a verification result; when the verification result is that the IP to be called is consistent with the actual IP and the port to be called is consistent with the actual port, executing the step of calling the IP to be called and the port to be called to run the simulated running environment process.

[0091] It should be noted that environment variables refer to some parameters used to specify the operating environment in the operating system. These parameters can be read by the operating system or application to determine certain behaviors or configurations. In this embodiment, the environment variables are set with the actual IP and actual port pre-configured for the simulated operating environment process. The actual IP and actual port represent the actual location of the simulated operating environment process in the device or network.

[0092] It is understandable that by reading the environment variables, the IP and port to be called input by the user or automatically obtained by the system can be verified to ensure that they match the actual location of the simulation running environment process. The call operation is performed only when the IP to be called is consistent with the actual IP and the port to be called is consistent with the actual port, which can effectively prevent the failure of the simulation running environment process to start or data communication problems caused by IP or port errors, further improving the stability and reliability of the large model operation.

[0093] Step S30, loading the preset large model file through the simulation running environment process to run the large model service corresponding to the preset large model file.

[0094] It should be noted that the preset large model file refers to a specific large model file that is pre-downloaded and stored. The file contains specific algorithms, model parameters and structures, and is used to perform specific tasks or functions, such as natural language processing, image recognition, etc. This embodiment does not impose specific restrictions on this.

[0095] It can be understood that by loading these preset large model files through the simulation running environment process, the large model service corresponding to the preset large model file can be quickly started without retraining or configuring the model, thereby improving the efficiency and convenience of using the large model.

[0096] It is worth noting that different big model files correspond to different big model services. For example, a big model file for natural language processing can provide services such as text generation and language understanding, and a big model file for image recognition can provide services such as image classification and object detection. This embodiment does not impose specific restrictions on this, and a suitable big model file can be selected and used according to actual needs.

[0097] In the specific implementation, a request instruction is sent to the simulation operation environment process through the IP to be called and the port to be called. The request instruction carries the path or identification information of the preset large model file, so that the simulation operation environment process can accurately find and load the preset large model file. After loading, the simulation operation environment process will parse the algorithm, model parameters and structure of the preset large model file, and configure and start the corresponding large model service based on this information, so that it can perform specific tasks or functions.

[0098] In a feasible implementation manner, before step S30, it also includes: obtaining the current network status; selecting a target large model file from a preset folder according to the network status; and using the target large model file as a preset large model file.

[0099] It should be noted that the current network status refers to the status of the network environment in which the device is currently located, including but not limited to network speed, bandwidth, stability, etc. The preset folder refers to a folder that is pre-set and stores multiple large model files. These large model files can be classified and stored according to the network conditions required. For example, large model files that require higher network speed and bandwidth are stored in one folder, and large model files that can run at lower network speed and bandwidth are stored in another folder. This embodiment does not impose specific restrictions on this.

[0100] It is understandable that selecting the target large model file from the preset folder according to the current network conditions can ensure that the most suitable large model file can be selected under the current network environment, avoiding the problem of low efficiency or failure of large model operation due to poor network conditions. At the same time, using the selected target large model file as the preset large model file can ensure that the large model file subsequently loaded and run is the best choice that matches the current network environment, which can reduce the real-time demand on the network.

[0101] In a specific implementation, the current network status can be obtained by detecting parameters such as the network connection status and network speed of the device, and these parameters are matched with the network requirements of each large model file in the preset folder to select the target large model file. After the selection is completed, the path or identification information of the target large model file is passed to the simulation running environment process to load and run the large model file.

[0102] In a feasible implementation manner, selecting the target large model file from a preset folder according to the network status includes: obtaining the network occupancy rate of each large model file in the preset folder; selecting the target large model file from the preset folder according to the current network status and the network occupancy rate of each large model file.

[0103] It should be noted that the network occupancy rate refers to the proportion of network resources occupied by large model files during operation, including but not limited to network bandwidth, data transmission volume, etc. By obtaining the network occupancy rate of each large model file in the preset folder, you can understand the specific requirements of each large model file for network resources.

[0104] In a specific implementation, corresponding tags or attributes can be set for each large model file in a preset folder to record its network occupancy rate. Then, a matching analysis is performed based on the current network status (such as network speed, bandwidth, etc.) and the network occupancy rate of each large model file. By comparing whether the current network status can meet the network requirements of each large model file, a large model file that can run normally in the current network environment and occupies relatively less network resources can be selected as the target large model file.

[0105] It is worth noting that after the selection is completed, the path or identification information of the target large model file is passed to the simulation running environment process so that the large model file can be loaded and run later. This method can ensure that the most suitable large model file is selected under the current network environment, avoiding the problem of low efficiency or failure of large model operation due to poor network conditions, and at the same time, it can make more reasonable use of network resources and improve network usage efficiency.

[0106] In a feasible implementation manner, after step S30, it also includes: responding to a dialogue instruction input in the browser, determining question information according to the dialogue instruction; generating corresponding answer information through the big model service according to the question information and displaying it.

[0107] It should be noted that the dialogue command refers to the command that the user inputs in a browser in a specific way (such as clicking a button, entering text, etc.) to trigger the big model to conduct a dialogue. The command carries the question information that the user wants to ask or discuss, and the big model service will generate corresponding reply information based on this information.

[0108] In the specific implementation, the browser will detect the user's input operation, and once the dialogue command is detected, it will be passed to the big model service. After receiving the dialogue command, the big model service will first parse the question information, and then call the corresponding big model algorithm for reasoning and analysis based on this information, and finally generate the reply information. After the reply information is generated, the browser will display it to the user, thus completing a dialogue interaction. This method allows users to have real-time dialogues with the big model through the browser and obtain the required information or help. At the same time, because the big model service has powerful data processing and reasoning capabilities, it can provide users with more accurate and comprehensive reply information, improving the quality and efficiency of dialogue interaction.

[0109] like Figure 2 As shown, Figure 2 The following is a detailed workflow diagram for the operation of the large model. The browser downloads the appropriate Ollama process and large model file according to the CPU configuration, thereby integrating the Ollama process in the browser, setting the IP and port to environment variables, and starting the Ollama process. The user accesses the Ollama process by reading the IP and port in the environment variables on the web page, and then sends a request to the large model process through the Ollama process to start the large model process, and the response of the large model process is fed back to the user through the Ollama process.

[0110] The present embodiment provides a large model operation method. The present embodiment first responds to a large model trigger instruction, extracts the IP to be called and the port to be called from the large model trigger instruction; calls the IP to be called and the port to be called to run a simulation operation environment process; loads a preset large model file through the simulation operation environment process to run the large model service corresponding to the preset large model file, which can effectively reduce the dependence on network connection, can quickly start the large model service, and effectively improves the large model processing efficiency. At the same time, since user data does not need to be uploaded to the cloud, user data leakage can be prevented, thereby enhancing data security.

[0111] In summary, this embodiment can quickly run the simulation operation environment process by extracting the IP and port to be called from the big model trigger instruction, and then calling the IP and port to be called. Since the simulation operation environment process is integrated in the browser, user data does not need to be uploaded to the cloud, which can prevent user data leakage. Then, the preset big model file can be loaded through the simulation operation environment process to quickly run the big model service corresponding to the preset big model file without relying on network connection, which effectively improves the processing efficiency and stability of the big model, overcomes the traditional big model deployment method that usually relies on cloud computing, user data needs to be uploaded to the cloud, there is a risk of privacy leakage, and cloud computing relies on network connection, resulting in low efficiency of big model processing. Technical defects, can effectively reduce dependence on network connection, can quickly start the big model service, effectively improve the processing efficiency of the big model, at the same time, since user data does not need to be uploaded to the cloud, it can prevent user data leakage and enhance data security.

[0112] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 3 , before step S10, steps S01-S03 are also included:

[0113] Step S01, obtaining the user's hardware configuration information.

[0114] It should be noted that hardware configuration information refers to the hardware parameters and configuration of the user's device, including but not limited to operating system type, processor architecture, processor model, memory size, storage space, etc.

[0115] In a specific implementation, when a user visits a web page or application that provides a large model service, the browser will automatically detect the user's hardware configuration information, which can be obtained through the interface or plug-in provided by the browser. These interfaces or plug-ins can read the hardware parameters and configuration status on the user's device. And send this information to the server. After receiving the hardware configuration information, the server will recommend suitable simulation operating environment processes and large model files to the user based on this information and the preset large model file running requirements. The recommended simulation operating environment processes and large model files can fully utilize the hardware performance of the user's device without causing waste of resources or low operating efficiency due to over-configuration. In this way, it can be ensured that users can get the best large model running experience on devices with different hardware configurations.

[0116] Step S02: integrating a simulated operating environment process in a browser according to the hardware configuration information.

[0117] It should be noted that the browser will select the appropriate simulation runtime process based on the hardware configuration information and integrate it into the browser. This simulation runtime process matches the hardware configuration of the user's device, and can fully utilize the hardware performance of the device to ensure the operating efficiency and stability of the large model file. At the same time, since the simulation runtime process is integrated into the browser, users can run the large model service directly through the browser without installing additional software or plug-ins, which improves the convenience of use and does not require uploading user data to the cloud, further enhancing data security.

[0118] In a feasible implementation, step S02 may include steps A10 to A11:

[0119] Step A10, matching the corresponding simulation running environment process compression package according to the hardware configuration information.

[0120] It should be noted that the simulation running environment process compressed package refers to a compressed file that contains all files and dependencies required for the running of the simulation running environment process.

[0121] In the specific implementation, the server will select a matching simulated operating environment process compression package from the preset simulated operating environment process library according to the user's hardware configuration information. By providing a compressed package, the simulated operating environment process can be easily deployed to the user's device, and it is also convenient for users to migrate and back up between different devices.

[0122] Step A11, integrating the simulated operating environment process in the browser based on the simulated operating environment process compression package.

[0123] It should be noted that the server will send the matched simulation running environment process compressed package to the user's browser. After receiving the compressed package, the browser will automatically decompress it and integrate the decompressed simulation running environment process into the browser, so that the user can directly run the large model service through the browser.

[0124] In a feasible implementation, the step A10 specifically includes: determining the operating system type and the processor architecture according to the hardware configuration information; and selecting a corresponding simulation operating environment process compression package from a preset simulation operating environment process compression package library according to the operating system type and the processor architecture.

[0125] It should be noted that, in this embodiment, the hardware configuration information includes at least an operating system type and a processor architecture. The operating system type refers to the operating system used by the user device, such as Windows, MacOS, Linux, etc.; the processor architecture refers to the type of processor used by the user device, such as x86, ARM, etc. This embodiment does not impose specific restrictions on this.

[0126] It is understandable that the preset simulation operating environment process compression package library refers to a database containing simulation operating environment process compression packages corresponding to multiple operating system types and processor architectures.

[0127] In a specific implementation, a simulated operating environment process compression package that fully matches the user's device is selected from the preset simulated operating environment process compression package library according to the operating system type and processor architecture of the user's device. For example, if the user's device is a Windows operating system and the processor architecture is x86, the server will select a simulated operating environment process compression package that matches the Windows x86 version from the library. In this way, it can ensure that the simulated operating environment process is highly compatible with the hardware configuration of the user's device, thereby giving full play to the hardware performance of the device and improving the operating efficiency and stability of the large model file. At the same time, since the simulated operating environment process compression package is prepared in advance, it can be quickly deployed to the user's device, reducing the user's waiting time and improving the convenience of use. In addition, the most suitable large model file will be recommended to the user based on the user's hardware configuration information to ensure that the operation effect of the large model service is optimal.

[0128] In a feasible implementation, the step A11 specifically includes: downloading the simulated operating environment process compressed package to the download cache area of ​​the browser; decompressing the simulated operating environment process compressed package in the download cache area to obtain the simulated operating environment process file; and integrating the simulated operating environment process file into the installation directory of the browser.

[0129] It should be noted that the download cache area refers to the area where the browser temporarily stores downloaded files. The simulated operating environment process file refers to the .exe file, which is an executable file. This file contains all the code and dependencies of the simulated operating environment process and is the core component of the simulated operating environment process. The browser installation directory refers to the folder where the browser stores and manages all its related files and components.

[0130] In the specific implementation, the browser will first download the received simulation operation environment process compressed package to its download cache area to ensure that the user's normal browsing experience will not be affected during the download process. After the download is completed, the browser will automatically decompress the simulation operation environment process compressed package in the download cache area to obtain the simulation operation environment process file. The decompressed simulation operation environment process file will be integrated into the browser's installation directory, so that the browser can directly call and run the simulation operation environment process. In this way, it can be ensured that the simulation operation environment process is tightly integrated with the browser, and the user can directly run the large model service through the browser without additional configuration or operation, which further improves the convenience of use. At the same time, since the simulation operation environment process file is directly integrated in the browser's installation directory, it can also effectively prevent user data leakage and enhance data security.

[0131] In a feasible implementation manner, after step S02, it may also include: matching corresponding multiple large model files according to the hardware configuration information; downloading the multiple large model files to a preset folder associated with the simulation running environment process.

[0132] It should be noted that the preset folder refers to a specific folder used to store and manage large model files. This folder is associated with the simulation running environment process, which can ensure that the large model files can be correctly loaded and run by the simulation running environment process. The preset folder is also located in the browser's installation directory so that it can be called at any time.

[0133] In the specific implementation, the server will recommend multiple suitable large model files to the user based on the user's hardware configuration information and the preset large model file running requirements. These large model files can fully meet the needs of users without wasting resources or inefficient operation due to over-configuration. The recommended large model files will be sorted according to priority so that users can choose according to actual needs. After the user selects the large model files, the server will download these files to the preset folder associated with the simulation running environment process. When the simulation running environment process is started, the large model file will be selected and loaded, so that the user can run the large model service directly through the browser without additional configuration or operation. In this way, users can be provided with a more flexible and convenient large model service experience, while also further improving data security and the efficiency of large model processing.

[0134] In the specific implementation, the server will match multiple suitable large model files for the user based on the user's hardware configuration information and the user's usage habits and needs. These large model files can be different versions, different application scenarios or different functional modules to meet the diverse needs of users. After the matching is completed, the server will send these large model files to the user's browser, and the browser will download them to the preset folder associated with the simulation operation environment process. In this way, when the user needs to run a large model service, the simulation operation environment process can directly load the corresponding large model file from the preset folder without the user performing additional searches or configurations, further improving the convenience of use.

[0135] In a feasible implementation, matching the corresponding multiple large model files according to the hardware configuration information includes: determining the computing requirements and storage requirements of the large model according to the hardware configuration information; and selecting the corresponding multiple large model files from a preset large model file library according to the computing requirements and the storage requirements.

[0136] It should be noted that computing requirements refer to the computing power required by the large model during operation, including processor speed, memory size, etc.; storage requirements refer to the storage space required by the large model during operation, including the capacity of the hard disk or solid-state drive, etc. The preset large model file library refers to a database containing large model files corresponding to various computing requirements and storage requirements.

[0137] In a specific implementation, the computing and storage requirements of the large model are determined according to the user's hardware configuration information. Then, multiple matching large model files are selected from the preset large model file library. These large model files can not only meet the computing and storage requirements of the user, but also will not cause waste of resources or low operating efficiency due to over-configuration. In this way, it can be ensured that users can get the best large model running experience on devices with different hardware configurations. At the same time, these large model files can also be sorted or classified according to the user's hardware configuration information and the user's usage habits and needs, so that users can choose according to actual needs. For example, for devices with strong computing power, large model files with higher computing requirements can be recommended; for devices with larger storage space, large model files with higher storage requirements can be recommended. In this way, users can be provided with a more personalized and customized large model service experience.

[0138] It is worth noting that since the large model file is matched according to the user's hardware configuration information, it can also effectively avoid large model operation problems caused by configuration incompatibility, further improving the stability and reliability of the large model service.

[0139] Step S03, adding the preset IP and the preset port to the environment variables, and binding the environment variables to the simulation running environment process.

[0140] It should be noted that the preset IP is the IP address that can access the simulation running environment process, and the preset port is the network port used to communicate with the simulation running environment process.

[0141] It is understandable that by adding the preset IP and the preset port to the environment variables, it can be ensured that the simulated operating environment process can be easily accessed from any location in the system. At the same time, binding the environment variables to the simulated operating environment process can ensure that the simulated operating environment process can automatically load and configure the relevant network parameters when it is started, thereby eliminating the need for the user to perform additional manual configuration.

[0142] In a specific implementation, the preset IP and preset port can be added by modifying the system's environment variable configuration file, and the corresponding environment variable name and value can be set. Then, in the startup script or configuration file of the simulation running environment process, these environment variables are referenced to achieve binding with the preset IP and preset port. In this way, it can be ensured that the simulation running environment process can correctly listen to and respond to network requests from the preset IP and preset port, thereby achieving communication and data exchange with user devices or other services.

[0143] In this embodiment, by selecting a suitable simulation operating environment process according to the user's hardware configuration information and integrating it into the browser, the browser can directly call and run the simulation operating environment process without the user having to perform additional configuration or operation, further improving the convenience of use. The preset IP and preset port are then bound to the simulation operating environment process through environment variables to ensure that the simulation operating environment process can automatically load and configure related network parameters when it starts, thereby eliminating the need for the user to perform additional manual configuration, further simplifying the user's operating process and improving the user experience.

[0144] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the large model operation method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0145] This application also provides a large model running device, please refer to Figure 4 , the large model operation device comprises:

[0146] The extraction module 10 is used to extract the IP to be called and the port to be called from the large model trigger instruction in response to the large model trigger instruction.

[0147] The calling module 20 is used to call the IP to be called and the port to be called to run the simulation running environment process.

[0148] The loading module 30 is used to load the preset large model file through the simulation operation environment process to run the large model service corresponding to the preset large model file.

[0149] The present embodiment provides a large model running device. In response to a large model trigger instruction, the present embodiment extracts the IP to be called and the port to be called from the large model trigger instruction; calls the IP to be called and the port to be called to run a simulation running environment process; loads a preset large model file through the simulation running environment process to run the large model service corresponding to the preset large model file, which can effectively reduce the dependence on network connection, can quickly start the large model service, and effectively improve the large model processing efficiency. At the same time, since user data does not need to be uploaded to the cloud, it can prevent user data leakage and enhance data security.

[0150] In summary, this embodiment can quickly run the simulation operation environment process by extracting the IP and port to be called from the big model trigger instruction, and then calling the IP and port to be called. Since the simulation operation environment process is integrated in the browser, user data does not need to be uploaded to the cloud, which can prevent user data leakage. Then, the preset big model file can be loaded through the simulation operation environment process to quickly run the big model service corresponding to the preset big model file without relying on network connection, which effectively improves the processing efficiency and stability of the big model, overcomes the traditional big model deployment method that usually relies on cloud computing, user data needs to be uploaded to the cloud, there is a risk of privacy leakage, and cloud computing relies on network connection, resulting in low efficiency of big model processing. Technical defects, can effectively reduce dependence on network connection, can quickly start the big model service, effectively improve the processing efficiency of the big model, at the same time, since user data does not need to be uploaded to the cloud, it can prevent user data leakage and enhance data security.

[0151] Optionally, the large model operation device also includes an integration module, which is used to obtain the user's hardware configuration information; integrate the simulation operation environment process in the browser according to the hardware configuration information; add the preset IP and preset port to the environment variables, and bind the environment variables to the simulation operation environment process.

[0152] Optionally, the integration module is further used to match a corresponding simulation operating environment process compression package according to the hardware configuration information; and integrate the simulation operating environment process in the browser based on the simulation operating environment process compression package.

[0153] Optionally, the integration module is also used to download the simulated operating environment process compressed package to the download cache area of ​​the browser; decompress the simulated operating environment process compressed package in the download cache area to obtain the simulated operating environment process file; and integrate the simulated operating environment process file into the installation directory of the browser.

[0154] Optionally, the integrated module is also used to determine the operating system type and processor architecture based on the hardware configuration information; and select a corresponding simulation operating environment process compression package from a preset simulation operating environment process compression package library based on the operating system type and the processor architecture.

[0155] Optionally, the integration module is further used to match the corresponding multiple large model files according to the hardware configuration information; and download the multiple large model files to a preset folder associated with the simulation running environment process.

[0156] Optionally, the integration module is further used to determine the computing requirements and storage requirements of the large model based on the hardware configuration information; and select corresponding multiple large model files from a preset large model file library based on the computing requirements and the storage requirements.

[0157] Optionally, the large model running device also includes a selection module, which is used to obtain the current network status; select the target large model file from a preset folder according to the network status; and use the target large model file as the preset large model file.

[0158] Optionally, the selection module is also used to obtain the network occupancy rate of each large model file in a preset folder; and select the target large model file from the preset folder according to the current network status and the network occupancy rate of each large model file.

[0159] Optionally, the large model operation device also includes a generation module, which is used to respond to dialogue instructions input in the browser, determine question information according to the dialogue instructions; generate corresponding answer information through the large model service according to the question information and display it.

[0160] Optionally, the large model running device also includes a verification module, which is used to read the actual IP and actual port corresponding to the simulated running environment process in the environment variables; verify the IP to be called and the port to be called according to the actual IP and the actual port to obtain a verification result; when the verification result is that the IP to be called is consistent with the actual IP and the port to be called is consistent with the actual port, execute the step of calling the IP to be called and the port to be called to run the simulated running environment process.

[0161] The large model operation device provided by the present application adopts the large model operation method in the above embodiment, which can solve the technical problems that the traditional large model deployment method usually relies on cloud computing, user data needs to be uploaded to the cloud, there is a risk of privacy leakage, and cloud computing relies on network connection, resulting in low efficiency of large model processing. Compared with the prior art, the beneficial effects of the large model operation device provided by the present application are the same as the beneficial effects of the large model operation method provided by the above embodiment, and the other technical features in the large model operation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0162] The present application provides a large model operation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the large model operation method in the above-mentioned embodiment one.

[0163] Reference below Figure 5 , which shows a schematic diagram of the structure of a large model running device suitable for implementing the embodiment of the present application. The large model running device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The large model operation device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0164] like Figure 5As shown, the large model running device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the large model running device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the large model operation device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a large model operation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0165] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0166] The large model operation device provided by the present application adopts the large model operation method in the above embodiment, which can solve the technical problems that the traditional large model deployment method usually relies on cloud computing, user data needs to be uploaded to the cloud, there is a risk of privacy leakage, and cloud computing relies on network connection, resulting in low efficiency of large model processing. Compared with the prior art, the beneficial effects of the large model operation device provided by the present application are the same as the beneficial effects of the large model operation method provided by the above embodiment, and the other technical features in the large model operation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0167] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0168] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0169] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the large model operation method in the above-mentioned embodiment.

[0170] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0171] The computer-readable storage medium may be included in the large-scale model operation device; or it may exist independently without being assembled into the large-scale model operation device.

[0172] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the big model running device, the big model running device: responds to the big model trigger instruction, extracts the IP to be called and the port to be called from the big model trigger instruction; calls the IP to be called and the port to be called to run the simulation running environment process; loads the preset big model file through the simulation running environment process to run the big model service corresponding to the preset big model file.

[0173] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0174] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0175] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0176] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned large model operation method, which can solve the technical problems that the traditional large model deployment method usually relies on cloud computing, user data needs to be uploaded to the cloud, there is a risk of privacy leakage, and cloud computing relies on network connection, resulting in low efficiency of large model processing. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the large model operation method provided in the above-mentioned embodiment, which will not be repeated here.

[0177] The present application also provides a computer program product, including a computer program, which implements the steps of the large model operation method as described above when executed by a processor.

[0178] The computer program product provided by this application can solve the technical problems that the traditional large model deployment method usually relies on cloud computing, user data needs to be uploaded to the cloud, there is a risk of privacy leakage, and cloud computing relies on network connection, resulting in low efficiency of large model processing. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the large model operation method provided in the above embodiment, which will not be repeated here.

[0179] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

[0180] The present invention discloses A1. a large model operation method, the method comprising:

[0181] In response to the large model trigger instruction, extracting the IP to be called and the port to be called from the large model trigger instruction;

[0182] Calling the IP to be called and the port to be called to run the simulation running environment process;

[0183] The preset large model file is loaded through the simulation operation environment process to run the large model service corresponding to the preset large model file.

[0184] A2. The method as described in A1, wherein in response to the large model trigger instruction, before extracting the IP to be called and the port to be called from the large model trigger instruction, further comprises:

[0185] Get the user's hardware configuration information;

[0186] Integrate a simulated operating environment process in the browser according to the hardware configuration information;

[0187] Add the preset IP and preset port to the environment variables, and bind the environment variables to the simulation running environment process.

[0188] A3. The method as described in A2, wherein the simulated operating environment process is integrated in the browser according to the hardware configuration information, comprising:

[0189] Matching the corresponding simulation running environment process compression package according to the hardware configuration information;

[0190] The simulated operating environment process is integrated in the browser based on the simulated operating environment process compression package.

[0191] A4. The method as described in A3, wherein the simulated operating environment process is integrated in the browser based on the simulated operating environment process compression package, comprising:

[0192] Downloading the simulated operating environment process compressed package to the download cache area of ​​the browser;

[0193] Decompressing the simulated operating environment process compressed package in the download cache area to obtain a simulated operating environment process file;

[0194] Integrate the simulation running environment process file into the browser's installation directory.

[0195] A5. The method as described in A3, wherein the compressed package of the simulated operating environment process corresponding to the hardware configuration information is matched, comprising:

[0196] Determine the operating system type and processor architecture according to the hardware configuration information;

[0197] A corresponding simulated operating environment process compression package is selected from a preset simulated operating environment process compression package library according to the operating system type and the processor architecture.

[0198] A6. The method as described in A2, after integrating the simulated operating environment process in the browser according to the hardware configuration information, further comprising:

[0199] Matching corresponding multiple large model files according to the hardware configuration information;

[0200] A plurality of large model files are downloaded to a preset folder associated with the simulation running environment process.

[0201] A7. The method as described in A6, wherein the matching of the corresponding multiple large model files according to the hardware configuration information comprises:

[0202] Determine the computing requirements and storage requirements of the large model according to the hardware configuration information;

[0203] According to the computing requirements and the storage requirements, a corresponding plurality of large model files are selected from a preset large model file library.

[0204] A8. The method as described in A1, before loading the preset large model file through the simulation running environment process to run the large model service corresponding to the preset large model file, further includes:

[0205] Get the current network status;

[0206] Selecting a target large model file from a preset folder according to the network status;

[0207] The target large model file is used as a preset large model file.

[0208] A9. The method as described in A8, wherein the target large model file is selected from a preset folder according to the network status, comprising:

[0209] Get the network usage of each model file in the preset folder;

[0210] A target large model file is selected from the preset folder according to the current network status and the network occupancy rate of each large model file.

[0211] A10. The method as described in A1, after loading the preset large model file through the simulation running environment process to run the large model service corresponding to the preset large model file, further includes:

[0212] In response to a dialogue instruction inputted in the browser, determining question information according to the dialogue instruction;

[0213] According to the question information, corresponding answer information is generated and displayed through the large model service.

[0214] A11. As described in the method of A1, before calling the IP to be called and the port to be called to run the simulated operating environment process, it also includes:

[0215] Read the actual IP and actual port corresponding to the simulated running environment process in the environment variables;

[0216] Verify the to-be-called IP and the to-be-called port according to the actual IP and the actual port to obtain a verification result;

[0217] When the verification result shows that the IP to be called is consistent with the actual IP and the port to be called is consistent with the actual port, the step of calling the IP to be called and the port to be called to run the simulation running environment process is executed.

[0218] The present invention also discloses B12. a large model operation device, the large model operation device comprising:

[0219] An extraction module, configured to extract the IP to be called and the port to be called from the large model trigger instruction in response to the large model trigger instruction;

[0220] A calling module, used to call the IP to be called and the port to be called to run the simulation running environment process;

[0221] The loading module is used to load the preset large model file through the simulation running environment process to run the large model service corresponding to the preset large model file.

[0222] B13. The device as described in B12, wherein the large model operation device further comprises an integration module, wherein the integration module is used to obtain the user's hardware configuration information;

[0223] Integrate a simulated operating environment process in the browser according to the hardware configuration information;

[0224] Add the preset IP and preset port to the environment variables, and bind the environment variables to the simulation running environment process.

[0225] B14. The apparatus as described in B13, wherein the integrated module is further used to match the corresponding simulated operating environment process compression package according to the hardware configuration information;

[0226] The simulated operating environment process is integrated in the browser based on the simulated operating environment process compression package.

[0227] B15. The apparatus as described in B14, wherein the integrated module is further used to download the compressed package of the simulated operating environment process to the download cache of the browser;

[0228] Decompressing the simulated operating environment process compressed package in the download cache area to obtain a simulated operating environment process file;

[0229] Integrate the simulation running environment process file into the browser's installation directory.

[0230] B16. The apparatus as described in B14, wherein the integrated module is further used to determine the operating system type and the processor architecture according to the hardware configuration information;

[0231] A corresponding simulated operating environment process compression package is selected from a preset simulated operating environment process compression package library according to the operating system type and the processor architecture.

[0232] B17. The apparatus as described in B13, wherein the integrated module is further used to match the corresponding multiple large model files according to the hardware configuration information;

[0233] A plurality of large model files are downloaded to a preset folder associated with the simulation running environment process.

[0234] B18. The apparatus as described in B17, wherein the integrated module is further used to determine the computing requirements and storage requirements of the large model according to the hardware configuration information;

[0235] According to the computing requirements and the storage requirements, a corresponding plurality of large model files are selected from a preset large model file library.

[0236] The present invention also discloses C19. a large model running device, which includes: a memory, a processor, and a large model running program stored in the memory and executable on the processor, wherein the large model running program is configured to implement the large model running method as described above.

[0237] The present invention also discloses D20. A storage medium, on which a large model running program is stored. When the large model running program is executed by a processor, the large model running method as described above is implemented.

Claims

1. A large model operation method, characterized in that: The method comprises: In response to the large model trigger instruction, extracting the IP to be called and the port to be called from the large model trigger instruction; Calling the IP to be called and the port to be called to run the simulation running environment process; The preset large model file is loaded through the simulation operation environment process to run the large model service corresponding to the preset large model file.

2. The method according to claim 1, characterized in that In response to the large model trigger instruction, before extracting the IP to be called and the port to be called from the large model trigger instruction, the method further includes: Get the user's hardware configuration information; Integrate a simulated operating environment process in the browser according to the hardware configuration information; Add the preset IP and preset port to the environment variables, and bind the environment variables to the simulation running environment process.

3. The method according to claim 2, characterized in that The step of integrating the simulated operating environment process in the browser according to the hardware configuration information includes: Matching the corresponding simulation running environment process compression package according to the hardware configuration information; The simulated operating environment process is integrated in the browser based on the simulated operating environment process compression package.

4. The method according to claim 3, characterized in that The step of integrating the simulated operating environment process in the browser based on the simulated operating environment process compression package includes: Downloading the simulated operating environment process compressed package to the download cache area of ​​the browser; Decompressing the simulated operating environment process compressed package in the download cache area to obtain a simulated operating environment process file; Integrate the simulation running environment process file into the browser's installation directory.

5. The method according to claim 3, characterized in that The matching of the corresponding simulated operating environment process compression package according to the hardware configuration information includes: Determine the operating system type and processor architecture according to the hardware configuration information; A corresponding simulated operating environment process compression package is selected from a preset simulated operating environment process compression package library according to the operating system type and the processor architecture.

6. The method according to claim 2, characterized in that After integrating the simulated operating environment process in the browser according to the hardware configuration information, the process further includes: Matching corresponding multiple large model files according to the hardware configuration information; A plurality of large model files are downloaded to a preset folder associated with the simulation running environment process.

7. The method according to claim 6, characterized in that The matching of the corresponding multiple large model files according to the hardware configuration information includes: Determine the computing requirements and storage requirements of the large model according to the hardware configuration information; According to the computing requirements and the storage requirements, a corresponding plurality of large model files are selected from a preset large model file library.

8. A large model operation device, characterized in that: The large model operation device comprises: An extraction module, configured to extract the IP to be called and the port to be called from the large model trigger instruction in response to the large model trigger instruction; A calling module, used to call the IP to be called and the port to be called to run the simulation running environment process; The loading module is used to load the preset large model file through the simulation running environment process to run the large model service corresponding to the preset large model file.

9. A large model operation device, characterized in that: The large model running device includes: a memory, a processor, and a large model running program stored in the memory and executable on the processor, wherein the large model running program is configured to implement the large model running method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a large model running program, and when the large model running program is executed by the processor, the large model running method according to any one of claims 1 to 7 is implemented.

Images