Data value evaluation method and device, equipment and computer readable storage medium

Abstract

Embodiments of the present application disclose a data value evaluation method, device and equipment and a computer readable storage medium. The method comprises: determining a first data value of a to-be-evaluated data set; the to-be-evaluated data set comprises at least one to-be-evaluated data held by a data owner, and the first data value represents an inherent value of the to-be-evaluated data set itself; determining a second data value of the to-be-evaluated data set; the second data value represents a derivative value generated by the to-be-evaluated data set in an application process, and the second data value is associated with an application scenario in which the to-be-evaluated data set is located and an efficiency generated by the to-be-evaluated data set to a collaborative task; based on the first data value and the second data value, comprehensively evaluating a third data value of the to-be-evaluated data set; and the third data value represents an actual data value of the to-be-evaluated data set.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data value assessment method, apparatus, device, and computer-readable storage medium. Background Technology

[0002] In the face of the increasingly important demand for the marketization of data elements, assessing the data value held by various data owners is of great significance for building a data trading ecosystem.

[0003] Currently, relevant technologies primarily measure data value from the following aspects: intrinsic value, commercial value, performance value, cost value, economic value, and market value. However, these approaches to measuring data value suffer from numerous problems, including a single dimension for evaluation, isolated evaluation methods, and a lack of dynamic adjustment mechanisms. This leads to inaccurate and unreliable assessments of data value, thereby affecting the rational allocation and use of data resources. Summary of the Invention

[0004] To address the technical problems existing in related technologies, embodiments of this application provide a data value assessment method, apparatus, device, and computer-readable storage medium.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] This application provides a data value assessment method, the method comprising:

[0007] Determine a first data value for the dataset to be evaluated; the dataset to be evaluated includes at least one piece of data to be evaluated held by a data owner, and the first data value characterizes the inherent value of the dataset itself.

[0008] Determine the second data value of the dataset to be evaluated; the second data value characterizes the derived value generated by the dataset to be evaluated during the application process, and the second data value is related to the application scenario of the dataset to be evaluated and the effectiveness of the dataset to be evaluated in the collaborative task.

[0009] Based on the first data value and the second data value, a third data value is comprehensively evaluated for the dataset to be evaluated; the third data value represents the actual data value of the dataset to be evaluated.

[0010] In the above scheme, determining the first data value of the dataset to be evaluated includes:

[0011] Determine an initial value for the first data value of the dataset to be evaluated, and an adjustment value for the first data value;

[0012] The first data value of the dataset to be evaluated is determined based on the initial value of the first data value and the adjustment value.

[0013] In the above scheme, determining the initial value of the first data value of the dataset to be evaluated includes:

[0014] Determine the value assessment index parameters for at least one dimension corresponding to the dataset to be evaluated;

[0015] Based on the combination of the value assessment index parameters of the at least one dimension and the weight values ​​corresponding to each value assessment index parameter, the initial value of the first data value of the dataset to be evaluated is obtained.

[0016] In the above scheme, determining the value assessment index parameter parameters for at least one dimension corresponding to the dataset to be evaluated includes:

[0017] Obtain the application scenario of the dataset to be evaluated; the application scenario is one of multiple application scenarios included in the application domain of the dataset to be evaluated;

[0018] Based on the obtained application scenarios and the correspondence between the established application scenarios and value assessment index parameters, the value assessment index parameters corresponding to the obtained application scenarios are determined as the value assessment index parameters of at least one dimension of the dataset to be evaluated.

[0019] The method in the above scheme further includes:

[0020] Determine the overall contribution of the dataset to be evaluated to at least one collaborative task in which it participates; the overall contribution is correlated with the value of the second data.

[0021] Based on the determined overall contribution, the value of the first data is adjusted accordingly.

[0022] In the above scheme, determining the second data value of the dataset to be evaluated includes:

[0023] Determine the marginal benefit value of the dataset to be evaluated in the collaborative task; the marginal benefit value characterizes the difference in the effectiveness of the collaborative task caused by the presence or absence of the dataset to be evaluated.

[0024] Based on the determined marginal benefit value, the second data value of the dataset to be evaluated is determined.

[0025] In the above scheme, determining the marginal benefit value of the dataset to be evaluated in the collaborative task includes:

[0026] Determine the value metric generated by the first dataset; the first dataset is a set of data participating in the collaborative task that includes the dataset to be evaluated;

[0027] Determine the value metric generated by the second dataset; the second dataset is a set of data in the collaborative task that does not include the dataset to be evaluated;

[0028] Based on the difference between the value metric generated by the first dataset and the value metric generated by the second dataset, the marginal benefit value of the dataset to be evaluated in the collaborative task is obtained.

[0029] The method in the above scheme further includes:

[0030] Determine the attenuation adjustment function used to adjust the value of the second data;

[0031] Based on the determined attenuation adjustment function, the value of the second data is adjusted accordingly.

[0032] In the above scheme, the step of comprehensively evaluating the third data value of the dataset to be evaluated based on the first data value and the second data value includes:

[0033] Determine the weight value corresponding to the first data value and the weight value corresponding to the second data value;

[0034] The third data value of the dataset to be evaluated is obtained based on the combination of the first data value and the weight value corresponding to the first data value, and the second data value and the weight value corresponding to the second data value.

[0035] This application embodiment also provides a data value assessment device, the device comprising:

[0036] The first determining unit is used to determine a first data value of the dataset to be evaluated; the dataset to be evaluated includes at least one piece of data to be evaluated held by the data owner, and the first data value characterizes the inherent value of the dataset to be evaluated itself.

[0037] The second determining unit is used to determine the second data value of the dataset to be evaluated; the second data value represents the derived value generated by the dataset to be evaluated during the application process, and the second data value is related to the application scenario of the dataset to be evaluated and the effectiveness of the dataset to be evaluated in the collaborative task.

[0038] An evaluation unit is used to comprehensively evaluate a third data value of the dataset to be evaluated based on the first data value and the second data value; the third data value represents the actual data value of the dataset to be evaluated.

[0039] This application embodiment also provides a data value assessment device, the device comprising:

[0040] A processor is configured to determine a first data value of a dataset to be evaluated, wherein the dataset to be evaluated includes at least one piece of data held by a data owner, and the first data value characterizes the inherent value of the dataset itself; determine a second data value of the dataset to be evaluated, wherein the second data value characterizes the derived value generated by the dataset during application, and the second data value is related to the application scenario of the dataset and the effectiveness of the dataset in participating collaborative tasks; and comprehensively evaluate a third data value of the dataset to be evaluated based on the first data value and the second data value, wherein the third data value characterizes the actual data value of the dataset to be evaluated.

[0041] This application embodiment also provides a data value assessment device, including: a processor and a memory for storing a computer program capable of running on the processor;

[0042] When the processor runs the computer program, it executes the steps of any of the above methods.

[0043] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above methods.

[0044] The data value assessment method, apparatus, device, and computer-readable storage medium provided in this application embodiment determine a first data value of a dataset to be assessed; the dataset to be assessed includes at least one piece of data held by a data owner, and the first data value represents the inherent value of the dataset itself; determine a second data value of the dataset to be assessed; the second data value represents the derived value generated by the dataset during application, and the second data value is related to the application scenario of the dataset and the effectiveness of the dataset in participating collaborative tasks; based on the first data value and the second data value, comprehensively assess a third data value of the dataset; the third data value represents the actual data value of the dataset.

[0045] The solution adopted in this application takes into account the inherent value of the dataset to be evaluated, as well as the derived value related to the application scenario and the effectiveness generated by participating in collaborative tasks, and comprehensively determines the actual data value of the dataset to be evaluated. In this way, the static inherent value and dynamic derived value of the data are combined to jointly measure the actual data value, improve the accuracy of the actual data value assessment, make the obtained actual data value credible, and enhance the rational allocation and use of data resources. Attached Figure Description

[0046] Figure 1 A flowchart illustrating a data value assessment method provided in this application embodiment;

[0047] Figure 2 A schematic diagram of a data value assessment system architecture provided in an embodiment of this application;

[0048] Figure 3 A flowchart illustrating a method for determining the inherent value of a dataset to be evaluated, provided in an embodiment of this application;

[0049] Figure 4 A flowchart illustrating a method for determining the derived value of a dataset to be evaluated, provided in an embodiment of this application;

[0050] Figure 5 A schematic diagram of the structure of a data value assessment device provided in an embodiment of this application;

[0051] Figure 6 This is a schematic diagram of the structure of a data value assessment device provided in an embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] In the following description, "some embodiments" are referred to, which describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subset of all possible embodiments, and the technical solutions described in the embodiments of this application may be combined with each other without conflict.

[0054] Before introducing the technical solutions of the embodiments of this application, the relevant technologies will be described below.

[0055] In the era of big data, data, possessing immense economic value, is likened to a new form of oil resource. By deeply mining cross-sectoral data resources and uncovering the economic patterns behind the data, we can powerfully promote industrial upgrading and leapfrog development. Big data has become a focus of attention for countries worldwide, and my country has also included it in its national strategic development direction, hoping to combine it with artificial intelligence to boost China's economic take-off.

[0056] In practical applications, breakthroughs in machine learning algorithms and the widespread adoption of artificial intelligence technologies rely heavily on a massive supply of high-quality data. Organizations across the board seek valuable data resources to optimize performance and support decision-making. However, current data sharing and circulation rules and technologies fail to meet the strong demand for data resources from various applications, resulting in numerous isolated data silos—a significant waste of data resources. Therefore, there is an urgent need for open platforms and related technologies that support data sharing and trading to break down data barriers, connect data silos, and promote data circulation on the internet, thereby unlocking the economic value of big data and releasing the application potential of various types of data.

[0057] Data circulation and trading, as an emerging business model, has attracted significant attention from both the business and academic communities. For example, Company A in the US operates data sharing within the financial industry, Company B sells data from social networks, and Company C trades subscriptions and query information from traveling users. The domestic data trading market is also experiencing explosive growth. The Big Data Exchange in Province D was the first of its kind in China, followed by similar platforms such as the Data Exchange Center in City E and the Donghu Big Data Exchange Center in City F. Recently, blockchain-based distributed data trading markets have generated considerable buzz, with examples including the IOTA IoT Data Market, DatabrokerDao, and BAIC. In academia, Professor A's research group at the University of Washington is a pioneer in data trading, having produced a series of related works. Professor B's team at the University of Science and Technology of China analyzed the opportunities in the domestic data sharing and trading market, as well as the challenges faced in data preprocessing, data quality assessment, data pricing, data security and privacy, and data traceability, in their columns for the *Communications of the ACM* and the *China Computer Federation Communications*.

[0058] Furthermore, the term "big data trading" frequently appears in various national documents on big data development. For example, the "Action Plan for Promoting Big Data Development" issued by the State Council in 2015 explicitly proposed "guiding and cultivating the big data trading market, conducting pilot projects for application-oriented data trading markets, exploring the trading of big data derivative products, encouraging market entities at all stages of the industrial chain to exchange and trade data, promoting the circulation of data resources, establishing and improving data resource trading mechanisms and pricing mechanisms, and standardizing trading behavior, among other ideas and measures to improve the market development mechanism." The "Big Data Industry Development Plan (2016-2020)" released by the Ministry of Industry and Information Technology in January 2017 pointed out that "it is necessary to carry out the development of basic and general standards for data resource classification, open sharing, trading, identification, statistics, product evaluation, data capabilities, and data security, as well as relevant national standards for key application areas such as industrial big data."

[0059] It is evident that, unlike traditional commodities, data, as a non-exclusive and unique resource, possesses characteristics such as rapid growth, low replication costs, unknown potential value, difficulty in determining ownership, and difficulty in controlling distribution channels. These characteristics pose numerous challenges to building an efficient, reliable, fair, and secure data sharing and trading market. Simultaneously, big data possesses a rare attribute—synergy. This means that the entire set of multiple datasets, when combined and collaborated, can generate greater value than the simple sum of the individual datasets' values. Therefore, opening up, circulating, and integrating this previously isolated and closed data can significantly enhance the utilization value of data resources, which is also the trend of development in the big data era.

[0060] However, the current field of big data sharing and trading faces some problems and challenges, mainly including the following aspects:

[0061] 1. Preprocessing of heterogeneous massive data

[0062] To turn data into a valuable asset, it needs to be properly preprocessed, such as data cleaning, standardization, calibration, fusion, and desensitization, so that the data is processed into a state that is consistent in specifications, clearly defined, complete in data items, and suitable for unified algorithm processing. The data preprocessing operations include, but are not limited to, some of the operations listed above.

[0063] 2. Data ownership confirmation and data verification

[0064] Before sharing transaction data, the rights to data assets should be clearly defined, including ownership and usage rights. Ownership refers to the right to possess, possess, and dispose of the data; usage rights include the rights to access, read, transform, and compute the data. The data market needs efficient and accurate data traceability methods to ensure that data sources, usage, and flow are traceable, and actively promotes relevant legislation for data rights protection. On the other hand, feasible verification methods are also needed to ensure the authenticity of data sources, the reliability of data quality, and the accuracy of data calculation results.

[0065] 3. Data security and privacy protection

[0066] The vast amounts of data contain a wealth of sensitive information, making transaction security and privacy protection in the data market particularly important for the sharing of sensitive data. Data trading platforms have the responsibility and obligation to securely store and transmit user data, and to use data resources reasonably while fully protecting user privacy, finding a suitable balance between open circulation and privacy protection.

[0067] 4. Data quality and value assessment

[0068] To protect the rights and interests of data transaction participants, build a fair and credible standardized market, and maintain a healthy data transaction ecosystem, data quality assessment and value assessment have become urgent challenges. Quality assessment focuses on the multi-dimensional characteristics of the data content itself, such as completeness, accuracy, precision, consistency, and timeliness, while value assessment, while evaluating data quality, further considers the costs of data production and its output in different tasks.

[0069] Related research currently faces many challenges, such as the difficulty in quantifying and evaluating features, the low efficiency of dataset quality assessment, the difficulty in calculating data costs, the difficulty in predicting the value of data use, and the dynamic changes in data value. The ease with which data can be copied and the difficulty in controlling distribution channels make it difficult to return sold data, further increasing the requirements for accurate and reliable quality and value assessment of data before it is sold.

[0070] 5. Data pricing and revenue distribution

[0071] In practical applications, the sustainable and healthy development of the data sharing and trading market requires a reasonable data pricing mechanism and a fair revenue distribution strategy. First, as a byproduct of information systems, data is difficult to estimate in terms of cost. Second, the market value of data is influenced by the application scenario and similar products on the market; for example, GPS data is valuable in navigation applications but less so in financial credit reporting. The diversity and dynamism of data application scenarios significantly increase the difficulty of assessing the market value of data.

[0072] Furthermore, the complex interrelationships of data make arbitrage more prevalent in the data market: data buyers can infer the content of high-priced data from low-priced data. Overcoming these difficulties and designing a reasonable data pricing mechanism to ensure a more equitable allocation of data resources and achieve a win-win situation for all participants is a crucial issue for the sustainable development of data trading.

[0073] Based on the assessment and calculation of data value in the above-mentioned problems, the main approaches and perspectives for measuring data value in related technologies are as follows, and their characteristics and existing problems are analyzed separately:

[0074] 1. The intrinsic value of data

[0075] This method completely disregards business value, focusing instead on the intrinsic value of the data. It breaks down data quality into characteristics such as accuracy, accessibility, scarcity, and completeness. Each characteristic is evaluated, and weighting factors can be assigned to each, followed by a final weighted score.

[0076] The problem with this method is that it does not take into account the statistical characteristics of the data itself. In actual applications, the statistical characteristics of the data determine whether the data is reliable and usable during the modeling process, and are one of the most basic characteristics of the data itself.

[0077] 2. The commercial value of data

[0078] This method is used to measure the impact of data characteristics about one or more business processes on business implementation and the value of products and services.

[0079] The problem with this method is that the measurement of data commercial value is influenced by many factors. The same data often presents drastically different commercial values ​​under different application scenarios and business purposes, and there is no quantifiable and clear calculation method for commercial value.

[0080] 3. The performance value of data

[0081] This approach is more "empirical" because it measures the impact of data over time on one or more key performance indicators (KPIs).

[0082] The problem with this method is that there is often no quantitative assessment method for the impact of single or multiple KPI indicators. Moreover, the comprehensive impact of multiple KPI indicators that are interrelated and interdependent is even more difficult to quantify.

[0083] 4. The cost value of data

[0084] This method measures the cost of acquiring or replacing lost data. Value is assigned to the data by measuring the lost revenue and the cost required to acquire it. This is how valuation experts assess most intangible assets that do not have an apparent market value or are generating a market.

[0085] This method is essentially measuring the marginal gain or marginal loss in the case of gaining or losing data. The problem is that there is no clear method for quantifying the marginal effect of data value, which presents a significant challenge.

[0086] 5. The economic value of data

[0087] This method measures how information assets contribute to an organization's revenue.

[0088] The problem with this method is that it uses a relatively single dimension to measure the economic value of data. The economic value of data is not only reflected in revenue, but also in search and retrieval costs, holding and maintenance costs, replication and circulation costs, auditing and tracing costs, and verification and credibility costs.

[0089] 6. The Market Value of Data

[0090] This method measures the revenue generated from “selling, renting, or exchanging” company data, which is one of the best ways to assess data assets.

[0091] The problem with this method is that the market value of data changes dynamically, and it does not take into account or model the fluctuations in the market value of data.

[0092] Regarding the approaches and methods for measuring data value in the aforementioned related technologies, their shortcomings and limitations are summarized and analyzed as follows:

[0093] 1. Lack of accurate definition

[0094] Currently, there is no accurate definition of the value of data assets, which leads to inconsistencies and diversity in the industry's definition and understanding of data assets. This makes it difficult to establish an effective data asset assessment and management system and to determine a reliable method for assessing the value of data assets.

[0095] 2. Using a single dimension

[0096] The ideas and methods of related technologies use a relatively single dimension to measure the value of data. A comprehensive and accurate measurement of data value depends on the integrated evaluation and calculation of the value of data across multiple dimensions. Currently, there is a lack of usable, feasible, and reliable methods.

[0097] 3. Value assessment is isolated.

[0098] The value of data lies not only in its inherent value but also in the actual integration of it with specific business application scenarios. Therefore, the value of data should be comprehensively evaluated by combining its inherent value with the value generated during its application.

[0099] 4. Value assessment is static.

[0100] The value of data is not static. On the one hand, the connotation and value of the same data will be continuously enriched and enhanced as the data is used, expanded in dimensions, further processed and refined, and further detailed in annotation. On the other hand, with the advancement of technology and the evolution of application needs, the same data may no longer generate its original value, and its importance and usability will decline, and its value judgment will also change accordingly.

[0101] 5. Lack of a data asset valuation system

[0102] Currently, there is no authoritative data asset valuation model or system, no data asset valuation indicator system, and no quantifiable valuation methods or standards.

[0103] In summary, in the face of the increasingly important market demand for data elements, there is currently no universally recognized, mature, and systematic data value assessment method. Moreover, the approaches to measuring data value in related technologies suffer from many problems, such as a single data value assessment dimension, isolated assessment methods, and a lack of dynamic adjustment mechanisms. This leads to inaccurate and unreliable data value assessments, thereby affecting the rational allocation and use of data resources.

[0104] Based on this, in various embodiments of this application, a first data value of the dataset to be evaluated is determined; the dataset to be evaluated includes at least one piece of data to be evaluated held by a data owner, and the first data value represents the inherent value of the dataset itself; a second data value of the dataset to be evaluated is determined; the second data value represents the derived value generated by the dataset during application, and the second data value is related to the application scenario of the dataset and the effectiveness of the dataset in participating collaborative tasks; based on the first data value and the second data value, a third data value of the dataset to be evaluated is comprehensively evaluated; the third data value represents the actual data value of the dataset to be evaluated.

[0105] The solution adopted in this application takes into account the inherent value of the dataset to be evaluated, as well as the derived value related to the application scenario and the effectiveness generated by participating in collaborative tasks, and comprehensively determines the actual data value of the dataset to be evaluated. In this way, the static inherent value and dynamic derived value of the data are combined to jointly measure the actual data value, improve the accuracy of the actual data value assessment, make the obtained actual data value credible, and enhance the rational allocation and use of data resources.

[0106] The present application will now be described in further detail with reference to the accompanying drawings and embodiments.

[0107] This application provides a data value assessment method. Figure 1 A flowchart illustrating a data value assessment method provided in this application embodiment is shown below. Figure 1 As shown, the method includes:

[0108] Step 101: Determine the first data value of the dataset to be evaluated.

[0109] In this embodiment of the application, the dataset to be evaluated includes at least one dataset to be evaluated held by the data owner, and the first data value characterizes the inherent value of the dataset to be evaluated itself.

[0110] In some embodiments, the first data value of the dataset to be evaluated can be determined in the following manner:

[0111] Determine an initial value for the first data value of the dataset to be evaluated, and an adjustment value for the first data value;

[0112] The first data value of the dataset to be evaluated is determined based on the initial value of the first data value and the adjustment value.

[0113] Here, determining the initial value of the first data value of the dataset to be evaluated includes:

[0114] Determine the value assessment index parameters for at least one dimension corresponding to the dataset to be evaluated;

[0115] Based on the combination of the value assessment index parameters of the at least one dimension and the weight values ​​corresponding to each value assessment index parameter, the initial value of the first data value of the dataset to be evaluated is obtained.

[0116] In practical applications, corresponding value assessment index parameters are defined for each application scenario, and the value assessment index parameters corresponding to different application scenarios are not the same.

[0117] Based on this, in some embodiments, determining the value assessment index parameters for at least one dimension corresponding to the dataset to be evaluated includes:

[0118] Obtain the application scenario of the dataset to be evaluated; the application scenario is one of multiple application scenarios included in the application domain of the dataset to be evaluated;

[0119] Based on the obtained application scenarios and the correspondence between the established application scenarios and value assessment index parameters, the value assessment index parameters corresponding to the obtained application scenarios are determined as the value assessment index parameters of at least one dimension of the dataset to be evaluated.

[0120] Here, "application domain" is a broader concept, encompassing several application scenarios. For example, finance, a common application domain, includes different application scenarios such as credit assessment, risk warning, credit reporting, and anti-fraud. Each application scenario corresponds to different value assessment indicator parameters. The correspondence between application scenarios and value assessment indicator parameters can be pre-stored in a database, facilitating the retrieval of the value assessment indicator parameters corresponding to the current application scenario of the dataset to be evaluated.

[0121] Here, the value assessment index parameters for at least one dimension of the dataset to be evaluated include, but are not limited to, statistical characteristics, authenticity, completeness, accuracy, data cost, and security.

[0122] In this embodiment, each value assessment indicator parameter corresponds to a weight value. Based on the combination of the at least one dimension's value assessment indicator parameter and the corresponding weight values, the initial value of the first data value of the dataset to be assessed is obtained. This includes, but is not limited to, obtaining the initial value of the first data value of the dataset to be assessed based on a weighted average of the at least one dimension's value assessment indicator parameters and the corresponding weight values. In other words, the initial value of the first data value of the dataset to be assessed can be determined based on various combinations of the at least one dimension's value assessment indicator parameters and the corresponding weight values, and is not limited to weighted averages.

[0123] In practical applications, after determining the initial value of the first data value of the dataset to be evaluated and the adjustment value for the first data value, the initial value of the first data value and the adjustment value can be added together to obtain the first data value of the dataset to be evaluated. However, the embodiments of this application are not limited to the addition method.

[0124] Taking addition as an example, the first data value (intrinsic value) of the dataset to be evaluated = the initial value of the first data value of the dataset to be evaluated + the adjustment value. The adjustment value can be evaluated and determined according to different situations (time, other conditions that trigger adjustment). If no adjustment is needed to the intrinsic value at a certain moment and under a certain state, that is, the adjustment value is zero, then the initial value of the intrinsic value of the dataset to be evaluated at this time is the intrinsic value of the dataset to be evaluated.

[0125] In practical applications, once the data owner completes a collaborative task, the contribution of the data owner's dataset to be evaluated to the overall contribution of at least one collaborative task during the collaborative task directly affects the first data value of the dataset to be evaluated, and the first data value of the dataset to be evaluated can be adjusted.

[0126] Based on this, in some embodiments, the method further includes:

[0127] Determine the overall contribution of the dataset to be evaluated to at least one collaborative task in which it participates; the overall contribution is correlated with the value of the second data.

[0128] Based on the determined overall contribution, the value of the first data is adjusted accordingly.

[0129] Here, the contributions of the dataset to be evaluated to multiple collaborative tasks can be summarized and synthesized. This includes, for example, accumulating the contributions of each collaborative task historically, accumulating within a selected time window, and compiling comprehensive evaluation opinions. The primary data value is then adjusted based on the summarized contribution. In practice, the overall contribution can be calculated by accumulating the contributions of the dataset to be evaluated to multiple collaborative tasks. Then, depending on whether the contribution of the dataset to be evaluated to the current collaborative task iteration cycle is positive or negative, a corresponding positive or negative adjustment factor can be determined. Based on the weighted average of the determined overall contribution and the adjustment factor, the primary data value is adjusted accordingly.

[0130] Step 102: Determine the second data value of the dataset to be evaluated.

[0131] In this embodiment of the application, the second data value represents the derived value generated by the dataset to be evaluated during the application process. The second data value is related to the application scenario of the dataset to be evaluated and the effectiveness of the dataset to be evaluated in the collaborative task.

[0132] Here, the second data value may include the effectiveness value of the dataset to be evaluated in the execution of the collaborative task, as well as the value generated by the data initiator when using the results of the collaborative task after its completion.

[0133] In practical applications, the secondary data value of the dataset to be evaluated can be assessed by quantitatively measuring the marginal benefit of the dataset in the collaborative task.

[0134] Based on this, in some embodiments, determining the second data value of the dataset to be evaluated can be achieved in the following way:

[0135] Determine the marginal benefit value of the dataset to be evaluated in the collaborative task; the marginal benefit value characterizes the difference in the effectiveness of the collaborative task caused by the presence or absence of the dataset to be evaluated; based on the determined marginal benefit value, determine the second data value of the dataset to be evaluated.

[0136] In some embodiments, the marginal benefit value of the dataset to be evaluated in the collaborative task can be determined in the following manner:

[0137] Determine the value metric generated by the first dataset; the first dataset is a set of data participating in the collaborative task that includes the dataset to be evaluated;

[0138] Determine the value metric generated by the second dataset; the second dataset is a set of data in the collaborative task that does not include the dataset to be evaluated;

[0139] Based on the difference between the value metric generated by the first dataset and the value metric generated by the second dataset, the marginal benefit value of the dataset to be evaluated in the collaborative task is obtained.

[0140] It should be noted that, in determining the marginal benefit value of the dataset to be evaluated in the collaborative task, the embodiments of this application include, but are not limited to, calculating the difference between the value metric value generated by the first dataset and the value metric value generated by the second dataset.

[0141] For example, suppose the dataset to be evaluated is d. i The remaining datasets in all data sets participating in the collaborative task, excluding the dataset to be evaluated, are denoted as D. The value metric generated by the first dataset is v. i Specifically, the dataset participating in the collaborative task includes the dataset d to be evaluated. i The value measure v generated by the set i (d i ∪D), the value metric generated by the second dataset is v' i Specifically, the dataset d to be evaluated was not included in the dataset used for the collaborative task. i The value measure v' generated by the set i (D), then the difference between the value measure generated by the first dataset and the value measure generated by the second dataset can be expressed by the following formula: Diff(vi (d i ∪D),v' i (D)), where the Diff function is used to compare the difference between the value measure generated by the first dataset and the value measure generated by the second dataset. However, embodiments of this application include, but are not limited to, calculating the difference between the two value measures.

[0142] In practical applications, considering the relative value improvement in data performance evaluation and the greater importance of early data collaboration contributions, the secondary data value of the dataset to be evaluated can be adjusted.

[0143] Based on this, in some embodiments, the method further includes:

[0144] Determine the attenuation adjustment function used to adjust the value of the second data;

[0145] Based on the determined attenuation adjustment function, the value of the second data is adjusted accordingly.

[0146] Here, the decay adjustment function includes, but is not limited to, functions with time as the independent variable, such as various common time decay functions with time as the independent variable, such as linear / exponential time decay functions; it can also be a function of transformation and mapping relationship.

[0147] Step 103: Based on the first data value and the second data value, comprehensively evaluate the third data value of the dataset to be evaluated.

[0148] In this embodiment of the application, the third data value represents the actual data value of the dataset to be evaluated.

[0149] In some embodiments, the step of comprehensively evaluating the third data value of the dataset to be evaluated based on the first data value and the second data value includes:

[0150] Determine the weight value corresponding to the first data value and the weight value corresponding to the second data value;

[0151] The third data value of the dataset to be evaluated is obtained based on the combination of the first data value and the weight value corresponding to the first data value, and the second data value and the weight value corresponding to the second data value.

[0152] It should be noted that the embodiments of this application include, but are not limited to, comprehensively evaluating the third data value of the dataset to be evaluated based on the weight value corresponding to the first data value and the weight value corresponding to the second data value; that is, the embodiments of this application include, but are not limited to, calculating the weight value, and can also use other reasonable methods to obtain the third data value of the dataset to be evaluated.

[0153] In practical applications, a first data value corresponds to a weight value, and a second data value also corresponds to a weight value. Based on the first data value and its corresponding weight value, and the combination of the second data value and its corresponding weight value, the third data value of the dataset to be evaluated is obtained. This includes, but is not limited to, multiplying the first data value by its corresponding weight value, multiplying the second data value by its corresponding weight value, and then weighting the two multiplication results to obtain the third data value of the dataset to be evaluated. In other words, the third data value of the dataset to be evaluated can be determined based on various combinations of the first data value and its corresponding weight value, and the second data value and its corresponding weight value; it is not limited to weighted methods.

[0154] The data value assessment method provided in this application comprehensively determines the actual data value of the dataset by considering the inherent value of the dataset itself and the derived value related to the application scenario and the effectiveness generated by participating in collaborative tasks. In this way, the static inherent value and dynamic derived value of the data are combined to jointly measure the actual data value, improve the accuracy of the actual data value assessment, make the obtained actual data value credible, and enhance the rational allocation and use of data resources.

[0155] The present application will be further described in detail below with reference to application examples.

[0156] In this application embodiment, for a joint modeling scenario involving multiple parties (including the data initiator and multiple data participants, where the data participants are data owners who have reached an agreement with the data initiator and whose data needs are met), evaluating the value of the data held by each data owner plays a fundamental role and has great value in promoting data collaboration and sharing and building a data trading ecosystem.

[0157] The technical solution described in this application is a method for calculating and evaluating the value of each data owner's data and its contribution to the joint modeling task in a multi-party collaborative modeling scenario. Compared with the problems existing in related technologies and approaches, the method described in this application has the following characteristics:

[0158] 1) Use multiple dimensions to measure and evaluate the value of the data;

[0159] 2) Take into account the inherent value of the dataset to be evaluated, as well as the derived value related to the application scenario and processing task performance;

[0160] 3) Establish a dynamic adjustment mechanism for data value based on utility feedback and the fading of popularity.

[0161] Here, utility feedback refers to the process by which the data initiator evaluates the value of collaborative task results or joint modeling results when using them in subsequent business activities or production operations, and then provides value feedback to the data participants who initially contributed to the results based on that value. The decline in popularity refers to adjusting the utility and value of the aforementioned results by introducing a moderating factor that changes and decays over time, so that the longer the time, the lower the utility and value, similar to a gradual decline in popularity.

[0162] In this application example, a multi-dimensional and dynamic data value assessment system architecture is proposed for multi-party collaborative modeling scenarios. Figure 2 This is a schematic diagram of a data value assessment system architecture provided in an embodiment of this application, such as... Figure 2 As shown, the actual data value of the dataset to be evaluated is determined by separately assessing its inherent value and derived value (including at least one piece of data held by the data owner), and then combining the inherent value and derived value of the dataset to be evaluated for a comprehensive judgment. Furthermore, a data value adjustment mechanism can be established to adjust the inherent value and derived value of the dataset to be evaluated accordingly.

[0163] Based on the above Figure 2 The data value assessment system architecture shown below will be explained in the following section regarding the process of determining the inherent value (first data value) and the derived value (second data value) of the dataset to be assessed.

[0164] Figure 3 A flowchart illustrating a method for determining the inherent value of a dataset to be evaluated, provided in an embodiment of this application, is shown below. Figure 3 As shown, firstly, at least one dimension of the value assessment index parameter corresponding to the dataset to be evaluated is determined. This at least one dimension of the value assessment index parameter includes, but is not limited to, statistical characteristics, data volume, completeness, and diversity. Each value assessment index parameter corresponds to a weight value; for example, the weight value for statistical characteristics is w1, the weight value for data volume is w2, the weight value for completeness is w3, the weight value for diversity is w4, and so on, with the weight value for the i-th value assessment index parameter being w... i Then, the values ​​of at least one dimension of the value assessment index parameter are weighted together with the weight values ​​corresponding to the values ​​of each value assessment index parameter to obtain the intrinsic value of the dataset to be assessed.

[0165] The following section details the method for determining the inherent value of the dataset to be evaluated, mainly including the following steps:

[0166] Step 1: Based on the characteristics and needs of data in different fields, establish a data value evaluation index system for different fields and scenarios. This can be abstracted into the following (including but not limited to) value assessment index parameters across several dimensions:

[0167] Statistical characteristics: The distribution characteristics that the data itself follows (including but not limited to: central tendency characteristics (location mean, numerical mean, mean, mode, median, arithmetic mean, harmonic mean, geometric mean and power mean, etc.), dispersion characteristics (range, mean deviation, interquartile range, variance and standard deviation and coefficient of variation, etc.), distribution shape (distribution type, moments, skewness and kurtosis, etc.));

[0168] Authenticity: This refers to the degree of authenticity of the data. If the data is biased or even falsified, it will lose its statistical significance and value.

[0169] Completeness: This refers to the completeness of the data-related indicator records. If key indicator dimensions are missing or abnormal, it will seriously affect the value of data assets and their assessment.

[0170] Accuracy: This refers to the accuracy of data records, including cleaning up outliers, blank values, invalid values, and duplicate values ​​in the original data, thereby improving data accuracy and enhancing the value of data assets.

[0171] Data cost: This refers to the quantifiable and assessable cost required for data acquisition, management, and maintenance. It is also an important factor and indicator for enhancing the value and bargaining power of data assets.

[0172] Security: This refers to the ability of data not to be stolen or destroyed. The higher the data security, the more it can provide a continuous and stable contribution of value, while reducing the additional costs of data protection.

[0173] Step 2: For the given dataset whose value is to be evaluated (i.e. the dataset to be evaluated), determine the value evaluation index system to be adopted based on its application field and application scenario.

[0174] Here, the value assessment indicator system includes value assessment indicator parameters across multiple dimensions. Application domains are a broader concept, encompassing several application scenarios. For example, finance, a common application domain, includes different application scenarios such as credit assessment, risk warning, credit reporting, and anti-fraud. In this application embodiment, different application domains are defined and established, each containing different application scenarios. Corresponding value assessment indicator parameters are defined for each application scenario, thereby establishing a hierarchical and categorized value assessment indicator system.

[0175] Step 3: Based on the values ​​of the value assessment index parameters in the value assessment index system and the combination of the weight values ​​corresponding to the values ​​of each value assessment index parameter, obtain the initial value of the static inherent value of the dataset to be assessed.

[0176] Step 4: Adjust the initial value of the intrinsic value by means of heat dissipation, domain scene matching degree, etc., and determine the static intrinsic value estimate at a given time in the future;

[0177] Here are some ways to achieve the cooling-off of popularity: An adjustment factor that decays over time can be introduced, and the value of this adjustment factor at a certain point in time can be used as the factor for value-weighted adjustment. Another way to achieve domain-scene matching is to define the architecture of the domain and scene on a tree structure, calculate the distance between two specific application scenarios in the tree structure (the number of steps taken from one node back to its common parent node, and then to another node), and use this as the matching degree between the two application scenarios.

[0178] Step 5: After the data collaboration task is completed, the static intrinsic value of the dataset to be evaluated is adjusted according to the magnitude of its contribution to the overall collaboration task. The contribution reputation of the dataset to be evaluated in multiple different collaboration tasks can be summarized and integrated (e.g., overall historical accumulation, time window sampling accumulation, comprehensive evaluation opinions, etc.), and the static intrinsic value is adjusted according to the summary and integration.

[0179] Here, the intrinsic value of the dataset to be evaluated is weighted and adjusted based on the magnitude of the cumulative contribution of the dataset to the collaborative task and whether the adjustment factor is positive or negative.

[0180] The process of determining the derived value of the dataset to be evaluated is described below.

[0181] Figure 4 A flowchart illustrating a method for determining the derived value of a dataset to be evaluated, provided in an embodiment of this application, is shown below. Figure 4 As shown, it includes the following steps:

[0182] Step 1: Build a library of performance evaluation rules / methods for different collaborative tasks;

[0183] For example, for collaborative tasks of jointly trained models, the loss function / cost function in the core algorithm optimization stage can be used as the performance evaluation function. However, different collaborative tasks and different algorithms have different performance evaluation rules and algorithms. Therefore, a library can be constructed, and selection can be made according to the actual situation to select the appropriate performance evaluation rule / method library.

[0184] Step 2: Calculate the marginal benefit value of the dataset to be evaluated in the collaborative task, that is, the change in the efficiency of the collaborative task caused by removing or adding the given dataset to be evaluated through efficiency evaluation; based on the marginal benefit value, evaluate the efficiency value of the dataset to be evaluated, that is, the derived value, through efficiency evaluation rules / method library.

[0185] Here, the marginal benefit value represents the difference in the effectiveness of collaborative tasks caused by the presence or absence of the dataset to be evaluated.

[0186] Step 3: Adjust the derived value of performance evaluation through (including but not limited to) sub-modules such as relative performance and performance reduction to achieve the adjustment of derived value;

[0187] Here, relative effectiveness mainly considers factors such as the contribution, value, and effectiveness brought by data, and the ratio of the input of data in terms of quantity, scale, and number of participations. In other words, it is the effectiveness that can be brought by a unit of input, such as contribution and value. Effectiveness reduction refers to the fact that the effectiveness brought by data in the current task iteration cycle is negative, and its cumulative contribution should also be reduced. That is to say, if data disrupts collaborative tasks and joint models, it should bear the corresponding losses and penalties.

[0188] Step 4: Feed back the collaborative tasks and overall effectiveness of the dataset to be evaluated to the intrinsic value assessment module.

[0189] The process of the above data value assessment method can be completed through the following modules:

[0190] 1. Intrinsic Value Assessment Module

[0191] The intrinsic value assessment module is used to assess the intrinsic value of the dataset to be assessed. The intrinsic value is static, and it is quantitatively assessed based on the composition and characteristics of the data in the dataset, taking into account the application domain and application scenario.

[0192] 2) Derivative Value Assessment Module

[0193] The derived value assessment module is used to evaluate the derived value of the dataset to be evaluated. The evaluation of derived value can be (but is not limited to) through a quantitative measure of the marginal effect of the data, for example: for the dataset d to be evaluated... i The remaining dataset (i.e., the datasets remaining in all data sets participating in the collaborative task excluding the dataset to be evaluated) is denoted as D. Specifically, it is the calculation of the dataset d to be evaluated. i The first dataset generates the value metric v i , and the dataset d to be evaluated is not included i The value metric v' generated by the second dataset iDifference values ​​between:

[0194] Diff(v i (d i ∪D),v' i (D));

[0195] The Diff function is used to compare the difference between two value measures, including but not limited to calculating the difference.

[0196] 3) Regulation mechanism module

[0197] The adjustment mechanism module (considering the changes in data value over time in terms of popularity, attention, and importance, adjusts the inherent value of the dataset to be evaluated; and considering the improvement of relative value in data performance evaluation and the greater importance of early data contribution to collaboration, a decay adjustment function with time as the independent variable (e.g., various common linear / exponential time decay functions) can be introduced to adjust the derived value of the dataset to be evaluated through models and formulas; furthermore, considering the greater potential value of data and its collaborative task-related results, the derived value of data is adjusted).

[0198] 4) Comprehensive Judgment Module

[0199] The comprehensive judgment module is used to comprehensively judge the inherent value and derived value of the dataset to be evaluated, so as to obtain the actual data value of the dataset to be evaluated.

[0200] To implement the data value assessment method of this application, this application also provides a data value assessment device. Figure 5 This is a schematic diagram of the structure of a data value assessment device provided in an embodiment of this application, as shown below. Figure 5 As shown, the device includes:

[0201] The first determining unit 51 is used to determine a first data value of the dataset to be evaluated; the dataset to be evaluated includes at least one piece of data to be evaluated held by the data owner, and the first data value represents the inherent value of the dataset to be evaluated itself.

[0202] The second determining unit 52 is used to determine the second data value of the dataset to be evaluated; the second data value represents the derived value generated by the dataset to be evaluated during the application process, and the second data value is related to the application scenario of the dataset to be evaluated and the effectiveness of the dataset to be evaluated in the collaborative task.

[0203] Evaluation unit 53 is used to comprehensively evaluate the third data value of the dataset to be evaluated based on the first data value and the second data value; the third data value represents the actual data value of the dataset to be evaluated.

[0204] In some embodiments, the first determining unit 51 includes: a third determining subunit and a fourth determining subunit; wherein,

[0205] The third determining subunit is used to determine an initial value of the first data value of the dataset to be evaluated, and an adjustment value for the first data value.

[0206] The fourth determining subunit is used to determine the first data value of the dataset to be evaluated based on the determined initial value of the first data value and the adjustment value.

[0207] In some embodiments, the third determining subunit is specifically used for:

[0208] Determine the value assessment index parameters for at least one dimension corresponding to the dataset to be evaluated;

[0209] Based on the combination of the value assessment index parameters of the at least one dimension and the weight values ​​corresponding to each value assessment index parameter, the initial value of the first data value of the dataset to be evaluated is obtained.

[0210] In practical applications, the third determining subunit is specifically used for:

[0211] Obtain the application scenario of the dataset to be evaluated; the application scenario is one of multiple application scenarios included in the application domain of the dataset to be evaluated;

[0212] Based on the obtained application scenarios and the correspondence between the established application scenarios and value assessment index parameters, the value assessment index parameters corresponding to the obtained application scenarios are determined as the value assessment index parameters of at least one dimension of the dataset to be evaluated.

[0213] In some embodiments, the method further includes:

[0214] Determine the overall contribution of the dataset to be evaluated to at least one collaborative task in which it participates; the overall contribution is correlated with the value of the second data.

[0215] Based on the determined overall contribution, the value of the first data is adjusted accordingly.

[0216] In some embodiments, the second determining unit 52 includes: a fifth determining subunit and a sixth determining subunit; wherein,

[0217] The fifth determining subunit is used to determine the marginal benefit value of the dataset to be evaluated in the collaborative task; the marginal benefit value characterizes the difference in the effectiveness of the collaborative task caused by the presence or absence of the dataset to be evaluated.

[0218] The sixth determining subunit is used to determine the second data value of the dataset to be evaluated based on the determined marginal benefit value.

[0219] In practical applications, the fifth determining subunit is specifically used for:

[0220] Determine the value metric generated by the first dataset; the first dataset is a set of data participating in the collaborative task that includes the dataset to be evaluated;

[0221] Determine the value metric generated by the second dataset; the second dataset is a set of data in the collaborative task that does not include the dataset to be evaluated;

[0222] Based on the difference between the value metric generated by the first dataset and the value metric generated by the second dataset, the marginal benefit value of the dataset to be evaluated in the collaborative task is obtained.

[0223] In some embodiments, the method further includes:

[0224] Determine the attenuation adjustment function used to adjust the value of the second data;

[0225] Based on the determined attenuation adjustment function, the value of the second data is adjusted accordingly.

[0226] In some embodiments, the evaluation unit 53 is specifically used for:

[0227] Determine the weight value corresponding to the first data value and the weight value corresponding to the second data value;

[0228] The third data value of the dataset to be evaluated is obtained based on the combination of the first data value and the weight value corresponding to the first data value, and the second data value and the weight value corresponding to the second data value.

[0229] In practical applications, the first determining unit 51, the second determining unit 52, and the evaluation unit 53 can be implemented by the processor in the data value evaluation device.

[0230] It should be noted that the data value assessment device provided in the above embodiments is only illustrated by the division of the above program modules when performing data value assessment. In practical applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the data value assessment device and the data value assessment method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0231] Based on the hardware implementation of the above program modules, and in order to implement the data value assessment method of this application embodiment, this application embodiment also provides a data value assessment device. Figure 6 This is a schematic diagram of the structure of a data value assessment device provided in an embodiment of this application, as shown below. Figure 6 As shown, the data value assessment device 60 includes:

[0232] Communication interface 61 enables data interaction with data participants;

[0233] The processor 62 is connected to the communication interface 61 to enable data interaction with data participants and to execute the methods provided by one or more of the above-described technical solutions when running a computer program. The computer program is stored in the memory 63.

[0234] Specifically, processor 62 is configured to determine a first data value of a dataset to be evaluated; the dataset to be evaluated includes at least one piece of data to be evaluated held by a data owner, and the first data value represents the inherent value of the dataset itself; determine a second data value of the dataset to be evaluated; the second data value represents the derived value generated by the dataset during application, and the second data value is related to the application scenario of the dataset and the effectiveness of the dataset in participating collaborative tasks; and comprehensively evaluate a third data value of the dataset based on the first data value and the second data value; the third data value represents the actual data value of the dataset.

[0235] In some embodiments, the processor 62 is specifically used for:

[0236] Determine an initial value for the first data value of the dataset to be evaluated, and an adjustment value for the first data value;

[0237] The first data value of the dataset to be evaluated is determined based on the initial value of the first data value and the adjustment value.

[0238] In some embodiments, the processor 62 is specifically used for:

[0239] Determine the value assessment index parameters for at least one dimension corresponding to the dataset to be evaluated;

[0240] Based on the combination of the value assessment index parameters of the at least one dimension and the weight values ​​corresponding to each value assessment index parameter, the initial value of the first data value of the dataset to be evaluated is obtained.

[0241] In some embodiments, the processor 62 is specifically used for:

[0242] Obtain the application scenario of the dataset to be evaluated; the application scenario is one of multiple application scenarios included in the application domain of the dataset to be evaluated;

[0243] Based on the obtained application scenarios and the correspondence between the established application scenarios and value assessment index parameters, the value assessment index parameters corresponding to the obtained application scenarios are determined as the value assessment index parameters of at least one dimension of the dataset to be evaluated.

[0244] In some embodiments, the processor 62 is further configured to:

[0245] Determine the overall contribution of the dataset to be evaluated to at least one collaborative task in which it participates; the overall contribution is correlated with the value of the second data.

[0246] Based on the determined overall contribution, the value of the first data is adjusted accordingly.

[0247] In some embodiments, the processor 62 is specifically used for:

[0248] Determine the marginal benefit value of the dataset to be evaluated in the collaborative task; the marginal benefit value characterizes the difference in the effectiveness of the collaborative task caused by the presence or absence of the dataset to be evaluated.

[0249] Based on the determined marginal benefit value, the second data value of the dataset to be evaluated is determined.

[0250] In some embodiments, the processor 62 is specifically used for:

[0251] Determine the value metric generated by the first dataset; the first dataset is a set of data participating in the collaborative task that includes the dataset to be evaluated;

[0252] Determine the value metric generated by the second dataset; the second dataset is a set of data in the collaborative task that does not include the dataset to be evaluated;

[0253] Based on the difference between the value metric generated by the first dataset and the value metric generated by the second dataset, the marginal benefit value of the dataset to be evaluated in the collaborative task is obtained.

[0254] In some embodiments, the processor 62 is further configured to:

[0255] Determine the attenuation adjustment function used to adjust the value of the second data;

[0256] Based on the determined attenuation adjustment function, the value of the second data is adjusted accordingly.

[0257] In some embodiments, the processor 62 is specifically used for:

[0258] Determine the weight value corresponding to the first data value and the weight value corresponding to the second data value;

[0259] The third data value of the dataset to be evaluated is obtained based on the combination of the first data value and the weight value corresponding to the first data value, and the second data value and the weight value corresponding to the second data value.

[0260] It should be noted that the specific processing procedure of processor 62 is detailed in the method embodiment and will not be repeated here.

[0261] Of course, in practical applications, the various components in the data value assessment device 60 are coupled together via a bus system 64. It can be understood that the bus system 64 is used to achieve communication between these components. In addition to a data bus, the bus system 64 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 6 The general labeled all buses as Bus System 64.

[0262] The memory 63 in this embodiment is used to store various types of data to support the operation of the data valuation device 60. Examples of such data include any computer program used to operate on the data valuation device 60.

[0263] The methods disclosed in the embodiments of this application can be applied to processor 62, or implemented by processor 62. Processor 62 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 62 or by instructions in the form of software. The processor 62 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 62 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory 63. Processor 62 reads the information in memory 63 and, in conjunction with its hardware, completes the steps of the aforementioned data value assessment method.

[0264] In an exemplary embodiment, the data value assessment device 60 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned data value assessment method.

[0265] It is understood that the memory 63 in the embodiments of this application can be volatile memory or non-volatile memory, or both. Specifically, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); the magnetic surface memory can be disk storage or magnetic tape storage.

[0266] Volatile memory can be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), sync link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memory.

[0267] In an exemplary embodiment, this application also provides a computer-readable storage medium, such as a memory 63 storing a computer program, which can be executed by the processor 62 of the data value assessment device 60 to complete the steps described in the aforementioned data value assessment method. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM; or it can be various devices including one or any combination of the above-mentioned memories.

[0268] In the embodiments of this application, the terms "first", "second", etc. are used only to distinguish similar objects and do not represent a specific order or sequence of objects. It is understood that "first", "second", etc. can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0269] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating data value, characterized by, The method comprises: determining a first data value of a to-be-evaluated data set; the to-be-evaluated data set comprises at least one to-be-evaluated data held by a data owner, and the first data value represents an inherent value of the to-be-evaluated data set itself; determining a second data value of the to-be-evaluated data set; the second data value represents a derived value generated by the to-be-evaluated data set in an application process, and the second data value is associated with an application scenario in which the to-be-evaluated data set is located and an efficiency generated by the to-be-evaluated data set to a collaborative task; based on the first data value and the second data value, comprehensively evaluating a third data value of the to-be-evaluated data set; the third data value represents an actual data value of the to-be-evaluated data set; wherein the determining of the first data value of the to-be-evaluated data set comprises: determining a plurality of dimension value evaluation index parameters corresponding to the to-be-evaluated data set; based on the plurality of dimension value evaluation index parameters and a combination of weight values corresponding to each value evaluation index parameter, an initial value of the first data value is obtained; based on the initial value of the first data value and an adjustment value for the first data value, the first data value of the to-be-evaluated data set is determined; the determining of the second data value of the to-be-evaluated data set comprises: determining a marginal benefit value of the to-be-evaluated data set in a collaborative task; the marginal benefit value represents a difference in efficiency generated by the to-be-evaluated data set to the collaborative task; based on the marginal benefit value, the second data value of the to-be-evaluated data set is determined through an efficiency evaluation rule / method library; based on the first data value and the first data value corresponding weight value, and the second data value and the second data value corresponding weight value, a combination is obtained, and the third data value of the to-be-evaluated data set is obtained. the determining of the first data value of the to-be-evaluated data set comprises: obtaining an application scenario in which the to-be-evaluated data set is located; the application scenario is one of a plurality of application scenarios included in an application field of the to-be-evaluated data set; 2. The method of claim 1, wherein, based on the obtained application scenario and a set corresponding relationship between the application scenario and the value evaluation index parameter, the value evaluation index parameter corresponding to the obtained application scenario is determined as the plurality of dimension value evaluation index parameters corresponding to the to-be-evaluated data set. The method further comprises: determining a total contribution degree of the to-be-evaluated data set to at least one collaborative task; the total contribution degree is associated with the second data value; 3. The method of claim 1, wherein, based on the determined total contribution degree, the first data value is adjusted correspondingly. the determining of the marginal benefit value of the to-be-evaluated data set in a collaborative task comprises: ​ 4. The method of claim 1, wherein, ​ determining a value metric value generated by a first data set; the first data set being a set of data sets participating in a collaborative task and including the to-be-evaluated data set; determining a value metric value generated by a second data set; the second data set being a set of data sets participating in the collaborative task and not including the to-be-evaluated data set; obtaining a marginal benefit value of the to-be-evaluated data set in the collaborative task based on a difference value between the value metric value generated by the first data set and the value metric value generated by the second data set.

5. The method of claim 1, wherein, The method further comprises: determining an attenuation adjustment function for adjusting the second data value; correspondingly adjusting the second data value based on the determined attenuation adjustment function.

6. A data value evaluation device characterized by comprising: The apparatus comprises: a first determining unit configured to determine a first data value of a to-be-evaluated data set; the to-be-evaluated data set including at least one to-be-evaluated data held by a data owner, and the first data value representing an inherent value of the to-be-evaluated data set itself; a second determining unit configured to determine a second data value of the to-be-evaluated data set; the second data value representing a derivative value generated by the to-be-evaluated data set in an application process, and the second data value being associated with an application scenario in which the to-be-evaluated data set is located and an efficiency generated by the to-be-evaluated data set to a collaborative task participated by the to-be-evaluated data set; an evaluation unit configured to comprehensively evaluate a third data value of the to-be-evaluated data set based on the first data value and the second data value; the third data value representing an actual data value of the to-be-evaluated data set; The first determining unit is specifically configured to: determine a plurality of dimension value evaluation index parameters corresponding to the to-be-evaluated data set; and obtain an initial value of the first data value based on a combination of the plurality of dimension value evaluation index parameters and weight values corresponding to the value evaluation index parameters; determine the first data value of the to-be-evaluated data set based on the initial value of the first data value and an adjustment value for the first data value; The second determining unit is specifically configured to: determine a marginal benefit value of the to-be-evaluated data set in the collaborative task; the marginal benefit value representing a difference in efficiency generated by the to-be-evaluated data set to the collaborative task; determine the second data value of the to-be-evaluated data set based on the marginal benefit value and through an efficiency evaluation rule / method library; The evaluation unit is specifically configured to: determine a weight value corresponding to the first data value and a weight value corresponding to the second data value; the weight value corresponding to the first data value and the weight value corresponding to the second data value being adapted to an application scenario in which the to-be-evaluated data set is located; obtain the third data value of the to-be-evaluated data set based on a combination of the first data value and the weight value corresponding to the first data value, and the second data value and the weight value corresponding to the second data value.

7. A data value evaluation device characterized by comprising: The device comprises: The processor is configured to determine a first data value of a data set to be evaluated, wherein the data set to be evaluated comprises at least one data to be evaluated held by a data owner, and the first data value represents an inherent value of the data set to be evaluated itself; determine a second data value of the data set to be evaluated, wherein the second data value represents a derivative value generated by the data set to be evaluated in an application process, and the second data value is associated with an application scenario of the data set to be evaluated and an efficiency of the data set to be evaluated to a collaborative task; and comprehensively evaluate a third data value of the data set to be evaluated based on the first data value and the second data value, wherein the third data value represents an actual data value of the data set to be evaluated. The processor is specifically configured to: determine a plurality of value evaluation index parameters of a plurality of dimensions corresponding to the data set to be evaluated; and obtain an initial value of the first data value based on a combination of the plurality of value evaluation index parameters and weight values corresponding to the value evaluation index parameters; determine the first data value of the data set to be evaluated based on the initial value of the first data value and an adjustment value for the first data value; determine a marginal benefit value of the data set to be evaluated in the collaborative task, wherein the marginal benefit value represents a difference in efficiency of the collaborative task caused by the data set to be evaluated; determine the second data value of the data set to be evaluated based on the marginal benefit value and an efficiency evaluation rule / method library; determine a weight value corresponding to the first data value and a weight value corresponding to the second data value, wherein the weight value corresponding to the first data value and the weight value corresponding to the second data value are adapted to an application scenario of the data set to be evaluated; obtain the third data value of the data set to be evaluated based on a combination of the first data value, the weight value corresponding to the first data value, the second data value, and the weight value corresponding to the second data value.

8. A data value evaluation device characterized by comprising: The processor and a memory for storing a computer program capable of running on the processor are included. When the processor runs the computer program, the processor performs the steps of the method according to any one of claims 1 to 5. A computer program is stored on the computer readable medium, and the computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, ​

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