A multi-channel extended warranty data management method and platform

Abstract

The present invention discloses a multi-channel extended warranty data management method and platform, which relates to data management-related fields. The method comprises: performing data cycle positioning and data source feature analysis on multiple data collection channels to obtain the cycle positioning and data source features of each channel; performing time-series alignment and period sorting on the cycle positioning of each channel according to the life cycle temporal relationship of the extended warranty product, establishing a periodic data map, and performing graph fitting using the data source features as attributes of the graph nodes; performing data source feature comparison on the time-series aligned channel data according to the periodic data map, and configuring import rules for each data source; obtaining extended warranty collection data, performing extended warranty risk analysis on each life cycle, obtaining extended warranty risk information; and generating extended warranty management feedback. This method solves the technical problem of existing data management, which lacks accurate data support and makes it difficult to formulate effective extended warranty strategies, and achieves the technical effect of providing accurate data support and improving the effectiveness of extended warranty strategies.

Description

Technical Field

[0001] The present application relates to data management related fields, and in particular to a multi-channel extended warranty data management method and platform. Background Art

[0002] The effective implementation of extended warranty services relies on the precise management and in-depth analysis of multi-channel data. This data comes from multiple channels, including sales, after-sales, customer service, and third-party agencies, each with distinct data cycles and characteristics. Traditional approaches to extended warranty data management focus on collecting and analyzing data from a single channel, simply aggregating data from different channels. The independence of data from each channel leads to severe data silos, making it difficult to develop a comprehensive data view. Furthermore, data from different channels suffers from inconsistent formats and mismatched time periods, reducing data reliability and hindering the formulation of extended warranty strategies.

[0003] Among the current related technologies, extended warranty data management faces technical issues such as lack of accurate data support and difficulty in formulating effective extended warranty strategies. Summary of the Invention

[0004] This application provides a multi-channel extended warranty data management method and platform. The method uses period positioning and data source feature analysis of data from multiple channels to align and sort the period positioning of each channel according to the life cycle time relationship of the extended warranty product. A period data map is established. Based on the period data map, data source feature comparison is performed on the time-aligned channel data. Import rules for each data source are configured. Extended warranty collection data is obtained based on the import rules. Extended warranty risk analysis is performed for each life cycle to obtain extended warranty risk information. Extended warranty management feedback is generated based on the extended warranty risk information. These technical means achieve the technical effects of providing accurate and comprehensive data support, improving the effectiveness of extended warranty strategies, and optimizing extended warranty management.

[0005] The present application provides a multi-channel extended warranty data management method, comprising: performing data cycle positioning and data source feature analysis on multiple data collection channels to obtain the cycle positioning and data source features of each channel; performing time alignment and period sorting on the cycle positioning of each channel according to the time sequence relationship of the extended warranty product's life cycle, establishing a cycle data graph, and performing graph fitting using the data source features as attributes of the graph nodes; performing data source feature comparison on the time-aligned channel data based on the cycle data graph, and configuring import rules for each data source; obtaining extended warranty collection data based on the import rules, performing extended warranty risk analysis on the extended warranty collection data for each life cycle to obtain extended warranty risk information; and generating extended warranty management feedback based on the extended warranty risk information.

[0006] In a possible implementation, data cycle positioning and data source feature analysis are performed on multiple channels of data collection to obtain the cycle positioning and data source features of each channel, and the following processing is performed: the correlation between the data of each channel and the product life cycle is analyzed to establish an associated response relationship between the data of each channel and the life cycle; data format and extended warranty traceability reliability analysis is performed on the data source based on the historical data of each channel to obtain data format features and reliability features; cycle positioning is performed based on the associated response relationship between the data of each channel and the life cycle to obtain the cycle positioning of each channel, and the data source features are obtained based on the data format features and reliability features.

[0007] In a possible implementation, the correlation between each channel data and the product life cycle is analyzed, and a correlation response relationship between each channel data and the life cycle is established. The following processing is performed: life cycle nodes of the extended warranty product are established, and the life cycle nodes at least include extended warranty enrollment, extended warranty execution, renewal, and extended warranty end; correlation mining is performed using each channel data and life cycle nodes to obtain correlation coefficients and correlation characteristics between each channel data and life cycle nodes; and relationship sorting is performed based on the correlation coefficients and correlation characteristics between each channel data and life cycle nodes to establish a correlation response relationship between each channel data and the life cycle.

[0008] In a possible implementation, data formats and extended warranty traceability reliability analysis are performed on the data sources based on the historical data of each channel to obtain data format characteristics and reliability characteristics, and the following processing is performed: format characteristics of the historical data of each channel are analyzed from multiple dimensions such as data type, data structure, time format, and field definition to obtain the data format characteristics; a complete data chain is established, wherein the complete data chain includes a data integrity characteristic chain for extended warranty traceability, corresponding to the extended warranty lifecycle node; the complete data chain is converted to the format characteristic standards of each channel using the data format characteristics; product tracking information is extracted, wherein the product tracking information is an identity characteristic for identifying the product; the historical data of each channel is tracked and identified based on the product tracking information and the complete data chain to obtain the tracking identification probability of each channel; a mapping relationship between the tracking identification probability of each channel and the channel collection data is established to obtain the reliability characteristics.

[0009] In a possible implementation, based on the periodic data map, the data source characteristics of the time-aligned channel data are compared, the import rules of each data source are configured, and the following processing is performed: based on the periodic data map, the channel data of each period is extracted, and the channel data of the same period are time-aligned; the format conversion rules are analyzed according to the data source characteristics of each channel to determine the data format conversion rules; based on the time alignment relationship, the reliability of the channel data is analyzed for data import rules, the channel data with reliability higher than the import threshold is extracted and set to directly import rules, and the channel data with reliability lower than the import threshold is extracted and imported according to the reliability setting feedback loop; according to the data format conversion rules and data import rules, the import rules of each data source are obtained.

[0010] In a possible implementation, based on the import rules, extended warranty collection data is obtained, and each life cycle extended warranty risk analysis is performed on the extended warranty collection data to obtain extended warranty risk information, and the following processing is performed: risk identification targets are configured for each life cycle node, an influence relationship between the risk identification targets and channel data is established, and a risk identification model is trained; the risk identification model for each life cycle node is added to the cycle node, channel data is obtained according to the import rules, and cycle risk identification is performed using the corresponding risk identification model to obtain a cycle node risk identification result; based on the life cycle time relationship of the extended warranty product, the cycle node risk identification result is subjected to a full-cycle time series analysis to obtain a risk assessment result; and the cycle node risk identification result and the risk assessment result are integrated to obtain the extended warranty risk information.

[0011] In a possible implementation, the risk assessment result is obtained and the following processing is also performed: the risk identification target of the extended warranty multi-party cooperative users is obtained; the fusion coefficient of each period node is configured according to the risk identification target of the extended warranty multi-party cooperative users; and the risk identification results of the period nodes are weightedly fused according to the fusion coefficient to obtain the risk assessment result.

[0012] The present application also provides a multi-channel extended warranty data management platform, comprising: a data parsing module for performing data cycle location and data source feature analysis on multiple data collection channels to obtain the cycle location and data source features of each channel; a periodic data map establishment module for performing time-series alignment and period-sequencing on the cycle locations of each channel according to the life cycle temporal relationship of the extended warranty product, establishing a periodic data map, and performing map fitting using the data source features as attributes of the graph nodes; an import rule configuration module for performing data source feature comparison on the time-series aligned channel data according to the periodic data map and configuring import rules for each data source; an extended warranty risk analysis module for obtaining extended warranty collection data based on the import rules, performing extended warranty risk analysis on the extended warranty collection data for each life cycle, and obtaining extended warranty risk information; and an extended warranty management feedback module for generating extended warranty management feedback based on the extended warranty risk information.

[0013] This application proposes a multi-channel extended warranty data management method and platform. First, data cycle location and data source feature analysis are performed on multiple data collection channels to obtain the cycle location and data source features for each channel. Then, the cycle locations of each channel are aligned and sorted according to the lifecycle chronological relationship of the extended warranty product. A cycle data graph is constructed, and the data source features are used as graph node attributes for graph fitting. Next, based on the cycle data graph, the data source features of the aligned channel data are compared, and import rules are configured for each data source. Based on these import rules, the extended warranty data is collected, and lifecycle extended warranty risk analysis is performed on this data to obtain extended warranty risk information. Finally, extended warranty management feedback is generated based on this extended warranty risk information. This method achieves the technical benefits of providing accurate and comprehensive data support, improving the effectiveness of extended warranty strategies, and optimizing extended warranty management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings of the embodiments of the present invention. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact sequence. On the contrary, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A flowchart of a multi-channel extended warranty data management method provided in an embodiment of the present application.

[0016] Figure 2 This is a structural diagram of a multi-channel extended warranty data management platform provided in an embodiment of the present application.

[0017] Description of the accompanying drawings: data analysis module 10, periodic data map establishment module 20, import rule configuration module 30, extended warranty risk analysis module 40, extended warranty management feedback module 50. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments” which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms “including” and “having” and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, platform, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices, and unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The present application embodiment provides a multi-channel warranty data management method, such as Figure 1 As shown, the method includes:

[0022] Step S100 , performing data cycle positioning and data source feature analysis on multiple data collection channels to obtain the cycle positioning and data source features of each channel.

[0023] Specifically, use ETL (Extract, Transform, Load) tools or write custom scripts to collect data from multiple channels. These data exist in different formats and storage methods. Preprocess the collected data, including data cleaning and format conversion, to ensure data consistency and availability, and load the processed data into the designated storage location.

[0024] By developing algorithms or leveraging existing time series analysis tools, the collected data can be periodized. This involves identifying timestamps or time intervals within the data and matching the data with the lifecycle phase of the extended warranty product. For example, sales data includes the purchase date, which can be used as the starting point for extended warranty enrollment; after-sales service and repair data includes the service date, which can be mapped to the extended warranty execution phase.

[0025] After determining the data cycle, use data quality assessment tools or write custom scripts to analyze the data source characteristics, including analyzing the data structure, attributes, completeness, and accuracy, and assessing the data's value and reliability for extended warranty management decisions. For example, sales data contains information such as customer purchase history and extended warranty selection, which is valuable for assessing customer extended warranty needs and risks.

[0026] In one possible implementation, data collection from multiple channels is performed on data cycle location and data source characteristics to obtain the cycle location and data source characteristics of each channel. Step S100 further includes step S110, which analyzes the correlation between each channel's data and the product life cycle, and establishes a correlation response relationship between each channel's data and the life cycle. Specifically, predefined rules are used to deeply analyze the data collected from multiple channels. This channel data includes customer records from the sales platform (including information such as purchase time, whether to purchase extended warranty service, and purchase frequency), after-sales service records (recording customer repairs, consultations, etc. during product use), data from maintenance channels (recording product repair history), and customer feedback and evaluation data (including customer satisfaction and suggestions for the product). By analyzing information such as timestamps and event types in the above data, a mapping relationship is established between each data point and the product life cycle stage. The product life cycle includes the introduction stage, growth stage, maturity stage, and decline stage. For example, the purchase time can be determined by the timestamp to determine which life cycle stage the customer record belongs to.

[0027] Step S120, based on the historical data of each channel, the data source is analyzed for data format and extended warranty traceability reliability to obtain data format characteristics and reliability characteristics. Specifically, the format consistency of the data from each channel and the reliability of extended warranty traceability are evaluated. Among them, format consistency refers to whether the data conforms to a unified format standard, so as to facilitate subsequent processing and analysis; extended warranty traceability reliability refers to whether the data can accurately reflect the purchase, use and claims of extended warranty services. Through the data cleaning and formatting module, the data format is automatically checked, and the rule engine is used to verify the key information in the data, such as the order number for purchasing the extended warranty, the serial number of the maintenance record, etc., to evaluate the reliability of the data. For example, in the data of a certain maintenance channel, some maintenance records lack serial number information, resulting in the inability to accurately trace the product. These records can be automatically marked as low reliability and given a lower weight in subsequent analysis.

[0028] Step S130 performs cycle location based on the correlation between the data from each channel and the lifecycle, obtaining the cycle location for each channel. The data source characteristics are then derived based on the data format characteristics and reliability characteristics. Specifically, combining the results of steps S110 and S120, cycle location is performed on the data from each channel, and data source characteristics are extracted based on the data format characteristics and reliability characteristics. Cycle location refers to determining the lifecycle stage to which each piece of data belongs; data source characteristics include multiple dimensions such as data format and reliability. For example, if analysis reveals that among the customer records of a sales platform, customers who purchase extended warranty services have a lower maintenance frequency during the growth and maturity stages, and their maintenance records are more reliable, this information can be extracted as data source characteristics and utilized in subsequent extended warranty risk analysis. This implementation approach, by analyzing the correlation between data and the product lifecycle, enables a more accurate understanding of the business logic behind the data. By analyzing the data format and reliability, high-quality data can be selected for subsequent analysis. Cycle location and data source feature extraction provide strong support for extended warranty risk analysis and strategy formulation, thereby optimizing extended warranty management.

[0029] In one possible implementation, the correlation between each channel's data and the product lifecycle is analyzed, and a correlation-response relationship between each channel's data and the lifecycle is established. Step S110 further includes step S111, which establishes lifecycle nodes for the extended warranty product. These lifecycle nodes include at least warranty enrollment, warranty execution, renewal, and warranty expiration. Specifically, based on a preset algorithm or rule, the lifecycle nodes of the extended warranty product are defined. These nodes include key time points or states such as warranty enrollment, warranty execution (i.e., use of the service within the extended warranty period), renewal (the customer chooses to extend the warranty period before the end of the extended warranty period), and warranty expiration. These nodes represent the entire process of the extended warranty product, from purchase to service termination, and serve as the foundation for data analysis and management. For example, for an extended warranty service for a household appliance, the following lifecycle nodes can be defined: warranty enrollment at purchase (node ​​1), first repair within the extended warranty period (node ​​2, which may occur multiple times), renewal decision point (node ​​3, which occurs before the end of the extended warranty period), and official expiration of the extended warranty (node ​​4).

[0030] Step S112, use the data from each channel to conduct correlation mining with the lifecycle nodes to obtain the correlation coefficient and correlation features between the data from each channel and the lifecycle nodes. Specifically, extract key information from the data of each channel, such as timestamp, event type, customer ID, etc. Use statistical methods (such as Pearson correlation coefficient) to calculate the correlation strength between data features and lifecycle nodes, where the correlation coefficient is an indicator that quantifies the correlation strength between two variables. Use machine learning algorithms (such as decision trees) to identify patterns in the data, which can indicate the correlation between data and lifecycle nodes.

[0031] Step S113, based on the correlation coefficients and correlation characteristics between the channel data and the life cycle nodes, the relationships are sorted out to establish a correlation response relationship between the channel data and the life cycle. Specifically, a correlation coefficient threshold is set according to the characteristics of the data and business needs. All data and life cycle nodes are traversed, and associations are established based on whether the correlation coefficient exceeds the threshold. The sorted-out relationships are stored in a data structure (such as a dictionary, database table) for subsequent use. This implementation method can identify which data is more reliable and more valuable by analyzing the correlation between the channel data and the product life cycle. After establishing the correlation response relationship, the customer's behavior and needs at different life cycle stages can be more accurately understood, thereby optimizing the extended warranty strategy.

[0032] In one possible implementation, data formats and extended warranty traceability reliability are analyzed for each channel's historical data to obtain data format characteristics and reliability characteristics. Step S120 further includes step S121, where format characteristics of each channel's historical data are analyzed from multiple dimensions, including data type, data structure, time format, and field definition, to obtain the data format characteristics. Specifically, historical data from each channel is read, and this data is stored in various file formats, such as CSV, Excel, or database tables. Next, identify the data type, that is, analyze the data type of each field, such as numeric, character, date, etc.; parse the data structure, that is, understand the logical structure of data in storage and organization, such as the relationship between tables, rows, and columns, and possible nested structures; identify and convert different time formats to ensure that all time data follows a unified format standard. For example, the timestamps of sales data and maintenance data are in ISO format, and social media feedback uses Unix timestamps; match the field definitions against the predefined field list to ensure that each field has a clear meaning and purpose. For example, the "maintenance record" field contains "maintenance date", but the name of this field may be different in different channels (such as "service time" or "maintenance completion time"), so field definition matching is required.

[0033] Step S122, establish a complete data chain, which includes a data integrity feature chain for extended warranty traceability, corresponding to the extended warranty life cycle nodes. Specifically, based on the life cycle nodes of the extended warranty product (such as extended warranty joining, extended warranty execution, renewal, and extended warranty end), a complete data chain is constructed. This data chain describes all key events and data points from the beginning to the end of the extended warranty, including data consistency and the integrity of historical data. Data consistency refers to whether the records of the same product in different channels are consistent, for example, whether the purchase date and the maintenance date are reasonably connected, and whether customer complaints correspond to maintenance records. The integrity of historical data refers to whether there are omissions in the historical data, especially key extended warranty time nodes (such as purchase time, extended warranty start date, maintenance records, etc.). If a data source has many missing records, the reliability of this data source will be reduced.

[0034] Step S123 uses the data format characteristics to convert the complete data chain to the format characteristic standards of each channel. Specifically, using the data format characteristics analyzed in step S121, each data point in the complete data chain is formatted, including data type conversion, time format adjustment, and field name unification, to ensure that all data follows a unified format standard.

[0035] Step S124: Extract product tracking information. The product tracking information is an identity feature used to identify the product. Based on the product tracking information and the complete data chain, the historical data of each channel is tracked and identified to obtain the tracking and identification probability of each channel. Specifically, information that can uniquely identify the product, such as the product serial number, model number, purchase date, etc., is extracted from the historical data of each channel. This information is called product tracking information. That is, product tracking information is a set of information that can uniquely identify a product, and the identity feature is an attribute or set of attributes used to uniquely identify an entity. Using the product tracking information and the complete data chain, the historical data of each channel is tracked and identified. For each piece of data, the product tracking information is used to search for a match in the complete data chain, and the probability of its correct identification and tracking is calculated (the number of times each piece of data is correctly identified is counted and the ratio of the number to the total number of times is calculated to obtain the tracking and identification probability), that is, the tracking and identification probability.

[0036] Step S125, establish a mapping relationship between the tracking and identification probability of each channel and the data collected by the channel to obtain the reliability characteristics. Specifically, establish a mapping relationship between the tracking and identification probability of each channel and the data collected by the channel, that is, associate the tracking and identification probability of each channel with the data collected by the channel to form a mapping relationship, thereby obtaining the reliability characteristics. This implementation method ensures that all data follows a unified format standard before integration by analyzing the format characteristics of each channel data in detail, establishing a complete data chain, performing format conversion, extracting product tracking information and calculating the tracking and identification probability, and establishing a mapping relationship. By building a complete data chain, it ensures that all key events and data points from the beginning to the end of the extended warranty are covered. By calculating the tracking and identification probability and establishing a mapping relationship, it is identified which channels have more reliable data, thereby giving them a higher weight in subsequent analysis, and ultimately ensuring that the data integrated from multiple channels has a high degree of reliability and consistency.

[0037] In step S200 , the cycle positioning of each channel is aligned and sorted according to the time sequence relationship of the life cycle of the extended warranty product, a cycle data graph is established, and the data source characteristics are used as attributes of the graph nodes for graph fitting.

[0038] Specifically, based on the extended warranty product lifecycle (such as purchase, use, repair, and expiration), the data cycles across various channels are time-series aligned. This means defining time intervals based on the lifecycle phases and assigning data to corresponding intervals, ensuring all data is on the same timeline. Then, using timestamps or date fields, the data is sorted chronologically, integrating data from different channels in chronological order to create a cyclical data graph. Furthermore, data source characteristics are used as attributes of nodes in the graph, and a graph database (such as Neo4j) is used to store data nodes and relationships. For example, using sales records and IoT data as an example, the system sorts all sales records by purchase date and IoT data by receipt time. Then, based on the product lifecycle (e.g., warranty period within one year after purchase), sales records are marked as "purchase phase," and IoT data is assigned to corresponding usage phases based on time intervals. Finally, a data graph is created using Neo4j, with nodes including sales records and IoT data and attributes such as data type and timestamp.

[0039] Step S300 : performing data source feature comparison on the time-series aligned channel data according to the periodic data map, and configuring import rules for each data source.

[0040] Specifically, based on the periodic data map, the data source characteristics of each channel are compared, and data verification algorithms (such as hash verification, checksum, etc.) are used to compare the similarities and differences of data fields to identify data differences and potential conflicts. According to the comparison results, the data import rules are configured, that is, the data import script is automatically generated, including data cleaning, conversion, merging and other operations to ensure the consistency and integrity of the data. For example, when comparing sales records and IoT data, the system found that the "product ID" in the sales records was inconsistent with the "device ID" in the IoT data. After analysis, it was determined that the two were different names for the same identifier. Therefore, the system configured the import rules to map the "device ID" in the IoT data to the "product ID" in the sales records.

[0041] In one possible implementation, based on the periodic data map, the time-aligned channel data is compared for data source characteristics, and import rules for each data source are configured. Step S300 further includes step S310, which extracts channel data for each period based on the periodic data map and time-aligns the channel data for the same period. Specifically, based on the periodic data map established in step S200, the data periods corresponding to each extended warranty product lifecycle node are identified and extracted. Subsequently, by parsing and comparing timestamps, the data from different channels within the same period are time-aligned to ensure that all data points are arranged according to a unified timeline.

[0042] Step S320 analyzes format conversion rules based on the data source characteristics of each channel to determine data format conversion rules. Specifically, after determining the data for each period, the data source characteristics of this data are analyzed, including data type (e.g., integer, floating point, string), data structure (e.g., flat table, nested structure), time format, and field definition. Based on this analysis, a set of data format conversion rules is determined to convert all data into a unified format for subsequent processing and analysis.

[0043] In step S330, based on the time alignment relationship, the reliability of the channel data is analyzed by data import rules, and the channel data with reliability higher than the import threshold is extracted to set direct import rules, and the channel data with reliability lower than the import threshold is extracted to set the import feedback loop according to the reliability. Specifically, based on the reliability characteristics obtained in step S120, an import threshold is set to distinguish the reliability of the data. For data with reliability higher than the import threshold, a direct import rule is set, that is, this data is directly used for subsequent analysis and processing. For data with reliability lower than the import threshold, different import feedback loops are set according to the different reliabilities, that is, these data will trigger some additional verification or review steps when imported, or will be marked as data that requires special attention.

[0044] In step S340, based on the data format conversion rules and data import rules, import rules for each data source are obtained. Specifically, the analysis results of steps S320 and S330 are combined to generate a complete set of import rules for each data source. These rules not only include the specific steps and methods for data format conversion, but also cover the standards for data reliability assessment and the processing logic during import. These rules are used to guide data import and processing. This implementation ensures that data integrated from multiple channels can be imported and processed in a unified, reliable, and efficient manner, ensuring the controllability and efficiency of the entire process.

[0045] In step S400 , based on the import rule, warranty extension collection data is obtained, and warranty extension risk analysis of each life cycle is performed on the warranty extension collection data to obtain warranty extension risk information.

[0046] Specifically, after obtaining extended warranty data based on imported rules, machine learning algorithms are used to train this data to build a failure prediction model. This model analyzes extended warranty risks at each lifecycle stage, including identifying potential failure modes, predicting failure probabilities, and estimating repair costs. For example, a random forest algorithm was used to train sales records and IoT data to build a failure prediction model. The model's predictions indicated that a certain product model had a high failure rate within 6-12 months of purchase. Therefore, extended warranty risk information for this product model during this period was extracted.

[0047] In one possible implementation, based on the import rules, extended warranty data is obtained, and extended warranty risk analysis is performed on the data for each lifecycle to obtain extended warranty risk information. Step S400 further includes step S410, which configures risk identification targets for each lifecycle node, establishes an impact relationship between the risk identification targets and channel data, and trains a risk identification model. Specifically, based on the lifecycle characteristics of the extended warranty product, the potential risks at each lifecycle node (e.g., extended warranty enrollment, extended warranty execution, renewal, and extended warranty expiration) are determined. These risks include customer churn risk, repair cost overrun risk, and renewal rate decline risk. Clear identification targets and thresholds are set for each risk. The correlation between each channel data (e.g., customer records from the sales platform, after-sales service records, repair channel data, and customer feedback and evaluation data) and the risk identification targets is analyzed. Data mining techniques, such as association rule mining and decision tree analysis, are used to establish a mapping relationship between the channel data characteristics and the risk identification targets. Based on the established mapping relationship, the risk identification model is trained using historical data. Through iterative optimization, the model is enabled to accurately identify risks at each lifecycle node.

[0048] Step S420, add the risk identification model of each life cycle node to the cycle node, obtain channel data according to the import rules, perform cycle risk identification through the corresponding risk identification model, and obtain the cycle node risk identification result. Specifically, associate the trained risk identification model with the corresponding life cycle node, and configure the corresponding risk identification model at each life cycle node. According to the import rules, obtain data related to the current life cycle node from each channel. Preprocess the data, such as data cleaning, format conversion, etc., to ensure that the data meets the model input requirements. Input the preprocessed data into the corresponding risk identification model, and the model outputs the risk identification result (the risk identification result obtained by the risk identification model at the current life cycle node) based on the input data, such as the probability of risk occurrence, risk level, etc.

[0049] In step S430, the risk identification results for each cycle node are analyzed over the entire extended warranty product lifecycle, based on the time series relationship of the extended warranty product. Specifically, the risk identification results for each cycle node are concatenated according to the time series relationship of the extended warranty product lifecycle, and the changing trends and patterns of the risk identification results over the entire lifecycle are analyzed. Based on these changing trends and patterns of the risk identification results, a comprehensive evaluation method, such as a weighted average method or a fuzzy comprehensive evaluation method, is used to assess the overall risk of the extended warranty product.

[0050] In step S440, the risk identification results and risk assessment results of the cycle nodes are integrated to obtain the extended warranty risk information. Specifically, the risk identification results and risk assessment results of each cycle node are integrated in the form of tables, charts, etc. to intuitively display the overall risk status of the extended warranty product. The integrated extended warranty risk information is output to relevant personnel or systems for decision-making reference. This implementation method can accurately identify the risks of each life cycle node by training the risk identification model, thereby improving the accuracy of risk identification. Through time-series full-cycle analysis, the overall risk status of the extended warranty product is comprehensively and systematically understood, achieving full-cycle risk management and promoting the optimization of extended warranty services.

[0051] In one possible implementation, step S430 further includes step S431, where the risk assessment results are obtained for the multiple extended warranty cooperating parties. Specifically, through user surveys and historical data analysis, the risk indicators of interest to different user groups participating in the extended warranty cooperation (e.g., manufacturers, retailers, consumers, and repair service providers) at different lifecycle points (e.g., extended warranty enrollment, extended warranty execution, renewal, and extended warranty expiration) are obtained. Table 1 below provides a specific example:

[0052]

[0053] Table 1

[0054] In step S432, fusion coefficients are configured for each periodic node based on the risk identification objectives of the extended warranty multi-party cooperative user. Specifically, based on user research and historical data analysis, a fusion coefficient is assigned to each risk indicator in the database. These coefficients reflect the importance of different risk indicators in the overall risk assessment.

[0055] In step S433, the risk identification results of the lifecycle nodes are weighted and fused based on the fusion coefficient to obtain the risk assessment result. Specifically, the risk identification results and corresponding fusion coefficients for each lifecycle node are read and calculated according to the weighted fusion formula. The fused risk assessment results are output to the user interface or stored in a database. This implementation method comprehensively analyzes the risk concerns of different users at different lifecycle nodes of the extended warranty product, thereby obtaining more comprehensive and accurate risk assessment results, which helps to formulate more effective extended warranty strategies and improve customer satisfaction and service quality.

[0056] Step S500: Generate extended warranty management feedback based on the extended warranty risk information.

[0057] Specifically, based on the extended warranty risk information and level, extended warranty management feedback is generated, including early warning notifications, maintenance plans, customer care, etc. Feedback is sent to relevant personnel or customers through emails, text messages, App push, etc., to optimize extended warranty management and improve customer satisfaction. For example, for the high failure rate risk of a certain model of product, the system generates an early warning notification, recommending strengthening the quality inspection of this model of product and preparing repair spare parts in advance. At the same time, caring emails are sent to affected customers to inform them of possible failure risks and countermeasures. The embodiment of the present application uses period positioning and data source feature analysis of data from multiple channels, and aligns and sorts the period positioning of each channel according to the time series relationship of the life cycle of the extended warranty product, establishes a periodic data map, and compares the data source features of the time-aligned channel data based on the periodic data map. The import rules of each data source are configured, and the extended warranty collection data is obtained based on the import rules. The extended warranty risk analysis of each life cycle is performed to obtain extended warranty risk information, and the extended warranty management feedback is generated based on the extended warranty risk information. The technical means achieve the technical effect of providing accurate and comprehensive data support, improving the effectiveness of the extended warranty strategy, and optimizing extended warranty management.

[0058] In the above, refer to Figure 1 A multi-channel warranty data management method according to an embodiment of the present invention is described in detail. Figure 2 A multi-channel extended warranty data management platform according to an embodiment of the present invention is described.

[0059] A multi-channel extended warranty data management platform according to an embodiment of the present invention addresses the technical issues of existing technologies, such as a lack of accurate data support and the difficulty in formulating effective extended warranty strategies. This platform provides accurate and comprehensive data support, improves the effectiveness of extended warranty strategies, and optimizes extended warranty management. The multi-channel extended warranty data management platform includes a data analysis module 10, a periodic data map creation module 20, an import rule configuration module 30, an extended warranty risk analysis module 40, and an extended warranty management feedback module 50.

[0060] The data analysis module 10 is used to perform data cycle location and data source feature analysis on multiple data collection channels to obtain the cycle location and data source features of each channel. The cycle data map establishment module 20 is used to align and sort the cycle locations of each channel according to the life cycle temporal relationship of the extended warranty product, establish a cycle data map, and use the data source features as attributes of the graph nodes for graph fitting. The import rule configuration module 30 is used to compare the data source features of the time-aligned channel data according to the cycle data map and configure import rules for each data source. The extended warranty risk analysis module 40 is used to obtain extended warranty collection data based on the import rules, perform extended warranty risk analysis on the extended warranty collection data according to each life cycle, and obtain extended warranty risk information. The extended warranty management feedback module 50 is used to generate extended warranty management feedback based on the extended warranty risk information.

[0061] The specific configuration of the data parsing module 10 will be described in detail below. As described above, the data collection module 10 performs data cycle location and data source feature analysis on multiple channels to obtain the cycle location and data source features of each channel. The data parsing module 10 may further include: a correlation analysis unit for analyzing the correlation between each channel's data and the product life cycle, and establishing a correlation response relationship between each channel's data and the life cycle; a data source feature analysis unit for performing data format, extended warranty traceability, and reliability analysis on the data source based on the historical data of each channel to obtain data format features and reliability features; and a cycle location unit for performing cycle location based on the correlation response relationship between each channel's data and the life cycle to obtain the cycle location of each channel, and obtaining the data source features based on the data format features and reliability features.

[0062] Among them, the correlation between each channel data and the product life cycle is analyzed, and the correlation response relationship between each channel data and the life cycle is established. The correlation analysis unit may further include: a life cycle node establishment subunit for establishing the life cycle node of the extended warranty product, and the life cycle node at least includes extended warranty joining, extended warranty execution, renewal, and extended warranty end; a correlation mining subunit for performing correlation mining with each channel data and the life cycle node to obtain the correlation coefficient and correlation characteristics between each channel data and the life cycle node; a relationship combing subunit for performing relationship combing based on the correlation coefficient and correlation characteristics between each channel data and the life cycle node, and establishing the correlation response relationship between each channel data and the life cycle.

[0063] Among them, the data source is analyzed for data format and extended warranty traceability reliability based on the historical data of each channel to obtain data format characteristics and reliability characteristics. The data source characteristic analysis unit may further include: a format characteristic analysis subunit for performing format characteristic analysis on the historical data of each channel from multiple dimensions such as data type, data structure, time format, and field definition to obtain the data format characteristics; a complete data chain establishment subunit for establishing a complete data chain, wherein the complete data chain includes a data integrity characteristic chain for extended warranty traceability, corresponding to the extended warranty lifecycle node; a standard conversion subunit for performing standard conversion of format characteristics of each channel on the complete data chain using the data format characteristics; a tracking and identification subunit for extracting product tracking information, wherein the product tracking information is an identity characteristic for identifying the product, and the historical data of each channel is tracked and identified based on the product tracking information and the complete data chain to obtain the tracking and identification probability of each channel; a reliability characteristic acquisition subunit for establishing a mapping relationship between the tracking and identification probability of each channel and the channel collection data to obtain the reliability characteristics.

[0064] The specific configuration of the import rule configuration module 30 will be described in detail below. As described above, according to the periodic data map, the data source characteristics of the time-aligned channel data are compared, and the import rules of each data source are configured. The import rule configuration module 30 may further include: a timing alignment unit for extracting the channel data of each period according to the periodic data map, and performing time alignment on the channel data of the same period; a format conversion rule analysis unit for performing format conversion rule analysis according to the data source characteristics of each channel, and determining the data format conversion rule; a reliability import rule analysis unit for performing data import rule analysis on the reliability of the channel data based on the time alignment relationship, extracting the channel data with reliability higher than the import threshold and setting direct import rules, extracting the channel data with reliability lower than the import threshold and setting import feedback loop according to reliability; an import rule acquisition unit for obtaining the import rules of each data source according to the data format conversion rules and data import rules.

[0065] The specific configuration of the extended warranty risk analysis module 40 will be described in detail below. As described above, based on the import rules, extended warranty collection data is obtained, and lifecycle extended warranty risk analysis is performed on the collected extended warranty data to obtain extended warranty risk information. The extended warranty risk analysis module 40 may further include: a risk identification model training unit for configuring risk identification targets for each lifecycle node, establishing the impact relationship between the risk identification targets and channel data, and training the risk identification model; a periodic risk identification unit for adding the risk identification model for each lifecycle node to the period node, obtaining channel data according to the import rules, and performing periodic risk identification using the corresponding risk identification model to obtain periodic node risk identification results; a time series full-cycle analysis unit for performing time series full-cycle analysis on the periodic node risk identification results based on the time series relationship of the extended warranty product's lifecycle to obtain risk assessment results; and a result integration unit for integrating the periodic node risk identification results and the risk assessment results to obtain the extended warranty risk information.

[0066] Among them, to obtain the risk assessment result, the time series full-cycle analysis unit may further include: a risk identification target acquisition subunit for obtaining the risk identification target of the extended warranty multi-party cooperation user; a fusion coefficient configuration subunit for configuring the fusion coefficient of each cycle node according to the risk identification target of the extended warranty multi-party cooperation user; a weighted fusion subunit for weightedly fusing the risk identification results of the cycle nodes according to the fusion coefficient to obtain the risk assessment result.

[0067] A multi-channel extended warranty data management platform provided by an embodiment of the present invention can execute a multi-channel extended warranty data management method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0068] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0069] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A multi-channel extended warranty data management method, characterized in that: include: Perform data cycle positioning and data source feature analysis on multiple data collection channels to obtain the cycle positioning and data source features of each channel; According to the time series relationship of the extended warranty product's life cycle, the cycle positioning of each channel is time-sequentially aligned and cycle-sorted, a cycle data graph is established, and the data source characteristics are used as attributes of the graph nodes for graph fitting; According to the periodic data map, the data source characteristics of the time-series aligned channel data are compared, and the import rules of each data source are configured; Based on the import rules, obtaining warranty extension collection data, performing warranty extension risk analysis for each life cycle on the warranty extension collection data, and obtaining warranty extension risk information; generating extended warranty management feedback based on the extended warranty risk information; Perform data cycle positioning and data source feature analysis on multiple data collection channels to obtain the cycle positioning and data source features of each channel, including: Analyze the correlation between each channel data and product life cycle, and establish the correlation response relationship between each channel data and life cycle; Analyze the data format and extended warranty traceability reliability of the data source based on historical data from each channel to obtain data format characteristics and reliability characteristics; Performing period positioning according to the correlation response relationship between the data of each channel and the life cycle to obtain the period positioning of each channel, and obtaining the data source characteristics according to the data format characteristics and reliability characteristics; Based on the periodic data graph, the data source characteristics of the time-aligned channel data are compared, and the import rules for each data source are configured, including: Extracting channel data of each period according to the periodic data map, and performing time sequence alignment on the channel data of the same period; Analyze the format conversion rules based on the data source characteristics of each channel and determine the data format conversion rules; Based on the time series alignment relationship, the reliability of channel data is analyzed for data import rules. Channel data with reliability higher than the import threshold is extracted and set up for direct import. Channel data with reliability lower than the import threshold is extracted and imported according to the reliability settings and feedback loop. Obtaining import rules for each data source according to the data format conversion rules and data import rules; Based on the import rules, extended warranty collection data is obtained, and extended warranty risk analysis of each life cycle is performed on the extended warranty collection data to obtain extended warranty risk information, including: Configure risk identification targets for each lifecycle node, establish the impact relationship between the risk identification targets and channel data, and train the risk identification model; Add the risk identification model of each lifecycle node to the cycle node, obtain channel data according to the import rules, perform cycle risk identification through the corresponding risk identification model, and obtain the cycle node risk identification result; Based on the time series relationship of the extended warranty product's life cycle, the risk identification results of the cycle nodes are analyzed in a time series throughout the entire cycle to obtain a risk assessment result; The period node risk identification result and the risk assessment result are integrated to obtain the extended warranty risk information.

2. The multi-channel extended warranty data management method according to claim 1, characterized in that: Analyze the correlation between each channel data and the product life cycle, and establish the correlation response relationship between each channel data and the life cycle, including: Establish lifecycle nodes for extended warranty products, including at least warranty enrollment, warranty execution, warranty renewal, and warranty expiration. Use the data from each channel and the life cycle nodes to conduct correlation mining to obtain the correlation coefficient and correlation characteristics between the data from each channel and the life cycle nodes; According to the correlation coefficients and correlation characteristics between the channel data and the life cycle nodes, the relationships are sorted out to establish the correlation response relationships between the channel data and the life cycle.

3. The multi-channel extended warranty data management method according to claim 2, characterized in that: Based on the historical data from each channel, the data source is analyzed for data format and extended warranty traceability reliability to obtain data format characteristics and reliability characteristics, including: Analyze the format characteristics of historical data from each channel from multiple dimensions such as data type, data structure, time format, and field definition to obtain the data format characteristics; Establish a complete data chain, which includes a data integrity feature chain for warranty extension tracing, corresponding to the warranty extension lifecycle node; Using the data format characteristics, the complete data chain is converted into the format characteristics standard of each channel; Extract product tracking information, which is the identity feature for identifying the product, and track and identify the historical data of each channel based on the product tracking information and the complete data chain to obtain the tracking and identification probability of each channel; A mapping relationship between the tracking and identification probability of each channel and the channel collection data is established to obtain the reliability feature.

4. The multi-channel extended warranty data management method according to claim 1, characterized in that: Obtaining risk assessment results also includes: Obtain risk identification targets for extended warranty multi-party cooperation users; Configure the fusion coefficient of each period node according to the risk identification target of the extended warranty multi-party cooperation users; The risk assessment result is obtained by weighted fusion of the periodic node risk identification results according to the fusion coefficient.

5. A multi-channel extended warranty data management platform, characterized by: The platform is used to implement the multi-channel extended warranty data management method according to any one of claims 1 to 4, and the platform includes: The data analysis module is used to locate the data cycle and analyze the characteristics of the data source for multiple channels of data collection, and obtain the cycle location and data source characteristics of each channel; A cycle data graph establishment module is used to align and sort the cycle locations of the channels according to the time series relationship of the extended warranty product's life cycle, establish a cycle data graph, and use the data source characteristics as attributes of the graph nodes for graph fitting; An import rule configuration module is used to compare data source characteristics of the time-series aligned channel data according to the periodic data map and configure import rules for each data source; An extended warranty risk analysis module is used to obtain extended warranty collection data based on the import rules, perform extended warranty risk analysis on the extended warranty collection data in each life cycle, and obtain extended warranty risk information; The warranty extension management feedback module is used to generate warranty extension management feedback based on the warranty extension risk information.

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