Diagnostic method and device for warehouse distribution rates

Abstract

The application discloses a warehouse distribution rate diagnosis method and device, and relates to the technical field of warehouse and logistics management. A specific embodiment of the method comprises the following steps: in response to a warehouse distribution rate diagnosis request, determining a diagnosis target, wherein the diagnosis target comprises a cost subject to be diagnosed and at least one influencing factor; obtaining index association data, wherein the index association data is generated by associating financial cost data and supply chain operation data on a unified dimension; according to the index association data, calculating a fluctuation contribution value of each influencing factor in the at least one influencing factor to the cost subject to be diagnosed, and sorting the fluctuation contribution values of the influencing factors to perform attribution analysis; and based on the attribution analysis result, generating a structured diagnosis report through a large model and a natural language processing template. The embodiment simplifies the attribution analysis process, improves the efficiency of the attribution analysis, can accurately locate the business link that can be optimized, and has strong explainability of the diagnosis report.

Description

Technical Field

[0001] This invention relates to the field of warehousing and logistics management technology, and in particular to a method and apparatus for diagnosing warehousing and distribution rates. Background Technology

[0002] In current warehousing, logistics, and supply chain management, warehousing and distribution rates are key indicators for measuring operational efficiency and cost control, and their accurate analysis and diagnosis are crucial for a company's profitability. With intensifying market competition, companies urgently need a systematic solution that can deeply understand the root causes of rate fluctuations and provide actionable decision support.

[0003] Existing methods for analyzing warehousing and distribution rates typically rely on visual reporting systems built upon financial data. The typical technical approach involves periodically extracting core cost data from financial databases, cleaning and aggregating it to generate predefined, fixed multi-dimensional analysis reports (e.g., expense rate reports summarized by department, time, and product category). Business analysts manually review these reports, comparing them with historical data to identify rate fluctuations, and, based on their personal experience, attempting to attribute causes by cross-referencing multiple supply chain business reports at different granularities.

[0004] However, the aforementioned existing technical solutions have the following significant drawbacks: because financial cost data and supply chain operation data are stored in different data marts or systems, they fail to achieve effective integration, resulting in a disconnect between data and business, and limited depth of attribution analysis; the analysis process is cumbersome and delayed, and the attribution results are mostly qualitative descriptions, unable to accurately locate business links that can be optimized; existing solutions are merely simple data visualization dashboards, lacking independent reasoning, planning, and decision support capabilities. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a diagnostic method and apparatus for warehousing and distribution rates. This method generates indicator-related data by correlating financial cost data and supply chain operation data on a unified dimension, achieving multimodal data fusion. Based on the fused indicator-related data, it calculates the fluctuation contribution value of influencing factors for attribution analysis, reducing the complexity of attribution analysis and establishing an automatic and accurate traceability link from financial results to operational drivers. This solves the problems of data and business separation and limited attribution depth, simplifies the attribution analysis process, improves its efficiency, and accurately identifies optimizable business processes. By generating structured diagnostic reports through large models and natural language processing templates, the attribution analysis results can be automatically transformed into structured, interpretable natural language insights and specific optimization suggestions, realizing an intelligent link from data perception to decision support. Furthermore, the diagnostic reports are highly interpretable.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for diagnosing warehousing and distribution rates is provided, comprising: In response to a warehousing and distribution rate diagnosis request, a diagnosis objective is determined, which includes the expense item to be diagnosed and at least one influencing factor. Obtain indicator-related data, which is generated by associating financial cost data with supply chain operation data in a unified dimension; Based on the correlation data of the indicators, calculate the fluctuation contribution value of each of the at least one influencing factors to the expense item to be diagnosed, and sort the fluctuation contribution values ​​of each influencing factor for attribution analysis. Based on the attribution analysis results, a structured diagnostic report is generated using a large model and natural language processing templates.

[0007] Optionally, calculating the fluctuation contribution value of each of the at least one influencing factors to the expense item to be diagnosed includes: calculating the self-rate contribution value and the contribution value of the change in the proportion of total merchandise transaction volume for each of the at least one influencing factors; wherein, the self-rate contribution value is used to characterize the fluctuation contribution of the expense item to be diagnosed due to the change in the efficiency of each business unit under the current business structure; the contribution value of the change in the proportion of total merchandise transaction volume is used to characterize the fluctuation contribution of the expense item to be diagnosed due to the change in business structure; and determining the fluctuation contribution value of each influencing factor to the expense item to be diagnosed based on the sum of the self-rate contribution value and the contribution value of the change in the proportion of total merchandise transaction volume for each influencing factor.

[0008] Optionally, the fluctuation contribution value of each influencing factor is sorted for attribution analysis, including: sorting the fluctuation contribution value of each influencing factor to identify the main and secondary influencing factors that cause the fluctuation of the expense item to be diagnosed; and identifying the abnormal influencing factors that cause abnormal fluctuations in the expense item to be diagnosed based on the fluctuation contribution value and sorting results of each influencing factor.

[0009] Optionally, ranking the fluctuation contribution value of each influencing factor for attribution analysis further includes: drilling down the at least one influencing factor based on the pre-built correlation between influencing factors, business dimensions and business entities to determine the business dimensions and business entities that cause fluctuations in the expense item to be diagnosed.

[0010] Optionally, based on the attribution analysis results, a structured diagnostic report is generated using a large model and a natural language processing template, including: extracting the influencing factor with the largest fluctuation contribution value from the at least one influencing factor according to the ranking results as the key influencing factor; generating prompt words corresponding to the key influencing factor based on a preset natural language processing template; and generating a structured diagnostic report using a large model based on the prompt words. The diagnostic report includes a set of core business indicators associated with the key influencing factor and a formula for quantifying its financial impact.

[0011] Optionally, the diagnostic objective further includes at least one comparison dimension; calculating the fluctuation contribution value of each of the at least one influencing factors to the expense item to be diagnosed includes: calculating the fluctuation contribution value of each of the at least one influencing factors to the expense item to be diagnosed in the at least one comparison dimension; ranking the fluctuation contribution values ​​of each influencing factor for attribution analysis includes: ranking the fluctuation contribution values ​​of each influencing factor in each comparison dimension; and identifying the abnormal influencing factors and abnormal comparison dimensions that cause abnormal fluctuations in the expense item to be diagnosed based on the fluctuation contribution values ​​of each influencing factor in each comparison dimension and the ranking results.

[0012] Optionally, the at least one influencing factor is selected from a plurality of preset influencing factors, which include supply chain intervention factors and business strategy adaptation factors. The supply chain intervention factors include cross-regional fulfillment, fulfillment type, weight range, item type, turnover days range, order splitting, packaging standards, value-added item range, and distance type. The business strategy adaptation factors include price range, returns, average order value range, and order structure.

[0013] Optionally, the method further includes: based on the at least one influencing factor, matching and generating targeted optimization strategy suggestions from a knowledge base using large model and retrieval enhancement generation techniques.

[0014] According to another aspect of the present invention, a diagnostic device for warehousing and distribution rates is provided, comprising: The parameter determination module is used to determine the diagnostic target in response to the warehousing and distribution rate diagnostic request. The diagnostic target includes the expense item to be diagnosed and at least one influencing factor. The data acquisition module is used to acquire indicator-related data, which is generated by associating financial cost data and supply chain operation data on a unified dimension. The contribution value calculation module is used to calculate the fluctuation contribution value of each of the at least one influencing factors to the expense item to be diagnosed based on the index-related data, and to sort the fluctuation contribution values ​​of each influencing factor for attribution analysis. The results generation module is used to generate structured diagnostic reports based on attribution analysis results, using large models and natural language processing templates.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the warehousing and distribution rate diagnosis method provided in the embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the warehousing and distribution rate diagnosis method provided in the embodiments of the present invention.

[0017] According to another aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the warehousing and distribution rate diagnosis method provided in the embodiments of the present invention.

[0018] One embodiment of the above invention has the following advantages or beneficial effects: In response to a warehousing and distribution rate diagnosis request, a diagnosis objective is determined, including the expense item to be diagnosed and at least one influencing factor; indicator correlation data is obtained, generated by correlating financial cost data and supply chain operation data in a unified dimension; based on the indicator correlation data, the fluctuation contribution value of each influencing factor to be diagnosed is calculated for the expense item, and the fluctuation contribution values ​​of each influencing factor are ranked for attribution analysis; based on the attribution analysis results, a technical solution for generating a structured diagnosis report through a large model and natural language processing template can achieve this by correlating financial cost data and supply chain operation data in a unified dimension. The system generates correlation data for indicators, achieving multimodal data fusion. Based on the fused correlation data, it calculates the fluctuation contribution value of influencing factors for attribution analysis. This reduces the complexity of attribution analysis and establishes an automatic and accurate traceability link from financial results to operational drivers. It solves the problems of data and business separation and limited attribution depth, simplifies the attribution analysis process, improves the efficiency of attribution analysis, and can accurately locate optimizable business links. Through large models and natural language processing templates, it generates structured diagnostic reports, which can automatically transform attribution analysis results into structured, interpretable natural language insights and specific optimization suggestions. This realizes an intelligent link from data perception to decision support, and the diagnostic reports are highly interpretable.

[0019] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0020] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein: Figure 1 This is a schematic diagram of the main steps of the method for diagnosing warehousing and distribution rates according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the impact factor decomposition process according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the diagnostic process for warehousing and distribution rates according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the main modules of a diagnostic device for warehousing and distribution rates according to an embodiment of the present invention; Figure 5 This is an exemplary system architecture diagram in which embodiments of the present invention can be applied; Figure 6 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0021] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] It should be noted that the technical solutions disclosed in this invention, regarding the collection, updating, analysis, processing, use, transmission, and storage of user personal information, all comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0023] During the research and development process, the inventors discovered that existing methods for analyzing warehousing and distribution rates have the following main shortcomings: Data is fragmented from business operations, resulting in limited attribution depth: Financial cost data and supply chain operation data (such as item type, weight range, transportation distance, return reasons, etc.) are stored in different data marts or systems. Furthermore, financial data, being highly sensitive, cannot be moved outside the data mart, making it difficult to integrate the two sets of data, thus leading to data fragmentation. This results in the analysis process heavily relying on analysts manually looking up tables and relating data offline, a complex process that makes it difficult to establish an automated and accurate traceability link from financial results to operational drivers. Therefore, the attribution process can only describe "which indicators changed," failing to quantify the specific contribution of each influencing factor (such as item type structure, cross-regional fulfillment, inventory turnover, etc.) to total cost fluctuations, and unable to answer core questions such as "why did it increase?" and "what are the weights of each driving factor?" The analysis process is cumbersome and delayed: the existing process relies heavily on manual labor, with long cycles from data collection, cleaning, and modeling to report generation, representing a typical "post-mortem review." By the time management receives the analysis report, the problem may have already persisted for weeks, missing the optimal intervention window. Furthermore, the attribution results are mostly qualitative descriptions, failing to accurately pinpoint optimizable business processes. The system suffers from low intelligence and poor interpretability: the existing solution is merely a simple data visualization dashboard, lacking autonomous reasoning, planning, and decision support capabilities. Its output still requires interpretation by business analysis and operations experts based on experience, failing to automatically transform diagnostic results into structured, interpretable natural language insights and specific optimization suggestions, resulting in a break in the link from "data perception" to "decision support."

[0024] Therefore, how to achieve deep integration of financial and supply chain operation data, and on this basis build a complete diagnostic chain that can automatically and intelligently complete the process from rate fluctuation perception, multi-factor quantitative attribution to root cause location and decision-making recommendations, is a technical problem that urgently needs to be solved in this field.

[0025] Accordingly, this invention provides a diagnostic method for warehousing and distribution rates. By correlating financial cost data and supply chain operation data on a unified dimension to generate indicator correlation data, it achieves multimodal data fusion. Based on the fused indicator correlation data, it calculates the fluctuation contribution value of influencing factors for attribution analysis, reducing the complexity of attribution analysis and establishing an automatic and accurate traceability link from financial results to operational drivers. This solves the problems of data and business separation and limited attribution depth, simplifies the attribution analysis process, improves the efficiency of attribution analysis, and can accurately locate optimizable business links. By generating structured diagnostic reports through large models and natural language processing templates, it can automatically transform attribution analysis results into structured, interpretable natural language insights and specific optimization suggestions, realizing an intelligent link from data perception to decision support, and the diagnostic reports are highly interpretable.

[0026] Figure 1 This is a schematic diagram illustrating the main steps of a warehousing and distribution rate diagnosis method according to an embodiment of the present invention. Figure 1 As shown, the diagnostic method for warehousing and distribution rates in this embodiment of the invention mainly includes the following steps S101 to S104.

[0027] Step S101: In response to the warehousing and distribution rate diagnosis request, determine the diagnosis objectives, which include the expense items to be diagnosed and at least one influencing factor.

[0028] The influencing factors in this invention are constructed based on the extraction and structuring of in-depth business knowledge in the field of supply chain management. Specifically, they are constructed by systematically reviewing historical business analysis practices, standardizing and quantifying the core business dimensions (such as part type, transportation distance, and inventory turnover days) that business experts frequently focus on for cost attribution, and mapping them into quantitative driving factors that can be identified and processed by mathematical models.

[0029] When a user needs to perform a warehousing and logistics fee rate diagnosis, they will select at least one influencing factor that needs to be analyzed in this diagnosis, as well as the corresponding expense item to be diagnosed. Then, a warehousing and logistics fee rate diagnosis request is sent, allowing the system to determine the diagnostic targets based on the received request. The diagnostic targets include the expense item to be diagnosed and at least one influencing factor. For the selected influencing factor, drill-down analysis can be performed to more comprehensively and deeply analyze the impact of the factors of interest to the user on the warehousing and logistics fee rate.

[0030] In other embodiments of the present invention, the diagnostic target may also include at least one comparison dimension to describe the data dimension that is expected to be compared in this warehousing and distribution rate diagnosis, such as year-on-year, month-on-month, overall, etc.

[0031] Step S102: Obtain indicator correlation data. This indicator correlation data is generated by linking financial cost data and supply chain operation data on a unified dimension. After receiving the warehousing and distribution fee rate diagnosis request, the system also needs to obtain indicator correlation data. This indicator correlation data is generated by linking financial cost data and supply chain operation data on a unified dimension.

[0032] Financial cost data refers to warehousing and distribution profit and loss expenses under financial accounting standards, serving as the basis for calculating expenses or fluctuation contribution values. Supply chain operation data refers to key information such as departments, categories, and products within the existing business permissions, used to select the data scope and ensure that the data scope and dimensions for this warehousing and distribution rate diagnosis meet business expectations. For example, if the warehousing and distribution rate needs to be diagnosed for a specific second-level department, the input supply chain operation data will be the supply chain operation data corresponding to that second-level department; if the diagnosis needs to be performed on a specific product, the input will be the supply chain operation data corresponding to that product. In addition, supply chain operation data can also include operational data for business dimensions such as city, business model, and network type to ensure that the business can have more granular queries. At the same time, supply chain operation data can also include various expense items under warehousing and distribution expenses, including but not limited to: Expense_Change_Warehousing_Actual, Expense_Change_Delivery_Actual, etc., mainly used to obtain the expense items that the business actually needs to diagnose, thereby enabling drill-down analysis.

[0033] Step S103: Based on the index correlation data, calculate the fluctuation contribution value of each influencing factor in the expense item to be diagnosed, and sort the fluctuation contribution values ​​of each influencing factor for attribution analysis.

[0034] According to one embodiment of the present invention, before calculating the fluctuation contribution value of each of the at least one influencing factors to the expense item to be diagnosed, the index-related data can also be aligned and verified to ensure that the obtained index-related data is consistent in time, scope and granularity, and to complete the data quality check, such as removing extreme outliers.

[0035] Subsequently, based on the configured comparison dimensions (year-on-year, month-on-month, overall), calculations are performed in parallel to quantify the warehousing and distribution fee rates and the volatility contribution and volatility of each level of expense item.

[0036] According to one embodiment of the present invention, the at least one influencing factor is selected from a plurality of preset influencing factors. The plurality of preset influencing factors may include, for example, supply chain intervention factors and business strategy adaptation factors. The supply chain intervention factors may include, for example, cross-regional fulfillment, fulfillment type, weight range, item type, turnover days range, order splitting, packaging standards, value-added item range, and distance type. The business strategy adaptation factors may include, for example, price range, returns, average order value range, and order structure.

[0037] In the embodiments of this invention, based on the complex and diverse business ecosystem, 13 types of factors affecting warehousing and distribution rates were systematically identified and divided into two main categories according to their operability and optimization logic: 1. Supply Chain Intervention Factors: These factors originate from supply chain network planning, resource deployment, and process design. Their cost impact can be directly offset through proactive strategy adjustments and execution optimization. For example, shortening the average transportation distance through route optimization and reducing the proportion of irregularly shaped parts through packaging standardization can achieve cost reduction and efficiency improvement. 2. Business strategies must adapt to specific factors: These factors reflect the actual behavior of end consumers and market characteristics (such as price range, return rate, order structure, etc.). Although they cannot be directly intervened in, their cost contribution must be accurately identified. Understanding these factors helps in designing better operational strategies (such as cross-selling and promotional program design), achieving the best balance between cost and experience, and realizing structural optimization.

[0038] The classification system of this influencing factor clarifies the management logic of "where to proactively optimize" and "where to adapt strategies", helping teams to continuously improve the user experience while controlling costs.

[0039] In embodiments of the present invention, based on the quantitative model of the 13 preset influencing factors (such as part type, weight range, transportation distance, price range, inventory turnover days, etc.), a fluctuation contribution value algorithm can be used to calculate the fluctuation contribution value and relative contribution of each influencing factor to be diagnosed expense item under the selected comparison dimension. The relative contribution can be determined based on the absolute value of the fluctuation contribution value of each influencing factor. In one embodiment, the relative contribution of a certain influencing factor is, for example, the ratio of the absolute value of the fluctuation contribution value of that influencing factor to the sum of the absolute values ​​of the fluctuation contribution values ​​of all influencing factors.

[0040] According to one embodiment of the present invention, calculating the fluctuation contribution value of each of the at least one influencing factors to the expense item to be diagnosed may specifically include: calculating the self-rate contribution value and the contribution value of the change in the proportion of total commodity transaction volume for each of the at least one influencing factors; wherein, the self-rate contribution value is used to characterize the fluctuation contribution of the expense item to be diagnosed due to the change in the efficiency of each business unit under the current business structure; the contribution value of the change in the proportion of total commodity transaction volume is used to characterize the fluctuation contribution of the expense item to be diagnosed due to the change in the business structure; and determining the fluctuation contribution value of each influencing factor to the expense item to be diagnosed based on the sum of the self-rate contribution value and the contribution value of the change in the proportion of total commodity transaction volume for each influencing factor.

[0041] In embodiments of the present invention, the overall contribution value is divided into the contribution value of changes in its own fee rate and the contribution value of changes in the proportion of total merchandise transaction volume (i.e., the contribution value of changes in the proportion of GMV).

[0042] Regarding the contribution value of each factor to its own fee rate, this invention introduces the proportion of GMV (Gross Merchandise Volume) as a weight to construct a "contribution value converter." This converter can assess, under the current business structure, how much responsibility each influencing factor should bear for changes in the overall fee rate due to its own efficiency improvement or decline. The sum of the contribution values ​​of all influencing factors to changes in their own fee rates constitutes the portion of the overall warehousing and distribution fee rate change driven by the internal efficiency changes of each influencing factor, assuming the existing business structure remains unchanged. In one embodiment of this invention, the influencing factors can be calculated using the following formula. The contribution of its own rate change: = Among them, rate changes Current period GMV percentage , This represents the current period's GMV. For current period expenses, For the HMV of the control period, Costs for the comparison period, This represents the GMV value for the current period. This represents the total GMV.

[0043] The contribution value for changes in GMV share is calculated based on the impact of changes in business structure (changes in GMV share) on the overall warehousing and logistics fee rate. That is, if a business unit's fee rate is higher than the overall average, then an increase in its GMV share will push up the overall warehousing and logistics fee rate (positive contribution). If a business unit's fee rate is lower than the overall average, then an increase in its GMV share will lower the overall warehousing and logistics fee rate (negative contribution).

[0044] Finally, the contribution of the influencing factors to the fluctuation of the expense items to be diagnosed is calculated: this is done by summing the contribution of the business unit's own expense rate and the contribution of changes in GMV share, to represent the total contribution to the overall warehousing and distribution expense rate change. This total contribution fully reflects the net impact of this business unit on the overall expense rate through both efficiency changes and changes in scale and structure.

[0045] According to one embodiment of the present invention, the fluctuation contribution value of each influencing factor is sorted for attribution analysis. Specifically, this may include: sorting the fluctuation contribution value of each influencing factor to identify the primary and secondary influencing factors causing the fluctuation of the expense item to be diagnosed; and identifying the abnormal influencing factors causing abnormal fluctuations in the expense item to be diagnosed based on the fluctuation contribution value and the sorting results of each influencing factor. After calculating the fluctuation contribution value of each influencing factor to the change in warehousing and distribution rates, the factors can be sorted from largest to smallest based on their fluctuation contribution values, thus determining the primary and secondary influencing factors causing the fluctuation of the expense item to be diagnosed. Specifically, for example, the factors with the largest fluctuation contribution values ​​can be identified as primary influencing factors, and the factors with the smallest fluctuation contribution values ​​as secondary influencing factors, and so on. Then, based on whether the fluctuation contribution value of each influencing factor is positive or negative, the abnormal influencing factors causing abnormal fluctuations in the expense item to be diagnosed can be identified. Specifically, for nodes with positive fluctuation contribution values, it means that the node contributes to the negative change in warehousing and distribution rates, and these need to be identified by the business department as abnormal influencing factors for subsequent judgment and management. For example, if the system identifies an overall increase in rates across regions, the business needs to conduct further attribution analysis on the system. If the attribution analysis determines that the factors can be improved through supply chain strategy adjustments, then the purchasing and sales personnel will adjust the strategy and implement it.

[0046] It should be noted that since there may be correlations between various influencing factors, an anomaly in one influencing factor can lead to anomalies in all subsequent influencing factors. Therefore, when identifying anomalous influencing factors, only the first identified anomalous influencing factor needs to be considered, and subsequent attribution analysis can be performed.

[0047] According to one embodiment of the present invention, ranking the fluctuation contribution value of each influencing factor for attribution analysis may further include: drilling down at least one influencing factor based on a pre-constructed correlation between influencing factors, business dimensions, and business entities to determine the business dimensions and business entities that cause fluctuations in the expense item to be diagnosed. According to the technical solution of the present invention, a three-layer correlation network can be constructed in advance based on the needs of the business scenario, combining the correlation between influencing factors, business dimensions, and business entities to achieve intelligent penetration from macro-level attribution to micro-level root causes. This network map starts with the influencing factors of the business, clearly associating them with drill-downable business dimensions and underlying business entities, supporting accurate and operable root cause localization.

[0048] For example, for the influencing factor "cross-regional fulfillment factor," the associated business dimensions are "shipping and distribution center" and "receiving and distribution center," and the associated business entities are "shipping warehouse," "receiving warehouse," and "goods." By combining the relationships between this influencing factor and the business dimensions and entities, drill-down analysis can be performed to identify the business dimensions and entities causing fluctuations in this expense item. Specifically, by drilling down into this influencing factor, we can penetrate to specific "shipping-receiving" distribution center pairs, accurately quantify the cross-regional fulfillment rate of each regional pair, and thus identify the key flows that drive up cross-regional delivery costs. This provides data support for network planning, inter-warehouse transfers, and source product selection strategies, optimizing the fulfillment structure from the source to reduce ineffective flows.

[0049] For example, regarding the influencing factor "reverse return rate factor," the associated business dimensions include "reason for return," "product category," and "customer level," while the associated business entities include "shipping warehouse," "customer level," and "product." By combining the relationships between this influencing factor and business dimensions and entities, drill-down analysis can be performed to identify the business dimensions and entities causing fluctuations in this expense item. Specifically, by drilling down on this influencing factor, we can penetrate to specific reasons for returns (such as quality issues or description discrepancies). Combining product category and customer dimensions, we can analyze the contribution of reverse logistics to cost fluctuations in different scenarios, thereby identifying the core return scenarios that lead to abnormal warehousing and distribution rates. This can drive quality control, page information optimization, or after-sales policy adjustments, reducing reverse costs at the source.

[0050] For example, regarding the influencing factor "inventory turnover factor," its associated business dimensions include "region," "warehouse," and "product," and its associated business entities include "order region," "shipping warehouse," "receiving warehouse," and "product." By combining the relationships between this influencing factor and its business dimensions and entities, drill-down analysis can be performed to identify the business dimensions and entities causing fluctuations in this expense item. Specifically, by performing drill-down analysis on this influencing factor, we can first locate the region-warehouse combination with abnormal turnover days, and then drill down to specific products to identify the top N slow-moving products affecting overall turnover efficiency. This provides a clear tool for inventory management, supports the development of targeted promotional clearance, allocation, or replacement strategies, accelerates inventory turnover, and directly reduces warehousing and holding costs.

[0051] Through this three-layered network, the system combines the abstract factors affecting warehousing and distribution rates with specific business entities and operational actions, achieving a closed-loop analysis capability from "discovering cost problems" to "locating actionable root causes." Businesses only need to perform warehousing and distribution rate diagnosis through the system and then click to interpret the analysis and diagnosis report to obtain the attribution results under different influencing factors, thereby guiding actual business strategies.

[0052] According to one embodiment of the present invention, the diagnostic target further includes at least one comparison dimension. Specifically, calculating the fluctuation contribution value of each of the at least one influencing factors for the expense item to be diagnosed may include: calculating the fluctuation contribution value of each of the at least one influencing factor for the expense item to be diagnosed in at least one comparison dimension. The fluctuation contribution values ​​of each influencing factor are then sorted for attribution analysis, specifically including: sorting the fluctuation contribution values ​​of each influencing factor in each comparison dimension; based on the fluctuation contribution values ​​of each influencing factor in each comparison dimension and the sorting results, identifying the abnormal influencing factors and abnormal comparison dimensions that cause abnormal fluctuations in the expense item to be diagnosed. For example, for the influencing factor "cross-regional performance factor," there are two comparison dimensions: "cross-regional" and "same-regional." In this case, the system calculates the fluctuation contribution value under each dimension and integrates them to obtain the fluctuation contribution value corresponding to the influencing factor. During sorting, the influencing factors are first sorted according to their fluctuation contribution values, and then each dimension under the influencing factor is further sorted according to its fluctuation contribution value. Finally, the abnormal influencing factors and abnormal comparison dimensions that cause abnormal fluctuations in the expense item to be diagnosed can be identified.

[0053] Figure 2 This is a schematic diagram illustrating the impact factor decomposition process of one embodiment of the present invention. For example... Figure 2 As shown, in one embodiment of the present invention, when a user needs to perform warehousing and distribution rate diagnosis, the user-inputted influencing factors and comparison dimensions are first obtained. Here, it is assumed that each influencing factor's comparison dimensions include dimension 1 and dimension 2. Then, the fluctuation contribution value of each influencing factor is calculated based on the obtained indicator correlation data. The fluctuation contribution value of each influencing factor is determined by the fluctuation contribution values ​​of dimension 1 and dimension 2 of that influencing factor. For example, the fluctuation contribution values ​​of the two dimensions can be weighted and summed to calculate the fluctuation contribution value of the influencing factor. Afterwards, the influencing factors are sorted according to their fluctuation contribution values. Based on the sorting results, the fluctuation contribution values ​​corresponding to each dimension of each influencing factor are sequentially broken down and displayed to facilitate the identification of abnormal influencing factors and abnormal comparison dimensions under abnormal influencing factors.

[0054] Step S104: Based on the attribution analysis results, generate a structured diagnostic report using a large model and natural language processing templates. The attribution analysis results include the fluctuation contribution values ​​of each influencing factor and their ranking, major and minor influencing factors, abnormal influencing factors, business dimensions, and business entities, etc.

[0055] According to one embodiment of the present invention, based on the attribution analysis results, a structured diagnostic report is generated through a large model and a natural language processing template. Specifically, this may include: extracting the influencing factor with the largest fluctuation contribution value from at least one influencing factor based on the ranking results as the key influencing factor; generating prompt words corresponding to the key influencing factor based on a preset natural language processing template; and generating a structured diagnostic report based on the prompt words through a large model. The diagnostic report includes a set of core business indicators associated with the key influencing factor and a quantitative formula for financial impact. After completing the quantitative attribution analysis and root cause drill-down of the influencing factors, in order to effectively transform the attribution analysis results into actionable business insights, the system does not directly output tabular data results. Instead, it generates a structured diagnostic report through a natural language processing template and a large model, thereby achieving the goal of transforming data results into business-understandable text within the intelligent warehousing and distribution rate diagnostic system.

[0056] In one embodiment, when generating a diagnostic report, each influencing factor is selected as a key influencing factor in descending order of its fluctuation contribution value. Then, based on the prompt word template (natural language processing template) corresponding to the key influencing factor, prompt words are generated for the key influencing factor. By inputting the prompt words into the large model, the diagnostic report for the key influencing factor can be generated. Finally, the diagnostic reports for each influencing factor are integrated to obtain the final diagnostic report. This invention can pre-configure a corresponding prompt word template for each influencing factor, defining the core business indicator set and financial impact quantification formula associated with the influencing factor through the prompt word template. For example, for the "cross-regional fulfillment factor," the associated core business indicator set includes "cross-regional order volume ratio," "related GMV ratio," and "average transportation distance," etc.; the associated financial impact quantification formula is, for example: Additional cost = fluctuation contribution value of the influencing factor × GMV of the comparison period for the influencing factor, where the comparison period is relative to the current period; if the comparison dimension is year-on-year, then the comparison period refers to the same period last year.

[0057] In one embodiment of the present invention, taking "cross-regional performance factor" as a key influencing factor as an example, a structured diagnostic report is generated through a large model as follows, which can be rendered as a report card for display: "

Impact Factor

[0058] Financial impact: It incurred additional costs of RMB 850,000, resulting in an increase of RMB 0.32 in the average delivery cost per order.

[0059] Impact on user experience: The average delivery time for related orders increased by 6 hours, and the 24-hour completion rate decreased by 15%.

[0060] Root cause analysis: The flow from East China warehouses to South China stations is a significant issue, with a cross-regional rate of 28%, contributing 40% of the cost increase for this factor.

[0061] Based on the above embodiments, by automatically translating the attribution analysis results of engineering calculations into standardized summaries that conform to the business management context through a configurable rule system, seamless integration of technical insights and business decisions is achieved. This solves the technical challenges of poor interpretability and long decision support paths in directly outputting detailed attribution analysis results, significantly improving the system's usability and action guidance value in actual business management, and completing an intelligent closed loop from "data perception" to "decision support".

[0062] According to one embodiment of the present invention, the warehousing and distribution rate detection method may further include: based on at least one influencing factor, matching and generating targeted optimization strategy suggestions from a knowledge base using a large model and retrieval enhancement generation technology. The knowledge base stores prompt word templates and optimization strategy suggestions corresponding to the influencing factors. Through retrieval enhancement generation technology, recommended measures corresponding to the influencing factors can be matched from the knowledge base. For example, if the attribution analysis results indicate that "the proportion of irregularly shaped parts is too high," then it is suggested to "collaborate with procurement to optimize the packaging specifications of specific commodities."

[0063] Figure 3 This is a schematic diagram of the diagnostic process for warehousing and distribution rates according to an embodiment of the present invention. Figure 3 As shown, in one embodiment of the present invention, the diagnostic process for warehousing and distribution rates mainly includes several steps: data input, diagnostic engine calculation, diagnostic result output, and optimization suggestion generation.

[0064] 1. Data Input: Obtain the diagnostic objectives and related indicator data required by the warehousing and distribution fee rate diagnostic engine, and send them to the diagnostic engine for processing as the current business input. The related indicator data is generated by linking financial cost data and supply chain operation data on a unified dimension. The diagnostic objectives include the expense items to be diagnosed, at least one influencing factor, and comparison dimensions. Financial cost data, such as warehousing and distribution profit and loss expenses under financial accounting standards, is the most critical input item, serving as the basis for calculating expense or volatility contribution values. Supply chain operation data includes key data such as departments, categories, and products under existing business permissions, used to select the data scope and ensure that the scope and dimensions of this diagnosis meet business expectations. Comparison dimensions refer to the data dimensions that the business expects to compare, including year-on-year, month-on-month, and overall comparisons. The expense items to be diagnosed refer to the items that the business actually needs to diagnose, facilitating subsequent drill-down analysis, such as expense_change_warehousing_actual, expense_change_delivery_actual, etc. Influencing factors are the main influencing factors that the user focuses on in this diagnosis; generally, 1-4 factors are sufficient.

[0065] 2. Diagnostic Engine Calculation: This step is executed in the core computing layer of intelligent diagnosis. The diagnostic engine executes the following steps in sequence: (1) Data alignment and verification: Ensure that all input data are consistent in time, scope and granularity, and complete data quality checks, such as removing extreme outliers; (2) Multidimensional comparison calculation: Calculation is performed in parallel according to the comparison dimensions (year-on-year, month-on-month, overall) of the business configuration to quantify the total expense ratio and the fluctuation contribution value and volatility of each expense item; (3) Analysis of the contribution of influencing factors: Based on the quantitative model of multiple influencing factors (such as piece type, weight range, transportation distance, price range, inventory turnover days, etc.), the contribution value algorithm is used to calculate the fluctuation contribution value and relative contribution of each influencing factor in the diagnostic target to the target expense item under the selected comparison dimension. (4) Root cause localization and ranking: Based on the absolute value of the fluctuation contribution of each influencing factor, the main and secondary factors causing the fluctuation are identified, and the root cause localization is completed.

[0066] 3. Diagnostic Result Output: The results of the diagnostic engine's calculations are encapsulated into a structured, interpretable diagnostic report, which mainly includes the following parts: (1) Core conclusions: Output the overall rate change performance, and based on the user-selected influencing factors, provide a one-sentence overview of the fluctuation contribution value of the financial item changes under the core dimensions, as well as the impact of core data indicators, such as the impact on GMV, cost, and timeliness; (2) Visualized data dashboard: Generates data dashboards including trend charts, stacked charts, etc., and supports visualization viewing in the form of average cost per piece, average cost per unit, total cost, etc., providing intuitive insights; (3) Attribution analysis results: The influencing factors are ranked based on the fluctuation contribution value, and the fluctuation contribution value, relative contribution and influence direction of the influencing factors are clearly displayed, such as pushing up or lowering costs. (4) Drill-down analysis results: Supports drill-down analysis of influencing factors, which can be located to specific products, business departments, distribution centers and warehouses, revealing the scenarios where the problems are most concentrated.

[0067] 4. Optimization Suggestion Generation: To form a closed loop of "diagnosis-decision," the system generates targeted optimization strategy suggestions based on the large model. Specifically, for scenarios under different influencing factors, a systematic suggestion engineering framework is constructed using the large model's suggestion engineering. This framework, based on a domain knowledge graph, predefines structured suggestion templates for each type of influencing factor (such as "cross-regional fulfillment" and "inventory turnover"), ensuring that the large model can follow a predetermined professional analysis path in any scenario to generate structured, standardized, and interpretable business insights. Furthermore, based on root causes, it matches recommended measures corresponding to the influencing factors from the knowledge base through retrieval-enhanced generation technology. For example, if the diagnosis is "excessive proportion of irregularly shaped parts," the suggestion is "to collaborate with procurement to optimize the packaging specifications of specific products."

[0068] The above process forms a complete closed loop from data to insights to recommendations, transforming passive manual diagnosis of rates into proactive, precise intelligent diagnosis and management.

[0069] Figure 4 This is a schematic diagram of the main modules of a diagnostic device for warehousing and distribution rates according to an embodiment of the present invention. Figure 4 As shown, the diagnostic device 400 for warehousing and distribution rates in this embodiment of the invention mainly includes a parameter determination module 401, a data acquisition module 402, a contribution value calculation module 403, and a result generation module 404.

[0070] The parameter determination module 401 is used to determine the diagnostic target in response to the warehousing and distribution rate diagnostic request. The diagnostic target includes the expense item to be diagnosed and at least one influencing factor. The data acquisition module 402 is used to acquire indicator-related data, which is generated by associating financial cost data with supply chain operation data in a unified dimension. The contribution value calculation module 403 is used to calculate the fluctuation contribution value of each of the at least one influencing factors for the expense items to be diagnosed based on the index correlation data, and to sort the fluctuation contribution values ​​of each influencing factor for attribution analysis. Result generation module 404 is used to generate structured diagnostic reports based on attribution analysis results, using large models and natural language processing templates.

[0071] According to one embodiment of the present invention, the contribution value calculation module 403 can be specifically used to: calculate the self-rate contribution value and the contribution value of the change in the proportion of total commodity transaction volume for each of at least one influencing factor; wherein, the self-rate contribution value is used to characterize the fluctuation contribution of the expense items to be diagnosed due to the change in the efficiency of each business unit under the current business structure; the contribution value of the change in the proportion of total commodity transaction volume is used to characterize the fluctuation contribution of the expense items to be diagnosed due to the change in business structure; and determine the fluctuation contribution value of each influencing factor for the expense items to be diagnosed based on the sum of the self-rate contribution value and the contribution value of the change in the proportion of total commodity transaction volume for each influencing factor.

[0072] According to one embodiment of the present invention, the contribution value calculation module 403 can be specifically used to: sort the fluctuation contribution values ​​of each influencing factor to identify the main and secondary influencing factors that cause the fluctuation of the expense item to be diagnosed; and identify the abnormal influencing factors that cause the abnormal fluctuation of the expense item to be diagnosed based on the fluctuation contribution value and sorting results of each influencing factor.

[0073] According to one embodiment of the present invention, the contribution value calculation module 403 can also be used to: drill down on at least one influencing factor based on the pre-built relationship between the influencing factor, business dimension and business entity, to determine the business dimension and business entity that cause the fluctuation of the expense item to be diagnosed.

[0074] According to one embodiment of the present invention, the result generation module 404 can be specifically used to: extract the influence factor with the largest fluctuation contribution value from at least one influence factor based on the ranking result as the key influence factor; generate prompt words corresponding to the key influence factor based on a preset natural language processing template; and generate a structured diagnostic report through a large model based on the prompt words. The diagnostic report includes a set of core business indicators associated with the key influence factor and a formula for quantifying the financial impact.

[0075] According to one embodiment of the present invention, the diagnostic target further includes at least one comparison dimension; the contribution value calculation module 403 can be specifically used to: calculate the fluctuation contribution value of each of the at least one influencing factors in the expense item to be diagnosed in at least one comparison dimension; sort the fluctuation contribution values ​​of each influencing factor in each comparison dimension; and based on the fluctuation contribution value of each influencing factor in each comparison dimension and the sorting results, identify the abnormal influencing factors and abnormal comparison dimensions that cause the expense item to be diagnosed to have abnormal fluctuations.

[0076] According to one embodiment of the present invention, at least one influencing factor is selected from a plurality of preset influencing factors. The plurality of preset influencing factors include supply chain intervention factors and business strategy adaptation factors. The supply chain intervention factors include cross-regional fulfillment, fulfillment type, weight range, item type, turnover days range, order splitting, packaging standards, value-added item range, and distance type. The business strategy adaptation factors include price range, returns, average order value range, and order structure.

[0077] According to one embodiment of the present invention, the diagnostic device 400 for warehousing and distribution rates may further include an optimization suggestion generation module (not shown in the figure) for: matching and generating targeted optimization strategy suggestions from a knowledge base based on at least one influencing factor, using large model and retrieval enhancement generation techniques.

[0078] According to the technical solution of this invention, in response to a warehousing and distribution rate diagnosis request, a diagnosis target is determined, which includes the expense item to be diagnosed and at least one influencing factor; indicator correlation data is obtained, which is generated by correlating financial cost data and supply chain operation data in a unified dimension; based on the indicator correlation data, the fluctuation contribution value of each influencing factor in the at least one influencing factor to be diagnosed is calculated, and the fluctuation contribution values ​​of each influencing factor are ranked for attribution analysis; based on the attribution analysis results, a structured diagnosis report is generated through a large model and natural language processing templates, which correlates financial cost data and supply chain operation data in a unified dimension. By integrating data related to indicators, multimodal data fusion is achieved. Based on the fused data, the fluctuation contribution value of influencing factors is calculated for attribution analysis. This reduces the complexity of attribution analysis and establishes an automatic and accurate traceability link from financial results to operational drivers. It solves the problems of data and business separation and limited attribution depth, simplifies the attribution analysis process, improves the efficiency of attribution analysis, and can accurately locate optimizable business links. Through large models and natural language processing templates, structured diagnostic reports are generated, which can automatically transform attribution analysis results into structured, interpretable natural language insights and specific optimization suggestions. This realizes an intelligent link from data perception to decision support, and the diagnostic reports are highly interpretable.

[0079] Figure 5 An exemplary system architecture 500 is shown that can be applied to the diagnostic method or apparatus for warehousing and distribution rates according to embodiments of the present invention.

[0080] like Figure 5As shown, system architecture 500 may include terminal devices 501, 502, and 503, a network 504, and a server 505. Network 504 serves as the medium for providing communication links between terminal devices 501, 502, and 503 and server 505. Network 504 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0081] Users can use terminal devices 501, 502, and 503 to interact with server 505 via network 504 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 501, 502, and 503, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0082] Terminal devices 501, 502, and 503 can be various electronic devices with displays that support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0083] Server 505 can be a server providing various services, such as a backend management server supporting shopping websites browsed by users using terminal devices 501, 502, and 503 (for example only). The backend management server can process received product information query requests and other data by determining diagnostic targets, obtaining indicator correlation data, calculating fluctuation contribution values, performing attribution analysis, and generating diagnostic reports, and then feed back the processing results (such as the generated diagnostic report - for example only) to the terminal devices.

[0084] It should be noted that the diagnostic method for warehousing and distribution rates provided in this embodiment of the invention is generally executed by server 505, and correspondingly, the diagnostic device for warehousing and distribution rates is generally set in server 505.

[0085] It should be understood that Figure 5 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0086] The following is for reference. Figure 6 It shows a schematic diagram of the structure of a computer system 600 suitable for implementing terminal devices or servers of the present invention. Figure 6 The terminal device or server shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0087] like Figure 6As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0088] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0089] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined above in the system of this invention.

[0090] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0092] The units or modules described in the embodiments of the present invention can be implemented in software or hardware. The described units or modules can also be housed in a processor; for example, a processor can be described as including a parameter determination module, a data acquisition module, a contribution value calculation module, and a result generation module. The names of these units or modules do not necessarily limit the specific unit or module itself; for example, the parameter determination module can also be described as "a module for determining diagnostic targets in response to a warehousing and distribution rate diagnostic request."

[0093] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs that, when executed by the device, cause the device to: in response to a warehousing and distribution rate diagnosis request, determine a diagnosis target, the diagnosis target including the expense item to be diagnosed and at least one influencing factor; acquire indicator correlation data, the indicator correlation data being generated by correlating financial cost data and supply chain operation data on a unified dimension; calculate, based on the indicator correlation data, the fluctuation contribution value of each of the at least one influencing factor to the expense item to be diagnosed, and rank the fluctuation contribution values ​​of each influencing factor for attribution analysis; and generate a structured diagnostic report based on the attribution analysis results using a large model and a natural language processing template.

[0094] According to the technical solution of this invention, in response to a warehousing and distribution rate diagnosis request, a diagnosis target is determined, which includes the expense item to be diagnosed and at least one influencing factor; indicator correlation data is obtained, which is generated by correlating financial cost data and supply chain operation data in a unified dimension; based on the indicator correlation data, the fluctuation contribution value of each influencing factor in the at least one influencing factor to be diagnosed is calculated, and the fluctuation contribution values ​​of each influencing factor are ranked for attribution analysis; based on the attribution analysis results, a structured diagnosis report is generated through a large model and natural language processing templates, which correlates financial cost data and supply chain operation data in a unified dimension. By integrating data related to indicators, multimodal data fusion is achieved. Based on the fused data, the fluctuation contribution value of influencing factors is calculated for attribution analysis. This reduces the complexity of attribution analysis and establishes an automatic and accurate traceability link from financial results to operational drivers. It solves the problems of data and business separation and limited attribution depth, simplifies the attribution analysis process, improves the efficiency of attribution analysis, and can accurately locate optimizable business links. Through large models and natural language processing templates, structured diagnostic reports are generated, which can automatically transform attribution analysis results into structured, interpretable natural language insights and specific optimization suggestions. This realizes an intelligent link from data perception to decision support, and the diagnostic reports are highly interpretable.

[0095] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for diagnosing warehousing and distribution rates, characterized in that, include: In response to a warehousing and distribution rate diagnosis request, a diagnosis objective is determined, which includes the expense item to be diagnosed and at least one influencing factor. Obtain indicator-related data, which is generated by associating financial cost data with supply chain operation data in a unified dimension; Based on the correlation data of the indicators, calculate the fluctuation contribution value of each of the at least one influencing factors to the expense item to be diagnosed, and sort the fluctuation contribution values ​​of each influencing factor for attribution analysis. Based on the attribution analysis results, a structured diagnostic report is generated using a large model and natural language processing templates.

2. The method according to claim 1, characterized in that, Calculating the contribution of each of the at least one influencing factor to the volatility of the expense item to be diagnosed includes: Calculate the self-rate contribution value and the contribution value of the change in the proportion of total commodity transaction volume for each of the at least one influencing factor; wherein, the self-rate contribution value is used to characterize the fluctuation contribution of the change in the efficiency of each business unit to the expense item to be diagnosed under the current business structure; the contribution value of the change in the proportion of total commodity transaction volume is used to characterize the fluctuation contribution of the change in the business structure to the expense item to be diagnosed. The fluctuation contribution of each influencing factor to the expense item to be diagnosed is determined by summing the contribution value of each influencing factor's own rate contribution value and the contribution value of the change in the proportion of total commodity transaction amount.

3. The method according to claim 2, characterized in that, The fluctuation contribution values ​​of each influencing factor are ranked for attribution analysis, including: Based on the fluctuation contribution value of each influencing factor, the primary and secondary influencing factors causing the fluctuation of the expense item to be diagnosed are identified. Based on the fluctuation contribution value and ranking results of each influencing factor, the abnormal influencing factors that cause abnormal fluctuations in the expense items to be diagnosed are identified.

4. The method according to claim 3, characterized in that, The process of ranking the fluctuation contribution values ​​of each influencing factor for attribution analysis also includes: Based on the pre-built relationships between influencing factors, business dimensions, and business entities, drill down on at least one influencing factor to determine the business dimensions and business entities that cause fluctuations in the expense item to be diagnosed.

5. The method according to claim 3 or 4, characterized in that, Based on the attribution analysis results, a structured diagnostic report is generated using a large model and natural language processing templates, including: Based on the ranking results, the influence factor with the largest fluctuation contribution value is selected as the key influence factor from the at least one influence factor in sequence; Based on a preset natural language processing template, prompt words corresponding to the key influencing factors are generated; Based on the aforementioned prompts, a structured diagnostic report is generated using a large model. The diagnostic report includes a set of core business indicators associated with the key influencing factors and a formula for quantifying their financial impact.

6. The method according to claim 1, characterized in that, The diagnostic objectives also include at least one comparative dimension; Calculating the contribution of each of the at least one influencing factor to the volatility of the expense item to be diagnosed includes: Calculate the fluctuation contribution value of each of the at least one influencing factor to the expense item to be diagnosed in the at least one comparison dimension; The fluctuation contribution values ​​of each influencing factor are ranked for attribution analysis, including: The fluctuation contribution values ​​of each influencing factor in each comparison dimension are sorted separately; Based on the fluctuation contribution value and ranking results of each influencing factor in each comparison dimension, the abnormal influencing factors and abnormal comparison dimensions that cause abnormal fluctuations in the expense item to be diagnosed are identified.

7. The method according to claim 1, characterized in that, The at least one influencing factor is selected from a set of preset influencing factors, which include supply chain intervention factors and business strategy adaptation factors. The supply chain intervention factors include cross-regional fulfillment, fulfillment type, weight range, item type, turnover days range, order splitting, packaging standards, value-added items, and distance type. The business strategy adaptation factors include price range, returns, average order value range, and order structure.

8. The method according to claim 1, characterized in that, The method further includes: Based on at least one of the influencing factors, targeted optimization strategy suggestions are generated from the knowledge base through large model and retrieval-enhanced generation techniques.

9. A diagnostic device for warehousing and distribution rates, characterized in that, include: The parameter determination module is used to determine the diagnostic target in response to the warehousing and distribution rate diagnostic request. The diagnostic target includes the expense item to be diagnosed and at least one influencing factor. The data acquisition module is used to acquire indicator-related data, which is generated by associating financial cost data and supply chain operation data on a unified dimension. The contribution value calculation module is used to calculate the fluctuation contribution value of each of the at least one influencing factors to the expense item to be diagnosed based on the index-related data, and to sort the fluctuation contribution values ​​of each influencing factor for attribution analysis. The results generation module is used to generate structured diagnostic reports based on attribution analysis results, using large models and natural language processing templates.

10. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-8.

11. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

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