Data configuration method and device, equipment, medium and program product

Abstract

The invention provides a data configuration method which can be applied to the technical field of big data technologies. The data configuration method comprises the steps of obtaining at least one data demand dimension corresponding to a target business scene based on the target business scene; based on the data demand dimension, at least one piece of candidate sub-data is obtained from a data pool, the data pool is constructed based on m pieces of sub-data subjected to data annotation, the m pieces of sub-data are from at least one data product, and m is an integer larger than or equal to 1; combining the candidate sub-data to obtain at least one candidate configuration scheme; based on the value score and the configuration cost of the candidate configuration scheme, the candidate configuration scheme is evaluated to obtain a target configuration scheme, and the value score is obtained at least based on one of the application range, the timeliness and the data precision of the candidate sub-data. The invention further provides a data configuration device, equipment, a storage medium and a program product.

Description

Technical Field

[0001] This application relates to the field of big data technology, and more specifically to a data configuration method, apparatus, device, medium, and program product. Background Technology

[0002] Enterprises typically rely on external data products for business decision-making and operational optimization, such as operator data, credit data, and e-commerce data. These are widely used in core business scenarios such as risk control, market competition insights, and dormant user reactivation, becoming key to enhancing enterprise competitiveness.

[0003] However, enterprises currently face the following problems in procuring, integrating, and applying external data products: 1. The number of external data vendors is vast, and the data products offered by different vendors vary in data format, interface protocols, quality standards, and pricing models. This difference necessitates enterprises investing significant resources in developing interfaces, formulating data cleaning rules, and adapting billing logic for each vendor, significantly impacting work efficiency; 2. For the same business scenario, different external data vendors offer differentiated data combination solutions based on their own data resources. Enterprise decision-makers often rely on past experience or subjective recommendations from vendors for procurement decisions, making it difficult to objectively assess the suitability of different solutions for business needs. This can lead to the final solution potentially having redundant data dimensions or missing key dimensions, failing to guarantee optimal business results; 3. Enterprises' core objective is to minimize external data procurement costs while meeting business scenario requirements. However, in practice, enterprises are prone to over-purchasing data products beyond their needs, resulting in unnecessary cost waste. Alternatively, the purchased data may have insufficient coverage or substandard accuracy, affecting the accuracy of business decisions. Summary of the Invention

[0004] In view of the above problems, this application provides a data configuration method, apparatus, device, medium and program product that can balance configuration effect and cost.

[0005] According to a first aspect of this application, a data configuration method is provided, comprising: obtaining at least one data requirement dimension corresponding to a target business scenario; obtaining at least one candidate sub-data from a data pool based on the data requirement dimension, wherein the data pool is constructed based on m data-annotated sub-data, the m sub-data coming from at least one data product, and m being an integer greater than or equal to 1; combining the candidate sub-data to obtain at least one candidate configuration scheme; and evaluating the candidate configuration scheme based on its value score and configuration cost to obtain a target configuration scheme, wherein the value score is obtained based on at least one of the following: applicability, timeliness, and data accuracy of the candidate sub-data.

[0006] According to an embodiment of this application, constructing a data pool includes: acquiring at least one data product, each data product including n sub-data with different functional attributes, the functional attributes corresponding one-to-one with the data requirement dimensions, and n being an integer greater than or equal to 1; labeling the sub-data based on the metadata of the sub-data, the metadata including at least one of functional attributes, scope of application, timeliness, data accuracy, and data cost; and dividing the m sub-data into at least one functional unit data pool based on the different functional attributes, the at least one functional unit data pool constituting the data pool.

[0007] According to an embodiment of this application, obtaining at least one candidate sub-data includes: obtaining a functional unit data pool with corresponding functional attributes based on the data requirement dimension; and obtaining at least one candidate sub-data from the functional unit data pool.

[0008] According to an embodiment of this application, obtaining a value score includes: quantifying the scope of application based on the total number of covered objects corresponding to the target business scenario and the actual number of covered objects of the candidate sub-data to obtain a first value; quantifying the timeliness based on the update cycle of the candidate sub-data to obtain a second value; quantifying the data accuracy based on the error rate and source of the candidate sub-data to obtain a third value; and assigning corresponding weights to the first value, the second value, and the third value based on the data demand priority of the target business scenario to calculate the value score.

[0009] According to embodiments of this application, obtaining the target configuration scheme includes: assigning value scores and configuration costs corresponding weights to data configuration targets based on the target business scenario; obtaining a comprehensive score for candidate configuration schemes based on the weighted value scores and configuration costs; and obtaining the target configuration scheme based on the comprehensive score.

[0010] According to an embodiment of this application, after obtaining the target configuration scheme, the method further includes: running the target configuration scheme and receiving the running results; and adjusting the proportion of sub-data in the target configuration scheme based on the running results to update the target configuration scheme.

[0011] A second aspect of this application provides a data configuration apparatus, comprising: a demand module for acquiring at least one data demand dimension corresponding to a target business scenario; a sub-data module for acquiring at least one candidate sub-data from a data pool based on the data demand dimension, wherein the data pool is constructed based on m data-annotated sub-data, the m sub-data coming from at least one data product, and m being an integer greater than or equal to 1; a candidate module for combining the candidate sub-data to obtain at least one candidate configuration scheme; and a target module for evaluating the candidate configuration scheme based on its value score and configuration cost to obtain a target configuration scheme, wherein the value score is obtained based on at least one of the candidate sub-data's applicability, timeliness, and data accuracy.

[0012] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0013] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0014] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method. Attached Figure Description

[0015] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0016] Figure 1 The illustrations depict application scenarios of data configuration methods, apparatuses, devices, media, and program products according to embodiments of this application.

[0017] Figure 2 A flowchart illustrating a data configuration method according to an embodiment of this application is shown schematically;

[0018] Figure 3 The illustration shows a schematic diagram of constructing a data pool according to an embodiment of this application;

[0019] Figure 4 A flowchart illustrating the process of obtaining candidate sub-data according to an embodiment of this application is shown schematically;

[0020] Figure 5 A flowchart illustrating the process of obtaining a value score according to an embodiment of this application is shown schematically;

[0021] Figure 6 A flowchart illustrating the process of obtaining the target configuration scheme according to an embodiment of this application is shown schematically;

[0022] Figure 7 A schematic diagram illustrating the structure of a data configuration apparatus according to an embodiment of this application is shown; and

[0023] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a data configuration method according to an embodiment of this application. Detailed Implementation

[0024] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0027] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0028] Most existing data configuration solutions focus on the governance of internal enterprise data, such as the cleaning, integration, and storage of internal data, or the simple listing and display of external data products, such as only providing basic information and procurement access to data products. They fail to provide in-depth technical support for the core need of intelligently filtering and combining massive heterogeneous external data products to form the most cost-effective solution for specific business scenarios. They lack standardized encapsulation mechanisms for external data products and have not built demand decomposition and multi-objective optimization algorithms adapted to business scenarios. They cannot fundamentally solve the problems of fragmentation, non-standardization, and value-cost imbalance in the application of external data products.

[0029] An embodiment of this application provides a data configuration method, comprising: obtaining at least one data requirement dimension corresponding to a target business scenario; obtaining at least one candidate sub-data from a data pool based on the data requirement dimension, wherein the data pool is constructed based on m data-annotated sub-data, the m sub-data coming from at least one data product, and m being an integer greater than or equal to 1; combining the candidate sub-data to obtain at least one candidate configuration scheme; and evaluating the candidate configuration scheme based on its value score and configuration cost to obtain a target configuration scheme, wherein the value score is obtained based on at least one of the following: the applicability scope, timeliness, and data accuracy of the candidate sub-data.

[0030] Figure 1 The diagram illustrates an application scenario of the data configuration method according to an embodiment of this application.

[0031] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0032] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0033] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0034] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0035] It should be noted that the data configuration method provided in this application embodiment can generally be executed by server 105. Correspondingly, the data configuration device provided in this application embodiment can generally be located in server 105. The data configuration method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the data configuration device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0036] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0037] The following will be based on Figure 1 The described scene, through Figures 2-6 The data configuration method according to the embodiments of this application will be described in detail.

[0038] Figure 2 A flowchart illustrating a data configuration method according to an embodiment of this application is shown schematically.

[0039] like Figure 2 As shown, the data configuration method of this embodiment includes operations S210 to S240.

[0040] In operation S210, based on the target business scenario, at least one data requirement dimension corresponding to the target business scenario is obtained.

[0041] According to embodiments of this application, business scenarios may include: 1. Financial risk control, such as credit approval, qualification assessment, account security protection, and compliance risk verification; 2. Marketing and operations, such as dormant user reactivation, potential high-value customer discovery, user churn warning, personalized recommendations, and customer satisfaction improvement; 3. User management, such as user profile building, identity verification, membership level assessment, and access control. Furthermore, customized business scenarios can be created based on individual business needs, covering various application scenarios driven by data.

[0042] According to the embodiments of this application, corresponding data requirement dimensions are pre-configured for each business scenario. These data requirement dimensions are used to concretize abstract business scenarios into quantifiable data support standards, clarifying the core direction of the data required for each business scenario. For example, for a credit approval scenario, data requirement dimensions need to be constructed around identity authenticity and credit history, such as "identity authenticity verification," "historical credit assessment," and "income capacity assessment." For a dormant user reactivation scenario, data requirement dimensions need to be constructed focusing on user profiles and historical interaction records, such as "basic user profile" and "historical interaction records." For a potential high-value customer mining scenario, data requirement dimensions need to be constructed emphasizing spending power and demand matching, such as "spending power assessment" and "demand matching." It should be noted that the above are merely examples, and the specific data requirement dimensions can be selected according to the actual situation.

[0043] In this embodiment, the credit approval scenario is used as the target business scenario for illustration. The data requirements dimensions include "identity authenticity verification", "historical credit assessment" and "income capacity assessment".

[0044] In operation S220, based on the data requirement dimension, at least one candidate sub-data is obtained from the data pool, wherein the data pool is constructed based on m sub-data that have been labeled with data, and the m sub-data come from at least one data product, where m is an integer greater than or equal to 1.

[0045] According to embodiments of this application, for the "identity authenticity verification" dimension, the data pool includes multiple candidate sub-data that can be used for identity authenticity verification, and these multiple candidate sub-data may come from different data products. For example, ID card recognition results from data product A, mobile phone number real-name information verification results from data product B, bank card opening bank information verification data from data product C, etc.

[0046] According to embodiments of this application, for the "historical credit assessment" dimension, the data pool also includes multiple candidate sub-data that can be used for historical credit assessment. For example, credit report data from data product C, loan repayment records from data product D, and consumption record verification results from data product E, etc.

[0047] According to embodiments of this application, for the "income capacity assessment" dimension, the data pool also includes multiple candidate sub-data that can be used for income capacity assessment. For example, payroll data from data product C, industry and job salary benchmarking data from data product F, etc.

[0048] In operation S230, the candidate sub-data are combined to obtain at least one candidate configuration scheme.

[0049] According to the embodiments of this application, in order to meet the assessment needs of credit approval scenarios, assessments need to be conducted based on three data requirement dimensions: "identity authenticity verification," "historical credit assessment," and "income capacity assessment." As described in operation S220, candidate sub-data from different data products can be combined to obtain candidate configuration schemes. For example, the first candidate configuration scheme includes a combination of ID card recognition results (from data product A), loan repayment records (from data product D), and industry and job salary benchmarking data (from data product F); the second candidate configuration scheme includes a combination of mobile phone number real-name information verification results (from data product B), credit report data (from data product C), and salary slip data (from data product C). It should be noted that the above candidate configuration schemes are merely examples and are not limited to the above candidate configuration schemes.

[0050] In operation S240, the candidate configuration schemes are evaluated based on their value scores and configuration costs to obtain the target configuration scheme. The value scores are obtained based on at least one of the following: the applicability of the candidate sub-data, its timeliness, and its accuracy.

[0051] According to embodiments of this application, the applicable scope is the ratio of the actual number of objects covered by the candidate sub-data to the total number of objects covered by the target business scenario. For example, if the total number of objects covered by the target business scenario is 1 million, and the data source of the ID card recognition results from data product A covers mainstream household registration verification channels nationwide, and the actual number of objects covered that can complete authenticity verification is 950,000, then the applicable scope of the candidate sub-data is 95%; if the number of objects covered by the mobile phone number real-name information verification results from data product B is 600,000, then its applicable scope is 60%.

[0052] According to embodiments of this application, timeliness refers to the update cycle of candidate sub-data. For example, loan repayment records from data product D are updated daily; credit report data from data product C is updated monthly.

[0053] According to embodiments of this application, data accuracy refers to the degree to which candidate sub-data reflects the true state of the target object, and is quantified by data error rate and source authority.

[0054] According to an embodiment of this application, the configuration cost is calculated based on the procurement cost of candidate sub-data. For example, for the first candidate configuration scheme, the procurement cost of ID card recognition results (from data product A) is 1 yuan / time, the procurement cost of loan repayment records (from data product D) is 2 yuan / time, and the procurement cost of industry and job salary benchmarking data (from data product F) is 1 yuan / time, so the configuration cost of the first candidate configuration scheme is 4 yuan / time; for the second candidate configuration scheme, the procurement cost of mobile phone number real-name information verification results (from data product B) is 3 yuan / time, the procurement cost of credit report data (from data product C) is 2 yuan / time, and the procurement cost of salary transaction data (from data product C) is 1 yuan / time, so the configuration cost of the second candidate configuration scheme is 6 yuan / time.

[0055] According to the embodiments of this application, a value score is obtained by weighting calculation based on the scope of application, timeliness, and data accuracy. The candidate configuration scheme is then evaluated using a two-dimensional comprehensive evaluation model of "value score - configuration cost" to obtain a target configuration scheme that can satisfy the balance between value score and configuration cost.

[0056] According to the embodiments of this application, by constructing a data pool that integrates multi-source labeled sub-data, unified governance and standardized adaptation of data products from different external data providers are achieved. This eliminates the need to develop interface connections, formulate cleaning rules, and adapt billing logic for each individual provider, reducing the cost of cross-provider data access and improving data integration efficiency. Simultaneously, by anchoring to the data needs of business scenarios, candidate sub-data is selectively filtered and combined to form diverse candidate configuration schemes. Value scores are quantified based on core dimensions such as applicability, timeliness, and data accuracy. A two-dimensional comprehensive evaluation model is constructed, combining configuration costs to replace the decision-making model relying on subjective recommendations. This objectively judges the matching degree between different schemes and business needs, avoiding data redundancy or missing key dimensions. Furthermore, by dynamically allocating value and cost weights based on business configuration objectives, a precise balance is achieved between meeting business needs and minimizing procurement costs. This prevents cost waste caused by over-purchasing and avoids the problem of insufficient data coverage or substandard accuracy affecting the accuracy of business decisions.

[0057] According to an embodiment of this application, after obtaining the target configuration scheme, the target configuration scheme is run, the running results are received, and based on the running results, the proportion of sub-data in the target configuration scheme is adjusted to update the target configuration scheme. Assuming the first candidate configuration scheme is the target configuration scheme, and its running result shows a credit approval accuracy rate of 90%, based on this running result, the proportions of ID card recognition results (from data product A), loan performance records (from data product D), and industry and job salary benchmarking data (from data product F) can be adjusted. For example, the proportion of loan performance records (from data product D) can be increased to optimize the "value scoring-configuration cost" dual-dimensional comprehensive evaluation model.

[0058] By adjusting the weighting of sub-data within the target configuration scheme based on actual business performance after its implementation and updating the scheme accordingly, a closed-loop mechanism is formed, encompassing implementation, result feedback, weight optimization, and scheme iteration. This achieves dynamic adaptation between the data configuration scheme and actual business performance. It strengthens the decision-making weight of high-contribution sub-data, mitigates the negative impact of inefficient or biased sub-data, and corrects deviations between the initial configuration and actual business needs in real time. This ensures that the updated target configuration scheme maintains an optimal balance between value, cost, and effectiveness, significantly improving the accuracy, flexibility, and long-term effectiveness of data in supporting business decisions.

[0059] Figure 3 The schematic diagram illustrates the construction of a data pool according to an embodiment of the present application, which includes operations S310 to S330.

[0060] In operation S310, at least one data product is acquired. Each data product includes n sub-data with different functional attributes. The functional attributes correspond one-to-one with the data requirement dimensions, and n is an integer greater than or equal to 1.

[0061] According to embodiments of this application, application programming interfaces (APIs) for connecting to various external data providers or direct data sources are set up. By being compatible with and handling differences in authentication mechanisms, data transmission protocols, and communication formats of different providers, technical barriers to data access are eliminated, thereby enabling compliant acquisition and stable access to at least one data product.

[0062] According to embodiments of this application, each data product includes n sub-data with different functional attributes, and the functional attributes correspond one-to-one with the data requirement dimensions. For example, data product C includes bank card account opening bank information verification data for "identity authenticity verification", credit report data for "historical credit assessment", and salary slip data for "income capacity assessment"; data product B includes mobile phone number real-name information verification results for "identity authenticity verification" and historical bills for "spending capacity assessment".

[0063] When operating S320, data annotation is performed on the sub-data based on the metadata of the sub-data. The metadata includes at least one of the following: functional attributes, scope of application, timeliness, data accuracy, and data cost.

[0064] According to the embodiments of this application, the functional attributes, applicable scope, timeliness, data accuracy, and data cost of the sub-data are labeled. Taking the payroll data of data product C as an example, its functional attribute is "income capacity assessment", its applicable scope is 80%, its timeliness is updated monthly, its data accuracy is an error rate of ≤1% and it is an authoritative data source, and its data cost is 1 yuan / time.

[0065] In operation S330, based on different functional attributes, m sub-data are divided into at least one functional unit data pool, and at least one functional unit data pool constitutes a data pool.

[0066] According to embodiments of this application, based on different functional attributes, m sub-data are divided into at least one functional unit data pool. For example, bank card account opening information verification data from data product C and mobile phone number real-name information verification results from data product B can both be used for "identity authenticity verification," thus they belong to the same functional unit data pool. At least one functional unit data pool constitutes a data pool, meaning that the data pool includes multiple different functional unit data pools, each with different functional attributes, and can be used for different data requirement dimensions.

[0067] By acquiring multi-source data products and differentiated sub-data with functional attributes that meet data requirements, and combining them with metadata to standardize and label the sub-data, and constructing a total data pool by dividing functional unit data pools according to functional attributes, the unified integration, standardized governance, and structured classification of multi-source heterogeneous data are achieved. This ensures accurate alignment between sub-data and data requirements, and reduces the difficulty of subsequent candidate sub-data retrieval, filtering, and configuration scheme combination, providing high-quality, quantifiable, and easily reusable basic data support for business scenarios.

[0068] Figure 4 A flowchart illustrating the acquisition of candidate sub-data according to an embodiment of this application is shown.

[0069] like Figure 4 As shown, the acquisition of candidate sub-data in this embodiment includes operations S410 to S420.

[0070] When operating S410, a data pool of functional units with corresponding functional attributes is obtained based on the data requirement dimension.

[0071] In operation S420, at least one candidate sub-data is obtained from the functional unit data pool.

[0072] According to embodiments of this application, based on the functional requirements of each data requirement dimension, a corresponding functional unit data pool is matched within the data pool. For each matched functional unit data pool, using the metadata of the sub-data as the filtering basis, combined with business constraints (such as compliance qualification requirements, coverage ratio thresholds, response time limits, etc.), at least one sub-data that meets the basic adaptation requirements is selected from the functional unit data pool as candidate sub-data for the corresponding data requirement dimension. For example, in a credit approval scenario, candidate sub-data is obtained from the functional unit data pools corresponding to "identity verification," "historical credit assessment," and "income capacity assessment," with at least one candidate sub-data obtained from each functional unit data pool.

[0073] By first accurately matching the functional unit data pool with the corresponding functional attributes based on the data requirement dimension, and then filtering candidate sub-data from this targeted data pool, the precise alignment of candidate sub-data with the data requirement dimension is achieved, avoiding invalid data retrieval across functional dimensions and significantly improving the filtering efficiency and matching accuracy of candidate sub-data.

[0074] Figure 5 A flowchart illustrating the process of obtaining a value score according to an embodiment of this application is shown.

[0075] like Figure 5 As shown, the value score obtained in this embodiment includes operations S510 to S540.

[0076] When operating S510, the scope of application is quantified based on the total number of covered objects corresponding to the target business scenario and the actual number of covered objects of the candidate sub-data, thus obtaining the first value.

[0077] According to the embodiments of this application, firstly, the total number of all covered objects that need to be supported by data through the current data requirement dimension in the target business scenario is determined. Then, the number of covered objects that the candidate sub-data can provide complete and effective data support is counted. The applicable scope of each candidate sub-data is calculated using the formula "actual number of covered objects of candidate sub-data / total number of covered objects in the target business scenario". Since the candidate configuration scheme includes at least one candidate sub-data, the applicable scope of the candidate configuration scheme obtained by integrating the applicable scope of at least one candidate sub-data is the first value. The value of the first value ranges from 0 to 100. The closer the value is to 100, the more comprehensive the coverage of the target business scenario by the candidate configuration scheme and the stronger its adaptability.

[0078] In operation S520, based on the update cycle of candidate sub-data, the timeliness is quantified to obtain the second value.

[0079] According to the embodiments of this application, the actual update cycle of the candidate sub-data (such as real-time update, daily update, weekly update, monthly or higher update, etc.) is first determined. Then, a graded quantification standard is set based on the data real-time requirement threshold of the target business scenario, and corresponding scores are assigned to different update cycles, with higher scores for shorter update cycles. For example, real-time updates are assigned 100 points, daily updates 75 points, weekly updates 50 points, and monthly or higher updates 25 points. Since the candidate configuration scheme includes at least one candidate sub-data, the timeliness of the candidate configuration scheme obtained by integrating the update cycles of at least one candidate sub-data is the second value. The higher the second value, the stronger the timeliness of the candidate configuration scheme.

[0080] In operation S530, based on the error rate and source of candidate sub-data, the data accuracy is quantified to obtain a third value.

[0081] According to an embodiment of this application, a third value is obtained based on the data accuracy of the candidate sub-data. Specifically, the core evaluation indicators of data accuracy are first defined, including the error rate and source of the candidate sub-data, and then a graded quantitative standard is set in combination with the data accuracy requirements of the target business scenario. For example, if the error rate is ≤1% and it is an authoritative data source, a score of 100 is assigned; if the error rate is 1% < ≤5% and it is an authoritative / quasi-authoritative data source, a score of 75 is assigned; if the error rate is 5% < ≤10% and it is a compliant third-party data source, a score of 50 is assigned; and if the error rate is >10% or it is a non-authoritative data source, a score of 25 is assigned. Since the candidate configuration scheme includes at least one candidate sub-data, the data accuracy of the candidate configuration scheme obtained by integrating the data accuracy of at least one candidate sub-data is the third value. The higher the third value, the better the data accuracy.

[0082] When operating S540, based on the priority of data requirements in the target business scenario, assign corresponding weights to the first, second, and third values, and calculate the value score.

[0083] According to the embodiments of this application, the data requirements are prioritized based on the importance of the following three factors: scope of application, timeliness, and data accuracy, to adapt to the core needs of different business scenarios. For example, prioritizing data accuracy is suitable for risk control and compliance review scenarios (such as credit approval, anti-fraud, and qualification certification). The core requirement is that the data is true and reliable to avoid decision-making bias or compliance risks, with the highest requirement for data accuracy, followed by coverage of the core target audience, and relatively lower requirements for real-time performance. Prioritizing timeliness is suitable for real-time decision-making and dynamic monitoring scenarios (such as instant credit granting, real-time marketing, and public opinion monitoring). The core requirement is to quickly obtain the latest data to support immediate decision-making, with the highest requirements for data update speed and response efficiency, followed by ensuring basic data accuracy, and accepting a certain range of coverage gaps. Prioritizing coverage is suitable for user mining and market research scenarios (such as potential customer screening and industry trend analysis). The core requirement is to comprehensively cover the target group to ensure sample representativeness, with the highest requirement for data coverage ratio, followed by ensuring that data accuracy meets the standards, and accepting low to medium frequency updates.

[0084] According to embodiments of this application, based on data requirement priority, a dynamic weight is assigned to the first, second, and third values, with a total weight of 100%. For example, for a target business scenario prioritizing data accuracy, the first value is configured with a weight of 30%, the second value with a weight of 20%, and the third value with a weight of 50%; for a target business scenario prioritizing timeliness, the first value is configured with a weight of 20%, the second value with a weight of 50%, and the third value with a weight of 30%. Finally, the value score is calculated using the formula: "Value Score = First Value × Corresponding Weight + Second Value × Corresponding Weight + Third Value × Corresponding Weight".

[0085] Assuming the target business scenario prioritizes data accuracy, for the first candidate configuration scheme, the weights of the first value are set to 30%, the weight of the second value to 20%, and the weight of the third value to 50%, resulting in a value score of 85 points. For the second candidate configuration scheme, the weights of the first value to 30%, the weight of the second value to 20%, and the weight of the third value to 50% are also set, resulting in a value score of 88 points.

[0086] By quantifying the applicability, timeliness, and accuracy of candidate sub-data into standardized first, second, and third values, and dynamically configuring weights to calculate value scores based on the data requirement priority of the target business scenario, an objective, quantifiable, and scenario-based assessment of the suitability value of candidate sub-data is achieved. This avoids the one-sidedness of single-dimensional evaluation, ensures the comparability of different sub-data / configuration schemes through standardization and quantification, and allows the scenario-based weight configuration to accurately align the scores with core business needs. This provides a unified and reliable decision-making basis for subsequent "value-cost" trade-offs and the scientific selection of target configuration schemes, effectively improving the accuracy of data configuration schemes in matching business needs and the efficiency of screening.

[0087] Figure 6 A flowchart illustrating the process of obtaining the target configuration scheme according to an embodiment of this application is shown.

[0088] like Figure 6 As shown, the target configuration scheme obtained in this embodiment includes operations S610 to S630.

[0089] When operating S610, data configuration targets are assigned based on the target business scenario, and corresponding weights are given to value scores and configuration costs.

[0090] According to embodiments of this application, data configuration objectives include three categories: value-first, configuration cost-first, and value-cost balance. The core objective of value-first is to maximize the supporting value of data for business decisions, accepting reasonable cost input; therefore, it assigns a weight of 70%-80% to value score and 20%-30% to configuration cost, suitable for scenarios such as high-risk control and core business decisions. The core objective of configuration cost-first is to strictly control the investment throughout the entire data lifecycle, only needing to meet the basic business value threshold; therefore, it assigns a weight of 30%-40% to value score and 60%-70% to configuration cost, suitable for cost-sensitive businesses (such as inclusive services for micro and small enterprises, and user profile maintenance in non-core scenarios). The core objective of value-cost balance is to maximize business value within controllable costs, pursuing the optimal cost-effectiveness between the two; therefore, it assigns a weight of 50% to value score and 50% to configuration cost, suitable for routine business operations (such as daily customer segmentation and general qualification review).

[0091] When operating the S620, a comprehensive score for the candidate configuration scheme is obtained based on the weighted value score and configuration cost.

[0092] According to an embodiment of this application, based on the weighted value score and configuration cost, a comprehensive evaluation model of "value score - configuration cost" is used to obtain the comprehensive score of the candidate configuration scheme. For example, if the data configuration objective of the target business scenario is value priority, then for the candidate configuration scheme, its value score is assigned a weight of 80%, and its configuration cost is assigned a weight of 20%. Using the above weights, the comprehensive score of the first candidate configuration scheme is calculated to be 90 points, and the comprehensive score of the second candidate configuration scheme is calculated to be 88 points.

[0093] When operating the S630, the target configuration scheme is obtained based on the comprehensive score.

[0094] According to an embodiment of this application, the comprehensive scores of the candidate configuration schemes are sorted from high to low, and the top k candidate configuration schemes are output as the target configuration schemes, where k is an integer greater than or equal to 1.

[0095] By dynamically assigning value scores and corresponding weights to configuration costs based on the target business scenario's data configuration objectives, and then calculating the comprehensive score of candidate configuration schemes to select the optimal solution, the system achieves scenario-based, precise adaptation and quantitative decision-making for data configuration schemes. This approach balances the core business demands for data value with the practical constraints of cost control, while avoiding subjective decision-making biases through standardized scoring and selection logic. It efficiently selects the target configuration scheme with the optimal "value-cost" ratio that meets business constraints, significantly improving the scientific rigor, adaptability, and feasibility of data configuration.

[0096] Based on the above data configuration method, this application also provides a data configuration device. The following will be combined with... Figure 7 The device is described in detail.

[0097] Figure 7 A schematic block diagram of a data configuration apparatus according to an embodiment of this application is shown.

[0098] like Figure 7 As shown, the data configuration device 700 of this embodiment includes a demand module 710, a sub-data module 720, a candidate module 730, and a target module 740.

[0099] The requirement module 710 is used to obtain at least one data requirement dimension corresponding to the target business scenario. In one embodiment, the requirement module 710 can be used to perform the operation S210 described above, which will not be repeated here.

[0100] The sub-data module 720 is used to obtain at least one candidate sub-data from a data pool based on the data requirement dimension. The data pool is constructed based on m labeled sub-data, each sub-data coming from at least one data product, where m is an integer greater than or equal to 1. In one embodiment, the sub-data module 720 can be used to perform the operation S220 described above, which will not be repeated here.

[0101] The candidate module 730 is used to combine candidate sub-data to obtain at least one candidate configuration scheme. In one embodiment, the candidate module 730 can be used to perform the operation S230 described above, which will not be repeated here.

[0102] The target module 740 is used to evaluate candidate configuration schemes based on their value scores and configuration costs to obtain a target configuration scheme. The value score is obtained based on at least one of the following: the applicability of the candidate sub-data, its timeliness, and its accuracy. In one embodiment, the target module 740 can be used to perform the operation S240 described above, which will not be repeated here.

[0103] According to embodiments of this application, any plurality of modules among the demand module 710, sub-data module 720, candidate module 730, and target module 740 can be merged into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this application, at least one of the demand module 710, sub-data module 720, candidate module 730, and target module 740 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the requirement module 710, sub-data module 720, candidate module 730, and target module 740 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0104] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a data configuration method according to an embodiment of this application.

[0105] like Figure 8As shown, an electronic device 800 according to an embodiment of this application includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage portion 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0106] RAM 803 stores various programs and data required for the operation of electronic device 800. Processor 801, ROM 802, and RAM 803 are interconnected via bus 804. Processor 801 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 802 and / or RAM 803. It should be noted that the programs may also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0107] According to embodiments of this application, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to a bus 804. The electronic device 800 may also include one or more of the following components connected to the input / output (I / O) interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output (I / O) interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0108] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0109] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM 802 and / or RAM 803 and / or one or more memories other than ROM 802 and RAM 803 described above.

[0110] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to enable the computer system to implement the data configuration method provided in the embodiments of this application.

[0111] When the computer program is executed by the processor 801, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0112] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 809, and / or installed from a removable medium 811. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0113] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, it performs the functions defined in the system of this application embodiment. According to the embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0114] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0116] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A data configuration method, characterized in that, include: Based on the target business scenario, obtain at least one data requirement dimension corresponding to the target business scenario; Based on the data requirement dimension, at least one candidate sub-data is obtained from the data pool, wherein the data pool is constructed based on m sub-data that have been labeled with data, and the m sub-data come from at least one data product, where m is an integer greater than or equal to 1. The candidate sub-data are combined to obtain at least one candidate configuration scheme; The candidate configuration schemes are evaluated based on their value scores and configuration costs to obtain the target configuration scheme. The value scores are obtained based on at least one of the following: the applicability, timeliness, and data accuracy of the candidate sub-data.

2. The data configuration method according to claim 1, characterized in that, Constructing the data pool includes: Obtain at least one data product, each of which includes n sub-data with different functional attributes, wherein the functional attributes correspond one-to-one with the data requirement dimension, and n is an integer greater than or equal to 1; Based on the metadata of the sub-data, the sub-data is labeled, and the metadata includes at least one of the following: functional attributes, scope of application, timeliness, data accuracy, and data cost. Based on the different functional attributes, the m sub-data are divided into at least one functional unit data pool, and the at least one functional unit data pool constitutes the data pool.

3. The data configuration method according to claim 2, characterized in that, Obtaining at least one candidate sub-data includes: Based on the data requirement dimension, a data pool of functional units with corresponding functional attributes is obtained; At least one candidate sub-data is obtained from the data pool of the functional unit.

4. The data configuration method according to claim 1, characterized in that, The value score is obtained by: Based on the total number of covered objects corresponding to the target business scenario and the actual number of covered objects of the candidate sub-data, the scope of application is quantified to obtain a first value; Based on the update cycle of the candidate sub-data, the timeliness is quantified to obtain a second value; Based on the error rate and source of the candidate sub-data, the data accuracy is quantified to obtain a third value; Based on the data demand priority of the target business scenario, the first value, the second value, and the third value are assigned corresponding weights, and the value score is calculated.

5. The data configuration method according to claim 1, characterized in that, The target configuration scheme includes: Based on the data configuration objectives of the target business scenario, assign corresponding weights to the value score and the configuration cost; Based on the weighted value score and the configuration cost, a comprehensive score for the candidate configuration scheme is obtained; Based on the comprehensive score, the target configuration scheme is obtained.

6. The data configuration method according to claim 1, characterized in that, After obtaining the target configuration scheme, it also includes: Run the target configuration scheme and receive the results; Based on the results of the operation, the proportion of the sub-data in the target configuration scheme is adjusted to update the target configuration scheme.

7. A data configuration device, characterized in that, include: The requirements module is used to obtain at least one data requirement dimension corresponding to the target business scenario, based on the target business scenario. The sub-data module is used to obtain at least one candidate sub-data from the data pool based on the data requirement dimension, wherein the data pool is constructed based on m sub-data that have been labeled with data, and the m sub-data come from at least one data product, where m is an integer greater than or equal to 1. A candidate module is used to combine the candidate sub-data to obtain at least one candidate configuration scheme; The target module is used to evaluate the candidate configuration schemes based on their value scores and configuration costs to obtain a target configuration scheme, wherein the value scores are obtained based on at least one of the applicability, timeliness, and data accuracy of the candidate sub-data.

8. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

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

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