Logistics order data correlation analysis method, device and equipment and storage medium

Abstract

This invention relates to the field of logistics technology and discloses a method for correlation analysis of logistics order data. The method includes: processing differentiated lists by performing differentiated processing on customer list data based on its source channel; calling an external address resolution service based on its address information to obtain point-of-interest (POI) names, codes, and geographic coordinates; performing semantic matching by inputting the customer list address, POI information, and geographic coordinates into a semantic understanding matching model to calculate the matching confidence of the address and POI; predicting order trends by predicting the daily order volume and fluctuation range for a specified number of days in the future based on historical reverse order data; and generating statistical indicators. This invention achieves high-precision address identification by differentiated processing of customer list data and combining external address resolution with semantic matching, effectively solving the problems of low address identification accuracy and weak reverse logistics correlation analysis capabilities, and improving the accuracy, correlation, and timeliness of logistics order data analysis.

Description

Technical Field

[0001] This invention relates to the field of logistics information technology, and in particular to a method, apparatus, equipment and storage medium for logistics order data correlation analysis. Background Technology

[0002] In the e-commerce platform and logistics industry, the cleaning and processing of customer list data is fundamental to supporting business decisions and operational optimization. Currently, relevant data processing technologies mainly focus on channel source identification, customer address parsing, and e-commerce platform identification, but the following limitations still exist: First, address recognition accuracy is insufficient. Existing methods typically only resolve addresses to the province, city, and district levels, failing to pinpoint detailed levels such as streets and towns. This results in limited accuracy when spatially matching specific logistics orders, hindering refined analysis based on geographic location. Second, cross-data source correlation analysis capabilities are lacking, particularly in deep integration with reverse logistics (such as returns, exchanges, and repair item recycling) order data. This makes it impossible to effectively assess customer activity, identify consumption behavior patterns, or provide early warnings of related business trends. Furthermore, order trend statistics are limited in scope. Existing systems mostly provide basic order quantity statistics, making it difficult to support the construction and analysis of complex business indicators such as "whether there are reverse orders within a specific period." Finally, the data status update mechanism is inadequate, lacking dynamic response capabilities to changes in the business identifier status in the customer list. Data updates rely on periodic batch processing, leading to lag in analysis results and impacting the timeliness and accuracy of decision-making.

[0003] Therefore, it is necessary to invent a method, device, equipment, and storage medium for logistics order data association analysis that has high address recognition accuracy, strong correlation analysis capability, diverse statistical dimensions, and the ability to be updated. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for logistics order data correlation analysis, which is used to perform high-precision address identification, correlation analysis, statistical analysis of diverse dimensions, and updates to logistics order data.

[0005] The first aspect of this invention provides a method for correlation analysis of logistics order data, the method comprising: Process the differentiated list by performing differentiated processing on the customer list data based on the source channel of the customer list data; To obtain point of interest information, for customer list data that lacks specific business identifiers, an external address resolution service is called based on the address information to obtain the point of interest name, code, and geographic coordinates; Perform semantic matching by inputting customer list addresses, points of interest information and geographic coordinates into the semantic understanding matching model, calculating the matching confidence between the address and the point of interest, and determining the matching validity based on a preset threshold; Predict order trends by using a fusion time series forecasting model based on historical reverse order data to predict the daily order volume and fluctuation range for a specified number of days in the future. Generate statistical indicators, integrate the results of differential processing, semantic matching and order prediction, calculate and output statistical indicators.

[0006] Optionally, in a first implementation of the first aspect of the present invention, the process of processing the differentiated list, which involves performing differentiated processing on the customer list data based on the source channel of the customer list data, includes: Identify the source of the customer list data; The address collection interface is called to parse the customer's address and identify the address at the province, city, district, and street level. For addresses whose districts cannot be identified, their original data is retained. Based on a pre-defined keyword database, the system identifies the platform to which a customer belongs and marks customers whose platforms cannot be identified as having no platform.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the step of obtaining point-of-interest information, for customer list data lacking specific business identifiers, involves calling an external address resolution service based on the address information to obtain the point-of-interest name, code, and geographic coordinates, including: Detect whether a specific business identifier exists in the customer list data; For customer list data where the specific business identifier is empty, extract their address information; Use the address information as input parameters to call the address resolution interface; Receive and parse the response returned by the interface to obtain the point of interest name, point of interest code and geographic coordinates.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the step of performing semantic matching, which involves inputting customer list addresses, points of interest information, and geographic coordinates into a semantic understanding matching model, calculating the matching confidence between the addresses and the points of interest, and determining the matching validity based on a preset threshold, includes: Input the customer list, which includes province, city, district, street, address, point of interest name, and geographic coordinates, into the semantic understanding matching model; The model performs semantic analysis on the address text and point of interest names, and integrates geographic coordinate information to calculate the matching confidence. If the matching confidence level exceeds a preset threshold, it is determined to be a valid match; otherwise, it is marked as requiring manual review.

[0009] Optionally, in the fourth implementation of the first aspect of the present invention, the prediction of order trends, based on historical reverse order data and using a fused time series prediction model, predicts the daily order volume and fluctuation range for a specified number of days in the future, including: Obtain the volume of reverse orders placed by customers within a preset historical period and construct time-series data; The time series data is input into a fusion time series forecasting model to predict the daily order volume and fluctuation range within a specified number of days in the future. Based on the forecast results, calculate and output the recent forecast average daily order volume and forecast error rate.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the step of generating statistical indicators, integrating the results of differential processing, semantic matching, and order prediction, and calculating and outputting statistical indicators includes: Integrate the differentiated processing results, semantic matching judgment results, and order trend prediction results of customer list data; Based on the integrated results, correlation statistical calculations are performed; Calculate and generate core statistical indicators.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, the step of performing semantic matching, which involves inputting customer list addresses, points of interest information, and geographic coordinates into a semantic understanding matching model, calculating the matching confidence between the addresses and the points of interest, and determining the matching validity based on a preset threshold, further includes: Track and statistically analyze the matching success rate of each address field during the semantic matching process between addresses and points of interest; When a change in the matching success rate of a specific address field is detected, the weight of that field in the semantic understanding matching model is automatically adjusted when calculating the matching confidence.

[0012] A second aspect of the present invention provides a logistics order data correlation analysis device, comprising: The differential list processing module is used to perform differential processing on the customer list data according to the source channel of the customer list data; The module for processing the differential list includes: The identification unit is used to identify the source channel of the customer list data; The calling unit is used to call the address collection interface to parse the client address, identify the address at the province, city, district and street level, and retain the original data for addresses that cannot be identified by district. The tagging unit is used to identify the platform to which a customer belongs based on a preset keyword library, and to mark the list of customers that cannot be identified as having no platform.

[0013] The Point of Interest (POI) information acquisition module is used for customer list data that lacks specific business identifiers. Based on their address information, it calls an external address resolution service to obtain the POI name, code, and geographic coordinates. The module for obtaining point of interest information includes: The detection unit is used to detect whether a specific business identifier exists in the customer list data; The extraction unit is used to extract the address information of customer list data where the specific business identifier is empty; The parsing unit is used to call the address parsing interface by taking the address information as input parameters; The acquisition unit is used to receive and parse the response returned by the interface to obtain the point of interest name, point of interest code and geographic coordinates.

[0014] The semantic matching module is used to input customer list addresses, points of interest information and geographic coordinates into the semantic understanding matching model, calculate the matching confidence between the address and the point of interest, and determine the matching validity based on a preset threshold. The semantic matching module includes: The input unit is used to input the customer list address, point of interest name and geographic coordinates containing the province, city, district and street of the customer into the semantic understanding and matching model; The calculation unit is used to perform semantic analysis on the address text and point of interest names by the model, and to calculate the matching confidence by integrating geographic coordinate information; The determination unit is used to determine a valid match if the matching confidence exceeds a preset threshold; otherwise, it marks the match as requiring manual review.

[0015] The order trend prediction module is used to predict the daily order volume and fluctuation range for a specified number of days in the future based on historical reverse order data and a fusion time series prediction model. The order trend prediction module includes: The building unit is used to obtain the number of reverse orders from customers within a preset historical period and build time series data; The prediction unit is used to input the time series data into the fusion time series prediction model to predict the daily order volume and fluctuation range within a specified number of days in the future. The output unit is used to calculate and output the recent forecast average daily order volume and forecast error rate based on the forecast results.

[0016] The statistical indicator generation module integrates the results of differential processing, semantic matching, and order prediction to calculate and output statistical indicators.

[0017] The module for generating statistical indicators includes: The integration unit is used to integrate the differentiated processing results, semantic matching judgment results, and order trend prediction results of customer list data; Statistical units are used to perform correlation statistical calculations based on the integrated results; The generation unit is used to calculate and generate core statistical indicators.

[0018] The semantic matching process involves inputting customer list addresses, points of interest information, and geographic coordinates into a semantic understanding matching model, calculating the matching confidence between the addresses and the points of interest, and determining the matching validity based on a preset threshold. It also includes: Track and statistically analyze the matching success rate of each address field during the semantic matching process between addresses and points of interest; When a change in the matching success rate of a specific address field is detected, the weight of that field in the semantic understanding matching model is automatically adjusted when calculating the matching confidence.

[0019] A third aspect of the present invention provides a logistics order data correlation analysis device, including a memory and at least one processor, wherein the memory stores computer-readable instructions; The at least one processor invokes the computer-readable instructions in the memory to perform the various steps of the logistics order data association analysis method described above.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the various steps of the logistics order data association analysis method described above.

[0021] This invention achieves high-precision address identification by differentially processing customer list data, combining external address parsing and semantic matching, integrating reverse logistics data to complete order trend prediction, and integrating multi-dimensional results to generate composite statistical indicators. It effectively solves the problems of low address identification accuracy, weak reverse logistics correlation analysis capability, single order statistical dimensions, and lagging data updates in existing technologies. It significantly improves the accuracy, correlation, and timeliness of logistics order data analysis, increases statistical dimensions, and provides comprehensive and reliable data support for logistics business decision-making and operational optimization. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the logistics order data correlation analysis method of the present invention; Figure 2 This is a flowchart illustrating the process of differentiating the customer list according to the present invention. Figure 3 This is a schematic diagram of the process for obtaining point of interest information according to the present invention; Figure 4This is a schematic diagram illustrating the semantic matching process of the present invention; Figure 5 This is a schematic diagram of the process for predicting order trends according to the present invention; Figure 6 This is a flowchart illustrating the process of generating statistical indicators for this invention. Figure 7 This is a schematic diagram of the structure of the logistics order data correlation analysis device of the present invention; Figure 8 This is a schematic diagram of the logistics order data correlation analysis device of the present invention. Detailed Implementation

[0023] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 The first embodiment of the logistics order data association analysis method in this invention includes: S101. Process the differentiated list: Perform differentiated processing on the customer list data according to the source channel of the customer list data; S102. Obtain point of interest information. For customer list data that lacks specific business identifiers, call the external address resolution service based on their address information to obtain the point of interest name, code and geographic coordinates. S103. Perform semantic matching by inputting the customer list address, point of interest information and geographic coordinates into the semantic understanding matching model, calculating the matching confidence between the address and the point of interest, and determining the matching validity based on a preset threshold. S104. Predict order trends: Based on historical reverse order data, use a fusion time series forecasting model to predict the daily order volume and fluctuation range for a specified number of days in the future. S105. Generate statistical indicators, integrate the results of differential processing, semantic matching and order prediction, calculate and output statistical indicators.

[0025] This invention achieves high-precision address identification by differentially processing customer list data and combining external address parsing and semantic matching. It integrates reverse logistics data to complete order trend prediction and combines multi-dimensional results to generate composite statistical indicators. This effectively solves the problems of low address identification accuracy, weak reverse logistics correlation analysis capability, single order statistical dimension, and lagging data update in the prior art. It greatly improves the accuracy, correlation, and timeliness of logistics order data analysis, increases statistical dimensions, and provides comprehensive and reliable data support for logistics business decision-making and operational optimization.

[0026] Please see Figure 2 In a second embodiment of the logistics order data association analysis method of the present invention, the processing of the differentiated list, which involves performing differentiated processing on the customer list data based on the source channel of the customer list data, includes: S201. Identify the source channel of the customer list data; S202. Call the address collection interface to parse the client address, identify the address at the province, city, district and street level, and retain the original data for addresses that cannot be identified by district. S203. Based on a preset keyword database, identify the platform to which the customer belongs, and mark the list of customers that cannot be identified as having no platform.

[0027] This invention first identifies the source channels of customer list data, then parses addresses down to the street level, and automatically identifies the platform to which the customer belongs based on a keyword database. This achieves standardized and differentiated processing of list data from different sources. This not only improves the granularity of address parsing and makes up for the shortcomings of traditional address identification hierarchy, but also standardizes the data format through unified platform identification rules, reduces manual processing costs, and lays a high-quality data foundation for subsequent address semantic matching and cross-source association analysis.

[0028] Please see Figure 3 The third embodiment of the logistics order data association analysis method in this invention includes obtaining point of interest information. For customer list data lacking specific business identifiers, the method calls an external address resolution service based on the address information to obtain the point of interest name, code, and geographic coordinates, including: S301. Detect whether a specific business identifier exists in the customer list data; S302. For customer list data where the specific business identifier is empty, extract its address information; S303. Use the address information as input parameters to call the address resolution interface; S304. Receive and parse the response returned by the interface to obtain the point of interest name, point of interest code and geographic coordinates.

[0029] In this embodiment of the invention, for customer lists lacking specific business identifiers, an external address resolution service is invoked to obtain the name, code, and geographic coordinates of points of interest. This not only improves the high-precision geographic data of addresses and breaks through the limitations of traditional address resolution hierarchy, but also avoids invalid processing and improves the targeting and efficiency of data processing.

[0030] Please see Figure 4 In the fourth embodiment of the logistics order data association analysis method of this invention, the step of performing semantic matching involves inputting customer list addresses, points of interest information, and geographic coordinates into a semantic understanding matching model, calculating the matching confidence between the address and the points of interest, and determining the matching validity based on a preset threshold, including: S401. Input the customer list, including province, city, district, street, address, point of interest name, and geographic coordinates, into the semantic understanding matching model. S402. The model performs semantic analysis on the address text and point of interest names, and integrates geographic coordinate information to calculate the matching confidence. S403. If the matching confidence level exceeds the preset threshold, it is determined to be a valid match; otherwise, it is marked as requiring manual review.

[0031] This invention integrates multi-dimensional address information and point-of-interest data into a semantic understanding matching model. It combines text semantics and geographic coordinates to calculate matching confidence and determine matching validity, achieving high-precision address matching from the provincial, municipal, and district levels down to the street and township levels. At the same time, it distinguishes between automatic matching and manual review scenarios by using threshold judgment, improving the accuracy of address matching, reducing unnecessary manual intervention, and ensuring matching efficiency and accuracy.

[0032] Please see Figure 5 The fifth embodiment of the logistics order data correlation analysis method in this invention includes predicting order trends based on historical reverse order data and using a fusion time series prediction model to predict the daily order volume and fluctuation range for a specified number of days in the future, comprising: S501. Obtain the volume of reverse orders from customers within a preset historical period and construct time series data; S502. Input the time series data into the fusion time series prediction model to predict the daily order volume and fluctuation range within a specified number of days in the future; S503. Based on the prediction results, calculate and output the recent predicted daily average order volume and prediction error rate.

[0033] In this embodiment of the invention, a time series is constructed based on historical reverse order data of a preset period. By integrating the time series prediction model, the daily order volume and fluctuation range of a specified number of days in the future are accurately predicted, and the average daily order volume and error rate are output, which breaks through the limitation of the existing technology that lacks in-depth analysis of reverse logistics data.

[0034] Furthermore, this not only enables quantitative prediction of reverse order trends, but also improves the credibility of prediction results through error rate feedback. It can effectively assess customer activity, provide early warning of business trends, and provide accurate quantitative basis for reverse logistics resource allocation and operational decision-making.

[0035] Please see Figure 6 The sixth embodiment of the logistics order data association analysis method in this invention includes generating statistical indicators, integrating the results of differential processing, semantic matching, and order prediction, and calculating and outputting the statistical indicators, including: S601, integrate the differentiated processing results, semantic matching judgment results, and order trend prediction results of customer list data; S602. Based on the integrated results, perform correlation statistical calculations; S603. Calculate and generate core statistical indicators.

[0036] Furthermore, the step of performing semantic matching, which involves inputting customer list addresses, points of interest information, and geographic coordinates into a semantic understanding matching model, calculating the matching confidence between the addresses and the points of interest, and determining the matching validity based on a preset threshold, also includes: Track and statistically analyze the matching success rate of each address field during the semantic matching process between addresses and points of interest; When a change in the matching success rate of a specific address field is detected, the weight of that field in the semantic understanding matching model is automatically adjusted when calculating the matching confidence.

[0037] This invention integrates multi-dimensional data to generate composite core statistical indicators, enriching the dimensions of order analysis; at the same time, it dynamically adjusts the address field weight of the semantic matching model to achieve adaptive optimization of the model, which not only solves the problem of single traditional statistical dimensions, but also ensures the accuracy of address matching, and improves the practicality and precision of data analysis.

[0038] The logistics order data correlation analysis method in the embodiments of the present invention has been described above. The apparatus in the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 7 The implementation method of the logistics order data correlation analysis device in this embodiment of the invention includes: The differential list processing module 701 is used to perform differential processing on the customer list data according to the source channel of the customer list data; In some embodiments, the processing differential list module 701 includes: Identification unit 7011 is used to identify the source channel of the customer list data; Calling unit 7012 is used to call the address collection interface to parse customer addresses, identify addresses at the province, city, district, and street levels, and retain the original data for addresses whose districts cannot be identified. The tagging unit 7013 is used to identify the platform to which a customer belongs based on a preset keyword library, and to mark the list of customers that cannot be identified as having no platform.

[0039] This invention first identifies the source channels of customer list data, then parses addresses down to the street level, and automatically identifies the platform to which the customer belongs based on a keyword database. This achieves standardized and differentiated processing of list data from different sources. This not only improves the granularity of address parsing and makes up for the shortcomings of traditional address identification hierarchy, but also standardizes the data format through unified platform identification rules, reduces manual processing costs, and lays a high-quality data foundation for subsequent address semantic matching and cross-source association analysis.

[0040] The Point of Interest Information Acquisition Module 702 is used for customer list data that lacks specific business identifiers. Based on their address information, it calls an external address resolution service to obtain the name, code, and geographic coordinates of the Point of Interest. In some embodiments, the point of interest information acquisition module 702 includes: Detection unit 7021 is used to detect whether a specific business identifier exists in the customer list data; Extraction unit 7022 is used to extract address information from customer list data where the specific business identifier is empty; The parsing unit 7023 is used to call the address parsing interface by taking the address information as input parameters; The acquisition unit 7024 is used to receive and parse the response returned by the interface to obtain the point of interest name, point of interest code and geographic coordinates.

[0041] In this embodiment of the invention, for customer lists lacking specific business identifiers, an external address resolution service is invoked to obtain the name, code, and geographic coordinates of points of interest. This not only improves the high-precision geographic data of addresses and breaks through the limitations of traditional address resolution hierarchy, but also avoids invalid processing and improves the targeting and efficiency of data processing.

[0042] The semantic matching module 703 is used to input customer list addresses, points of interest information and geographic coordinates into the semantic understanding matching model, calculate the matching confidence of the address and the points of interest, and determine the matching validity based on a preset threshold. In some embodiments, the semantic matching module 703 includes: Input unit 7031 is used to input the customer list address, point of interest name and geographic coordinates containing province, city, district and street into the semantic understanding matching model; The calculation unit 7032 is used to perform semantic analysis on the address text and point of interest name by the model, and to calculate the matching confidence by integrating geographic coordinate information; The determination unit 7033 is used to determine a valid match if the matching confidence exceeds a preset threshold; otherwise, it is marked as requiring manual review.

[0043] This invention integrates multi-dimensional address information and point-of-interest data into a semantic understanding matching model. It combines text semantics and geographic coordinates to calculate matching confidence and determine matching validity, achieving high-precision address matching from the provincial, municipal, and district levels down to the street and township levels. At the same time, it distinguishes between automatic matching and manual review scenarios by using threshold judgment, improving the accuracy of address matching, reducing unnecessary manual intervention, and ensuring matching efficiency and accuracy.

[0044] The order trend prediction module 704 is used to predict the daily order volume and fluctuation range for a specified number of days in the future based on historical reverse order data and a fusion time series prediction model. In some embodiments, the order trend prediction module 704 includes: Construction unit 7041 is used to obtain the number of reverse orders from customers within a preset historical period and construct time series data; Prediction unit 7042 is used to input the time series data into a fused time series prediction model to predict the daily order volume and fluctuation range within a specified number of days in the future; Output unit 7043 is used to calculate and output the recent forecast average daily order volume and forecast error rate based on the forecast results.

[0045] In this embodiment of the invention, a time series is constructed based on historical reverse order data of a preset period. By integrating the time series prediction model, the daily order volume and fluctuation range of a specified number of days in the future are accurately predicted, and the average daily order volume and error rate are output, which breaks through the limitation of the existing technology that lacks in-depth analysis of reverse logistics data.

[0046] The statistical indicator generation module 705 is used to integrate the results of differential processing, semantic matching and order prediction, and calculate and output statistical indicators.

[0047] In some embodiments, the statistical indicator generation module 705 includes: Integration unit 7051 is used to integrate the differential processing results, semantic matching judgment results, and order trend prediction results of customer list data; Statistical unit 7052 is used to perform correlation statistical calculations based on the integrated results; Generation unit 7053 is used to calculate and generate core statistical indicators.

[0048] Furthermore, the step of performing semantic matching, which involves inputting customer list addresses, points of interest information, and geographic coordinates into a semantic understanding matching model, calculating the matching confidence between the addresses and the points of interest, and determining the matching validity based on a preset threshold, also includes: Track and statistically analyze the matching success rate of each address field during the semantic matching process between addresses and points of interest; When a change in the matching success rate of a specific address field is detected, the weight of that field in the semantic understanding matching model is automatically adjusted when calculating the matching confidence.

[0049] This invention integrates multi-dimensional data to generate composite core statistical indicators, enriching the dimensions of order analysis; at the same time, it dynamically adjusts the address field weight of the semantic matching model to achieve adaptive optimization of the model, which not only solves the problem of single traditional statistical dimensions, but also ensures the accuracy of address matching, and improves the practicality and precision of data analysis.

[0050] Figure 7 The structure of the logistics order data association analysis device shown does not constitute a limitation on the logistics order data association analysis device, and can implement the steps of the logistics order data association analysis method provided in the above method embodiments.

[0051] above Figure 7 The logistics order data association analysis device in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The logistics order data association analysis device in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0052] Figure 8 This is a schematic diagram of the structure of a logistics order data correlation analysis device provided in an embodiment of the present invention. The device 800 can vary significantly depending on its configuration and performance, and may include one or more central processing units (CPUs) 810 (e.g., one or more processors) and a memory 820, and one or more storage media 830 (e.g., one or more mass storage devices) for storing application programs 833 or data 832. The memory 820 and storage media 830 can be temporary or persistent storage. The program stored in the storage media 830 may include one or more modules (not shown), each module including a series of instruction operations on the device 800. Furthermore, the processor 810 may be configured to communicate with the storage media 830 and execute the series of instruction operations in the storage media on the device 800.

[0053] Device 800 may also include one or more power supplies 840, one or more wired or wireless network interfaces 850, one or more input / output interfaces 860, and / or one or more operating systems 831, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.

[0054] This invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of a logistics order data association analysis method.

[0055] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0056] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0057] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for correlation analysis of logistics order data, characterized in that, include: Process the differentiated list by performing differentiated processing on the customer list data based on the source channel of the customer list data; To obtain point of interest information, for customer list data that lacks specific business identifiers, an external address resolution service is invoked based on their address information to obtain the point of interest name, code, and geographic coordinates. Perform semantic matching by inputting customer list addresses, points of interest information and geographic coordinates into the semantic understanding matching model, calculating the matching confidence between the address and the point of interest, and determining the matching validity based on a preset threshold; Predict order trends by using a fusion time series forecasting model based on historical reverse order data to predict the daily order volume and fluctuation range for a specified number of days in the future. Generate statistical indicators, integrate the results of differential processing, semantic matching and order prediction, calculate and output statistical indicators.

2. The logistics order data correlation analysis method according to claim 1, characterized in that, The process of creating a differentiated list involves performing differentiated processing on the customer list data based on the source channel of the customer list data, including: Identify the source channels of the customer list data; The address collection interface is called to parse the customer's address and identify the address at the province, city, district, and street level. For addresses whose districts cannot be identified, their original data is retained. Based on a pre-defined keyword database, the system identifies the platform to which a customer belongs and marks customers whose platforms cannot be identified as having no platform.

3. The logistics order data correlation analysis method according to claim 2, characterized in that, The acquisition of Points of Interest (POI) information, for customer list data lacking specific business identifiers, involves calling an external address resolution service based on their address information to obtain the POI name, code, and geographic coordinates, including: Detect whether a specific business identifier exists in the customer list data; For customer list data where the specific business identifier is empty, extract their address information; Use the address information as input parameters to call the address resolution interface; Receive and parse the response returned by the interface to obtain the point of interest name, point of interest code and geographic coordinates.

4. The logistics order data correlation analysis method according to claim 3, characterized in that, The semantic matching process involves inputting customer list addresses, points of interest (POIs), and geographic coordinates into a semantic understanding matching model, calculating the matching confidence between the addresses and POIs, and determining the matching validity based on a preset threshold. This includes: Input the customer list, which includes province, city, district, street, address, point of interest name, and geographic coordinates, into the semantic understanding matching model; The model performs semantic analysis on the address text and point of interest names, and integrates geographic coordinate information to calculate the matching confidence. If the matching confidence level exceeds a preset threshold, it is determined to be a valid match; otherwise, it is marked as requiring manual review.

5. The logistics order data correlation analysis method according to claim 4, characterized in that, The predicted order trend, based on historical reverse order data, utilizes a fusion time series forecasting model to predict the daily order volume and fluctuation range for a specified number of days in the future, including: Obtain the volume of reverse orders placed by customers within a preset historical period and construct time-series data; The time series data is input into a fusion time series forecasting model to predict the daily order volume and fluctuation range within a specified number of days in the future. Based on the prediction results, calculate and output the recent predicted average daily order volume and prediction error rate.

6. The logistics order data correlation analysis method according to claim 5, characterized in that, The generated statistical indicators integrate the results of differential processing, semantic matching, and order prediction, and calculate and output statistical indicators, including: Integrate the differentiated processing results, semantic matching judgment results, and order trend prediction results of customer list data; Based on the integrated results, correlation statistical calculations are performed; Calculate and generate core statistical indicators.

7. The logistics order data correlation analysis method according to claim 4, characterized in that, The semantic matching process involves inputting customer list addresses, points of interest information, and geographic coordinates into a semantic understanding matching model, calculating the matching confidence between the addresses and the points of interest, and determining the matching validity based on a preset threshold. It also includes: Track and statistically analyze the matching success rate of each address field during the semantic matching process between addresses and points of interest; When a change in the matching success rate of a specific address field is detected, the weight of that field in the semantic understanding matching model is automatically adjusted when calculating the matching confidence.

8. A logistics order data correlation analysis device, characterized in that, include: The differential list processing module is used to perform differential processing on the customer list data according to the source channel of the customer list data; The Point of Interest (POI) information acquisition module is used for customer list data that lacks specific business identifiers. Based on their address information, it calls an external address resolution service to obtain the POI name, code, and geographic coordinates. The semantic matching module is used to input customer list addresses, points of interest information and geographic coordinates into the semantic understanding matching model, calculate the matching confidence between the address and the point of interest, and determine the matching validity based on a preset threshold. The order trend prediction module is used to predict the daily order volume and fluctuation range for a specified number of days in the future based on historical reverse order data and a fusion time series prediction model. The statistical indicator generation module integrates the results of differential processing, semantic matching, and order prediction to calculate and output statistical indicators.

9. A logistics order data correlation analysis device, characterized in that, It includes a memory and at least one processor, wherein the memory stores computer-readable instructions; The at least one processor invokes the computer-readable instructions in the memory to perform the various steps of the logistics order data association analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer-readable instructions thereon, characterized in that, When the computer-readable instructions are executed by the processor, they implement the various steps of the logistics order data association analysis method as described in any one of claims 1-7.

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