Digital asset risk information processing method and system

Abstract

The present invention provides a digital asset risk information processing method and system. The method comprises: acquiring web2 multimodal source data (including but not limited to various modalities such as text, images, speech, and videos) and web3 source data; converting web2 non-text source data into web2 text processing data; filtering web2 text source data and the web2 text processing data to obtain web2-related risk information involving a digital asset; acquiring web2 statistical information from the web2 text source data and the web2 text processing data; extracting from the web3 source data web3-related risk information involving the digital asset; inputting the web2 statistical information and the web3-related risk information into a first machine learning model to obtain a risk score involving the digital asset and a corresponding risk information feature; and inputting the filtered web2 text data and the web3-related risk information into a second machine learning model to obtain a risk analysis and prevention and control strategy involving the digital asset.

Description

Digital asset risk information processing method and system

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese invention patent application No. 202311553089.X filed on November 20, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present invention relates to an information processing method and system, and in particular to a digital asset risk information processing method and system. Background Art

[0004] While the probability of a digital asset risk event may be low, once it occurs, it can have a significant impact on asset holders. For example, a risk event in the business sector could lead to corporate bankruptcy or a stock market crash. Early warning signals before a risk event occurs are often overlooked, missing the window for risk prevention and resulting in significant losses. Summary of the Invention

[0005] In one aspect, the present invention provides a method for processing digital asset risk information. According to one embodiment, the method includes obtaining web2 text source data, web2 non-text source data, and web3 source data; converting the web2 non-text source data into web2 text processed data; filtering the web2 text source data and the web2 text processed data to obtain web2-related risk information related to the digital asset; obtaining web2 statistical information from the web2 text source data and the web2 text processed data; extracting web3-related risk information related to the digital asset from the web3 source data; and inputting the web2 statistical information and the web3-related risk information into a first machine learning model to obtain a risk score related to the digital asset and corresponding risk information features.

[0006] Preferably, the method further comprises, after obtaining the risk score related to the digital asset, converting the risk score into a graph, and displaying the graph.

[0007] Preferably or additionally, if the risk score exceeds a preset threshold, the method further comprises generating and issuing risk warning information.

[0008] According to another embodiment, the digital asset risk information processing method of the present invention includes obtaining web2 text source data, web2 non-text source data, and web3 source data; converting the web2 non-text source data into web2 text processing data; filtering the web2 text source data and the web2 text processing data to obtain web2-related risk information involving the digital asset; embedding the web2-related risk information into a prompt template to generate first-level prompt information; extracting web3-related risk information involving the digital asset from the web3 source data; inputting the first-level prompt information into a second machine learning model to summarize key points; merging the web3-related information with the key points to generate second machine learning model prompt information; and generating risk insight information involving the digital asset based on the second machine learning model prompt information.

[0009] In another aspect, the present invention provides a digital asset risk information processing system. According to one embodiment, the digital asset risk information processing system of the present invention includes an information processing unit and a non-volatile information storage medium coupled to the information processing unit. The non-volatile information storage medium stores instructions that cause the information processing unit to perform the following steps: obtaining web2 text source data, web2 non-text source data, and web3 source data; converting the web2 non-text source data into web2 text processing data; filtering the web2 text source data and the web2 text processing data to obtain web2-related risk information related to the digital asset; obtaining web2 statistical information from the web2 text source data and the web2 text processing data; extracting web3-related risk information related to the digital asset from the web3 source data; and inputting the web2 statistical information and the web3-related risk information into a first machine learning model to obtain a risk score related to the digital asset.

[0010] According to another embodiment, a digital asset risk information processing system of the present invention includes an information processing unit and a non-volatile information storage medium coupled to the information processing unit. The non-volatile information storage medium stores instructions that cause the information processing unit to perform the following steps: obtaining web2 text source data, web2 non-text source data, and web3 source data; converting the web2 non-text source data into web2 text processed data; filtering the web2 text source data and the web2 text processed data to obtain web2-related risk information related to the digital asset; embedding the web2-related risk information into a prompt template to obtain first prompt information; extracting web3-related risk information related to the digital asset from the web3 source data, inputting the first prompt information into a second machine learning model to summarize key points; merging the web3-related information with the key points to generate second machine learning model prompt information; and generating risk insight information related to the digital asset based on the second machine learning model prompt information.

[0011] In some embodiments of the method and / or system according to the present invention, the web2-related risk information includes at least one of the following: negative tweets, negative comments, and negative reports involving the digital asset.

[0012] In some embodiments of the method and / or system according to the present invention, filtering the web2 text source data and the web2 text processed data comprises applying at least one of the following filters: sentiment score negativity, popularity index, authenticity index, time range.

[0013] In some embodiments of the method and / or system according to the present invention, extracting web3-related risk information involving the digital asset from the web3 source data includes primary feature extraction and secondary feature extraction, wherein the primary feature extraction includes directly generating web3-related risk information from the on-chain transaction records, and the secondary feature extraction includes detecting abnormal transaction information from the on-chain transaction records, as well as statistical information based on the abnormal transaction information. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Various embodiments of the present invention are described below with reference to the accompanying drawings, in which:

[0015] FIG1 is a flowchart of a method for processing digital asset risk information according to an embodiment of the present invention;

[0016] FIG2 is a flowchart of a method for processing digital asset risk information according to another embodiment of the present invention;

[0017] FIG3 is a framework diagram of a digital asset risk information processing system according to one embodiment of the present invention;

[0018] FIG4 is a flow chart of a digital asset risk information processing system and method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention provides a method and system for obtaining digital asset risk information and risk indicators with reference value based on data and information obtained from web2 data sources and web3 data sources through artificial intelligence methods.

[0020] The technical solution provided by this invention automates the collection, organization, aggregation, development of risk indicators, and presentation of risk insights. The method and system according to this invention aggregate structured and unstructured data from real-time data sources, including web2 and web3, to provide integrated risk datasets. Based on these datasets, machine learning techniques, such as large language models (LLMs) or graph neural networks (GNNs), are combined to provide more in-depth digital asset risk indicators.

[0021] On one hand, the technical solution provided by the present invention uses a dedicated machine learning model to extract risk indicators from data of various structures to obtain an overall risk score for digital asset risk identification, prediction, and management. The results of the information processing can also be presented in the form of visualizations such as charts. On the other hand, the technical solution provided by the present invention uses a large language model to summarize the key points of negative feedback in online information to understand the mainstream negative information about entities or assets. Subsequently, the technical solution provided by the present invention uses a large language model to generate and provide digital asset risk insights, such as potential risks, comparable historical events, and risk mitigation action strategies, by combining key points in web2 data and web3 statistical features extracted from web3 data sources. In some embodiments, the method and system according to the present invention provide at least one of the following solutions: (1) Notification and warning function: When the risk score obtained exceeds a preset threshold, the method and system according to the present invention can provide risk warning notification / information to assist users in taking corresponding measures and actions to reduce financial losses; (2) Visualization function: The method and system according to the present invention can present digital risk information in the form of a chart for easy user access; (3) Understanding function: Users can view key negative arguments, potential risks, comparable events, and risk response and mitigation strategies to obtain actionable risk warnings and risk prevention and control insights.

[0022] The present invention provides a method and system for integrating web2 and web3 data processing flows to comprehensively assess an entity's digital asset risk. The method and system also provide for converting multimodal data (including but not limited to text, images, voice, video, audio, and other modalities) into text format and employing natural language processing techniques to filter relevant digital asset risk feeds from large amounts of data with diverse structures. The method and system process heterogeneous data structures and complete online information, thereby providing users with more valuable insights into digital asset risk. By utilizing big data analysis and real-time detection technologies, the method and system can automatically monitor digital asset risk-related information and detect potential risks. The specific machine learning model employed by the method and system can extract digital asset risk indicators with varying update frequencies from web2 and web3 data sources, thereby improving the model's ability to obtain more comprehensive risk-related information. By combining prompt engineering with a general large-scale language model to convert complex web2 text data into a data format that can be easily combined with on-chain structured data in web3, the method and system can provide more comprehensive insights into digital asset risk.

[0023] According to one embodiment, as shown in FIG1 , the digital asset risk information processing method 100 of the present invention includes, at step 110, selecting a digital asset to be risk-assessed. The digital asset can be an enterprise-level digital asset of interest to the user, or a specific digital asset itself, such as "FTX," a digital asset derivatives trading platform, or "Bored Ape Yacht Club." At step 120, multimodal web2 data related to the selected digital asset is extracted from a web2 data source. This extraction step 120 can be performed using methods such as web crawlers, application programming interfaces (APIs), or direct database connections to obtain relevant information and data from the web2 data source based on information such as keywords and digital asset names.

[0024] Web2 data sources may include, but are not limited to, social media platforms such as Twitter, WeChat, Facebook, and Reddit, public websites such as www.bbc.com and www.coindesk.com, search engines such as Google, Baidu, and Bing, public chains such as Bitcoin, Ethereum, BSC, and Solana, and / or market venues such as OpenSea, Rarible, and SuperRare, etc.

[0025] The extracted web2 data can be structured data, such as tabular data, or unstructured data, such as text, images, video, audio, etc. Unstructured data can be processed using artificial intelligence technologies such as natural language processing (NLP) and combined with structured data for analysis.

[0026] At step 130, method 100 converts the extracted web2 non-text source data into web2 processed text data and filters the web2 text source data and the web2 processed text data to obtain web2-related risk information related to the selected digital asset. The web2-related risk information may include negative tweets, negative comments, and / or negative reports related to the digital asset. This filtering step may be performed using at least one of the following filters: sentiment score negativity, popularity index, authenticity index, time range, etc.

[0027] In step 140 , the method 100 employs natural language processing technology to perform feature engineering and obtain web2 statistical information from the web2 text source data and the web2 text processed data, such as obtaining statistical information related to negative tweets.

[0028] At step 150, method 100 extracts web3-related risk information related to the selected digital asset from the web3 source data, such as on-chain transaction records. Specifically, information extraction step 150 includes a primary feature extraction step 152 and a secondary feature extraction step 154. For information that can be directly obtained from on-chain transaction records, such as information related to market volatility risk, executing primary feature extraction step 152 can extract web3-related risk information for the digital asset. For more complex features, such as information related to wash trading (also known as virtual trading), information extraction step 150 further executes a secondary feature extraction step 154 ​​to obtain information that cannot be directly obtained from on-chain transaction records, such as in-depth information such as the number of wash trades.

[0029] In step 170, method 100 inputs the web2 statistical information and web3 related risk information into a first machine learning model, such as a dedicated machine learning model, to obtain a risk score involving the digital asset and a corresponding risk information feature.

[0030] Additionally, the method 100 may further include, at step 180 , generating corresponding visualization information, such as statistical charts or other types of visualization information, based on the risk score and the corresponding risk information features, and displaying the visualization information on a screen.

[0031] The method 100 may further include determining whether the obtained risk score exceeds a preset threshold at step 190. If so, the method 100 may generate and issue a risk warning message at step 192. If not, the method 100 may stop or repeat the previous steps.

[0032] According to another embodiment, as shown in FIG2 , the digital asset risk information processing method 200 of the present invention includes, at step 210, selecting a digital asset for risk assessment. The digital asset may be a digital asset of interest to the user, such as "FTX," "Bored Ape Yacht Club," or the like. At step 220, multimodal web2 data related to the selected digital asset is extracted from a web2 data source. This extraction step 220 can be performed using methods such as web crawlers, application programming interfaces (APIs), or direct database connections to obtain relevant information and data from the web2 data source based on information such as keywords and digital asset names.

[0033] Non-limiting web2 data sources may include, for example, social media platforms such as Twitter, WeChat, Facebook, Reddit, public websites such as www.bbc.com, www.coindesk.com, search engines such as Google, Baidu, Bing, public chains such as Bitcoin, Ethereum, BSC, Solana, and / or market venues such as OpenSea, Rarible, SuperRare, etc.

[0034] The extracted web2 data can be structured data, such as tabular data, or unstructured data, such as text, images, video, audio, etc. Unstructured data can be processed using artificial intelligence technologies such as natural language processing (NLP) and combined with structured data for analysis.

[0035] At step 230, method 200 converts the extracted web2 non-text source data into web2 processed text data, and filters the web2 text source data and the web2 processed text data to obtain web2-related risk information related to the selected digital asset. The web2-related risk information may include negative tweets, negative comments, and / or negative reports related to the digital asset. This filtering step may be performed using at least one of the following filters: sentiment score negativity, popularity index, authenticity index, time range, etc.

[0036] In step 240, method 200 embeds the web2-related risk information into a prompt template, such as a customized prompt template, to generate primary prompt information for input into a second machine learning model in a subsequent step.

[0037] At step 250, method 200 extracts web3-related risk information related to the selected digital asset from the web3 source data, such as on-chain transaction records. Specifically, information extraction step 250 includes a primary feature extraction step 252 and a secondary feature extraction step 254. For information that can be directly obtained from on-chain transaction records, such as information related to market volatility risk, executing primary feature extraction step 252 can extract web3-related risk information for the digital asset. For more complex features, such as information related to wash trading (also known as virtual trading), information extraction step 250 further executes a secondary feature extraction step 254 to obtain information that cannot be directly obtained from on-chain transaction records, such as in-depth information such as wash trade volume statistics.

[0038] At step 270, method 200 inputs the primary prompt information generated at step 240 into a second machine learning model to summarize the key points. The second machine learning model may be, for example, a general large language model.

[0039] In step 280, method 200 merges the key points obtained in step 270 with the web3 related information obtained in step 260 to generate second machine learning model prompt information.

[0040] At step 290, method 200 generates risk insight information related to the digital asset based on the second machine learning model prompt information obtained at step 280. The risk insight information may include, for example, at least one of the following: potential risk information, comparable historical event information, and risk prevention strategy or risk mitigation action strategy information.

[0041] In another aspect, the present invention provides a digital asset risk information processing system for extracting information related to digital asset risks from an information source and generating a risk indicator for the digital asset. In some embodiments, the digital asset risk indicator may include a risk score for a selected digital asset to be assessed, risk insights about the digital asset, such as potential risks, potential consequences of the risk, and risk prevention strategies or risk mitigation action plans.

[0042] According to one embodiment, the digital asset risk information processing system 300 of the present invention may include all or part of the modules shown in Figure 3. As shown in Figure 3, the system 300 includes a data storage module 310, a data processing module 320, a model module 330, and an application module 340.

[0043] The data storage module 310 may include an SQL database 312 and a non-SQL database 314. The SQL database 312 may include an Oracle database, a MySQL database, or the like, and is used to store structured table data. The non-SQL database 314 may include a MongoDB, Couchbase, or the like, and is used to store unstructured data such as text, images, video, and audio data.

[0044] The data processing module 320 may include multiple submodules. In some embodiments, the data processing module 320 may include a prompt engineering submodule 322 , an indicator formulation submodule 323 , a data collection submodule 324 , a multi-modal transformation submodule 325 , and a natural language processing submodule 326 .

[0045] The data acquisition submodule 324 can acquire data from stream processing platforms such as Apache Kafka or Amazon Kinesis and manage it using pre-defined data preprocessing workflow scripts and / or programs. These scripts can clean and perform exploratory data analysis, as well as perform quality checks on each data type. The preprocessed data can then be processed using SQL streams or non-SQL streams and stored in a database. For each stream processing platform, a separate preprocessing script and database storage can be provided for each data type (text, images, audio and video content, etc.).

[0046] The multi-mode conversion submodule 325 can convert each data type in the multimedia data into a text format. For example, an audio file can be converted into a plain text format for further processing.

[0047] The natural language processing submodule 326 analyzes text data and creates an index to filter out the information feeds most relevant to risk. Natural language processing methods employed may include sentiment analysis, topic modeling, named entity recognition, tokenization, stemming, lemmatization, and bag-of-words. Indexing may include scoring negative sentiment, popularity indexes, authenticity indexes, and more.

[0048] The prompt engineering submodule 322 can embed the filtered information feed into a preset prompt template to generate a first-level prompt information for input into the large language model. In one example, the first-level prompt information can be "Provide the key meaning of the following tweet: (specific content of the tweet)".

[0049] The indicator formulation submodule 323 can be a machine learning model for generating and / or calculating statistical features. For web2 data, features (e.g., statistical features) are primarily obtained by the natural language processing submodule 326 through additional processing, such as aggregation. For web3 data, steps including primary feature extraction and secondary feature extraction can be performed. For example, after detecting risky activities in on-chain transactions through primary feature extraction using a machine learning model such as a graph neural network, secondary feature extraction is performed to obtain final feature information.

[0050] The model module 330 may include two submodules: a general large-scale language model submodule 332 and a specialized machine learning model submodule 334. In some embodiments, the general large-scale language model submodule 332 may receive the prompt information from the prompt engineering submodule 322, namely, web2 key points and web3 statistical features, and generate risk insights. In some instances, the risk insights may include potential risks, potential outcomes (by reference to comparable historical events), and / or risk mitigation action strategies. In some embodiments, web2 key points and web3 statistical features may be stored in a general database and serve as input to the general large-scale language model submodule 332. In some embodiments, risk insights may be generated by utilizing commercial APIs (such as OpenAI GPT4) or by running a locally developed large-scale language model. In some embodiments, the specialized machine learning model submodule 334 may be customized to the desired use case to assess the overall risk level, for example, by calculating a risk score. In some embodiments, the input to the specialized machine learning model submodule 334 may be derived from web2 statistical features and web3 statistical features. In some embodiments, the output of the specialized machine learning model submodule 334 may be a risk score and corresponding key features.

[0051] The application module 340 may include multiple submodules. In some embodiments, the application module 340 includes a notification submodule 342, a visualization submodule 344, and an understanding submodule 346. The notification submodule 342 may monitor the risk score of the dedicated machine learning model and issue a risk warning message when the risk score is greater than a preset threshold. In some embodiments, the visualization submodule 344 may generate corresponding visual risk diagrams, such as charts and tables, based on the statistical functions / outputs of the dedicated machine learning model submodule 334. In some instances, the visualization submodule 104 may generate relevant risk charts and / or risk tables based on the risk score and / or corresponding key features. In some embodiments, the understanding submodule 346 may display the output of the general large language model submodule 332, such as risk insights, in the form of natural language.

[0052] Figure 4 illustrates a process 400 of a digital asset risk information processing method based on the system shown in Figure 3, corresponding to the embodiment described above with reference to Figures 1 and 2. As shown in Figure 4, corresponding to steps 120 and 220 of the methods shown in Figures 1 and 2, process 400 utilizes a data collection submodule 324, employing methods such as web crawlers, application programming interfaces, or direct database connections, to collect relevant data from a variety of different data sources based on keywords (e.g., the names of selected data assets to be risk assessed), and continuously tracks information and status updates. Data sources may include web2 and web3 data sources. Data sources may include, but are not limited to, the following web2 data sources: social media platforms 410, such as Twitter, WeChat, Facebook, and Reddit; search engines 420, such as Google, Baidu, and Bing; and public websites 430, such as www.bbc.com and www.coindesk.com. Data sources may also include, but are not limited to, the following web3 data sources: public chains 440, such as Bitcoin, Ethereum, BSC, and Solana; trading markets 450, such as OpenSea, Rarible, and SuperRare; and other data sources 460. The data can be multimodal. The data can include structured data, such as tabular data, as well as unstructured data, such as text, images, videos, and audio. Machine learning techniques such as the natural language processing submodule 325, image processing, or video analysis are used to analyze the unstructured data and combine the unstructured data with the structured data.

[0053] Corresponding to steps 130 and 230 of the method shown in Figures 1 and 2, process 400 uses a multimodal transformation submodule 325 to convert multimodal data, such as images, videos, audio, etc., into text data, and then filters a large amount of web2 data to obtain web2-related risk information involving the selected digital assets. The filter may include a variety of pre-set filters, including but not limited to sentiment score negativity, popularity index, authenticity index, time range, etc. Corresponding to steps 140 and 240 of the method shown in Figures 1 and 2, process 400 uses natural language processing technology to perform feature engineering and develop statistical features based on text data (e.g., the number of negative tweets, etc.). The statistical features can be used in subsequent machine learning models.

[0054] Corresponding to steps 150 and 250 of the method shown in Figures 1 and 2, process 400, through the indicator formulation submodule 323, employs a feature engineering module (e.g., feature formulation) and a deep neural network approach to perform primary and secondary feature extraction on the web3 data and obtain web3 statistical features. Furthermore, the steps for the web2 data and the steps for the web3 data can be performed in parallel, serially, and / or independently.

[0055] Corresponding to step 170 of the method shown in FIG1 , process 400 inputs the extracted statistical features into a dedicated machine learning model 334. This model outputs a risk score and corresponding risk information features. Subsequently, corresponding to step 180 of the method shown in FIG1 , process 400 generates risk-related charts and tables based on predefined templates via visualization submodule 344 and displays them on a display screen. If the risk score exceeds a preset threshold, notification submodule 342 may issue a risk notification and / or risk warning.

[0056] Independently of or in addition to the aforementioned process, corresponding to step 270 of the method shown in FIG2 , process 400 inputs the primary prompt information generated in step 240 into a general large-scale language model 332 to summarize key points and output web2 key points. The web2 key points are then merged with identified risk data from web3 statistical features (e.g., the number of wash trades). In some embodiments, the merged risk data may be stored in a general database and then prompted using the large-scale language model to generate risk insights in text, charts, or other suitable representations. Risk insights may include one or more of potential risks, historically similar events, and mitigation strategies.

[0057] The present invention has been presented above for purposes of illustration and description, but is not intended to be exhaustive or limiting. The exemplary embodiments are selected and described in order to explain the technical solutions and practical applications of the present invention and to enable those skilled in the art to understand the various embodiments of the present invention, which may include various modifications suitable for the specific intended use.

[0058] Some functional units in this document take modules as examples as execution units of corresponding technical features to describe relevant technical solutions and technical features. Those skilled in the art will understand that modules can be implemented as circuits, logic chips or any kind of discrete components. Modules should be understood to not be limited to physical or hardware forms. Modules can also be implemented in software form and / or functional form, and the software or functions can be executed or implemented by different types of processor architectures. In an embodiment of the present invention, a module may also include computer instructions or executable code, and may instruct a computer processor to perform a series of operations according to the instructions received. Those skilled in the art can select a specific implementation module according to the technical solution provided by the present invention. The execution units and modules described in the present invention are exemplary and not restrictive or exhaustive. Therefore, the scope of protection defined in the claims of the present invention should not be understood as being limited to the technical features and technical solutions presented in each embodiment.

Claims

1. A method for processing digital asset risk information, comprising: Obtaining web2 multimodal source data and web3 source data, wherein the web2 multimodal source data includes web2 non-text source data and web2 text source data; Converting the web2 non-text source data into web2 text processed data; Filtering the web2 text source data and the web2 text processed data to obtain web2 related risk information involving the digital asset; Obtaining web2 statistical information from the web2 text source data and the web2 text processed data; Extracting web3-related risk information involving the digital asset from the web3 source data; The web2 statistical information and the web3 related risk information are input into a first machine learning model to obtain a risk score involving the digital asset and a corresponding risk information feature.

2. A method for processing digital asset risk information, comprising: Obtaining web2 multimodal source data and web3 source data, wherein the web2 multimodal source data includes web2 non-text source data and web2 text source data; Converting the web2 non-text source data into web2 text processed data; Filtering the web2 text source data and the web2 text processed data to obtain web2 related risk information involving the digital asset; Embedding the web2-related risk information into a prompt template to generate first-level prompt information; Extracting web3-related risk information involving the digital asset from the web3 source data; Inputting the primary prompt information into a second machine learning model to summarize key points; Merging the web3 related information with the key point to generate second machine learning model prompt information; Based on the second machine learning model prompt information, risk insight information related to the digital assets is generated.

3. The method according to claim 1 or 2, wherein the web2-related risk information includes at least one of the following: negative tweets, negative comments, and negative reports involving the digital asset.

4. The method according to claim 1 or 2, wherein filtering the web2 text source data and the web2 text processed data comprises using at least one of the following filters: sentiment score negativity, popularity index, authenticity index, time range.

5. The method according to claim 1 or 2, wherein the web3-related risk information includes on-chain transaction records.

6. The method according to claim 5, wherein extracting web3-related risk information involving the digital asset from the web3 source data includes primary feature extraction and secondary feature extraction, the primary feature extraction includes directly generating web3-related risk information from the on-chain transaction records, and the secondary feature extraction includes detecting abnormal transaction information from the on-chain transaction records, as well as statistical information based on the abnormal transaction information.

7. The method of claim 1 further comprising, after obtaining the risk score related to the digital asset, converting the risk score into a graph, and displaying the graph.

8. The method according to claim 1 or 7, wherein: If the risk score exceeds a preset threshold, the method further includes generating and issuing risk warning information.

9. A digital asset risk information processing system, comprising: Information processing unit; and A non-volatile information storage medium coupled to the information processing unit, wherein the non-volatile information storage medium stores instructions, wherein the instructions cause the information processing unit to perform the following steps: Obtain web2 multimodal source data and web3 source data, the web2 multimodal Modal source data includes web2 non-text source data and web2 text source data; Converting the web2 non-text source data into web2 text processed data; Filtering the web2 text source data and the web2 text processed data to obtain web2 related risk information involving the digital asset; Obtaining web2 statistical information from the web2 text source data and the web2 text processed data; Extract web3 related risk information involving the digital asset from the web3 source data The web2 statistical information and the web3 related risk information are input into a first machine learning model to obtain a risk score involving the digital asset.

10. A digital asset risk information processing system, comprising: Information processing unit; and A non-volatile information storage medium coupled to the information processing unit, wherein the non-volatile information storage medium stores instructions, wherein the instructions cause the information processing unit to perform the following steps: Obtaining web2 multimodal source data and web3 source data, wherein the web2 multimodal source data includes web2 non-text source data and web2 text source data; Converting the web2 non-text source data into web2 text processed data; Filtering the web2 text source data and the web2 text processed data to obtain web2 related risk information involving the digital asset; Embedding the web2-related risk information into a prompt template to obtain first prompt information; Extracting web3 related risk information involving the digital asset from the web3 source data, Inputting the first prompt information into a second machine learning model to summarize key points; Merging the web3 related information with the key point to generate second machine learning model prompt information; Based on the second machine learning model prompt information, risk insight information related to the digital assets is generated.

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