Customer waybill straightening method, apparatus, computer device, and storage medium

Abstract

The application relates to the technical field of logistics information processing, and provides a customer waybill straightening method and device, computer equipment and a storage medium. The method obtains to-be-straightened waybill data by classifying customer waybill data according to sending companies and sending points; excludes waybill data with a distribution flow frequency less than or equal to 1 from the to-be-straightened waybill data, and obtains the corresponding attribution distribution information of each to-be-straightened waybill data; generates a distribution and goods flow direction table according to the attribution distribution information; extracts to-be-straightened waybill data with a customer type of a sending company or a payment company and a weight greater than or equal to a preset weight from the distribution and goods flow direction table, and obtains corresponding non-straightened goods quantity information and should-straightened goods quantity information; generates analysis result information according to the to-be-straightened waybill data, the non-straightened goods quantity information and the should-straightened goods quantity information, and completes straightening of the customer waybill data. The method is suitable for transportation line optimization and distribution resource allocation in a logistics transportation network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics information processing, and in particular to a customer waybill straightening method and device, computer equipment and a storage medium. BACKGROUND

[0002] In logistics transportation management, the straightening processing of customer waybills (i.e. reducing unnecessary distribution links and improving transportation efficiency by optimizing transportation routes) is a key link to reduce transportation costs and shorten transportation time. In the prior art, the following problems exist in the processing of customer waybills:

[0003] 1. Extensive data preprocessing: the traditional method does not perform accurate filtering for specific transportation modes (such as whole vehicle transportation, direct flight / air-to-air transportation), resulting in a large amount of invalid data in subsequent analysis, and lacking a unified data format and field mapping standard, poor data compatibility;

[0004] 2. Blind distribution flow configuration: the selection rules for the number of waybill distribution flows are not clear, which cannot effectively exclude low-value distribution data, and the determination of the attribution distribution information relies on manual experience, without forming a systematic attribution rule based on actual flow logs, resulting in insufficient scientificity of the distribution of goods flow direction table;

[0005] 3. Lack of quantitative basis for straightening strategy: the existing scheme does not analyze the volume of goods in combination with customer types (such as sending companies and payment companies) and weight thresholds, which cannot accurately assess the difference between the volume of goods that are not straightened and the volume of goods that should be straightened, resulting in a lack of data support for the priority ranking and effect evaluation of straightening operations, making it difficult to achieve optimal allocation of transportation resources.

[0006] Therefore, there is an urgent need for a method to solve at least one of the above problems. SUMMARY

[0007] The present application provides a customer waybill straightening method, device, computer equipment and storage medium, aiming to solve the problems of extensive data preprocessing, blind distribution flow configuration and lack of quantitative basis for straightening strategy in the prior art.

[0008] In a first aspect, the present application provides a customer waybill straightening method, comprising:

[0009] performing general pre-filtering on the obtained customer waybill data, excluding waybills with service mode of whole vehicle transportation, and excluding waybills with transportation mode of direct flight and air-to-air, and unifying the data format and field mapping of the customer waybill data; obtaining waybill data to be straightened according to the dimensions of sending company and sending point;

[0010] Excluding the waybill data with the number of distribution flow-throughs less than or equal to 1 from the to-be-straightened waybill data, obtaining the corresponding home distribution information of each of the to-be-straightened waybill data; and generating a distribution-configured cargo flow direction table according to the plurality of home distribution information.

[0011] Extracting the to-be-straightened waybill data with the customer type being a sending company or a payment company and the weight being greater than or equal to a preset weight from the distribution-configured cargo flow direction table, and obtaining the corresponding un-straightened cargo volume information and the should-straightened cargo volume information;

[0012] Generating analysis result information according to the to-be-straightened waybill data, the un-straightened cargo volume information, and the should-straightened cargo volume information, and completing the straightening of the customer waybill data according to the analysis result information.

[0013] In some embodiments, the obtained customer waybill data is subjected to general pre-filtering, the waybills with the service mode being whole vehicle transportation are excluded, and the waybills with the transportation mode being direct flight and air-to-air are excluded, the data format and field mapping of the customer waybill data are unified, including: analyzing the service mode field and the transportation mode field in the customer waybill data, matching the value of the service mode field with a preset whole vehicle transportation service identifier, and matching the value of the transportation mode field with a preset direct flight transportation mode identifier and an air-to-air transportation mode identifier; logically deleting or marking the filtered state of the waybill data with the service mode matching the whole vehicle transportation identifier or the transportation mode matching the direct flight or air-to-air identifier; performing field standardization processing on the unfiltered waybill data, including mapping data items with the same meaning but different field names in different customer systems to a unified standard field, converting unstructured data into a structured data format, and filling in default values or marking abnormal data for missing fields.

[0014] In some embodiments, the exclusion of the waybill data with the number of distribution flow-throughs less than or equal to 1 from the to-be-straightened waybill data and the obtaining of the corresponding home distribution information of each of the to-be-straightened waybill data include: extracting the distribution node flow log of the waybill for the to-be-straightened waybill data filtered according to the sending company and the sending point, counting the number of distribution center nodes actually passed by each waybill in the transportation process as the number of distribution flow-throughs; retaining the waybill data with the number of distribution flow-throughs greater than 1 and excluding the waybill data with the number of distribution flow-throughs less than or equal to 1; and for the retained waybill data, determining the home distribution center corresponding to the waybill as the home distribution information according to the last actual operation distribution center node information in the distribution node flow log and combining a preset distribution center home rule, the distribution center home rule including home matching according to the geographical area, the operation subject, or the network level of the distribution node.

[0015] In some embodiments, obtaining the corresponding unstraightened cargo volume information and straightened cargo volume information includes: filtering waybill data from the cargo flow table where the customer type field is the sending company or the payment company, and extracting the cargo weight field from the waybill; comparing the cargo weight with a preset weight threshold, and retaining waybill data where the cargo weight is greater than or equal to the preset weight threshold; for the retained waybill data, calculating the total cargo weight of waybills not marked as straightened as unstraightened cargo volume information, and calculating the total cargo weight of all waybills that meet the customer type and weight conditions as straightened cargo volume information, wherein the preset weight threshold is pre-configured based on the straightening cost and efficiency parameters of the transportation network.

[0016] In some embodiments, generating a cargo flow table based on multiple attribution and distribution information includes: grouping the to-be-directed waybill data with the same attribution and distribution information into groups; aggregating and statistically analyzing the transportation destination, transportation route, and transportation timeliness requirements of each group of waybill data; generating cargo flow records with the attribution and distribution center as the starting point and the transportation destination as the ending point based on the aggregation results corresponding to the aggregation statistics; each flow record includes the number of waybills, cargo weight distribution, and configuration information of commonly used transportation route types for that flow; and performing deduplication and standardization processing on the flow records to form a cargo flow table containing distribution center codes, destination codes, transportation route rules, and cargo type adaptation conditions. The cargo flow table is used to guide cargo sorting and transportation route planning at the distribution center.

[0017] In some embodiments, generating analysis results based on the data of waybills to be straightened, the information on unstraightened cargo volume, and the information on cargo volume to be straightened includes: establishing an analysis data model to associate the information of the sending company, sending point, and affiliated distribution center in the data of waybills to be straightened with the information on unstraightened cargo volume and the information on cargo volume to be straightened; calculating the proportion of unstraightened cargo volume to cargo volume to be straightened as the straightening rate indicator, and analyzing the differences in straightening rates among different sending companies, sending points, or affiliated distribution centers; combining transportation cost data and distribution center processing efficiency data to generate a cargo volume straightening priority ranking under each dimension, forming analysis results including straightening potential assessment, cost-benefit analysis, and operational suggestions, wherein the analysis results are stored in the form of structured data tables or visual reports.

[0018] In some embodiments, the step of straightening the customer waybill data based on the analysis results includes: generating straightening operation instructions for high-priority shipping companies or shipping points based on the straightening priority ranking in the analysis results; the operation instructions include adjusting the cargo sorting rules of the distribution center to match the optimal route in the cargo flow table of the cargo allocation configuration, and sending a configuration request for straightening the transportation route to the transportation scheduling system; marking the waybill data that has undergone straightening operation as straightened, and recording the execution time, operation subject, and transportation route change information of the straightening operation; periodically backtracking and verifying the straightened waybill data, comparing the actual transportation timeliness and transportation cost with the expected indicators in the analysis results, and forming a straightening effect evaluation report for optimizing subsequent straightening strategies.

[0019] Secondly, this application provides a customer waybill straightening device, comprising:

[0020] The pre-filtering unit is used to perform general pre-filtering on the acquired customer waybill data, excluding waybills with full truckload transportation as the service method, and excluding waybills with direct flight and air-to-air transportation modes, and unifying the data format and field mapping of customer waybill data; and to obtain the waybill data to be straightened out by the dimensions of the sending company and the sending point of the customer waybill data.

[0021] The flow direction generation unit is used to exclude waybill data with a distribution flow count of less than or equal to 1 from the data of waybills to be straightened, obtain the distribution information corresponding to each of the waybill data to be straightened, and generate a cargo flow direction table for cargo allocation configuration based on the multiple distribution information;

[0022] The information acquisition unit is used to extract the data of the waybills to be straightened that are customer type sender company or payer company and have a weight greater than or equal to the preset weight from the cargo flow table of the cargo distribution configuration, and to obtain the corresponding information on the quantity of goods not straightened and the quantity of goods to be straightened.

[0023] The straightening completion unit is used to generate analysis result information based on the straightening data of the waybill to be straightened, the unstraightened cargo quantity information, and the cargo quantity to be straightened information, and to complete the straightening of the customer waybill data based on the analysis result information.

[0024] Thirdly, this application also provides a computer device, comprising:

[0025] Memory and processor;

[0026] The memory is used to store computer programs;

[0027] The processor is configured to execute the computer program and, in executing the computer program, implement the steps of the customer waybill straightening method as described in the first aspect above.

[0028] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the customer waybill straightening method described in the first aspect above.

[0029] This application provides a method, apparatus, computer equipment, and storage medium for straightening customer waybills. The method eliminates invalid waybills through a general pre-filter and standardizes the data format, improving the accuracy and efficiency of subsequent analysis and avoiding interference from invalid data. Based on the number of times the goods pass through the distribution center and the matching of the distribution rules, it ensures that the cargo flow table for cargo allocation only contains waybill data with actual distribution value, providing a scientific basis for sorting and route planning in the distribution center. Combining customer type and weight threshold, it statistically analyzes the quantity of goods that have not been straightened and the quantity that should be straightened, and generates analysis results information through a data model to realize the priority ranking and effect evaluation of straightening operations, significantly improving the resource utilization efficiency of the transportation network, reducing transportation costs, and shortening cargo turnaround time.

[0030] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic flowchart illustrating the steps of a customer waybill straightening method provided in an embodiment of this application;

[0033] Figure 2 This is a schematic diagram of the structure of a customer waybill straightening device provided in one embodiment of this application;

[0034] Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0035] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0036] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0037] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0038] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0039] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0040] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0041] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0042] In logistics and transportation management, streamlining customer waybills (i.e., optimizing transportation routes to reduce unnecessary distribution steps and improve transportation efficiency) is a key step in reducing transportation costs and shortening delivery times. Current technologies for processing customer waybills generally suffer from the following problems:

[0043] Data preprocessing is crude: Traditional methods do not perform precise filtering for specific transportation modes (such as full truckload transportation, direct flight / air-to-air transportation), resulting in a large amount of invalid data in subsequent analysis, and lack of unified data format and field mapping standards, leading to poor data compatibility;

[0044] Blindly configuring distribution flow: The filtering rules for the number of times a waybill is distributed are unclear, making it impossible to effectively exclude low-value distribution data. The determination of distribution information relies on manual experience, and no systematic attribution rules based on actual flow logs have been formed, resulting in insufficient scientificity in configuring cargo flow tables.

[0045] The straightening strategy lacks quantitative basis: the existing solution does not combine customer type (such as sending company, payment company) and cargo weight threshold for cargo volume analysis, and cannot accurately assess the difference between the unstraightened cargo volume and the cargo volume that should be straightened. As a result, the priority ranking and effect evaluation of straightening operations lack data support, making it difficult to achieve the optimal allocation of transportation resources.

[0046] While some existing technologies exist for waybill data processing or route optimization, none have developed a complete and systematic approach encompassing "multi-dimensional waybill filtering - sorting through-time screening - matching of attribution information - generation of cargo flow tables - analysis of cargo volume differences." In particular, they lack precise filtering for specific transportation modes, attribution analysis based on actual sorting node logs, and quantitative assessment combining customer type and weight thresholds. Therefore, existing technologies do not provide technical insights for achieving efficient straightening of customer waybills through the collaborative implementation of these multi-step processes. An improved method is urgently needed that can enhance data processing accuracy, scientifically configure sorting flows, and quantify straightening strategies.

[0047] To resolve the above issues, please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic flowchart illustrating a customer waybill straightening method according to an embodiment of this application. This customer waybill straightening method can be implemented using computer equipment, which can be deployed on a single server or a server cluster. It can also be deployed on handheld terminals, laptops, wearable devices, or robots, etc.

[0048] It should be noted that the acquisition of any information involved in the provided methods is in compliance with relevant regulations and is carried out with the user's consent, and will not infringe on the user's privacy or violate relevant laws and regulations.

[0049] Specifically, such as Figure 1 As shown, the provided method for straightening customer waybills includes steps S101 to S104, which are detailed below:

[0050] Step S101. Perform general pre-filtering on the acquired customer waybill data to exclude waybills with full truckload transportation as the service method, and to exclude waybills with direct flight and air-to-air transportation modes, and unify the data format and field mapping of customer waybill data; obtain the waybill data to be straightened out by the dimensions of the sending company and the sending point of the customer waybill data.

[0051] Specifically, targeting the inherent "direct" nature of certain transportation modes (full truckload transportation, direct flight / air-to-air transportation) in logistics, the system accurately filters out waybill data that needs to be streamlined and optimized through data cleaning and format standardization. It also divides the objects to be processed according to business dimensions (sending company, sending point) to provide a high-quality dataset for subsequent distribution analysis.

[0052] Precise filtering by transport mode: Define filtering rules: Exclude full truckload transport orders by using the "Service Method" label (e.g., "Full Truckload Transport" or "Less-than-Truckload Transport") in the waybill fields; exclude direct flight and air-to-air transport orders by using the "Transport Mode" label (e.g., "Direct Flight," "Air-to-Air," or "Transit Transport"), as these types of transport have no room for sorting optimization. Use regular expressions or database query statements (e.g., SQL WHERE conditions) to batch filter the original waybill data, generating a preliminary effective dataset.

[0053] Unified data format and field mapping are achieved by establishing a standardized data model: defining unified field specifications (such as sender address, recipient address, cargo weight, distribution node, etc.) and mapping fields from different data sources (such as ERP systems, third-party platforms) (for example, mapping "shipping company" to "sender company", and "originating point" to "sender point"). Field cleaning and type conversion (such as converting string weights to numeric types) are performed using tools such as ETL (Extract-Transform-Load) or programming scripts (Python / PySpark) to eliminate data heterogeneity.

[0054] Extracting waybills to be streamlined by dimension includes: Dimensional division: "Sending company" (customer entity) and "Sending point" (shipping network) are used as grouping dimensions to ensure that subsequent analysis focuses on optimizing batch waybills from the same customer or network. The cleaned dataset is then grouped and aggregated by sending company + sending point, and waybill data with non-direct (requiring distribution) transportation mode is extracted from each group to generate the "Waybill to be streamlined dataset".

[0055] Step S102. Exclude waybill data with a distribution flow count less than or equal to 1 from the data of waybills to be straightened, and obtain the distribution information corresponding to each of the waybill data to be straightened; generate a cargo flow table for cargo allocation configuration based on the multiple distribution information.

[0056] Specifically, by quantitatively filtering the number of times a shipment passes through a distribution center, low-value distribution data (with too few passes and little room for optimization) is excluded. Based on the actual distribution node flow logs, a systematic attribution rule is established to automatically determine the distribution node to which a waybill belongs. Finally, a scientific cargo flow table is generated to provide data support for distribution center configuration.

[0057] The sorting process filtering is achieved by defining filtering rules and extracting the "number of sorting nodes" (i.e., the number of sorting centers a waybill passes through from dispatch to receipt) from the waybill's transit log. Waybills with ≤1 sorting node count are excluded (because these waybills are close to direct delivery, and there is limited room for further streamlining). The waybill data is correlated with the transit log (using the waybill number as the primary key), and the sorting node count for each waybill is calculated (e.g., the number of rows in the "Sorting Center Operation Records" section of the log). Data is then filtered using the WHERE clause: Sorting Flow Count > 1.

[0058] Automatic matching of distribution center attribution information includes: establishing an attribution rule model: based on historical shipment logs, using machine learning (such as decision trees) or a rule engine, defining the determination logic for "attributed distribution center" (e.g., if the origin of a waybill belongs to a certain distribution center A, and more than 80% of waybills from the same network have A as their initial distribution node, then the default attribution is A). For each waybill to be straightened out, based on data such as the sender's location, historical distribution records, and geofencing (the service area of ​​the distribution center), the "attributed distribution center" field is automatically filled in through database association queries or algorithm matching, replacing manual experience-based judgment.

[0059] Generating a cargo flow table for distribution configuration includes: Data integration: The filtered waybill data (including attribution distribution information) is structured according to the flow chain of "shipping point → attribution distribution center → destination distribution center → recipient address" to generate a cargo flow table containing distribution node levels and transportation routes. Pivot tables or graph databases (such as Neo4j) are used to construct a distribution node relationship diagram to visualize the cargo flow and provide an intuitive basis for subsequent route optimization.

[0060] Step S103. Extract the data of the waybills to be straightened from the cargo flow table in the cargo distribution configuration table. The customer type is the sending company or the payment company and the weight is greater than or equal to the preset weight. Obtain the corresponding information on the unstraightened cargo volume and the cargo volume to be straightened.

[0061] Specifically, by combining customer attributes (sending company / payment company) and cargo weight thresholds, high-value optimization targets are accurately identified. By quantifying the difference between "undirected cargo volume" (actual distribution volume) and "directed cargo volume" (theoretically reachable cargo volume), priority ranking is provided for directing strategies.

[0062] Customer type and weight threshold filtering extracts waybills with customer types of "Sender Company" (actual shipper) or "Payment Company" (payer), as these customers are typically more sensitive to transportation costs and timeliness. Based on transportation vehicle weight limits (such as maximum load for long-haul trucks and single-piece limits for air freight) and cost-benefit analysis, a preset weight threshold (e.g., ≥300kg) is used to filter out "heavy" waybills requiring priority optimization. Data is filtered using WHERE Customer Type IN ('Sender Company', 'Payment Company') AND Cargo Weight ≥ Preset Threshold.

[0063] Information on unstraightened cargo volume includes: the actual distribution volume of the filtered waybills (e.g., total weight, number of pieces), i.e., the total amount of goods that need to be distributed multiple times under the current process. Information on cargo volume to be straightened includes: the theoretical cargo volume that could be directly transported if these waybills were straightened (reducing distribution steps) (consistent with the unstraightened cargo volume, the difference being the number of distribution steps), used for comparative analysis and optimization potential. The filtered waybill data is aggregated and calculated (SUM(cargo weight), COUNT(waybill quantity)) to generate indicators such as "Total unstraightened weight" and "Total weight to be straightened".

[0064] Step S104. Generate analysis result information based on the data of waybills to be straightened, the information of unstraightened cargo volume, and the information of cargo volume to be straightened, and complete the straightening of the customer waybill data based on the analysis result information.

[0065] By integrating data on direct waybills awaiting shipment and cargo volume discrepancies, the system generates analytical results that include priority ranking and expected outcomes, guiding adjustments to transportation routes and achieving streamlined distribution processes and optimal resource allocation.

[0066] The analysis results include: core indicators such as "number of waybills to be straightened," "difference between unstraightened and straightened cargo volume," "estimated time reduction" (calculated based on the reduction in sorting frequency), and "estimated cost savings" (based on the sorting cost model). Priority is assigned based on "cargo volume difference × customer value coefficient" (customer value can be calculated by weighting historical order volume, profit contribution, etc.), generating a priority list to prioritize waybills from high-volume, high-value customers. Structured analysis reports are generated using data reporting tools (such as Tableau) or custom algorithms, supporting visualization. Waybill straightening execution includes: route optimization: designing direct routes for high-priority waybills based on the cargo flow table (e.g., skipping redundant intermediate sorting centers), and adjusting sorting routing rules (e.g., configuring "prohibit access to a certain sorting center" in the WMS system). By recording the actual sorting frequency, transportation time, and cost data after straightening, and comparing them with the analysis results, a closed-loop optimization is formed (e.g., periodically updating preset weight thresholds and assigned sorting rules). The optimized routing instructions are synchronized to the Transportation Management System (TMS) via API, triggering route scheduling updates.

[0067] In some embodiments, the general pre-filtering of the acquired customer waybill data, excluding waybills with full truckload transportation as the service method and excluding waybills with direct flight and air-to-air transportation modes, and unifying the data format and field mapping of customer waybill data, includes: parsing the service method field and transportation mode field in the customer waybill data; matching the value of the service method field with a preset full truckload transportation service identifier; matching the value of the transportation mode field with preset direct flight transportation mode identifiers and air-to-air transportation mode identifiers; logically deleting or marking the filtering status of waybill data whose service method matches the full truckload transportation identifier or whose transportation mode matches the direct flight or air-to-air identifier; and performing field standardization processing on unfiltered waybill data, including mapping data items with the same meaning but different field names from different customer systems to a unified standard field, converting unstructured data into structured data format, and filling missing fields with default values ​​or marking abnormal data.

[0068] By parsing the service method and transportation mode identifiers in the waybill fields, we can accurately filter out transportation types that do not require straightening (full truckload, direct flight / air-to-air), and perform field standardization on the remaining data to solve data compatibility issues and provide a unified data foundation for subsequent analysis.

[0069] Field parsing and identifier matching extract field values ​​(such as "full truckload transportation", "less-than-truckload transportation", "direct flight", "air-to-air") by parsing the [service method field] (such as "transportation type" and "service type") and [transportation mode field] (such as "transportation method" and "transportation route") in the waybill data.

[0070] A pre-defined identifier dictionary is established, including: Full Truckload Transportation Identifiers: containing keywords such as "full truckload," "full truckload transportation," and "charter"; Direct Flight Transportation Mode Identifiers: containing keywords such as "direct flight," "direct air freight," and "no transshipment"; Air-to-Air Transportation Mode Identifiers: containing keywords such as "air-to-air," "direct air transport," and "airport direct delivery." String matching algorithms (such as regular expressions and fuzzy matching) are used to compare field values ​​and identify waybills that meet the filtering conditions.

[0071] Data filtering is performed by either "logical deletion" (marking the waybill as "no need to straighten" rather than physical deletion, for easier auditing) or "marking the filtering status" (adding a "filtering reason" label, such as "full truckload transportation" or "direct flight mode") on waybills that match the filter flag.

[0072] Field standardization includes: Field mapping: Establishing a cross-system field mapping table (e.g., mapping "Shipping Company" in System A and "Shipper Name" in System B to the standard field "Sending Company"), and batch replacing field names using ETL tools or code scripts. Structured transformation: Splitting unstructured data (such as address text) into structured fields (province / city / district / street), and using Natural Language Processing (NLP) to parse fuzzy addresses. Missing value handling: For waybills missing key fields (such as weight, sending point), filling in default values ​​(such as "unknown") or marking them as "data abnormal" for subsequent manual verification.

[0073] In some embodiments, excluding waybill data with a distribution flow count less than or equal to 1 from the data of waybills to be straightened, and obtaining the corresponding distribution information for each waybill to be straightened, includes: for the waybill data to be straightened filtered by the dimensions of the sending company and the sending point, extracting the distribution node flow log of the waybill, and counting the number of distribution center nodes actually passed by each waybill in the transportation process as the distribution flow count; retaining waybill data with a distribution flow count greater than 1, and removing waybill data with a distribution flow count less than or equal to 1; for the retained waybill data, determining the corresponding distribution center as the distribution information based on the last actual operation of the distribution center node in the distribution node flow log, combined with the preset distribution center attribution rules, the distribution center attribution rules include attribution matching based on the geographical area, operating entity, or network level of the distribution node.

[0074] By statistically analyzing the actual number of times a waybill passes through the distribution node's flow log, low-value waybills (those passing through ≤1 time) are excluded. The distribution center to which the waybill belongs is determined based on the last operation node and preset rules, replacing manual experience judgment and improving the accuracy of distribution information.

[0075] The sorting flow count includes: linking waybill data with sorting node logs (using waybill number as the primary key), and extracting all records in the logs with the operation type "sorting center processing". The number of sorting center nodes for each waybill (i.e., the number of times different sorting center codes appear in the logs) is counted as the "sorting flow count".

[0076] Waybill screening includes: retaining waybills that have passed through the distribution center more than once (there are redundant distribution links, and there is room for optimization), and removing waybills that have passed through the distribution center less than once (they are close to direct delivery and have low optimization value).

[0077] The attribution and distribution information is determined by extracting the "last actual operation of the distribution center node" (i.e., the distribution center closest to the recipient address) from the distribution node flow log of the retained waybill.

[0078] The application uses preset attribution rules to match the attribution of distribution centers, including: Geographical region rules: Based on the service area of ​​the distribution center (e.g., the East China distribution center covers Jiangsu, Zhejiang and Shanghai), the distribution center corresponding to the geographical area where the parcel is located is matched; Operating entity rules: Based on the company to which the distribution center belongs (e.g., self-operated distribution center, franchised distribution center), the logistics network attribution of the waybill is matched; Network hierarchy rules: Trunk distribution centers (rather than last-mile delivery centers) are prioritized as the attribution nodes to ensure coverage of core distribution links.

[0079] In some embodiments, obtaining the corresponding unstraightened cargo volume information and straightened cargo volume information includes: filtering waybill data from the cargo flow table where the customer type field is the sending company or the payment company, and extracting the cargo weight field from the waybill; comparing the cargo weight with a preset weight threshold, and retaining waybill data where the cargo weight is greater than or equal to the preset weight threshold; for the retained waybill data, calculating the total cargo weight of waybills not marked as straightened as unstraightened cargo volume information, and calculating the total cargo weight of all waybills that meet the customer type and weight conditions as straightened cargo volume information, wherein the preset weight threshold is pre-configured based on the straightening cost and efficiency parameters of the transportation network.

[0080] By combining customer type (sending / payment company) and cargo weight threshold, high-value optimization targets are screened. By statistically analyzing the total weight of eligible waybills, the difference between "undirected cargo volume" (current sorted cargo volume) and "cargo volume that should be directed" (theoretically reachable cargo volume) is quantified, providing data support for strategy formulation.

[0081] Customer type and weight filtering are used to filter waybills from the cargo flow table where the [Customer Type Field] is "Sending Company" or "Paying Company" (these customers are more sensitive to cost and timeliness). The [Cargo Weight Field] of the waybill is extracted and compared with a preset weight threshold (e.g., 300kg, based on parameters such as the minimum load limit of trunk transport vehicles and the critical point of air freight costs). Waybills with a weight ≥ the threshold are retained (heavy cargo has higher distribution costs, and straightening the process is more effective).

[0082] Cargo volume calculation includes: Unstraightened cargo volume information: This calculates the total weight of goods in waybills not marked as "straightened" (i.e., the total amount of heavy cargo that still needs multiple sorting). Straightened cargo volume information: This calculates the total weight of goods in all waybills that meet the customer type and weight criteria (regardless of whether they are straightened, representing the theoretically optimizable potential cargo volume). The filtered waybill data is summed using SQL aggregate functions (SUM) or data frameworks (such as Pandas), and the "straightened" status is distinguished (e.g., filtered by the boolean field "is_straightened").

[0083] In some embodiments, generating a cargo flow table based on multiple attribution and distribution information includes: grouping the to-be-directed waybill data with the same attribution and distribution information into groups; aggregating and statistically analyzing the transportation destination, transportation route, and transportation timeliness requirements of each group of waybill data; generating cargo flow records with the attribution and distribution center as the starting point and the transportation destination as the ending point based on the aggregation results corresponding to the aggregation statistics; each flow record includes the number of waybills, cargo weight distribution, and configuration information of commonly used transportation route types for that flow; and performing deduplication and standardization processing on the flow records to form a cargo flow table containing distribution center codes, destination codes, transportation route rules, and cargo type adaptation conditions. The cargo flow table is used to guide cargo sorting and transportation route planning at the distribution center.

[0084] By grouping waybills belonging to the same distribution center, aggregating information such as transportation destination, route, and timeliness, a standardized cargo flow table is generated, clarifying the optimal route configuration from the distribution center to the destination, and providing rule guidance for sorting and scheduling.

[0085] Grouping and aggregation statistics are performed by grouping the retained outgoing waybill data according to [originating distribution center] (each group corresponds to an outgoing waybill from one distribution center). The following information is aggregated for each group of waybills: destination (province / city / district level of the recipient address, or a custom area code); frequently used transportation routes (e.g., "distribution center A → trunk transportation → destination distribution center B"); delivery time requirements (latest delivery time specified by the customer, historical delivery time statistics); number of waybills; and cargo weight distribution (e.g., percentage by weight range).

[0086] The flow record generation generates a flow record for each [originating distribution center → destination] combination, which includes: basic information: distribution center code, destination code; flow information: number of waybills, average weight, weight distribution range; route information: common transportation route types (highway trunk line, railway, air transport), route cost and timeliness parameters.

[0087] Deduplication and standardization are achieved by deduplicating flow records according to [distribution center code + destination code], merging statistical data of the same flow direction (such as taking average delivery time and total volume); standardizing field formats (such as unifying route types as "trunk highway" and "regional distribution"), and finally forming a structured cargo flow table containing distribution center code, destination code, transportation route rules, and cargo type adaptation conditions (such as applicable trunk highway for weight ≤500kg), which is stored in a database or configuration file.

[0088] In some embodiments, generating analysis results based on the data of waybills to be straightened, the information on unstraightened cargo volume, and the information on cargo volume to be straightened includes: establishing an analysis data model to associate the information of the sending company, sending point, and affiliated distribution center in the data of waybills to be straightened with the information on unstraightened cargo volume and the information on cargo volume to be straightened; calculating the proportion of unstraightened cargo volume to cargo volume to be straightened as the straightening rate indicator, and analyzing the differences in straightening rates among different sending companies, sending points, or affiliated distribution centers; combining transportation cost data and distribution center processing efficiency data to generate a cargo volume straightening priority ranking under each dimension, forming analysis results including straightening potential assessment, cost-benefit analysis, and operational suggestions, wherein the analysis results are stored in the form of structured data tables or visual reports.

[0089] By linking multi-dimensional data to establish an analytical model, calculating the straightening rate index, analyzing the straightening potential of different dimensions (shipping companies, branches, distribution centers), and combining cost and efficiency data to generate a priority ranking, a report that can guide operations is generated.

[0090] The data model is established by constructing a multidimensional analysis table, linking the following data: basic information of waybills to be straightened: sending company, sending point, and affiliated distribution center; cargo volume difference indicators: unstraightened cargo volume, cargo volume to be straightened, straightening rate (unstraightened cargo volume / cargo volume to be straightened × 100%); additional attributes: customer's historical order volume, distribution center processing cost (RMB / kg), and average transportation time (hours).

[0091] Straightening rate analysis and priority calculation are performed by grouping shipments by sending company, sending point, and affiliated distribution center, etc., and calculating the straightening rate for each group to identify high-potential groups with low straightening rates (i.e., a high proportion of unstraightened shipments). A weighted factor is introduced to calculate priority: Priority = (Straightened Shipment Volume × Customer Value Coefficient) + (1 - Straightening Rate) × Distribution Cost Saving Coefficient; where the customer value coefficient is assigned based on historical order amount, years of cooperation, etc., and the distribution cost saving coefficient is calculated based on the reduction in distribution times after straightening × cost per distribution.

[0092] The analysis results output includes: generating structured tables containing fields such as group, straightening rate, quantity to be straightened, expected cost savings, and priority level; visual reports: displaying the differences in straightening rates among different groups through bar charts, and showing the distribution of straightening potential among distribution centers through heatmaps, supporting interactive filtering and drill-down analysis; storage method: storing the results in a data warehouse or generating PDF reports for business departments to refer to.

[0093] In some embodiments, the step of straightening the customer waybill data based on the analysis results includes: generating straightening operation instructions for high-priority shipping companies or shipping points based on the straightening priority ranking in the analysis results; the operation instructions include adjusting the cargo sorting rules of the distribution center to match the optimal route in the cargo flow table of the cargo allocation configuration, and sending a configuration request for straightening the transportation route to the transportation scheduling system; marking the waybill data that has undergone straightening operation as straightened, and recording the execution time, operation subject, and transportation route change information of the straightening operation; periodically backtracking and verifying the straightened waybill data, comparing the actual transportation timeliness and transportation cost with the expected indicators in the analysis results, and forming a straightening effect evaluation report for optimizing subsequent straightening strategies.

[0094] Based on the analysis results, straightening operation instructions are generated, sorting rules and transportation routes are adjusted, operation records are marked, and the effect is evaluated through backtracking verification to form a closed loop of strategy optimization.

[0095] Straightening operation instruction generation includes: sorting by priority and generating operation instructions for high-priority groups (such as shipping companies with a straightening rate of <30% and a straightening volume of >10 tons); adjusting sorting rules by configuring sorting rules in WMS (Warehouse Management System), such as "Waybills from shipping point X, belonging to distribution center Y, and destined for Z, are prohibited from entering intermediate distribution center M"; and route configuration requests by sending route optimization instructions to TMS (Transportation Management System) via API to specify direct routes (such as trunk transportation routes from distribution center Y to destination Z).

[0096] Operation logs and status markers record key information by marking waybills that have undergone straightening operations as "straightened": operation time, operation subject (automatically triggered by the system or confirmed manually); route change details (original distribution route and new direct route); and expected indicators from the correlation analysis results (e.g., expected time reduction of 2 hours and cost saving of 5%). Backtracking verification and effect evaluation: Actual transportation data (number of distributions, timeliness, cost) of straightened waybills are periodically (e.g., weekly / monthly) extracted and compared with expected indicators from the analysis results: Timeliness evaluation: actual transportation time and expected reduction in time; Cost evaluation: actual transportation cost and expected cost savings (calculated based on the reduction in the number of distributions); An effect evaluation report is generated to summarize the optimization results, identify abnormal cases (e.g., if the timeliness fails to meet the standard after straightening a route, analyze whether it is due to bottlenecks in the processing efficiency of the distribution center), and adjust parameters such as preset weight thresholds and attribution rules accordingly to optimize subsequent strategies.

[0097] In some embodiments, by addressing the limitations of traditional rule matching in parsing fuzzy or non-standard fields, natural language processing (NLP) technology is introduced to perform semantic understanding on unstructured text fields such as service methods and transportation modes, thereby improving the robustness of filtering rules and automatically completing missing key information (such as implicit transportation modes).

[0098] The domain-specific corpus is constructed by collecting service method and transportation mode description texts (such as "full truckload charter", "direct air flight without transit", "air-to-air transit") from historical waybill data, and labeling them with standard categories (full truckload / non-full truckload, direct flight / non-direct flight) to build a corpus dedicated to the logistics domain. A "transportation mode classification model" is trained using pre-trained models such as BERT for fine-tuning, supporting multi-label classification (such as simultaneously recognizing "air-to-air" and "direct flight" labels).

[0099] Intelligent parsing and completion includes: performing NLP parsing on the free text fields of the original waybill (such as "remarks" and "special requirements") to extract implicit transportation mode information (e.g., inferring "direct flight mode" from "do not transfer, direct to destination"). For waybills missing key fields (such as those without clearly marked service methods), completion is inferred through contextual semantics (e.g., combining the distance between the sender and recipient addresses and the weight of the goods to determine whether it is a full truckload shipment).

[0100] Dynamic rule optimization includes: cross-validating the classification results output by the model with the matching results of traditional rules, automatically labeling samples with a classification accuracy below a threshold (such as 90%), and back-optimizing the corpus to form a closed loop of "data labeling-model training-rule update".

[0101] In some embodiments, by constructing a distribution node relationship graph, a graph neural network (GNN) is used to model the complex relationships between distribution centers, drop-off points, and geographical regions, breaking through the limitations of traditional rule engines, automatically learning dynamic attribution rules, and adapting to changes in network structure (such as the activation of new distribution centers or adjustments to regional service areas).

[0102] The construction of the distribution network map includes: Node definition: distribution center (attributes: coordinates, service area, processing capacity), dispatch point (attributes: coordinates, historical distribution preferences), and geographical region (province / city / district). Edge definition: service relationship between distribution centers and dispatch points (historical attribution records), trunk transportation connection between distribution centers, and coverage relationship between geographical regions and distribution centers.

[0103] GNN model training includes: Input: Coordinates of the sender's location and historical distribution node logs (forming a path sequence). Output: Predict the optimal distribution center for the waybill corresponding to the sender's location (considering the current distribution center's load, transportation distance, and time constraints). Training data: The actual distribution centers of historical waybills are used as labels, and a Graph Convolutional Network (GCN) or Graph Attention Network (GAT) is used to learn the dependencies between nodes.

[0104] Real-time attribution matching includes: for new waybills, inputting the sender location information, and using a trained GNN model to output the attribution distribution center in real time, replacing the static matching of traditional rules, which is especially suitable for dynamic adjustments of newly opened areas or abnormal distribution routes.

[0105] In some embodiments, by modeling the straightening strategy formulation as a sequential decision problem, reinforcement learning (RL) is used to dynamically optimize the priority ranking, taking into account the real-time transportation resource status (such as the congestion of distribution centers and the empty load rate of trunk vehicles) and dynamic customer demand (such as the proportion of time-sensitive and urgent orders), the real-time optimal allocation of transportation resources is achieved.

[0106] The state space definition includes: environmental state: distribution center processing load (current backlog of goods / processing capacity), real-time freight rates of trunk transportation routes, customer historical directing response rate (such as a customer's complaint rate after directing), and available transportation capacity for the current time period.

[0107] The action space definition includes: Optional actions: Perform straightening operations on waybill groups with different priorities (grouped by customer type / weight threshold), and adjust straightening strategy parameters (such as temporarily increasing the weight threshold of a certain area to match vehicle load).

[0108] The reward function design includes: positive rewards: actual cost savings after straightening, time reduction, and improvement in the processing efficiency of the distribution center; negative rewards: costs of line conflicts caused by straightening (such as penalties for temporary line adjustments) and reputational damage caused by customer complaints.

[0109] Strategy optimization involves training a model using deep reinforcement learning algorithms (such as DQN and PPO), taking the real-time state as input, and outputting the optimal priority ranking strategy. The reward function weights are updated periodically based on historical straightening effect data (such as backtesting results) to adapt to changes in business scenarios (such as timeliness being more important than cost during e-commerce promotions).

[0110] In some embodiments, by introducing a spatiotemporal sequence model (such as LSTM+graph network) and combining historical straightening data (number of distributions, timeliness, cost) and geospatial features (location of distribution centers, distance of transportation routes), the expected effects of different straightening strategies can be predicted, thus solving the problem of lag in traditional methods that rely on historical average data.

[0111] The spatiotemporal characteristics engineering includes: temporal characteristics: waybill creation time (hour / working day / quarter), and historical same period straightening time data; spatial characteristics: distance between the sending point and the originating distribution center, and distribution of trunk transportation time between the distribution center and the destination.

[0112] The prediction model construction includes: input: customer type, cargo weight, and current distribution route (node ​​sequence) of the waybill to be straightened; output: predicted reduction in the number of distributions, the shortening of delivery time, and the amount of cost savings after straightening; the model architecture uses LSTM to process time series dependencies and combines graph embedding to encode the distribution network structure to build an end-to-end spatiotemporal prediction model.

[0113] The application of the prediction results includes: in step S103, the predicted "expected cost savings" replaces the traditional estimation based on historical averages, providing a more accurate quantitative basis for priority ranking; in the backtracking verification in step S104, the predicted effect is compared with the actual effect, and the prediction accuracy is continuously optimized through model updates (transfer learning), forming an intelligent closed loop of "prediction-execution-feedback".

[0114] In some embodiments, to address the limitations of traditional grouping that relies on manually preset dimensions, clustering algorithms (such as DBSCAN and K-means) are used to perform multidimensional clustering of transportation destinations, cargo weights, and timeliness requirements, automatically discovering implicit distribution flow patterns and generating dynamic, fine-grained cargo flow tables.

[0115] Multidimensional feature clustering includes: feature selection includes: destination latitude and longitude (spatial distance), cargo weight range, customer-required delivery time window, and historical transportation route preferences (such as whether air freight is preferred). The clustering algorithm identifies groups of waybills with similar transportation characteristics (such as "short-distance heavy cargo group" and "long-distance time-sensitive group") by performing unsupervised clustering on the retained waybill data to be straightened.

[0116] Flow pattern mining analyzes the optimal distribution path for each cluster: for example, short-distance heavy cargo groups are suitable for bypassing regional distribution centers and going directly via trunk lines; long-distance time-sensitive groups are suitable for prioritizing matching with air transport nodes.

[0117] The generation of dynamic flow rules includes: automatically adjusting cargo allocation configuration based on clustering results (such as adding a rule that "time-sensitive goods are given priority for direct flight routes"), replacing the traditional fixed route configuration.

[0118] The dynamic update of the flow table is achieved by periodically (e.g., weekly) re-clustering based on the latest waybill data, detecting changes in flow patterns (such as changes in weight distribution caused by seasonal goods), and automatically updating the cargo flow table to adapt to business fluctuations.

[0119] In some embodiments, by utilizing anomaly detection algorithms such as isolated forest and One-Class SVM, inefficient or invalid straightening operations can be identified from backtracking verification data, solving the problem of low efficiency in traditional manual investigation and accurately locating policy vulnerabilities (such as load imbalance in distribution centers caused by excessive straightening).

[0120] Abnormal indicators include: core indicators: the number of distributions does not decrease after straightening (abnormal type: invalid straightening), the timeliness increases beyond the threshold (e.g., +2 hours), and the cost increases instead (abnormal type: negative benefit straightening).

[0121] The anomaly detection model deployment includes: training data including: actual effect data of historical straightening operations (changes in the number of distributions, timeliness change rate, cost change rate), and labeling known anomalous samples (such as manually confirmed inefficient cases). The model selection combines supervised (such as random forest classification) and unsupervised (such as isolated forest) algorithms to detect anomalous patterns that have never appeared before (such as a distribution center experiencing warehouse overload due to straightening).

[0122] Closed-loop optimization triggers automatically mark detected abnormal operations, associate them with information such as distribution paths and customer types, and generate optimization suggestions (such as restoring the original distribution path for the customer or adjusting the weight threshold); it also periodically summarizes abnormal cases and reverse-optimizes the input parameters of the straightening strategy (such as correcting the geographical region division error in the distribution rules).

[0123] In some embodiments, a scalable customer straightening identification algorithm model is built, capable of: covering three core business scenarios (unstraightened receiving customers, unstraightened originating distribution centers, and unstraightened backend planning); automatically determining whether the volume of unstraightened goods in different distance ranges reaches the business threshold; verifying the consistency between the waybill path and the planned path based on a path matching mechanism that integrates multiple data sources; and uniformly outputting periodic statistical results by sending time and sending point dimensions. A general pre-filter module excludes waybills with "service mode = full truckload" transportation; excludes waybills with transportation mode = direct flight or air-to-air; and unifies data format and field mapping to ensure that subsequent modules make judgments under the same data premise. Based on the dimension of "shipping company + shipping point", we statistically analyze waybills from the same recipient who meet any of the following conditions for ≥3 days within a week: Shipping point-to-receiver distance < 400km and unsorted cargo volume ≥ 6T; Shipping point-to-receiver distance ∈ [400km, 1000km] and unsorted cargo volume ≥ 9T; Shipping point-to-receiver distance ≥ 1000km and unsorted cargo volume ≥ 13T. Recipient consistency is determined by: ① Completely identical recipient addresses; ② Or, any field of the recipient's mobile phone / signature mobile phone / receiver's company is completely identical, and the recipient addresses are ≤ 1km apart (same grid code). Unsorted cargo volume is determined by the cumulative weight of [site cargo] unloading records.

[0124] After excluding waybills with ≤1 distribution transit frequency, compare the unloading distribution of the waybill with the inbound distribution or route allocation in the [Cargo Distribution Information] to see if they are consistent; identify the distribution node to which it should belong by using the line binding relationship of trunk type = point branch / secondary branch in the [Route Backend]; if there are multiple return distribution points, select the distribution point with the most bound "return pickup and delivery points" as the allocation; set the unstraightened cargo volume threshold (6T, 9T, 13T) according to the distance range for judgment. Extract the straightened waybills that meet the conditions (customer type = sending company or payment company, weight ≥ 4T) from the [Cargo Configuration - Cargo Flow] table; calculate the straightened cargo volume: match "flow to final origin + associated final origin", and the final origin organization is consistent, with a weight ≥ 2T; if the unstraightened cargo volume / the straightened cargo volume is ≥ 30% for any 3 out of 7 consecutive days, it is judged as "back-end planning not straightened"; definition of unstraightened cargo volume: the first unloading site ≠ the final origin and ≠ the distribution organization.

[0125] Statistical time: shipping time; organizational dimension: shipping point; period: sliding one-week time window; output fields include: problem type, number of days without straightening, average daily weight, quantity of goods without straightening, analyst information, etc.; supports association with analysis records to form a closed-loop management.

[0126] Compared to existing path analysis methods, this invention offers the following significant advantages: Multi-scenario coverage: The unified model framework can simultaneously identify three main "unstraightened" scenarios, reducing maintenance costs caused by fragmented business rules. Refined threshold control: Cargo volume thresholds are set based on different distance ranges, providing a scientific and scalable judgment standard. Multi-data source integration: Automatically integrates data from multiple systems such as waybills, routes, sites, and cargo allocation configurations, achieving cross-system path consistency verification. Periodic statistics and attribution: Periodic statistics are uniformly performed based on dispatch time and dispatch point dimensions, facilitating performance evaluation and operational analysis. Scalability and maintainability: The modular design allows for flexible addition / adjustment of business rules to meet the needs of different business stages.

[0127] In summary, the customer straightening algorithm model of this invention can significantly improve the efficiency and accuracy of path anomaly identification, providing strong data support for logistics companies to optimize transportation routes, reduce operating costs, and improve customer satisfaction. It has high industrial application value and promotion prospects.

[0128] Please see Figure 2 As shown, Figure 2This is a schematic diagram of the customer waybill straightening device 200 provided in this application embodiment. The customer waybill straightening device 200 is used to execute the steps of the customer waybill straightening method shown in the above embodiments. The customer waybill straightening device 200 can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, laptop computer, wearable device, or robot.

[0129] like Figure 2 As shown, the customer waybill straightening device 200 includes:

[0130] The pre-filtering unit 201 is used to perform general pre-filtering on the acquired customer waybill data, exclude waybills with full truckload transportation as the service method, and exclude waybills with direct flight and air-to-air transportation modes, and unify the data format and field mapping of customer waybill data; and obtain the waybill data to be straightened by the dimensions of the sending company and the sending point of the customer waybill data.

[0131] The flow direction generation unit 202 is used to exclude waybill data with a distribution flow count of less than or equal to 1 from the waybill data to be straightened, obtain the distribution information corresponding to each waybill data to be straightened, and generate a cargo flow direction table for cargo allocation configuration based on the multiple distribution information.

[0132] Information acquisition unit 203 is used to extract data of waybills to be straightened that are customer type sender company or payer company and have a weight greater than or equal to a preset weight from the cargo flow table of the cargo distribution configuration, and to obtain the corresponding information on the quantity of goods not straightened and the quantity of goods to be straightened.

[0133] The straightening completion unit 204 is used to generate analysis result information based on the straightening order data, the unstraightened cargo quantity information and the cargo quantity to be straightened information, and to complete the straightening of the customer order data based on the analysis result information.

[0134] In some embodiments, the general pre-filtering of the acquired customer waybill data, excluding waybills with full truckload transportation as the service method and excluding waybills with direct flight and air-to-air transportation modes, and unifying the data format and field mapping of customer waybill data, includes: parsing the service method field and transportation mode field in the customer waybill data; matching the value of the service method field with a preset full truckload transportation service identifier; matching the value of the transportation mode field with preset direct flight transportation mode identifiers and air-to-air transportation mode identifiers; logically deleting or marking the filtering status of waybill data whose service method matches the full truckload transportation identifier or whose transportation mode matches the direct flight or air-to-air identifier; and performing field standardization processing on unfiltered waybill data, including mapping data items with the same meaning but different field names from different customer systems to a unified standard field, converting unstructured data into structured data format, and filling missing fields with default values ​​or marking abnormal data.

[0135] In some embodiments, excluding waybill data with a distribution flow count less than or equal to 1 from the data of waybills to be straightened, and obtaining the corresponding distribution information for each waybill to be straightened, includes: for the waybill data to be straightened filtered by the dimensions of the sending company and the sending point, extracting the distribution node flow log of the waybill, and counting the number of distribution center nodes actually passed by each waybill in the transportation process as the distribution flow count; retaining waybill data with a distribution flow count greater than 1, and removing waybill data with a distribution flow count less than or equal to 1; for the retained waybill data, determining the corresponding distribution center as the distribution information based on the last actual operation of the distribution center node in the distribution node flow log, combined with the preset distribution center attribution rules, the distribution center attribution rules include attribution matching based on the geographical area, operating entity, or network level of the distribution node.

[0136] In some embodiments, obtaining the corresponding unstraightened cargo volume information and straightened cargo volume information includes: filtering waybill data from the cargo flow table where the customer type field is the sending company or the payment company, and extracting the cargo weight field from the waybill; comparing the cargo weight with a preset weight threshold, and retaining waybill data where the cargo weight is greater than or equal to the preset weight threshold; for the retained waybill data, calculating the total cargo weight of waybills not marked as straightened as unstraightened cargo volume information, and calculating the total cargo weight of all waybills that meet the customer type and weight conditions as straightened cargo volume information, wherein the preset weight threshold is pre-configured based on the straightening cost and efficiency parameters of the transportation network.

[0137] In some embodiments, generating a cargo flow table based on multiple attribution and distribution information includes: grouping the to-be-directed waybill data with the same attribution and distribution information into groups; aggregating and statistically analyzing the transportation destination, transportation route, and transportation timeliness requirements of each group of waybill data; generating cargo flow records with the attribution and distribution center as the starting point and the transportation destination as the ending point based on the aggregation results corresponding to the aggregation statistics; each flow record includes the number of waybills, cargo weight distribution, and configuration information of commonly used transportation route types for that flow; and performing deduplication and standardization processing on the flow records to form a cargo flow table containing distribution center codes, destination codes, transportation route rules, and cargo type adaptation conditions. The cargo flow table is used to guide cargo sorting and transportation route planning at the distribution center.

[0138] In some embodiments, generating analysis results based on the data of waybills to be straightened, the information on unstraightened cargo volume, and the information on cargo volume to be straightened includes: establishing an analysis data model to associate the information of the sending company, sending point, and affiliated distribution center in the data of waybills to be straightened with the information on unstraightened cargo volume and the information on cargo volume to be straightened; calculating the proportion of unstraightened cargo volume to cargo volume to be straightened as the straightening rate indicator, and analyzing the differences in straightening rates among different sending companies, sending points, or affiliated distribution centers; combining transportation cost data and distribution center processing efficiency data to generate a cargo volume straightening priority ranking under each dimension, forming analysis results including straightening potential assessment, cost-benefit analysis, and operational suggestions, wherein the analysis results are stored in the form of structured data tables or visual reports.

[0139] In some embodiments, the step of straightening the customer waybill data based on the analysis results includes: generating straightening operation instructions for high-priority shipping companies or shipping points based on the straightening priority ranking in the analysis results; the operation instructions include adjusting the cargo sorting rules of the distribution center to match the optimal route in the cargo flow table of the cargo allocation configuration, and sending a configuration request for straightening the transportation route to the transportation scheduling system; marking the waybill data that has undergone straightening operation as straightened, and recording the execution time, operation subject, and transportation route change information of the straightening operation; periodically backtracking and verifying the straightened waybill data, comparing the actual transportation timeliness and transportation cost with the expected indicators in the analysis results, and forming a straightening effect evaluation report for optimizing subsequent straightening strategies.

[0140] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the customer waybill straightening device and each module described above can be referred to the corresponding process in the customer waybill straightening method embodiments described above, and will not be repeated here.

[0141] The above-mentioned method for straightening customer waybills can be implemented as a computer program, which can be used in, for example... Figure 2 It runs on the device shown.

[0142] Please see Figure 3 , Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0143] The storage medium may store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any customer waybill straightening method.

[0144] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0145] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any customer waybill straightening method.

[0146] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0147] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0148] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0149] The acquired customer waybill data is subjected to general pre-filtering to exclude waybills with full truckload transportation service mode and waybills with direct flight and air-to-air transportation mode, and to unify the data format and field mapping of customer waybill data; the customer waybill data is then processed by sending company and sending point to obtain the waybill data to be straightened out.

[0150] Exclude waybill data with a distribution flow count less than or equal to 1 from the data of waybills to be straightened, and obtain the distribution information corresponding to each of the waybill data to be straightened; generate a cargo flow table for cargo allocation configuration based on the multiple distribution information;

[0151] Extract the data of waybills to be straightened from the cargo flow table of the cargo distribution configuration. The customer type is either the sending company or the paying company and the weight is greater than or equal to the preset weight. Obtain the corresponding information on the unstraightened cargo volume and the cargo volume to be straightened.

[0152] Based on the data of waybills to be straightened, the information of unstraightened cargo volume, and the information of cargo volume to be straightened, an analysis result is generated, and the straightening of the customer waybill data is completed based on the analysis result.

[0153] In some embodiments, the general pre-filtering of the acquired customer waybill data, excluding waybills with full truckload transportation as the service method and excluding waybills with direct flight and air-to-air transportation modes, and unifying the data format and field mapping of customer waybill data, includes: parsing the service method field and transportation mode field in the customer waybill data; matching the value of the service method field with a preset full truckload transportation service identifier; matching the value of the transportation mode field with preset direct flight transportation mode identifiers and air-to-air transportation mode identifiers; logically deleting or marking the filtering status of waybill data whose service method matches the full truckload transportation identifier or whose transportation mode matches the direct flight or air-to-air identifier; and performing field standardization processing on unfiltered waybill data, including mapping data items with the same meaning but different field names from different customer systems to a unified standard field, converting unstructured data into structured data format, and filling missing fields with default values ​​or marking abnormal data.

[0154] In some embodiments, excluding waybill data with a distribution flow count less than or equal to 1 from the data of waybills to be straightened, and obtaining the corresponding distribution information for each waybill to be straightened, includes: for the waybill data to be straightened filtered by the dimensions of the sending company and the sending point, extracting the distribution node flow log of the waybill, and counting the number of distribution center nodes actually passed by each waybill in the transportation process as the distribution flow count; retaining waybill data with a distribution flow count greater than 1, and removing waybill data with a distribution flow count less than or equal to 1; for the retained waybill data, determining the corresponding distribution center as the distribution information based on the last actual operation of the distribution center node in the distribution node flow log, combined with the preset distribution center attribution rules, the distribution center attribution rules include attribution matching based on the geographical area, operating entity, or network level of the distribution node.

[0155] In some embodiments, obtaining the corresponding unstraightened cargo volume information and straightened cargo volume information includes: filtering waybill data from the cargo flow table where the customer type field is the sending company or the payment company, and extracting the cargo weight field from the waybill; comparing the cargo weight with a preset weight threshold, and retaining waybill data where the cargo weight is greater than or equal to the preset weight threshold; for the retained waybill data, calculating the total cargo weight of waybills not marked as straightened as unstraightened cargo volume information, and calculating the total cargo weight of all waybills that meet the customer type and weight conditions as straightened cargo volume information, wherein the preset weight threshold is pre-configured based on the straightening cost and efficiency parameters of the transportation network.

[0156] In some embodiments, generating a cargo flow table based on multiple attribution and distribution information includes: grouping the to-be-directed waybill data with the same attribution and distribution information into groups; aggregating and statistically analyzing the transportation destination, transportation route, and transportation timeliness requirements of each group of waybill data; generating cargo flow records with the attribution and distribution center as the starting point and the transportation destination as the ending point based on the aggregation results corresponding to the aggregation statistics; each flow record includes the number of waybills, cargo weight distribution, and configuration information of commonly used transportation route types for that flow; and performing deduplication and standardization processing on the flow records to form a cargo flow table containing distribution center codes, destination codes, transportation route rules, and cargo type adaptation conditions. The cargo flow table is used to guide cargo sorting and transportation route planning at the distribution center.

[0157] In some embodiments, generating analysis results based on the data of waybills to be straightened, the information on unstraightened cargo volume, and the information on cargo volume to be straightened includes: establishing an analysis data model to associate the information of the sending company, sending point, and affiliated distribution center in the data of waybills to be straightened with the information on unstraightened cargo volume and the information on cargo volume to be straightened; calculating the proportion of unstraightened cargo volume to cargo volume to be straightened as the straightening rate indicator, and analyzing the differences in straightening rates among different sending companies, sending points, or affiliated distribution centers; combining transportation cost data and distribution center processing efficiency data to generate a cargo volume straightening priority ranking under each dimension, forming analysis results including straightening potential assessment, cost-benefit analysis, and operational suggestions, wherein the analysis results are stored in the form of structured data tables or visual reports.

[0158] In some embodiments, the step of straightening the customer waybill data based on the analysis results includes: generating straightening operation instructions for high-priority shipping companies or shipping points based on the straightening priority ranking in the analysis results; the operation instructions include adjusting the cargo sorting rules of the distribution center to match the optimal route in the cargo flow table of the cargo allocation configuration, and sending a configuration request for straightening the transportation route to the transportation scheduling system; marking the waybill data that has undergone straightening operation as straightened, and recording the execution time, operation subject, and transportation route change information of the straightening operation; periodically backtracking and verifying the straightened waybill data, comparing the actual transportation timeliness and transportation cost with the expected indicators in the analysis results, and forming a straightening effect evaluation report for optimizing subsequent straightening strategies.

[0159] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement the steps of the customer waybill straightening method provided in the above embodiments of this application.

[0160] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0161] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for straightening customer waybills, characterized in that, include: The acquired customer waybill data is subjected to general pre-filtering to exclude waybills with full truckload transportation service mode and waybills with direct flight and air-to-air transportation mode, and to unify the data format and field mapping of customer waybill data; the customer waybill data is then processed by sending company and sending point to obtain the waybill data to be straightened out. In the data of waybills awaiting direct shipment, waybill data with a distribution flow count less than or equal to 1 is excluded. The distribution information corresponding to each waybill is then obtained, including: for waybill data filtered by sending company and sending point, the distribution node flow log of the waybill is extracted, and the number of distribution center nodes actually passed through by each waybill in the transportation process is counted as the distribution flow count; waybill data with a distribution flow count greater than 1 is retained, and waybill data with a distribution flow count less than or equal to 1 is removed; for the retained waybill data, based on the last actual operation of the distribution center node in the distribution node flow log, combined with a preset distribution center attribution rule, the corresponding distribution center is determined as the attribution information for that waybill. The attribution rule for the distribution center includes factors such as the geographical region of the distribution node, The system performs attribution matching at the operational entity or network level; it generates a cargo flow table based on multiple attribution and distribution information, including: grouping the order data to be straightened with the same attribution and distribution information into groups, and aggregating and statistically analyzing the transportation destination, transportation route, and transportation timeliness requirements of each group of order data; generating cargo flow records with the attribution and distribution center as the starting point and the transportation destination as the ending point based on the aggregation results of the aggregation statistics, with each flow record containing the number of order data, cargo weight distribution, and configuration information of commonly used transportation route types for that flow; and performing deduplication and standardization processing on the flow records to form a cargo flow table containing distribution center code, destination code, transportation route rules, and cargo type adaptation conditions, which is used to guide cargo sorting and transportation route planning at the distribution center. Extract the data of waybills to be straightened from the cargo flow table of the cargo distribution configuration. The customer type is either the sending company or the paying company and the weight is greater than or equal to the preset weight. Obtain the corresponding information on the unstraightened cargo volume and the cargo volume to be straightened. Based on the data of waybills to be straightened, the information of unstraightened cargo volume, and the information of cargo volume to be straightened, an analysis result is generated, and the straightening of the customer waybill data is completed based on the analysis result.

2. The method according to claim 1, characterized in that, The process involves performing a general pre-filter on the acquired customer waybill data, excluding waybills with full truckload transportation as the service method, and excluding waybills with direct flights and air-to-air transportation modes. This standardizes the data format and field mapping of the customer waybill data, including: The service method field and transportation mode field in the customer waybill data are parsed. The value of the service method field is matched with the preset full truckload transportation service identifier, and the value of the transportation mode field is matched with the preset direct flight transportation mode identifier and air-to-air transportation mode identifier. Logically delete or mark as filtered waybill data that match the service method with the full truckload transportation identifier or the transportation mode with the direct flight or air-to-air identifier. Perform field standardization processing on unfiltered waybill data, including mapping data items with the same meaning but different field names from different customer systems to a unified standard field, converting unstructured data into structured data format, and filling missing fields with default values ​​or marking abnormal data.

3. The method according to claim 1, characterized in that, The process of obtaining the corresponding information on unstraightened goods and the amount of goods to be straightened includes: Filter waybill data from the cargo flow table where the customer type field is either the sending company or the paying company, and extract the cargo weight field from the waybill. The cargo weight is compared with a preset weight threshold, and waybill data with cargo weight greater than or equal to the preset weight threshold are retained. For the retained waybill data, the total cargo weight of waybills not marked as straightened is calculated as the unstraightened cargo volume information, and the total cargo weight of all waybills that meet the customer type and weight conditions is calculated as the straightened cargo volume information. The preset weight threshold is pre-configured based on the straightening cost and efficiency parameters of the transportation network.

4. The method according to claim 1, characterized in that, The step of generating analysis results based on the data of waybills to be straightened, the information on the quantity of goods not yet straightened, and the information on the quantity of goods to be straightened includes: Establish an analytical data model to associate the information of the shipping company, shipping point, and distribution center in the data of waybills to be straightened with the information of the volume of goods that have not been straightened and the volume of goods that should be straightened. The proportion of unstraightened cargo to straightened cargo is calculated as the straightening rate indicator. The differences in straightening rates among different shipping companies, shipping points, or affiliated distribution centers are analyzed. Combined with transportation cost data and distribution center processing efficiency data, a cargo straightening priority ranking is generated under each dimension, forming an analysis result information that includes straightening potential assessment, cost-benefit analysis, and operational suggestions. The analysis result information is stored in the form of structured data tables or visual reports.

5. The method according to claim 4, characterized in that, The step of straightening the customer waybill data based on the analysis results includes: Based on the priority ranking in the analysis results, a straightening operation instruction is generated for high-priority parcel delivery companies or parcel delivery points. The operation instruction includes adjusting the cargo sorting rules of the distribution center to match the optimal route in the cargo flow table of the cargo distribution configuration and sending a configuration request for straightening the transportation route to the transportation scheduling system. The waybill data that has undergone straightening operation is marked as straightened, and the execution time, operation subject, and transportation route change information of the straightening operation are recorded; Regularly backtest and verify the data of straightened waybills, compare the actual transportation time and cost with the expected indicators in the analysis results, and generate a straightening effect evaluation report to optimize subsequent straightening strategies.

6. A customer waybill straightening device, characterized in that, include: The pre-filtering unit is used to perform general pre-filtering on the acquired customer waybill data, excluding waybills with full truckload transportation as the service method, and excluding waybills with direct flight and air-to-air transportation modes, and unifying the data format and field mapping of customer waybill data; and to obtain the waybill data to be straightened out by the dimensions of the sending company and the sending point of the customer waybill data. The flow generation unit is used to exclude waybill data with a distribution flow count less than or equal to 1 from the data of waybills to be straightened, and to obtain the corresponding distribution information for each waybill to be straightened, including: for the waybill data to be straightened filtered by the dimensions of sending company and sending point, extracting the distribution node flow log of the waybill, and counting the number of distribution center nodes actually passed by each waybill in the transportation process as the distribution flow count; retaining waybill data with a distribution flow count greater than 1, and removing waybill data with a distribution flow count less than or equal to 1; for the retained waybill data, determining the corresponding distribution center as the distribution information based on the last actual operation of the distribution center node in the distribution node flow log, combined with the preset distribution center attribution rules, the distribution center attribution rules include the distribution node's geographical area, operating entity, or network. The process involves: hierarchical attribution matching; generating a cargo flow configuration table based on multiple attribution and distribution information, including: grouping the order data to be straightened with the same attribution and distribution information into groups; aggregating and statistically analyzing the transportation destination, transportation route, and transportation timeliness requirements of each group of order data; generating cargo flow records with the attribution and distribution center as the starting point and the transportation destination as the ending point based on the aggregation results; each flow record containing the number of orders, cargo weight distribution, and configuration information of commonly used transportation route types for that flow; deduplicating and standardizing the flow records to form a cargo flow configuration table containing distribution center codes, destination codes, transportation route rules, and cargo type adaptation conditions, which is used to guide cargo sorting and transportation route planning at the distribution center; and generating the cargo flow configuration table based on multiple attribution and distribution information. The information acquisition unit is used to extract the data of the waybills to be straightened that are customer type sender company or payer company and have a weight greater than or equal to the preset weight from the cargo flow table of the cargo distribution configuration, and to obtain the corresponding information on the quantity of goods not straightened and the quantity of goods to be straightened. The straightening completion unit is used to generate analysis result information based on the straightening data of the waybill to be straightened, the unstraightened cargo quantity information, and the cargo quantity to be straightened information, and to complete the straightening of the customer waybill data based on the analysis result information.

7. A computer device, characterized in that, The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and, in executing the computer program, implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the method as described in any one of claims 1 to 5.

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