Logistics single quantity fluctuation analysis system

Abstract

The invention provides a logistics order quantity fluctuation analysis system, and the system comprises a data collection module which is used for collecting internal operation data of logistics business through an internal data interface, and obtaining unstructured external environment data from a plurality of external data sources through an external data interface; the data preprocessing module is used for cleaning and segmenting the internal operation data to generate time sequence data, and performing semantic extraction on the external environment data to convert the external environment data into structured factors representing different types of external events; the single-quantity fluctuation analysis and attribution module is used for calculating the single-quantity fluctuation ratio of the current time period and triggering anomaly analysis when the single-quantity fluctuation ratio exceeds a preset threshold value; the anomaly analysis comprises the steps of performing semantic reasoning and causal matching on the structured factor through an AI large model, and calculating an external event intensity factor; and introducing the external event intensity factor into an original fluctuation calculation function, calculating a new single-quantity fluctuation ratio, and performing attribution judgment on fluctuation causes.

Description

Technical Field

[0001] This invention relates to the field of logistics technology, and more specifically to a logistics order volume fluctuation analysis system. Background Technology

[0002] With the rapid development of global cross-border e-commerce and intelligent logistics systems, the volume of data in the logistics chain has increased dramatically. The delivery, customs clearance, warehousing, and transportation of various parcels across different countries and nodes generate a large amount of real-time data. Enterprises are increasingly relying on data analysis to judge business trends and anomalies in their operational decisions. However, traditional analysis systems mostly rely on internal operational data, such as order volume, delivery volume, and delay rates, lacking the ability to perceive the external environment. When faced with uncontrollable external factors such as policy changes, social events, traffic emergencies, or safety accidents, the system cannot provide dynamic interpretation, resulting in delayed and biased analysis results. Management struggles to distinguish whether delays or fluctuations are caused by internal operational problems or external objective factors, making it impossible to take timely and targeted measures.

[0003] In emerging markets like South Africa, the logistics operating environment is complex and volatile. External factors such as traffic congestion, strikes, social security issues, large-scale events during holidays, and even temporary government policy adjustments can all cause order fluctuations and delivery delays. Due to the lack of unified data integration and analysis models, companies often rely on manual experience or single-point data monitoring, making it difficult to accurately identify the causes of fluctuations. For example, during the same period, there may be a contradictory phenomenon of increased business volume but decreased deliveries; manual analysis cannot determine whether this is due to internal resource imbalances or external environmental anomalies. Traditional methods also cannot quantify the contribution of external events to order volume fluctuations, making it difficult for management to formulate precise response strategies.

[0004] Therefore, improving the accuracy and comprehensiveness of logistics data anomaly analysis is a problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to propose a logistics order volume fluctuation analysis system, which can more comprehensively and accurately assess the causes of logistics order volume fluctuations.

[0006] To achieve the above objectives, the present invention provides a logistics order volume fluctuation analysis system, comprising:

[0007] The data acquisition module is used to collect internal operational data of logistics business through internal data interfaces, and to obtain unstructured external environment data from multiple external data sources through external data interfaces.

[0008] The data preprocessing module is used to clean and segment the internal operational data to generate time series data, and to perform semantic extraction on the external environment data to transform it into structured factors that characterize different types of external events.

[0009] The single-volume fluctuation analysis and attribution module is used to calculate the single-volume volatility of the current time period based on the time series data according to the single-volume fluctuation calculation function, and to trigger anomaly analysis when the single-volume volatility exceeds a preset threshold.

[0010] The anomaly analysis includes:

[0011] The structured factors are semantically reasoned and causally matched using an AI big data model to calculate the external event intensity factor.

[0012] The external event intensity factor is introduced into the original volatility calculation function to calculate a new single-volume volatility, and the cause of volatility is attributed to determine whether the volatility is dominated by internal or external issues.

[0013] The report generation module automatically generates structured reports that include data analysis, root cause diagnosis, and decision recommendations.

[0014] In the optional scheme, the external event intensity factor is calculated according to the following formula:

[0015]

[0016] Among them, F t This represents the intensity factor of external events, where m is the number of types of external events, and w... i E represents the weight of the i-th type of external event. i,t This represents the impact value of the i-th type of event within the time period t.

[0017] In the optional scheme, E is calculated using the following formula. i,t

[0018] E i,t =s i,t ×d i,t ×g i,t

[0019] Among them, s i,t Let d be the normalized intensity of event type i at time t. i,t g is a time decay factor based on the time interval between the event occurrence time and the current time. i,t This is a geographical relevance factor based on the distance between the event location and the logistics operation location.

[0020] In the optional scheme, the single-quantity fluctuation calculation function is:

[0021]

[0022] Among them, W t Q represents the volatility of a single unit. t The number of orders within time period t. This is the historical average.

[0023] In the optional scheme, the formula after incorporating the external event intensity factor into the original fluctuation calculation function is:

[0024]

[0025] in, For the new single-volume volatility, F t λ represents the intensity factor of external events, and λ is the coefficient that adjusts the weight of external factors.

[0026] Among the optional solutions, the rules for attribution judgment are as follows:

[0027] When the weight matrix shows that external event factors are not significant and the internal resource data contradicts the trend of single-item change, a marker indicating that internal problems dominate is output.

[0028] When the weight matrix shows that there are external events that overlap with the fluctuation period and have a significant impact, a marker indicating that the external problem is dominant is output.

[0029] In an optional configuration, the structured report includes:

[0030] A visual chart showing the trend of order volume fluctuations;

[0031] A list of internal abnormal diagnostics, marked with primary visual markers;

[0032] An analysis list of external events and their impact weights, presented using second visual markers;

[0033] Decision-making suggestion text generated by a large AI model.

[0034] In an optional configuration, the data preprocessing module integrates a large AI model to perform semantic analysis on unstructured external environment data obtained from news, policy announcements, and social media content, and to identify different types of external events.

[0035] In an optional embodiment, the logistics order volume fluctuation analysis system further includes a dynamic learning and prediction module; the dynamic learning and prediction module is used to optimize the weights of various events in the weight matrix based on the analysis results of historical data, and to predict future order volume fluctuation trends.

[0036] In the optional solution, the AI ​​big model also evaluates events and internal factors, and constructs a weight matrix of internal and external factors on the causes of single-quantity fluctuations.

[0037] The beneficial effects of this invention are as follows: through the fusion analysis of AI large-scale models and multi-source data, it is possible to achieve synchronous access and semantic parsing of internal operational data and external API data (news, policies, social events, traffic, public security). The system automatically calculates the volatility of individual quantities and integrates external event intensity factors to determine whether the volatility is caused by internal problems (highlighted in red) or external problems (highlighted in yellow), thereby providing accurate cause analysis and decision-making suggestions. Attached Figure Description

[0038] The above and other objects, features and advantages of the present invention will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0039] Figure 1 This is a framework diagram of a logistics order volume fluctuation analysis system in one embodiment of the present invention. Detailed Implementation

[0040] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and drawings. However, it should be noted that the concept of the technical solution of the present invention can be implemented in many different forms and is not limited to the specific embodiments described herein. The accompanying drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0041] It should be understood that when an element or layer is referred to as "on," "adjacent to," "connected to," or "coupled to" other elements or layers, it may be directly on, adjacent to, connected to, or coupled to other elements or layers, or there may be intervening elements or layers. Conversely, when an element is referred to as "directly on," "directly adjacent to," "directly connected to," or "directly coupled to" other elements or layers, there are no intervening elements or layers. It should be understood that although the terms first, second, third, etc., may be used to describe various elements, components, areas, layers, and / or portions, these elements, components, areas, layers, and / or portions should not be limited by these terms. These terms are only used to distinguish one element, component, area, layer, or portion from another element, component, area, layer, or portion. Therefore, without departing from the teachings of this invention, the first element, component, area, layer, or portion discussed below may be referred to as the second element, component, area, layer, or portion.

[0042] Spatial relation terms such as “below,” “under,” “below,” “under,” “above,” “above,” etc., are used herein for convenience of description to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms are intended to also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, then the element or feature described as “below” or “under” the other element or feature will be oriented “above” the other element or feature. Therefore, the exemplary terms “below” and “under” can include both upper and lower orientations. The device may be otherwise oriented (rotated 90 degrees or otherwise) and the spatial descriptive terms used herein will be interpreted accordingly.

[0043] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising” and / or “including,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0044] Example

[0045] Reference Figure 1 This embodiment provides a logistics order volume fluctuation analysis system, including:

[0046] The data acquisition module is used to collect internal operational data of logistics business through internal data interfaces, and to obtain unstructured external environment data from multiple external data sources through external data interfaces.

[0047] The data preprocessing module is used to clean and segment the internal operational data to generate time series data, and to perform semantic extraction on the external environment data to transform it into structured factors that characterize different types of external events.

[0048] The single-volume fluctuation analysis and attribution module is used to calculate the single-volume volatility of the current time period based on the time series data according to the single-volume fluctuation calculation function, and to trigger anomaly analysis when the single-volume volatility exceeds a preset threshold.

[0049] The anomaly analysis includes:

[0050] The structured factors are semantically reasoned and causally matched using an AI big data model to calculate the external event intensity factor.

[0051] The external event intensity factor is introduced into the original volatility calculation function to calculate a new single-volume volatility, and the cause of volatility is attributed to determine whether the volatility is dominated by internal or external issues.

[0052] The report generation module automatically generates structured reports that include data analysis, root cause diagnosis, and decision recommendations.

[0053] Specifically, the data acquisition module collects internal operational data on logistics operations, such as order volume, delivery delay rate, vehicle dispatch, and employee scheduling, through an internal data interface. The external data interface acquires unstructured external data, including news, policy announcements, traffic incidents, and public security data, via API. The data preprocessing module cleans and segments the internal data to generate time series data and performs semantic extraction on the external data, transforming it into structured factors (such as "traffic congestion," "policy restrictions," and "public security"). The data preprocessing module integrates a large AI model for semantic analysis of unstructured external environmental data obtained from news, policy announcements, and social media content, identifying different types of external events (traffic, policy, and public security).

[0054] Calculate the single-volume volatility for the current time period using the single-volume volatility calculation function:

[0055]

[0056] Among them, W t Q represents the volatility of a single unit. t The number of orders within time period t. This is the historical average.

[0057] When (W) t When the value exceeds θ (θ threshold, such as 10%), the abnormal fluctuation analysis process begins. This abnormal analysis involves introducing an external event intensity factor into the original fluctuation calculation function to calculate a new single-volume volatility Wt*(“True Volatility”). The “True Volatility” tells management the overall volatility of the current logistics business after considering the impact of external events, and is not merely a result of average calculations. The goal is to obtain a comprehensive volatility index that not only reflects internal business fluctuations but also quantifies the interference of external events, thereby helping the system determine whether the current fluctuation is reasonable, whether it is caused by internal problems (highlighted in red), or by external factors (highlighted in yellow), and the proportion of external influence in the total volatility.

[0058] After incorporating the external event intensity factor into the original volatility calculation function, the new formula for calculating single-unit volatility is:

[0059]

[0060] in, For the new single-volume volatility, F t λ represents the intensity factor of external events, and λ is the coefficient that adjusts the weight of external factors.

[0061] The external event intensity factor is calculated using the following formula:

[0062]

[0063] Among them, F t This represents the intensity factor of external events, where m is the number of types of external events, and w... i E represents the weight of the i-th type of external event. i,t This represents the impact value of the i-th type of event within time period t. In this embodiment, m = 3, and the types of external events include: traffic, policy, and public security.

[0064] Furthermore, in order to depict the impact of the event in greater detail, E i,t It can be decomposed into the product of intensity, timeliness, and spatial correlation:

[0065] E i,t =s i,t ×d i,t ×g i,t

[0066] Among them, s i,t Let d be the normalized intensity of event type i at time t. i,t g is a time decay factor based on the time interval between the event occurrence time and the current time. i,t This is a geographical relevance factor based on the distance between the event location and the logistics operation location.

[0067] Refer to Table 1, which provides the definitions and values ​​of each parameter.

[0068] Table 1

[0069]

[0070]

[0071] In addition, d i,t It can also be simplified to the following value selection methods, all of which can be calculated manually.

[0072]

[0073] The causes of fluctuations are attributed and judged, categorizing them as either primarily internal or primarily external. The attribution rules are as follows: when the weight matrix shows that external event factors are not significant, and the trend of internal resource data contradicts the trend of order volume changes (e.g., an increase in the number of employees or vehicles, but a decrease in order volume), a marker indicating that internal problems dominate is output, such as a red marker. When the weight matrix shows that there are external events that overlap with the fluctuation period and have a significant impact, a marker indicating that external problems dominate is output, such as a yellow marker.

[0074] In this embodiment, the AI ​​big model also evaluates events and internal factors, and constructs a weight matrix of internal and external factors on the causes of single-quantity fluctuations. The matrix form is shown in Table 2.

[0075] Table 2

[0076]

[0077] The structured report includes: a visual chart showing the trend of single-volume fluctuations; a list of internal anomaly diagnoses listed with first visual markers; a list of external event analyses and their impact weights listed with second visual markers; and decision-making suggestion text generated by an AI big model.

[0078] The report first compares the order volume change curves for different time periods with historical averages, outputting data such as fluctuation amplitude, fluctuation direction (upward or downward), and duration. The AI ​​model compares the volatility changes between previous and subsequent periods, generating graphical representations (such as line charts, heatmaps, and time series analysis charts). Example: The report indicates that the average order volume volatility over the past three months was 12%, significantly higher than the 6% average over the past 12 months. For internal anomalies detected by the system, such as vehicle availability shortages, warehouse loading and unloading delays, low employee scheduling efficiency, and delivery route congestion, the report will highlight them in red, listing the time period of occurrence, the degree of impact, and key indicators. The AI ​​will provide quantitative indicators (such as a 15% decrease in vehicle utilization and a 10% decrease in average delivery volume per person) and indicate suspicious causes in the report (such as overlapping shifts and lagging scheduling algorithms). Example: System analysis found a high correlation between the order volume decrease in mid-August and the extended warehouse loading time (+25%). The system combines external API data to identify and label external influences such as policy adjustments, social security incidents, weather changes, and traffic congestion during the same period. The report displays the impact weight, relevant time window, and affected area map for each event. The contribution of external factors to the fluctuation range will be presented as a percentage in the report. Example: The report notes "High incidence of traffic accidents in the Johannesburg area from August 15th to 18th (impact weight 0.32)," and indicates that the fluctuation is within a reasonable range due to external events. The system integrates the weights of internal and external factors to determine the cause of overall fluctuations: when the weight of internal causes is greater than that of external causes, the report highlights in red, indicating "Internal operations need optimization"; when the weight of external causes is significant, the report highlights in yellow, indicating "Fluctuations are dominated by external factors." The report will automatically generate a list of optimization suggestions, including: scheduling and capacity optimization suggestions (such as temporarily adding vehicles or adjusting shift coverage); warehousing and transshipment efficiency improvement plans (such as activating backup warehouses and improving sorting speed); and external collaboration suggestions (such as avoiding high-risk areas in advance, optimizing delivery routes, and adjusting delivery rhythm).

[0079] The report is designed to ensure that management can quickly understand the source, scope, and response to abnormal fluctuations, thus achieving a closed loop from data discovery to decision-making.

[0080] In this embodiment, the logistics order volume fluctuation analysis system further includes a dynamic learning and prediction module; the dynamic learning and prediction module is used to optimize the weights of various events in the weight matrix based on the analysis results of historical data, and to predict future order volume fluctuation trends.

[0081] This embodiment combines historical averages with real-time data to identify abnormal fluctuations and distinguish between reasonable and abnormal fluctuations. This mechanism directly addresses the problem in the background that "traditional systems can only perform static analysis and lack dynamic adjustment capabilities," achieving real-time analysis and intelligent identification across multiple time periods and dimensions. Through semantic understanding and causal reasoning using a large AI model, internal business data is correlated with external events (policies, security, traffic, etc.) to construct a red and yellow label early warning system. This model solves the pain point of "difficulty in distinguishing between internal operational problems and external objective events," enabling the system to accurately determine the source of fluctuations and intuitively present the risk type. External unstructured data (news, announcements, public opinion, social media content) is transformed into quantitative indicators (F) through AI semantic parsing. t After multi-dimensional weighting based on intensity, timeliness, and spatial correlation, external events are standardized and calculable. This innovation effectively solves the problem of traditional analysis's inability to quantify external impacts, improving the model's interpretability and predictive accuracy. A fully automated process from data analysis to report output is established, with reports covering fluctuation trends, cause diagnosis, and AI suggestions, supporting graphical, semantic, and interactive displays. Through continuous data updates and model self-learning capabilities, the system can adaptively optimize fluctuation characteristics for different regions and business types, achieving short-term predictions and anomaly warnings for future trends. The AI ​​module in this embodiment can track the causal path of fluctuations, identify the impact chain from external events to changes in internal indicators, and generate optimized decision-making solutions, such as dynamic scheduling, capacity allocation, and regional avoidance strategies. This innovation enables the system not only to identify problems but also to guide actions, achieving a closed loop from "intelligent diagnosis" to "intelligent decision-making."

[0082] This embodiment has the following beneficial effects:

[0083] Multi-source factor fusion: This embodiment solves the problem of traditional systems having a single analytical dimension and being unable to integrate external information by establishing a fusion analysis framework that integrates internal operational data with external multi-source data (policy, news, transportation, public security, etc.). The AI ​​big data model can perform semantic parsing and weighted processing on multi-dimensional data, enabling multi-angle explanations of the causes of single-quantity fluctuations, and significantly improving the accuracy and comprehensiveness of anomaly analysis.

[0084] Intelligent Anomaly Detection and Visualized Diagnosis: This embodiment introduces an internal and external factor weight matrix and a dynamic fluctuation calculation function, using red (internal problems) and yellow (external problems) to visually distinguish anomaly types. This mechanism solves the technical bottleneck of "difficulty in distinguishing the source of problems" in traditional systems, enabling management to intuitively grasp the dominant factors of fluctuations and make targeted management decisions.

[0085] Dynamic Adaptability and Real-Time Feedback Mechanism: This embodiment achieves rapid system response to changes in the external environment (such as emergencies, policy updates, traffic congestion, etc.) through real-time API interface calls and continuous data learning mechanisms. The system can update fluctuation analysis results and regenerate reports within minutes, solving the problems of "delayed reporting and slow response" in traditional solutions and ensuring that managers can make decisions based on the latest data in real time.

[0086] Enhanced causal attribution and prediction capabilities: Compared to historical retrospective analysis based solely on statistics, this embodiment, through the semantic reasoning and causal analysis modules of the AI ​​large model, can identify the key influencing chains behind fluctuations, make short-term predictions and early warnings of future fluctuation trends, thereby deploying resources in advance and avoiding the escalation of delays.

[0087] In summary, this embodiment can effectively solve the problems of "lack of external data, insufficient causal analysis, and delayed decision response" in traditional technologies, and realize the intelligent and automated process of the entire process from data collection, analysis, judgment to decision output, which greatly improves the efficiency and accuracy of management decision-making in cross-border and complex logistics environments.

[0088] The above description is merely a description of preferred embodiments of the present invention and is not intended to limit the scope of the present invention in any way. Any changes or modifications made by those skilled in the art based on the above disclosure shall fall within the protection scope of the claims.

Claims

1. A logistics item quantity fluctuation analysis system characterized by comprising: Comprise: a data collection module for collecting internal operation data of logistics business through internal data interface and obtaining unstructured external environment data from multiple external data sources through external data interface; a data preprocessing module for cleaning and segmenting the internal operation data to generate time series data and performing semantic extraction on the external environment data to convert into structured factors representing different types of external events; a single-quantity fluctuation analysis and attribution module for calculating single-quantity fluctuation rate of the current time period based on the time series data according to a single-quantity fluctuation calculation function, and triggering abnormality analysis when the single-quantity fluctuation rate exceeds a preset threshold; the abnormality analysis comprises: performing semantic reasoning and causal matching on the structured factors by an AI large model to calculate external event intensity factors; introducing the external event intensity factors into the original fluctuation calculation function to calculate new single-quantity fluctuation rate and make attribution judgment on fluctuation causes, and marking the fluctuation as internal problem dominant or external problem dominant; a report generation module for automatically generating a structured report containing data analysis, cause diagnosis and decision suggestion.

2. The logistics sheet quantity fluctuation analysis system according to Claim 1, wherein The external event intensity factors are calculated according to the following formula: where F t represents the external event intensity factor, m is the number of categories of external events, w i represents the weight of the ith category of external events, E i,t represents the impact value of the ith category of events in the time period t.

3. The logistics sheet quantity fluctuation analysis system according to Claim 2, wherein E is calculated by the following equation i,t E i,t = s i,t × d i,t × g i,t where s i,t is the normalized intensity of event type i at time t, d i,t is a time decay factor based on the interval between the event occurrence time and the current time, g i,t is a geographic relevance factor based on the distance between the event occurrence location and the logistics operation location.

4. The logistics sheet quantity fluctuation analysis system according to Claim 1, wherein the single-quantity fluctuation calculation function is: where W t is the single quantity volatility, Q t is the single quantity, is the historical mean.

5. The logistics sheet quantity fluctuation analysis system according to Claim 4, wherein the formula after introducing the external event intensity factors into the original fluctuation calculation function is: wherein, is the new single volatility, F t denotes the external event intensity factor, and λ is a coefficient that adjusts the weight of the external factor.

6. The logistics sheet quantity fluctuation analysis system according to Claim 1, wherein the attribution judgment rule is: when the weight matrix shows that the external event factors are not significant, and the internal resource data is contrary to the single-quantity change trend, output the internal problem dominant mark; when the weight matrix shows that there is an external event that coincides with the fluctuation period and has significant impact, output the external problem dominant mark.

7. The logistics sheet quantity fluctuation analysis system according to Claim 1, wherein The structured report comprises: a visual chart showing single-quantity fluctuation trend; an internal abnormality diagnosis list listed with a first visual mark; an external event analysis list and its impact weight listed with a second visual mark; decision suggestion text generated by the AI large model.

8. The logistics sheet quantity fluctuation analysis system according to Claim 1, wherein The AI large model is integrated in the data preprocessing module for semantic analysis of unstructured external environment data obtained from news, policy announcements and social media content to identify different types of external events.

9. The logistics sheet quantity fluctuation analysis system according to Claim 1, wherein The logistics single-quantity fluctuation analysis system further comprises a dynamic learning and prediction module; the dynamic learning and prediction module is used for optimizing the weight of each type of event in the weight matrix according to historical data analysis results, and predicting future single-quantity fluctuation trend.

10. The logistics document quantity fluctuation analysis system according to Claim 1, wherein The AI large model also evaluates events and internal factors to build a weight matrix of internal and external factors on single-quantity fluctuation causes.

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