Data element management method and device

Abstract

The invention discloses a data element management method and device. The method comprises the steps that target subscription theme information pushed by message-oriented middleware of a data transaction system is acquired, and the type of the target subscription theme information comprises at least one of the following information: shelving information of data elements, transaction information and evaluation information; determining a target data element corresponding to the target subscription theme information, and obtaining a plurality of sub-scores of the target data element corresponding to different types of subscription theme information and a comprehensive score obtained by weighting calculation of the plurality of sub-scores from a database; target sub-scores of the corresponding types are updated according to the target subscription theme information, and comprehensive scores of the target data elements are updated according to the target sub-scores; and adjusting a management strategy of the target data element according to the updated comprehensive score. The technical problem that a data transaction system lacks real-time objective and comprehensive evaluation for data elements and is difficult to effectively manage is solved.

Description

Technical Field

[0001] The present application relates to the field of data management technology, and more specifically, to a method and device for managing data elements. Background Art

[0002] In the era of digital economy, data, as a new type of production factor, has become a new driving force for the development of productivity, showing the unique advantage of increasing returns to scale. Data providers transform raw data into tradable data assets through a series of processes, including data collection, cleaning, analysis and processing, and register, list and trade on data trading platforms. However, the current data element quality assessment mechanism has shortcomings, mainly relying on data similarity comparison or collaborative filtering methods based on historical transaction records for recommendation and retrieval matching. This method can only provide limited guidance and lacks a comprehensive, objective, dynamic and real-time data quality evaluation system. Therefore, it is difficult to accurately identify and recommend high-quality data elements that meet user needs, thereby limiting the activity and efficiency of the data market. Specifically, the existing methods focus on static data analysis and fail to fully consider the quality fluctuations of data elements over time and their impact on actual application scenarios. This makes it impossible to effectively screen out truly valuable resources in the system, hindering the effective trading and widespread application of data elements. Establishing a more comprehensive, accurate and dynamic data quality assessment framework is crucial to improving the transparency of the data element market and promoting the effective allocation of data resources. This can not only improve the success rate of data transactions, but also enhance the trust of market participants and further promote the development of the digital economy.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method and device for managing data elements, so as to at least solve the technical problem that the data trading system lacks real-time, objective and comprehensive evaluation of data elements and is difficult to manage effectively.

[0005] According to one aspect of an embodiment of the present application, a method for managing data elements is provided, including: obtaining target subscription topic information pushed by a message middleware of a data transaction system, wherein the type of the target subscription topic information includes at least one of the following: listing information, transaction information, and evaluation information of the data element; determining a target data element corresponding to the target subscription topic information, and obtaining from a database multiple sub-scores of the target data element corresponding to different types of subscription topic information and a comprehensive score obtained by weighted calculation of the multiple sub-scores; updating a target sub-score of a corresponding type according to the target subscription topic information, and updating the comprehensive score of the target data element according to the target sub-score; and adjusting the management strategy of the target data element according to the updated comprehensive score.

[0006] Optionally, obtaining target subscription topic information pushed by a message middleware of a data transaction system includes: obtaining target subscription topic information in a preset data format pushed by a message middleware of a data transaction system, wherein the listing information includes a variety of registration attribute information of target data elements, and the type of registration attribute information includes at least one of the following: restrictive required item information, restrictive optional item information, non-restrictive required item information, and non-restrictive optional item information; the transaction information includes at least one of the following: the transaction completion volume, transaction application volume, transaction cancellation volume, transaction return volume, transaction return volume, transaction return and cancellation volume, and transaction completion time of the target data element; the evaluation information includes at least one of the following: the data purchaser's evaluation star rating and evaluation content for the target data element.

[0007] Optionally, updating a target sub-score of a corresponding type based on the target subscription subject information includes: when the type of the target subscription subject information is shelf information, updating the target subscription subject information to a database; determining a preset type of registration attribute information from a variety of registration attribute information as a scoring indicator, and determining an attribute scoring model corresponding to the type of the target data element; analyzing the scoring indicator from multiple dimensions using the attribute scoring model to obtain a first score for each dimension, wherein the dimension includes at least one of the following: attribute configuration completeness, attribute content completeness, and attribute content relevance; performing a weighted summation of each first score using a preset weight coefficient to obtain a target sub-score corresponding to the shelf information type; and updating the sub-score corresponding to the shelf information type in the database based on the target sub-score.

[0008] Optionally, updating a target sub-score of a corresponding type based on the target subscription subject information includes: when the type of the target subscription subject information is transaction information, updating historical transaction information corresponding to the target data element in the database based on the target subscription subject information; analyzing the updated historical transaction information from multiple dimensions using a transaction scoring model to obtain a second score for each dimension, wherein the dimension includes at least one of the following: transaction success rate, transaction return rate, and average transaction time; performing a weighted summation of each second score using a preset weight coefficient to obtain a target sub-score corresponding to the transaction information type; and updating the sub-score corresponding to the transaction information type in the database based on the target sub-score.

[0009] Optionally, updating a target sub-score of a corresponding type based on the target subscription subject information includes: when the type of the target subscription subject information is evaluation information, updating the target subscription subject information to a database, and obtaining historical evaluation information of the target data element within a preset historical time period from the database; analyzing the target subscription subject information and the historical evaluation information from multiple dimensions using an evaluation scoring model to obtain a third score for each dimension, wherein the evaluation scoring model has natural language processing capabilities, and the dimensions include at least one of the following: data integrity, data timeliness, transaction response time, and sentiment tendency in the evaluation; performing a weighted summation of each third score using a preset weight coefficient to obtain a target sub-score corresponding to the evaluation information type; and updating the sub-score corresponding to the evaluation information type in the database based on the target sub-score.

[0010] Optionally, the comprehensive score of the target data element is updated based on the target sub-score, including: using a preset weight coefficient to weightedly sum the target sub-score corresponding to the target subscription subject information and the sub-scores corresponding to other types of subscription subject information in the database to obtain a target comprehensive score for the target data element; and updating the comprehensive score of the target data element in the database based on the target comprehensive score.

[0011] Optionally, adjusting the management strategy of the target data element according to the updated comprehensive score includes: determining a target score interval corresponding to the updated comprehensive score from a preset mapping relationship table, and determining a target management strategy corresponding to the target score interval, wherein the mapping relationship table stores different management strategies corresponding to different score intervals, and the management strategy is used to configure at least one of the following attributes of the data element: review level, recommendation level, and transaction price; adjusting the management strategy of the target data element to the target management strategy.

[0012] Optionally, for any data element, if the comprehensive score of the data element is lower than a preset score threshold and continues to be lower than a preset time period, the data element will be removed from the shelves.

[0013] According to another aspect of an embodiment of the present application, a data element management device is also provided, including: an acquisition module, used to acquire target subscription topic information pushed by a message middleware of a data transaction system, wherein the type of the target subscription topic information includes at least one of the following: listing information, transaction information, and evaluation information of the data element; a determination module, used to determine the target data element corresponding to the target subscription topic information, and obtain from a database multiple sub-scores of the target data element corresponding to different types of subscription topic information and a comprehensive score obtained by weighted calculation of the multiple sub-scores; a scoring module, used to update the target sub-score of the corresponding type according to the target subscription topic information, and to update the comprehensive score of the target data element according to the target sub-score; and a management module, used to adjust the management strategy of the target data element according to the updated comprehensive score.

[0014] According to another aspect of an embodiment of the present application, a computer program product is also provided, the computer program product comprising: a computer program, wherein when the computer program is executed by a processor, the above-mentioned data element management method is implemented.

[0015] According to another aspect of an embodiment of the present application, there is further provided an electronic device, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above-mentioned data element management method through the computer program.

[0016] In the embodiment of the present application, the target subscription subject information (such as the listing, transaction, and evaluation information of the data element) pushed by the message middleware in the data transaction environment is received through the subscription function. This technical feature directly ensures that the scoring mechanism can respond to the dynamic changes in the data element market in real time, and enhances the real-time and effectiveness of the scoring; after determining the data element corresponding to the target subscription subject information, multiple sub-scores (such as initial scores, transaction scores, and evaluation scores) and the comprehensive score obtained by weighted calculation can be obtained from the database. This method ensures the comprehensiveness and objectivity of the scoring results by integrating information from different sources, so that the evaluation of the data element more accurately reflects its quality; the corresponding type of sub-score is updated according to the target subscription subject information (for example, the listing information updates the initial score, and the transaction score updates the evaluation score). The comprehensive score is recalculated based on the changes in the sub-scores. This approach allows the score to be dynamically adjusted with the new information of the data element, ensuring the continued accuracy of the score and the effectiveness of the data element management strategy. According to the updated comprehensive score, the management strategy of the data element is automatically adjusted, such as adjusting the review level, recommendation level or transaction price. This approach directly links the score with the performance of the data element in the trading environment. By driving the strategy adjustment through the score, it promotes the improvement of the quality of data elements and the healthy operation of the market, ensuring the priority display and circulation of high-quality data elements, and thus solving the technical problem that the data trading system lacks real-time, objective and comprehensive evaluation of data elements and is difficult to effectively manage. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a flowchart of an optional data element management method according to an embodiment of the present application;

[0019] Figure 2 is a schematic diagram of the structure of an optional data element management device according to an embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] Example 1

[0024] According to an embodiment of the present application, a method for managing data elements is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 is a flow chart of a data element management method provided according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0026] Step S102, obtaining target subscription topic information pushed by the message middleware of the data transaction system, wherein the type of the target subscription topic information includes at least one of the following: listing information, transaction information, and evaluation information of data elements.

[0027] Step S104, determining the target data element corresponding to the target subscription subject information, and obtaining from the database multiple sub-scores of the target data element corresponding to different types of subscription subject information and a comprehensive score obtained by weighted calculation of the multiple sub-scores.

[0028] Step S106, updating the target sub-score of the corresponding type according to the target subscription subject information, and updating the comprehensive score of the target data element according to the target sub-score;

[0029] Step S108, adjusting the management strategy of the target data element according to the updated comprehensive score.

[0030] The following describes each step of the data element management method in conjunction with the specific implementation process.

[0031] First, the target subscription subject information pushed by the message middleware of the data transaction system is obtained, wherein the type of the target subscription subject information includes at least one of the following: listing information, transaction information, and evaluation information of data elements.

[0032] As an optional implementation, the following steps may be taken to obtain the target subscription topic information pushed by the message middleware of the data transaction system:

[0033] Obtain target subscription topic information in a preset data format pushed by the message middleware of the data transaction system, wherein the listing information includes a variety of registration attribute information of the target data element, and the type of the registration attribute information includes at least one of the following: restrictive required item information, restrictive optional item information, non-restrictive required item information, and non-restrictive optional item information; the transaction information includes at least one of the following: the target data element's transaction completion volume, transaction application volume, transaction cancellation volume, transaction return volume, transaction return volume, transaction return cancellation volume, and transaction completion time; the evaluation information includes at least one of the following: the data buyer's evaluation star rating and evaluation content for the target data element.

[0034] Among them, restricted mandatory information refers to the information that must be filled in during the data element registration process. This information is essential for the integrity and usability of the data element, such as the name, description, data format, data source, etc. of the data element. Restricted optional information, although not mandatory, can increase the attractiveness and practicality of the data element by providing such information, such as data update frequency, data coverage, data examples, etc. Non-restricted mandatory information, although this type of information is required in some scoring models, may not be in other models. For example, the creation date and update date of a data element are very important in the scoring of data timeliness, but may have a lower weight in the initial scoring. Non-restricted optional information is similar to restricted optional information, but emphasizes its selectivity in specific scenarios. For example, the use cases of data elements, data quality reports, etc., although not all users pay attention to them, for users with specific needs, this information may be extremely important.

[0035] Completed transactions: refers to the number of successful transactions of data elements, reflecting the market acceptance of data elements. Transaction application volume: refers to the number of transaction applications currently being processed, which can indirectly evaluate the transaction popularity of data elements. Transaction cancellation volume: refers to the number of transaction applications canceled due to various reasons, which may involve the reliability or quality of data elements. Transaction return volume: the amount of data returned by users after a successful transaction, reflecting the user's satisfaction with the data elements. Transaction return volume: the number of unfinished transactions during the return process, which may involve customer service issues or disputes over data elements. Transaction return cancellation volume: the number of transactions that are canceled after the user initiates a return, which may reflect fluctuations in user demand or understanding of the return policy. Time required to complete a transaction: the average time from the start to the completion of a single transaction, which can evaluate the efficiency of the transaction process.

[0036] Rating star: The user's subjective satisfaction rating of the data element, which can be expressed from 1 to 5 stars, with 1 star indicating very dissatisfied and 5 stars indicating very satisfied. Rating content: The user's detailed feedback on the data element, which may include specific evaluation of the data's completeness, timeliness, response time or data quality, is key information for calculating the rating score.

[0037] After obtaining the target subscription subject information, the target data element corresponding to the target subscription subject information is determined, and multiple sub-scores corresponding to different types of subscription subject information of the target data element and a comprehensive score obtained by weighted calculation of the multiple sub-scores are obtained from the database.

[0038] Update the target sub-score of the corresponding type based on the target subscription topic information, and update the comprehensive score of the target data element based on the target sub-score.

[0039] As an optional implementation, the following steps may be taken to update the target sub-score of the corresponding type based on the target subscription subject information: when the type of the target subscription subject information is shelf information, update the target subscription subject information to the database; determine a preset type of registration attribute information from a variety of registration attribute information as a scoring indicator, and determine an attribute scoring model corresponding to the type of the target data element; use the attribute scoring model to analyze the scoring indicator from multiple dimensions to obtain a first score for each dimension, wherein the dimension includes at least one of the following: attribute configuration completeness, attribute content completeness, and attribute content relevance; use a preset weight coefficient to perform a weighted summation on each first score to obtain a target sub-score corresponding to the shelf information type; and update the sub-score corresponding to the shelf information type in the database based on the target sub-score.

[0040] Among them, the formula of target sub-score can be expressed as:

[0041]

[0042] Among them, Q(m) represents the target sub-score corresponding to the listed information type, Q(i) represents the first score of the i-th dimension, and R(i) represents the weight coefficient of the i-th dimension, wherein the weight coefficient of each dimension = the ideal highest score of the dimension / the sum of the ideal highest scores of all dimensions, wherein the first score of each dimension is the actual score.

[0043] For example, when a data element is listed or re-listed in the trading system for the first time, the system will receive the listing information, which will be stored in the database. According to the registered attribute information of the data element, the scoring indicators are determined. These indicators include attribute configuration completeness, attribute content completeness and attribute content relevance. They respectively evaluate whether the attribute settings of the data element are comprehensive, whether the attribute description is complete, and whether the attribute content is consistent with the description. Select a scoring model corresponding to the data element type (such as API (Application Programming Interface) service, data set, etc.) and analyze the scoring indicators. The scoring model needs to be able to handle the characteristics of different types of data elements, and the scoring rules should be able to cover the full picture of the data elements. Use the scoring model to quantitatively evaluate each scoring indicator and obtain the first score. In order to ensure the accuracy of the scoring, the rules in the scoring model should be verified and adjusted in advance, and the correlation between different indicators should be considered in the scoring process. The weighted sum of each first score is calculated according to the preset weight coefficient to obtain the target sub-score corresponding to the listing information type. The setting of the weight coefficient should be able to reflect the importance of different scoring indicators in the listing score, and may be adjusted according to market feedback and business needs.

[0044] As an optional implementation, the following steps may be taken to update the target sub-score of the corresponding type based on the target subscription subject information: when the type of the target subscription subject information is transaction information, update the historical transaction information corresponding to the target data element in the database based on the target subscription subject information; use the transaction scoring model to analyze the updated historical transaction information from multiple dimensions to obtain a second score for each dimension, wherein the dimension includes at least one of the following: transaction success rate, transaction return rate, and average transaction time; use a preset weight coefficient to perform a weighted summation on each second score to obtain a target sub-score corresponding to the transaction information type; and update the sub-score corresponding to the transaction information type in the database based on the target sub-score.

[0045] Among them, the calculation formula of the target sub-score corresponding to the transaction information type can be expressed as:

[0046]

[0047] Wherein, T(m) represents the target sub-score corresponding to the transaction information type, T(i) represents the second score of the i-th dimension, and P(i) represents the weight coefficient of the i-th dimension, wherein the weight coefficient of each dimension = the ideal highest score of the dimension / the sum of the ideal highest scores of all dimensions, wherein the second score of each dimension is the actual score.

[0048] For example, when transaction information arrives, the system updates this information to the database to reflect the latest transaction dynamics. It is necessary to ensure the timeliness and accuracy of the transaction information to avoid the impact of data delays or errors on the scoring results. The transaction scoring model is used to analyze the updated historical transaction information to obtain a second score in terms of transaction success rate, transaction return rate, average transaction time, etc. The scoring model needs to be able to process dynamic transaction data, and the scoring results should be able to reflect the market performance of the data elements in a timely manner. The second score is weighted and summed to obtain the target sub-score corresponding to the transaction information type. The setting of the weight coefficient should take into account the timeliness of the transaction data and the degree of influence of different transaction indicators on the quality of data elements.

[0049] As an optional implementation, the following steps can also be taken to update the target sub-score of the corresponding type based on the target subscription subject information: when the type of the target subscription subject information is evaluation information, the target subscription subject information is updated to the database, and the historical evaluation information of the target data element within a preset historical time period is obtained from the database; the evaluation scoring model is used to analyze the target subscription subject information and the historical evaluation information from multiple dimensions to obtain a third score for each dimension, wherein the evaluation scoring model has natural language processing capabilities, and the dimensions include at least one of the following: data integrity, data timeliness, transaction response time, and emotional tendency in the evaluation; each third score is weighted and summed using a preset weight coefficient to obtain the target sub-score corresponding to the evaluation information type; and the sub-score corresponding to the evaluation information type in the database is updated based on the target sub-score.

[0050] Among them, the calculation formula of the target sub-score corresponding to the evaluation information type can be expressed as:

[0051]

[0052] Wherein, E(m) represents the target sub-score corresponding to the transaction information type, E(i) represents the second score of the i-th dimension, and W(i) represents the weight coefficient of the i-th dimension, where the weight coefficient of each dimension = the ideal highest score of the dimension / the sum of the ideal highest scores of all dimensions, where the second score of each dimension is the actual score.

[0053] For example, when evaluation information arrives, the system not only processes the current evaluation information, but also extracts historical evaluation information within a preset historical time period from the database. It is necessary to set a reasonable evaluation history time range to balance the timeliness and comprehensiveness of the score. The evaluation content is analyzed using natural language processing (NLP) technology to extract information related to dimensions such as data integrity, data timeliness, and transaction response time, and perform sentiment analysis on it. The NLP model should be able to accurately identify keywords and sentiment in the evaluation to achieve accurate scoring. According to the evaluation scoring model, the evaluation information and historical evaluation information are analyzed to obtain the third score of each dimension. The score should reflect the overall tendency of the evaluation. At the same time, considering the balance of positive and negative evaluations, the third score is weighted and summed to obtain the target sub-score corresponding to the evaluation information type. The weight setting should be able to distinguish the importance of different dimensions. For example, the user's evaluation of data integrity may be more important than transaction response time.

[0054] As an optional implementation, the comprehensive score of the target data element can be updated based on the target sub-score by taking the following steps: using a preset weight coefficient to weightedly sum the target sub-score corresponding to the target subscription subject information and the sub-scores corresponding to other types of subscription subject information in the database to obtain the target comprehensive score of the target data element; and updating the comprehensive score of the target data element in the database based on the target comprehensive score.

[0055] Among them, the calculation formula of the target comprehensive score can be expressed as:

[0056] V(m)=Q(m)*ω 1 +T(m)*ω 2 +E(m)*ω 3

[0057] Where V(m) represents the comprehensive score of the target, ω 1 ,ω 2 ,ω 3 is the weight coefficient.

[0058] For example, after updating the sub-scores of listing, transaction and evaluation, the system needs to recalculate the comprehensive score. A set of comprehensive weights should be set to integrate different types of sub-scores into a comprehensive score through weighted summation to fully reflect the real-time quality of data elements. The calculated comprehensive score should be updated to the database to ensure that the transaction system and other related systems can access the latest score information in real time. The database update should be efficient and have high concurrent processing capabilities to cope with the update needs of a large number of real-time scores.

[0059] Adjust the management strategy of the target data element based on the updated comprehensive score. The process can be carried out in the following steps:

[0060] Determine the target score interval corresponding to the updated comprehensive score from a preset mapping relationship table, and determine the target management strategy corresponding to the target score interval, wherein the mapping relationship table stores different management strategies corresponding to different score intervals, and the management strategy is used to configure at least one of the following attributes of the data element: review level, recommendation level, and transaction price; adjust the management strategy of the target data element to the target management strategy.

[0061] For example, when the comprehensive score of the target data element is updated, the system will refer to a preset mapping relationship table to map the score to different target score intervals. These score intervals can be set according to the score value, such as 0-50, 51-80, 81-100, etc. Each interval corresponds to a different quality level of the data element. The mapping relationship table stores different management strategies corresponding to different score intervals. The management strategies may include review levels, recommendation levels, and adjustments to transaction prices to ensure that the management and market performance of the data element match its quality. For example, data elements in high score intervals (such as 81-100) may be assigned higher recommendation levels and lower review levels so that they can be quickly brought to market; while data elements in low score intervals (such as 0-50) may require stricter review procedures or even be temporarily removed from the shelves for improvement.

[0062] As an optional implementation, for any data element, if the comprehensive score of the data element is lower than a preset score threshold and continues to be lower than a preset time period, the data element will be removed from the shelves.

[0063] For example, the setting of preset thresholds and durations should be based on market feedback and data element quality standards. It may take multiple adjustments and tests to find the most appropriate parameters. Once the conditions for delisting are met, the system should automatically execute the delisting operation to avoid the continuous display of data elements affecting the overall data quality of the platform. In terms of implementation details, this may require close integration with the front-end display, recommendation algorithm and other modules of the trading system to ensure the seamless execution of the delisting operation. After the data element is delisted, the system should provide feedback to the data element provider on the improvement mechanism, including specific scoring indicators and improvement suggestions. In terms of implementation details, a feedback system can be designed to inform the data element provider of the reasons for delisting and the direction of improvement, to help it improve the data quality before relisting.

[0064] In the embodiment of the present application, the target subscription subject information (such as the listing, transaction, and evaluation information of the data element) pushed by the message middleware in the data transaction environment is received through the subscription function. This technical feature directly ensures that the scoring mechanism can respond to the dynamic changes in the data element market in real time, and enhances the real-time and effectiveness of the scoring; after determining the data element corresponding to the target subscription subject information, multiple sub-scores (such as initial scores, transaction scores, and evaluation scores) and the comprehensive score obtained by weighted calculation can be obtained from the database. This method ensures the comprehensiveness and objectivity of the scoring results by integrating information from different sources, so that the evaluation of the data element more accurately reflects its quality; the corresponding type of sub-score is updated according to the target subscription subject information (for example, the listing information updates the initial score, and the transaction score updates the evaluation score). The comprehensive score is recalculated based on the changes in the sub-scores. This approach allows the score to be dynamically adjusted with the new information of the data element, ensuring the continued accuracy of the score and the effectiveness of the data element management strategy. According to the updated comprehensive score, the management strategy of the data element is automatically adjusted, such as adjusting the review level, recommendation level or transaction price. This approach directly links the score with the performance of the data element in the trading environment. By driving the strategy adjustment through the score, it promotes the improvement of the quality of data elements and the healthy operation of the market, ensuring the priority display and circulation of high-quality data elements, and thus solving the technical problem that the data trading system lacks real-time, objective and comprehensive evaluation of data elements and is difficult to effectively manage.

[0065] Example 2

[0066] According to an embodiment of the present application, a data element management device for implementing the data element management method in embodiment 1 is also provided, such as Figure 2 As shown, the data element management device at least includes: an acquisition module 21, a determination module 22, a scoring module 23 and a management module 24, wherein:

[0067] The acquisition module 21 is used to acquire the target subscription topic information pushed by the message middleware of the data transaction system, wherein the type of the target subscription topic information includes at least one of the following: listing information, transaction information, and evaluation information of the data element;

[0068] A determination module 22 is used to determine a target data element corresponding to the target subscription subject information, and obtain from a database a plurality of sub-scores of the target data element corresponding to different types of subscription subject information and a comprehensive score obtained by weighted calculation of the plurality of sub-scores;

[0069] The scoring module 23 is used to update the target sub-score of the corresponding type according to the target subscription subject information, and update the comprehensive score of the target data element according to the target sub-score;

[0070] The management module 24 is used to adjust the management strategy of the target data element according to the updated comprehensive score.

[0071] The functions of each module of the data element management device are explained below in conjunction with the specific implementation process.

[0072] The acquisition module acquires target subscription subject information pushed by the message middleware of the data transaction system, wherein the type of the target subscription subject information includes at least one of the following: listing information, transaction information, and evaluation information of data elements.

[0073] As an optional implementation, the following steps may be taken to obtain the target subscription topic information pushed by the message middleware of the data transaction system:

[0074] Obtain target subscription topic information in a preset data format pushed by the message middleware of the data transaction system, wherein the listing information includes a variety of registration attribute information of the target data element, and the type of the registration attribute information includes at least one of the following: restrictive required item information, restrictive optional item information, non-restrictive required item information, and non-restrictive optional item information; the transaction information includes at least one of the following: the target data element's transaction completion volume, transaction application volume, transaction cancellation volume, transaction return volume, transaction return volume, transaction return cancellation volume, and transaction completion time; the evaluation information includes at least one of the following: the data buyer's evaluation star rating and evaluation content for the target data element.

[0075] After obtaining the target subscription subject information, the determination module determines the target data element corresponding to the target subscription subject information, and obtains from the database multiple sub-scores of the target data element corresponding to different types of subscription subject information and a comprehensive score obtained by weighted calculation of the multiple sub-scores.

[0076] The scoring module updates the target sub-score of the corresponding type according to the target subscription topic information, and updates the comprehensive score of the target data element according to the target sub-score.

[0077] As an optional implementation, the following steps may be taken to update the target sub-score of the corresponding type based on the target subscription subject information: when the type of the target subscription subject information is shelf information, update the target subscription subject information to the database; determine a preset type of registration attribute information from a variety of registration attribute information as a scoring indicator, and determine an attribute scoring model corresponding to the type of the target data element; use the attribute scoring model to analyze the scoring indicator from multiple dimensions to obtain a first score for each dimension, wherein the dimension includes at least one of the following: attribute configuration completeness, attribute content completeness, and attribute content relevance; use a preset weight coefficient to perform a weighted summation on each first score to obtain a target sub-score corresponding to the shelf information type; and update the sub-score corresponding to the shelf information type in the database based on the target sub-score.

[0078] As an optional implementation, the following steps may be taken to update the target sub-score of the corresponding type based on the target subscription subject information: when the type of the target subscription subject information is transaction information, update the historical transaction information corresponding to the target data element in the database based on the target subscription subject information; use the transaction scoring model to analyze the updated historical transaction information from multiple dimensions to obtain a second score for each dimension, wherein the dimension includes at least one of the following: transaction success rate, transaction return rate, and average transaction time; use a preset weight coefficient to perform a weighted summation on each second score to obtain a target sub-score corresponding to the transaction information type; and update the sub-score corresponding to the transaction information type in the database based on the target sub-score.

[0079] As an optional implementation, the following steps can also be taken to update the target sub-score of the corresponding type based on the target subscription subject information: when the type of the target subscription subject information is evaluation information, the target subscription subject information is updated to the database, and the historical evaluation information of the target data element within a preset historical time period is obtained from the database; the evaluation scoring model is used to analyze the target subscription subject information and the historical evaluation information from multiple dimensions to obtain a third score for each dimension, wherein the evaluation scoring model has natural language processing capabilities, and the dimensions include at least one of the following: data integrity, data timeliness, transaction response time, and emotional tendency in the evaluation; each third score is weighted and summed using a preset weight coefficient to obtain the target sub-score corresponding to the evaluation information type; and the sub-score corresponding to the evaluation information type in the database is updated based on the target sub-score.

[0080] As an optional implementation, the comprehensive score of the target data element can be updated based on the target sub-score by taking the following steps: using a preset weight coefficient to weightedly sum the target sub-score corresponding to the target subscription subject information and the sub-scores corresponding to other types of subscription subject information in the database to obtain the target comprehensive score of the target data element; and updating the comprehensive score of the target data element in the database based on the target comprehensive score.

[0081] The management module adjusts the management strategy of the target data element according to the updated comprehensive score. The process can be carried out in the following steps:

[0082] Determine the target score interval corresponding to the updated comprehensive score from a preset mapping relationship table, and determine the target management strategy corresponding to the target score interval, wherein the mapping relationship table stores different management strategies corresponding to different score intervals, and the management strategy is used to configure at least one of the following attributes of the data element: review level, recommendation level, and transaction price; adjust the management strategy of the target data element to the target management strategy.

[0083] As an optional implementation, for any data element, if the comprehensive score of the data element is lower than a preset score threshold and continues to be lower than a preset time period, the data element will be removed from the shelves.

[0084] It should be noted that each module in the data element management device in the embodiment of the present application corresponds one by one to each implementation step of the data element management method in Example 1. Since a detailed description has been given in Example 1, some details not reflected in this embodiment can be referred to Example 1 and will not be elaborated here.

[0085] Example 3

[0086] According to an embodiment of the present application, a computer program product is also provided, which includes a computer program, wherein when the computer program is executed by a processor, the data element management method in Example 1 is implemented.

[0087] According to an embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the data element management method in Example 1 by running the computer program.

[0088] According to an embodiment of the present application, a processor is also provided, which is used to run a computer program, wherein the data element management method in Example 1 is executed when the computer program is running.

[0089] According to an embodiment of the present application, an electronic device is also provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the data element management method in Example 1 through the computer program.

[0090] Specifically, the computer program executes the following steps when it is running: obtaining target subscription topic information pushed by the message middleware of the data transaction system, wherein the type of the target subscription topic information includes at least one of the following: listing information, transaction information, and evaluation information of the data element; determining the target data element corresponding to the target subscription topic information, and obtaining from the database multiple sub-scores of the target data element corresponding to different types of subscription topic information and a comprehensive score obtained by weighted calculation of the multiple sub-scores; updating the target sub-score of the corresponding type according to the target subscription topic information, and updating the comprehensive score of the target data element according to the target sub-score; and adjusting the management strategy of the target data element according to the updated comprehensive score.

[0091] As an optional implementation, the electronic device may be in the form of a mobile terminal, a computer terminal or a similar computing device. Figure 3FIG. 1 shows a hardware structure block diagram of an electronic device for implementing a data element management method. Figure 3 As shown, the electronic device 30 may include one or more (302a, 302b, ..., 302n are used to illustrate) processors 302 (the processor 302 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 304 for storing data, and a transmission device 306 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations are shown.

[0092] It should be noted that the one or more processors 302 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the electronic device 30. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0093] The memory 304 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the data element management method in the embodiment of the present application. The processor 302 executes various functional applications and data processing by running the software programs and modules stored in the memory 304, that is, realizing the vulnerability detection method of the above-mentioned application. The memory 304 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 304 may further include a memory remotely arranged relative to the processor 302, and these remote memories may be connected to the electronic device 30 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0094] The transmission device 306 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the electronic device 30. In one example, the transmission device 306 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 306 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0095] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the electronic device 30 .

[0096] The serial numbers of the above embodiments are only for description and do not represent the advantages or disadvantages of the embodiments.

[0097] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0100] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0102] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for managing data elements, characterized in that: include: Obtaining target subscription subject information pushed by the message middleware of the data transaction system, wherein the type of the target subscription subject information includes at least one of the following: listing information, transaction information, and evaluation information of data elements; Determine the target data element corresponding to the target subscription subject information, and obtain from a database a plurality of sub-scores of the target data element corresponding to different types of subscription subject information and a comprehensive score obtained by weighted calculation of the plurality of sub-scores; updating the target sub-score of the corresponding type according to the target subscription topic information, and updating the comprehensive score of the target data element according to the target sub-score; Adjust the management strategy of the target data element based on the updated comprehensive score.

2. The method according to claim 1, characterized in that: Get the target subscription topic information pushed by the message middleware of the data transaction system, including: Obtain the target subscription topic information in a preset data format pushed by the message middleware of the data transaction system, wherein: The listing information includes a variety of registration attribute information of the target data element, and the type of the registration attribute information includes at least one of the following: restrictive mandatory information, restrictive optional information, non-restrictive mandatory information, and non-restrictive optional information; The transaction information includes at least one of the following: the transaction completion volume, transaction application volume, transaction cancellation volume, transaction return volume, transaction return volume, transaction return cancellation volume, and transaction completion time of the target data element; The evaluation information includes at least one of the following: the star rating and evaluation content of the target data element by the data purchaser.

3. The method according to claim 2, characterized in that The target sub-score of the corresponding type is updated according to the target subscription topic information, including: In a case where the type of the target subscription subject information is listing information, updating the target subscription subject information to the database; Determine a preset type of registered attribute information from the plurality of registered attribute information as a scoring indicator, and determine an attribute scoring model corresponding to the type of the target data element; Analyzing the scoring indicators from multiple dimensions using the attribute scoring model to obtain a first score for each dimension, wherein the dimension includes at least one of the following: attribute configuration completeness, attribute content completeness, and attribute content relevance; Using a preset weight coefficient to perform a weighted summation on each of the first scores to obtain a target sub-score corresponding to the type of listing information; The sub-score corresponding to the listed information type in the database is updated according to the target sub-score.

4. The method according to claim 2, characterized in that: The target sub-score of the corresponding type is updated according to the target subscription topic information, including: In the case where the type of the target subscription subject information is transaction information, updating the historical transaction information corresponding to the target data element in the database according to the target subscription subject information; Analyze the updated historical transaction information from multiple dimensions using the transaction scoring model to obtain a second score for each dimension, wherein the dimension includes at least one of the following: transaction success rate, transaction return rate, and average transaction time; Using a preset weight coefficient to perform a weighted summation on each of the second scores to obtain a target sub-score corresponding to the transaction information type; The sub-score corresponding to the transaction information type in the database is updated according to the target sub-score.

5. The method according to claim 2, characterized in that: The target sub-score of the corresponding type is updated according to the target subscription topic information, including: In the case where the type of the target subscription subject information is evaluation information, updating the target subscription subject information to the database, and obtaining historical evaluation information of the target data element within a preset historical time period from the database; Analyzing the target subscription topic information and the historical evaluation information from multiple dimensions using an evaluation scoring model to obtain a third score of each dimension, wherein the evaluation scoring model has a natural language processing capability, and the dimensions include at least one of the following: data integrity, data timeliness, transaction response time, and sentiment tendency in the evaluation; Performing a weighted summation on each of the third scores using a preset weight coefficient to obtain a target sub-score corresponding to the evaluation information type; The sub-score corresponding to the evaluation information type in the database is updated according to the target sub-score.

6. The method according to claim 1, characterized in that Updating the comprehensive score of the target data element according to the target sub-score includes: Using a preset weight coefficient, weighted sum is performed on the target sub-score corresponding to the target subscription subject information and the sub-scores corresponding to other types of subscription subject information in the database to obtain a target comprehensive score for the target data element; The comprehensive score of the target data element in the database is updated according to the target comprehensive score.

7. The method according to claim 1, characterized in that Adjust the management strategy of the target data element based on the updated comprehensive score, including: Determine a target score interval corresponding to the updated comprehensive score from a preset mapping relationship table, and determine a target management strategy corresponding to the target score interval, wherein the mapping relationship table stores different management strategies corresponding to different score intervals, and the management strategy is used to configure at least one of the following attributes of the data element: review level, recommendation level, and transaction price; The management policy of the target data element is adjusted to the target management policy.

8. The method according to claim 7, characterized in that The method further comprises: For any data element, if the comprehensive score of the data element is lower than the preset score threshold and continues to be lower than the preset time, the data element will be removed from the shelves.

9. A data element management device, characterized in that: include: An acquisition module, used to acquire target subscription topic information pushed by the message middleware of the data transaction system, wherein the type of the target subscription topic information includes at least one of the following: listing information, transaction information, and evaluation information of data elements; A determination module, used to determine the target data element corresponding to the target subscription subject information, and obtain from a database a plurality of sub-scores of the target data element corresponding to different types of subscription subject information and a comprehensive score obtained by weighted calculation of the plurality of sub-scores; A scoring module, used to update the target sub-score of the corresponding type according to the target subscription subject information, and update the comprehensive score of the target data element according to the target sub-score; A management module is used to adjust the management strategy of the target data element according to the updated comprehensive score.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the data element management method described in any one of claims 1 to 8 through the computer program.

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