Data processing method, system and device

Abstract

The embodiment of the invention provides a data processing method, system and device.The data processing method comprises the steps that a published content list associated with a target guarantee project is obtained, and the published content list is a list composed of recommended published contents published by a target user on multiple interaction platforms for the target guarantee project; according to preset time information, collecting content interaction data associated with recommended release content on each interaction platform, and obtaining multi-source heterogeneous data of the target guarantee project; updating the multi-source heterogeneous data into guarantee project data, and storing the guarantee project data into a distributed database according to an incremental storage strategy; and in response to a project request submitted for the target guarantee project, calculating target return information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of data processing technology, and in particular to data processing methods, systems, and devices. Background Art

[0002] With the development of computer and Internet technology, the ways to promote projects are becoming more and more diverse. With the rise of social media, promoting projects through social platforms has become a more cost-effective way. By inviting users with high following on the platform to assist in the promotion of the project, it is possible to effectively improve the promotion effect through the social attributes of users on the platform, thereby reaching more users. However, there are many social platforms in the existing technology, and users may have accounts on different platforms. When users publish recommended content related to related projects through multiple platforms, it will become more difficult for the project party to collect interaction data from multiple platforms. At the same time, the data related to the project also involves multiple dimensions, which leads to lower data processing efficiency. At the same time, because the interaction data is related to multiple platforms, the calculation of the feedback information needs to be completed by the operation and maintenance personnel, which is inefficient. Therefore, an effective solution is urgently needed to solve the above problems. Summary of the Invention

[0003] In view of this, embodiments of this specification provide a data processing method. One or more embodiments of this specification also relate to a data processing system, a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product to address technical deficiencies in the prior art.

[0004] According to a first aspect of an embodiment of this specification, there is provided a data processing method, including: Obtaining a list of published content associated with a target guarantee project, wherein the list of published content is a list of recommended published content published by a target user on multiple interactive platforms for the target guarantee project; Collect content interaction data related to recommended content on each interactive platform according to preset time information, and obtain multi-source heterogeneous data of the target security project; Updating the multi-source heterogeneous data into security project data, and storing the security project data in a distributed database according to an incremental storage strategy; In response to a project request submitted for the target guarantee project, target reward information corresponding to the target guarantee project is calculated based on the content interaction data and the guarantee project data stored in the distributed database.

[0005] According to a second aspect of an embodiment of this specification, a data processing system is provided, including a data processing end and a security project end, including: The data processing end is configured to obtain a list of published content associated with a target security project, wherein the list of published content is a list of recommended published content published by a target user for the target security project on multiple interactive platforms, collect content interaction data associated with the recommended published content on each interactive platform according to preset time information, and send a data acquisition request to the security project end; The security project end is configured to respond to the data acquisition request, read the multi-source heterogeneous data corresponding to the target security project, and send the multi-source heterogeneous data to the data processing end; The data processing end is used to update the multi-source heterogeneous data into guarantee project data, and store the guarantee project data in a distributed database according to an incremental storage strategy. In response to a project request submitted for the target guarantee project, the target return information corresponding to the target guarantee project is calculated based on the content interaction data and the guarantee project data stored in the distributed database.

[0006] According to a third aspect of the embodiments of this specification, there is provided a data processing device, including: an acquisition module configured to acquire a list of published contents associated with a target guarantee project, wherein the list of published contents is a list of recommended published contents published by a target user on multiple interactive platforms for the target guarantee project; A collection module is configured to collect content interaction data related to recommended published content on each interactive platform according to preset time information, and obtain multi-source heterogeneous data of the target security project; An updating module is configured to update the multi-source heterogeneous data into guarantee project data, and store the guarantee project data in a distributed database according to an incremental storage strategy; The calculation module is configured to calculate target reward information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database in response to a project request submitted for the target guarantee project.

[0007] According to a fourth aspect of the embodiments of this specification, there is provided a computing device, including: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above-mentioned data processing method are implemented.

[0008] According to a fifth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned data processing method are implemented.

[0009] According to a sixth aspect of the embodiments of this specification, a computer program product is provided, comprising a computer program or instructions, which implement the steps of the above-mentioned data processing method when executed by a processor.

[0010] To automate the calculation of reward information and improve data processing efficiency, the data processing method provided in this embodiment can obtain a list of published content associated with a target security project. The published content list is a list of recommended content published by target users on multiple interactive platforms for the target security project. Content interaction data associated with the recommended content on each interactive platform can then be collected according to preset time information, enabling regular collection and management of content interaction data associated with the target security project, making it more convenient and flexible. Furthermore, multi-source heterogeneous data for the target security project can be obtained. Given the format inconsistencies of multi-source heterogeneous data, this multi-source heterogeneous data can be updated to security project data and stored in a distributed database according to an incremental storage strategy. When reward information calculation is required, the target reward information corresponding to the target security project can be calculated based on the content interaction data and the security project data stored in the distributed database in response to a project request submitted for the target security project. This automates both data processing and reward information calculation, saving human resources while effectively improving data processing efficiency and facilitating business adjustments or updates. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a flow chart of a data processing method provided by one embodiment of this specification; Figure 2 This is a flowchart of a data processing method provided by one embodiment of this specification; Figure 3 This is a schematic diagram of the structure of a data processing system provided by one embodiment of this specification; Figure 4 This is a schematic diagram of the structure of a data processing device provided by one embodiment of this specification; Figure 5 This is a structural block diagram of a computing device provided by one embodiment of this specification. DETAILED DESCRIPTION

[0012] The following description sets forth many specific details to facilitate a thorough understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0013] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a," "the," and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0014] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0015] In addition, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0016] This specification provides a data processing method. One or more embodiments of this specification also relate to a data processing system, a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, each of which is described in detail in the following embodiments.

[0017] See also Figure 1 , Figure 1 A flow chart of a data processing method provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0018] Step S102: obtaining a list of published contents associated with the target security project, wherein the list of published contents is a list of recommended published contents published by the target user on multiple interactive platforms for the target security project.

[0019] The data processing method provided in this embodiment can be applied to the calculation of reward information for any type of insurance program, where an insurance program refers to a program that provides insurance services, such as auto insurance, health insurance, and mobile phone screen insurance. After users on an interactive platform promote an insurance program, the reward information can be calculated by combining the interaction data on the platform and the data corresponding to the program.

[0020] Among them, the target protection project specifically refers to the insurance project that needs to be promoted by the target user on the interactive platform. The target user specifically refers to the user found by the business party to which the target protection project belongs on each interactive platform for cooperation, and is used for the user to publish recommended content of the target protection project on the interactive platform. The interactive platform specifically refers to a social media platform that supports users to publish videos, pictures and texts, audio, etc. on the platform and has a dissemination characteristic. Accordingly, the recommended content specifically refers to the content published by the target user on each platform when promoting the target protection project, including but not limited to pictures and texts, videos or audios, and the promotional content for the target protection project can be promoted by video, pictures and texts or audios alone, or inserted into other content published by the user. The specific form can be set according to actual needs, and this embodiment does not make any restrictions here. Accordingly, the published content list specifically refers to a list composed of corresponding identifiers of the recommended content published by the target user on each interactive platform related to the target protection project, which facilitates the subsequent statistics of content interaction data related to the target content for subsequent reward information calculation.

[0021] Based on this, in order to automate the calculation of reward information and improve data processing efficiency, a list of published content associated with the target security project can be obtained. This list of published content consists of recommended content published by the target user for the target security project on multiple interactive platforms. Content interaction data associated with the recommended content on each interactive platform can then be collected according to preset time information, enabling regular collection and management of content interaction data associated with the target security project, making it more convenient and flexible. Multi-source heterogeneous data for the target security project can also be obtained. Given the format inconsistencies of multi-source heterogeneous data, this multi-source heterogeneous data can be updated to security project data and stored in a distributed database according to an incremental storage strategy. When reward information calculation is required, the target reward information corresponding to the target security project can be calculated based on the content interaction data and the security project data stored in the distributed database in response to a project request submitted for the target security project. This automates both data processing and reward information calculation, saving human resources while effectively improving data processing efficiency and facilitating business adjustments or updates.

[0022] Furthermore, in order to make it more convenient for target users to publish content related to target security projects on various interactive platforms, content publishing guidance information can be pre-built. In this embodiment, the specific implementation method is as follows: Obtain project information of the target guarantee project and collect platform prompt words submitted for the project information; input the platform prompt words and the project information into a large language model for processing to obtain content publishing guidance information corresponding to each interactive platform; send the content publishing guidance information to the target user, wherein the content guidance information is used by the target user to publish recommended content on each interactive platform.

[0023] Specifically, project information refers to project introduction information associated with the target guarantee project, including but not limited to the project's coverage, timeframe, price, and other information. Accordingly, platform prompts refer to the prompts corresponding to the interactive platform on which the target guarantee project is to be published. Considering that different platforms have different content publishing styles, inputting platform prompts into a large language model allows the large language model to generate content publishing guidance information that aligns with the platform's style based on the prompts, thereby enabling target users to reach more users when publishing recommended content. The large language model can be implemented using an LLM (Large Language Model) model. Content publishing guidance information specifically refers to guidance information that guides users in publishing recommended content on different platforms, such as informing users of dialogue lines, appropriate video length, and speaking style.

[0024] Based on this, in order to enable users to publish recommended content related to target protection projects on various platforms to reach more users, the project information of the target protection project can be obtained, and the platform prompt words submitted for the project information can be collected; thereafter, the platform prompt words and project information can be input into the large language model for processing, and then the large language model automatically generates content publishing guidance information corresponding to each interactive platform; thereafter, the content publishing guidance information can be sent to the target user, and the content guidance information can be used by the target user to publish recommended content on each interactive platform, making it more convenient for users to publish recommended content and more in line with the target protection project.

[0025] For example, a business entity invites User A, who has 10 million followers across various platforms, to promote the newly released health insurance product, Health Insurance A. To facilitate User A's ability to post promotional videos about Health Insurance A on social platforms X, Y, and Z, the business entity uses a large language model, taking into account the characteristics of each platform, to generate three types of promotional copy. Copy 1, in the form of images and text, compares Health Insurance A with Health Insurance B and is applied to social platform X. Copy 2, in the form of a video, provides a detailed introduction to Health Insurance A and is applied to social platform Y. Copy 3, in the form of a video, inserts a promotional description of Health Insurance A into a video explaining the movie S and is applied to social platform Z. Copy 1-3 are then sent to User A, allowing User A to post text and images about Health Insurance A on social platform X, a video promoting Health Insurance A on social platform Y, and a movie commentary video that includes Health Insurance A on social platform Z. Among them, the corresponding identifier for the text and picture publishing content is X1, the corresponding identifier for the promotional video is Y1, and the corresponding identifier for the movie commentary video containing disease insurance A is Z1. The above identifiers can then be recorded to facilitate the subsequent collection of interaction data corresponding to each identifier from social platform X, social platform Y, and social platform Z, which is used to calculate the corresponding return rate of disease insurance A.

[0026] In summary, the large language model automatically generates personalized publishing guidance information adapted to each platform, achieving accurate matching and efficient dissemination of project information, significantly reducing manual compilation costs, and improving multi-channel content publishing efficiency and user convenience.

[0027] Step S104 : collecting content interaction data related to recommended published content on each interactive platform according to preset time information, and acquiring multi-source heterogeneous data of the target security project.

[0028] Specifically, after determining the list of published content associated with the target security project as mentioned above, the content interaction data of the associated recommended published content on each interactive platform can be collected according to the preset time information, and the multi-source heterogeneous data of the target security project can be obtained at the same time, so that the target return information can be calculated in combination with the content interaction data in the future. This calculation processing also needs to be combined with the project-related data of the target security project, so it is also necessary to obtain the multi-source heterogeneous data of the target security project for subsequent use.

[0029] Among them, the preset time information specifically refers to the time for regularly collecting interactive data related to the recommended content released by the target user on each interactive platform related to the target protection project. It can be set according to actual needs, and this embodiment does not impose any restrictions on this. Correspondingly, content interaction data specifically refers to the number of likes, comments, back searches, readings, etc. received by the recommended content on the platform, which is used to characterize the exposure of the target protection project through the recommended content. Correspondingly, multi-source heterogeneous data specifically refers to the multi-dimensional data involved in the target protection project, including but not limited to the project's underwriting data, surrender data, claims data, behavior logs, etc.

[0030] Furthermore, when collecting content interaction data, in order to avoid the situation where the target user has abnormal means to increase the interaction data, the collected content interaction data can be detected. In this embodiment, the specific implementation method is as follows: Initial content interaction data of the associated recommended published content on each interactive platform is collected according to preset time information; the initial content interaction data is tested according to the user attribute information of the target user; if the test passes, the initial content interaction data is used as content interaction data; if the test fails, the initial content interaction data is verified in batches, and the content interaction data is determined based on the test results.

[0031] Specifically, initial content interaction data refers to the interaction data collected for recommended content published on various interactive platforms, and this data needs to be tested. The purpose of the test is to prevent the target user from inflating the interaction data for recommended content through abnormal means. Correspondingly, user attribute information specifically refers to information that represents the popularity of the target user on the interactive platform. Generally, the popularity of the target user on the interactive platform is proportional to the interaction data they can receive after publishing recommended content. For example, if a user has 1,000 followers on platform A, the recommended content they publish will generally receive around 100 comments. If the number of comments is much higher than this value, it indicates that the user is engaging in comment manipulation and needs to be tested.

[0032] Based on this, for the recommended content published on each platform, the initial content interaction data of the associated recommended content published on each interactive platform can be collected according to the preset time information; thereafter, the initial content interaction data can be tested based on the user attribute information of the target user; if the test passes, it means that the initial content interaction data is consistent with the normal number of comments of the target user, that is, the interaction data is within the safe range, so the initial content interaction data can be used as content interaction data; if the test fails, it means that there are abnormalities in the initial content interaction data, and at this time, the initial content interaction data needs to be verified in batches to determine whether the current initial content interaction data is increased by abnormal means or meets the situation of sudden increase, so as to determine the real content interaction data based on the test results.

[0033] Continuing with the above example, after user A publishes videos and graphic content on social platforms X, Y, and Z respectively, the number of likes for the content published for disease insurance A on each platform can be collected regularly at a set time, such as 12:00 every day. At this time, the number of likes for the graphic content corresponding to the X1 identifier is determined to be n1, the number of likes for the promotional video corresponding to the Y1 identifier is n2, and the number of likes for the movie commentary video corresponding to the Z1 identifier is n3. Then, it can be determined whether the number of likes for disease insurance A on each platform is normal. If they are all normal, the data can be used to calculate the rate of return. If not, it can be further verified whether user A has engaged in inflating the number of likes.

[0034] In summary, through user attribute detection and batch verification, high-quality interaction data can be effectively screened, ensuring data accuracy and compliance, improving processing efficiency, reducing resource consumption, and ensuring content recommendation effectiveness.

[0035] Step S106: updating the multi-source heterogeneous data into security project data, and storing the security project data in a distributed database according to an incremental storage strategy.

[0036] Specifically, after obtaining the multi-source heterogeneous data as mentioned above, in order to facilitate subsequent use, the multi-source heterogeneous data can be updated to security project data, and the security project data can be stored in a distributed database according to an incremental storage strategy, so that the data of the associated target security project can be stored in the same location in the distributed database, so as to facilitate subsequent return rate calculation.

[0037] Among them, the security project data specifically refers to the standardized data obtained after preprocessing multi-source heterogeneous data, and the incremental storage strategy specifically refers to the strategy of storing security project data in a distributed database.

[0038] Furthermore, multi-dimensional pre-processing can be performed on multi-source heterogeneous data, so that the data format is unified for subsequent use. In this embodiment, the specific implementation is as follows: The sub-data contained in the multi-source heterogeneous data are respectively subjected to data cleaning, denoising and standardization processing through a dynamic rule engine; the standard sub-data corresponding to the target format is determined according to the processing results, and the guarantee project data is generated based on the standard sub-data.

[0039] Specifically, the dynamic rule engine refers to an engine that can perform data cleaning, denoising and standardization processing on sub-data in multi-source heterogeneous data at the millisecond level, and is configured with data cleaning rules, denoising rules and standardization rules.

[0040] Based on this, in order to facilitate the subsequent use of standardized data for rate of return calculation, the sub-data contained in the multi-source heterogeneous data can be cleaned, denoised and standardized through the dynamic rule engine; the standard sub-data corresponding to the target format can be determined according to the processing results, and the guarantee project data can be generated based on the standard sub-data.

[0041] In summary, the dynamic rule engine can achieve efficient data processing, improve the efficiency of cleaning, denoising and standardization, ensure data quality and consistency, reduce the cost of manual intervention, and provide accurate and reliable data support for the project.

[0042] Furthermore, when storing data, a mapping relationship can be established so that related data is stored in the same location for easy access and use. In this embodiment, the specific implementation is as follows: Entity recognition is performed on the security project data to obtain multiple project entities; based on the security project map associated with the target security project and the multiple project entities, a data mapping relationship is established between the security project data, the target user, and the recommended content published on each interactive platform; based on the data mapping relationship, the security project data is stored in a distributed database according to an incremental storage strategy.

[0043] Specifically, entity recognition refers to the processing operation of extracting entities from insurance project data. This can be achieved through an entity recognition model. Multiple project entities are entities associated with the target insurance project in the project insurance data, such as insurance name, compensation amount, user name, and other entities. Accordingly, the insurance project graph refers to a pre-established data lineage graph associated with insurance business semantics. Correspondingly, data mapping relationships specifically refer to the establishment of mapping relationships between insurance project data, target users, and recommended content, facilitating the direct reading of relevant data from distributed databases when using the data.

[0044] Based on this, when storing data, entity recognition can be performed on the security project data to obtain multiple project entities; at this time, a data mapping relationship can be established between the security project data, target users, and recommended content published on each interactive platform based on the security project map and multiple project entities associated with the target security project; this establishment operation can be understood as binding data with semantic level associations, and then the security project data can be stored in a distributed database according to an incremental storage strategy based on the data mapping relationship, and the associated data can be stored in the same area for subsequent use.

[0045] In practical applications, given the heterogeneous nature of data associated with insurance projects, potentially involving multiple dimensions such as contracting, surrender, claims, and user behavior logs, a lightweight data pipeline can be designed. This leverages a dynamic rule engine to achieve millisecond-level data cleansing, denoising, and standardization, addressing integration bottlenecks caused by varying data formats (e.g., JSON, XML, and relational tables). Furthermore, a data lineage graph based on insurance business semantics can be constructed. Through entity resolution and graph computing techniques, multi-dimensional data such as user ID, policy number, and corresponding identifiers of published content in the published content list can be automatically linked, ensuring the integrity and traceability of metric calculations (e.g., pre-surrender conversion rate and post-surrender retention rate). Furthermore, a tiered storage strategy (hot-warm-cold tiering) can be employed to store real-time incremental data (e.g., daily surrender records) in a distributed in-memory database (e.g., Redis Cluster), enabling efficient aggregate queries on a wider range of data.

[0046] Continuing with the above example, when calculating the ROI corresponding to the promotion of disease insurance A through user A, we can obtain multi-source heterogeneous data corresponding to disease insurance A, including underwriting data, surrender data, claims data, and user behavior logs. The dynamic rule engine can then be used to clean, denoise, and standardize the above data to obtain standardized insurance data. Combined with entity recognition and graph computing, the standardized insurance data can be incrementally stored in a distributed memory database for use in subsequent ROI calculations.

[0047] In summary, by accurately building data associations through entity recognition and graph mapping, combined with incremental storage and distributed architecture, data processing efficiency, system scalability and data consistency can be significantly improved, ensuring that project data management is more intelligent and efficient.

[0048] Step S108 : In response to the project request submitted for the target guarantee project, target reward information corresponding to the target guarantee project is calculated based on the content interaction data and the guarantee project data stored in the distributed database.

[0049] Specifically, when a project request is received for a targeted guaranteed project, it indicates that the corresponding return information for the targeted guaranteed project needs to be calculated. Therefore, the target return information for the targeted guaranteed project can be calculated based on the content interaction data and the guaranteed project data stored in the distributed database for downstream business adjustments. The target return information specifically refers to the return on investment (ROI) corresponding to promoting the targeted guaranteed project through target users.

[0050] Furthermore, when calculating the return information, it is necessary to first construct the publishing content cost information and guarantee the project income information. In this embodiment, the specific implementation method is as follows: Receive a project request submitted for the target security project; extract local security project data related to the target security project in the distributed database according to the project request; generate publishing content cost information according to the content interaction data, and generate security project revenue information according to the local security project data; calculate the target return information corresponding to the target security project based on the publishing content cost information and the security project revenue information.

[0051] Specifically, the local guarantee project data specifically refers to the guarantee project data of the related target guarantee projects collected from the time the recommended content is released to the current time, the release content cost information specifically refers to the cost information calculated by combining the number of recommended release contents and the average cost, and the guarantee project revenue information specifically refers to the revenue information corresponding to the target guarantee project calculated based on the local guarantee project information.

[0052] Based on this, when receiving a project request submitted for a target security project, local security project data related to the target security project can be extracted from the distributed database according to the project request; then, the publishing content cost information can be generated based on the content interaction data, and the security project benefit information can be generated based on the local security project data; and then, based on the publishing content cost information and the security project benefit information, the target return information corresponding to the target security project can be calculated.

[0053] In actual applications, it is considered that the annualized premium of the target protection project is composed of multiple factors, including policy conversion rate (before policy cancellation), average premium per case (before policy cancellation), retention rate (after policy cancellation), etc. These factors comprehensively consider the sales of insurance products and the subsequent retention of customers, and can more accurately reflect the actual benefits brought by the cooperation with experts. Therefore, the data processing method provided in this embodiment can be combined with multiple conversion rates and content interaction data for calculation to further refine the evaluation of benefits. In specific implementation, the target return information ROI can be calculated as follows: Calculate the search UV by multiplying the number of published content, the average number of readings of published content, and the search conversion rate; calculate the annualized premium (after policy cancellation) by multiplying the search UV, policy conversion rate, average premium per case, and retention rate (after policy cancellation). At the same time, calculate the cost by multiplying the number of published content and the average cost of published content; finally, divide the product of the annualized premium (after policy cancellation) and the set contribution value by the cost to obtain the target return information ROI corresponding to the target protection project for use by the business side.

[0054] Continuing with the above example, when it is necessary to calculate the ROI corresponding to Disease Insurance A, the number of published content related to Disease Insurance A on each platform can be collected, and the insurance data corresponding to Disease Insurance A can be obtained at the same time. Then, the ROI corresponding to Disease Insurance A can be calculated according to the above calculation method, thereby providing a quantitative indicator for insurance companies to evaluate the effectiveness of cooperation with User A.

[0055] In summary, by extracting distributed data in real time and dynamically analyzing costs and benefits, accurate return calculation can be achieved, data processing efficiency and decision-making accuracy can be improved, resource allocation can be optimized, and project benefit evaluation capabilities can be enhanced.

[0056] In addition, considering that the feedback information may not meet the needs of the business party, the published content can be adjusted. In this embodiment, the specific implementation method is as follows: In the case that the target reward information does not meet the reward conditions of the target guarantee project, a published content adjustment strategy is constructed based on the content interaction data and the guarantee project data stored in the distributed database; the published content adjustment strategy is sent to the target user, wherein the published content adjustment strategy is used to update the recommended published content published on each interactive platform.

[0057] Specifically, reward conditions refer to the conditions that determine whether the target reward information meets the business needs. Content adjustment strategies refer to strategies for adjusting recommended content published on various platforms, such as increasing video length, changing dialogue, or replacing images and text.

[0058] Based on this, after obtaining the target return information, the target return information can be tested. If the target return information does not meet the return conditions of the target guarantee project, it means that the recommended content published by the current user is not compliant or does not meet the needs of the business party. Therefore, a content adjustment strategy can be constructed based on the content interaction data and the guarantee project data stored in the distributed database; thereafter, the content adjustment strategy can be sent to the target user, so that the user can update the recommended content published on each interactive platform according to the content adjustment strategy, so as to reach more users to participate in the target guarantee project.

[0059] To automate the calculation of reward information and improve data processing efficiency, the data processing method provided in this embodiment can obtain a list of published content associated with a target security project. The published content list is a list of recommended content published by target users on multiple interactive platforms for the target security project. Content interaction data associated with the recommended content on each interactive platform can then be collected according to preset time information, enabling regular collection and management of content interaction data associated with the target security project, making it more convenient and flexible. Furthermore, multi-source heterogeneous data for the target security project can be obtained. Given the format inconsistencies of multi-source heterogeneous data, this multi-source heterogeneous data can be updated to security project data and stored in a distributed database according to an incremental storage strategy. When reward information calculation is required, the target reward information corresponding to the target security project can be calculated based on the content interaction data and the security project data stored in the distributed database in response to a project request submitted for the target security project. This automates both data processing and reward information calculation, saving human resources while effectively improving data processing efficiency and facilitating business adjustments or updates.

[0060] The following combined Figure 2 , taking the application of the data processing method provided in this specification in an insurance project as an example, the data processing method is further explained. Figure 2 A flowchart of a data processing method provided in one embodiment of this specification is shown, which specifically includes the following steps.

[0061] Step S202: Obtain a list of published contents associated with the target security project, wherein the list of published contents is a list of recommended published contents published by the target user on multiple interactive platforms for the target security project.

[0062] Step S204 : collecting content interaction data related to recommended published content on each interactive platform according to preset time information, and acquiring multi-source heterogeneous data of the target security project.

[0063] Step S206 : performing data cleaning, denoising, and standardization on the sub-data contained in the multi-source heterogeneous data through a dynamic rule engine.

[0064] Step S208: determining the standard sub-data corresponding to the target format according to the processing result, and generating the guarantee item data based on the standard sub-data.

[0065] Step S210: perform entity recognition on the security project data to obtain multiple project entities.

[0066] Step S212: establishing a data mapping relationship between the guarantee project data, the target user, and the recommended content published on each interactive platform based on the guarantee project map and multiple project entities associated with the target guarantee project.

[0067] Step S214: store the security project data in the distributed database according to the incremental storage strategy based on the data mapping relationship.

[0068] Step S216: receiving a project request submitted for the target guarantee project, and extracting local guarantee project data related to the target guarantee project from the distributed database according to the project request.

[0069] Step S218: Generate content publishing cost information based on the content interaction data, and generate guarantee project benefit information based on the local guarantee project data.

[0070] Step S220 , calculating target return information corresponding to the target guarantee project based on the publishing content cost information and the guarantee project income information.

[0071] To automate the calculation of reward information and improve data processing efficiency, the data processing method provided in this embodiment can obtain a list of published content associated with a target security project. The published content list is a list of recommended content published by target users on multiple interactive platforms for the target security project. Content interaction data associated with the recommended content on each interactive platform can then be collected according to preset time information, enabling regular collection and management of content interaction data associated with the target security project, making it more convenient and flexible. Furthermore, multi-source heterogeneous data for the target security project can be obtained. Given the format inconsistencies of multi-source heterogeneous data, this multi-source heterogeneous data can be updated to security project data and stored in a distributed database according to an incremental storage strategy. When reward information calculation is required, the target reward information corresponding to the target security project can be calculated based on the content interaction data and the security project data stored in the distributed database in response to a project request submitted for the target security project. This automates both data processing and reward information calculation, saving human resources while effectively improving data processing efficiency and facilitating business adjustments or updates.

[0072] Corresponding to the above method embodiment, this specification also provides a data processing system embodiment, Figure 3 FIG1 shows a schematic diagram of a data processing system provided by an embodiment of this specification. Figure 3 As shown, the data processing system 300 includes a data processing end 310 and a security project end 320, including: The data processing terminal 310 is configured to obtain a list of published content associated with the target guarantee project, wherein the list of published content is a list of recommended published content published by the target user for the target guarantee project on multiple interactive platforms, collect content interaction data associated with the recommended published content on each interactive platform according to preset time information, and send a data acquisition request to the guarantee project terminal; The security project end 320 is configured to respond to the data acquisition request, read the multi-source heterogeneous data corresponding to the target security project, and send the multi-source heterogeneous data to the data processing end; The data processing end 310 is used to update the multi-source heterogeneous data into guarantee project data, and store the guarantee project data in a distributed database according to an incremental storage strategy. In response to a project request submitted for the target guarantee project, the target return information corresponding to the target guarantee project is calculated based on the content interaction data and the guarantee project data stored in the distributed database.

[0073] In an optional embodiment, updating the multi-source heterogeneous data into support project data includes: The sub-data contained in the multi-source heterogeneous data are respectively subjected to data cleaning, denoising and standardization processing through a dynamic rule engine; the standard sub-data corresponding to the target format is determined according to the processing results, and the guarantee project data is generated based on the standard sub-data.

[0074] In an optional embodiment, storing the security project data in a distributed database according to an incremental storage strategy includes: Entity recognition is performed on the security project data to obtain multiple project entities; based on the security project map associated with the target security project and the multiple project entities, a data mapping relationship is established between the security project data, the target user, and the recommended content published on each interactive platform; based on the data mapping relationship, the security project data is stored in a distributed database according to an incremental storage strategy.

[0075] In an optional embodiment, in response to a project request submitted for the target guarantee project, calculating target reward information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database includes: Receive a project request submitted for the target security project; extract local security project data related to the target security project in the distributed database according to the project request; generate publishing content cost information according to the content interaction data, and generate security project revenue information according to the local security project data; calculate the target return information corresponding to the target security project based on the publishing content cost information and the security project revenue information.

[0076] In an optional embodiment, after the step of calculating target reward information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database in response to the project request submitted for the target guarantee project is executed, the method further includes: In the case that the target reward information does not meet the reward conditions of the target guarantee project, a published content adjustment strategy is constructed based on the content interaction data and the guarantee project data stored in the distributed database; the published content adjustment strategy is sent to the target user, wherein the published content adjustment strategy is used to update the recommended published content published on each interactive platform.

[0077] In an optional embodiment, before the step of obtaining a list of published contents associated with the target guarantee project is performed, the method further includes: Obtain project information of the target guarantee project and collect platform prompt words submitted for the project information; input the platform prompt words and the project information into a large language model for processing to obtain content publishing guidance information corresponding to each interactive platform; send the content publishing guidance information to the target user, wherein the content guidance information is used by the target user to publish recommended content on each interactive platform.

[0078] In an optional embodiment, collecting content interaction data related to recommended published content on each interactive platform according to preset time information includes: Initial content interaction data of the associated recommended published content on each interactive platform is collected according to preset time information; the initial content interaction data is tested according to the user attribute information of the target user; if the test passes, the initial content interaction data is used as content interaction data; if the test fails, the initial content interaction data is verified in batches, and the content interaction data is determined based on the test results.

[0079] In summary, to automate the calculation of reward information and improve data processing efficiency, a list of published content associated with the target security project can be obtained. This list of published content consists of recommended content published by the target user on multiple interactive platforms for the target security project. Content interaction data associated with the recommended content on each interactive platform can then be collected according to preset time information, enabling regular collection and management of content interaction data associated with the target security project, making it more convenient and flexible. Multi-source heterogeneous data for the target security project can also be obtained. Given the format inconsistencies of multi-source heterogeneous data, this multi-source heterogeneous data can be updated to security project data and stored in a distributed database according to an incremental storage strategy. When reward information calculation is required, the target reward information corresponding to the target security project can be calculated based on the content interaction data and the security project data stored in the distributed database in response to a project request submitted for the target security project. This automates both data processing and reward information calculation, saving human resources while effectively improving data processing efficiency and facilitating business adjustments or updates.

[0080] The above is a schematic diagram of a data processing system according to this embodiment. It should be noted that the technical solution of the data processing system and the technical solution of the above-mentioned data processing method are based on the same concept. For details not described in detail in the technical solution of the data processing system, please refer to the description of the technical solution of the above-mentioned data processing method.

[0081] Corresponding to the above method embodiment, this specification also provides a data processing device embodiment, Figure 4FIG1 shows a schematic diagram of the structure of a data processing device provided by an embodiment of this specification. Figure 4 As shown, the device includes: The acquisition module 402 is configured to acquire a list of published contents associated with the target guarantee project, wherein the list of published contents is a list of recommended published contents published by the target user on multiple interactive platforms for the target guarantee project; The collection module 404 is configured to collect content interaction data related to the recommended published content on each interactive platform according to preset time information, and obtain multi-source heterogeneous data of the target guarantee project; An updating module 406 is configured to update the multi-source heterogeneous data into assurance project data, and store the assurance project data in a distributed database according to an incremental storage strategy; The calculation module 408 is configured to calculate target reward information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database in response to the project request submitted for the target guarantee project.

[0082] In an optional embodiment, updating the multi-source heterogeneous data into support project data includes: The sub-data contained in the multi-source heterogeneous data are respectively subjected to data cleaning, denoising and standardization processing through a dynamic rule engine; the standard sub-data corresponding to the target format is determined according to the processing results, and the guarantee project data is generated based on the standard sub-data.

[0083] In an optional embodiment, storing the security project data in a distributed database according to an incremental storage strategy includes: Entity recognition is performed on the security project data to obtain multiple project entities; based on the security project map associated with the target security project and the multiple project entities, a data mapping relationship is established between the security project data, the target user, and the recommended content published on each interactive platform; based on the data mapping relationship, the security project data is stored in a distributed database according to an incremental storage strategy.

[0084] In an optional embodiment, in response to a project request submitted for the target guarantee project, calculating target reward information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database includes: Receive a project request submitted for the target security project; extract local security project data related to the target security project in the distributed database according to the project request; generate publishing content cost information according to the content interaction data, and generate security project revenue information according to the local security project data; calculate the target return information corresponding to the target security project based on the publishing content cost information and the security project revenue information.

[0085] In an optional embodiment, after the step of calculating target reward information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database in response to the project request submitted for the target guarantee project is executed, the method further includes: In the case that the target reward information does not meet the reward conditions of the target guarantee project, a published content adjustment strategy is constructed based on the content interaction data and the guarantee project data stored in the distributed database; the published content adjustment strategy is sent to the target user, wherein the published content adjustment strategy is used to update the recommended published content published on each interactive platform.

[0086] In an optional embodiment, before the step of obtaining a list of published contents associated with the target guarantee project is performed, the method further includes: Obtain project information of the target guarantee project and collect platform prompt words submitted for the project information; input the platform prompt words and the project information into a large language model for processing to obtain content publishing guidance information corresponding to each interactive platform; send the content publishing guidance information to the target user, wherein the content guidance information is used by the target user to publish recommended content on each interactive platform.

[0087] In an optional embodiment, collecting content interaction data related to recommended published content on each interactive platform according to preset time information includes: Initial content interaction data of the associated recommended published content on each interactive platform is collected according to preset time information; the initial content interaction data is tested according to the user attribute information of the target user; if the test passes, the initial content interaction data is used as content interaction data; if the test fails, the initial content interaction data is verified in batches, and the content interaction data is determined based on the test results.

[0088] In summary, to automate the calculation of reward information and improve data processing efficiency, a list of published content associated with the target security project can be obtained. This list of published content consists of recommended content published by the target user on multiple interactive platforms for the target security project. Content interaction data associated with the recommended content on each interactive platform can then be collected according to preset time information, enabling regular collection and management of content interaction data associated with the target security project, making it more convenient and flexible. Multi-source heterogeneous data for the target security project can also be obtained. Given the format inconsistencies of multi-source heterogeneous data, this multi-source heterogeneous data can be updated to security project data and stored in a distributed database according to an incremental storage strategy. When reward information calculation is required, the target reward information corresponding to the target security project can be calculated based on the content interaction data and the security project data stored in the distributed database in response to a project request submitted for the target security project. This automates both data processing and reward information calculation, saving human resources while effectively improving data processing efficiency and facilitating business adjustments or updates.

[0089] The above is a schematic diagram of a data processing device according to this embodiment. It should be noted that the technical solution of the data processing device and the technical solution of the above-mentioned data processing method are based on the same concept. For details not described in detail in the technical solution of the data processing device, please refer to the description of the technical solution of the above-mentioned data processing method.

[0090] Figure 5 The block diagram of a computing device 500 according to one embodiment of the present disclosure is shown. Components of the computing device 500 include, but are not limited to, a memory 510 and a processor 520. The processor 520 is connected to the memory 510 via a bus 530, and a database 550 is used to store data.

[0091] The computing device 500 also includes an access device 540 that enables the computing device 500 to communicate via one or more networks 560. Examples of such networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 540 may include one or more of any type of network interface (e.g., a network interface card (NIC)) whether wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, or a near field communication (NFC) interface.

[0092] In one embodiment of the present specification, the above components of the computing device 500 and Figure 5 Other components not shown in the figure may also be connected to each other, for example, via a bus. Figure 5 The computing device structure block diagram shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art may add or replace other components as needed.

[0093] Computing device 500 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, personal digital assistant, laptop computer, notebook computer, netbook computer, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or personal computer (PC). Computing device 500 can also be a mobile or stationary server.

[0094] The processor 520 is configured to execute the following computer-executable instructions, which implement the steps of the above-mentioned data processing method when executed by the processor.

[0095] The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned data processing method are of the same concept. For details not described in detail in the technical scheme of the computing device, please refer to the description of the technical scheme of the above-mentioned data processing method.

[0096] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions, which implement the steps of the above-mentioned data processing method when executed by a processor.

[0097] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the above-mentioned data processing method are based on the same concept. For details not described in detail in the technical scheme of the storage medium, please refer to the description of the technical scheme of the above-mentioned data processing method.

[0098] An embodiment of the present specification further provides a computer program product, including a computer program or instructions, which implement the steps of the above-mentioned data processing method when executed by a processor.

[0099] The above is a schematic solution of a computer program product of this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the above-mentioned data processing method are based on the same concept. For details not described in detail in the technical solution of the computer program product, please refer to the description of the technical solution of the above-mentioned data processing method.

[0100] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The computer instructions include computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium may be appropriately increased or decreased based on the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0102] It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0103] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] The preferred embodiments disclosed above are intended only to help illustrate this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of the embodiments described herein. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification.

Claims

1. A data processing method, characterized in that: include: Obtaining a list of published content associated with a target guarantee project, wherein the list of published content is a list of recommended published content published by a target user on multiple interactive platforms for the target guarantee project; Collect content interaction data related to recommended content on each interactive platform according to preset time information, and obtain multi-source heterogeneous data of the target security project; Updating the multi-source heterogeneous data into security project data, and storing the security project data in a distributed database according to an incremental storage strategy; In response to a project request submitted for the target guarantee project, target reward information corresponding to the target guarantee project is calculated based on the content interaction data and the guarantee project data stored in the distributed database.

2. The data processing method according to claim 1, wherein: Updating the multi-source heterogeneous data into support project data includes: Performing data cleaning, denoising, and standardization on the sub-data contained in the multi-source heterogeneous data through a dynamic rule engine; Standard sub-data corresponding to the target format is determined according to the processing results, and support item data is generated based on the standard sub-data.

3. The data processing method according to claim 1, wherein: Storing the security project data in a distributed database according to an incremental storage strategy includes: Performing entity recognition on the security project data to obtain multiple project entities; Establishing a data mapping relationship between the guarantee project data, the target user, and the recommended content published on each interactive platform according to the guarantee project map associated with the target guarantee project and the multiple project entities; According to the data mapping relationship, the security project data is stored in a distributed database according to an incremental storage strategy.

4. The data processing method according to claim 1, wherein: The step of calculating target reward information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database in response to the project request submitted for the target guarantee project includes: receiving a project request submitted for the target safeguard project; extracting, from the distributed database, local guarantee project data associated with the target guarantee project according to the project request; generating content publishing cost information based on the content interaction data, and generating guarantee project benefit information based on the local guarantee project data; Calculate target return information corresponding to the target guarantee project based on the content publishing cost information and the guarantee project benefit information.

5. The data processing method according to any one of claims 1 to 4, characterized in that: After the step of calculating target reward information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database in response to the project request submitted for the target guarantee project is executed, the method further includes: In the case where the target reward information does not meet the reward condition of the target guarantee project, constructing a published content adjustment strategy based on the content interaction data and the guarantee project data stored in the distributed database; The published content adjustment strategy is sent to the target user, wherein the published content adjustment strategy is used to update the recommended published content published on each interactive platform.

6. The data processing method according to any one of claims 1 to 4, characterized in that: Before the step of obtaining a list of published contents associated with the target guarantee project is executed, the method further includes: Obtaining project information of the target guarantee project and collecting platform prompt words submitted for the project information; Inputting the platform prompt words and the project information into a large language model for processing to obtain content publishing guidance information corresponding to each interactive platform; The content publishing guidance information is sent to the target user, wherein the content guiding information is used by the target user to publish recommended content on each interactive platform.

7. The data processing method according to any one of claims 1 to 4, characterized in that: The collecting of content interaction data related to the recommended published content on each interactive platform according to the preset time information includes: Collecting initial content interaction data related to recommended published content on each interactive platform according to preset time information; detecting the initial content interaction data according to user attribute information of the target user; If the detection is passed, the initial content interaction data is used as content interaction data; In the case that the detection fails, the initial content interaction data is verified in batches, and the content interaction data is determined according to the verification results.

8. A data processing system, characterized in that: It includes data processing and project assurance, including: The data processing end is configured to obtain a list of published content associated with a target security project, wherein the list of published content is a list of recommended published content published by a target user for the target security project on multiple interactive platforms, collect content interaction data associated with the recommended published content on each interactive platform according to preset time information, and send a data acquisition request to the security project end; The security project end is configured to respond to the data acquisition request, read the multi-source heterogeneous data corresponding to the target security project, and send the multi-source heterogeneous data to the data processing end; The data processing end is used to update the multi-source heterogeneous data into guarantee project data, and store the guarantee project data in a distributed database according to an incremental storage strategy. In response to a project request submitted for the target guarantee project, the target return information corresponding to the target guarantee project is calculated based on the content interaction data and the guarantee project data stored in the distributed database.

9. A data processing device, characterized in that: include: an acquisition module configured to acquire a list of published contents associated with a target guarantee project, wherein the list of published contents is a list of recommended published contents published by a target user on multiple interactive platforms for the target guarantee project; A collection module is configured to collect content interaction data related to recommended published content on each interactive platform according to preset time information, and obtain multi-source heterogeneous data of the target security project; An updating module is configured to update the multi-source heterogeneous data into guarantee project data, and store the guarantee project data in a distributed database according to an incremental storage strategy; The calculation module is configured to calculate target reward information corresponding to the target guarantee project based on the content interaction data and the guarantee project data stored in the distributed database in response to a project request submitted for the target guarantee project.

10. A computing device, characterized in that include: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer-readable storage medium, characterized in that It stores computer-executable instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

12. A computer program product, characterized in that The method comprises a computer program or instructions, which implements the steps of the method according to any one of claims 1 to 7 when executed by a processor.

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