Data asset value evaluation method and system based on large language model

Abstract

The invention relates to a data asset value evaluation method and system based on a large language model, and the method comprises the steps: data collection: collecting multi-source data related to data assets, the multi-source data comprising structured data and unstructured data; identifying and constructing features related to data asset values to obtain key features; constructing an evaluation model based on a large language model based on the key features, and carrying out quantitative evaluation on the value of the data assets; and determining a data asset value according to an evaluation result, and loading the data asset value into a table. The method aims at solving the problem that a traditional data asset evaluation method is limited in the aspects of accuracy, efficiency and adaptability, the accuracy and efficiency of data asset value evaluation are improved through a large language model, deeper and more comprehensive data analysis is provided, and the data asset value evaluation efficiency is improved. Meanwhile, the invention further provides a general evaluation framework capable of adapting to different industries and data types, and a customized and automatic evaluation process is supported.

Description

Technical Field

[0001] The present invention relates to the technical field of large language models, and in particular to a data asset value assessment method and system based on a large language model. Background Art

[0002] In the digital economy era, data, as a key asset, plays an irreplaceable role in the strategic decision-making of enterprises and organizations. Data assets include but are not limited to user information, transaction records, market analysis, etc. They can be used to improve operational efficiency, drive product innovation and optimize customer experience. With the rapid development of technologies such as big data, cloud computing and artificial intelligence, the value of data assets has become increasingly prominent and has become an important part of the core competitiveness of enterprises.

[0003] However, compared with physical assets, the particularity of data assets is that their value is not easy to quantify. Traditional asset valuation methods often have difficulty adapting to the immateriality and dynamic characteristics of data assets. Therefore, developing a scientific and efficient data asset value assessment method is of great significance for fully exploring and utilizing the potential of data assets.

[0004] Currently, data asset value assessment mainly relies on traditional assessment methods such as financial indicators, market comparison method or cost method. These methods often ignore the unique attributes of data, such as diversity, timeliness and relevance. In addition, the value of data is also affected by data quality, data scarcity and application scenarios, which are difficult to fully reflect in traditional assessment models. Therefore, existing methods often have certain limitations when assessing data assets and it is difficult to accurately reflect the true value of data assets. Summary of the invention

[0005] In order to address the deficiencies of the prior art, the present invention proposes an efficient data asset value assessment method, which improves the accuracy and efficiency of data asset assessment through a large language model.

[0006] To achieve the above objectives, the technical solutions adopted by the present invention include:

[0007] A data asset value assessment method based on a large language model, characterized by comprising:

[0008] S1. Data collection: collecting multi-source data related to data assets, where the multi-source data includes structured data and unstructured data;

[0009] S2. Identify and construct features related to the value of data assets to obtain key features;

[0010] S3. Based on the key features, an evaluation model based on a large language model is constructed to quantitatively evaluate the value of data assets;

[0011] S4. Determine the value of data assets based on the evaluation results and enter the data asset value into the table.

[0012] Furthermore, the data collection step also includes data preprocessing, which specifically includes:

[0013] Data cleaning, removing irrelevant or erroneous data and handling missing values;

[0014] Data transformation, converting unstructured data into structured data;

[0015] Standardization processing: standardize data from different sources and formats.

[0016] Furthermore, the identification and construction of features related to the value of data assets to obtain key features include:

[0017] Use large language models to analyze data and extract key features related to the value of data assets;

[0018] The key features are analyzed in depth using statistical analysis and machine learning algorithms to obtain the correlation between the key features and the changing trends of the key features.

[0019] Furthermore, the evaluation model based on the large language model is constructed based on the key features to quantitatively evaluate the value of the data assets, including:

[0020] Select a large language model and select an adaptation algorithm according to the key features;

[0021] Adjust model parameters and optimize the model through cross-validation and iterative optimization. The adjustment of model parameters includes adjusting network structure, learning rate, and loss function.

[0022] Furthermore, after the step of constructing an evaluation model based on a large language model based on the key features to quantitatively evaluate the value of the data assets, the step further includes:

[0023] Use statistical indicators to evaluate the model and adjust the model structure and parameters based on the evaluation results.

[0024] Furthermore, the statistical indicators include accuracy, recall rate, and F1 score.

[0025] The present invention also relates to a data asset value assessment system based on a large language model, which is characterized by comprising:

[0026] A data collection module, used for data collection, collecting multi-source data related to data assets, wherein the multi-source data includes structured data and unstructured data;

[0027] Feature extraction module, used to identify and construct features related to the value of data assets to obtain key features;

[0028] A model building module, used to build an evaluation model based on a large language model based on the key features, and to quantitatively evaluate the value of data assets;

[0029] The result output module is used to determine the value of data assets based on the evaluation results and enter the data asset value into a table.

[0030] The present invention also relates to a computer-readable storage medium, characterized in that a computer program is stored on the storage medium, and the computer program implements the above method when executed by a processor.

[0031] The present invention also relates to an electronic device, characterized in that it comprises a processor and a memory;

[0032] The memory is used to store program instructions;

[0033] The processor is used to execute the above method by calling program instructions.

[0034] The present invention also relates to a computer program product, comprising a computer program and / or instructions, characterized in that the computer program and / or instructions implement the steps of the above method when executed by a processor.

[0035] The beneficial effects of the present invention are:

[0036] The present invention aims to provide a data asset value assessment method based on a large language model, which is used to address the limitations of traditional data asset assessment methods in terms of accuracy, efficiency and adaptability. Through the large language model, the accuracy and efficiency of data asset value assessment are improved, and more in-depth and comprehensive data analysis is provided. At the same time, the present invention also provides a general assessment framework that can adapt to different industries and data types, and supports customized and automated assessment processes.

[0037] Traditional data asset valuation methods often find it difficult to accurately quantify the intangible value of data, such as the timeliness, uniqueness and relevance of data. The present invention, by introducing a large language model, can more deeply understand and analyze the data content, thereby providing a more accurate value assessment; as the amount of data continues to grow, traditional methods are less efficient in processing large-scale and diverse data. The present invention uses a large language model to effectively process and analyze large-scale and complex data sets; traditional evaluation methods are time-consuming and require a lot of manual intervention. The present invention greatly improves the efficiency of evaluation through automated data processing and analysis processes; the present invention provides a flexible evaluation framework that can be adjusted and optimized according to different application scenarios and data characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in describing the embodiments of the present application are briefly introduced below.

[0039] Figure 1 This is a flow chart of a method for evaluating the value of data assets based on a large language model according to the present invention;

[0040] Figure 2 This is a schematic diagram of the structure of a data asset value assessment system based on a large language model according to the present invention;

[0041] Figure 3 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. It should be understood that the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments, and these embodiments are only used to illustrate the present application and are not used to limit the scope of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0043] The theoretical basis and related technologies of the present invention are explained below.

[0044] Large Language Models (LLMs) are advanced artificial intelligence models based on deep learning and natural language processing techniques. These models learn the deep structure and semantic regularities of language by training on large-scale text data. They are usually based on neural network architectures, such as the Transformer model, which can handle long-range dependencies and complex language patterns.

[0045] In terms of development history, large language models have evolved from early simple network architectures (such as RNN and LSTM) to today's highly complex models (such as GPT and BERT series). With the improvement of computing power and the increase in data volume, the scale and performance of these models are also constantly improving, and they can perform well in a variety of language tasks.

[0046] In addition, regarding the theory of data asset value assessment, data asset value assessment is a highly comprehensive discipline that covers theories from multiple fields such as economics, management, and information science. The core of assessing the value of data assets lies in determining the economic value of data, which usually involves quantitative analysis of the scarcity, uniqueness, application potential, and economic benefits of data. In theory, this assessment aims to convert the intangible value of data assets into measurable economic value.

[0047] In practice, the valuation of data assets often relies on the estimation of their direct benefits, cost savings, risk reduction and future potential value, which requires comprehensive consideration of multiple factors such as data quality, relevance, timeliness and reliability.

[0048] Related technologies include the combination of big language models and data asset value assessment, which is an innovative integration of information technology and financial assessment. In this process, big language models not only serve as tools for data processing and analysis, but also provide the ability to deeply understand the value of data.

[0049] In the field of financial evaluation, the application of related technologies includes but is not limited to text analysis, trend prediction, market sentiment analysis, etc. For example, large language models can evaluate the market influence and potential value of specific data assets by analyzing corporate financial reports, market news, and social media content. The application of this technology can improve evaluation efficiency while providing a more in-depth and comprehensive analysis perspective.

[0050] like Figure 1 As shown, the embodiment of the present invention discloses a data asset value assessment method based on a large language model, and the steps of the process include:

[0051] Step S1. Data collection: collecting multi-source data related to data assets, wherein the multi-source data includes structured data and unstructured data.

[0052] In the embodiment of the present invention, data collection includes internal data collection and external data integration. The internal data includes customer transaction records and loan application data within the bank, and the external data includes market data (such as market trends, competitor analysis), public data (such as social media data, news reports), etc.

[0053] Specifically, multi-source data can be divided into structured data (such as database tables) and unstructured data (such as text and images) according to the data type.

[0054] After the data collection step, data preprocessing is also included, including:

[0055] Data cleaning, removing irrelevant or erroneous data and handling missing values;

[0056] Data transformation, converting unstructured data into structured data;

[0057] Standardization processing: standardize data from different sources and formats.

[0058] Data preprocessing includes removing noise, processing missing values, data normalization, etc. In the present invention, data preprocessing can ensure the accuracy and consistency of data.

[0059] Step S2. Identify and construct features related to the value of data assets to obtain key features.

[0060] In the embodiment of the present invention, the characteristics related to the value of data assets include relevance, timeliness, influence, etc.

[0061] By using large language models to analyze text data, we can obtain key features related to the value of data assets, including sentiment tendencies, trend changes, etc.

[0062] Statistical analysis and machine learning algorithms are used to conduct in-depth analysis of the extracted features, obtain the correlation between the key features and the changing trends of the key features, and explore the data value and potential influencing factors.

[0063] Step S3: construct an evaluation model based on a large language model based on the key features to quantitatively evaluate the value of the data assets.

[0064] Specifically, an evaluation model based on a large language model is constructed based on the key features to quantitatively evaluate the value of data assets, including:

[0065] Select a large language model and select an adaptation algorithm according to the key features;

[0066] Adjust model parameters and optimize the model through cross-validation and iterative optimization. The adjustment of model parameters includes adjusting network structure, learning rate, and loss function.

[0067] In an embodiment of the present invention, a natural language processing model such as BERT may be selected.

[0068] In the embodiment of the present invention, the use of the cross-validation method can avoid overfitting and optimize model performance. By continuously iterative training and adjusting model parameters, the model can achieve the optimal evaluation effect.

[0069] Preferably, after the step of constructing an evaluation model based on a large language model based on the key features and quantitatively evaluating the value of the data assets, the method further includes:

[0070] The model is evaluated using statistical indicators, and the structure and parameters of the model are adjusted according to the evaluation results. The statistical indicators include accuracy, recall rate, and F1 score.

[0071] In an embodiment of the present invention, this step provides a method for evaluating a model, and the method further includes:

[0072] Use independent test data sets to evaluate the accuracy and generalization ability of the model, and test the performance of the model in actual data asset evaluation scenarios to ensure the practicality and reliability of the evaluation results;

[0073] Adjust the model structure and parameters according to the evaluation results to improve the evaluation efficiency and accuracy of the model. Consider using ensemble learning or multi-model fusion technology to further improve the stability and accuracy of the model.

[0074] Step S4. Determine the data asset value based on the evaluation results and enter the data asset value into a table.

[0075] In the embodiment of the present invention, by combining key feature analysis and industry standards, the value of data assets can be estimated, and ultimately a comprehensive evaluation report can be generated to display the evaluation results and key findings.

[0076] Through the method described in the present invention, an efficient and accurate data asset value assessment system based on a large language model can be constructed. This system can comprehensively process and analyze data from different sources, provide in-depth and comprehensive value assessments, and thus help companies better understand and utilize their data assets. At the same time, through continuous model training and optimization, the timeliness and accuracy of the assessment results can be ensured to adapt to the rapidly changing market environment and data conditions. In addition, the use of scientific and rigorous assessment standards and methods can also help improve the transparency and credibility of the assessment process and provide strong data support for decision makers.

[0077] like Figure 2 As shown, the embodiment of the present invention discloses a data asset value assessment system based on a large language model, and its structure includes:

[0078] A data collection module, used for data collection, collecting multi-source data related to data assets, wherein the multi-source data includes structured data and unstructured data;

[0079] Feature extraction module, used to identify and construct features related to the value of data assets to obtain key features;

[0080] A model building module, used to build an evaluation model based on a large language model based on the key features, and to quantitatively evaluate the value of data assets;

[0081] The result output module is used to determine the value of data assets based on the evaluation results and enter the data asset value into a table.

[0082] By using this system, the above-mentioned operation processing method can be executed and the corresponding technical effects can be achieved.

[0083] The present invention provides an embodiment for application to a bank to assess the value of its huge customer data assets. By using the present invention, the bank can optimize risk management, enhance customer service, and provide data support for new product development through in-depth analysis of customer data assets. The implementation process is as follows:

[0084] Step 1: Collect various types of data including customers’ credit records, transaction histories, online behavior logs, and customer service interaction records. Ensure that data collection complies with privacy regulations and data protection standards.

[0085] Preprocess the data, clean the collected data, and remove invalid or irrelevant data; standardize unstructured data, such as converting text data into a format that can be analyzed.

[0086] Step 2: Use large language models to process text data, such as customer transaction descriptions, customer service communications, etc., to extract key features; analyze key indicators such as customer behavior patterns, preference trends, and credit risk.

[0087] Step 3: Establish a valuation model. Combining the characteristics of banking business and customer data, build a valuation model that can estimate the value of customer data assets and use machine learning algorithms to further analyze the relationship between data characteristics and asset value.

[0088] Train and optimize the model. Train the model on the bank's historical data set to adjust parameters and improve the accuracy of the assessment; update the model regularly to reflect the latest market and customer data changes.

[0089] Step 4: Determine the value of data assets based on the evaluation results, conduct a value assessment on the current customer database, identify high-value customer groups and key value drivers; analyze the changes in data asset value over time and predict future trends.

[0090] Apply assessment results to risk management, marketing strategies and product development; provide data-supported decision-making recommendations to help banks optimize resource allocation and business strategies.

[0091] Through this embodiment, banks will be able to more accurately assess the value of their customer data assets, gain a deeper understanding of customer needs and behavior patterns, and thus more effectively formulate market strategies and optimize risk management. In addition, this large language model-based evaluation method can provide continuous data asset value analysis, helping banks adapt to market changes and adjust business strategies in a timely manner. The present invention can greatly improve the competitiveness of banks and promote their business growth and innovative development. At the same time, the use of this method can also enhance customer satisfaction and loyalty, and further increase the market share and brand value of banks by providing more personalized services and products.

[0092] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps in the method in the above embodiment, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps in the method in the above embodiment are implemented.

[0093] The embodiment of the present invention further provides an electronic device for executing the above method, as a device for implementing the method, and its structure is as follows: Figure 3 As shown, the electronic device 300 includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one. It is understandable that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the specific structure of the electronic device 300. In other embodiments of the present application, the electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware. Optionally, the electronic device may also include a display screen 305 for displaying images, or receiving user operation instructions when necessary.

[0094] The processor 301 is applied in the embodiment of the present application to implement the method shown in the above method embodiment. The transceiver 304 may include a receiver and a transmitter. The transceiver 304 is applied in the embodiment of the present application to implement the function of the electronic device of the embodiment of the present application communicating with other devices when executed.

[0095] Processor 301 may be a CPU (Central Processing Unit), a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0096] The processor 301 may also include one or more processing units, for example, the processor 301 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processor (NPU). Among them, different processing units may be independent devices or integrated into one or more processors. Among them, the controller may be the nerve center and command center of the electronic device 300. The controller may generate an operation control signal according to the instruction opcode and the timing signal to complete the control of fetching and executing instructions. A memory may also be set in the processor 301 for storing instructions and data. In some embodiments, the memory in the processor 301 is a high-speed cache memory. The memory may store instructions or data that the processor 301 has just used or circulated. If the processor 301 needs to use the instruction or data again, it may be directly called from the memory. Repeated access is avoided, the waiting time of the processor 301 is reduced, and the efficiency of the system is improved.

[0097] The processor 301 can run the method provided in the embodiment of the present application. The processor 301 can include different devices. For example, when a CPU and a GPU are integrated, the CPU and the GPU can cooperate to execute the method provided in the embodiment of the present application, for example, part of the algorithm in the method is executed by the CPU, and another part of the algorithm is executed by the GPU to obtain faster processing efficiency.

[0098] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0099] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or may include a high-speed random access memory, or may include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0100] Optionally, the memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the method provided in any embodiment of the present application.

[0101] The memory 303 can be used to store computer executable program codes, which include instructions. The processor 301 executes various functional applications and data processing of the electronic device 300 by running the instructions stored in the memory 303. The memory 303 may include a program storage area and a data storage area. Among them, the program storage area may store the code of the operating system, application program, etc. The data storage area may store data created during the use of the electronic device 300 (such as images, videos, etc. collected by the camera application), etc.

[0102] The memory 303 may also store one or more computer programs corresponding to the method provided in the embodiment of the present application. The one or more computer programs are stored in the above-mentioned memory 303 and are configured to be executed by the one or more processors 301. The one or more computer programs include instructions, and the above-mentioned instructions can be used to execute each step in the above-mentioned corresponding embodiment.

[0103] Of course, the code of the method provided in the embodiment of the present application can also be stored in an external memory. In this case, the processor 301 can run the code of the method stored in the external memory through the external memory interface, and the processor 301 can control the running process.

[0104] The display screen 305 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 300 may include 1 or N display screens 305, where N is a positive integer greater than 1. The display screen 305 can be used to display information input by a user or information provided to a user and various graphical user interfaces (GUI). For example, the display screen 305 can display photos, videos, web pages, or files.

[0105] The electronic device provided in the embodiment of the present application is applicable to any embodiment of the above method. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, which will not be repeated here.

[0106] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0108] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0110] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A data asset value assessment method based on a large language model, characterized in that: include: S1. Data collection: collecting multi-source data related to data assets, where the multi-source data includes structured data and unstructured data; S2. Identify and construct features related to the value of data assets to obtain key features; S3. Based on the key features, an evaluation model based on a large language model is constructed to quantitatively evaluate the value of data assets; S4. Determine the value of data assets based on the evaluation results and enter the data asset value into the table.

2. The method according to claim 1, characterized in that The data collection step also includes data preprocessing, which specifically includes: Data cleaning, removing irrelevant or erroneous data and handling missing values; Data transformation, converting unstructured data into structured data; Standardization processing: standardize data from different sources and formats.

3. The method according to claim 1, characterized in that The identification and construction of features related to the value of data assets to obtain key features include: Use large language models to analyze data and extract key features related to the value of data assets; The key features are analyzed in depth using statistical analysis and machine learning algorithms to obtain the correlation between the key features and the changing trends of the key features.

4. The method according to claim 1, characterized in that The evaluation model based on the large language model is constructed based on the key features to quantitatively evaluate the value of the data assets, including: Select a large language model and select an adaptation algorithm based on the key features; Adjust model parameters and optimize the model through cross-validation and iterative optimization. The adjustment of model parameters includes adjusting network structure, learning rate, and loss function.

5. The method according to claim 1, characterized in that After the step of constructing an evaluation model based on a large language model based on the key features to quantitatively evaluate the value of the data assets, the method further includes: Use statistical indicators to evaluate the model and adjust the model structure and parameters based on the evaluation results.

6. The method according to claim 5, characterized in that The statistical indicators include accuracy, recall, and F1 score.

7. A data asset value assessment system based on a large language model, characterized in that: include: A data collection module, used for data collection, collecting multi-source data related to data assets, wherein the multi-source data includes structured data and unstructured data; Feature extraction module, used to identify and construct features related to the value of data assets to obtain key features; A model building module, used to build an evaluation model based on a large language model based on the key features, and to quantitatively evaluate the value of data assets; The result output module is used to determine the value of data assets based on the evaluation results and enter the data asset value into a table.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store program instructions; The processor is configured to execute the method according to any one of claims 1 to 6 by calling program instructions.

10. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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