Cloud service systems and methods applied to logistics integration

Abstract

This invention discloses a cloud service system and method for logistics integration, relating to the field of cloud service technology. The method includes: receiving order data from e-commerce platforms and distributors in real time, and obtaining inventory data from each warehouse; analyzing order data to extract communication features, analyzing the correlation between order placement time and promotional activities, and obtaining the proportion of urgent orders in a specific time period in a target region; associating order communication features with corresponding regional warehouse inventory data, and generating an inventory allocation trigger signal when, within a preset time period after the start of a promotional activity in the target region, the order volume exceeds an order surge threshold, and the proportion of urgent orders exceeds the threshold but falls below the inventory safety threshold; based on the inventory allocation trigger signal, selecting surrounding warehouses, sending scheduling instructions to initiate emergency replenishment; and calling historical promotional order data from the same period, analyzing order growth trends and replenishment volumes, predicting subsequent replenishment volumes, and notifying relevant warehouses to prepare in advance.

Description

Technical Field

[0001] This invention relates to the field of cloud service technology, specifically to a cloud service system and method for logistics integration. Background Technology

[0002] In the field of logistics and e-commerce collaboration, structured processing and dynamic coordination of data communication are key to improving scheduling efficiency. Traditional methods suffer from inconsistent data coding systems among e-commerce platforms, distributors, and warehouses. The heterogeneous coding of item specifications in e-commerce orders and warehouse inventory specifications necessitates manual intervention for matching inventory with order specifications, severely hindering automated processing efficiency. Emergency order information transmission is isolated, using only simple text tags to indicate urgency without a structured link to total order volume or inventory data, making it impossible to quantify the proportion of emergency orders and accurately trigger priority scheduling. Furthermore, the correlation analysis between promotional activities and order data lags behind, lacking a systematic mapping of promotional timeframes and coverage areas, making it difficult to quickly identify peak order periods and surge regions, resulting in replenishment timing judgments lagging behind actual demand fluctuations. Summary of the Invention

[0003] The purpose of this invention is to provide a cloud service system and method for logistics integration, in order to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a cloud service method applied to logistics integration, the method comprising the following steps:

[0005] S100 receives order data from e-commerce platforms and distributors in real time. The order data includes the unique identifier of the order, the order time, the region code of the order, the urgency level of the order, and the order information. At the same time, it obtains the inventory data of each warehouse, which includes the specifications, quantity, and inventory safety threshold of the items currently stored in each warehouse.

[0006] S200: Analyze order data to extract communication features, analyze the correlation between order time and promotional activities, identify promotional periods with concentrated orders, statistically analyze order distribution by region to determine the target regions with surges, and obtain the proportion of urgent orders in the target regions within a preset time period;

[0007] S300, associated order communication features and corresponding regional warehouse inventory data, when the order volume exceeds the order volume surge threshold, the proportion of emergency orders exceeds the emergency order proportion threshold, and the corresponding specification inventory is lower than the inventory safety threshold within a preset time after the start of the promotional activity in the target region, an inventory allocation trigger signal is generated.

[0008] S400: Based on the inventory allocation trigger signal, select surrounding warehouses with matching specifications, sufficient inventory and distance from the target area within a preset distance threshold, and send a dispatch command to start emergency replenishment.

[0009] S500 retrieves historical promotional order data from the same period, analyzes order growth trends and replenishment quantities, predicts subsequent replenishment quantities, and notifies relevant warehouses to prepare in advance.

[0010] According to the above plan, the urgency of orders includes regular orders, expedited orders, and extremely urgent orders;

[0011] Order information includes item name, item specifications, order quantity, and payment completion status indicator; the item specifications and the specifications of items currently stored in the warehouse use the same signal coding system; the payment completion status includes paid and unpaid.

[0012] According to the above scheme, step S200 includes:

[0013] S210. Construct a promotional activity schedule, which includes a unique identifier for the promotional activity, start time, end time, and coverage area.

[0014] S220. Extracting communication features includes extracting the order's unique identifier, order time, and order's region code; traversing the promotional activity schedule; when t... j ∈[Ts,Te] and L j ∈L i When, then determine order Q j Orders placed during the promotional period are considered orders placed outside the promotional period; where t j Let L represent the order placement time, j represent the order index, [Ts, Te] represent the time interval of the promotional activity, Ts represent the start time of the promotion, Te represent the end time of the promotion, and L represent the end time of the promotion. j Indicates the region where the order is located, L i This represents the set of regions covered by the promotional activity, Q. j Represented as a unique identifier for the order;

[0015] S230. Divide the promotional activity time interval into multiple consecutive unit time periods Δt. n (n=1,2,…,m), where n represents the index of the time period and m represents the total number of time periods; the formula for obtaining the number of orders per unit time is as follows:

[0016] C n =N n / Δt;

[0017] Where, N n Δt represents the total number of orders per unit time period, and Δt represents the unit time period.

[0018] The formula for calculating the average number of orders per unit time during a promotional campaign is as follows:

[0019] C avg =N p / (Te-Ts);

[0020] Where, N p This represents the total number of orders during the promotional period; if there exists a unit of time where the order volume satisfies C... n >θ1×C avg When θ1 represents the order concentration threshold, and θ1>1, then the unit of time is determined to be a promotional period of order concentration;

[0021] S240. Obtain the order volume growth rate for the regions covered by the promotional activity, using the following formula:

[0022] G k =(R k -R 0k ) / R 0k ×100%;

[0023] Among them, G k R represents the order volume growth rate; k R represents the total number of orders placed in the region covered by the promotion during the peak promotional period; 0k This represents the average total number of orders during the same time period as the promotional period, outside of promotional events.

[0024] When the order volume growth rate G k When the value is greater than θ2, θ2 represents the order volume growth rate threshold, and the corresponding region L is determined. k For target regions experiencing a surge in orders;

[0025] S250, Obtain Region L k Urgent orders account for P k =E k / T k ×100%, where P k E represents the percentage of urgent orders within a preset time period for a given region. k This represents the total number of orders marked as rush orders and express orders; T k Indicates region L during the promotional period k Total number of orders.

[0026] According to the above scheme, step S300 includes:

[0027] S310. Determine the corresponding target region according to the region code of the order, analyze the warehouses located in the target region or capable of providing logistics services for the target region, determine the specification model of the items in the order, and match it with the specifications of the items stored in the warehouse;

[0028] S320. Obtain the order volume R of region Lk within the preset time period T promo and the average order volume benchmark value R of the same time period T during the non-promotion period promo ; promo ; avg ;

[0029] S330. When R promo > θ order × R avg , it is determined that the order volume exceeds the surge threshold; where R promo represents the order volume of the region within the preset time period, θ order represents the order volume surge threshold, and R avg represents the average order volume benchmark value of the same time period during the non-promotion period;

[0030] When P k > θ emergency , it is determined that the proportion of emergency orders exceeds the threshold; P k represents the proportion of emergency orders in the region within the preset time period; θ emergency represents the emergency order proportion threshold;

[0031] When (S ks - Q s ) < Sas, it is determined that the inventory is lower than the safety threshold; where S ks represents the current inventory quantity of the corresponding item specification in the region; Q s represents the total required quantity of the item specification s in the region orders within the preset time period; S as represents the inventory safety threshold for the corresponding item specification; s represents the item specification;

[0032] S340. When within the preset time period after the start of the promotion activity in the target region, the order volume exceeds the order volume surge threshold compared with the average order volume benchmark value of the same time period during the non-promotion period, the proportion of emergency orders exceeds the emergency order proportion threshold, and the inventory of the corresponding specification is lower than the inventory safety threshold, generate an inventory allocation trigger signal.

[0033] According to the above solution, step S400 includes:

[0034] S410. Construct a surrounding warehouse screening model. The surrounding warehouse screening model is based on the geographical location coordinates of the target region and the geographical location coordinates of each warehouse, and screens out those that meet D i ≤ D thA collection of nearby warehouses, where D th Represented as a preset distance threshold, D i Represented as the straight-line distance between the warehouse and the target area;

[0035] S420. Based on the set of nearby warehouses, filter out the inventory quantity S. ws ≥S as The set of candidate repositories, where S ws This indicates the current inventory quantity of item specification s in warehouse w;

[0036] S430. Based on the candidate warehouse set, generate an emergency replenishment dispatch instruction. The instruction includes the target area identifier, the required item specifications, the replenishment quantity, and the estimated delivery time.

[0037] Replenishment quantity Q=R promo ×(1+γ), where R promo Represented as preset duration T promo The order quantity within the range, γ represents the replenishment redundancy coefficient, and γ>0; the estimated delivery time T d =T now +T t T now Represented as instruction generation time, T t T represents the logistics transportation time. t Based on the distance D between the warehouse and the target area i The value is calculated from the average speed v of the transport vehicle, i.e., T. t =D i / v.

[0038] According to the above scheme, step S500 includes:

[0039] S510. Using historical order data from the same period of the promotion, construct a time series forecasting model. The model is based on the historical order volume H(t) at time t, and uses exponential smoothing to predict the subsequent replenishment quantity H(t+1) = α × H(t) + (1-α) × H(t-1), where α represents the smoothing coefficient, 0 < α < 1. Simultaneously, considering the current order growth trend of the promotional activity, introduce a trend adjustment factor β to correct the forecast result to H'(t+1) = H(t+1) × (1 + β), where β is obtained based on the historical order growth rate for the same period, i.e., β = G. his / G avg Among them, G his G represents the historical order growth rate. avg Expressed as average growth rate;

[0040] S520. Combine the predicted subsequent replenishment quantity H'(t+1) with the inventory capacity C of each warehouse. w and replenishment cycle T cPerform correlation analysis to determine the advance preparation quantity Q for each warehouse. p =H'(t+1)×(T c / T promo ), where T c This represents the warehouse replenishment cycle, which includes transportation and loading / unloading time, T. promo This is a preset duration; a replenishment preparation notification is sent to the relevant warehouse via the cloud service platform, including the item specification 's' and the advance preparation quantity 'Q'. p And warehouse replenishment cycle T c .

[0041] According to the above plan, the replenishment execution status will be monitored in real time, and when the actual replenishment quantity Q... a When the deviation from the predicted replenishment quantity H'(t+1) exceeds the preset threshold δ, the subsequent replenishment plan is automatically adjusted; the adjusted replenishment quantity Q'=H'(t+1)×(1+δ), and the updated replenishment instruction is resent to the relevant warehouse.

[0042] A cloud service system for logistics integration, which includes a data receiving module, an order feature analysis module, an inventory allocation module, a replenishment execution module, and a replenishment monitoring module;

[0043] The system comprises the following modules: a data receiving module, which receives order data from e-commerce platforms and distributors, as well as inventory data from various warehouses in real time; an order feature analysis module, which performs in-depth analysis of order data and extracts key features related to logistics allocation; an inventory allocation module, which correlates order features with warehouse inventory data, determines whether inventory allocation conditions are met, and generates an inventory allocation trigger signal; a replenishment execution module, which executes emergency replenishment based on the inventory allocation trigger signal sent by the inventory allocation module, predicts subsequent demand, and prepares replenishment resources in advance; and a replenishment monitoring module, which monitors the replenishment execution process in real time and dynamically adjusts the replenishment plan.

[0044] According to the above scheme, the order feature analysis module includes a promotion association module and a demand forecasting module; the promotion association module constructs a promotion activity schedule, matches the order placement time with the region to the corresponding promotion activity, and distinguishes between promotional and non-promotional orders; the demand forecasting module analyzes warehouses with a surge in order volume, an emergency order ratio exceeding the emergency order ratio threshold, and corresponding specification inventory below the inventory safety threshold.

[0045] The replenishment execution module includes a warehouse screening module, an emergency replenishment module, and a replenishment quantity prediction module. The warehouse screening module filters out nearby warehouses with matching specifications, sufficient inventory, and a distance within a preset threshold based on the geographical location of the target area. The emergency replenishment module sends emergency replenishment dispatch instructions to the screened warehouses. The replenishment quantity prediction module calls up historical promotional order data from the same period, analyzes order growth trends through a time series prediction model, predicts subsequent replenishment quantities, and determines the advance preparation quantity by combining warehouse inventory capacity and replenishment cycle.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] 1. This invention extracts communication features, associates emergency order identifiers with total order volume to calculate the proportion, and triggers scheduling in conjunction with order volume and inventory threshold;

[0048] 2. This invention constructs a promotion schedule, calculates order growth rates, and enables real-time identification of peak order periods and regions, quickly pinpointing replenishment targets;

[0049] 3. This invention utilizes historical data and real-time trends to construct a time series forecasting model, predicting replenishment quantities in advance and notifying the warehouse to prepare. At the same time, it dynamically corrects the plan through deviation monitoring, reducing the risk of stockouts and logistics costs. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the steps of the cloud service method for logistics integration according to the present invention;

[0051] Figure 2 This is a schematic diagram of the cloud service system for logistics integration according to the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Example: Figures 1-2 As shown, the present invention provides a technical solution for a cloud service method for logistics integration, the method comprising the following steps:

[0054] S100 receives order data from e-commerce platforms and distributors in real time. The order data includes the unique identifier of the order, the order time, the region code of the order, the urgency level of the order, and the order information. At the same time, it obtains the inventory data of each warehouse, which includes the specifications, quantity, and inventory safety threshold of the items currently stored in each warehouse.

[0055] Specifically, order urgency levels include regular orders, expedited orders, and urgent orders; order information includes item name, item specifications, quantity ordered, and payment completion status; item specifications and the specifications of items currently stored in the warehouse use the same signal coding system; payment completion status includes paid and unpaid.

[0056] For example: An e-commerce platform is conducting a promotional activity. Within two hours of the activity starting, it received 1200 orders from the Nanjing area. Some order information is as follows:

[0057] Order ID: 00001, Order Time: 00:00, Area Code: 00001, Urgency Level: Urgent, Item Specifications: XL-1kg-001, Order Quantity: 3 bottles, Payment Status: Paid;

[0058] Order ID: 00002, Order Time: 00:01, Region Code: 00001, Urgency Level: Normal, Item Specifications: XL-1kg-001, Order Quantity: 1 bottle, Payment Status: Paid;

[0059] Get the Nanjing local warehouse, warehouse ID: NJ-001, item specifications: XL-1kg-001, current inventory: 300 bottles, inventory safety threshold: 200 bottles.

[0060] S200: Analyze order data to extract communication features, analyze the correlation between order time and promotional activities, identify promotional periods with concentrated orders, statistically analyze order distribution by region to determine the target regions with surges, and obtain the proportion of urgent orders in the target regions within a preset time period;

[0061] Specifically, step S200 includes:

[0062] S210. Construct a promotional activity schedule, which includes a unique identifier for the promotional activity, start time, end time, and coverage area.

[0063] For example: Unique identifier for promotional activities: Double11-2024, time range: [2024-11-01 00:00, 2024-11-11 23:59], coverage area: [00001,00002,00003,00004], where 00001 is Nanjing, 00002 is Yangzhou, 00003 is Suzhou, and 00004 is Wuxi; this is just an example and is not a restriction.

[0064] S220. Extracting communication features includes extracting the order's unique identifier, order time, and order's region code; traversing the promotional activity schedule; when t... j ∈[Ts,Te] and L j ∈Li When, then determine order Q j Orders placed during the promotional period are considered orders placed outside the promotional period; where t j Let L represent the order placement time, j represent the order index, [Ts, Te] represent the time interval of the promotional activity, Ts represent the start time of the promotion, Te represent the end time of the promotion, and L represent the end time of the promotion. j Indicates the region where the order is located, L i This represents the set of regions covered by the promotional activity, Q. j Represented as a unique identifier for the order;

[0065] For example: The order times of the 1200 orders received from Nanjing were all within [2024-11-01 00:00, 2024-11-11 23:59], and the area code was 00001, which is within the coverage area. Therefore, they were all determined to be orders placed during the promotion period.

[0066] S230. Divide the promotional activity time interval into multiple consecutive unit time periods Δt. n (n=1,2,…,m), where n represents the index of the time period and m represents the total number of time periods; the formula for obtaining the number of orders per unit time is as follows:

[0067] C n =N n / Δt;

[0068] Where, N n Δt represents the total number of orders per unit time period, and Δt represents the unit time period.

[0069] The formula for calculating the average number of orders per unit time during a promotional campaign is as follows:

[0070] C avg =N p / (Te-Ts);

[0071] Where, N p This represents the total number of orders during the promotional period; when there exists a unit of time where the order volume satisfies C... n >θ1×C avg When θ1 represents the order concentration threshold, and θ1>1, then the unit of time is determined to be a promotional period of order concentration;

[0072] For example: Divide the promotional time period into 1-hour time segments, Δt=1h, and calculate the order volume for each time segment:

[0073] 00:00-01:00: 700 orders, N1=700, order volume per unit time C1=700 / 1=700 orders / hour; 01:00-02:00: 500 orders, N2=500, order volume per unit time C2=500 / 1=500 orders / hour;

[0074] The promotion lasts for 11 days, totaling 264 hours, with an estimated total of 1500 orders and an average order volume of C per unit time. avg =1500 / 264≈56.82 units / hour.

[0075] Taking the order concentration threshold θ1=2, since C1=700>2×56.82≈113.64 and C2=500>113.64, 00:00-01:00 and 01:00-02:00 are both promotional periods with concentrated orders;

[0076] S240. Obtain the order volume growth rate for the regions covered by the promotional activity, using the following formula:

[0077] G k =(R k -R 0k ) / R 0k ×100%;

[0078] Among them, G k R represents the order volume growth rate; k R represents the total number of orders placed in the region covered by the promotion during the peak promotional period; 0k This represents the average total number of orders during the same time period as the promotional period, outside of promotional events.

[0079] When the order volume growth rate G k When the value is greater than θ2, θ2 represents the order volume growth rate threshold, and the corresponding region L is determined. k For target regions experiencing a surge in orders;

[0080] For example: In Nanjing, the total number of orders during the peak period: 00:00-02:00 is R. k =1200 orders; during non-promotional periods, the average total number of orders R 0k =300 orders; Order volume growth rate G k =(1200-300) / 300×100%=300%, take the growth rate threshold θ2=80%, since 300%>80%, Nanjing is determined to be the target area for the surge in order volume;

[0081] S250, Obtain Region L k Urgent orders account for P k =E k / T k ×100%, where Pk E represents the percentage of urgent orders within a preset time period for a given region. k This represents the total number of orders marked as rush orders and express orders; T k Indicates region L during the promotional period k Total number of orders;

[0082] For example: E, the total number of expedited and express orders in the Nanjing area during peak periods. k =280 orders, total number of orders T k =1200 orders, with urgent orders accounting for P k =280 / 1200×100%≈23.3%.

[0083] S300, associated order communication features and corresponding regional warehouse inventory data, when the order volume exceeds the order volume surge threshold, the proportion of emergency orders exceeds the emergency order proportion threshold, and the corresponding specification inventory is lower than the inventory safety threshold within a preset time after the start of the promotional activity in the target region, an inventory allocation trigger signal is generated.

[0084] Specifically, step S300 includes:

[0085] S310. Determine the corresponding target region based on the region code of the order, analyze the warehouses located in the target region or capable of providing logistics services to the target region, determine the specifications and models of the items in the order, and match them with the specifications of the items stored in the warehouse.

[0086] For example: the target region is Nanjing, the matching warehouse ID is NJ-001, and the order item specification is XL-1kg-001;

[0087] S320, Obtain region Lk within a preset time T promo Order volume R promo And the same duration T during non-promotional periods promo Average order volume benchmark R avg ;

[0088] For example: preset duration T promo (00:00-02:00), the order volume R in Nanjing during this period promo =1200 orders; the benchmark value R for the average order volume in the same 2 hours during non-promotional periods. avg =300 orders;

[0089] S330, when R is satisfied promo >θ order ×R avg When the order volume exceeds the surge threshold, it is determined that the order volume exceeds the surge threshold; among which, R promo θ represents the number of orders placed in a region within a preset time period. orderDenoted as the surge threshold of order volume, R avg Denoted as the average order volume benchmark value for the same duration in non-promotion periods;

[0090] When P k >θ emergency is satisfied, it is determined that the proportion of emergency orders exceeds the threshold; P k Denoted as the proportion of emergency orders in the region within the preset duration; θ emergency Denoted as the threshold of the proportion of emergency orders;

[0091] When (S ks -Q s ) < Sas is satisfied, it is determined that the inventory is lower than the safety threshold; where S ks Denoted as the current inventory quantity of the corresponding item specification in the region; Q s Denoted as the total required quantity of item specification s in the orders in the region within the preset duration; S as Denoted as the inventory safety threshold for the corresponding item specification; s denotes the item specification;

[0092] For example: Order volume threshold: Take θ order = 3, since 1200 > 3 × 300 = 900, it is determined that the order volume exceeds the surge threshold;

[0093] Threshold of the proportion of emergency orders: Take θ emergency = 20%, since 23.3% > 20%, it is determined that the proportion of emergency orders exceeds the threshold;

[0094] Inventory threshold: The current inventory S ks = 300 bottles in the Nanjing warehouse, and the total required quantity Q s = 1500 bottles within the preset duration, then S ks -Q s = 300 - 1500 = -1200 bottles, and the inventory safety threshold S as = 200 bottles, since -1200 < 200, it is determined that the inventory is lower than the safety threshold;

[0095] S340. When within the preset duration after the start of the promotion activity in the target region, the order volume exceeds the order volume surge threshold compared to the average order volume benchmark value for the same duration in the non-promotion period, the proportion of emergency orders exceeds the emergency order proportion threshold, and the inventory of the corresponding specification is lower than the inventory safety threshold, a stock allocation trigger signal is generated;

[0096] For example: After the start of the promotion activity in the Nanjing region within the preset duration, the order volume exceeds the surge threshold, the proportion of emergency orders exceeds the threshold, and the inventory is lower than the safety threshold, a stock allocation trigger signal is generated.

[0097] S400: Based on the inventory allocation trigger signal, select surrounding warehouses with matching specifications, sufficient inventory and distance from the target area within a preset distance threshold, and send a dispatch command to start emergency replenishment.

[0098] Specifically, S410, construct a surrounding warehouse screening model. This model, based on the geographic coordinates of the target area and the geographic coordinates of each warehouse, filters out warehouses that meet the requirements of D. i ≤D th A collection of nearby warehouses, where D th Represented as a preset distance threshold, D i Represented as the straight-line distance between the warehouse and the target area;

[0099] For example: Warehouses surrounding Nanjing include the Yangzhou warehouse (Warehouse ID: YZ-001) and the Zhenjiang warehouse (Warehouse ID: ZJ-001); calculate the straight-line distance: Nanjing warehouse to Yangzhou warehouse: D i ≈100km; Nanjing warehouse to Zhenjiang warehouse: D i ≈80km; Preset distance threshold D th =150km, Yangzhou warehouse and Zhenjiang warehouse are both considered close-range warehouses;

[0100] S420. Based on the set of nearby warehouses, filter out the inventory quantity S. ws ≥S as The set of candidate repositories, where S ws This indicates the current inventory quantity of item specification s in warehouse w;

[0101] For example: Check the inventory data of a nearby warehouse: Yangzhou warehouse: Item specifications: XL-1kg-001, current inventory S ws =5000 bottles, inventory safety threshold S as =200 bottles, satisfying S ws ≥S as Zhenjiang Warehouse: Item Specifications: XL-1kg-001, Current Inventory: S ws =180 bottles, lower than S as =200 bottles, which does not meet the requirement; therefore, the candidate warehouse set only includes the Yangzhou warehouse, warehouse ID: YZ-001;

[0102] S430. Based on the candidate warehouse set, generate an emergency replenishment dispatch instruction. The instruction includes the target area identifier, the required item specifications, the replenishment quantity, and the estimated delivery time.

[0103] Replenishment quantity Q=R promo ×(1+γ), where R promo Represented as preset duration T promo The order quantity within the range, γ represents the replenishment redundancy coefficient, and γ>0; the estimated delivery time Td =T now +T t T now Represented as instruction generation time, T t T represents the logistics transportation time. t Based on the distance D between the warehouse and the target area i The value is calculated from the average speed v of the transport vehicle, i.e., T. t =D i / v;

[0104] For example: Target region identifier: Nanjing, region code 00001;

[0105] Required item specifications: XL-1kg-001; Replenishment quantity: using a redundancy factor γ=0.15 (considering fluctuations in temporary orders), Q=1200×(1+0.15)=1380 bottles; Estimated delivery time: instruction generation time T now =02:00, average speed of the transport vehicle v=60km / h, transport time T t =100 / 60≈1.67h, therefore T d =02:00+1.67h≈03:40;

[0106] Send instruction to Yangzhou warehouse: Target area 00001, item XL-1kg-001, replenish 1380 bottles, estimated delivery time 03:40;

[0107] S500 retrieves historical promotional order data from the same period, analyzes order growth trends and replenishment quantities, predicts subsequent replenishment quantities, and notifies relevant warehouses to prepare in advance.

[0108] Specifically, step S500 includes:

[0109] S510. Using historical order data from the same period of the promotion, construct a time series forecasting model. The model is based on the historical order volume H(t) at time t, and uses exponential smoothing to predict the subsequent replenishment quantity H(t+1) = α × H(t) + (1-α) × H(t-1), where α represents the smoothing coefficient, 0 < α < 1. Simultaneously, considering the current order growth trend of the promotional activity, introduce a trend adjustment factor β to correct the forecast result to H'(t+1) = H(t+1) × (1 + β), where β is obtained based on the historical order growth rate for the same period, i.e., β = G. his / G avg Among them, G his G represents the historical order growth rate. avg Expressed as average growth rate;

[0110] For example: Using historical data from Nanjing during the 2023 Double 11 shopping festival: Historical order volume H(t) = 1100 orders at time t = 2023-11-01 02:00; Historical order volume H(t-1) = 900 orders at time t-1 = 2023-11-01 01:00; Taking a smoothing coefficient α = 0.6, the predicted order volume H(t+1) = 0.6 × 1100 + (1 - 0.6) × 900 = 660 + 360 = 1020 orders; Historical order growth rate G... his =300%, average growth rate G avg =250%, trend adjustment factor β=300% / 250%=1.2, after correction H'(t+1)=1020×(1+1.2)=2244 units;

[0111] S520. Combine the predicted subsequent replenishment quantity H'(t+1) with the inventory capacity C of each warehouse. w and replenishment cycle T c Perform correlation analysis to determine the advance preparation quantity Q for each warehouse. p =H'(t+1)×(T c / T promo ), where T c This represents the warehouse replenishment cycle, which includes transportation and loading / unloading time, T. promo This is a preset duration; a replenishment preparation notification is sent to the relevant warehouse via the cloud service platform, including the item specification 's' and the advance preparation quantity 'Q'. p And warehouse replenishment cycle T c ;

[0112] For example: Yangzhou warehouse inventory capacity C w =8000 bottles, replenishment cycle Tc=48h, preset duration T promo =2h, 2244 orders per hour, average order is one bottle, advance preparation quantity Q p =2244×48=107712 bottles, which need to be carried out in batches;

[0113] Send a notification to the Yangzhou warehouse: Item specifications: XL-1kg-001, prepare 107,712 bottles in advance, to be supplied in batches within 48 hours;

[0114] Furthermore, real-time monitoring of replenishment execution is conducted, when the actual replenishment quantity Q... a When the deviation from the predicted replenishment quantity H'(t+1) exceeds the preset threshold δ, the subsequent replenishment plan is automatically adjusted; the adjusted replenishment quantity Q'=H'(t+1)×(1+δ), and the updated replenishment instruction is resent to the relevant warehouse.

[0115] For example: Actual replenishment quantity Q a=1350 bottles, due to the temporary allocation of some goods, the predicted replenishment quantity H'(t+1)=2244 orders;

[0116] Deviation = |1350-2244| / 2244≈40%, preset threshold δ=25%, since 40%>25%, adjust the subsequent replenishment quantity Q'=2244×(1+25%)=2805 bottles, send an update instruction to Yangzhou warehouse: Item specification: XL-1kg-001, add 2805 bottles, expected delivery at 12:00 on 2024-11-01.

[0117] This invention provides another technical solution for a cloud service system for logistics integration, which includes a data receiving module, an order feature analysis module, an inventory allocation module, a replenishment execution module, and a replenishment monitoring module.

[0118] The system comprises the following modules: a data receiving module, which receives order data from e-commerce platforms and distributors, as well as inventory data from various warehouses in real time; an order feature analysis module, which performs in-depth analysis of order data and extracts key features related to logistics allocation; an inventory allocation module, which correlates order features with warehouse inventory data, determines whether inventory allocation conditions are met, and generates an inventory allocation trigger signal; a replenishment execution module, which executes emergency replenishment based on the inventory allocation trigger signal sent by the inventory allocation module, predicts subsequent demand, and prepares replenishment resources in advance; and a replenishment monitoring module, which monitors the replenishment execution process in real time and dynamically adjusts the replenishment plan.

[0119] The order feature analysis module includes a promotion association module and a demand forecasting module. The promotion association module constructs a promotion activity schedule, matches order placement time with region to the corresponding promotion activity, and distinguishes between promotional and non-promotional orders. The demand forecasting module analyzes warehouses with a surge in order volume, an emergency order ratio exceeding the emergency order ratio threshold, and corresponding specification inventory below the inventory safety threshold.

[0120] The replenishment execution module includes a warehouse screening module, an emergency replenishment module, and a replenishment quantity prediction module. The warehouse screening module filters out nearby warehouses with matching specifications, sufficient inventory, and a distance within a preset threshold based on the geographical location of the target area. The emergency replenishment module sends emergency replenishment dispatch instructions to the screened warehouses. The replenishment quantity prediction module calls up historical promotional order data from the same period, analyzes order growth trends through a time series prediction model, predicts subsequent replenishment quantities, and determines the advance preparation quantity by combining warehouse inventory capacity and replenishment cycle.

[0121] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A cloud service method applied to logistics integration, characterized by: The method includes the following steps: S100: Receive order data sent by e-commerce platforms and distributors in real time. The order data includes a unique order identifier, order time, order region code, order urgency level identifier, and order information. Simultaneously, acquire inventory data from each warehouse. The inventory data includes the specifications, quantity, and inventory safety threshold of the items currently stored in each warehouse. S200: Analyze order data to extract communication features, analyze the correlation between order time and promotional activities, identify promotional periods with concentrated orders, statistically analyze order distribution by region to determine the target regions with surges, and obtain the proportion of urgent orders in the target regions within a preset time period; S300, associated order communication features and corresponding regional warehouse inventory data, when the order volume exceeds the order volume surge threshold, the proportion of emergency orders exceeds the emergency order proportion threshold, and the corresponding specification inventory is lower than the inventory safety threshold within a preset time after the start of the promotional activity in the target region, an inventory allocation trigger signal is generated. S310. Determine the corresponding target region based on the region code of the order, analyze the warehouses located in the target region or capable of providing logistics services to the target region, determine the specifications and models of the items in the order, and match them with the specifications of the items stored in the warehouse; S320, Obtain region Lk within a preset time T promo Order volume R promo And the same duration T during non-promotional periods promo Average order volume benchmark R avg ; S330, when R is satisfied promo >θ order ×R avg When the order volume exceeds the surge threshold, it is determined that the order volume exceeds the surge threshold; among which, R promo θ represents the number of orders placed in a region within a preset time period. order R represents the threshold for a surge in order volume. avg This represents the baseline value of the average order volume for the same duration during non-promotional periods; When P is satisfied k >θ emergency When the proportion of urgent orders exceeds a certain threshold, it is determined that P... k This is represented by the percentage of urgent orders within a preset timeframe for that region; θ emergency This is represented as the threshold for the proportion of urgent orders; When (S ks - Q s ) < Sas, it is determined that the inventory is below the safety threshold; where S ks represents the current inventory quantity of the item specification corresponding to the region; Q s represents the total required quantity of the item specification s in the regional orders within the preset duration; S as represents the inventory safety threshold for the corresponding item specification; s represents the item specification; S340. When, within a preset time period after the start of a promotional activity in the target region, the order volume exceeds the average order volume benchmark value for the same period during non-promotional periods, the proportion of urgent orders exceeds the emergency order proportion threshold, and the corresponding specification inventory is lower than the inventory safety threshold, an inventory allocation trigger signal is generated. S400: Based on the inventory allocation trigger signal, select surrounding warehouses with matching specifications, sufficient inventory and distance from the target area within a preset distance threshold, and send a dispatch command to start emergency replenishment. S500 retrieves historical promotional order data from the same period, analyzes order growth trends and replenishment quantities, predicts subsequent replenishment quantities, and notifies relevant warehouses to prepare in advance.

2. The cloud service method for logistics integration according to claim 1, characterized in that: Order urgency levels include regular orders, expedited orders, and express orders; The order information includes the item name, item specifications, quantity ordered, and payment completion status. The specifications and models of the items and the specifications of the items currently stored in the warehouse use the same signal coding system; the payment completion status includes paid and unpaid.

3. The cloud service method for logistics integration according to claim 1, characterized in that: Step S200 includes: S210. Construct a promotional activity schedule, wherein the promotional activity schedule includes a unique identifier for the promotional activity, a start time, an end time, and a coverage area; S220, The extraction of communication features includes extracting the order's unique identifier, order time, and order's region code, and traversing the promotional activity schedule. When t... j ∈[Ts,Te] and L j ∈L i When, then determine order Q j Orders placed during the promotional period are considered orders placed outside the promotional period; where t j Let L represent the order placement time, j represent the order index, [Ts, Te] represent the time interval of the promotional activity, Ts represent the start time of the promotion, Te represent the end time of the promotion, and L represent the end time of the promotion. j Indicates the region where the order is located, L i This represents the set of regions covered by the promotional activity, Q. j Represented as a unique identifier for the order; S230. Divide the promotional activity time interval into multiple consecutive unit time periods Δt. n (n=1,2,…,m), where n represents the index of the time period and m represents the total number of time periods; the formula for obtaining the number of orders per unit time is as follows: C n =N n / Δt; Where, N n Δt represents the total number of orders per unit time period, and Δt represents the unit time period. The formula for calculating the average number of orders per unit time during a promotional campaign is as follows: C avg =N p / (Te-Ts); Where, N p This represents the total number of orders during the promotional period; if there exists a unit of time where the order volume satisfies C... n >θ1×C avg When θ1 represents the order concentration threshold, and θ1>1, then the unit of time is determined to be a promotional period of order concentration; S240. Obtain the order volume growth rate for the regions covered by the promotional activity, using the following formula: G k =(R k -R 0k ) / R 0k ×100%; Among them, G k R represents the order volume growth rate; k R represents the total number of orders placed in the region covered by the promotion during the peak promotional period; 0k This represents the average total number of orders during the same time period as the promotional period, outside of promotional events. When the order volume growth rate G k When the value is greater than θ2, θ2 represents the order volume growth rate threshold, and the corresponding region L is determined. k For target regions experiencing a surge in orders; S250, Obtain Region L k Urgent orders account for P k =E k / T k ×100%, where E k This represents the total number of orders marked as rush orders and express orders; T k Indicates region L during the promotional period k Total number of orders.

4. The cloud service method for logistics integration according to claim 1, characterized in that: Step S400 includes: S410. Construct a surrounding warehouse screening model, which is based on the geographical coordinates of the target area and the geographical coordinates of each warehouse, and screens out those warehouses that meet the requirements of D. i ≤D th A collection of nearby warehouses, where D th Represented as a preset distance threshold, D i Represented as the straight-line distance between the warehouse and the target area; S420. Based on the set of nearby warehouses, filter out the inventory quantity S. ws ≥S as The set of candidate repositories, where S ws This indicates the current inventory quantity of item specification s in warehouse w; S430. Based on the candidate warehouse set, generate an emergency replenishment dispatch instruction, which includes the identifier of the target area, the specifications of the required items, the replenishment quantity, and the estimated delivery time.

5. The cloud service method for logistics integration according to claim 1, characterized in that: Step S500 includes: S510. Call historical promotional order data for the same period and construct a time series prediction model. The model is based on the historical order volume H(t) at time t and uses exponential smoothing to predict the subsequent replenishment volume H(t+1)=α×H(t)+(1-α)×H(t-1), where α represents the smoothing coefficient, 0<α<1; at the same time, combined with the order growth trend of the current promotional activity, a trend adjustment factor β is introduced to correct the prediction result to H'(t+1)=H(t+1)×(1+β), where β is obtained according to the historical order growth rate for the same period. S520. Combine the predicted subsequent replenishment quantity H'(t+1) with the inventory capacity C of each warehouse. w and replenishment cycle T c Perform correlation analysis to determine the advance preparation quantity Q for each warehouse. p =H'(t+1)×(T c / T promo ), where T c This refers to the warehouse replenishment cycle, which includes transportation and loading / unloading time, T. promo This is indicated by a preset duration; a replenishment preparation notification is sent to the relevant warehouse via the cloud service platform, the notification including the item specification s and the advance preparation quantity Q. p And warehouse replenishment cycle T c .

6. The cloud service method for logistics integration according to claim 5, characterized in that: The system monitors replenishment execution in real time. When the deviation between the actual replenishment quantity and the predicted replenishment quantity exceeds a preset threshold, it automatically adjusts the subsequent replenishment plan and resends updated replenishment instructions to the relevant warehouses.

7. A cloud service system for logistics integration, applicable to the cloud service method for logistics integration as described in any one of claims 1-6, characterized in that: The system includes a data receiving module, an order feature analysis module, an inventory allocation module, a replenishment execution module, and a replenishment monitoring module; The data receiving module receives order data and inventory data from e-commerce platforms and distributors in real time; the order feature analysis module performs in-depth analysis of order data and extracts key features related to logistics allocation; the inventory allocation module associates order features with warehouse inventory data, determines whether inventory allocation conditions are met, and generates an inventory allocation trigger signal. The replenishment execution module performs emergency replenishment and predicts subsequent demand based on the inventory allocation trigger signal sent by the inventory allocation module, and prepares replenishment resources in advance; the replenishment monitoring module monitors the replenishment execution process in real time and dynamically adjusts the replenishment plan.

8. The cloud service system for logistics docking according to claim 7, characterized in that: The order feature analysis module includes a promotion association module and a demand forecasting module. The promotion association module constructs a promotion activity schedule, matches order placement time with region to the corresponding promotion activity, and distinguishes between promotional and non-promotional orders. The demand forecasting module analyzes warehouses with a surge in order volume, an emergency order ratio exceeding the emergency order ratio threshold, and corresponding specification inventory below the inventory safety threshold. The replenishment execution module includes a warehouse screening module, an emergency replenishment module, and a replenishment quantity prediction module; The warehouse screening module filters out surrounding warehouses with matching specifications, sufficient inventory, and a distance within a preset threshold based on the geographical location of the target area; the emergency replenishment module sends emergency replenishment scheduling instructions to the screened warehouses; the replenishment quantity prediction module calls up historical promotional order data from the same period, analyzes the order growth trend through a time series prediction model, predicts the subsequent replenishment quantity, and determines the advance preparation quantity by combining the warehouse inventory capacity and replenishment cycle.

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