Data processing method and device based on large model, equipment and medium

Abstract

The invention discloses a data processing method and device based on a large model, equipment and a medium, and relates to the technical field of smart home / smart home, and the data processing method based on the large model comprises the steps: obtaining problem data; obtaining a tool and a processing flow according to the problem data; wherein the tool comprises external software and a preset artificial intelligence model; generating a problem analysis program according to the tool and the processing flow; and inputting the problem data into the problem analysis program to obtain a problem analysis result.

Description

Technical Field

[0001] The present application relates to the field of smart home / intelligent home technology, and more specifically, to a data processing method, apparatus, device, and medium based on a large model. Background Art

[0002] Currently, artificial intelligence technology has made people's lives more convenient. People can input problem data into artificial intelligence models. After the artificial intelligence models analyze the problem data, they will display the analysis results to users.

[0003] In existing technology, AI models are typically trained using collected data based on specific question-answering tasks. This generates an AI model, and then a question analysis system is developed based on this AI model and the task. However, when the question-answering task changes, a new question analysis system needs to be redesigned and developed, leading to high development costs. Summary of the Invention

[0004] In order to overcome the above-mentioned defects, this application is proposed to provide a solution or at least partially solve the technical problem in the prior art that when the question-answering task changes, a new question analysis system needs to be redesigned and developed, which leads to high development costs.

[0005] In a first aspect, the present application provides a data processing method based on a large model, comprising:

[0006] Get problem data;

[0007] Obtaining tools and processing procedures based on the problem data; wherein the tools include external software and preset artificial intelligence models;

[0008] generating a problem analysis program according to the tool and the processing flow;

[0009] The problem data is input into the problem analysis program to obtain a problem analysis result.

[0010] In one technical solution of the above-mentioned data processing method based on a large model, the acquisition tool and processing flow based on the problem data include:

[0011] Decomposing the problem data to obtain multiple sub-problems;

[0012] According to the multiple sub-problems, obtaining preset artificial intelligence models and external software corresponding to the multiple sub-problems;

[0013] A processing flow is obtained based on the multiple sub-problems, the preset artificial intelligence model corresponding to each sub-problem, and the external software.

[0014] In one technical solution of the above-mentioned large-model-based data processing method, generating a problem analysis program according to the tool and the processing flow includes:

[0015] According to the processing flow, a calling sequence of the tools is obtained;

[0016] According to the calling sequence, a problem analysis program is obtained.

[0017] In one technical solution of the above-mentioned data processing method based on a large model, inputting the problem data into the problem analysis program to obtain the problem analysis result includes:

[0018] Inputting the question data into the question analysis program to obtain a plurality of answer data;

[0019] Performing correlation analysis on the plurality of answer data and the question data respectively to obtain correlation values ​​of the plurality of answer data;

[0020] Obtain target answer data with a correlation value greater than a preset threshold;

[0021] The target answer data are integrated and processed to obtain a question analysis result.

[0022] In one technical solution of the above-mentioned large-model-based data processing method, obtaining the preset artificial intelligence model includes:

[0023] Get text data;

[0024] Preprocessing the text data to obtain training data;

[0025] Classify the training data to obtain training data of different categories;

[0026] According to the different categories of training data, multiple large models are trained separately to obtain a preset artificial intelligence model.

[0027] In one technical solution of the above-mentioned data processing method based on a large model, the method further includes:

[0028] Obtain user needs;

[0029] Adjusting the problem analysis program according to the user's needs to obtain an adjusted program;

[0030] The problem data is input into the adjustment program to obtain analysis results.

[0031] In one technical solution of the above-mentioned data processing method based on a large model, the method further includes:

[0032] Obtain system information and environment information of the large model platform;

[0033] Evaluating and processing the system information and environmental information to obtain status information;

[0034] The large model platform is adjusted according to the status information.

[0035] In a second aspect, the present application provides a data processing device based on a large model, comprising:

[0036] Acquisition module, used to obtain problem data;

[0037] An analysis module, configured to obtain tools and processing procedures based on the problem data; wherein the tools include external software and a preset artificial intelligence model;

[0038] A generation module, configured to generate a problem analysis program based on the tool and the processing flow;

[0039] The processing module is used to input the problem data into the problem analysis program to obtain a problem analysis result.

[0040] In a third aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute any one of the methods according to the first aspect through the computer program.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the methods in the first aspect when running.

[0042] The present application provides a data processing method, device, equipment and medium based on a large model. The method specifically comprises: obtaining problem data; obtaining tools and processing procedures based on the problem data; wherein the tools include external software and a preset artificial intelligence model; generating a problem analysis program based on the tools and the processing procedures; inputting the problem data into the problem analysis program to obtain problem analysis results, thereby improving the development efficiency of the problem analysis system and reducing development costs and development time. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 This is a structural diagram of a first embodiment of a large model platform according to an embodiment of the present application;

[0046] Figure 2 1 is a flow chart of a first embodiment of a data processing method based on a large model according to an embodiment of the present application;

[0047] Figure 3 1 is a flow chart of a second embodiment of a data processing method based on a large model according to an embodiment of the present application;

[0048] Figure 4 1 is a flow chart of a third embodiment of a data processing method based on a large model according to an embodiment of the present application;

[0049] Figure 5 1 is a flow chart of a fourth embodiment of a data processing method based on a large model according to an embodiment of the present application;

[0050] Figure 6 1 is a flow chart of a fifth embodiment of a data processing method based on a large model according to an embodiment of the present application;

[0051] Figure 7 1 is a flow chart of a sixth embodiment of a data processing method based on a large model according to an embodiment of the present application;

[0052] Figure 8 1 is a flow chart of a seventh embodiment of a data processing method based on a large model according to an embodiment of the present application;

[0053] Figure 9 1 is a structural diagram of a first embodiment of a data processing device based on a large model according to an embodiment of the present application;

[0054] Figure 10 1 is a structural diagram of a first embodiment of an electronic device according to an embodiment of the present application.

[0055] List of reference numerals:

[0056] 11: Data module; 12: Orchestration module; 13: Development module; 14: Problem analysis module; 21: Acquisition module; 22: Analysis module; 23: Generation module; 24: Processing module; 31: Memory; 32: Processor. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0058] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0059] Current large-model platforms typically feature multiple AI models, each capable of answering different types of questions. This requires training multiple AI models and designing and developing a question analysis system based on these models. When a new question-answering task emerges, it's necessary to collect questions and answers of that type, train the AI ​​model with them, and then redesign and develop a new question analysis system. This leads to technical issues such as high development costs and long development cycles for large-model platforms.

[0060] Based on this, in order to solve the above technical problems, the technical concept of this application is: how to provide a new data processing method based on large models to reduce the development cost of large model platforms.

[0061] Figure 1 This is a structural diagram of a large model platform embodiment 1 according to the embodiment of the present application, such as Figure 1 As shown, the system includes: a data module 11, an orchestration module 12, a development module 13 and a problem analysis module 14.

[0062] The data module 11 obtains the problem data, and then the orchestration module 12 obtains the tool based on the problem data. The orchestration module 13 obtains the processing flow based on the problem data and the tool. The development module 13 generates the problem analysis program based on the tool and the processing flow. Finally, the problem analysis module 14 inputs the problem data into the problem analysis program to obtain the problem analysis results.

[0063] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0064] Figure 2 This is a flow chart of a first embodiment of a data processing method based on a large model according to an embodiment of the present application. Figure 2 Specifically, the method includes:

[0065] Step S201: Obtain question data.

[0066] In this embodiment, the question data may be text data or voice data. The text data may also be mixed data, such as text data and image data, text data and document data, voice data and image data, voice data and text data, or voice data and document data.

[0067] In this embodiment, various types of data can be parsed, and contextual coherence can be preserved through a sharding strategy.

[0068] Step S202: Obtain tools and processing procedures based on the problem data; wherein the tools include external software and preset artificial intelligence models.

[0069] In this embodiment, based on the problem data, a preset artificial intelligence model and external software that can analyze the problem data are obtained, and based on the obtained preset artificial intelligence model, external software and processing logic of the problem data, a processing flow is obtained.

[0070] In this embodiment, for example, the external software may be a third-party plug-in or software.

[0071] In this embodiment, for example, the preset artificial intelligence model may support mainstream models such as OpenAI, Claude, Llama, and may integrate local models.

[0072] In this embodiment, the processing flow is a process of analyzing data.

[0073] Step S203: Generate a problem analysis program based on the tool and processing flow.

[0074] In this embodiment, according to the tool and the processing flow, a visual process design tool can be used to generate a problem analysis program.

[0075] In this embodiment, the problem analysis program can be run on a computer, a server or a large model platform.

[0076] Step S204: Input the problem data into the problem analysis program to obtain the problem analysis results.

[0077] In this embodiment, after the problem is input into the problem analysis program, the problem analysis result is obtained through analysis by external software and a preset artificial intelligence model.

[0078] In this embodiment, problem data is obtained; tools and processing procedures are obtained based on the problem data; wherein the tools include external software and a preset artificial intelligence model; a problem analysis program is generated based on the tools and processing procedures; the problem data is input into the problem analysis program to obtain a problem analysis result. Compared with the existing technology, when the problem category changes, a new problem analysis system needs to be redesigned and developed, which leads to high development costs. The present application obtains problem data, and based on the problem data, obtains tools and processing procedures, and then inputs the problem data into the problem analysis program to obtain a problem analysis result, thereby improving the development efficiency of the problem analysis system and reducing development costs and development time.

[0079] Figure 3 This is a flow chart of a second embodiment of a data processing method based on a large model according to an embodiment of the present application. On the basis of the above embodiment, Figure 3 Specifically, one implementation of step S202 is as follows:

[0080] Step S301: Decompose the question data to obtain multiple sub-questions.

[0081] In this embodiment, the question data is analyzed by a large language model to obtain multiple sub-questions corresponding to the question data.

[0082] In this embodiment, for example, the question data is: What is the weather like today? After decomposing the question data, the multiple sub-questions are: (1) Where is the location? (2) What month and day is today? (3) What is the weather like?

[0083] Step S302: Based on the multiple sub-problems, obtain preset artificial intelligence models and external software corresponding to the multiple sub-problems.

[0084] In this embodiment, based on the type of sub-problem, a preset artificial intelligence model corresponding to the sub-problem is obtained, and based on the problem that the sub-problem solves, external software is obtained.

[0085] In this embodiment, for example, for the question: What is the weather like?, the preset weather artificial intelligence model corresponds to it, and for the question: Where is the location?, the location service is called.

[0086] Step S303: Obtain a processing flow based on multiple sub-problems, a preset artificial intelligence model corresponding to each sub-problem, and external software.

[0087] In this embodiment, according to the order of multiple sub-questions, the preset artificial intelligence model and external software corresponding to each word problem are arranged in order to obtain a processing flow.

[0088] In this embodiment, the problem data is decomposed and processed to obtain multiple sub-problems; based on the multiple sub-problems, preset artificial intelligence models and external software corresponding to the multiple sub-problems are obtained; based on the multiple sub-problems and the preset artificial intelligence model and external software corresponding to each sub-problem, a processing flow is obtained, and then the task processing flow can be automatically obtained, thereby improving the efficiency and accuracy of task analysis.

[0089] Figure 4 This is a flow chart of a third embodiment of a data processing method based on a large model according to an embodiment of the present application. On the basis of the above embodiment, Figure 4 As shown, a specific implementation of step S203 includes:

[0090] Step S401: According to the processing flow, the calling sequence of the tools is obtained.

[0091] In this embodiment, the called tool and the steps to which the called tool belongs can be obtained from the processing flow, and then the calling sequence of the tools can be obtained.

[0092] Step S402: Obtain a problem analysis program according to the calling sequence.

[0093] In this embodiment, the calling of the tools and the logical relationship between the tools are converted into computer language, thereby obtaining a problem analysis program.

[0094] In this embodiment, the calling sequence of the tools is obtained according to the processing flow; according to the calling sequence, the problem analysis program is obtained, thereby realizing automatic conversion of tasks into commands executed by the computer, thereby improving task processing efficiency.

[0095] Figure 5 This is a flow chart of a fourth embodiment of a data processing method based on a large model according to an embodiment of the present application. On the basis of the above embodiment, Figure 5 As shown, one implementation of step S204 is as follows:

[0096] Step S501: Input question data into the question analysis program to obtain a plurality of answer data.

[0097] Step S502: performing correlation analysis on the plurality of answer data and the question data respectively to obtain correlation values ​​of the plurality of answer data.

[0098] In this embodiment, there is answer data irrelevant to the question data among the multiple answer data. In order for the user to quickly determine the answer data, it is necessary to perform correlation analysis on the multiple answer data and the question data respectively to obtain correlation values ​​of the multiple answer data.

[0099] Step S503: Acquire target answer data whose correlation value is greater than a preset threshold.

[0100] In this embodiment, for example, the preset threshold is 0.8.

[0101] Step S504: Integrate and process the target answer data to obtain the question analysis result.

[0102] In this embodiment, the content of the target answer data is integrated, and the answer data with low relevance can be supplemented with the answer data with high relevance to obtain a question analysis result with sufficient content.

[0103] In this embodiment, question data is input into a question analysis program to obtain multiple answer data; the multiple answer data are respectively subjected to correlation analysis with the question data to obtain correlation values ​​of the multiple answer data; target answer data whose correlation value is greater than a preset threshold is obtained; the target answer data is integrated and processed to obtain a question analysis result, thereby improving the quality and readability of the question analysis result.

[0104] Figure 6 This is a flow chart of a fifth embodiment of a data processing method based on a large model according to an embodiment of the present application. On the basis of the above embodiment, Figure 6 As shown, one implementation method of obtaining the preset artificial intelligence model in step S202 includes:

[0105] Step S601: Acquire text data.

[0106] In this embodiment, text data can be obtained through crawlers or public datasets.

[0107] Step S602: pre-process the text data to obtain training data.

[0108] In this embodiment, the text data is cleaned, segmented, and annotated to obtain preprocessed data.

[0109] Step S603: Classify the training data to obtain training data of different categories.

[0110] In this embodiment, the training data is classified according to the data classification model to obtain training data of different categories.

[0111] Step S604: Based on different categories of training data, multiple large models are trained separately to obtain a preset artificial intelligence model.

[0112] In this embodiment, different categories of training data are input into the corresponding large model for training to obtain a preset artificial intelligence model.

[0113] In this embodiment, the preset artificial intelligence model is stored in the database through distributed deployment.

[0114] In this embodiment, text data is obtained; the text data is preprocessed to obtain training data; the training data is classified to obtain training data of different categories; and according to the training data of different categories, multiple large models are trained separately to obtain a preset artificial intelligence model.

[0115] Figure 7 This is a flow chart of a sixth embodiment of a data processing method based on a large model according to an embodiment of the present application. Based on the above embodiment, Figure 7 As shown, after step S204, the method further includes:

[0116] Step S701: Obtain user needs.

[0117] Step S702: Adjust the problem analysis program according to user needs to obtain an adjusted program.

[0118] In this embodiment, the logical relationship and the called tools of the problem analysis program are adjusted according to user needs to obtain an adjusted program.

[0119] Step S703: Input the problem data into the adjustment program to obtain analysis results.

[0120] In this embodiment, user needs are obtained; according to the user needs, the problem analysis program is adjusted and processed to obtain an adjustment program; the problem data is input into the adjustment program to obtain analysis results, and then the problem analysis program can be efficiently adjusted according to the user needs, and then a task system that meets the user needs can be quickly developed, thereby improving the flexibility and usability of the platform.

[0121] Figure 8 This is a flow chart of a seventh embodiment of a data processing method based on a large model according to an embodiment of the present application. Based on the above embodiment, Figure 8 As shown, after step S204, the method further includes:

[0122] Step S801: Obtain system information and environment information of the large model platform.

[0123] In this embodiment, the system information includes the hardware information of the large model platform, specifically the operating data of the memory, central processing unit, and hard disk. The environmental information includes the operating environment of the large model platform, specifically the temperature, humidity, pressure, etc.

[0124] Step S802: Evaluate and process the system information and environmental information to obtain status information.

[0125] In this embodiment, the operating status of the large model platform is determined by evaluating and processing the system information and environmental information.

[0126] In this embodiment, the status information includes normal and abnormal.

[0127] Step S803: Adjust the large model platform according to the status information.

[0128] In this embodiment, if the status information is abnormal, the parameters of the large model platform are adjusted or an alarm is sent to maintenance personnel.

[0129] Furthermore, the present application also provides a data processing device based on a large model.

[0130] Figure 9 This is a structural diagram of a data processing device based on a large model according to an embodiment of the present application. Figure 9 As shown, the data processing device based on the large model in the embodiment of the present application mainly includes an acquisition module 21, an analysis module 22, a generation module 23 and a processing module 24. In some embodiments, one or more of the acquisition module 21, the analysis module 22, the generation module 23 and the processing module 24 can be combined into one module. In some embodiments, the acquisition module 21 can be configured to acquire problem data. The analysis module 22 can be configured to acquire tools and processing procedures based on the problem data; wherein the tools include external software and a preset artificial intelligence model. The generation module 23 can be configured to generate a problem analysis program based on the tools and the processing procedure. The processing module 24 can be configured to input the problem data into the problem analysis program to obtain a problem analysis result.

[0131] Those skilled in the art will appreciate that all or part of the processes in the methods for implementing the above-mentioned embodiments of the present invention may also be accomplished by instructing the relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, it may implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal, and software distribution medium capable of carrying the computer program code. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0132] Furthermore, the present application also provides an electronic device.

[0133] Figure 10 1 is a structural diagram of an electronic device according to an embodiment of the present application. Figure 10 As shown, in an embodiment of an electronic device according to the present application, the electronic device includes a memory 31 and a processor 32, the memory 31 stores a computer program, and the processor 32 is configured to execute the above-mentioned Figures 2 to 8 The program for the data processing method based on a large model of the illustrated embodiment includes, but is not limited to, a program for executing the data processing method based on a large model of the aforementioned method embodiment. For ease of illustration, only the portion relevant to the embodiment of the present application is shown. For specific technical details not disclosed, please refer to the method section of the embodiment of the present application. The electronic device can be a control device formed by various electronic devices.

[0134] This method is widely applicable to whole-house intelligent digital control application scenarios such as smart homes, smart homes, smart home device ecosystems, and smart residential ecosystems. The network may include, but is not limited to, at least one of the following: a wired network or a wireless network. The wired network may include, but is not limited to, at least one of the following: a wide area network (WAN), a metropolitan area network (MAN), or a local area network (LAN). The wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity) or Bluetooth. Terminal devices may include, but are not limited to, PCs, mobile phones, tablets, smart air conditioners, smart range hoods, smart refrigerators, smart ovens, smart stoves, smart washing machines, smart water heaters, smart laundry appliances, smart dishwashers, smart projectors, smart TVs, smart clothes drying racks, smart curtains, smart audio and video equipment, smart sockets, smart speakers, smart fresh air equipment, smart kitchen and bathroom appliances, smart bathroom appliances, smart sweeping robots, smart window cleaning robots, smart mopping robots, smart air purifiers, smart steamers, smart microwave ovens, smart kitchen appliances, smart purifiers, smart water dispensers, and smart door locks.

[0135] Furthermore, the present application also provides a computer-readable storage medium.

[0136] In a computer-readable storage medium embodiment according to the present application, the computer-readable storage medium may be configured to store a computer program that executes the above method. Figures 2 to 8 The program of the data processing method based on the large model of the embodiment shown can be loaded and run by the processor to implement the above-mentioned data processing method based on the large model. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-transitory computer-readable storage medium.

[0137] Furthermore, it should be understood that since the configuration of each module is merely for the purpose of illustrating the functional units of the apparatus of the present invention, the physical devices corresponding to these modules may be the processor itself, or a portion of the software in the processor, a portion of the hardware, or a combination of software and hardware. Therefore, the number of modules in the figure is merely illustrative.

[0138] Those skilled in the art will appreciate that the various modules in the device can be adaptively split or merged. Such splitting or merging of specific modules does not cause the technical solution to deviate from the principles of the present invention. Therefore, the technical solutions after splitting or merging will fall within the scope of protection of the present invention.

[0139] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A data processing method based on a large model, characterized in that: include: Get problem data; Obtaining tools and processing procedures based on the problem data; wherein the tools include external software and preset artificial intelligence models; generating a problem analysis program according to the tool and the processing flow; The problem data is input into the problem analysis program to obtain a problem analysis result.

2. The data processing method based on a large model according to claim 1, characterized in that: The tool and processing flow for obtaining the problem data include: Decomposing the problem data to obtain multiple sub-problems; According to the multiple sub-problems, obtaining preset artificial intelligence models and external software corresponding to the multiple sub-problems; A processing flow is obtained based on the multiple sub-problems, the preset artificial intelligence model corresponding to each sub-problem, and the external software.

3. The data processing method based on a large model according to claim 1, characterized in that: Generating a problem analysis program according to the tool and the processing flow includes: According to the processing flow, a calling sequence of the tools is obtained; According to the calling sequence, a problem analysis program is obtained.

4. The data processing method based on a large model according to claim 1, characterized in that: The step of inputting the problem data into the problem analysis program to obtain a problem analysis result includes: Inputting the question data into the question analysis program to obtain a plurality of answer data; Performing correlation analysis on the plurality of answer data and the question data respectively to obtain correlation values ​​of the plurality of answer data; Obtain target answer data with a correlation value greater than a preset threshold; The target answer data are integrated and processed to obtain a question analysis result.

5. The data processing method based on a large model according to claim 1, characterized in that: Obtaining the preset artificial intelligence model includes: Get text data; Preprocessing the text data to obtain training data; Classify the training data to obtain training data of different categories; According to the different categories of training data, multiple large models are trained separately to obtain a preset artificial intelligence model.

6. The data processing method based on a large model according to claim 1, characterized in that: The method further comprises: Obtain user needs; Adjusting the problem analysis program according to the user's needs to obtain an adjusted program; The problem data is input into the adjustment program to obtain analysis results.

7. The data processing method based on a large model according to claim 1, characterized in that: The method further comprises: Obtain system information and environment information of the large model platform; Evaluating and processing the system information and environmental information to obtain status information; The large model platform is adjusted according to the status information.

8. A data processing device based on a large model, characterized in that: include: Acquisition module, used to obtain problem data; An analysis module, configured to obtain tools and processing procedures based on the problem data; wherein the tools include external software and a preset artificial intelligence model; A generation module, configured to generate a problem analysis program based on the tool and the processing flow; The processing module is used to input the problem data into the problem analysis program to obtain a problem analysis result.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 7 when executed.

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