An e-commerce business data automatic matching and association method and system

Abstract

The present application relates to the technical field of data mining software, in particular to an automatic matching and association method and system for e-commerce business data, comprising the following steps: obtaining cross-border e-commerce influencer business data, standardizing the influencer business data to generate business records, associating sample clothing application data and delivery orders based on the business records to generate sample clothing order records, generating candidate matching relationships according to the influencer records, sample clothing order records and video records, generating three-party binding relationships when the video publishing time is later than the signing time and the first-in first-out sequence is met, writing to the to-be-processed records for multi-video and data missing cases, writing back the advertising intent state, updating the influencer state and outputting the overdue performance warning record. The present application forms a continuous association link among multi-source data through business records throughout matching, advertising linkage, state updating and warning output, so that orders, sample clothing orders, videos and risks have consistent processing basis.

Description

Technical Field

[0001] This invention relates to the field of data mining software technology, and in particular to an automatic matching and association method and system for e-commerce business data. Background Technology

[0002] Data mining software technology typically revolves around collecting, cleaning, indexing, comparing, associating, storing, and retrieving data such as transaction records, product information, user behavior logs, order status, inventory records, payment records, and after-sales records. E-commerce terminals, business servers, databases, caches, and interface programs work together to process data through field transmission, table entry writing, and query requests. Among these, traditional e-commerce business data automatic matching and association methods and systems refer to the programmed processing of the correspondence between orders, products, users, stores, logistics, payment, and after-sales records on e-commerce platforms. Generally, the terminal or interface obtains business tables, log files, and message queue data. The server then performs field comparison, deduplication, sorting, mapping, and association writing based on product number, order number, user identifier, timestamp, status field, and preset matching conditions, and stores the association results in the business database for subsequent querying.

[0003] Traditional e-commerce data processing often revolves around comparing and writing data based on order numbers, product numbers, user identifiers, timestamps, and status fields. However, when dealing with cross-border e-commerce influencer collaborations, data sources are scattered, business times are inconsistent, order and video correspondences overlap, abnormal records rely on manual verification, influencer status and performance risks are difficult to maintain continuously in the same data chain, and there are breakpoints between the associated results and subsequent operational records. Summary of the Invention

[0004] The purpose of this invention is to provide an automatic matching and association method and system for e-commerce business data. In cross-border e-commerce influencer cooperation business, it enables influencer information, sample garment applications, shipping orders, logistics receipts, video releases, sales figures, and advertising records to be matched, bound, updated in status, and issued with early warnings in a unified data link. This reduces the impact of scattered data sources, inconsistent business timelines, cross-correspondence between orders and videos, and fragmented processing of abnormal records on the business data association process.

[0005] To achieve the above objectives, the present invention provides an automatic matching and association method for e-commerce business data, comprising the following steps: The process involves acquiring cross-border e-commerce influencer business data, including influencer information, responsible personnel email addresses, sample garment application data, shipping orders, logistics receipt data, video posting data, video sales revenue, and advertising placement data. The acquisition process includes: receiving influencer information and responsible personnel email addresses via batch import or single push notifications, and writing data source identifiers and collection times to the received influencer information; acquiring sample garment application data, shipping orders, and logistics receipt data via a scheduled interface, and using the receipt time in the logistics receipt data as the business time basis for video matching; and acquiring video posting data, video sales revenue, and advertising placement data via scheduled capture, and writing these data into the same business record according to the influencer identifier.

[0006] The cross-border e-commerce influencer business data is standardized to generate business records containing the influencer identifier, the responsible person's email address, order number, sample garment application number, receipt time, video identifier, video release time, sales amount, and advertising placement identifier. The field standardization process includes: extracting the influencer identifier, responsible person's email address, order number, sample garment application number, receipt time, video identifier, video release time, sales amount, and advertising placement identifier from the cross-border e-commerce influencer business data; writing the extracted fields into corresponding field slots according to the data source identifier; when multiple source values ​​exist for the same field, determining the retained value according to the collection time and business time; when a missing field affects the sample garment order record or the generation of candidate matching relationships, the corresponding business record is marked as pending supplementation. For the reserved value, when the value obtained from the interface and the value captured at a timed time correspond to the same influencer identifier and the business time is consistent, the field value with the later collection time is selected as the reserved value; when the value obtained from the interface and the value captured at a timed time correspond to the same influencer identifier but the business time is inconsistent, a field conflict record is generated according to the order of the business time; when the field conflict record is not processed, the corresponding business record is restricted from entering the third-party binding relationship generation process.

[0007] Based on the business records, the sample garment application data is associated with the shipping order to form a sample garment order record, and candidate matching relationships are generated based on the influencer records, sample garment order records, and video records in the business records. The generation process of the candidate matching relationships includes: reading the influencer identifier, sample garment application number, order number, receipt time, video identifier, and video release time from the business records; using the same influencer identifier as a first filtering condition, writing the influencer records, sample garment order records, and video records corresponding to the same influencer identifier into the same candidate set; using the existence of a corresponding relationship between the sample garment application number and the order number as a second filtering condition, retaining record combinations with a correlation basis from the candidate set; when neither the first nor the second filtering condition is met, excluding the corresponding record combination from the candidate matching relationships.

[0008] When the video's release time is later than the receipt time and the first-in-first-out (FIFO) order is satisfied, a three-way binding relationship is generated between the influencer, the sample garment order, and the video. The generation process of this three-way binding relationship includes: sorting the sample garment order records according to the receipt time, and sorting the video records according to the video's release time; within the same candidate set, selecting video records whose release time is later than the receipt time as bindable video records; when multiple bindable video records correspond to the same sample garment order record, selecting the earliest-ranked bindable video record according to the FIFO order to generate the three-way binding relationship; when no bindable video record exists, writing the corresponding sample garment order record into the exception pending processing record.

[0009] When a shipping order corresponds to multiple videos, the first video that satisfies the candidate matching relationship is bound, and the remaining videos are written into the pending candidate record. The process of generating the pending candidate record includes: when a shipping order corresponds to multiple videos, reading the video release time, video identifier, and corresponding candidate matching relationship for each video; sorting the multiple videos according to the video release time or the time it was written into the business record; writing the video that ranks first and satisfies the candidate matching relationship into the third-party binding relationship; writing the remaining videos not written into the third-party binding relationship into the pending candidate record, and retaining the correspondence between the shipping order, the video identifier, and the influencer identifier.

[0010] When the shipping order has no video, the video has no shipping order, or the order number is missing, an abnormal pending record is generated and written back to the three-party binding relationship after being supplemented. The generation process of the abnormal pending record includes: when the shipping order does not have a corresponding video, the shipping order, the influencer identifier, and the receipt time are written to the "no video" status; when the video does not have a corresponding shipping order, the video identifier, the influencer identifier, and the video release time are written to the "no order" status; when the order number is missing, the corresponding business record is written to the "order number pending supplementation" status; after the order number is supplemented, the candidate matching relationship generation process is re-triggered based on the supplemented order number.

[0011] When the video recording and the advertising delivery data satisfy the correspondence between the influencer identifier, the video identifier, or the advertising delivery identifier, the advertising intent status is updated and written to the business record. The influencer status is updated based on the video release time, video sales generation time, responsible personnel email address, and store relationship in the business record. A late payment warning record is generated based on the receipt time, video search results, responsible personnel email address, and advertising intent status in the business record. The generation process of the advertising intent status, the influencer status, and the overdue performance warning record includes: when the video record and the advertising delivery data satisfy the correspondence between the influencer identifier, the video identifier, or the advertising delivery identifier, the corresponding advertising delivery data is written into the business record and the advertising intent status is updated; an influencer status judgment result is generated based on the video release time, the video sales generation time, the responsible person's email address, and the store relationship in the business record, and the influencer status is updated according to the influencer status judgment result; when the receipt time and the video search result meet the overdue triggering condition, the overdue performance warning record is generated based on the responsible person's email address, the influencer status, and the advertising intent status.

[0012] This invention also provides an automatic matching and association system for e-commerce business data, supporting the automatic matching and association processing of the aforementioned e-commerce business data. The system includes a communication interface, a processor, a memory, and a business database. The communication interface receives the cross-border e-commerce influencer business data; the processor calls a program in the memory to standardize the fields of the cross-border e-commerce influencer business data and writes it into the business database to form the business record; the processor associates the sample garment application data with the shipping order based on the business record to form the sample garment order record, and generates the candidate matching relationship based on the influencer record, sample garment order record, and video record in the business database; the processor generates the three-party binding relationship when the video release time is later than the receipt time and the first-in-first-out order is satisfied, and binds the first video that meets the first-in-first-out order when one shipping order corresponds to multiple videos. The processor selects videos with matching relationships and writes them into the candidate records to be processed; when data is missing, it writes the abnormal pending records; when the video records and the advertising placement data satisfy the correspondence between the influencer identifier, the video identifier, or the advertising placement identifier, it updates the advertising intent status and writes it into the business record; the processor updates the influencer status based on the video release time, the video sales generation time, the responsible person's email address, and the store relationship in the business record, and outputs the overdue performance warning record through the communication interface based on the receipt time, the video search results, the responsible person's email address, and the advertising intent status in the business record.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, cross-border e-commerce influencer business data is standardized into business records, and these records drive the association between sample garment application data and shipping orders. A traceable candidate matching foundation is formed between influencer records, sample garment order records, and video records. Candidate matching relationships are filtered by signing time, video release time, and first-in-first-out (FIFO) order, reducing mismatches caused by relying solely on order numbers or manual judgment. Through pending candidate records, abnormal pending records, and the writing back of advertising intent status, multiple videos, missing data, and advertising linkage results can be integrated into a unified business chain. Influencer status updates and overdue performance warning records provide clear triggering criteria for subsequent follow-up. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is the main flowchart for the automatic matching and association of e-commerce business data in this invention; Figure 2 This is a data flow diagram for multi-source service data access in this invention; Figure 3 This is a logic diagram of field standardization and field conflict handling in this invention; Figure 4 A partial flowchart is generated for the candidate matching relationship and the three-party binding relationship of this invention; Figure 5 This is a feedback diagram for the advertising linkage, influencer status update, and overdue performance warning of the present invention. Detailed Implementation

[0016] The embodiments of the present invention will be described below with reference to the technical solutions and accompanying drawings described in the application documents. The following embodiments are based on an automatic matching and association method for e-commerce business data, and are explained in conjunction with the execution carrier of the corresponding system. This explains the data sources, processing rules, intermediate states, and output results of cross-border e-commerce influencer business data during the processes of collection, standardization, matching, binding, status updating, and early warning output. The cross-border e-commerce influencer business data in the following embodiments includes influencer information, responsible personnel email addresses, sample garment application data, shipping orders, logistics receipt data, video release data, video sales revenue, and advertising placement data. Business records are the unified carrier objects formed after the above data has been standardized by fields. Candidate matching relationships are the definable relationships formed by influencer records, sample garment order records, and video records after meeting the filtering conditions. Three-way binding relationships are the association results formed after influencers, sample garment orders, and videos meet the time sequence conditions and sorting rules. Candidate records to be processed and abnormal records to be processed are used to carry multiple video candidate scenarios and data missing scenarios, respectively. Advertising intent status, influencer status, and overdue performance early warning records continue to be carried or output by business records as association results. This implementation method does not limit specific e-commerce platforms, specific interface protocols, specific database products, or specific notification channels. As long as the same data access, field standardization, matching judgment, write-back, and early warning output can be completed according to the technical solution described in this invention, they all belong to the technical forms that this invention can implement.

[0017] Please see Figures 1 to 5This embodiment provides an automatic matching and association method for e-commerce business data, applied to the data processing scenario of cross-border e-commerce influencer collaboration business. The method is executed by a business server or a computing device capable of executing the same program logic. The business server receives data from the influencer backend, order interface, logistics interface, video data interface, advertising placement interface, and batch import entry through communication interfaces, and writes the received data into the business database. During processing, the business server uses influencer identifier, responsible personnel email, order number, sample garment application number, receipt time, video identifier, video release time, sales amount, and advertising placement identifier as main fields, enabling sample garment application data, shipping orders, logistics receipt data, video release data, video sales amount, and advertising placement data to be retrieved, filtered, judged, written back, and output within the same business chain.

[0018] During the data access phase, the business server acquires cross-border e-commerce influencer business data. Influencer information and the responsible personnel's email addresses can be received through either a batch import entry or a single push entry. The batch import entry is used to receive files or data tables containing multiple influencer records, while the single push entry is used to receive new or modified content for a single influencer record. After receiving the influencer information, the business server writes a data source identifier and collection time to the information. The data source identifier distinguishes between sources such as batch import, single push, API acquisition, or scheduled capture, while the collection time indicates the time when the business server received the corresponding data. The responsible personnel's email address is written to the business record along with the influencer information, enabling subsequent status updates and overdue performance warnings to pinpoint the responsible personnel. Sample garment application data, shipping orders, and logistics receipt data are acquired through a scheduled interface. The scheduled interface reads the sample garment application number, order number, shipping status, and receipt time according to a preset call cycle. The receipt time is used as the business time basis for video matching, defining the chronological relationship between the video release time and the sample garment receipt status. Video release data, video sales revenue, and advertising data are obtained through periodic data capture. The business server writes the video release data, video sales revenue, and advertising data into the same business record according to the influencer's identifier, so that the data generated by the same influencer in different business processes can be merged into a unified data carrier object.

[0019] During the field standardization phase, the business server extracts fields and writes them to field slots from the cross-border e-commerce influencer business data. The business server extracts influencer identifiers, responsible personnel emails, order numbers, sample garment application numbers, receipt times, video identifiers, video release times, sales revenue, and advertising placement identifiers from the cross-border e-commerce influencer business data, and writes the extracted fields to the corresponding field slots according to the data source identifiers. Field slots are used to maintain name consistency across different data sources, avoiding subsequent matching breakpoints caused by the same technical object having different field names in different sources. For cases where the same field has multiple source values, the business server compares the collection time and the business time; when the value obtained from the interface and the value captured on a scheduled basis correspond to the same influencer identifier and the business time is consistent, the field value with the later collection time is selected as the retained value, and the replaced field value is saved in the record change history; when the value obtained from the interface and the value captured on a scheduled basis correspond to the same influencer identifier but the business time is inconsistent, the business server generates field conflict records according to the order of the business time. When a field conflict record is not processed, the corresponding business record does not enter the third-party binding relationship generation process, but remains in the state of pending supplementation or pending verification. This ensures that the generation of candidate matching relationships is based on the fact that both the field source and the business time are traceable.

[0020] During the sample garment order record generation stage, the business server associates sample garment application data with shipping orders to form a sample garment order record based on business records. The business server prioritizes reading the sample garment application number and order number. When the sample garment application number can be located in the sample garment application data and the order number can be located in the shipping order, the business server writes the corresponding sample garment application data and shipping order into the same sample garment order record. A sample garment order record includes at least the influencer identifier, sample garment application number, order number, shipping status, and receipt time. The sample garment application number identifies the sample garment application process, the order number identifies the shipping order, and the receipt time is used for subsequent video timing verification. If the sample garment application number exists but the order number is missing, the business server writes the corresponding business record to an order number pending addition status; if the order number exists but the sample garment application number cannot be mapped to sample garment application data, the business server retains the shipping order and influencer identifier and writes the corresponding record into an abnormal pending record so that the candidate matching relationship generation process can be re-triggered after data addition or re-acquisition.

[0021] During the candidate matching relationship generation phase, the business server reads the influencer identifier, sample garment application number, order number, receipt time, video identifier, and video release time from the business records. The business server first uses the same influencer identifier as the first filtering condition, writing influencer records, sample garment order records, and video records corresponding to the same influencer identifier into the same candidate set. This first filtering condition ensures consistency across influencer dimensions, preventing orders and videos from different influencers from entering the same candidate set. Subsequently, the business server uses the existence of a correspondence between video records and sample garment order records as the second filtering condition, retaining record combinations with a basis for association from the candidate set. This second filtering condition determines whether there is a traceable correspondence between video records and sample garment order records. When either the first or second filtering condition is not met, the business server excludes the corresponding record combination from the candidate matching relationship, retaining the basis for exclusion. Candidate matching relationships only indicate that the record combination has the basis for further time-series verification and sorting determination; they are not directly used as the final binding result.

[0022] During the three-way binding relationship generation phase, the business server sorts the sample garment order records and video records in the candidate set. The business server sorts the sample garment order records by receipt time and the video records by video release time, comparing the video release time with the receipt time within the same candidate set. When the video release time is later than the receipt time, the corresponding video record is marked as a bindable video record; when the video release time is not later than the receipt time, the corresponding video record is not included in the range of bindable video records. If multiple bindable video records correspond to the same sample garment order record, the business server selects the earliest-ranked bindable video record to generate the three-way binding relationship in a first-in-first-out (FIFO) order. This FIFO order is based on a time-series queue formed by the receipt time and video release time; the sample garment order record that enters the matching range first is preferentially bound to the video record that meets the conditions. If no bindable video record exists, the business server writes the corresponding sample garment order record into an exception pending processing record. The exception pending processing record retains the influencer identifier, sample garment application number, order number, and receipt time, so that it can re-enter the candidate matching relationship generation process after subsequent video data arrives.

[0023] In the multi-video processing stage, when a shipping order corresponds to multiple videos, the business server reads the video's publication time, video identifier, and corresponding candidate matching relationship for each video. The business server sorts the multiple videos according to their publication time or the time they are written into the business record, and writes the video that ranks first and meets the candidate matching relationship into the three-party binding relationship. Remaining videos not written into the three-party binding relationship are not deleted but are written into the pending candidate record. The pending candidate record retains the correspondence between the shipping order, video identifier, and influencer identifier, and records the reason why the video was not written into the three-party binding relationship. If subsequent manual data entry, order number correction, video data re-capture, or field conflict record processing is completed, the business server can re-determine whether the remaining videos should enter a new three-party binding relationship based on the pending candidate record. Therefore, the multi-video scenario is limited to the processing boundary of the first video that meets the conditions being automatically bound, and the remaining videos entering the pending candidate record.

[0024] During the exception handling phase, the business server differentiates between three scenarios: no video for a shipping order, no shipping order for a video, and missing order number. When a shipping order lacks a corresponding video, the business server writes the shipping order, influencer ID, and delivery time to the "no video" status. When a video lacks a corresponding shipping order, the business server writes the video ID, influencer ID, and video release time to the "no order" status. When an order number is missing, the business server writes the corresponding business record to the "order number pending" status. All of these exceptions are recorded in the exception handling record, retaining the source field, trigger time, and corresponding influencer ID. After the order number is supplemented, the business server re-triggers the candidate matching relationship generation process based on the supplemented order number. If the supplemented order number can form a corresponding relationship with the sample garment application number or shipping order, the business server re-executes influencer ID filtering, association basic filtering, time sequence verification, and first-in-first-out sorting. If the supplemented order number still cannot form a corresponding relationship, the business server maintains the exception handling record and updates the processing status.

[0025] During the ad placement linkage phase, the business server compares the influencer identifier, video identifier, and ad placement identifier between the video recording and the ad placement data. If the video recording and ad placement data satisfy any of the above correspondences, and the corresponding record is not in a field conflict unresolved state, the business server writes the ad placement data into the business record and updates the ad intent status. Once written to the business record, the ad intent status becomes a callable field in the overdue performance warning record and subsequent operation records. If the video recording and ad placement data cannot establish a correspondence through the influencer identifier, video identifier, or ad placement identifier, the business server does not update the ad intent status and retains the unmatched state of the ad placement data. Through this process, ad placement data is not merely received as input but participates in the correspondence determination of the video recording and enters the subsequent links of the business record through the ad intent status.

[0026] During the influencer status update phase, the business server generates an influencer status judgment result based on the video posting time, video sales generation time, responsible person's email address, and store relationship in the business records. The video posting time determines if the influencer has made any new posts; the video sales generation time determines if the influencer has any sales records; the responsible person's email address determines the recipient of status updates and alerts; and the store relationship determines the influencer's current affiliation. The business server inputs these fields into preset status rules, which form a time-dimensional status judgment based on the video posting time and video sales generation time, and determine the affiliation of the status record based on the responsible person's email address and store relationship. After the influencer status judgment result is generated, the business server updates the influencer status according to the judgment result. If the responsible person's email address changes, the business server writes the change time to the business records and re-establishes the influencer status affiliation based on the changed responsible person's email address. The updated influencer status result is written back to the business records, allowing subsequent overdue performance alert records to reference the updated influencer status.

[0027] During the late delivery warning phase, the business server reads the receipt time, video search results, responsible personnel's email address, and advertising intent status from the business records. The receipt time determines the point in time when the sample garment arrived at the influencer's location; the video search results indicate whether there are matching video records within the scope of the corresponding influencer and sample garment order; the responsible personnel's email address identifies the recipient of the warning output; and the advertising intent status indicates the linkage result between the video and advertising data. When the receipt time and video search results meet the late delivery trigger conditions, the business server generates a late delivery warning record based on the responsible personnel's email address, influencer status, and advertising intent status. The late delivery warning record includes at least the influencer identifier, shipping order, sample garment application number, receipt time, video search results, responsible personnel's email address, influencer status, and advertising intent status. The business server outputs the late delivery warning record through a communication interface, which can be an email interface, system message interface, or business notification interface. If the video search results change after the warning record is generated, the business server can re-evaluate the late delivery trigger conditions based on the updated business records and update the processing status of the late delivery warning record.

[0028] Corresponding to the above method, this embodiment also provides an automatic matching and association system for e-commerce business data. The system provides support for the aforementioned automatic matching and association processing of e-commerce business data. The system includes a communication interface, a processor, a memory, and a business database. The communication interface receives cross-border e-commerce influencer business data, which includes influencer information, responsible personnel email addresses, sample garment application data, shipping orders, logistics receipt data, video posting data, video sales figures, and advertising placement data. The memory stores programs that can be called by the processor and intermediate data generated during processing. The business database stores business records, candidate matching relationships, third-party binding relationships, pending candidate records, abnormal pending records, advertising intent status, influencer status judgment results, influencer status, and overdue performance warning records. The processor calls the program in memory to standardize the fields of cross-border e-commerce influencer business data and write it into the business database to form business records. Based on the business records, the processor associates sample garment application data with shipping orders to form sample garment order records, and generates candidate matching relationships based on influencer records, sample garment order records, and video records in the business database. When the video release time is later than the receipt time and the first-in-first-out order is satisfied, the processor generates a three-party binding relationship. When a shipping order corresponds to multiple videos, the processor binds the first video that meets the candidate matching relationship and writes the remaining unbound videos into the pending candidate records. When data is missing, the processor writes an abnormal pending record. When the video records and advertising data meet the correspondence relationship of influencer identifier, video identifier, or advertising placement identifier, the processor updates the advertising intent status and writes it into the business records. The processor updates the influencer status based on the video release time, video sales generation time, responsible person's email, and store relationship in the business records, and outputs a performance overdue warning record through the communication interface based on the receipt time, video search results, responsible person's email, and advertising intent status in the business records.

[0029] In the execution of the above system, the communication interface serves as the entry point for cross-border e-commerce influencer business data, the processor acts as the execution entity for field standardization, matching judgment, status updates, and early warning output, the memory serves as the program storage location, and the business database serves as the storage location for data records and intermediate results. Data received by the communication interface is first written to the business database by the processor to form records to be standardized, and then the processor extracts fields and generates business records. These business records are called at different processing stages within the business database: first, they are used to form sample garment order records; then, they are used to generate candidate matching relationships and three-party binding relationships; and also, they are used to write back the advertising intent status, update influencer status, and output overdue fulfillment early warning records. If the communication interface receives supplementary order numbers, re-captured video publishing data, or updated advertising placement data, the processor re-executes the corresponding processing flow based on the existing business records in the business database, ensuring traceability of input data, processing procedures, intermediate states, output results, and feedback updates.

[0030] In terms of feasibility verification, the business server can verify the completion of each processing stage through the record status in the business database. The verification object for the data access stage is cross-border e-commerce influencer business data with data source identifiers and collection times; the verification object for the field standardization stage is business records containing unified field slots; the verification object for the sample garment order record generation stage is sample garment order records formed by associating sample garment application data with shipping orders; the verification object for the candidate matching relationship generation stage is a candidate set containing influencer records, sample garment order records, and video records; the verification object for the three-party binding relationship generation stage is bound records that meet the requirement that the video release time is later than the receipt time and conforms to the first-in-first-out order; the verification object for the multi-video processing stage is pending candidate records; the verification object for the abnormal pending processing stage is the order without video status, video without order status, and order number pending entry status; the verification object for the advertising placement linkage stage is the advertising intent status written into the business record; the verification object for the influencer status update stage is the influencer status judgment result and influencer status; and the verification object for the overdue performance warning stage is the overdue performance warning record output through the communication interface.

[0031] To more intuitively demonstrate the execution process of this application, a demonstration operation is provided using a cross-border e-commerce apparel influencer collaboration business as an example: The system first obtains the influencer information of 'KOL-Alice' and the email address of its responsible person 'manager@example.com' through an interface. Subsequently, the system periodically retrieves the influencer's sample garment application data (sample garment application number: REQ-001) and shipping order (order number: ORD-2026, 0901). The logistics interface indicates that the order has been signed for, with the signing time T1 being 10:00:00 on September 5, 2026. At this time, the system generates a sample garment order record. Subsequently, the system periodically captures video posting data. The capture record shows that the influencer posted a video (video identifier: VID-A01) on its social media account, with the video posting time T2 being 15:00:00 on September 10, 2026. During the candidate matching relationship generation phase, the system uses the influencer identifier 'KOL-Alice' as the first filtering condition, writing the influencer's record, the aforementioned sample garment order record, and the video record into the same candidate set. During the tripartite binding relationship generation phase, the system compares the time sequence and finds that the video release time T2 (September 10th) is later than the receipt time T1 (September 5th), satisfying the time sequence condition, thus automatically generating a tripartite binding relationship between 'KOL-Alice', order 'ORD-20260901', and video 'VID-A01'. Furthermore, when the system obtains advertising placement data and finds that its advertising placement identifier corresponds to 'VID-A01', the system updates the advertising intent status of that business record to 'placed'. In another scenario, if another order with a delivery date of August 1, 2026, still yields no video search results after the preset 30-day overdue threshold, the system will automatically send an overdue performance warning to 'manager@example.com', based on the influencer's status and the responsible personnel's email address, reminding the business personnel to expedite the order. This complete data operation achieves automated flow and anomaly warning of multi-source business data.

[0032] The interface forms, storage structures, execution entities, capture cycles, notification methods, and database products described in the above specific embodiments are only used to illustrate the implementable path of the present invention. Without departing from the technical solutions described in this invention, those skilled in the art can make equivalent substitutions or adjustments to the batch import entry point, single push entry point, timed interface, timed capture method, business database storage structure, communication interface output channel, field names, etc., and can also arrange the processor, memory, communication interface, and business database in the same computing device or multiple collaborative computing devices. The aforementioned equivalent substitutions, equivalent modifications, equivalent combinations, order adjustments, equivalent changes to field names, equivalent acceptance of execution entities, or equivalent changes to carrier forms should not alter the substantive technical relationship of cross-border e-commerce influencer business data through business record matching, advertising linkage, status updates, and overdue performance warning outputs, and should not deviate from the scope of protection of this application by changing names or carrier forms.

Claims

1. A method for automatically matching and associating e-commerce business data, characterized in that, Obtain cross-border e-commerce influencer business data, which includes influencer information, responsible personnel email, sample garment application data, shipping orders, logistics receipt data, video posting data, video sales, store relationship data, and advertising placement data; The cross-border e-commerce influencer business data is standardized to generate business records including influencer identifier, responsible person's email, order number, sample garment application number, receipt time, video identifier, video release time, sales amount, store relationship, and advertising placement identifier. The business records are then divided into influencer records and video records containing the corresponding identifiers. The sample garment application data is associated with the shipping order to form a sample garment order record, and candidate matching relationships are generated based on the influencer records in the business records, the sample garment order record, and the video records; When the video is published later than the receipt time and the chronological order is met, a three-way binding relationship is generated between the influencer, the sample garment order, and the video. When a shipping order corresponds to multiple videos, bind the first video that meets the candidate matching relationship, and write the remaining videos into the pending candidate record; When a shipping order has no video, a video has no shipping order, or the order number is missing, an abnormal pending record is generated and the three-party binding relationship is written back after the record is supplemented. When the video recording and the advertising data meet the correspondence between the influencer identifier, video identifier, or advertising placement identifier, the advertising intent status is updated and written to the business record. Based on the video release time, video sales generation time, responsible person's email address, and store relationship update status in the business records, the video release data is retrieved to obtain video search results. Combined with the receipt time, video search results, responsible person's email address, and advertising intent status, a late delivery warning record is generated.

2. The method for automatic matching and association of e-commerce business data according to claim 1, characterized in that, The process of acquiring the business data of cross-border e-commerce influencers includes: The system receives influencer information and the responsible personnel's email address through batch import or single push, writes data source identifier and collection time into the received influencer information, and obtains store relationships from the influencer information or store configuration data. The sample garment application data, the shipping order and the logistics receipt data are obtained through a timed interface, and the receipt time in the logistics receipt data is used as the business time basis for video matching. The video release data, video sales revenue, video sales revenue generation time, and advertising placement data are periodically captured and obtained. The video release data, video sales revenue, video sales revenue generation time, store relationship, and advertising placement data are then written into the corresponding business records or business record sets according to the influencer identifier.

3. The method for automatic matching and association of e-commerce business data according to claim 1, characterized in that, The field standardization process includes: Extract the influencer identifier, responsible person's email address, order number, sample garment application number, receipt time, video identifier, video release time, sales amount field, and advertising placement identifier from the cross-border e-commerce influencer business data, and extract or generate the video sales amount generation time and store relationship; The extracted fields are written into the corresponding field slots according to the data source identifier. When the same field has multiple source values, the retained value is determined according to the collection time and business time. When a missing field affects the sample garment order record or the generation of candidate matching relationships, the corresponding business record will be marked as pending supplementation.

4. The method for automatic matching and association of e-commerce business data according to claim 3, characterized in that, The process for determining the retention value includes: When the value obtained from the interface and the value captured at a time correspond to the same influencer identifier and the business time is consistent, the field value with the later collection time is selected as the reserved value. When the value obtained from the interface and the value captured on a timer correspond to the same influencer identifier but the business time is different, a field conflict record is generated according to the order of the business time. When a field conflict record is not processed, the corresponding business record is restricted from entering the third-party binding relationship generation process.

5. The method for automatic matching and association of e-commerce business data according to claim 1, characterized in that, The process of generating the candidate matching relationship includes: Retrieve the influencer identifier, sample garment application number, order number, receipt time, video identifier, and video release time from the aforementioned business records; Using the same influencer ID as the first filtering criterion, influencer records, sample garment order records, and video records corresponding to the same influencer ID are written into the same candidate set; Using the correspondence between sample garment application number or order number as the second filtering condition between sample garment application data and shipping order, sample garment order records with a correlation basis are generated, and video record combinations with the same influencer identifier corresponding to the sample garment order record are retained in the candidate set; If the first or second screening condition is not met, the corresponding record combination will be excluded from the candidate matching relationship.

6. The method for automatic matching and association of e-commerce business data according to claim 5, characterized in that, The process of generating the tripartite binding relationship includes: The sample garment order records are sorted according to the receipt time, and the video records are sorted according to the video release time; Within the same candidate set, video records whose video publication time is later than the receipt time are considered as bindable video records; When multiple bindable video records correspond to the same clothing order record, the first bindable video record in the first-in-first-out order is selected to generate the three-party binding relationship. If no video record can be bound, the corresponding sample garment order record will be written into the abnormal pending record.

7. The method for automatic matching and association of e-commerce business data according to claim 1, characterized in that, The process of generating the candidate records to be processed includes: When multiple videos correspond to the same shipping order, read the video publication time, video identifier, and corresponding candidate matching relationship for each video; Multiple videos are sorted according to their release time. When multiple videos have the same release time, they are sorted according to the time they are written to the business record. The video that is ranked first and meets the candidate matching relationship is written into the third-party binding relationship. The remaining videos that are not written into the aforementioned three-party binding relationship will be written into the pending candidate record, and the correspondence between the shipping order, video identifier, and influencer identifier will be preserved.

8. The method for automatic matching and association of e-commerce business data according to claim 1, characterized in that, The process of generating the abnormal pending records includes: When a shipping order does not have a corresponding video, the shipping order, influencer ID, and receipt time will be marked as an order without a video. When a video does not have a corresponding order, the video identifier, influencer identifier, and video posting time will be marked as having no orders. When an order number is missing, the corresponding business record will be marked as an order number pending addition. After the order number is supplemented, the candidate matching relationship generation process is re-triggered based on the supplemented order number, and the three-party binding relationship is written back after the three-party binding conditions are met.

9. The method for automatic matching and association of e-commerce business data according to claim 1, characterized in that, The generation process of the advertising intent status, the influencer status, and the overdue performance warning record includes: When the video recording and the advertising data meet the correspondence between the influencer identifier, video identifier, or advertising identifier, the corresponding advertising data will be written into the business record and the advertising intent status will be updated. By comparing whether the time difference between the video release time and the time when the video sales are generated meets a preset time difference threshold, and combining the matching judgment result of the relationship between the responsible person's email and the store, an influencer status judgment result is generated, and the influencer status is updated according to the influencer status judgment result. The video posting data is retrieved based on at least one of the following: influencer identifier, video identifier, and video posting time, to generate video search results. When the time difference between the receipt time and the time data in the video search results exceeds the preset overdue threshold and meets the overdue triggering condition, the overdue performance warning record is generated based on the responsible person's email address, the influencer's status, and the advertising intent status.

10. An automatic matching and association system for e-commerce business data, used to support the automatic matching and association method for e-commerce business data as described in any one of claims 1 to 9, characterized in that, It includes a communication interface, processor, memory, and business database. The communication interface receives cross-border e-commerce influencer business data, which includes influencer information, responsible personnel email, sample garment application data, shipping orders, logistics receipt data, video release data, video sales, store relationship data, and advertising placement data. The processor calls the program in memory to standardize the fields of the cross-border e-commerce influencer business data, divides it into influencer records and video records, and writes them into the business database to form business records. The processor associates sample garment application data with shipping orders to form sample garment order records based on business records, and generates candidate matching relationships based on influencer records, sample garment order records, and video records in the business records; When the video release time is later than the receipt time and the time sequence is satisfied, the processor generates a three-party binding relationship. When a shipping order corresponds to multiple videos, the processor binds the first video that meets the candidate matching relationship and writes it into the candidate record to be processed. When data is missing, the processor writes an exception pending record. When the video record and the advertising data meet the correspondence between the influencer identifier, video identifier, or advertising placement identifier, the processor updates the advertising intent status and writes it into the business record. The processor updates the influencer status based on the video release time, video sales generation time, responsible person's email address, and store relationship in the business records. After retrieving video search results from the video release data, the processor outputs a performance overdue warning record through the communication interface based on the receipt time, video search results, responsible person's email address, and advertising intent status in the business records.

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