SKU field-based customs clearance rhythm adjustment system

Abstract

The invention relates to the technical field of supply chain management, in particular to an SKU field-based customs clearance rhythm adjustment system, which comprises an SKU urgency identification module, a priority construction module, a field density comparison module, a channel pressure calculation module and a rhythm adjustment decision module. According to SKU transportation fields and node real-time requirements, a customs clearance urgency identification model is constructed, customs clearance priorities are dynamically divided, information density is calculated based on field filling conditions, a path is matched to reduce the declaration risk, SKU channel calling frequency and resource pressure are combined, a path allocation mechanism is optimized, centralized congestion is avoided, and the method has the advantages of being high in efficiency and high in reliability. The SKU rhythm recognition value is generated through field density and path load normalization, the ex-warehouse sequence and the customs clearance rhythm are scientifically arranged, off-peak customs clearance and efficient resource configuration are achieved, a regulation and control system with timeliness evaluation as the basis, information precision as the guarantee, channel pressure as the guide and rhythm coordination as the target is constructed, delay and waste are effectively reduced, and the efficiency of customs clearance is improved. And the customs clearance efficiency and the path utilization rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain management, and in particular to a customs clearance rhythm adjustment system based on SKU fields. Background Art

[0002] The technical field of supply chain management encompasses order scheduling and control, warehouse access management, product classification and distribution, logistics routing design, inventory distribution control, and inter-node resource linkage. The core of this technical field lies in the dynamic linkage and optimized configuration of physical, information, and capital flows between different distribution entities and links in the supply network, enabling precise control across organizations, regions, and multiple nodes. In the context of cross-border e-commerce, supply chain management must address challenges such as product category compliance, customs clearance rhythm matching, delivery timeliness control, and customs clearance capacity constraints. Therefore, it is necessary to build a rule-driven rhythm coordination system based on multi-party data to improve the overall efficiency of the supply chain.

[0003] Among them, the customs clearance rhythm adjustment system based on SKU fields refers to the situation in which different commodity SKUs correspond to different declaration paths, tariff levels and restriction levels due to coding differences in the cross-border warehousing outbound to customs clearance processing links. Data values are extracted based on the three key fields of category segment, tariff segment and customs clearance control segment in the SKU coding field, and the combination of this field is used as the basis to build a queue priority sequence for outbound batches according to the combination group. By comparing the congestion value of the clearance node of each combination group with the outbound scheduling time interval, the rhythm buffer threshold is set. If the combination group triggers a high-frequency outbound instruction at the same time and the congestion value is close to the warning threshold, the outbound execution order or rhythm offset of some SKUs in the combination group is adjusted. The system uses field value comparison, set merging, time interval judgment and conditional offset to complete the dynamic matching between warehouse release rhythm and customs clearance capacity.

[0004] In the existing supply chain customs clearance control, SKU batch sorting is only based on static product field combinations, ignoring the actual time requirements of the clearance nodes, and lacks a dynamic assessment of the urgency of cargo transportation, resulting in delays in urgent SKU customs declarations. In addition, the integrity of SKU field information has not been quantitatively analyzed, resulting in frequent path selection deviations for goods with missing information, increasing the probability of customs clearance declaration failure. In the channel allocation process, the real-time pressure status of each path resource in the customs clearance link and the frequency of cargo calls are not comprehensively considered, resulting in some SKUs being concentrated in high-load paths, exacerbating congestion and delays. Rhythm control has not established a linkage mechanism between SKU data density and path availability, but simply relies on batch or declaration path division, resulting in rhythm chaos and queue disorder in actual implementation of SKUs, reducing overall customs clearance efficiency. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a customs clearance rhythm adjustment system based on the SKU field.

[0006] To achieve the above objectives, the present invention adopts the following technical solutions: A customs clearance rhythm adjustment system based on the SKU field includes:

[0007] The SKU urgency identification module obtains the transportation field of the SKU to be cleared, compares the interval between the current time node and the planned warehouse departure time with the arrival requirements, confirms the customs clearance urgency indicator under the current time status, and obtains the customs clearance urgency indicator set;

[0008] The priority ladder construction module sorts the SKUs into layers according to the length of the countdown field based on the outbound countdown and transportation urgency of all SKUs in the customs clearance urgency mark set, and obtains the SKU priority ladder structure set;

[0009] The field density comparison module collects the total number of field items in the SKU priority ladder structure and the number of currently filled field items, calculates the SKU information density between the total number of field items and the number of currently filled field items, and obtains a SKU field information density label group;

[0010] The channel pressure calculation module analyzes the frequency of SKUs using warehouse channels within a specified time period based on the SKU field information density tag group, clarifies the SKU allocation channels based on the SKU information density, and obtains a SKU channel mapping list;

[0011] The rhythm adjustment decision module classifies the rhythm category label field of all SKUs in the SKU channel mapping list, adjusts the corresponding customs clearance rhythm according to the classification result, and obtains the SKU customs clearance rhythm adjustment result.

[0012] As a further solution of the present invention, the customs clearance urgency identification set includes transportation timeliness level, customs clearance time expectation, and batch priority; the SKU priority ladder structure set specifically includes warehouse urgency level, category scarcity sequence, and sorting priority label; the SKU field information density label group includes filling completeness level, field missing warning, and information quality assessment; the SKU channel mapping list specifically includes channel preferred path, traffic pressure level, and warehouse channel allocation code; the SKU customs clearance rhythm adjustment result includes synchronization rhythm identification, adaptation rhythm instruction, and slow-release rhythm label.

[0013] As a further solution of the present invention, the SKU urgent identification module includes:

[0014] The transportation field extraction submodule obtains the record fields of the SKU to be cleared, extracts the order planned delivery time field, the agreed arrival time field, and the current time node field, organizes them into a field set under the same time dimension, and establishes the SKU transportation time field set;

[0015] The time interval judgment submodule determines whether the time interval between the current time node and the planned warehouse departure time in the SKU transportation time field set is less than the countdown interval between the agreed arrival time and the current time node, combines the classification code of the batch to which the SKU belongs and the registered average customs clearance time field, classifies the urgency status of the SKU at the time node, and generates a customs clearance urgency identification set.

[0016] As a further embodiment of the present invention, the priority ladder building module includes:

[0017] The countdown sorting submodule extracts the warehouse countdown field and transportation urgency field of each SKU based on all SKUs in the customs clearance urgency flag set, groups and layers the SKUs according to the length of the countdown field, and divides the SKUs into multiple time layers in ascending order to generate SKU countdown layer information;

[0018] The scarcity sorting submodule is based on the SKU countdown level information, compares the category scarcity fields corresponding to the SKU in each time level, sets the sorting order within the same level according to the scarcity value of the category to which the SKU belongs, and combines the association structure of the time level and category sorting to obtain the SKU priority ladder structure set.

[0019] As a further solution of the present invention, the field density comparison module includes:

[0020] The density calculation submodule collects the total number of SKU field items and the number of currently filled field items in the SKU priority ladder structure set, using the formula:

[0021]

[0022] Calculate the field information density D of each SKU, summarize the density of all SKUs, and establish a SKU information density value set;

[0023] Among them, N total Indicates the total number of field items corresponding to a single SKU, N filled Indicates the number of fields currently filled in the SKU, K indicates the total number of field types, n k Indicates the number of fields of the kth field type that appear in the SKU.

[0024] The label generation submodule calls the field information density value of each SKU in the SKU information density value set, and compares it item by item with the average filling ratio of the classification group in the SKU cluster. Based on the comparison results, it determines whether there is a deviation in the field information filling of each SKU, and divides it into multiple categories to obtain the SKU field information density label group.

[0025] As a further solution of the present invention, the channel pressure calculation module includes:

[0026] The channel frequency extraction submodule extracts the in-and-out path field of the warehouse corresponding to each SKU, the loading frequency field of the SKU, and the turnover cycle field based on the SKU field information density tag group, counts the usage frequency of the channel called by the SKU within the specified period, records the channel type and the total number of SKU calls, and obtains the SKU channel usage frequency set;

[0027] Based on the SKU channel usage frequency set, the formula is used:

[0028]

[0029] Calculate the path pressure coefficient of the i-th SKU. This coefficient reflects the resource shortage degree by combining the channel activation frequency and the average number of queued SKUs in the unit scheduling cycle, and establish the SKU path pressure value group;

[0030] Among them, P i represents the path pressure coefficient of the i-th SKU, R i is the average activation frequency of the path selected by the i-th SKU, W i,j is the waiting queue length of the i-th SKU in the j-th channel, m is the total number of SKU channels, T i is the scheduling period of the i-th SKU in the current warehouse;

[0031] The channel allocation generation submodule performs path screening and channel clarification on the SKU based on the passage pressure of each SKU in the path in the SKU path pressure value group and the label corresponding to the field information density, establishes the correspondence between the SKU and the channel path, and obtains the SKU channel mapping list.

[0032] As a further solution of the present invention, the rhythm adjustment decision module includes:

[0033] The rhythm classification submodule classifies the rhythm category label fields of all SKUs in the SKU channel mapping list, extracts the field information density value, path pressure coefficient and channel path number of each SKU, and linearly normalizes the field information density and path pressure parameters according to the minimum value and range in the SKU set, respectively, using the formula:

[0034]

[0035] Calculate the rhythm recognition value E of the h-th SKU h, the SKUs are divided into three categories: synchronous rhythm SKUs, adaptable rhythm SKUs, and slow-release rhythm SKUs according to the rhythm recognition value. The synchronous rhythm SKUs are set as the priority declaration batches and pushed to the starting execution sequence of the allocated path channel to obtain the SKU rhythm tag group;

[0036] Among them, D h is the original field information density of the h-th SKU, and P h is the original path pressure coefficient of the h-th SKU. D min and D max respectively represent the minimum and maximum values of the field information density values in the current SKU set. P min and P max respectively represent the minimum and maximum values in the path pressure coefficient;

[0037] The rhythm execution sub-module calls the rhythm classification tag field in the SKU rhythm tag group, sequentially incorporates the adaptable rhythm SKUs into the middle execution sequence of the path channel, sets the slow-release rhythm SKUs as delayed processing identifiers, integrates the rhythm types and channel scheduling priorities of all SKUs, and obtains the SKU joint rhythm adjustment result.

[0038] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0039] In the present invention, by comparing the transportation fields of the SKUs with the current node time, the outwarehouse time, and the arrival requirements, an identification system for customs clearance urgency can be established, enabling the dynamic stratification of the urgency degree of the SKUs; on this basis, double sorting of the outwarehouse countdown and the category scarcity degree is performed to achieve the priority stratification of the SKUs in two dimensions of timeliness and resource allocation, providing accurate support for subsequent path scheduling and rhythm adjustment; the calculation of the field information density comprehensively compares the filled fields and the total number of field types, enabling the quantitative evaluation of the SKU information integrity and enhancing the pre-response ability of the field quality difference to the scheduling scheme; the combination of the channel call frequency, the channel congestion degree of the SKU within the scheduling period, and the information density label ensures that the path allocation decision takes into account the actual resource occupancy and information accuracy, enhancing the rationality of the path selection; by normalizing the field information density and the path pressure coefficient, a rhythm recognition value is constructed to achieve the synchronous, adaptable, and slow-release division of the SKUs in terms of rhythm, effectively solving the problem of chaotic rhythm queuing sequences of different SKUs; finally, the customs clearance urgency, field quality, channel pressure, and rhythm order are integrated as a whole to form a customs clearance queuing system with high-density and high-timeliness SKUs for priority declaration and stable rhythm distribution, greatly alleviating the outwarehouse congestion and resource waste, and improving the overall customs clearance efficiency and path utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is the system flow chart of the present invention;

[0041] Figure 2 This is a flow chart of the SKU urgent identification module of the present invention;

[0042] Figure 3 A flow chart of a priority ladder building module of the present invention;

[0043] Figure 4 This is a flow chart of the field density comparison module of the present invention;

[0044] Figure 5 This is a flow chart of the channel pressure calculation module of the present invention;

[0045] Figure 6 This is a flow chart of the rhythm adjustment decision module of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0048] See also Figure 1 The customs clearance rhythm adjustment system based on the SKU field includes:

[0049] The SKU urgency identification module obtains the transportation fields of the SKU to be cleared, including the planned order shipment time, the agreed arrival time, and the current time node. It compares the interval between the current time node and the planned shipment time with the arrival requirements, and makes a judgment based on the batch to which the SKU belongs and the registered average customs clearance time. It confirms the customs clearance urgency flag under the current time status and obtains the customs clearance urgency flag set.

[0050] The priority ladder construction module extracts the SKU delivery countdown and transportation urgency flag from all SKUs in the customs clearance urgency flag set, sorts the SKUs into layers according to the length of the countdown field, compares the scarcity of the corresponding categories of SKUs within the same time layer, and sets the sorting order based on the scarcity of the SKUs. This creates a sorting structure based primarily on delivery urgency and supplemented by category criticality, thus obtaining the SKU priority ladder structure set.

[0051] The field density comparison module collects the total number of field items in the SKU priority ladder structure and the number of currently filled field items, calculates the SKU information density between the total number of field items and the number of currently filled field items, compares the information density with the average filling ratio of the SKU cluster, determines whether the SKU information is filled in too much or too little, and obtains the SKU field information density label group;

[0052] The channel pressure calculation module extracts the corresponding warehouse entry and exit paths, loading frequency, and turnover cycle based on the SKU field information density tag group. It analyzes the frequency of SKU use of warehouse channels within a specified time period, obtains the activation frequency and waiting queue length fields of the current entry and exit channels in the corresponding destination warehouse, calculates the path pressure coefficient, and uses the path pressure coefficient to identify the degree of SKU traffic pressure under various channel paths. Combined with the SKU information density, it determines the SKU's allocation channel and obtains the SKU channel mapping list.

[0053] The rhythm adjustment decision module classifies the rhythm category label fields of all SKUs in the SKU channel mapping list, including synchronous rhythm SKUs, adaptive rhythm SKUs, and slow-release rhythm SKUs. The synchronous rhythm SKUs are set as priority declaration batches and pushed to the starting execution sequence of the assigned path channel in order. The adaptive rhythm SKUs are sequentially included in the middle execution sequence, and the slow-release rhythm SKUs are set as delayed processing flags to obtain the SKU clearance rhythm adjustment results.

[0054] The customs clearance urgency identification set includes transportation timeliness level, customs clearance time expectation, and batch priority. The SKU priority ladder structure set specifically includes warehouse urgency level, category scarcity sequence, and sorting priority label. The SKU field information density label group includes completion completeness level, field missing warning, and information quality assessment. The SKU channel mapping list specifically includes channel preferred path, traffic pressure level, and warehouse channel allocation code. The SKU customs clearance rhythm adjustment results include synchronization rhythm identification, adaptation rhythm instruction, and slow-release rhythm marking.

[0055] See also Figure 2 , the SKU urgent identification module includes:

[0056] The transportation field extraction submodule obtains the record fields of the SKU to be cleared, extracts the order planned delivery time field, the agreed arrival time field, and the current time node field, organizes them into a field set under the same time dimension, and establishes the SKU transportation time field set;

[0057] First, extract the "Planned Shipping Time" field based on the registered fields in the SKU data table, and confirm that the field format is unified into the year-month-day-hour-minute structure. For example, if the planned shipping time of a SKU is 8:00 on April 17, 2025, its value is "2025-04-1708:00". Then read the "Agreed Arrival Time" field of the SKU. For example, if the value is "2025-04-2220:00", it needs to be unified into the same format for subsequent comparison. The current time node can be directly generated by the current system time. In actual application, if it is currently 10:00 on April 15, 2025, the current time field is "2025-04-1510:00". All time fields need to be converted to timestamps for subsequent difference calculation. After conversion, the planned shipping time is "1713331200" and the agreed arrival time is "171379680 0", the current time is "1713175200", the conversion format needs to use a unified standard time zone to avoid time difference deviations caused by regional differences, and ensure that the time nodes are under the same standard time base. All time field extractions should be performed in the SKU master data table. If there are missing fields in the data table, they will be marked as "invalid SKU" and excluded from the subsequent process. After the fields are sorted, the three fields in each SKU record are integrated into a complete transportation time field group and numbered according to the SKU unique code for easy subsequent tracking and processing. After processing 100 SKU records in the table, a group of 100 transportation time field array structures is formed. Each group of structures contains the field name, original value, standard timestamp value, and field validity identifier. For example, the time field group in the 5th SKU record is: planned delivery time "2025-04-2009:00"

[0058] (1713519600), agreed arrival time "2025-04-23 23:00" (1713884400), current time "2025-04-15 10:00" (1713175200), where all fields are valid, the validity mark is 1. For records where any field is missing or formatted abnormally, it is set to 0 and excluded from subsequent operations, and finally the SKU transportation time field set is obtained.

[0059] The time interval judgment submodule determines whether the time interval between the current time node and the planned warehouse departure time in the SKU transportation time field set is less than the countdown interval between the agreed arrival time and the current time node. It then combines the classification code of the batch to which the SKU belongs with the registered average customs clearance time field to classify and identify the urgency status of the SKU at the time node and generate a customs clearance urgency identification set.

[0060] Call the current time and planned delivery time of each SKU record to calculate the difference. For example, the current time in the record with SKU number A105 is "1713175200" and the planned delivery time is "1713519600". The difference between the two is 344400 seconds, which is 95.7 hours. Convert this time interval into "remaining delivery time". Set the default delivery reminder benchmark value in the system to 96 hours. That is, the SKU enters the urgent identification judgment process within 96 hours before delivery. The current result is 95.7 hours. , it enters the judgment range, and then compares the arrival time of the SKU with the current time difference "1713884400-1713175200=709200 seconds", which is 197 hours. The arrival buffer benchmark value is set to 240 hours, which is the maximum allowable interval from the current time to the agreed arrival time. In this case, 197 hours is less than the benchmark value, and the batch attribute of the SKU needs to be further judged. The SKU belongs to the batch number "B021". The average customs clearance time for this batch in the registration database is 78 hours. The system presets an average customs clearance threshold of 80 hours. If the average time is less than the threshold, it is marked as "controllable", otherwise it is "tense". The current batch takes 78 hours, which is lower than the threshold and is marked as "controllable". However, considering that the remaining time for delivery is lower than the benchmark value, the system comprehensively evaluates the time difference ratio = (planned delivery time - current time) / (arrival time - current time) = 344400 / 709200 ≈ 0.485, and sets the urgency range as follows: ratio > 0.75 is "loose", 0.5-0.75 is "medium", <0.5 is "urgent", the current ratio is 0.485, which is classified as "urgent" status. The comprehensive three judgment results are: warehouse countdown < benchmark value, arrival buffer time < preset value, and time ratio is in the urgent range. The system marks the SKU as "urgent" and sets its customs clearance status classification flag to 1. Finally, a customs clearance urgency flag set is established in the system. This set records the customs clearance urgency flag value, batch number, current time difference, and customs clearance judgment status code of each SKU to form an array structure for subsequent level differentiation and sorting.

[0061] See also Figure 3 , the priority ladder building blocks include:

[0062] The countdown sorting submodule extracts the outbound countdown field and transportation urgency field of each SKU from all SKUs in the customs clearance urgency set. It then groups and layers the SKUs based on the length of the countdown field, sorting them into multiple time layers in ascending order to generate SKU countdown layer information.

[0063] First, extract the countdown for each SKU's shipment and the transportation urgency mark. The countdown for shipment is calculated by the difference between the system time and the SKU's scheduled shipment time. For example, if the current system time is 12:00 on April 15, 2025, and the SKU's scheduled shipment time is 8:00 on April 18, 2025, the countdown is 68 hours. After extracting the countdown fields of all SKUs, group the SKUs according to the countdown time and define the time level. The division method is set according to the interval standard, such as 024 hours, 2472 hours, and more than 72 hours. Each SKU is divided into the corresponding interval to form an initial time layer. Then, the SKUs within each time layer are further sorted according to their transportation urgency mark. The input urgency indicator is the customs clearance urgency assessment result formed in advance in the previous module. Generally, a three-level classification is adopted: urgent, moderate, and loose. The numerical quantification is 3, 2, and 1. The priority in the sorting is urgent SKU, moderate SKU, and loose SKU from high to low, respectively. For example, SKU-A and SKU-B are both in the 0-24 hour level, SKU-A's urgency level is 3, and SKU-B is 2, then SKU-A is ranked before SKU-B. This type of processing can be used in scenarios such as outbound priority judgment during major promotions. For example, in the 618 e-commerce node, SKU batches A and B are about to be shipped out, but batch A needs to be delivered within 48 hours and the inventory turnover is slow. In this case, it needs to be assigned an earlier outbound batch, and the SKU countdown level information is obtained.

[0064] The scarcity ranking submodule compares the category scarcity fields of the SKUs in each time level based on the SKU countdown level information, sets the ranking order within the same level according to the scarcity value of the category to which the SKU belongs, and establishes the association structure between the time level and the category ranking to obtain the SKU priority ladder structure set;

[0065] At each time level, the category scarcity value of the SKU is extracted. The category scarcity is set by reversely calculating the ratio of the current inventory quantity and sales demand of the category to which the SKU belongs. For example, the scarcity S is set to 1000 / (inventory quantity / average daily sales volume), where both inventory quantity and sales volume are collected from the inventory system. For example, if the inventory of SKU-X is 500 pieces and the average daily sales volume is 50 pieces, then S=1000 / (500 / 50)=100. If the inventory of SKU-Y is 100 pieces and the average daily sales volume is 25 pieces, then S=1000 / (100 / 25)=250, then the scarcity of SKU-Y is higher than that of SKU -X, should be given priority in the sorting. If there is SKU-Z with no sales data, it is set to the lowest scarcity level 0. In practice, for example, in the warehouse, the electric toothbrush SKU and the toothpaste SKU are both in the 24-72 hour level. The toothbrush inventory is 2,000 pieces and the daily sales volume is 200 pieces. The scarcity is 1,000 / (2,000 / 200)=100. The toothpaste inventory is 400 pieces and the daily sales volume is 40 pieces. The scarcity is 1,000 / (400 / 40)=100. If the scarcity is the same, the transportation urgency mark is used to determine the priority. The critical sorting at the same level is completed. The dual sorting relationship of warehouse urgency and category scarcity is combined to generate the SKU priority ladder structure set.

[0066] See also Figure 4 , the field density comparison module includes:

[0067] The density calculation submodule collects the total number of field items for each SKU in the SKU priority ladder structure and the number of currently filled field items, using the formula:

[0068]

[0069] Calculate the field information density D of each SKU, summarize the density of all SKUs, and establish a SKU information density value set;

[0070] Among them, N total Indicates the total number of field items corresponding to a single SKU, N filled Indicates the number of fields currently filled in for this SKU. It is the sum operation of field types, K represents the total number of field types, n k Indicates the number of fields of the kth field type that appear in the SKU, log(1+n k ) represents the logarithmic enhancement value of the number of the k-th type of fields in the density, which is used to adjust the contribution intensity of the field type to the information density.

[0071] Collect the total number of field items and the number of filled fields in the SKU priority ladder structure. Based on the actual requirement of the completeness of SKU field information, the number of fields is converted into a structural density index. In actual application, if a batch contains 5 SKU records, the total number of field items of each SKU is different, namely 12 items, 15 items, 11 items, 10 items and 13 items. Assume that the number of filled fields is 9 items, 14 items, 10 items, 8 items and 12 items respectively. During the execution process, it is necessary to first obtain the total number of fields and filled values of each SKU, identify and count the filled status of the field items, where fields can be classified by type, such as date fields, numerical fields, text fields, etc. Suppose the first SKU has 4 numerical fields, 3 text fields, and 5 time fields, and 3 numerical fields, 3 text fields, and 3 time fields are filled. After calling the field type statistics, the number of each type of field is used as a variable in the subsequent information density calculation. Parameters are set for type distribution and normalized. In this step, the field information density calculation formula is introduced:

[0072]

[0073] Among them, D is the field information density value, which measures the comprehensiveness of the fields filled in for a certain SKU, and N total N is the total number of field items for this SKU, set to 12, filled The number of items to be filled in is set to 9. The total number of field types is 3, namely numerical type, text type, and time type. If their numbers are n1=4, n2=3, and n3=5 respectively, then

[0074]

[0075] Taking the logarithm base as e, calculate log(5)≈1.609, log(4)≈1.386, log(6)≈1.792 respectively; ∑=1.609+1.386+1.792=4.787;

[0076] Substitute all the values into the formula:

[0077] The calculation of field information density is used to evaluate the completeness and structural diversity of field filling in SKU records, reflecting the data saturation level and structural richness of SKU in the information registration process. By constructing the field information density value, the information quality of each SKU can be quantified, which serves as an important basis for subsequent customs clearance channel allocation, priority sorting and process scheduling. The overall formula shows that the SKU information density is composed of two core factors: one is the actual number of filled-in fields of the SKU, which occupies the main proportion and directly reflects the completeness of the filling; the other is the distribution number of field types. The logarithmic function is used to measure the auxiliary enhancement of diversity to density to avoid misjudgment caused by the large number of a certain type of field. Through log(1+nk ) suppresses scale effects, making the increase in density associated with changes in the number of types more limited. This results in a comparable and analytically valuable density metric based on comprehensive information integrity and structural breadth. This density value is not only calculable but also serves as a quantitative basis for determining information bias, serving the multi-dimensional decision-making module of the customs clearance rhythm management system.

[0078] The information density value of this field is 1.149, indicating that the number of SKU fields filled in is relatively complete and has a certain diversity of field types, which can be used for subsequent density comparison analysis. The introduction of the logarithmic function makes the growth of the number of field types non-linear, avoiding abnormal increases caused by excessive number of types. The benefit of this formula is that by introducing the field type distribution Improved the ability to distinguish information density at a fine-grained level, thereby establishing a set of field information density values;

[0079] The label generation submodule calls the field information density value of each SKU in the SKU information density value set, compares it with the average filling ratio of the classification group in the SKU cluster, and determines whether there is any deviation in the field information filling of each SKU based on the comparison results. It then divides the SKU into multiple categories and obtains the SKU field information density label group;

[0080] Call the field information density value set and compare the density value of each SKU with the average filling ratio in its SKU cluster. Assume that the cluster contains 5 SKUs, namely A, B, C, D, and E, and their field information density values are 1.149, 1.200, 0.980, 1.013, and 0.876 respectively. The average density value of the cluster is calculated as

[0081]

[0082] The judgment interval for too much or too little is set to ±10% of the benchmark density value, that is, the density greater than 1.0436×1.1=1.148 is too much, less than 1.0436×0.9=0.939 is too little, and the others are moderate. According to this setting, the density value of A is 1.149, which is classified as too much, B is 1.200 and is also classified as too much, C is 0.980 and is classified as moderate, D is 1.013 and is classified as moderate, and E is 0.876 and is classified as too little. According to the classification standard, each SKU is marked with a field density label and an SKU field information density label group is established.

[0083] See also Figure 5 , the channel pressure calculation module includes:

[0084] The channel frequency extraction submodule extracts the warehouse entry and exit path fields, the SKU loading frequency fields, and the turnover cycle fields for each SKU based on the SKU field information density tag group. It then counts the frequency of channel usage called by the SKU within a specified period, records the channel type and the total number of SKU calls, and obtains the SKU channel usage frequency set.

[0085] First, call the warehouse identification field of each SKU to obtain the bound inbound and outbound path name. Path names such as A1 and B3 identify the physical channel in the outbound or inbound direction. Then read the average daily loading frequency field of each SKU. The unit of this field is times / day. For example, the average daily loading frequency of SKU123 is 4 times / day. Then extract the corresponding turnover cycle field. This value reflects the average cycle time from the SKU's entry to the exit. For example, the turnover cycle of SKU123 is 7 days. By multiplying the average daily loading frequency by the turnover cycle, the total number of times the SKU is called within the cycle can be calculated as: Call frequency = frequency × cycle = 4 × 7 = 28 times. This value is then mapped to the SKU distribution record on each channel, forming a one-to-many relationship. If SKU123 used channel A120 times and B38 times within the cycle, the record would be SKU123: A1-20, B3-8. At the same time, the call frequency data should be combined with the data in the SKU density label to complete The completeness levels are recorded in parallel. For example, SKU123 is marked as "complete", which means that its call data is representative and can be used in the subsequent channel pressure calculation process. The frequency records of different SKUs in different channels will be uniformly output as a SKU channel usage frequency set. The record samples are as follows: SKU123-A1:20, B3:8, SKU456-A1:15, B2:10, SKU789-B3:5, C2:9. Referring to these values, in the daily operation data of the logistics center, the frequency is generally within the actual controllable range of 5-50 times. Therefore, when setting the judgment frequency band, it is usually set that the frequency <10 is "low usage frequency", 10-30 is "medium frequency", and >30 is "high frequency". This frequency range will be used as a reference value in the subsequent module for channel resource matching strategy formulation. Finally, the periodic usage frequency of all SKUs and the channel path type are combined and output to obtain the SKU channel usage frequency set.

[0086] Based on the SKU channel usage frequency set, the formula is:

[0087]

[0088] Calculate the path pressure coefficient of the i-th SKU. This coefficient reflects the resource shortage degree by combining the channel activation frequency and the average number of queued SKUs in the unit scheduling cycle, and establish the SKU path pressure value group;

[0089] Among them, P irepresents the path pressure coefficient of the i-th SKU, R i The average activation frequency (times / day) of the path selected by the SKU, W i,j is the waiting queue length (number of SKUs) of the i-th SKU in the j-th channel, m is the total number of channels involved in the SKU, is the total waiting quantity of SKU in all optional channels, T i is the SKU's scheduling cycle (in days) within the current warehouse. This formula normalizes the waiting quantity to the unit scheduling cycle and then creates a reasonable ratio with the activation frequency, ensuring dimensional consistency and operability.

[0090] First, extract the enabled frequency field R for the channel path bound to each SKU i , the unit is times / day. For example, the average activation frequency of SKU123 on path A1 is 3.2 times / day. Then extract the waiting queue length field W of the SKU on this path. i,j , measured by the number of SKUs, such as the queue length of SKU123 on path A1 is 12 SKUs, and its channel scheduling period T is collected i , if it is 6 days, substitute the formula on this basis: , suppose SKU123 uses paths A1 and B3, and the waiting queue on B3 has 9 SKUs, then ∑W i,j =12+9=21, so the calculation steps are:

[0091] Calculate the average waiting queue length:

[0092] Add the formula as a whole:

[0093] The path pressure coefficient reflects the pressure level of SKU123 in the current path. The smaller the value, the greater the queue pressure under the unit frequency. The interval standard is set based on the channel scheduling data statistics of 9 major logistics centers across the country. The average value of the path pressure coefficient in the data distribution is 1.15, and the standard deviation is 0.34. Based on this, the interval is set as: P i <0.7 is high pressure, 0.7≤P i <1.2 is medium pressure, P i ≥1.2 is low pressure. This segmentation takes into account the load elasticity and resource redundancy in actual logistics and is applicable to general SKU pressure calculation grading. The benefit of the formula is that it normalizes the channel activation frequency and the number of queues in a unit period to avoid distortion caused by comparison in different time periods, thereby improving the comparability of path pressure assessment and ultimately deriving a group of SKU path pressure values.

[0094] The channel allocation generation submodule performs path screening and channel clarification for SKUs based on the passage pressure of each SKU in the SKU path pressure value group and the label corresponding to the field information density, establishes the correspondence between SKUs and channel paths, and obtains the SKU channel mapping list;

[0095] First, call the corresponding field information density label, such as SKU123 density label is "complete", and then determine the pressure coefficient P of SKU i Based on the aforementioned interval settings, if the pressure coefficient of SKU123 is 0.711, which is in the range of 0.7–1.2, that is, "medium pressure", SKU123 is marked as the "passage channel" type. If the pressure coefficient of SKU456 is 0.42, which falls into the high-pressure section <0.7, it is marked as the "avoidance channel". If the pressure coefficient of SKU789 is 1.45, it is classified as the "priority channel" type under the low-pressure path. This segmentation setting is derived from the throughput rhythm and bottleneck interval settings of a typical logistics warehouse. In channel resource management, differentiated processing strategies can be used to balance the overall path load. The system will generate a path allocation table for all SKUs based on pressure levels and their density labels, and ultimately establish corresponding records of SKUs and channel paths, such as SKU123→A1, SKU456→C3, and SKU789→B2, and output a SKU channel mapping list.

[0096] See also Figure 6 , the rhythm adjustment decision module includes:

[0097] The rhythm classification submodule classifies the rhythm category label fields of all SKUs in the SKU channel mapping list, extracts the field information density value, path pressure coefficient, and channel path number of each SKU, and linearly normalizes the field information density and path pressure parameters according to the minimum value and range in the SKU set, respectively, using the formula:

[0098]

[0099] Calculate the rhythm recognition value E of the h-th SKU h ,According to the rhythm identification value, the SKU is divided into three categories: synchronous rhythm SKU, adaptive rhythm SKU and slow-release rhythm SKU. The synchronous rhythm SKU is set as the priority declaration batch and pushed to the starting execution sequence of the assigned path channel to obtain the SKU rhythm label group;

[0100] Among them, E h represents the rhythm recognition value of the h-th SKU, D h is the original field information density value of the h-th SKU, P h is the original path pressure coefficient of the h-th SKU, D min 、D maxRespectively represent the minimum and maximum values of the field information density in the current SKU set, P min 、P max Respectively represent the minimum and maximum values of the path pressure coefficient, the numerator is the normalized value of field information density (dimensionless), the denominator is log It represents the logarithmic smoothing conversion term (dimensionless) of the normalized path pressure value. The overall formula combines the two proportionally to form a rhythm recognition factor that can be discriminated in sections.

[0101] First, we need to clarify the path pressure level and information density level of the SKU, and then calculate the rhythm recognition value based on this. During the execution process, we first collect the field information density value D h and path pressure coefficient P h , and extract the minimum information density D in the SKU set min , maximum value D max , minimum path pressure P min , maximum value P max Taking a certain SKU as an example, its original information density value is 0.68. The minimum information density value in the SKU set to which this SKU belongs is 0.45, and the maximum information density is 0.85. Therefore, the normalized density value is calculated to be (0.68-0.45) / (0.85-0.45)=0.575. Alternatively, the original path pressure value of this SKU is 2.2. The minimum path pressure in this set is 0.8, and the maximum path pressure is 4.0. After normalization, it is (2.2-0.8) / (4.0-0.8)=0.4375. After logarithmic processing, log(1+0.4375)≈0.362. Based on this, the above result is substituted into the rhythm recognition value formula:

[0102]

[0103] According to the rhythm recognition value layered threshold set by the system, if E h ≥0.6 is classified as synchronous rhythm, 0.4≤E h <0.6 is classified as adaptive rhythm, E h <0.4 is a slow-release rhythm. The E h=0.422, thus classified as an adaptive rhythm SKU. The information density value is calculated by the ratio of the total number of fields to the number of filled fields. For example, if a SKU has 20 fields but only 13 are filled, the information density is 13 / 20 = 0.65. The path pressure value is calculated by normalizing the activation frequency and wait length of the SKU's corresponding channel. For example, if a SKU has an average activation frequency of 3.5 times per day across three paths, and the wait queue lengths are 5, 6, and 4, respectively, the total is 15, and the total scheduling period is 5 days, then the average number of waiting SKUs is 15 / 5 = 3. The path pressure is then normalized to 2.2. Parameters in this process are set based on actual collected data. The upper and lower limits of information density are typically automatically generated from historical SKU data, while the upper and lower limits of path pressure are statistically derived from daily channel operation records. Manual constants are not used, thereby enhancing the objectivity of the classification. The classification results ultimately form the SKU rhythm label group.

[0104] The rhythm execution submodule calls the rhythm classification tag field in the SKU rhythm tag group, sequentially inserts the adapted rhythm SKU into the middle execution sequence of the path channel, sets the slow-release rhythm SKU as the delayed processing flag, integrates the rhythm types and channel scheduling order of all SKUs, and obtains the SKU clearance rhythm adjustment results;

[0105] First, read the rhythm tag type field corresponding to each SKU, and sort the synchronous rhythm SKUs in ascending order according to their warehouse delivery time fields, and insert them into the starting position of the execution sequence under their respective channel paths in sequence. For example, the planned warehouse delivery times of three synchronous rhythm SKUs are April 22, April 20, and April 25, respectively. The sorting results are SKU2→SKU1→SKU3, and they are inserted into the starting segment in sequence. For the adaptive rhythm SKUs, sort them from low to high according to their path pressure and then insert them into the middle execution sequence. The path pressure coefficients of three adaptive SKUs are 1.8, 2.5, and 2.1, respectively, and they are arranged as SKUA→SKUC→SKUB. The slow-release rhythm SKUs are uniformly marked as delayed identifiers and removed from the current scheduling batch and stored in the SKU cache queue of the next scheduling cycle. The entire sorting sequence is directly generated by reading the path ID and channel mapping list. For example, if SKU1 is mapped to channel T01 and SKU2 to channel T03, SKUs 1 and 2 with synchronized tempos are prioritized and placed at the top of the task stack for that channel, according to T01 and T03, respectively. Adaptive tempo SKUs are placed in the middle of the task stack, and slow-release tempos are not included in the task stack. Each tempo tag group acts as an index field in the channel execution system to generate the scheduling order. The scheduling execution sequence fields are ultimately aggregated into the SKU clearance tempo adjustment results. This result demonstrates that tempo tags can directly determine the execution position of SKU channels and influence resource allocation priorities in a hierarchical structure, thereby outputting a clear and executable SKU scheduling order list.

[0106] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The customs clearance rhythm adjustment system based on the SKU field is characterized by: The system comprises: The SKU urgency identification module obtains the transportation field of the SKU to be cleared, compares the interval between the current time node and the planned warehouse departure time with the arrival requirements, confirms the customs clearance urgency indicator under the current time status, and obtains the customs clearance urgency indicator set; The priority ladder construction module sorts the SKUs into layers according to the length of the countdown field based on the outbound countdown and transportation urgency of all SKUs in the customs clearance urgency mark set, and obtains the SKU priority ladder structure set; The field density comparison module collects the total number of field items in the SKU priority ladder structure and the number of currently filled field items, calculates the SKU information density between the total number of field items and the number of currently filled field items, and obtains a SKU field information density label group; The channel pressure calculation module analyzes the frequency of SKUs using warehouse channels within a specified time period based on the SKU field information density tag group, clarifies the SKU allocation channels based on the SKU information density, and obtains a SKU channel mapping list; The rhythm adjustment decision module classifies the rhythm category label field of all SKUs in the SKU channel mapping list, adjusts the corresponding customs clearance rhythm according to the classification result, and obtains the SKU customs clearance rhythm adjustment result.

2. The customs clearance rhythm adjustment system based on SKU field according to claim 1 is characterized in that: The customs clearance urgency identification set includes the transportation timeliness level, customs clearance time expectation, and batch priority. The SKU priority ladder structure set is specifically the warehouse urgency level, category scarcity sequence, and sorting priority label. The SKU field information density label group includes the filling completeness level, field missing warning, and information quality assessment. The SKU channel mapping list is specifically the channel preferred path, traffic pressure level, and warehouse channel allocation code. The SKU customs clearance rhythm adjustment result includes the synchronization rhythm identification, adaptation rhythm instruction, and slow release rhythm label.

3. The customs clearance rhythm adjustment system based on SKU field according to claim 2 is characterized in that: The SKU urgent identification module includes: The transportation field extraction submodule obtains the record fields of the SKU to be cleared, extracts the order planned delivery time field, the agreed arrival time field, and the current time node field, organizes them into a field set under the same time dimension, and establishes the SKU transportation time field set; The time interval judgment submodule determines whether the time interval between the current time node and the planned warehouse departure time in the SKU transportation time field set is less than the countdown interval between the agreed arrival time and the current time node, combines the classification code of the batch to which the SKU belongs and the registered average customs clearance time field, classifies the urgency status of the SKU at the time node, and generates a customs clearance urgency identification set.

4. The system for adjusting customs clearance rhythm based on SKU fields according to claim 3 is characterized in that: The priority ladder building block includes: The countdown sorting submodule extracts the warehouse countdown field and transportation urgency field of each SKU based on all SKUs in the customs clearance urgency flag set, groups and layers the SKUs according to the length of the countdown field, and divides the SKUs into multiple time layers in ascending order to generate SKU countdown layer information; The scarcity sorting submodule is based on the SKU countdown level information, compares the category scarcity fields corresponding to the SKU in each time level, sets the sorting order within the same level according to the scarcity value of the category to which the SKU belongs, and combines the association structure of the time level and category sorting to obtain the SKU priority ladder structure set.

5. The customs clearance rhythm adjustment system based on SKU field according to claim 4 is characterized in that: The field density comparison module includes: The density calculation submodule collects the total number of SKU field items and the number of currently filled field items in the SKU priority ladder structure set, using the formula: Calculate the field information density D of each SKU, summarize the density of all SKUs, and establish a SKU information density value set; Among them, N total Indicates the total number of field items corresponding to a single SKU, N filled Indicates the number of fields currently filled in the SKU, K indicates the total number of field types, n k Indicates the number of fields of the kth field type that appear in the SKU. The label generation submodule calls the field information density value of each SKU in the SKU information density value set, and compares it item by item with the average filling ratio of the classification group in the SKU cluster. Based on the comparison results, it determines whether there is a deviation in the field information filling of each SKU, and divides it into multiple categories to obtain the SKU field information density label group.

6. The customs clearance rhythm adjustment system based on SKU fields according to claim 5 is characterized in that: The channel pressure calculation module includes: The channel frequency extraction submodule extracts the in-and-out path field of the warehouse corresponding to each SKU, the loading frequency field of the SKU, and the turnover cycle field based on the SKU field information density tag group, counts the usage frequency of the channel called by the SKU within the specified period, records the channel type and the total number of SKU calls, and obtains the SKU channel usage frequency set; Based on the SKU channel usage frequency set, the formula is used: Calculate the path pressure coefficient of the i-th SKU. This coefficient reflects the resource shortage degree by combining the channel activation frequency and the average number of queued SKUs in the unit scheduling cycle, and establish the SKU path pressure value group; Among them, P i represents the path pressure coefficient of the i-th SKU, R i is the average activation frequency of the path selected by the i-th SKU, W i,j is the waiting queue length of the i-th SKU in the j-th channel, m is the total number of SKU channels, T i is the scheduling period of the i-th SKU in the current warehouse; The channel allocation generation submodule performs path screening and channel clarification on the SKU based on the passage pressure of each SKU in the path in the SKU path pressure value group and the label corresponding to the field information density, establishes the correspondence between the SKU and the channel path, and obtains the SKU channel mapping list.

7. The customs clearance rhythm adjustment system based on SKU field according to claim 6 is characterized in that: The rhythm adjustment decision module includes: The rhythm classification submodule classifies the rhythm category label fields of all SKUs in the SKU channel mapping list, extracts the field information density value, path pressure coefficient and channel path number of each SKU, and linearly normalizes the field information density and path pressure parameters according to the minimum value and range in the SKU set, respectively, using the formula: Calculate the rhythm recognition value E of the h-th SKU h ,According to the rhythm identification value, the SKU is divided into three categories: synchronous rhythm SKU, adaptive rhythm SKU and slow-release rhythm SKU. The synchronous rhythm SKU is set as the priority declaration batch and pushed to the starting execution sequence of the assigned path channel to obtain the SKU rhythm label group; Among them, D h is the original field information density of the h-th SKU, P h is the original path pressure coefficient of the h-th SKU, D min 、D max Respectively represent the minimum and maximum values of the field information density in the current SKU set, P min 、P max Respectively represent the minimum and maximum values of the path pressure coefficient; The rhythm execution submodule calls the rhythm classification tag field in the SKU rhythm tag group, and sequentially compiles the adapted rhythm SKU into the middle execution sequence of the path channel, sets the slow-release rhythm SKU as the delayed processing identifier, integrates the rhythm types and channel scheduling sequences of all SKUs, and obtains the SKU clearance rhythm adjustment results.

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