Block chain-based overall operation asset and sub-operation asset value evaluation method and program product

Abstract

The invention provides an operating asset value evaluation method based on a block chain, and the method comprises the steps: obtaining the income data information of operating assets in a distributed account book, and enabling the income data information to comprise an income value; obtaining regional income and total position income, and calculating a position aging coefficient based on the regional income and the total position income; obtaining operation asset evaluation data, the operation asset evaluation data at least comprising operation asset user basic data and user behavior data, and obtaining an operation asset evaluation data coefficient based on the operation asset evaluation data; and integrating the income value, the position timeliness coefficient and the operation asset evaluation data coefficient into an analyzable data set, and inputting an asset evaluation model in the smart contract to obtain an operation asset periodic value evaluation result. More reasonable and accurate asset value is provided by utilizing asset evaluation in the smart contract, and the method is applied to transaction management of operating assets.

Description

Technical Field

[0001] The present application relates to the technical field of asset value assessment, and specifically to a method and program product for assessing the value of overall operating assets and sub-operating assets based on blockchain. Background Art

[0002] With the development of the Internet and blockchain technology, the application scope of online transactions continues to expand. Online asset trading not only brings great convenience to people's asset investment, but also puts forward new technical requirements for the pricing method of trading assets. During the asset holding period, the factors associated with the asset value are constantly changing, causing the asset value to fluctuate accordingly. The asset pricing method in the existing technology has the problem of information asymmetry, which often leads to unreasonable pricing. Large technology companies monopolize user data and platform control, lack of transparency and trust mechanisms, making it difficult for users to evaluate whether the asset value pricing they are involved in is fair, and whether the potential return calculation of holding assets is reasonable.

[0003] There is no method in current technology that combines blockchain technology to evaluate operating assets in units of time. Traditional asset evaluation methods fail to fully utilize the decentralized, transparent and tamper-proof characteristics of blockchain to solve many problems in existing asset pricing, especially the inability to dynamically adjust the value of operating assets in a timely manner according to changes in relevant factors. Therefore, it is necessary to develop an operating asset value evaluation method based on blockchain technology that can obtain and process multi-dimensional data related to assets in real time and ensure the transparency, fairness and efficiency of the evaluation process. This is a key issue that needs to be urgently solved in the current technical field. Summary of the invention

[0004] The present application embodiment provides a method for evaluating the value of overall operating assets based on blockchain, including:

[0005] Obtaining the income data information of the operating assets in the distributed ledger, the income data information including the income value;

[0006] Obtain the regional income and total location income of the operating assets in the distributed ledger, and obtain the location time efficiency coefficient based on the regional income and the total location income;

[0007] Obtaining operating asset evaluation data, the operating asset evaluation data includes operating asset user basic data and user behavior data, and obtaining operating asset evaluation data coefficients based on the operating asset evaluation data;

[0008] The income value, location timeliness coefficient and operating asset assessment data coefficient are integrated into a data set through smart contracts, and the data set is input into the asset assessment model on the blockchain to obtain the operating asset value assessment results.

[0009] Through the overall operating asset value assessment method based on blockchain, multiple problems existing in the prior art are solved. First, by obtaining the operating asset income data information, regional income and total location income in the distributed ledger, the location time efficiency coefficient is obtained, so that the impact of the asset location on its value can be dynamically reflected. Secondly, the basic data of operating asset users and user behavior data are obtained, and the operating asset assessment data coefficient is calculated based on these data, making the assessment result more comprehensive and accurate. This technical solution integrates the income value, location time efficiency coefficient and operating asset assessment data coefficient into a data set through smart contracts, and inputs them into the asset assessment model on the blockchain to obtain the operating asset value assessment result. This method utilizes the decentralized, transparent and tamper-proof characteristics of blockchain, ensures the fairness of the assessment process and the reliability of data, and avoids the problems of information asymmetry and data monopoly in traditional assessment methods. Through this method, the value of operating assets can be dynamically adjusted in real time according to the changes in asset-related factors, solving the problems of unreasonable asset pricing, lack of transparency and trust mechanism in the prior art, and greatly improving the accuracy and fairness of asset value assessment. In addition, the implementation of this method relies on smart contracts and blockchain technology, which not only improves the degree of automation of the evaluation process, but also reduces the possibility of human intervention, bringing unexpected technical effects such as improved evaluation efficiency and increased credibility of evaluation results.

[0010] The embodiment of the present application also provides a method for evaluating the value of sub-operating assets based on blockchain, where the sub-operating assets are transaction units after the operating assets are divided, including:

[0011] Obtain the operating asset value assessment result according to the above-mentioned blockchain-based overall operating asset value assessment method by calling the smart contract;

[0012] Obtaining the preset sub-operation asset partition power value and the sum of all partition power values, and calculating the sub-operation asset time efficiency coefficient based on the sub-operation asset partition power value and the sum of all partition power values;

[0013] Obtain multi-source value data of sub-operating asset holders, and calculate the contribution value index based on the multi-source value data of the holders by using the weighted average method through smart contracts;

[0014] The operating asset value assessment results, sub-operating asset time efficiency coefficient and contribution value index are input into the sub-operating asset assessment model in the blockchain smart contract to obtain the sub-operating asset value assessment results.

[0015] The embodiment of the present application effectively solves many problems in the prior art through a sub-operating asset value assessment method based on blockchain. First, the method obtains the overall operating asset value assessment result based on blockchain by calling a smart contract, thereby providing reliable basic data for the assessment of sub-operating assets. This method utilizes the transparency and immutability of blockchain to ensure the reliability of the data source and the fairness of the assessment process. Secondly, the method obtains the preset sub-operating asset partition power value and the sum of all partition power values, and calculates the sub-operating asset time efficiency coefficient based on these data, so as to dynamically reflect the impact of each partition on the value of the sub-operating asset. This calculation method makes the assessment result more detailed and accurate, and can accurately capture the actual situation of different partitions. In addition, by obtaining multi-source value data of sub-operating asset holders, and calling the weighted average method based on these data to calculate the contribution value index, the assessment process comprehensively considers the actual contribution and influence of each holder. This method not only improves the comprehensiveness and fairness of the assessment results, but also avoids the unreasonable pricing problem caused by information asymmetry and data monopoly in traditional assessment methods. By inputting the operating asset value assessment results, sub-operating asset timeliness coefficient and contribution value index into the sub-operating asset assessment model in the blockchain smart contract, the sub-operating asset value assessment results are obtained. This process utilizes the automation characteristics of the blockchain, improves the assessment efficiency, reduces the possibility of human intervention, and ensures the transparency and fairness of the assessment process. In summary, this technical solution not only solves the problems of unreasonable asset pricing, lack of transparency and trust mechanism in the existing technology, but also brings unexpected technical effects, such as automation of the assessment process, improved credibility of the assessment results and significant improvement in assessment efficiency, thus providing a new and reliable solution for the assessment of sub-operating assets.

[0016] The embodiment of the present application also provides a blockchain node program product for operating asset value assessment, including a computer program, which implements the above-mentioned blockchain-based sub-operating asset value assessment method when executed by a processor. The blockchain node program product completely embeds the assessment process into the blockchain network, and uses the decentralized characteristics of the blockchain and the automatic execution mechanism of the smart contract to ensure the transparency, credibility and non-tamperability of the assessment results, thereby establishing an efficient, fair and credible operating asset value assessment system.

[0017] The embodiment of the present application also provides a blockchain node publishing method for operating asset value assessment, comprising the following steps:

[0018] Deploy the smart contract to the blockchain network, record the contract address and ABI, and the smart contract includes the overall operating asset value assessment method based on blockchain and the sub-operating asset value assessment method based on blockchain as described above;

[0019] Obtain operating asset revenue data, regional revenue, total location revenue, operating asset valuation data, and multi-source value data of sub-operating asset holders through blockchain nodes or preset programs, and input them into smart contracts;

[0020] Smart contracts are called periodically to conduct asset valuations, and periodic overall operating asset value valuation results and sub-operating asset value valuation results are obtained.

[0021] An embodiment of the present application also provides a blockchain node program product for operating asset value assessment, including a computer program, characterized in that when the computer program is executed by a processor, it implements the above-mentioned blockchain node publishing method for operating asset value assessment.

[0022] This application provides a method and program product for evaluating the value of overall operating assets and sub-operating assets based on blockchain, uses the uniqueness and immutability of blockchain to ensure the security of value evaluation, and uses the asset evaluation model in smart contracts to provide more reasonable and accurate asset values. It provides asset holders with fair value evaluation based on revenue value and decentralized, transparent and open assets. It can be applied to the transaction management of various types of assets and can encourage more people to participate in asset transaction management. This application combines and anchors the actual industry, which will change the defects of assets in value evaluation such as opacity, instability, and unpredictability, and provide a more fair and just pricing method for all contributors, rather than only asset owners obtaining the vast majority of benefits. This application promotes the digitization and decentralization of the industry, truly returns the rights and interests to the general public, and is conducive to the realization of an asset management form with the participation of all people. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings.

[0024] Figure 1 This is a flowchart of a method for evaluating the value of overall operating assets based on blockchain according to an embodiment of the present application;

[0025] Figure 2 This is a flowchart of a blockchain-based sub-business asset value assessment method according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in a variety of forms and should not be construed as being limited to the embodiments set forth herein; rather, these embodiments are provided so that the present application will be comprehensive and complete and the concept of the example embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their repeated descriptions will be omitted. "Or" and "or" in the specification may both mean "and" or "or". Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices. Although "first" or "second" etc. are used in this specification to represent certain features, they are only used to represent the role, and are not used as a limitation on the number and importance of specific features.

[0027] The existing asset transaction management system and method, after the operating assets are put on the chain, a unique identifier is assigned to each operating asset and registered on the blockchain. The operating assets are divided into sub-operating assets and put on the chain, each sub-operating asset is assigned a unique identifier, and the sub-operating assets are traded as holding units of the asset holder. This can be achieved by creating a smart contract, which contains the identifier and related information of the asset. Use smart contracts to define and execute the correspondence between assets and blockchains. Smart contracts can include asset attributes, owner information, historical transaction records, etc. Ensure that the code and logic of smart contracts can accurately reflect the characteristics of operating assets. The operating assets are divided and put on the chain. Implement authentication and permission control mechanisms on the blockchain to ensure that only authorized users can modify or access information about specific assets. This can be achieved by using encryption technology and public-private key mechanisms. In addition, key information of operating assets is put on the chain to ensure that this information is permanently recorded on the blockchain. This includes asset ownership transfer, transaction history, status changes, etc. Metadata processing: Includes metadata of assets, such as location, status, life cycle, etc., to provide a more comprehensive understanding of the situation of operating assets. These metadata can be associated with smart contracts and updated over time. The key data of operating assets is collected on the blockchain using sensors, external data sources, etc. This may include production data, sales data, maintenance records, etc. The data is written to the blockchain in the form of transactions.

[0028] The data is collected using the BAA dimension system, namely Before data (previous historical data of the same period), After (future data, referring to specific environmental factors, such as economic activities related to seasonality and special time periods, night economy, number of tourists, etc.), and Average (average, average of each year). The B-past, A-average, and A-future forecasts of operating data are analyzed and corrected by using methods such as artificial intelligence. This method can not only more accurately predict the value changes of operating assets, but also adjust the evaluation results in a timely manner, ensure the accuracy and reliability of asset value evaluation, and enable enterprises and other business application stakeholders to adjust business strategic decisions and resource allocation more timely. This data collection and processing method based on the BAA dimension system provides a more comprehensive and dynamic perspective for asset value evaluation, and further enhances the effectiveness and intelligence of operating asset management on the blockchain.

[0029] like Figure 1 As shown, this application provides a method for evaluating the value of overall operating assets based on blockchain, including:

[0030] S101. Obtaining the revenue data information of the operating assets in the distributed ledger, the revenue data information includes the revenue value. In this embodiment, the blockchain or other distributed ledger technology is interacted with, connected to the distributed ledger system, and the operating assets recorded in the ledger are determined. The operating assets can be advertising screens, parking spaces, dining tables or other forms of assets to ensure that these assets can be uniquely identified and located. For each operating asset, retrieve the operating income information related to it recorded in the distributed ledger of the blockchain. In the distributed ledger, this operating income information can exist in the form of transaction records, smart contract execution results or other forms.

[0031] Optionally, the revenue data information also includes the payer brand information of the operating income. In this embodiment, the source of the revenue data can be automatically acquired by the program, the asset holder or the relevant participant submits the data, which can be done through a decentralized application (DApp) or a user interface, including recording the revenue data of the revenue value of the operating assets in a distributed ledger. The revenue data includes the market performance, brand familiarity, brand reputation, and brand influence of the payer brand information. Based on these four parameters, the payer brand value is measured based on big data classification, and the payer brand-consumption value evaluation coefficient is calculated. Specifically, smart contracts can collect data by integrating with various data sources, including social media, transaction records, user evaluations and other data, and use various big data analysis technologies to analyze the data, including machine learning and natural language processing to analyze and process multi-source data. Based on the data analysis and processing results.

[0032] Optionally, a calculation method for obtaining a brand-consumption value evaluation coefficient by weighting the four parameters of the corporate brand is as follows:

[0033] The calculation of the market performance index MP is based on the market share. The market share of the payer enterprise in its relevant market recorded in the distributed ledger is obtained, and the market performance index is calculated based on the size of the market share. An evaluation coefficient between 0 and 1 is given based on the evaluation results. For example, the following formula can be used for calculation:

[0034] Market performance indicator MP = market share of the payer company / overall market share of the market

[0035] The brand familiarity index BF obtains the user's perception data of the payer enterprise recorded in the distributed ledger, calculates the brand familiarity index based on the degree of perception, and gives an evaluation coefficient between 0 and 1 based on the evaluation result. For example, the following formula can be used for calculation:

[0036] Brand familiarity index BF = number of users who know the payer company / total number of users

[0037] The brand reputation indicator BR is evaluated through brand favorability data, obtains the positive emotions of users towards the payer enterprise recorded in the distributed ledger, and calculates the brand reputation indicator based on the size of the emotion, and gives an evaluation coefficient between 0 and 1 based on the evaluation results. For example, the following formula can be used for calculation:

[0038] Brand reputation index BR = number of positive emotions / (number of positive emotions + number of negative emotions)

[0039] The brand influence index BI is evaluated through media exposure data, obtaining the media exposure to the payer enterprise recorded in the distributed ledger, and calculating the brand influence index based on the exposure. And an evaluation coefficient between 0 and 1 is given based on the evaluation results. For example, the following formula can be used for calculation:

[0040] Brand influence index BI = media exposure / overall media exposure

[0041] Therefore, the brand-consumer value evaluation coefficient BV is estimated as follows:

[0042] BV=RE*MP*BF*BR*BI

[0043] Optionally, the income value of the operating assets is corrected to the result after being influenced by the brand-consumer value assessment coefficient BV, that is, the product of the income value of the initial operating assets recorded in the distributed ledger and the brand-consumer value assessment coefficient BV is used as the income value of the operating assets after the correction.

[0044] S102. Obtain the regional income and total location income of the operating assets in the distributed ledger, and obtain the location time efficiency coefficient based on the regional income and the total location income. In this embodiment, the location time efficiency coefficient is determined by the location and positioning area of ​​the operating assets. The attractiveness and value of operating assets in different locations are different. In this embodiment, the location time efficiency coefficient LTV (Location's AD Time Value) is obtained by the proportion of the regional income of the operating assets in the total location income, and the location time efficiency coefficient LTV is used to evaluate the value of operating assets in different locations.

[0045] Optionally, the location time effectiveness coefficient LTV is calculated as follows:

[0046] LTV = (regional income / total location income) * 100%

[0047] S103. Obtain operating asset evaluation data, where the operating asset evaluation data includes operating asset user basic data and user behavior data, and obtain an operating asset evaluation data coefficient based on the operating asset evaluation data.

[0048] In this embodiment, the user portrait index can comprehensively reflect the attractiveness and influence of the operating assets on different users, and then periodically evaluate the value of the operating assets through the index to reflect the objective and true value of the operating assets. User basic data includes personal information, such as gender, age and geographic location, and device information, such as the type of device used, operating system and browser. User behavior data includes user stay behavior at the operating assets, user interaction behavior, user purchase behavior and user search behavior.

[0049] Optionally, the user behavior data also includes dwell time, touch interaction, interactive click heat map, QR code scanning, interactive feedback, mobile device networking and real-time feedback data.

[0050] Optionally, the stop duration index SDI (Stop Duration Index) that affects the valuation of operating assets is calculated based on the data of user behavior stay duration. In this embodiment, the behavioral data of the stay duration measures the time the user stays in front of the operating asset. A long stay duration may indicate that the user is interested in the operating asset, while a short stay duration may indicate a lack of attraction. Use a camera or other sensor to monitor the user's presence time in front of the operating asset. Start timing when the user enters the camera's field of view and end timing when the user leaves. Represents the average stay time, which can be used to understand the user's interest in the operating asset. Used to evaluate the user's attention to the operating asset.

[0051] Optionally, the stop duration index SDI (Stop Duration Index) is calculated as follows:

[0052] Stay duration index SDI = (actual stay duration / service duration) * 100%

[0053] Among them, the actual stay time refers to the time the user stays in front of the operating assets, and the actual service time is the total time the operating assets are put into service.

[0054] Optionally, the interaction rate IR (Interaction Rate) that affects the valuation of operating assets is calculated based on the interaction data between user behavior and operating assets. In this embodiment, the interactive behavior data records the user's interactive gestures on the operating assets, such as sliding, zooming, etc., for operating assets that support interactive interaction technology. The coordinates and types of interactive events can be captured and analyzed. Measuring the frequency of user interaction with operating assets reflects the attractiveness of operating assets and user engagement.

[0055] Optionally, the interaction rate IR (Interaction Rate) is calculated as follows:

[0056] Interaction rate IR = (interaction times / impression times) * 100%

[0057] Among them, the number of interactions refers to the number of times that users interact with business assets, and the number of impressions refers to the number of times that interactive content is displayed on business assets.

[0058] Optionally, a QR code scanning rate that affects the valuation of operating assets is obtained based on user behavior. In this embodiment, when an operating asset contains a QR code, the number of times the user scans the QR code is recorded. The QR code scanning rate helps to understand whether the user is interested in specific information in the operating asset. Use a camera or scanner to capture the user's action of scanning the QR code on the operating asset. Record the time of scanning and the content of the scan. Used to evaluate the user's interest in the QR code contained in the operating asset.

[0059] Optionally, the QR code scanning rate SR (Scan Rate) is calculated as follows:

[0060] SR = (number of QR code scans / number of QR code displays) * 100%

[0061] Among them, the number of QR code displays represents the number of potential users, and the number of QR code scans represents the number of times potential users actually scanned.

[0062] Optionally, a mobile device connection rate index that affects the valuation of operating assets is obtained based on user behavior. In this embodiment, when the operating asset has a network connection function, the user's connection or interaction with the operating asset through a mobile device is recorded, such as through Bluetooth or Wi-Fi. The device connection information is recorded by monitoring the Bluetooth or Wi-Fi signals around the operating asset. This requires the user to enable the relevant connection function on the mobile device.

[0063] Optionally, a mobile device connection rate index MR (Mobile device connection rate) is calculated according to the device connection information.

[0064] SR = (number of mobile device connections / number of mobile devices sniffed) * 100%

[0065] Among them, the number of mobile device connections is the number of devices connected to the network of the operating assets, and the number of mobile devices sniffed is the number of mobile devices sniffed by the network equipment of the operating assets, for example, when the operating assets use WIFI to provide the network, the number of mobile devices sniffed.

[0066] Optionally, the user basic data and user behavior data are integrated and normalized, features are extracted based on feature engineering, a user portrait model is established, and the user portrait index value is calculated. However, this application does not specifically limit the method of extracting features, and big data classification and artificial intelligence can also be used to extract features. Technical methods that can achieve the technical effects of this application are all technical solutions to be included in the present invention.

[0067] Optionally, the calculation method of the user profile index UPI (User Profile Index) is as follows:

[0068]

[0069] in, It is the weight of each indicator, which can be adjusted according to business needs. Each indicator is standardized to obtain comparable scores in different ranges, such as age score Age Score, gender score Gender Score, geographic location score Location Score, dwell time index score SDI Score, interaction rate score IR Score, and QR code scanning rate score SR Score. However, the calculation of the user portrait index UPI in the present invention is not limited to this. The selection of indicators and their corresponding weights can be increased or decreased according to the business demand scenario. In some embodiments, when the user portrait index does not include any of the age score Age Score, gender score GenderScore, geographic location score Location Score, dwell time index score SDI Score, interaction rate score IR Score, and QR code scanning rate score SR Score, the value score can be considered to be 0. The calculation indicators and corresponding weights that can achieve the technical effects of the present invention belong to the protection scope of the present invention. In some embodiments, It can also be obtained by expert scoring or by learning through other machine learning models, and its specific value is not limited here.

[0070] Optionally, the operating asset evaluation data also includes network traffic data, which includes social network data with operating asset attribute data, and the operating asset attribute data includes at least one or more of the name, address, and number of the operating asset. In this embodiment, an asset evaluation network is established based on the social network data for one or more of the operating asset information such as name, address, and number, and then the operating asset network value evaluation coefficient is obtained. In the Web3.0 environment, social and community factors are crucial to the value evaluation of assets. Consider analyzing factors such as discussions on social media, community activities, and developer participation.

[0071] Optionally, social network data also includes the number of shares, comments, and likes of operating assets on social media platforms, and the social network valuation index Social NetWork Coefficient is obtained based on social network data. These data can be collected through social media APIs or analysis tools. Here, the social network coefficient and network traffic coefficient are used to reflect the influence and traffic attraction of the operating asset on social media and the overall network. The introduction of these coefficients makes the evaluation of operating assets more comprehensive, taking into account the performance on social networks and the overall network. Social network influence index:

[0072] Social network sharing times (Share Count): records the number of times the operating asset-related content is shared on social networks.

[0073] Number of social network comments (Comment Count): counts the number of comments on business asset-related content on social networks.

[0074] Like Count on social networks: records the number of times the content related to operating assets is liked on social networks.

[0075] Social network valuation index SNC = (Share Count + Comment Count + Like Count) / Total Social Interactions * 100%

[0076] Among them, Total Social Interactions is the sum of the number of social network shares, social network comments, and social network likes.

[0077] Optionally, the network traffic data also includes traffic data generated based on the built-in identifier of the operating asset, and a built-in identifier traffic index is obtained. In this embodiment, the built-in identifier of the operating asset can be an address in the operating asset that can be accessed through other network devices, such as a QR code, an Internet IP address, a subroutine address based on other applications, an interface for entering a blockchain space, etc. The present invention does not specifically limit the type of built-in identifier. Based on the network data generated by the interaction between the operating asset audience and the built-in identifier of the operating asset, an operating asset content value assessment coefficient is obtained. In this embodiment, the traffic data includes access logs, exposure times, click-through rates, access device information, etc., which are used to evaluate the value of the operating assets.

[0078] The measurement and calculation method of the built-in identification flow index is as follows:

[0079] Built-in logo traffic index = (Number of built-in logo visits + Number of unique logo visitors + Number of built-in logo page views) / Total Traffic * 100%

[0080] Among them, the number of visits to built-in logos: records the number of times the operating assets are accessed through built-in logos; the number of unique visitors to the logo: counts the number of unique users who access the operating assets through built-in logos; the number of page views of built-in logos: counts the number of page views accessed through built-in logos. The built-in logo traffic index reflects the proportion of traffic generated by built-in logos in the total traffic.

[0081] Optionally, the business asset evaluation data also includes tracking the conversion rate of subsequent interactive behaviors. Subsequent interactive behaviors record the behaviors that users may take after interacting with business assets, such as viewing more information, purchasing products or services. Subsequent interactive behaviors are recorded by monitoring the user's behavior after the interactive behavior ends, such as visiting a website, purchasing a product, etc. The behaviors evaluated by the conversion rate can be conversion behaviors such as product purchases, user registrations, and content subscriptions. By analyzing the conversion rate, the value of business assets can be better evaluated.

[0082] Post-Interaction Conversion Rate PICR

[0083] PICR = (number of subsequent interactions / number of interactions) * 100%

[0084] In this embodiment, the operating asset evaluation data coefficient M satisfies:

[0085] M=λ1*M1+λ2*M2+λ3*M3

[0086] Among them, λ1, λ2, and λ3 are the weights of each index, which can be adjusted according to business needs. λ1, λ2, and λ3 are all greater than 0 and less than 1. M1 is the user portrait index in the normalized operating asset evaluation data, M2 is the social network valuation index in the normalized operating asset evaluation data, and M3 is the built-in identification traffic index in the normalized operating asset evaluation data.

[0087] In some embodiments, λ1, λ2, and λ3 can be obtained by expert scoring or learning through other machine learning models, and their specific values ​​are not limited here. In some embodiments, when the operating asset evaluation data does not include any of the user portrait index, social network valuation index, and built-in identification traffic index, the value parameter can be considered to be 0. In other embodiments, the operating asset evaluation data also includes other data that can be used to evaluate the value of operating assets, such as the interactive follow-up behavior conversion rate PICR, and can also refer to the above method to participate in the calculation of the operating asset evaluation data coefficient, which will not be repeated here.

[0088] Optionally, the regional income and total location income, and operating asset valuation data include valuation values ​​within the past operating cycle, historical data valuation values, and estimated valuation values ​​for a future operating cycle. In this embodiment, the valuation value within the past operating cycle is the valuation value of the average operating performance of the past evaluation cycle, for example, based on the actuarial calculation of the past year; the historical data valuation value is the historical data valuation value obtained based on the statistical analysis result of the historical operating performance and historical data analysis value obtained by statistical analysis using historical data records; the estimated valuation value for a future operating cycle is an estimate of events that may occur in a future operating cycle, such as the next month, such as the impact of holidays.

[0089] S104. Integrate the income value, location time coefficient and operating asset evaluation data coefficient into an analyzable data set, input the data set into the asset evaluation model in the blockchain smart contract, and obtain the periodic value evaluation results of the operating assets.

[0090] Optionally, the location time efficiency coefficients and operating asset valuation data coefficients are combined into one analyzable data set, including:

[0091] Normalizing the location timeliness coefficient and the operating asset evaluation data coefficient to obtain the normalized target parameters;

[0092] Input the income value and normalized target parameters into the asset valuation model in the smart contract to obtain the periodic value assessment results of the operating assets.

[0093] In this embodiment, the specific manner of normalizing the location time efficiency coefficient and the operating asset evaluation data coefficient is not limited. For example, in some embodiments, the location time efficiency coefficient and the operating asset evaluation data coefficient are converted into a percentage system through normalization.

[0094] Optionally, in some embodiments, the periodic value assessment result K of the operating assets satisfies:

[0095] K = K1*(r1*K2+r2*K3);

[0096] Among them, r1 and r2 are the weights of K2 and K3 indexes respectively, which can be adjusted according to business needs, and are both greater than 0 and less than 1, r1+r2=1. K1 is the income value, K2 is the position time coefficient after normalization, and K3 is the operating asset evaluation data coefficient after normalization.

[0097] In some embodiments, r1 and r2 can be obtained by expert scoring or by learning through other machine learning models, and their specific values ​​are not limited here.

[0098] In this embodiment, a smart contract based on periodic execution of value assessment is responsible for triggering a periodic value assessment method. The contract includes analysis of asset data, calculation of assessment indicators, and recording the results on the blockchain. For example, it can be set to perform value assessment once a month or quarter. Embed the business logic of value assessment in the smart contract. This may include calculations based on factors such as output, market conditions, and asset status. Ensure that the assessment logic meets business needs and standards. Join a consensus mechanism to ensure that the results of the value assessment are consensus-based by all participants in the network. This helps prevent malicious manipulation and ensure data consistency. Store the results of the value assessment on the blockchain to achieve transparency of the results. Participants can verify the process and results of the value assessment at any time to enhance trust. Use Solidity (Ethereum's smart contract language) or other appropriate smart contract languages ​​to write smart contracts for asset valuation models. Smart contracts need to contain logic for processing revenue data, location time coefficients, and operating asset valuation data.

[0099] In this embodiment, the programmable code based on the blockchain automatically executes the contract terms and adjustments in the smart contract, so that transactions and agreements can be carried out without the intervention of a third party, increasing the efficiency and reliability of the interaction.

[0100] Optionally, the results of the periodic evaluation of operating assets are visualized by making charts, reports or other forms of visualization so that users can better understand the sources and trends of income.

[0101] Optionally, the operating income information and operating asset assessment data are synchronized with the distributed ledger. In this embodiment, this is achieved through regular data uploads and transaction records.

[0102] like Figure 2 As shown, the embodiment of the present application also provides a method for evaluating the value of sub-operating assets based on blockchain, where the sub-operating assets are transaction units after the operating assets are divided, including:

[0103] S201, obtain the operating asset value assessment result according to the above-mentioned overall operating asset value assessment method based on blockchain. In this embodiment, the obtained operating asset value assessment result is the operating asset periodic value assessment value obtained according to the above-mentioned operating asset periodic value assessment method based on blockchain.

[0104] S202, obtaining the preset sub-operating asset partition power value and the sum of all partition power values, and calculating the sub-operating asset time efficiency coefficient based on the sub-operating asset partition power value and the sum of all partition power values. In this embodiment, the sub-operating asset time efficiency coefficient is determined by the partition position of the sub-operating asset in the overall operating asset division. The value of sub-operating assets in different positions is different. In this embodiment, the sub-operating asset time efficiency coefficient is obtained by the proportion of the sub-operating asset partition power value in the sum of all partition power values, and the value of sub-operating assets in different positions is evaluated by the sub-operating asset time efficiency coefficient.

[0105] Optionally, the result of the sub-operating asset aging coefficient satisfies the following formula:

[0106] Sub-operation asset time efficiency coefficient = (district power value / sum of all district power values)*100%

[0107] S203, obtain the multi-source value data of the sub-operating asset holder, specifically including the user's basic attribute data, social data, behavior data and consumption record data, and calculate the contribution value index based on the holder's multi-source value data. In this embodiment, the holder's conversion value parameter Conversion Value Score is calculated based on the user's basic attribute data, such as sub-operating asset transaction data, blockchain reputation data, etc.; the social activity is calculated based on the holder's social APP usage behavior data, and then converted into the holder's active value parameter Activity Value Score; the user's KOC (Key Opinion Consumer micro-influencer) influence is calculated through the conversion data and drainage data driven by the sub-operating asset holder forwarding and sharing the sub-operating asset related links, and then converted into the influence value parameter Influence Value Parameters. After the conversion value parameter, active value parameter and influence value parameter are integrated and normalized, the contribution value index CVI (Contribution Value Index) of the sub-operating asset holder is calculated by weighted summation.

[0108] Optionally, the Contribution Value Index (CVI) is calculated as follows:

[0109] CVI=α1×Conversion Value Score+α2×Activity Value Score+α3×Influence Value Score

[0110] Among them, α1, α2 and α3 are the weights of each parameter, which can be adjusted according to business needs and importance. The calculation of the contribution value index CVI of the sub-operating asset holders in the present invention is not limited to this. The parameter items and their corresponding weights can be increased or decreased according to the business demand scenarios. The parameter items and corresponding weights that can achieve the technical effects of the present invention belong to the protection scope of the present invention. This contribution value index integrates the contributions of users in different aspects, weighs the factors of conversion, activity and influence, and can more comprehensively and objectively reflect the positive impact of the contribution of the evaluation holder on the value of the sub-operating assets held by him.

[0111] S204. Input the operating asset valuation, sub-operating asset time efficiency coefficient and contribution value index into the sub-operating asset valuation model in the smart contract to obtain the sub-operating asset periodic valuation results.

[0112] Optionally, the operating asset value assessment, sub-operating asset time efficiency coefficient and contribution value index are integrated into an analyzable data set, including:

[0113] Normalize the sub-operating asset time efficiency coefficient and contribution value index to obtain the normalized target parameters;

[0114] The operating asset valuation and normalized target parameters are input into the asset valuation model in the smart contract to obtain the periodic valuation results of the sub-operating assets.

[0115] In this embodiment, the specific method of normalizing the sub-operation asset time efficiency coefficient and the contribution value index is not limited here. For example, in some embodiments, the sub-operation asset time efficiency coefficient and the contribution value index are converted into a percentage system through normalization.

[0116] In some embodiments, the periodic evaluation result J of the sub-operating assets satisfies:

[0117] J=J1*(t1*J2+t2*J3)

[0118] Among them, t1 and t2 are the weights of the corresponding J1 and J2 parameters and are both greater than 0 and less than 1, and t1+t2=1. J1 is the operating asset value assessment value, J2 is the normalized sub-operating asset time efficiency coefficient, and J3 is the normalized contribution value index.

[0119] In some embodiments, t1 and t2 can be obtained by expert scoring or learning through other machine learning models, and their specific values ​​are not limited here.

[0120] An embodiment of the present application also provides a blockchain node program product for operating asset value assessment, including a computer program, which, when executed by a processor, implements the above-mentioned blockchain-based sub-operating asset value assessment method.

[0121] The embodiment of the present application also provides a blockchain node publishing method for operating asset value assessment, comprising the following steps:

[0122] Deploy the smart contract to the blockchain network and record the contract address and ABI. The smart contract includes the entire contents of the above-mentioned blockchain-based operating asset periodic value assessment method and blockchain-based sub-operating asset value assessment method. In this embodiment, ABI (Application Binary Interface) is an interface specification that defines how to communicate at the binary level. In the context of blockchain and smart contracts, ABI describes the communication method between smart contracts and the external environment (such as blockchain nodes or other smart contracts), including function calls, data transmission, etc.

[0123] The revenue data, regional income, total location income, operating asset evaluation data and multi-source value data of sub-operating asset holders of operating assets are obtained through blockchain nodes or preset programs, and the brand-consumption value evaluation coefficient, location timeliness coefficient and user portrait index are calculated. The preset program adopts existing methods for obtaining data in the prior art, such as web crawlers, and the present invention does not specifically limit this.

[0124] Periodically call smart contracts to perform asset evaluation and obtain periodic value evaluation results.

[0125] An embodiment of the present application also provides a blockchain node program product for operating asset value assessment, including a computer program, which, when executed by a processor, implements the above-mentioned blockchain node publishing method for operating asset value assessment.

[0126] Example 2

[0127] like Figure 1 As shown, this application provides a method for evaluating the value of overall operating assets based on blockchain, including:

[0128] S101, obtain the income data information of the operating assets in the distributed ledger, and the income data information includes the income value. In this embodiment, by using blockchain or other distributed ledger technology to interact with the present invention, the acquisition and verification of the operating asset income data information is realized. In this embodiment, the blockchain technology is specifically used to interact and connect to the distributed ledger system to determine the operating assets recorded in the ledger. The operating assets can be advertising screens, parking spaces, dining tables or other forms of assets to ensure that these assets can be uniquely identified and located. For each operating asset, retrieve the operating income information related to it recorded in the distributed ledger of the blockchain. This operating income information can exist in the form of transaction records, smart contract execution results or other forms. Using the distributed ledger technology of the blockchain, all transactions and income data are securely recorded and stored, such as using Hyperledger Fabric to record and manage the income data information of operating assets, and automatically obtain and verify the income data through smart contracts, thereby solving the data tampering and inaccuracy problems that may exist in the traditional data acquisition and verification process. This embodiment of the present invention ensures the security, transparency and non-tamperability of the data. It is worth noting that the present invention is not limited to the implementation using Hyperledger Fabric, and other blockchain platforms or distributed ledger technologies, such as Ethereum, Corda, etc., can also be used. The specific implementation method can be adjusted and replaced according to actual needs. This embodiment can ensure the accuracy and security of operating asset income data information, improve the efficiency of data acquisition and verification, and effectively solve the security risks and data inaccuracy problems existing in the traditional data management process.

[0129] Optionally, the revenue data information also includes the payer brand information of the operating income. The brand information includes four parameters: market performance, brand familiarity, brand reputation, and brand influence. The smart contract is used to call the brand consumption decision analysis model, and the brand information including market performance, brand familiarity, brand reputation, brand influence and other parameters is processed to obtain the brand-consumption value evaluation coefficient, and the brand value evaluation is completed. In this embodiment, the calculation of the brand-consumption value evaluation coefficient corrects the revenue value through the brand consumption decision analysis model. The brand information is obtained by recording the market performance, familiarity, reputation and influence parameters of the brand on the blockchain. The smart contract is used to call the brand consumption decision analysis model, process the brand information, calculate the brand-consumption value evaluation coefficient, and complete the brand value evaluation. The brand-consumption value evaluation coefficient is applied to the revenue value, and the revenue value correction process is automatically completed through the smart contract. In a specific embodiment, sentiment analysis tools such as VADER (Valence Aware Dictionary and sentiment Reasoner) or TextBlob are used to process the market performance, brand familiarity, brand reputation, and brand influence in the brand information, and calculate the brand-consumption value evaluation coefficient. At the same time, the Herfindahl-Hirschman Index (HHI) market share model is used to correct the revenue value. The above technical means solve the problems of data inconsistency and difficulty in quantification in the traditional brand value evaluation process. Through blockchain and smart contract technology, the authenticity of the data and the automation of the evaluation process are ensured, and the accuracy and efficiency of brand value evaluation are improved. The sentiment analysis tool and market share model used in this embodiment can be replaced according to specific needs, such as other sentiment analysis tools (such as Sentiment140, AFINN, etc.) or other market share models (such as CR4, Concentration Ratio of the top 4firms). In addition, the calculation of the brand-consumption value evaluation coefficient and the correction process of the revenue value can also be replaced by other algorithms or models to achieve the same or similar technical effects.

[0130] S102, obtain the regional income and total location income of the operating assets in the distributed ledger, and obtain the location time efficiency coefficient based on the regional income and total location income. In this embodiment, the location time efficiency coefficient is calculated by calling the multi-factor regression analysis and spatiotemporal big data analysis model through the smart contract. Multi-factor regression analysis is used to evaluate the impact of multiple independent variables on a dependent variable, and to evaluate the impact of income in different regions on the location time efficiency coefficient. Spatiotemporal big data analysis models (such as GeoSpark, ST-Hadoop) are used to process and analyze large-scale geographic location information, and can accurately calculate the location time efficiency coefficient by combining regional income and total location income. Using the geographic location data recorded in the blockchain, combined with external data sources, the location time efficiency coefficient is calculated by calling the pre-set multi-factor regression analysis and spatiotemporal big data analysis model through the smart contract. For example, GeoSpark or ST-Hadoop is used to perform data analysis of regional income and total location income, and the location time efficiency coefficient is calculated by the multi-factor regression model. GeoSpark can quickly process and query large-scale geographic data, while ST-Hadoop can process spatiotemporal data. The combination of these tools can ensure the accuracy and real-time performance of the location time efficiency coefficient. The model for calculating the location timeliness coefficient used in this embodiment can be selected according to specific needs, and other data analysis models and tools such as Google BigQuery, Apache Sedona, etc. can also be used to achieve the same functions. Through the application of these models and tools, the present invention can be flexibly adapted to different application scenarios to ensure the efficiency and accuracy of the location timeliness coefficient calculation. By using multi-factor regression analysis and spatiotemporal big data analysis models, combined with the geographic location data recorded in the blockchain and external data sources, the location timeliness coefficient can be accurately calculated. This method can improve the accuracy and real-time performance of the location timeliness coefficient, making the value assessment of operating assets more accurate and reliable, and helping to optimize resource allocation and improve operating efficiency.

[0131] Optionally, the regional income and total location income, and the operating asset evaluation data include the evaluation value in the past operating cycle, the historical data evaluation value, and the estimated evaluation value in the future operating cycle. In this embodiment, the regional income and total location income, and the operating asset evaluation data include the evaluation value in the past operating cycle, the historical data evaluation value, and the estimated evaluation value in the future operating cycle. The blockchain technology is used to record and manage these data in a transparent and tamper-proof manner, which solves the security and credibility problems existing in traditional data management. The evaluation value in the past operating cycle is the evaluation value of the average operating performance after actuarial calculation based on the past year recorded on the blockchain; the historical data evaluation value is the historical operating performance and historical data analysis value obtained by statistical analysis of the historical data recorded on the blockchain, and the historical data evaluation value obtained based on the statistical analysis result; the estimated evaluation value of the future operating cycle is the estimated evaluation value obtained by predicting the possible events in the future operating cycle, such as the impact of holidays, through smart contracts. In actual use, the revenue data and operating asset evaluation data in the past operating cycle are transparently and tamper-proof recorded on the blockchain using blockchain technology. The data analysis model is automatically called using a smart contract, for example, a time series analysis model based on historical data (such as an ARIMA model) is used to calculate the historical data evaluation value, and combined with an external data source, a machine learning model (such as an LSTM neural network) is used to predict the expected evaluation value within the next operating cycle. The source traceability and authenticity of all data are ensured by blockchain technology, and smart contracts are used to realize automated data processing and analysis. This embodiment illustrates a non-limiting technical solution, and other data analysis models and tools such as Prophet, Facebook's prediction tools, etc. can also be used to achieve the same function. Through the application of these models and tools, the present invention can be flexibly adapted to different application scenarios to ensure the efficiency and accuracy of the evaluation data. The evaluation data of the past operating cycle, historical data, and the future operating cycle are recorded and managed by blockchain technology to ensure the transparency and non-tamperability of the data. The data analysis model is automatically called in combination with smart contracts to realize the automation and accuracy of historical data evaluation and future data prediction. This method improves the efficiency and credibility of data processing, makes the value assessment of operating assets more accurate and reliable, and helps to optimize resource allocation and improve operating efficiency.

[0132] S103. Obtain operating asset evaluation data, where the operating asset evaluation data includes operating asset user basic data and user behavior data, and obtain an operating asset evaluation data coefficient by performing weighted processing on the operating asset evaluation data.

[0133] Optionally, a multi-index comprehensive evaluation model is called through a smart contract on the blockchain to process the operating asset evaluation data including user basic data and user behavior data, and calculate the operating asset evaluation data coefficient. Specifically, the operating asset evaluation data (user basic data and user behavior data) is weighted using the Random Forest algorithm to obtain the operating asset evaluation data coefficient.

[0134] In this embodiment, the operating asset evaluation data is first obtained through the blockchain node, and the data includes the user base data and user behavior data of the operating assets. Then, based on the smart contract on the blockchain, the machine learning algorithm and the multi-index comprehensive evaluation method are called to process the data. For example, the user base data and user behavior data are weighted using the Random Forest algorithm and the Gradient Boosting algorithm to obtain the operating asset evaluation data coefficient. These calculated data coefficients are integrated into the blockchain smart contract, and the smart contract automatically performs asset evaluation and generates results based on the integrated data. This evaluation process makes full use of the advantages of the machine learning algorithm and improves the accuracy and reliability of the evaluation results.

[0135] In this embodiment, user basic data includes personal information, such as gender, age and geographic location, and device information, such as the type of device used, operating system and browser. User behavior data includes user stay behavior at the operating assets, user interaction behavior, user purchase behavior and user search behavior.

[0136] Optionally, by comprehensively collecting the basic data and user behavior data of users, an accurate user portrait model is established to more accurately evaluate the value of operating assets. By integrating and normalizing the basic data and user behavior data of users, the differences between different data sources can be eliminated, making the data more unified and standardized. The establishment of the user portrait model and the calculation of the user portrait index can reflect the comprehensive characteristics and behavior patterns of the user group, thereby providing an important reference for the evaluation of operating assets. In the specific implementation process, a variety of data processing and analysis techniques can be used to process the basic user data and user behavior data. For example, the user's gender, age and geographical location are normalized using a standardized method so that these data can be compared on the same scale. For the user's viewing behavior, user interaction behavior, user purchase behavior and user search behavior, the key features can be extracted by feature engineering methods and standardized. Then, based on these standardized data, a machine learning model (such as K-means clustering, PCA principal component analysis, etc.) is used to establish a user portrait model and calculate the user portrait index value.

[0137] Optionally, the calculation method of the user profile index UPI (User Profile Index) is as follows:

[0138] Data collection and preprocessing, collecting basic user data (gender, age, geographic location) and user behavior data (viewing behavior, interaction behavior, purchasing behavior, search behavior).

[0139] Clean the data to remove noise and outliers.

[0140] Normalize the data using a standardization method such as Z-score standardization.

[0141] Feature extraction and modeling: perform feature engineering on user behavior data to extract key features (such as viewing time, interaction frequency, number of purchases, search frequency, etc.).

[0142] Principal component analysis (PCA) is used to reduce the dimensionality of high-dimensional data and retain the main features.

[0143] The K-means clustering algorithm is used to group users and establish a user portrait model.

[0144] User portrait index calculation: Calculate each user's portrait index (such as user activity, user preference, etc.) based on the output of the user portrait model. Normalize the user portrait index to obtain a standardized user portrait index.

[0145] The user portrait index is used as a weight to adjust the operating asset valuation data coefficient so that it can more accurately reflect the comprehensive characteristics and behavioral patterns of the user group.

[0146] Based on the user portrait index, the coefficient of the operating asset evaluation data is corrected to make the evaluation result more accurate and scientific. In the specific embodiment of the present technical means, different standardization methods (such as Min-Max standardization), different feature engineering methods (such as feature selection, feature combination) and different machine learning models (such as K-means, DBSCAN) can be used to realize the establishment of the user portrait model and the calculation of the user portrait index. The above embodiment is only a preferred embodiment of the present invention. The method and calculation process of establishing the user portrait model based on user basic data and user behavior data can adopt other equivalent alternatives, such as using different machine learning algorithms (such as neural networks, random forests) to establish the user portrait model, using different data standardization methods (such as MaxAbs standardization) to process the data, etc. These changes do not affect the essential content of the present invention.

[0147] Optionally, the user behavior data includes the user's stay time. The stop duration index SDI (Stop Duration Index) that affects the valuation of operating assets is calculated based on the user's stay time data, and the operating asset valuation data coefficient is corrected according to the stop duration index. By monitoring and analyzing the length of time that users stay in front of operating assets, the user's attention and interest in the operating assets are evaluated, thereby more accurately reflecting the actual value of operating assets. In this embodiment, the behavioral data of the stay time is a measure of the time the user stays in front of the operating assets. A long stay time may indicate that the user is interested in the operating assets, while a short stay time may indicate a lack of attraction. Use a camera or other sensor to monitor the user's presence time in front of the operating assets. Start timing when the user enters the camera's field of view and end timing when the user leaves. It represents the average stay time, which can be used to understand the user's interest in operating assets.

[0148] Optionally, the stop duration index SDI (Stop Duration Index) is calculated as follows:

[0149] Data collection can use cameras or other sensors to monitor the time users stay in front of operating assets and record the time when users enter and leave the camera's field of view.

[0150] Data processing: calculate the stay time of each user to obtain the user stay time data. Perform statistical analysis on the stay time data of all users to calculate the average stay time.

[0151] The dwell time index SDI is calculated by comparing the user's dwell time with a preset benchmark value to calculate the dwell time index SDI of each user.

[0152] The formula is: SDI = (user stay time - benchmark value) / benchmark value * 100%

[0153] Perform statistical analysis on the SDI of all users to obtain the overall SDI value.

[0154] Correct the operating asset evaluation data coefficient, and correct the operating asset evaluation data coefficient according to the calculated SDI value. Use the SDI value as a weight to adjust the operating asset evaluation data coefficient to more accurately reflect the user's actual attention and interest.

[0155] The above embodiments are only preferred embodiments of the present invention. The method and calculation process for calculating the residence time index SDI based on the user's residence time may adopt other equivalent alternatives, such as using other sensors (such as infrared sensors) to monitor the user's residence time, using different data statistical analysis methods (such as median analysis) to calculate SDI, etc. These changes do not affect the essential content of the present invention.

[0156] Optionally, the user behavior data includes the interactive behavior data between the user and the operating assets. The interaction rate IR (Interaction Rate) index that affects the valuation of the operating assets is calculated based on the interactive behavior data, and the coefficient of the operating asset valuation data is corrected according to the interaction rate IR index. In this embodiment, by monitoring and analyzing the interactive behavior between the user and the operating assets, the user's participation and interest are evaluated, thereby more accurately reflecting the actual value of the operating assets. In this embodiment, the interactive behavior data records the user's interactive gestures on the operating assets, such as sliding, zooming, etc., for operating assets that support interactive interaction technology. The coordinates and types of interactive events can be captured and analyzed. Measuring the frequency of user interaction with operating assets reflects the attractiveness of operating assets and user engagement.

[0157] Optionally, the interaction rate IR (Interaction Rate) index calculation method is as follows:

[0158] Data collection uses sensors or software to record user interaction behavior data on operating assets, such as clicks, swipes, zooms, and other gestures. The timestamp, coordinates, and type of each interaction event are recorded.

[0159] Data processing: Analyze the interactive behavior data of each user and calculate the number of interactions of each user. Perform statistical analysis on the interactive behavior data of all users and calculate the average number of interactions.

[0160] The interaction rate IR index calculation compares the user's interaction times with the preset benchmark value to calculate the interaction rate IR index for each user.

[0161] The formula is: IR = (number of user interactions / benchmark value) * 100%

[0162] Perform statistical analysis on the IR of all users to obtain the overall IR value.

[0163] Correct the operating asset evaluation data coefficient, and correct the operating asset evaluation data coefficient according to the calculated IR value. Use the IR value as a weight to adjust the operating asset evaluation data coefficient to more accurately reflect the user's actual participation and interest.

[0164] The above embodiments are only preferred embodiments of the present invention. The method and calculation process for calculating the interaction rate IR index based on user interaction behavior can adopt other equivalent alternatives, such as using different types of sensors (such as touch screens, pressure sensors) to record interaction data, using different data statistical analysis methods (such as percentile analysis) to perform IR calculations, etc. These changes do not affect the essential content of the present invention.

[0165] Optionally, the user behavior data includes the user's scanning data of the QR code of the operating asset, and the QR code scanning rate index that affects the valuation of the operating asset is obtained based on the user's scanning data of the QR code of the operating asset, and the coefficient of the operating asset valuation data is corrected according to the QR code scanning rate index. In this embodiment, by analyzing the frequency of users scanning the QR code in the operating asset, the user's specific interest in the operating asset is evaluated, thereby more accurately reflecting the actual value of the operating asset. In the specific implementation process, when the operating asset contains a QR code, the number of times the user scans the QR code is recorded. The QR code scanning rate helps to understand whether the user is interested in specific information in the operating asset. Use a camera or scanner to capture the user's action of scanning the QR code on the operating asset, or record the scanning time and scanned content based on the connection data of the scanned link, which is used to evaluate the user's interest in the QR code contained in the operating asset.

[0166] Optionally, the QR code scanning rate SR (Scan Rate) index calculation method is as follows:

[0167] Data collection, using a camera or QR code scanner to record the number of times a user scans a QR code. The timestamp, user ID, and content of the scanned QR code are recorded for each scan.

[0168] Data processing: Analyze each user’s QR code scanning data and calculate the number of scans for each user. Perform statistical analysis on all users’ scanning data and calculate the average number of scans.

[0169] QR code scanning rate calculation compares the user's scanning times with the preset benchmark value to calculate the QR code scanning rate of each user.

[0170] The formula is: QR = (number of user scans / reference value) * 100%

[0171] Perform statistical analysis on the QR of all users to obtain the overall QR value.

[0172] Correct the operating asset evaluation data coefficient, and correct the operating asset evaluation data coefficient according to the calculated QR value. Use the QR value as a weight to adjust the operating asset evaluation data coefficient to more accurately reflect the user's actual interest in the operating assets.

[0173] Optionally, the user behavior data includes data on the network connection between the user and the operating assets through the mobile device. Based on the data on the network connection between the user and the operating assets, the mobile device connection rate index that affects the valuation of the operating assets is obtained, and the coefficient of the operating asset valuation data is corrected according to the mobile device connection rate index. By analyzing the frequency of the user's network connection with the operating assets through the mobile device, the user's specific interest in the operating asset is evaluated, thereby more accurately reflecting the actual value of the operating assets. In this embodiment, when the operating assets have a network connection function, the user's connection with the operating assets through the mobile device is recorded, such as through Bluetooth or Wi-Fi. The device connection information is recorded by monitoring the Bluetooth or Wi-Fi signals around the operating assets. This requires the user to enable the relevant connection function on the mobile device.

[0174] Optionally, a method for calculating a mobile device connection rate index MR (Mobile device connection rate) according to the device connection information is as follows.

[0175] Data collection, using Bluetooth or Wi-Fi monitoring equipment to record the connection between user devices and operating assets. Record the timestamp, user device ID and connection duration of each connection.

[0176] Data processing: Analyze the connection data of each user device and calculate the number of connections and connection duration of each user device. Perform statistical analysis on the connection data of all user devices and calculate the average number of connections and connection duration.

[0177] The mobile device connection rate index calculation compares the number of connections and connection duration of the user device with the preset benchmark value to calculate the connection rate index of each user device.

[0178] The formula is: CR = (number of user device connections / reference value) * 100%

[0179] Perform statistical analysis on the CR of all user devices to obtain an overall CR value.

[0180] Correct the operating asset evaluation data coefficient, and correct the operating asset evaluation data coefficient according to the calculated CR value. Use the CR value as a weight to adjust the operating asset evaluation data coefficient to more accurately reflect the user's actual interest in the operating assets.

[0181] The above embodiments are only preferred embodiments of the present invention. The method and calculation process for calculating the mobile device connection rate index based on the data of network connection between user devices and operating assets can adopt other equivalent alternatives, such as using different types of network connection technologies (such as NFC, Zigbee) to record connection data, using different data statistical analysis methods (such as median analysis) to calculate CR, etc. These changes do not affect the essential content of the present invention.

[0182] Optionally, the operating asset evaluation data also includes network traffic data, which includes social network data with operating asset attribute data, and the operating asset attribute data includes at least one or more of the name, address, and number of the operating asset. By analyzing the social network data with operating asset attributes, the influence and popularity of the operating asset in the social network are evaluated, so as to more accurately reflect the actual value of the operating asset. In this embodiment, based on the social network data, one or more of the names, addresses, and numbers that are marked with operating asset information are established to establish an asset evaluation network, and then the operating asset network value evaluation coefficient is corrected. In the Web3.0 environment, social and community factors are crucial to the value assessment of assets. Consider analyzing factors such as discussions on social media, community activities, and developer participation. Data with operating asset attributes can also be obtained from social network platforms (such as Twitter, Facebook, Weibo, etc.) through API or crawler technology. Collect data such as social media posts, comments, shares, and likes with information such as the name, address, and number of the operating asset.

[0183] Optionally, the operating asset valuation data also includes network traffic data, which includes the number of social network shares, the number of social network comments, and the number of social network likes. Based on the network traffic data, social network analysis tools and time series analysis models are selected to obtain the social network influence index, and the operating asset valuation data coefficient is corrected according to the social network influence index. By introducing the social network influence index, the performance of operating assets on social media is comprehensively evaluated, thereby more accurately reflecting the actual value of operating assets. Network traffic data includes operating assets, that is, the number of shares, comments, and likes of operating assets on social media platforms. Through the analysis of social network data (such as the number of shares, comments, and likes), the influence and popularity of operating assets on social networks are evaluated, and then the coefficient of operating asset valuation data is corrected, making the evaluation more comprehensive and accurate.

[0184] Social network sharing times (Share Count): records the number of times the operating asset-related content is shared on social networks.

[0185] Number of social network comments (Comment Count): counts the number of comments on business asset-related content on social networks.

[0186] Like Count on social networks: records the number of times the content related to operating assets is liked on social networks.

[0187] Social network analysis tools such as NodeXL are used to process social network data. By analyzing data such as the number of shares, comments, and likes, indicators such as the centrality and connection strength of each node are calculated. Time series analysis models such as the ARIMA model are used to perform time series analysis on these data to predict future social network activity. Finally, these analysis results are weighted to calculate the social network influence index SNII, which is used to correct the coefficient of operating asset evaluation data.

[0188] The above embodiments are only preferred embodiments of the present invention. The method and calculation process for calculating the social network influence index based on social network data can adopt other equivalent alternatives, such as using different social network analysis tools (such as Gephi), different time series analysis models (such as LSTM) to calculate SNII, etc. These changes do not affect the essential content of the present invention.

[0189] Optionally, the network traffic data also includes traffic data generated based on the built-in identifier of the operating asset, and the built-in identifier traffic index that affects the valuation of the operating asset is obtained based on the traffic data generated by the built-in identifier, and the operating asset valuation data coefficient is corrected according to the built-in identifier traffic index. To solve how to accurately reflect the actual influence and value of the operating assets on the network. In this embodiment, the built-in identifier of the operating asset can be an address in the operating asset that can be accessed through other network devices, such as a QR code, an Internet IP address, a subroutine address based on other applications, an interface for entering a blockchain space, and the like. The present invention does not specifically limit the type of built-in identifier. Based on the network data generated by the interaction between the operating asset audience and the built-in identifier of the operating asset, the operating asset content value assessment coefficient is obtained. Traffic data includes access logs, exposure times, click-through rates, access device information, etc., which are used to evaluate the value of operating assets.

[0190] Traffic data collection uses smart contracts to automatically record the behavioral data of users accessing operating assets through built-in identifiers (such as QR codes, IP addresses, etc.), including access logs, exposure times, click-through rates, access device information, etc.

[0191] Traffic prediction and analysis. The traffic prediction model uses the LSTM neural network to predict and analyze the traffic data generated by the built-in identifier. LSTM (Long Short-Term Memory Network) can process and predict time series data and is suitable for trend prediction of traffic data. Multi-dimensional traffic analysis combines the multi-dimensional traffic analysis method of Google Analytics to conduct detailed analysis of traffic data from multiple dimensions (such as geographic location, device type, access time, etc.).

[0192] Built-in traffic index calculation, using smart contracts to call LSTM models for traffic forecasting, to obtain traffic estimates for a period of time in the future. Using the multi-dimensional analysis results of Google Analytics, normalize the traffic data and calculate the weight of each dimension.

[0193] The predicted value and the weights of each dimension are calculated comprehensively through the weighted average method to obtain the Embedded Identifier Traffic Index (EITI).

[0194] The coefficient of operating asset valuation data is corrected based on the calculated EITI to make it more accurately reflect the actual influence and value of operating assets on the network.

[0195] The above embodiments are only preferred embodiments of the present invention. The method and calculation process for calculating the built-in identifier traffic index based on the built-in identifier traffic data may adopt other equivalent alternatives, such as using different traffic prediction models (such as ARIMA), different multi-dimensional traffic analysis tools (such as Adobe Analytics) for EITI calculation, etc. These changes do not affect the essential content of the present invention.

[0196] Optionally, the operating asset evaluation data also includes a conversion rate index for tracking subsequent interactive behaviors, and the operating asset evaluation data coefficient is corrected according to the conversion rate index for tracking subsequent interactive behaviors. This is to solve the problem of how to accurately evaluate the actual conversion effect after the user interacts with the operating assets, so as to more accurately reflect the value of the operating assets. In this embodiment, the subsequent interactive behaviors include recording the behaviors that the user may take after interacting with the operating assets, such as viewing more information, purchasing products or services. The subsequent interactive behaviors are recorded by monitoring the user's behavior after the interactive behavior ends, such as visiting a website, purchasing a product, etc. The behaviors evaluated by the conversion rate can be conversion behaviors such as product purchases, user registrations, and content subscriptions.

[0197] The collection of subsequent behavioral data after the interaction is completed, through the built-in tracking codes of the operating assets (such as Cookies, pixel tags, etc.), records all related operations of the user after the interaction ends, such as visiting the website, clicking on the link, purchasing the product, etc.

[0198] Conversion rate calculation, conversion definition: Determine which user behaviors are defined as conversion behaviors, such as product purchases, user registrations, content subscriptions, etc.

[0199] Conversion tracking uses tracking code to record how often conversions occur and associate them with interactions.

[0200] Conversion rate formula: Conversion rate = (number of conversion behaviors / total number of interactive behaviors) * 100%. Correction of operating asset valuation data coefficient. Integrate the calculated conversion rate with other valuation data and correct the operating asset valuation data coefficient to make it more accurately reflect the actual value.

[0201] The above embodiments are only preferred embodiments of the present invention. The method and calculation process for calculating the conversion rate based on tracking the conversion rate of subsequent interactive behaviors may adopt other equivalent alternatives, such as using different tracking technologies (such as user behavior analysis tool Hotjar), different conversion definitions and calculation methods, etc. These changes do not affect the essential content of the present invention.

[0202] Optionally, the user basic data and user behavior data, as well as the user portrait index, stay time index, interaction rate, QR code scanning rate index, mobile device connection rate index, social network influence index, built-in logo flow index, and conversion rate index for tracking subsequent interactive behaviors, are weighted by a multi-indicator comprehensive evaluation method (such as analytic hierarchy process AHP, entropy weight method, etc.) to calculate the operating asset evaluation data coefficient. The smart contract dynamically modifies the weights of each indicator parameter according to the predetermined rules to ensure the real-time and accuracy of the evaluation data coefficient.

[0203] In this embodiment, the analytic hierarchy process (AHP) includes;

[0204] A hierarchical model is constructed to decompose the operating asset evaluation problem into three levels: the target level, the criterion level, and the solution level. The target level is a comprehensive evaluation of operating assets, the criterion level includes one or more of user portrait indicators, dwell time index, interaction rate, QR code scanning rate, mobile device connection rate, social network influence index, built-in logo traffic index, and conversion rate index, and the solution level is specific operating assets.

[0205] Construct a judgment matrix, and construct a pairwise comparison judgment matrix based on the importance of each indicator. Evaluate the relative importance of each two indicators, with scores ranging from 1 to 9.

[0206] Calculate the weights, normalize the judgment matrix, and calculate the weights of each indicator. Use the eigenvector method or the root method to calculate the weight of each indicator.

[0207] Consistency test, perform consistency test on the judgment matrix to ensure the consistency of the judgment matrix. If the consistency index CR is less than 0.1, the judgment matrix passes the consistency test, otherwise the judgment matrix needs to be adjusted.

[0208] In this embodiment, the entropy weight method includes,

[0209] Collect data and construct the original data matrix based on the indicator data of each operating asset.

[0210] Data standardization is to standardize the original data so that the data can be compared under the same dimension. The standardization method can be the maximum value method, the minimum value method or the standard deviation method.

[0211] Calculate the entropy value. Calculate the entropy value of each indicator based on the standardized data. The entropy value reflects the information entropy of the indicator. The larger the information entropy, the higher the dispersion of the indicator, and vice versa.

[0212] Calculate the weight and calculate the weight of each indicator according to the entropy value.

[0213] The calculation formula of weight is: weight = 1-entropy value.

[0214] The weighted comprehensive score is obtained by multiplying the standardized value of each indicator by its corresponding weight and summing them up to obtain the comprehensive score of operating assets.

[0215] The calculation formula for the comprehensive score is: comprehensive score = ∑ (indicator value × weight).

[0216] Data integration summarizes the comprehensive scores of all indicators on operating assets to form a set of operating asset evaluation data coefficients.

[0217] Deployment of smart contracts, deploy smart contracts on the blockchain platform, and the smart contracts contain the calculation logic and rules of the operating asset evaluation data coefficients.

[0218] Dynamic modification rules: preset rules in smart contracts dynamically adjust the weights and scores of various indicators based on changes in the external environment and data. Preset rules can be set based on historical data and machine learning algorithms.

[0219] Real-time update and verification. Smart contracts update the operating asset assessment data coefficients based on real-time data and verify them through the consensus mechanism of the blockchain to ensure the accuracy and immutability of the data.

[0220] Optionally, for an operating asset, collect one or more of its user portrait index, dwell time index, interaction rate, QR code scanning rate, mobile device connection rate, social network influence index, built-in logo flow index and conversion rate index. Use the AHP method and entropy weight method to calculate the weight of each indicator and perform a weighted comprehensive score. The specific steps are as follows:

[0221] The AHP method was used to construct a judgment matrix that included at least one of the above indicators, score every two indicators, calculate the weights and perform consistency tests.

[0222] The entropy weight method is used for data standardization and entropy value calculation. The original data is standardized, the entropy value of each indicator is calculated, and the weight is calculated based on the entropy value.

[0223] The weighted comprehensive score uses the weights calculated by the AHP method and the entropy weight method to give a weighted comprehensive score to the standardized indicator values ​​of each operating asset.

[0224] For example, the comprehensive score of operating assets is: Comprehensive score = ∑ (index value × weight)

[0225] Applied to smart contracts, first, the calculation logic and weight settings are deployed in the smart contracts of the blockchain platform. Secondly, according to the preset rules, the smart contract dynamically adjusts the weights and scores of each indicator and updates the operating asset evaluation data coefficients in real time. Through the consensus mechanism of the blockchain, the accuracy and credibility of each evaluation result are verified and recorded.

[0226] S104. Integrate the income value, location time coefficient and operating asset evaluation data coefficient into a data set through a smart contract, input the data set into the asset evaluation model on the blockchain, and obtain the periodic value evaluation result of the operating assets.

[0227] Optionally, the revenue value, location time efficiency coefficient and operating asset evaluation data coefficient are integrated into an analyzable data set through smart contracts, including:

[0228] Deploy smart contracts on the blockchain platform, which contain data integration and calculation logic.

[0229] The smart contract receives the revenue value, location time efficiency coefficient and operating asset assessment data coefficient and integrates this data into an analyzable data set.

[0230] The data is verified through the consensus mechanism of the blockchain, and the verified data is stored on the blockchain.

[0231] Optionally, the construction of an asset valuation model on the blockchain includes:

[0232] A machine learning model is selected for asset valuation, wherein the machine learning model may adopt one or more of a regression model, a time series model (such as ARIMA), a neural network model (such as LSTM), and the present invention does not make specific limitations.

[0233] The asset valuation model is trained using historical data of earnings value, location time efficiency coefficient and the above-mentioned operating asset valuation data coefficient to ensure that the model can accurately predict the cyclical value of operating assets.

[0234] Deploy the trained model on the blockchain platform and combine it with smart contracts to achieve automated evaluation.

[0235] Optional, results of operating asset valuation, including:

[0236] The integrated data set is fed into the asset valuation model on the blockchain.

[0237] Smart contracts trigger valuation models based on preset periodic rules (e.g. monthly or quarterly) to conduct periodic valuation assessments of operating assets.

[0238] The evaluation results are output through smart contracts and recorded on the blockchain to ensure the transparency and immutability of the results.

[0239] Through the above technical implementation scheme, the present invention realizes the comprehensive, real-time and accurate periodic value assessment of operating assets. Combined with blockchain technology, the security, transparency and non-tamperability of data and assessment results are ensured. Through the automated processing of smart contracts and the periodic trigger mechanism, the value assessment of operating assets is made more efficient and scientific.

[0240] Optionally, the results of the periodic evaluation of operating assets are visualized by making charts, reports or other forms of visualization so that users can better understand the sources and trends of income.

[0241] Optionally, the operating income information and operating asset assessment data are synchronized with the distributed ledger. In this embodiment, this is achieved through regular data uploads and transaction records.

[0242] like Figure 2 As shown, the embodiment of the present application also provides a method for evaluating the value of sub-operating assets based on blockchain, where the sub-operating assets are transaction units after the operating assets are divided, including:

[0243] S201. Obtain the operating asset value assessment result according to the above-mentioned overall operating asset value assessment method based on blockchain by calling the smart contract. In this embodiment, the operating asset value assessment result recorded on the blockchain is queried by calling the smart contract, that is, the operating asset periodic value assessment value obtained according to the above-mentioned operating asset periodic value assessment method based on blockchain.

[0244] S202. Obtain the preset sub-operating asset partition power value and the sum of all partition power values, and calculate the sub-operating asset time efficiency coefficient based on the sub-operating asset partition power value and the sum of all partition power values. The sub-operating asset partition power value refers to the attraction or influence of the sub-operating asset in the partition corresponding to a specific sub-operating asset when the overall operating assets are divided into different sub-operating assets according to pre-set rules. The higher the partition power value, the higher the value of the partition corresponding to the sub-operating asset. In this embodiment, the sub-operating asset's partition position in the overall operating asset division determines its sub-operating asset time efficiency coefficient, and the value of sub-operating assets in different positions is different. In this embodiment, the sub-operating asset time efficiency coefficient is obtained by the proportion of the sub-operating asset partition power value in the sum of all partition power values, and the sub-operating asset time efficiency coefficient is used to evaluate the value of sub-operating assets in different partition positions of the overall operating assets.

[0245] Design a smart contract on the blockchain, including functional modules such as data set, data verification, and time efficiency coefficient calculation. The pre-defined operating asset partitioning rules in the smart contract ensure the transparency and immutability of the data. The partition power value and the total partition power value are integrated through the smart contract to calculate the time efficiency coefficient of each sub-operating asset. The value of the sub-operating asset is evaluated using the sub-operating asset time efficiency coefficient. The present invention realizes a scientific evaluation of the value of sub-operating assets in different locations by calculating the sub-operating asset time efficiency coefficient. Combined with blockchain technology, the transparency and immutability of the data are ensured, making the evaluation process more open, fair, and reliable. The evaluation efficiency and accuracy are improved through automated processing by smart contracts.

[0246] This embodiment is only a specific implementation of the present invention and is not intended to limit the present invention. For those skilled in the art, several modifications and improvements may be made without departing from the spirit and scope of the technical solution of the present invention, which shall be deemed as the protection scope of the present invention. For example, different smart contract designs, different partition power value calculation methods, different data collection methods, etc., may all be used to achieve the purpose of the present invention.

[0247] Optionally, the sub-operating asset time efficiency coefficient is affected by the partition power value, the life cycle of the sub-operating asset and external factors. In this embodiment, the release of operating assets has its life cycle, so the sub-operating assets as trading units also have their life cycle. The initial value of the sub-operating asset at the time of release depends on the proportion of its partition power value, that is, the initial sub-operating asset time efficiency coefficient. By calculating the initial partition power value and the initial time efficiency coefficient, the value of the sub-operating asset at the time of release is accurately reflected.

[0248] Sub-operation asset time efficiency coefficient = (district power value / sum of all district power values)*100%

[0249] In the subsequent trading cycle of sub-operating assets, the value of sub-operating assets is affected by external supply and demand conditions and the life cycle of operating assets, and the corresponding impact is reflected in the sub-operating asset time efficiency coefficient. The sub-operating asset time efficiency coefficient is an important parameter to measure the change in the value of sub-operating assets during its life cycle. This coefficient not only depends on the initial partition dynamics value at the time of its release, but also dynamically adjusts according to changes in market supply and demand and the life cycle of operating assets. Use data collection tools (such as APIs, sensors, etc.) to monitor market supply and demand change data, such as transaction volume, price fluctuations, etc. in real time. Analyze the life cycle stage of operating assets (such as release, growth, maturity, and decline), and adjust the value impact of sub-operating assets according to different stages of the life cycle.

[0250] Adopt dynamic adjustment models such as Kalman Filter to adjust the time efficiency coefficient of sub-operating assets according to real-time monitoring data and life cycle influencing factors. Kalman Filter is an algorithm used to dynamically adjust the time efficiency coefficient by combining prior information and observation data.

[0251] At=Kalman Filter(At-1,Zt);

[0252] At: the value of the adjusted time efficiency coefficient at time t; At-1: the time efficiency coefficient at the previous moment; Zt: real-time monitoring data, including market demand, transaction volume, etc.

[0253] Through the Kalman filter, the timeliness coefficient is dynamically adjusted according to the historical timeliness coefficient and real-time monitoring data to adapt to market changes.

[0254] The Simple Exponential Smoothing method is used to smooth the time efficiency coefficient to reduce the impact of short-term fluctuations.

[0255] St=α·Zt+(1-α)·St-1

[0256] St: the value of the smoothed time effectiveness coefficient at time t; St-1: the smoothed time effectiveness coefficient at the previous moment; Zt: real-time monitoring data; α: smoothing factor, ranging from 0 to 1, controls the degree of smoothing. The larger the value, the greater the impact of new data on the smoothed value; the smaller the value, the greater the impact of historical data.

[0257] Through exponential smoothing, the impact of short-term fluctuations on the time efficiency coefficient is reduced, making the adjusted time efficiency coefficient more stable.

[0258] The above embodiment and calculation method are only one specific implementation method, and other implementation methods and calculation methods are also within the protection scope of the present invention. For example, other dynamic adjustment models (such as particle filters) and data smoothing methods (such as weighted moving average) can be used.

[0259] S203. Obtain multi-source value data of the sub-operating asset holders, and calculate the contribution value index based on the multi-source value data of the holders by weighted average method. This technical means specifically solves the problem of how to evaluate the contribution value of the sub-operating asset holders through multi-dimensional data. The multi-source value data of the holders specifically includes the basic attribute data, social data, behavior data and transaction data of the holders, and the contribution value index is calculated based on the multi-source value data of the holders.

[0260] In this embodiment, the holder's conversion value parameter (Conversion Value Score, CVS) is calculated based on the basic attribute data of the holder, such as the blockchain reputation data of the sub-operating asset holder and the transaction data of the sub-operating asset holder; the social activity is calculated based on the holder's use-related behavior data such as social APP, and then converted into the holder's activity value parameter (Activity Value Score, AVS); the sub-asset holder's KOC (Key Opinion Consumer, micro-influencer) influence is calculated through the conversion data and drainage data driven by the sub-operating asset holder's forwarding and sharing of its sub-operating asset-related links, and then converted into the holder's influence value parameter (Influence Value Parameters, IVP). After integrating and normalizing the holder's conversion value parameter, the holder's active value parameter, and the influence value parameter, the sub-operating asset holder's contribution value index (CVI) is calculated by weighted summation.

[0261] Optionally, the basic attribute data of the holder includes information such as the age, gender and occupation of the holder, and the basic attribute score of the holder is calculated based on these basic attribute data. The transaction data of the holder's sub-operating assets includes transaction frequency and transaction amount. By analyzing the holder's transaction behavior, the holder's transaction frequency score and transaction amount score are calculated. The holder's blockchain reputation data includes the holder's historical reputation score and transaction success rate. The holder's reputation score is calculated through the reputation data recorded in the blockchain. Based on the holder's basic attribute data, the holder's sub-operating asset transaction data, the holder's blockchain reputation data, etc., the holder's conversion value parameters are obtained.

[0262] In this embodiment, the method for obtaining the holder conversion value parameter is as follows:

[0263] Data collection: collect basic attribute data of holders, such as age, gender, and occupation. Collect transaction data of holders, including transaction frequency and transaction amount. Collect blockchain reputation data of holders, including historical reputation score and transaction success rate.

[0264] Data processing: Process the basic attribute data of the holder, calculate the age score, gender score, and occupation score. Normalize the transaction data of the holder, calculate the transaction frequency score, transaction frequency score, and transaction amount score. Normalize the blockchain reputation data of the holder and calculate the reputation score.

[0265] The weighted sum of the parameters is used to obtain the holder conversion value parameter.

[0266] Conversion Value Parameter (CVS) of the holder:

[0267] CVS=α1×Age Score+α2×Gender Score+α3×Occupation Score+α4×Transaction Frequency Score+α5×Transaction Amount Score+α6×Reputation Score

[0268] Among them, α1, α2, α3, α4, α5, and α6 are weight coefficients of each score, and the specific values ​​are set according to the actual application.

[0269] Data analysis: Statistical analysis of the CVS data of all holders is performed to obtain the overall distribution of holder conversion value.

[0270] Through the above steps, the conversion value parameter (CVS) of each holder is calculated to evaluate the overall value of the holder, and the contribution value index is corrected according to the holder's conversion value parameter.

[0271] The above embodiments are only preferred embodiments of the present invention. The method and calculation process for calculating the holder's conversion value parameters based on the holder's basic attribute data, transaction data and blockchain reputation data may adopt other equivalent alternatives, such as using different basic attribute data (such as educational background, geographic location) or using different calculation methods (such as machine learning algorithms) for data processing and analysis, etc. These changes do not affect the essential content of the present invention.

[0272] Optionally, the behavior data of social APP usage includes information such as the holder's login frequency and number of interactions on the social platform. Based on the user's social APP usage behavior data, the holder's active value parameter is calculated. By analyzing the user's login frequency and number of interactions, the user's activity level on the social platform can be evaluated, thereby more accurately reflecting the holder's active value.

[0273] In this embodiment, the method for obtaining the active value parameter of the holder is as follows:

[0274] Data collection: collect user login frequency data on social apps, and record the number of times a user logs in within a period of time. Collect user interaction data on social apps, and record the number of user interaction behaviors within a period of time, such as likes, comments, and shares.

[0275] Data processing: Process the user's login frequency data and calculate each user's login frequency score LoginFrequency Score. Process the user's interaction count data and calculate each user's interaction count score Interaction Count Score.

[0276] The weighted sum of the parameters is used to obtain the active value parameter of the holder.

[0277] Active Value Parameter (AVS) of Holders: AVS = β1 × Login Frequency Score + β2 × Interaction Count Score

[0278] Among them, β1 and β2 are weight coefficients of each score, and the specific values ​​are set according to the actual application.

[0279] Data analysis: Statistical analysis of the AVS data of all holders is performed to obtain the overall distribution of active value of holders.

[0280] Through the above steps, the active value parameter (AVS) of each holder is calculated to evaluate the holder's activity level on the social platform, and the contribution value index is corrected according to the holder's active value parameter.

[0281] The above embodiments are only preferred embodiments of the present invention. The method and calculation process for calculating the active value parameters of the holder based on the holder's social APP usage behavior data may adopt other equivalent alternatives, such as using different social behavior data (such as the number of content releases, fan growth rate) or using different calculation methods (such as machine learning algorithms) for data processing and analysis, etc. These changes do not affect the essential content of the present invention.

[0282] Optionally, the holder's behavior data includes the forwarding and sharing behavior data on the social platform, and the holder's influence value parameter (IVP) is calculated based on the holder's forwarding and sharing behavior data. By analyzing the number of shares and link clicks on the holder on the social platform, the holder's influence in the social network can be evaluated, thereby more accurately reflecting the holder's influence value.

[0283] In this embodiment, the method for obtaining the holder influence value parameter is as follows:

[0284] Data collection: collect the forwarding and sharing data of users on social platforms, and record the number of times users share within a period of time. Collect the number of times the links shared by users are clicked, and record the number of clicks on the links.

[0285] Data processing: Process the user's sharing times data and calculate each user's sharing times score (Share Count Score). Process the click volume data of the links shared by the user and calculate each user's click-through rate score (Click Through Rate Score).

[0286] The weighted sum of the parameters is used to obtain the influence value parameter of the holder.

[0287] Influence Value Parameter (IVP) of the holder: IVP = γ1×Share Count Score+γ2×ClickThrough Rate Score

[0288] Among them, γ1 and γ2 are weight coefficients of each score, and the specific values ​​are set according to the actual application.

[0289] Data analysis: Statistical analysis of the IVP data of all holders is performed to obtain the overall distribution of the holder's influence value.

[0290] Through the above steps, the influence value parameter (IVP) of each holder is calculated to evaluate the influence of the holder in the social network, and the contribution value index is corrected according to the influence value parameter of the holder.

[0291] The above embodiments are only preferred embodiments of the present invention. The method and calculation process for calculating the influence value parameters of the holder based on the holder's forwarding and sharing data may adopt other equivalent alternatives, such as using different social behavior data (such as the number of comments, the number of likes) or adopting different calculation methods (such as machine learning algorithms) for data processing and analysis, etc. These changes do not affect the essential content of the present invention.

[0292] Optionally, in order to ensure that all parameters are on the same scale, the holder conversion value parameter (CVS), the holder active value parameter (AVS) and the holder influence value parameter (IVP) are normalized to obtain the normalized holder conversion value parameter Normalized CVS, the normalized holder active value parameter Normalized AVS and the normalized holder influence value parameter Normalized IVP. The normalized parameters are weighted and summed to obtain the final contribution value index (CVI).

[0293] The weight coefficients w1, w2 and w3 are set according to the specific application scenario.

[0294] Weighted sum:

[0295] CVI=w1×Normalized CVS+w2×Normalized AVS+w3×Normalized IVP

[0296] Through the above process, the contribution value index (CVI) of the sub-operating asset holders is finally calculated and used to evaluate the value of the sub-operating assets.

[0297] The above embodiments are only preferred embodiments of the present invention. The normalization and weighted summation methods and calculation processes can adopt other equivalent alternatives, such as using different normalization methods (such as Z-score standardization) or weighted methods (such as the Delphi method) for data processing and analysis, etc. These changes do not affect the essential content of the present invention. The contribution value index integrates the contributions of users in different aspects, weighs the factors of conversion, activity and influence, and can more comprehensively and objectively reflect the positive impact of the contribution of the evaluation holder on the value of the sub-operating assets held by him.

[0298] S204. Input the operating asset valuation value, sub-operating asset time efficiency coefficient and contribution value index into the sub-operating asset valuation model in the smart contract to obtain the sub-operating asset valuation result.

[0299] Optionally, the method for periodic evaluation of sub-operating assets is as follows:

[0300] Collect relevant data of sub-operating assets, including but not limited to the operating asset value assessment, sub-operating asset time efficiency coefficient and contribution value index. Standardize these data to ensure the consistency of input data.

[0301] The ensemble learning model is used as the sub-operating asset evaluation model for evaluation, which includes random forest and gradient boosting models. The specific implementation method is as follows:

[0302] Data preprocessing: standardization of operating asset value assessment, timeliness coefficient, contribution value index, etc. to ensure data uniformity.

[0303] Construct a random forest model containing multiple decision trees and train it on the training data set. Each decision tree is trained on a randomly sampled subset to reduce overfitting and improve the generalization ability of the model. Optimize model parameters through cross-validation and grid search.

[0304] Build a gradient boosting model by gradually adding weak learners, each trained on the residuals of the previous model. Optimize model parameters through cross-validation and grid search.

[0305] The prediction results of the random forest and gradient boosting models are weighted averaged or voted to generate the final evaluation results. The model performance is evaluated using an independent validation set, using indicators such as mean square error (MSE) and root mean square error (RMSE).

[0306] The evaluation data, model parameters and evaluation results of the sub-operation assets are stored on the blockchain to ensure the transparency and immutability of the data. Smart contracts are deployed on the blockchain platform, including the evaluation logic and rules of the model, to automatically execute the evaluation process. Smart contracts dynamically adjust model parameters and weights according to preset rules to ensure the real-time and accuracy of the evaluation results.

[0307] Through the above process, the periodic assessment results of the sub-operating assets are finally obtained.

[0308] The above embodiments are only preferred embodiments of the present invention. The evaluation model based on sub-operation assets can adopt other equivalent alternatives, such as using other machine learning models (such as support vector machines, neural networks) for evaluation, or using other blockchain technologies to implement smart contracts. These changes do not affect the essence of the present invention.

[0309] The embodiment of the present application also provides a blockchain node program product for operating asset value assessment, including a computer program, which implements the above-mentioned blockchain-based sub-operating asset value assessment method when executed by a processor. The blockchain node program product includes deploying a program product including a smart contract and a computing model, and executing the assessment method through a blockchain node. Specifically, the program product can be run in a blockchain network, using smart contracts to achieve dynamic assessment and tracking of the value of operating assets and sub-operating assets. The smart contract receives and processes data from operating assets, and then calculates the value assessment data coefficient of the operating assets or the timeliness coefficient of the sub-operating assets according to the preset assessment model and rules, and records the results on the distributed ledger of the blockchain.

[0310] The embodiment of the present application also provides a blockchain node publishing method for operating asset value assessment, comprising the following steps:

[0311] Deploy the smart contract to the blockchain network and record the contract address and ABI. Among them, the smart contract includes the entire content of the above-mentioned blockchain-based operating asset periodic value assessment method and blockchain-based sub-operating asset value assessment method. In this embodiment, ABI (Application Binary Interface) is an interface specification that defines how to communicate at the binary level. In the context of blockchain and smart contracts, ABI describes the communication method between smart contracts and the external environment (such as blockchain nodes or other smart contracts), including function calls, data transmission, etc. Through this technical means, efficient interaction between smart contracts and external data can be achieved, ensuring the accuracy and security of data transmission.

[0312] The revenue data, regional income, total location income, operating asset assessment data and multi-source value data of sub-operating asset holders of operating assets are obtained through blockchain nodes or preset programs, and input into smart contracts to calculate parameters such as brand-consumption value assessment coefficient, location timeliness coefficient and user portrait index. The preset program adopts existing methods for obtaining data in the prior art, such as data stored on the blockchain or web crawlers. The implementation of this technical means can realize the automatic acquisition and processing of data, reduce manual intervention, and improve the efficiency and accuracy of data acquisition. Specifically, the preset program can capture relevant data regularly or in real time, and accurately transmit it to the smart contract to ensure the timeliness and integrity of the data.

[0313] Periodically call smart contracts to perform asset evaluation and obtain periodic value evaluation results.

[0314] Smart contracts are called regularly to evaluate the value of comprehensive operating assets and sub-operating assets and generate evaluation results. The implementation of this technical means can achieve regular value evaluation of operating assets and sub-operating assets, ensuring the reliability and accuracy of the evaluation results. Specifically, smart contracts automatically calculate and generate asset evaluation results based on input data, using preset evaluation models and algorithms, and record the results on the blockchain to ensure the immutability and traceability of the data.

[0315] This embodiment provides a technical implementation scheme for a blockchain-based operating asset periodic value assessment method and a sub-operating asset assessment method, aiming to improve the accuracy and efficiency of asset assessment. In addition, other data acquisition methods can be considered, such as API interface calls, data interfaces provided by data service providers, etc., to further improve the flexibility and diversity of data acquisition. In addition, the algorithm and assessment model of the smart contract can be optimized according to actual needs to meet the assessment needs of different types of assets.

[0316] The embodiment of the present application also provides a blockchain node program product for operating asset value assessment, including a computer program, which implements the blockchain node publishing method for operating asset value assessment when executed by a processor. When the program product is executed by the processor, the function of operating asset value assessment is implemented through the following steps:

[0317] Deployment and recording of smart contracts, deploy smart contracts to the blockchain network, and record the contract address and ABI. Smart contracts include the entire contents of the blockchain-based operating asset periodic value assessment method and the blockchain-based sub-operating asset value assessment method. ABI (Application Binary Interface) defines the communication method between smart contracts and the external environment, including function calls and data transmission.

[0318] Data acquisition and input: Obtain operating asset revenue data, regional income, total location income, operating asset assessment data, and multi-source value data of sub-operating asset holders through blockchain nodes or preset programs, and input them into smart contracts. The preset program uses existing methods for obtaining data, such as public data recorded on the blockchain or web crawlers, to ensure the automation and accuracy of data acquisition.

[0319] Periodic asset evaluation, periodically calling smart contracts to perform asset evaluation, and obtaining periodic value evaluation results. Smart contracts automatically calculate and generate asset evaluation results based on input data, using preset evaluation models and algorithms, and record the results on the blockchain to ensure the data is tamper-proof and traceable.

[0320] The above implementation is only a specific embodiment of the present invention. Those skilled in the art can make various modifications and improvements to it without departing from the spirit and scope of the present invention. For example, the data acquisition method can adopt API interface calls, data interfaces provided by data service providers, etc. to improve the flexibility and diversity of data acquisition; the algorithm and evaluation model of the smart contract can be optimized according to actual needs to adapt to the evaluation needs of different types of assets. These modifications and improvements should all fall within the scope of protection of the present invention.

[0321] The above technical features realize the periodic value assessment of operating assets and sub-operating assets through the deployment and execution of smart contracts. Through the application of blockchain technology, the accuracy and security of data transmission are guaranteed, human intervention is reduced, and the efficiency of data acquisition and processing is improved. In addition, by recording the assessment results on the blockchain, the data is ensured to be tamper-proof and traceable, and the reliability and accuracy of asset assessment are improved.

Claims

1. A method for evaluating the value of overall operating assets based on blockchain, characterized in that: include: Obtaining income data information of operating assets in a distributed ledger, wherein the income data information includes income value; Obtaining the regional income and the total location income of the operating assets in the distributed ledger, and obtaining the location time efficiency coefficient based on the regional income and the total location income; Acquire operating asset evaluation data, the operating asset evaluation data including operating asset user basic data and user behavior data, and obtain an operating asset evaluation data coefficient based on the operating asset evaluation data; The income value, the location time efficiency coefficient and the operating asset evaluation data coefficient are integrated into a data set through a smart contract, and the data set is input into the asset evaluation model on the blockchain to obtain the operating asset value evaluation result.

2. The overall operating asset value assessment method based on blockchain according to claim 1 is characterized in that: The revenue data information also includes brand information of the operating income payer, and the brand information includes at least one of market performance, brand familiarity, brand reputation and brand influence; The method further comprises the steps of: A brand-consumption value evaluation coefficient is obtained through weighted processing based on the brand information, or a smart contract is used to call a brand consumption decision analysis model to process the brand information to obtain a brand-consumption value evaluation coefficient; The revenue value is corrected according to the brand-consumption value assessment coefficient.

3. The overall operating asset value assessment method based on blockchain according to claim 1 is characterized in that: The location time efficiency coefficient is calculated by calling a pre-set multi-factor regression analysis and spatiotemporal big data analysis model through a smart contract.

4. The overall operating asset value assessment method based on blockchain according to claim 1 is characterized in that: The method for obtaining the operating asset evaluation data coefficient based on the operating asset evaluation data includes calling a multi-indicator comprehensive evaluation model through a smart contract on the blockchain, and processing the operating asset evaluation data to obtain the operating asset evaluation data coefficient.

5. The overall operating asset value assessment method based on blockchain according to claim 4 is characterized in that: The user basic data includes gender, age and geographic location, and the user behavior data includes user viewing behavior, user interaction behavior, user purchasing behavior and user search behavior. The user basic data and the user behavior data are integrated and normalized, a user portrait model is established and user portrait indicators are calculated, and the operating asset evaluation data coefficient is corrected according to the user portrait index.

6. The overall operating asset value assessment method based on blockchain according to claim 4 is characterized in that: The user behavior data includes the user's stay time, a stay time index is calculated based on the user's stay time, and the operating asset evaluation data coefficient is corrected according to the stay time index; The user behavior data includes interactive behavior data between users and operating assets. An interactive rate index is calculated based on the interactive behavior data, and the operating asset evaluation data coefficient is corrected according to the interactive rate index.

7. The overall operating asset value assessment method based on blockchain according to claim 4 is characterized in that: The operating asset valuation data also includes network traffic data, which includes the number of social network shares, the number of social network comments and the number of social network likes. Based on the network traffic data, social network analysis tools and time series analysis models are selected to obtain a social network influence index, and the operating asset valuation data coefficient is corrected according to the social network influence index.

8. The overall operating asset value assessment method based on blockchain according to claim 7 is characterized in that: The network traffic data also includes traffic data generated based on the built-in identification of operating assets. The built-in identification traffic index is obtained by using the weighted average method based on the traffic data generated by the built-in identification. The operating asset evaluation data coefficient is corrected according to the built-in identification traffic index.

9. The method for evaluating the value of operating assets based on blockchain according to claim 1, characterized in that: The asset evaluation model adopts one or more of a regression model, a time series model, and a neural network model.

10. A method for evaluating the value of sub-operating assets based on blockchain, wherein the sub-operating assets are transaction units after the operating assets are divided, characterized in that: include: Obtaining the operating asset value assessment result by calling the smart contract to obtain the overall operating asset value assessment method based on blockchain according to any one of claims 1 to 9; Obtaining a preset sub-operation asset partition power value and the sum of all partition power values, and calculating the sub-operation asset time efficiency coefficient based on the sub-operation asset partition power value and the sum of all partition power values; Obtain multi-source value data of sub-operating asset holders, and calculate the contribution value index based on the multi-source value data of the holders by using the weighted average method through smart contracts; The operating asset value assessment result, the sub-operating asset time efficiency coefficient and the contribution value index are input into the sub-operating asset assessment model in the smart contract to obtain the sub-operating asset value assessment result.

11. The method for evaluating the value of sub-operating assets based on blockchain according to claim 10 is characterized in that: The holder's multi-source value data includes the holder's basic attribute data, behavior data and transaction data.

12. The method for evaluating the value of sub-operating assets based on blockchain according to claim 11 is characterized in that: The basic attribute data of the holder includes the age, gender and occupation of the holder, and the transaction data of the holder includes the transaction frequency and transaction amount. Based on the basic attribute data of the holder and the transaction data of the holder, the conversion value parameter of the holder is obtained by weighted average method, and the contribution value index is corrected according to the conversion value parameter of the holder; The holder's behavior data includes the frequency of login and the number of interactions on the social platform. The active value parameter of the holder is obtained by weighted average method based on the holder's behavior data, and the contribution value index is corrected according to the active value parameter of the holder; The holder's behavior data includes his / her forwarding and sharing behavior data on the social platform. The holder's influence value parameter is obtained by weighted average method based on the holder's behavior data, and the contribution value index is corrected according to the holder's influence value parameter.

13. A blockchain node program product for operating asset value assessment, comprising a computer program, characterized in that: When the computer program is executed by a processor, the blockchain-based sub-business asset value assessment method described in any one of claims 10 to 12 is implemented.

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