Digital asset updating method, electronic equipment, storage medium and computer program product

Abstract

The invention provides a digital asset updating method, electronic equipment, a storage medium and a computer program product, and relates to the technical field of digital assets, and the method comprises the steps: determining a growth value of a target digital asset based on multi-dimensional reference data periodically obtained for the target digital asset; an attribute of the target digital asset is updated based on the growth value. Therefore, the growth value of the digital asset can be dynamically updated according to the periodically acquired multi-dimensional reference data, and the attribute of the digital asset can be dynamically updated according to the growth value, so that diversified application of the digital asset according to the growth value can be realized.

Description

Technical Field

[0001] This application relates to the field of digital asset technology, and in particular to a digital asset update method, electronic device, storage medium, and computer program product. Background Technology

[0002] Digital assets have been widely used in fields such as art and collectibles. However, existing digital assets often have fixed attributes and values, unable to dynamically adjust to changes in external factors. With social development and the flow of information, the value and influence of what digital assets represent can also change. For example, a piece of art might appreciate in value due to increased attention or depreciate due to negative events. Therefore, a method is needed to track and reflect changes in the value of digital assets in real time.

[0003] Currently, while some digital asset platforms provide information such as transaction data and price trends, this information is often outdated and cannot fully reflect the true value of digital assets. Furthermore, existing digital assets lack an automated mechanism to dynamically change their growth value and presentation based on social mobility information. Summary of the Invention

[0004] The embodiments of this application provide a digital asset update method, electronic device, storage medium, and computer program product that can realize diversified applications of digital assets based on dynamically changing growth values.

[0005] The technical solution of this application is implemented as follows: This application provides a digital asset update method, including: The growth value of the target digital asset is determined based on multi-dimensional reference data acquired periodically for the target digital asset. The attributes of the target digital asset are updated based on the growth value.

[0006] In the above scheme, determining the growth value of the target digital asset based on multi-dimensional reference data acquired periodically for the target digital asset includes: Based on multi-dimensional credibility assessment data obtained periodically from multiple websites, multiple target websites that meet the credibility requirements are identified. The growth value is determined based on the multidimensional reference data obtained periodically from multiple target websites.

[0007] In the above scheme, the step of determining multiple target websites that meet the credibility requirements based on multi-dimensional credibility assessment data obtained periodically from multiple websites includes: For each dimension of the credibility assessment data of each website, data analysis is performed to determine the sub-credibility corresponding to each dimension of the credibility assessment data; The credibility of each website is determined by non-linearly combining multiple sub-credibility values. Among the multiple websites, those with a credibility level greater than a credibility threshold are identified as the multiple target websites.

[0008] In the above scheme, determining the growth value based on the multidimensional reference data obtained periodically from multiple target websites includes: The multidimensional reference data for each target website is standardized to determine the multidimensional features corresponding to each target website. A weighted average is performed on each dimension of the features corresponding to the multiple target websites to determine the multidimensional comprehensive features. The growth value is determined based on the time decay factor and the multidimensional comprehensive characteristics; wherein the time decay factor is determined by a dynamic index based on a preset value; the dynamic index is determined by the negative of the ratio of the time elapsed between the current time and the preset time elapsed for the multidimensional reference data.

[0009] In the above scheme, the multidimensional credibility assessment data includes one or more of the following: website influence data, website user behavior data, website social network related data, website real-time monitoring data, multi-source related data, website data similarity, and website security data; The multidimensional reference data in each of the target websites includes one or more of the following for the target website: user behavior data, community engagement data, metadata change data, recognized influence data, related event impact data, historical transaction value data, art style data, and community activity related data.

[0010] The method in the above scheme further includes: The growth value of the target digital asset is adjusted based on one or more of the following: growth value cap, change reduction mechanism, and growth value change cooldown time.

[0011] In the above scheme, the attributes include one or more of the following: metadata attributes, visual attributes, rights attributes, functional attributes, display permission attributes, certificate and record attributes, and social interaction attributes; The attribute of the target digital asset updated based on the growth value includes one or more of the following: The updates to the metadata attributes based on the smart contract and the growth value include adding new descriptive information, tags, and story background; The updates to the visual attributes based on the smart contract and the growth value include changing the color scheme, adding special effects, or transforming into different art styles; The updates to the equity attributes based on the smart contract and the growth value include automatic adjustment updates to profit distribution, automatic dividend mechanisms, and updates to multiple transaction dividend mechanisms. The updates to the functional attributes based on the smart contract and the growth value include automatic unlocking of special functions and enhancement of interactive functions; The update of the display permission attribute based on the smart contract and the growth value includes automatic adjustment of the display method and dynamic adjustment of usage permissions; The updates to the certificate and record attributes based on the smart contract and the growth value include the automatic generation and updating of the digital certificate for the target digital asset; The updates to the social interaction attributes based on the smart contract and the growth value include social media binding and enhanced community interaction.

[0012] This application also provides an electronic device, including a memory and a processor. The memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps in the above-described method.

[0013] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the above-described method.

[0014] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the above-described method.

[0015] In this embodiment, the growth value of the target digital asset is determined based on multi-dimensional reference data acquired periodically for the target digital asset; the attributes of the target digital asset are then updated based on the growth value. This allows for dynamic updates to the growth value of the digital asset based on the periodically acquired multi-dimensional reference data, and consequently, dynamic updates to the attributes of the digital asset based on the growth value, enabling diversified applications of the digital asset based on its growth value. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the digital asset update method provided in this application embodiment. Figure 1 ; Figure 2 A flowchart illustrating the digital asset update method provided in this application embodiment. Figure 2 ; Figure 3 A flowchart illustrating the digital asset update method provided in this application embodiment. Figure 3 ; Figure 4 A flowchart illustrating the digital asset update method provided in this application embodiment. Figure 4 ; Figure 5 A flowchart illustrating the digital asset update method provided in this application embodiment. Figure 5 ; Figure 6 This application provides an example of the effect of a digital asset update method. Figure 1 ; Figure 7 A flowchart illustrating the digital asset update method provided in this application embodiment. Figure 6 ; Figure 8 A block diagram illustrating the digital asset update method provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of the digital asset update device provided in the embodiments of this application; Figure 10 This is a schematic diagram of a hardware entity of an electronic device provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application are further described in detail below with reference to the accompanying drawings and embodiments. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0019] If the application documents contain similar descriptions such as "first / second", the following explanation shall be added: In the following description, the terms "first / second / third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first / second / third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Among related technologies, existing digital assets still have the following shortcomings: 1. The Fixed Nature of Digital Assets: Existing digital assets often possess fixed attributes and values, making it impossible to dynamically adjust them based on changes in external factors. This results in the value of digital assets often failing to reflect their true worth in a timely manner, causing market prices to often fail to accurately reflect their intrinsic value.

[0022] 2. Information lag: Although some digital asset platforms provide information such as transaction data and price trends, this information is often lagging and cannot reflect changes in the value of digital assets in real time.

[0023] 3. Limited valuation methods: Current valuation of digital assets mainly relies on transaction data and price trends, lacking comprehensive consideration of other factors such as social mobility information.

[0024] 4. Lack of automation mechanism: Existing digital assets lack an automated mechanism to dynamically change their growth value and presentation form based on social flow information.

[0025] 5. Limited profit-sharing mechanism: The existing secondary profit-sharing mechanism for digital assets is only available to the minter and cannot be implemented for subsequent collectors.

[0026] This application's embodiments utilize dynamically changing growth values ​​to achieve diversified applications of digital assets. Specifically, it addresses how to dynamically adjust the attributes and presentation of digital assets through changing growth values, enabling them to better reflect different application needs and user interactions. This technology acquires and analyzes big data, dynamically updates the growth value of digital assets, and automatically adjusts their metadata, presentation format, and profit distribution through smart contracts, thereby enhancing the trading activity and user engagement of digital assets.

[0027] This application provides a method for updating digital assets. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating the digital asset update method provided in this application embodiment. Figure 1 , will combine Figure 1 The steps shown are explained below: S101. Based on multi-dimensional reference data periodically acquired for the target digital asset, determine the growth value of the target digital asset.

[0028] In this embodiment, the digital asset update device acquires multi-dimensional reference data for the target digital asset from different websites or platforms, and performs structured joint processing on the multi-dimensional reference data to determine the growth value of the target digital asset. The digital asset update device can be any of a platform, server, or cloud server that manages digital assets. Multi-dimensional reference data consists of different types of descriptive data for the target digital asset acquired from different websites or platforms. The reference value represented by the multi-dimensional reference data is used to determine the value growth value of the target digital asset. If the reference value represented by the multi-dimensional reference data increases, the corresponding growth value increases. If the reference value represented by the multi-dimensional reference data decreases, the corresponding growth value decreases. There is a strong correlation between the reference value represented by the multi-dimensional reference data and the growth value.

[0029] In this embodiment of the application, the digital asset update device can obtain multi-dimensional reference data for the target digital asset from multiple websites or platforms that meet the credibility requirements through big data crawling technology, and perform structured joint processing based on the multi-dimensional reference data to determine the growth value.

[0030] In this embodiment, the digital asset update device can set a growth value cap for each digital asset. Once the growth value reaches the cap, it will no longer be increased through attribute changes or equity increases. This prevents the growth value from increasing indefinitely. Specifically, as the growth value of a digital asset increases, its growth rate should gradually slow down. A diminishing returns mechanism can be introduced to gradually reduce the effect of each attribute change and equity increase on the growth value. A cooldown period mechanism can also be introduced to prevent frequent attribute changes and equity increases within a certain period, thereby controlling the excessively rapid growth of the growth value. By incorporating the growth value cap, diminishing returns mechanism, and cooldown period into the overall credibility algorithm, the growth value becomes more reasonable and fair.

[0031] Specifically, when creating a digital asset, the issuer stores the asset's basic information and initial growth value on the blockchain. This basic information typically includes, but is not limited to: Unique Identifier (Token Identity Document (ID)): A unique identifier that distinguishes it from other digital assets.

[0032] Metadata: Descriptive information about digital assets, such as titles, descriptions, image links, etc. Although this information can exist on the blockchain, due to storage costs, it is usually stored on decentralized storage (InterPlanetary File System (IPFS)) and its Uniform Resource Identifier (URI) is recorded on the blockchain, pointing to the metadata.

[0033] Ownership: Information indicating the current owner of a digital asset.

[0034] Issuer (Minter): The address of the person or organization that creates and first publishes digital assets on the blockchain.

[0035] Growth Value (GV): Calculated from reference data across 8 dimensions.

[0036] The multidimensional reference data in each of the target websites includes one or more of the following for the target website: user behavior data, community participation data, metadata change data, recognized influence data, related event impact data, historical transaction value data, art style data, and community activity related data.

[0037] S102. Dynamically update the attributes of the target digital asset based on the growth value.

[0038] In this embodiment of the application, the digital asset update device can dynamically update the attributes of digital assets based on the growth value.

[0039] The attributes can include one or more of the following: metadata attributes, visual attributes, rights attributes, functional attributes, display permission attributes, certificate and record attributes, and social interaction attributes. The growth value is used to update the characteristics exhibited by each attribute, and to enable features or permissions for certain attributes. A higher growth value results in a more significant update effect on the corresponding attribute. A lower growth value results in a less significant update effect on the corresponding attribute.

[0040] Metadata attributes are used to characterize the description, tags, and backstory of the target digital asset. Visual attributes characterize the appearance, color scheme, and visual effects of the target digital asset. Equity attributes characterize the profit distribution ratio, automatic profit distribution mechanism, and profit distribution mechanism for minters and intermediary holders in multi-digital transactions of the target digital asset as its growth value changes. Functional attributes characterize the functions or animation effects that the target digital asset possesses at the corresponding growth value in the relevant application. Display permission attributes characterize the display attributes and permission attributes of the target digital asset on different platforms as its growth value changes. Social interaction attributes characterize the exposure and influence of the target digital asset on the platform as its growth value changes. Record and certificate attributes characterize the updated digital certificate content and all update operations of the target digital asset as its growth value changes.

[0041] In this embodiment, the growth value of the target digital asset is determined based on multi-dimensional reference data acquired periodically for the target digital asset; the attributes of the target digital asset are then updated based on the growth value. In this way, the growth value of the digital asset can be dynamically updated based on the periodically acquired multi-dimensional reference data, and consequently, the attributes of the digital asset can be dynamically updated based on the growth value, enabling diversified applications of the digital asset based on its growth value.

[0042] Please see Figure 2 This is a flowchart illustrating the digital asset update method provided in this application embodiment. Figure 2 , Figure 1 S101 can also be implemented via S201 to S202, which will be explained in conjunction with the steps shown: S201. Based on multi-dimensional credibility assessment data obtained periodically from multiple websites, identify multiple target websites that meet the credibility requirements.

[0043] In this embodiment, the digital asset update device can periodically acquire multi-dimensional credibility assessment data from multiple websites or platforms, perform structured processing on the multi-dimensional credibility assessment data for each website, and determine the credibility of each website. Multiple target websites whose credibility meets the requirements are then identified from among the multiple websites.

[0044] In this embodiment of the application, after the multidimensional credibility assessment data for each website is structured, the credibility of each website can be determined by a weighted combination method.

[0045] Among these methods, big data crawling technology can be used to obtain multi-dimensional credibility assessment data from multiple websites.

[0046] The multidimensional credibility assessment data includes one or more of the following: website influence data, website user behavior data, website social network related data, website real-time monitoring data, multi-source related data, website data similarity, and website security data.

[0047] S202. Determine the growth value based on the multidimensional reference data obtained periodically from multiple target websites.

[0048] In this embodiment, after identifying multiple target websites, multidimensional reference data can be periodically acquired from these websites. The features of each dimension are determined based on the reference data for each of the multiple target websites, and then the features from multiple dimensions are combined to determine the growth value.

[0049] In this embodiment of the application, the data acquisition period in S201 can be the same as or different from the data acquisition period in S202.

[0050] Please see Figure 3 This is a flowchart illustrating the digital asset update method provided in this application embodiment. Figure 3 , Figure 2 S201 to S202 can also be implemented via S11 to S18, which will be explained in conjunction with the steps shown: S11, Casting.

[0051] S12, Basic Information: Initial Growth Value.

[0052] In this embodiment of the application, when the issuer of the target digital asset creates the digital asset, the basic information and initial growth value of the digital asset can be stored on the blockchain.

[0053] S13. Website Selection and Evaluation.

[0054] In this embodiment of the application, a credibility evaluation function can be defined to evaluate and determine the credibility of multiple websites.

[0055] S14, Credibility Weighting.

[0056] In this embodiment, after determining the credibility of multiple websites, websites with credibility values ​​greater than a credibility threshold can be identified as target websites. Furthermore, the weights of the multi-dimensional reference data corresponding to the target website are determined based on its credibility.

[0057] S15. Select data crawling content.

[0058] In this embodiment, a customized crawler program can be used to periodically crawl multidimensional reference data from the target website. An IP pool and a timed random access strategy are employed to prevent the crawler from being identified and blocked, ensuring the continuity and integrity of the data. S16. Data cleaning and vectorization.

[0059] In this embodiment, the crawled data is deduplicated and denoised, and irrelevant information is filtered out. Natural Language Processing (NLP) is performed on the text information to extract keywords, sentiment indicators, etc. Metrics related to each digital asset are extracted, such as the number of comments, likes, shares, and transaction frequency. These metrics are standardized and quantitatively represented to reflect the social influence, market activity, and other characteristics of each digital asset.

[0060] S17, Growth Value Calculation.

[0061] In this embodiment, based on the credibility weight (W_site) of each previously evaluated website, different weights are assigned to the same indicator data from different websites. A weighted average or other suitable weighting method is used to calculate the multidimensional comprehensive characteristics of each digital asset. The growth value is then jointly determined based on these multidimensional comprehensive characteristics.

[0062] S18, Update.

[0063] In this embodiment of the application, multiple attributes corresponding to the digital asset can be updated according to the size of the growth value.

[0064] In this embodiment, multiple target websites that meet the credibility requirements are identified based on multidimensional credibility assessment data obtained periodically from multiple websites. The growth value is then determined by combining the multidimensional reference data obtained periodically from these target websites. This approach, by obtaining multidimensional reference data from websites that meet the credibility requirements, improves the credibility of the multidimensional reference data, resulting in higher accuracy of the growth value determined using more reliable multidimensional reference data.

[0065] Please see Figure 4 This is a flowchart illustrating the digital asset update method provided in this application embodiment. Figure 4 , Figure 2 S201 can also be implemented via S301 to S302, which will be explained in conjunction with the steps shown: S301. Perform data analysis on each dimension of the credibility assessment data for each website to determine the sub-credibility corresponding to each dimension of the credibility assessment data.

[0066] In this embodiment of the application, after obtaining the multi-dimensional credibility assessment data corresponding to each website, the digital asset update device can perform data analysis on each dimension of the credibility assessment data of each website according to a preset program or model to determine the sub-credibility corresponding to each dimension of the credibility assessment data of each website.

[0067] In this embodiment of the application, after obtaining the multidimensional credibility assessment data, data cleaning can be performed on the multidimensional credibility assessment data, that is, the data is deduplicated, denoised and filtered out irrelevant information.

[0068] In this embodiment, the digital asset update device can define a website credibility evaluation function, considering the following data to evaluate the credibility of the website, and prioritizing websites with high credibility: Website influence data: In addition to considering conventional factors such as website reputation, content quality, and activity, website authority metrics can be used to assess credibility. The PageRank algorithm of search engines and other similar weighting methods are used to determine a website's influence and authority in a specific field.

[0069] Website user behavior data: Analyzing user behavior patterns on a website, such as visit frequency, dwell time, and engagement, to assess the website's credibility. A website is likely more credible if a large number of users frequently visit and actively participate.

[0070] Website social media data: Utilize social media information to verify a website's credibility. Check if the website has official social media accounts and whether these accounts have a significant number of followers and active interaction. Additionally, analyze user reviews and recommendations of the website on social media.

[0071] Real-time website monitoring data: Websites crawled are monitored in real time to observe for any abnormal behavior or changes. If a website's credibility metrics change significantly, the assessment can be adjusted promptly.

[0072] Multi-source relevant data: This approach goes beyond relying solely on information from a single website to assess credibility; instead, it combines data from multiple sources for comprehensive analysis. For example, it references reviews and discussions from other relevant websites, social media, forums, etc.

[0073] Website data similarity: This compares the crawled content with the content from other reliable sources. A higher similarity score generally indicates a higher level of trust in the website.

[0074] Website security data: Check the website's security measures, such as Secure Sockets Layer (SSL) certificates, encrypted transmission, etc. Secure websites are generally more trustworthy.

[0075] Each evaluation data point is defined as a function, corresponding to the evaluation result of each factor: 1. F_weight - Website influence data, using PageRank or a similar method.

[0076] Input parameters: url: The URL of the website to be evaluated PageRank API: The API used to retrieve PageRank values. Output: The corresponding sub-confidence level.

[0077] Method body: def F_weight(url, PageRank_API): Calculate the website's authority value :param url: The URL of the website to be evaluated :param PageRank_API: The API for retrieving PageRank values :return: Website authority value # Call the PageRank API to get the website's PageRank value response = PageRank_API.get(url) if response.status_code == 200: pagerank = response.json().get('pagerank') else: pagerank = 0 # Default value when PageRank cannot be obtained #Returns website authority value return pagerank 2. F_user_behaviour - Website user behavior data.

[0078] Input parameters: user_data: A dictionary containing user behavior data. click_rate: Click-through rate average_stay_time: Average time spent on the page visit_frequency: frequency of visits Output: The corresponding sub-confidence level.

[0079] Method body: def F user behavior(user data): Calculate user behavior score :param user_data: A dictionary containing user behavior data : return: User behavior score click_rate-user data.get('click rate', 0) average_stay_time-user_data.get('average stay_time', 0) visit_frequency-user data.get('visit frequency', ®) #Calculate user behavior scores; weights can be adjusted as needed. score-(0.4*click_rate)+(0.3*average_stay_time)+(0.3*visit_frequency) return score 3. F_social_verification - Website social network related data.

[0080] Input parameters: social_data: A dictionary containing social media data. followers_count: Number of followers engagement_rate: engagement rate social_mentions: Number of mentions on social media Output: The corresponding sub-confidence level.

[0081] Method body: def F_social_verification(social_data): Calculate social network validation score :param social_data: A dictionary containing social media data : return: Social Network Validation Score followers_count = social_data.get('followers_count', 0) engagement_rate-social_data.get('engagement_rate', 0) social_mentions = social_data.get('social_mentions', 0) # Calculate the social network validation score; the weights can be adjusted as needed. score = (0.5*followers_count) + (0.3*engagement_rate) + (0.2*social_mentions) return score 4. F_realtime_monitoring - Real-time website monitoring data.

[0082] Input parameters: monitoring_data: A dictionary containing real-time monitoring data. uptime: Percentage of website uptime response_time: Website response time error_rate: error rate Output: The corresponding sub-confidence level.

[0083] Method body: def F realtime monitoring(monitoring_data): Calculate real-time monitoring score :param monitoring_data: A dictionary containing real-time monitoring data :return: Real-time monitoring score uptime-monitoring_data.get('uptime', 0) response time-monitoring data.get('response time 0) error_rate-monitoring_data.get('error rate'.0) #Calculate the real-time monitoring score; the weights can be adjusted as needed. score-(0.5 uptime)-(0.3-response_time)-(0.2 error rate) return score 5. F_data_fusion_score - Multi-source related data.

[0084] Input parameters: data_sources: A list containing multiple data sources, each with a credibility score. Output: The corresponding sub-confidence level.

[0085] Method body: def F_data_fusion_score(data_sources): Calculate the multi-source data fusion score :param data_sources: A list containing multiple data sources return: Multi-source data fusion score total_score = 0 total weight = 0 for source in data_sources :score-source.get('score', 0) weight = source.get('weight', 1) total_score += score*weight total_weight += weight #Calculate the weighted average score fusion_score = total_score / total_weight if total_weight! =0 else0 return fusion_score 6. F_content_similarity - Website data similarity.

[0086] Input parameters: content: The text of the content to be evaluated. reference_contents: A list of reference content, each containing a text and a weight. Output: The corresponding sub-confidence level.

[0087] Method body: from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity def F_content_similarity(content, reference_contents): Calculate content similarity score :param content: The text of the content to be evaluated :param reference_contents: List of reference content return: content similarity score contents-[content]+[ref['text']for ref in reference_contents] vectorizer-TfidfVectorizer() tfidf matrix-vectorizer.fit transform(contents) cosine_similarities = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:]) similarity_scores = cosine_similarities.flatten() weighted_score-sum(sim*ref['weight']for sim, ref in zip(similarity_scores, reference_contents)) return weighted score 7. F_security_evaluation - Website security data.

[0088] Input parameters: security_data: A dictionary containing website security data. has_ssl: Does the SSL certificate exist? encryption_level: Encryption level vulnerability_count: Number of vulnerabilities Output: The corresponding sub-confidence level.

[0089] Method body: def F_security_evaluation(security_data): Calculate website security assessment score :param security_data: A dictionary containing website security data Return: Website security assessment score has_ssl-security_data.get('has_ssl', False) encryption_level-security_data.get('encryption_level', 0) vulnerability_count-security_data.get('vulnerability_count', 0) ssl_score - 1 if has_ssl else 0 encryption_score-encryption_level / 10 # Assuming the encryption level is between 10 and 10 vulnerability_score = max(0, 10 - vulnerability_count) / 10 # Assuming the number of vulnerabilities is as low as possible. #Calculate security score score = (0.4*ssl_score) + (0.4*encryption_score) + (0.2*vulnerability_score) return score S302. The multiple sub-credibility values ​​are combined non-linearly to determine the credibility value corresponding to each website.

[0090] In this embodiment of the application, the digital asset update device can non-linearly combine multiple sub-credibility values ​​corresponding to each website to determine the credibility value corresponding to each website.

[0091] In this embodiment, the digital asset update device can perform a weighted summation of multiple sub-credibility scores corresponding to each website to determine the corresponding credibility score. The weight of each sub-credibility score can be determined based on the importance of the corresponding evaluation data.

[0092] The overall trustworthiness (C_trust) algorithm can be a non-linear combination of the outputs of these functions: C_trust=Fusion(F_weight,F_user_behaviour,F_social_verification, F_realtime_monitoring,F_data_fusion_score,F_content_similarity, F_security_evaluation) Fusion is an aggregation nonlinear function that uses a multi-layer neural network to fit a comprehensive score (confidence) of the sub-confidences corresponding to all these features.

[0093] Method body: def C_trust(F_weight, F_user_behaviour, F_social_verification, F_realtime_monitoring, F_data_fusion_score, F_content_similarity, F_security_evaluation): Comprehensive credibility algorithm return: credibility score from sklearn.neural_network import MLPRegressor Assuming a pre-trained multi-layer neural network model model = MLPRegressor() features-[F_weight, F_user_behaviour, F_social_verification, F_realtime_monitoring, F_data_fusion_score, F_content_similarity, F_security_evaluation]trust_score-model.predict([features]) return trust_score[0] S303. Among the multiple websites, determine the websites whose credibility is greater than the credibility threshold, and select them as the multiple target websites.

[0094] In this embodiment, the digital asset update device can compare the credibility of each website with a credibility threshold. If the corresponding credibility is greater than the threshold, the website is determined to be a target website. This process continues until the credibility of multiple websites has been compared, thus identifying multiple target websites.

[0095] In this embodiment, the digital asset update device can define a trustworthiness threshold: a threshold T is defined, which can be obtained based on actual needs and historical data statistics. Threshold judgment: when the trustworthiness C_trust of a website is higher than this threshold T, we consider the website to be trustworthy; if it is lower than this threshold, we consider the website to be untrustworthy. For example: if C_trust > T, the website is trustworthy and is the target website; if C_trust ≤ T, the website is untrustworthy.

[0096] In this embodiment, data analysis is performed on the credibility assessment data of each website for each dimension to determine sub-credibility; multiple sub-credibility values ​​are non-linearly combined to determine the credibility corresponding to each website; websites with credibility values ​​greater than a credibility threshold are identified from among the multiple websites, serving as multiple target websites. In this way, by identifying target websites that meet the credibility requirements from among multiple websites, the credibility of the multi-dimensional reference data obtained from the target websites is also higher, and the accuracy of the growth value determined by the high-credibility multi-dimensional reference data is also higher.

[0097] Please see Figure 5 This is a flowchart illustrating the digital asset update method provided in this application embodiment. Figure 5 , Figure 2 S202 can also be implemented via S401 to S403, which will be explained in conjunction with the steps shown: S401. Standardize the multidimensional reference data for each target website to determine the multidimensional features corresponding to each target website.

[0098] In this embodiment, the digital asset update device can extract keywords related to the target digital asset from the text information in the acquired multidimensional reference data, perform normalization transformation on the numerical and numerical information in the acquired multidimensional reference data, and then determine multiple sub-features corresponding to each dimension of reference data based on the keywords and the normalized numbers. For each sub-data in each dimension of reference data, multiple corresponding sub-features are determined, and the multiple sub-features of each dimension are non-linearly combined to determine the feature of each dimension, thereby determining the multidimensional features corresponding to each target website.

[0099] In this embodiment, digital assets can also deduplicate and denoise multidimensional reference data, filtering out irrelevant information. Natural Language Processing (NLP) is performed on the text information to extract keywords, sentiment indicators, etc. Relevant metrics for each digital asset are extracted, such as the number of comments, likes, shares, and transaction frequency. These metrics are standardized and quantitatively represented to reflect the social influence, market activity, and other characteristics of each digital asset.

[0100] In this embodiment, the digital asset update device can use a customized crawler program to periodically crawl multiple target websites. It employs an Internet Protocol (IP) pool and a timed random access strategy to prevent the crawler from being identified and blocked, ensuring the continuity and integrity of the data. The crawled content includes the following dimensions: 1. User Behavior Data: Tracks user behavior patterns when buying, selling, or browsing digital assets, including click-through rates, page dwell time, and browsing frequency. This information can provide insights into a user's level of interest in a specific digital asset.

[0101] Crawling Strategy: Use web crawlers to monitor user activity in the digital asset market and its platforms. Collect data such as click-through rate, page dwell time, and browsing frequency to determine the corresponding sub-features.

[0102] Calculation method: def get user behavior _score(user_data): return F_user behavior(user data) user_data -{ click rate: 0.05 average stay time: 120,##t 'Visit frequency': 10 times / day } user behavior_score -get user behavior_score(user_data) 2. Community Engagement Data: Crawling the number of discussions and activity levels in communities or forums related to specific digital assets, including the number of posts, replies, and user engagement. This data measures users' enthusiasm and investment in specific digital assets, and can help determine the sub-features corresponding to these data.

[0103] Crawling strategy: Monitor relevant forums and social media platforms.

[0104] Use APIs to extract post count, reply count, and user engagement data.

[0105] Calculation method: def get social_ verification_score(social_data): return F social verification(social_data) social data = { 'followers_count': 5000, engagement rate': 0.03, 'social mentions': 150 } social_verification_score -get_social_verification_score(social data) 3. Metadata Change Data: Track changes in the metadata of digital assets, such as artist information, ownership history, copyright issues, etc., which can affect the value of digital assets.

[0106] Crawling strategy: Use blockchain explorer APIs to retrieve the metadata history of digital assets.

[0107] Calculation method: def get metadata_changes_score(metadata_changes): #The specific implementation is calculated based on changes in actual metadata. return calculate metadata_changes_score(metadata_changes) metadata_changes - { artist_change': 2, ownership change: 5 } metadata_changes_score - get metadata_changes_score(metadata_changes) 4. Recognized Influence Data: This determines the frequency and sentiment of mentions of digital assets or their creators in mainstream media, social media, or blogs. This level of exposure can indicate the public awareness and market acceptance of digital assets.

[0108] Crawling strategy: Monitor digital assets and their creators' mentions through social media full-text search APIs and web search APIs.

[0109] Calculation method: def get_realtime_monitoring_score(monitoring_data): return F_realtime_monitoring(monitoring_data) monitoring_data ={ 'uptime': 99.9 'response_time': 200, #milliseconds 'error_rate': 0.01 } realtime_monitoring_score = get_realtime_monitoring_score(monitoring_data) 5. Data on the Impact of Relevant Events: Collect news reports and significant events related to digital assets or their creators. These events may have a positive or negative impact on the value of digital assets.

[0110] Crawling strategy: Use the news API and custom crawlers to monitor important news and related events.

[0111] Calculation method: def get_news_event_score(news_events): #Specific implementation: Scoring based on the positive and negative impact of news and events return calculate_news_event_score(news_events) news_events - { 'positive news': 3 Negative news: 1 } news_event_score - get_news_event_score(news_events) 6. Historical transaction data: Obtain valuation and transaction records of digital assets on different platforms or at different times. This data can help us understand the changing trends of digital asset value.

[0112] Crawling strategy: Scrape transaction records from digital asset markets and auction house APIs.

[0113] Calculation method: def get_historical_data_score(historical_data): #Specific implementation involves modeling and analyzing historical transaction data. return calculate_historical_data_score(historical_data). historical_data = { 'historical_prices': [100, 200, 150, 300] } historical_data_score = get_historical_data_score(historical_data) 7. Art Style Data: Through image and text analysis tools, analyze the art style, theme, color selection, etc. of digital assets. Popular art styles and categories may affect the value of digital assets within a specific time period.

[0114] Crawling strategy: Use image recognition APIs and text analysis tools to extract style, theme, and color data.

[0115] Calculation method: def get_content_similarity_score(content, reference_contents): return F_content_similarity(content, reference_contents) This is a modern artwork that uses bright colors and an abstract style.

[0116] reference_contents =[ {'text': 'This is another modern artwork, in a similar style.', 'weight': 0.8} {'text': 'This is a classical artwork, in a different style.', 'weight': 0.2} ] content_similarity_score = get_content_similarity_score(content, reference_contents) 8. Community activity data: Collect information on community activities, tutorials, sponsorships, announcements, etc. related to digital assets, as well as popular user polls and survey results, to understand the community's consensus and expectations for specific digital assets.

[0117] Crawling strategy: Monitor community activity announcements, tutorials, and sponsorship information.

[0118] Calculation method: def get_security_evaluation_score(security_data): return F_security_evaluation(security_data) security_data = { 'has_ssl': True, 'encryption_level': 9, 'vulnerability_count': 1 } security_evaluation_score = get_security_evaluation_score(security_data) S402. Perform a weighted average process on each dimension feature corresponding to the multiple target websites to determine the multidimensional comprehensive feature.

[0119] In this embodiment of the application, each target website corresponds to multiple dimensions. The digital asset update device can perform weighted average processing on each dimension for each dimension of multiple websites to determine the comprehensive feature of each dimension, and thus obtain the multidimensional comprehensive feature.

[0120] The weight of each feature dimension can be determined based on the credibility of the corresponding target website, or it can be determined manually.

[0121] In this embodiment, the digital asset update device assigns different weights to data of the same dimension from different websites based on the credibility weight (W_site) of each website previously evaluated. A weighted average or other suitable weighting method is used to calculate the comprehensive features of each dimension, thereby obtaining multi-dimensional comprehensive features. First, the data for each dimension may be standardized for comprehensive scoring.

[0122] Example data: user_behaviour_data = { 'click_rate': 0.05, 'average_stay_time': 120, 'visit_frequency': 10 } social_verification_data = { 'followers_count': 5000,' engagement_rate': 0.03, 'social_mentions': 150 } monitoring_data = { 'uptime': 99.9, 'response_time': 200, 'error_rate': 0.01 } metadata_changes ={ 'artist_change': 2, 'ownership_change': 5 } news_events = { 'positive_news': 3 'negative_news': 1 } historical_data ={ historical_prices:[100,200,150,300] } This is a modern artwork that uses bright colors and an abstract style. reference_contents =[ 'historical_prices': [100, 200, 150, 300] {'text': 'This is another modern artwork, in a similar style.', 'weight': 0.8} {'text': 'This is a classical artwork, in a different style.', 'weight': 0.2} ] security_data ={ 'has_ssl': True 'encryption_level': 9 'vulnerability_count': 1 } In this embodiment, the digital asset update device calculates scores for each dimension and uses previously defined functions to calculate the features of each dimension: The methods include: F_user_behaviour_score =F_user_behaviour(user_behaviour_data) F_social_verification_score = F_social_verification(social_verification_data) F_realtime_monitoring_score = F_realtime_monitoring(monitoring_data) F_metadata_changes_score = get_metadata_changes_score(metadata_changes) F_news_event_score = get_news_event_score(news_events) F_historical_data_score = get_historical_data_score(historical_data) F_content_similarity_score =F_content_similarity(content, reference_contents) F_security_evaluation_score = F_security_evaluation(security_data) In this embodiment, the weights for each dimension are defined and can be set by experts or automatically adjusted through machine learning. The following is an example weight allocation: weights = { 'user_behaviour': 0.15, 'social_verification': 0.15, 'realtime_monitoring': 0.15, 'metadata_changes': 0.1, 'news_events': 0.1, 'historical_data': 0.15, 'content_similarity': 0.1,'security_evaluation': 0.1 } After determining the weights, the digital asset update device can calculate the comprehensive characteristics based on the scores and weights of each item: def calculate_composite_score(scores, weights): composite_score = 0 for key in scores: composite_score += scores[key] * weights[key] return composite_score scores = { 'user_behaviour': F_user_behaviour_score, 'social_verification':F_social_verification_score, 'realtime_monitoring': F_realtime_monitoring_score, 'metadata_changes': F_metadata_changes_score, 'news_events': F_news_event_score, 'historical_data': F_historical_data_score, content_similarity':F_content_similarity_score, security_evaluation': F_security_evaluation_score } composite_score = calculate_composite_score(scores, weights) In this way, the digital asset update mechanism can assign different weights to the same indicator data from different websites based on the credibility weight (W_site) of each website previously evaluated. A weighted average or other suitable weighting method is used to calculate a comprehensive indicator vector for each digital asset. Simultaneously, a control mechanism is introduced to prevent the growth value from increasing indefinitely.

[0123] S403. Based on the time decay factor and the multidimensional comprehensive characteristics, determine the growth value; wherein, the time decay factor is determined by a dynamic index based on a preset value; the dynamic index is determined by the negative of the ratio of the time elapsed between the current time and the preset time elapsed for the multidimensional reference data.

[0124] In this embodiment, the digital asset update device can determine the growth value by weighted summation of multi-dimensional comprehensive features. Each comprehensive feature, when multiplied by its corresponding weight, can be multiplied by a time decay factor. The time decay factor is determined by a dynamic index based on a preset value; the dynamic index is determined by the negative of the ratio of the time elapsed since the current time to the multi-dimensional reference data to a preset time elapsed.

[0125] In this embodiment, the digital asset update device can normalize each dimension of the comprehensive feature and then perform a weighted sum to determine the growth value. Specifically, the normalized values ​​for each dimension need to be multiplied by a time decay factor during the weighted summation.

[0126] Combination Figure 6 The digital asset update mechanism can use a personalized growth value algorithm. Assume we assign a weight Wi to each composite feature and calculate a normalized value Vi for each (the value of each indicator divided by its maximum value). We will also add a time decay factor to reflect the staleness of the information. The calculated growth value (GV) is as follows: Comprehensive User Behavior Features V1 Community Engagement Comprehensive Characteristics V2 Metadata Change Comprehensive Features V3 Recognized Influence Comprehensive Characteristics V4 Comprehensive Characteristics of Related Event Impact V5 Historical Transaction Value Comprehensive Features V6 Artistic Style Comprehensive Characteristics V7 Community Activity Related Comprehensive Features V8 These values ​​are then incorporated into formula (1) for measuring the growth value of digital assets: Formula (1) Among them: W iIt is the weight of the i-th metric, which is set by experts or automatically adjusted through machine learning. It is the normalized value of the i-th metric, calculated using formula (2) as follows: Formula (2). t is the current time, t i This is the update time of the i-th metric. This is the given time unit (e.g., days). T is the set time decay half-life, which determines how quickly information becomes outdated. It is a time decay factor; as time goes by, the importance of information will gradually decrease.

[0127] Formula (1) above takes into account multidimensional characteristics, with each metric's importance in the overall growth value determined by its respective weight. The time decay factor ensures that the latest data has an impact on the calculation results. Note that all metric weights W i Need to meet To ensure normalization.

[0128] In this embodiment, the digital asset update device can periodically reassess the credibility of the target website and adjust the indicator weights according to market changes to ensure the accuracy and timeliness of the algorithm. Machine learning algorithms are used to analyze the relationship between digital asset growth value and actual market performance, continuously optimizing the calculation formula and parameter settings. In summary, this algorithm model achieves dynamic and accurate calculation of digital asset growth value by comprehensively analyzing multi-source data and evaluating website credibility. The core of this model lies in weighted processing and time decay, aiming to provide users with more accurate and real-time information on digital asset value changes.

[0129] In this embodiment of the application, after obtaining a new growth value, the digital asset update device can automatically update and improve the following attributes through a smart contract: The attribute of the target digital asset updated based on the growth value includes one or more of the following: The updates to the metadata attributes based on the smart contract and the growth value include adding new descriptive information, tags, and story background; The updates to the visual attributes based on the smart contract and the growth value include changing the color scheme, adding special effects, or transforming into different art styles; The updates to the equity attributes based on the smart contract and the growth value include automatic adjustment updates to profit distribution, automatic dividend mechanisms, and updates to multiple transaction dividend mechanisms. The updates to the functional attributes based on the smart contract and the growth value include automatic unlocking of special functions and enhancement of interactive functions; The update of the display permission attribute based on the smart contract and the growth value includes automatic adjustment of the display method and dynamic adjustment of usage permissions; The updates to the certificate and record attributes based on the smart contract and the growth value include the automatic generation and updating of the digital certificate for the target digital asset; The updates to the social interaction attributes based on the smart contract and the growth value include social media binding and enhanced community interaction.

[0130] Metadata attribute updates: Smart contracts can automatically add or update metadata based on changes in growth value. Adding new descriptive information, tags, and backstory enhances the cultural significance and uniqueness of digital assets.

[0131] Visual Attribute Updates: As the growth value of a digital asset increases, the artwork's visual effects can be automatically updated. This can change color schemes, add special effects, or transform it into different art styles. These changes can be automatically triggered based on different growth value stages, enhancing visual appeal and collectible value. Combined with... Figure 7 The following steps will be explained: S21, Obtain new and old growth values.

[0132] In this embodiment of the application, the digital asset update device obtains the newly generated growth value and retrieves the old growth value from the blockchain.

[0133] S22, Judgment and Adjustment.

[0134] In this embodiment, the digital asset update device compares the new growth value with the old growth value to determine whether the visual appearance of the digital asset needs to be adjusted.

[0135] S23. Adjust visual effects.

[0136] In this embodiment, if adjustments are needed, the digital asset update device adjusts the color of the digital asset's appearance and adds corresponding special effects based on the magnitude of the new growth value. For example, when the new growth value is greater than 50, the digital asset's appearance is adjusted so that the character is more defined, the eyes are half-open, and the background has a certain complexity, while still maintaining a two-dimensional (2D) style.

[0137] S24. Records and notifications.

[0138] In this embodiment of the application, the digital asset adjustment device can record the adjustment of appearance attributes.

[0139] Feature Attribute Updates: These may include automatic unlocking of special features and enhancements to interactive functions. Automatic Unlocking of Special Features: Digital assets can automatically unlock new features through growth points. In the game, when growth points reach a certain level, in-game digital asset items can automatically unlock new skills or upgrade existing skills, making them more practical and competitive. Enhanced Interactive Functions: For virtual pets or virtual character digital assets, increasing growth points can automatically unlock more interactive functions and animation effects, enhancing the user's interactive experience and sense of participation.

[0140] The equity attribute updates include: automatic adjustment and update of profit distribution, automatic dividend mechanism, and multiple transaction dividend mechanism.

[0141] Automatic Profit Distribution Adjustment: Based on the growth value of digital assets, smart contracts can automatically adjust the profit distribution ratio. The higher the growth value, the larger the profit distribution ratio received by the holder. This dynamic adjustment mechanism can incentivize holders to actively participate and promote the growth of digital assets.

[0142] Automatic dividend mechanism: When the growth value reaches a certain threshold, the smart contract can automatically execute the dividend operation, distributing a portion of the profits to holders or specific groups, thereby increasing users' economic returns and participation.

[0143] Multiple Transaction Profit Sharing Mechanism (Enhancing Revenue for Intermediate Users): In digital asset platforms and markets, while creators (original creators) can still enjoy a share of the profits when trading on the secondary market (secondary profit sharing), intermediate users (those who buy and then resell) generally do not receive this secondary profit sharing. This causes digital assets to lose their vitality with multiple transactions. We have introduced a growth value mechanism that allows not only creators to enjoy profit sharing as the value of digital assets increases, but also intermediate users to receive a portion of the profits.

[0144] Design of a multi-transaction dividend mechanism: Initial setup: When minting digital assets, define a smart contract that includes a multi-tiered profit-sharing mechanism, including the original minter and intermediary trading users.

[0145] Profit-sharing mechanism: For each transaction after a miner's growth value increases, the original miner receives a pre-set royalty share based on the current market value.

[0146] Intermediate transaction users: Design a progressive dividend mechanism. When a digital asset is resold, users can obtain a certain percentage of the profit based on the difference between the current growth value and the growth value before the previous holder resold it. The profit is automatically transferred to the previous owner according to the transaction amount.

[0147] Smart contract writing: Smart contracts need to record each transaction and owner information to ensure that the revenue can be correctly traced and distributed in each transaction.

[0148] Transaction history: Records information about each owner of a digital asset and the period during which they held it.

[0149] Profit distribution logic: Based on the increase in transaction price, profits will be distributed to the previous owners according to a predetermined ratio.

[0150] The social interaction attributes update includes enhanced social media integration and community interaction.

[0151] Social Media Integration: Changes in growth value can automatically trigger integration and promotion on social media platforms. When the growth value reaches a certain level, the digital asset will be automatically promoted on social media platforms, increasing its exposure and influence.

[0152] Enhanced Community Interaction: Smart contracts can automatically adjust community interaction features based on changes in growth value. Digital asset holders with high growth values ​​can automatically gain more community permissions and privileges, increasing their influence and participation in the community.

[0153] Certificate and record attribute updates: Dynamic certificate generation and updates: Based on changes in growth value, the system can automatically generate and update digital certificates for digital assets, including proof of ownership, transaction records, and growth history. These certificates enhance the credibility and transparency of digital assets.

[0154] Real-time recording and logging: Smart contracts automatically record every change in growth value and the corresponding digital asset update operation, generating detailed logs and reports to ensure that all changes are traceable, improving system reliability and user trust.

[0155] Updates to display permission attributes include: automatic adjustment of display methods and dynamic adjustment of usage permissions.

[0156] Automatic Display Adjustment: Based on changes in growth value, the display method of digital assets will automatically adjust across different platforms and scenarios. In virtual exhibitions, digital assets with high growth value will automatically receive more prominent positions and richer display effects, enhancing their attractiveness.

[0157] Dynamic usage permissions: Digital assets with high growth values ​​can automatically unlock more usage scenarios and permissions. In specific virtual worlds or platforms, only digital assets with high growth values ​​can participate in specific activities or enjoy special services.

[0158] To address the need for automated digital asset updates following growth value updates, a smart contract was written, which defines: The structure of digital asset attributes, including growth value, metadata of artworks, etc.

[0159] The data includes: struct NFTAttributes{ uint256 growthValue; / / Growth value string metadata; / / Artwork metadata / / This can include other attributes, such as the artwork's name, creator, and creation date. } The digital asset update device can form an update function that can receive new growth values ​​and logically describe how the growth values ​​affect the attributes and presentation of digital assets.

[0160] The functions include: function updateGrowthValue(uint256_newGrowthValue, string memory_newMetadata) public { / / Permission verification to ensure the caller has the right to update. require(hasPermission(msg.sender), "Caller lacks permission"); / / Validate the new growth value to ensure its validity. require(validateGrowthValue(_newGrowthValue), "Invalid growth value"); Update NFT property logic nftAttributes.growthValue =_newGrowthValue; nftAttributes.metadata =_newMetadata; / / Trigger dynamic adjustments to attributes and display formats, which may include adjusting colors, changing styles, unlocking new animation effects, etc. adjustPresentation(nftAttributes); } Example of permission verification function function hasPermission(address_caller) private view returns(bool){ / / Implement permission verification logic} Growth value verification function example function validateGrowthValue(uint256_growthValue) private pure returns(bool) { / / Implement growth value verification logic } Attribute and presentation adjustment functions function adjustPresentation(NFTAttributes memory_attributes) private{ / / Adjust the NFT's presentation based on growth value and metadata } This function also restricts the rules and conditions for attribute updates, including under what circumstances an update is triggered and who has the right to trigger an update.

[0161] The update is triggered when a new growth value is calculated and verified through an external API or some other mechanism.

[0162] Update permissions: Only the contract owner or users with specific permissions can trigger the update function.

[0163] Once a new growth value is received, the contract layer verifies it correctly and automatically executes the update function, submitting the new growth value to the smart contract. After receiving the new growth value, the smart contract dynamically adjusts the attributes of the digital asset according to the pre-written logic.

[0164] In this embodiment of the application, after determining the growth value, step S501 may also be included, which will be described in conjunction with the steps: S501. Based on one or more of the growth value cap, the change reduction mechanism, and the growth value change cooldown time, the growth value of the target digital asset is modified.

[0165] In this embodiment, adjusting the growth value based on the upper limit of the growth value may include determining that if the determined growth value is greater than the upper limit of the growth value, the growth value represented by the upper limit of the growth value is the final growth value. Adjusting the growth value based on the diminishing change mechanism includes: as the digital asset grows over time or the number of updates increases, the effect of multi-dimensional reference data obtained from multiple target websites on the change of growth value gradually decreases. Adjusting the growth value based on the growth value change cooldown time may include: no two changes of growth value can occur within the period represented by the cooldown time.

[0166] The digital asset update mechanism allows setting a growth value cap for each digital asset. Once the growth value reaches the cap, it will no longer be increased through attribute changes or equity increases. This prevents the growth value from increasing indefinitely.

[0167] The methods include: MAX_GROWTH_VALUE=1000 #Example value, can be adjusted according to actual needs. def cap_growth_value(growth_value): Control the growth value to not exceed the set upper limit. :param growth_value: Current growth value return: Growth value after restrictions return min(growth_value, MAX_GROWTH_VALUE) Specifically, as the growth value of digital assets increases, its growth rate should gradually slow down. A diminishing returns mechanism can be introduced to ensure that the effect of each attribute change and increase in equity on the growth value gradually decreases.

[0168] The methods include: def decremental_growth_increase(current_value, increment): As the growth value increases, its growth rate gradually slows down. :param current_value: Current growth value :param increment: The growth value increased this time return: The actual increase after decrementing. factor1 = 1 / (1 + 0.001 * current_value) # As the growth value increases, the growth factor decreases. return increment*factor One approach is to introduce a cooldown mechanism to prevent frequent attribute changes and rights increases within a certain period, thereby controlling the excessively rapid growth of growth values.

[0169] import time COOLDOWN_PERIOD = 86400#24 hours last_update_time ={} def can_update(nft_id): Determine if the NFT is within its cooling-off period :param nft_id: The unique identifier of the NFT return: Can it be updated? current_time = time.time() if nft_id in last_update_time: if current_time-last_update_time[nft_id] <COOLDOWN_PERIOD: return False last_update_time[nft_id]= current_time return True In this way, the digital asset update mechanism takes into account the growth value cap, the reduction mechanism, and the cooldown time, making the overall score more reasonable and fair.

[0170] The methods include: def evaluate_trustworthiness(url, user_data, social_data, monitoring_data, metadata_changes, news_events, historical_data, content, reference_contents, security_data, PageRank_API, threshold, site_trust_weights, site_scores): F_weight_score = F weight(url, PageRank_API) F_user_behaviour_score -F user_behaviour(user_data) F_social_verification_score = F_social_verification(social_data) F_realtime_monitoring_score = F realtime monitoring(monitoring_data) F_metadata_changes_score = get metadata changes_score(metadata_changes) F_news_event_score=get news event score(news_events) F_historical_data_score = get historical data score(historical_data) F_content_similarity_score =F content similarity(content, reference_contents) F_security_evaluation_score = F security evaluation(security_data) Scores= { 'user_behaviour': F_user_behaviour_score, social_verification': F_social_verification_score, 'realtime_monitoring': F_realtime_monitoring_score, 'metadata_changes': F_metadata_changes_score, 'news_events': F_news_event_score, 'historical_data': F_historical_data_score, 'content_similarity': F_content_similarity_score, 'security_evaluation': F_security_evaluation_score } composite_score = calculate composite score(scores,weights) # Control growth value composite_score = cap growth value(composite_score) composite_score = decremental growth increase(composite_score, composite_score) site_scores['current_site']= composite_score weighted_composite_score = calculate weighted composite score(site_scores, site_trust_weights) return weighted_composite_score nft_growth_value = evaluate_trustworthiness(url, user data, socialdata, monitoring data, metadata changes, news events, historical data, content, reference contents, security data, PageRank API, threshold, site trust weights, site scores) print(f"NFT Growth Value: {nft_growth_value}") In this embodiment, the growth value of the target digital asset is modified based on one or more of the following: a growth value cap, a change reduction mechanism, and a growth value change cooldown time. This effectively controls the unlimited increase of the growth value and prevents the target digital asset's attributes from being updated too quickly and excessively.

[0171] Please see Figure 8 The following is a block diagram illustrating the digital asset update method provided in this application embodiment, which will be described in conjunction with the stages shown: Detailed explanation of each stage: 1. Data crawling stage; Note: The system uses big data crawling technology to regularly retrieve information related to digital assets from various websites. The dimensions of data crawling include user behavior, community engagement, metadata changes, recognition and influence, news and events, historical valuations and transaction records, art styles and categories, community activities and sentiment feedback, etc.

[0172] process: Identify the target website and conduct a credibility assessment; Use a custom crawler to regularly crawl the target website; Crawle and store data; 2. Data processing stage; Note: The crawled data is cleaned, extracted, and vectorized to ensure its accuracy and validity.

[0173] process: Data cleaning: deduplication, noise reduction, and filtering out irrelevant information; Data extraction: Extract relevant metrics for each digital asset; Data vectorization: Standardizing the processing and quantifying the characteristics of each digital asset; 3. Credibility weighting stage; Note: Based on the credibility weight of each website previously evaluated, different weights were assigned to the same metric data from different websites. A weighted average or other suitable weighting method was used to calculate a comprehensive metric vector for each digital asset.

[0174] process: Calculate the scores for each indicator; Assign weights to each dimension; Calculate the comprehensive index vector; Weighted processing based on website credibility; 4. Growth value calculation stage; Note: The growth value of digital assets is dynamically calculated based on comprehensive indicators from various dimensions and time decay factors.

[0175] process: Set dynamic baseline values; Calculate the normalized values ​​for each dimension; The final growth value is calculated by incorporating a time decay factor. 5. Growth value control phase; Note: Growth value caps, diminishing returns mechanisms, and attribute change cooldowns are introduced to prevent growth values ​​from increasing indefinitely and to maintain market fairness and competitiveness.

[0176] process: Check if the growth value has reached the upper limit; Apply a decreasing mechanism to control the growth rate; Determine if it is within the cooling time; 6. Digital asset update phase; Note: Based on the new growth value, the metadata, functions, visual effects, and profit distribution of digital assets are automatically updated through smart contracts.

[0177] process: Update triggered: based on external events or conditions; Receive new data: including new growth values ​​and metadata; Permission verification: Check for update permissions; Growth value verification: review of rationality and validity; Data update: Back up the old data and update it; Display format adjustment: Adjust as needed; Audit log: Records update information; Update complete: Notify digital asset holders or publish an update.

[0178] Please see Figure 9 This is a schematic diagram of the structure of the digital asset update device provided in the embodiments of this application.

[0179] This application embodiment also provides a digital asset update device 800, including: a data acquisition unit 801 and an update unit 802.

[0180] The data acquisition unit 801 is used to determine the growth value of the target digital asset based on multi-dimensional reference data acquired periodically for the target digital asset. The update unit 802 is used to update the attributes of the target digital asset based on the growth value.

[0181] In this embodiment of the application, the data acquisition unit 801 in the digital asset update device 800 is used to determine multiple target websites that meet the credibility requirements based on multi-dimensional credibility assessment data acquired periodically from multiple websites; and to determine the growth value based on the multi-dimensional reference data acquired periodically from the multiple target websites.

[0182] In this embodiment of the application, the data acquisition unit 801 in the digital asset update device 800 is used to perform data analysis on each dimension of the credibility assessment data of each website and determine the sub-credibility corresponding to each dimension of the credibility assessment data. The credibility of each website is determined by non-linearly combining multiple sub-credibility values. Among the multiple websites, those with a credibility level greater than a credibility threshold are identified as the multiple target websites.

[0183] In this embodiment of the application, the data acquisition unit 801 in the digital asset update device 800 is used to perform standardization processing on the multidimensional reference data of each target website to determine the multidimensional features corresponding to each target website. A weighted average is performed on each dimension of the features corresponding to the multiple target websites to determine the multidimensional comprehensive features. The growth value is determined based on the time decay factor and the multidimensional comprehensive characteristics; wherein, the time decay factor is determined by a dynamic index based on a preset value; the dynamic index is determined by the negative of the ratio of the time elapsed between the current time and the preset time elapsed for the multidimensional reference data. In this embodiment of the application, the multidimensional credibility assessment data includes one or more of the following: website influence data, website user behavior data, website social network related data, website real-time monitoring data, multi-source related data, website data similarity, and website security data; The multidimensional reference data in each of the target websites includes one or more of the following for the target website: user behavior data, community engagement data, metadata change data, recognized influence data, related event impact data, historical transaction value data, art style data, and community activity related data.

[0184] In this embodiment of the application, the update unit 802 in the digital asset update device 800 is used to modify the growth value of the target digital asset based on one or more of the growth value upper limit, the change reduction mechanism, and the growth value change cooling time.

[0185] In this embodiment of the application, the attributes include one or more of the following: metadata attributes, visual attributes, rights attributes, functional attributes, display permission attributes, certificate and record attributes, and social interaction attributes; The attribute of the target digital asset updated based on the growth value includes one or more of the following: The updates to the metadata attributes based on the smart contract and the growth value include adding new descriptive information, tags, and story background; The updates to the visual attributes based on the smart contract and the growth value include changing the color scheme, adding special effects, or transforming into different art styles; The updates to the equity attributes based on the smart contract and the growth value include automatic adjustment updates to profit distribution, automatic dividend mechanisms, and updates to multiple transaction dividend mechanisms. The updates to the functional attributes based on the smart contract and the growth value include automatic unlocking of special functions and enhancement of interactive functions; The update of the display permission attribute based on the smart contract and the growth value includes automatic adjustment of the display method and dynamic adjustment of usage permissions; The updates to the certificate and record attributes based on the smart contract and the growth value include the automatic generation and updating of the digital certificate for the target digital asset; The updates to the social interaction attributes based on the smart contract and the growth value include social media binding and enhanced community interaction.

[0186] It should be noted that, in the embodiments of this application, if the above-described digital asset update method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a digital asset update device (which may be a personal computer, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0187] Correspondingly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the method of the digital asset update device 800.

[0188] It should be noted that the descriptions of the storage medium and device embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0189] It should be noted that, Figure 10 A schematic diagram of a hardware entity of an electronic device provided in an embodiment of this application, such as... Figure 10 As shown, this application embodiment provides an electronic device 900, including a memory 902 and a processor 901. The memory 902 stores a computer program that can run on the processor 901. When the processor 901 executes the program, it implements the steps in the above-described method, wherein; Processor 901 typically controls the overall operation of electronic device 900.

[0190] The memory 902 is configured to store instructions and applications executable by the processor 901, and can also cache data to be processed or already processed (e.g., image data, audio data, voice communication data and video communication data) in the processor 901 and various modules in the electronic device 900. It can be implemented by flash memory or random access memory (RAM).

[0191] Correspondingly, this application also provides a computer program product, including a computer program that can be executed by the processor 901 of the electronic device 900 to complete the steps in the method of the digital asset update device 800.

[0192] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0193] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0194] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the apparatus or units can be electrical, mechanical, or other forms.

[0195] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0196] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0197] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0198] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0199] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for updating a digital asset, the method comprising: include: The growth value of the target digital asset is determined based on multi-dimensional reference data acquired periodically for the target digital asset. The attributes of the target digital asset are updated based on the growth value.

2. The digital asset updating method of claim 1, wherein, The determination of the growth value of the target digital asset based on multi-dimensional reference data acquired periodically for the target digital asset includes: Based on multi-dimensional credibility assessment data obtained periodically from multiple websites, multiple target websites that meet the credibility requirements are identified. The growth value is determined based on the multidimensional reference data obtained periodically from multiple target websites.

3. The digital asset updating method of claim 2, wherein, The multi-dimensional credibility assessment data, obtained periodically from multiple websites, identifies multiple target websites that meet the credibility requirements, including: For each dimension of the credibility assessment data of each website, data analysis is performed to determine the sub-credibility corresponding to each dimension of the credibility assessment data; The credibility of each website is determined by non-linearly combining multiple sub-credibility values. Among the multiple websites, those with a credibility level greater than a credibility threshold are identified as the multiple target websites.

4. The digital asset updating method of claim 2, wherein, The determination of the growth value based on the multidimensional reference data obtained periodically from multiple target websites includes: The multidimensional reference data for each target website is standardized to determine the multidimensional features corresponding to each target website. A weighted average is performed on each dimension of the features corresponding to the multiple target websites to determine the multidimensional comprehensive features. The growth value is determined based on the time decay factor and the multidimensional comprehensive characteristics; wherein the time decay factor is determined by a dynamic index based on a preset value; the dynamic index is determined by the negative of the ratio of the time elapsed between the current time and the preset time elapsed for the multidimensional reference data.

5. The digital asset updating method of claim 2, wherein, The multidimensional credibility assessment data includes one or more of the following: website influence data, website user behavior data, website social network related data, website real-time monitoring data, multi-source related data, website data similarity, and website security data; The multidimensional reference data in each of the target websites includes one or more of the following for the target website: user behavior data, community engagement data, metadata change data, recognized influence data, related event impact data, historical transaction value data, art style data, and community activity related data.

6. The digital asset updating method according to claims 1 to 5, characterized in that, The method further includes: The growth value of the target digital asset is adjusted based on one or more of the following: growth value cap, change reduction mechanism, and growth value change cooldown time.

7. The digital asset updating method of any one of claims 1 to 5, characterized in that, The attributes include one or more of the following: metadata attributes, visual attributes, rights attributes, functional attributes, display permission attributes, certificate and record attributes, and social interaction attributes; The attribute of the target digital asset updated based on the growth value includes one or more of the following: The updates to the metadata attributes based on the smart contract and the growth value include adding new descriptive information, tags, and story background; The updates to the visual attributes based on the smart contract and the growth value include changing the color scheme, adding special effects, or transforming into different art styles; The updates to the equity attributes based on the smart contract and the growth value include automatic adjustment updates to profit distribution, automatic dividend mechanisms, and updates to multiple transaction dividend mechanisms. The updates to the functional attributes based on the smart contract and the growth value include automatic unlocking of special functions and enhancement of interactive functions; The update of the display permission attribute based on the smart contract and the growth value includes automatic adjustment of the display method and dynamic adjustment of usage permissions; The updates to the certificate and record attributes based on the smart contract and the growth value include the automatic generation and updating of the digital certificate for the target digital asset; The updates to the social interaction attributes based on the smart contract and the growth value include social media binding and enhanced community interaction.

8. An electronic device, comprising: It includes a memory and a processor, the memory storing a computer program that can run on the processor, the processor executing the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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