Automatic stacking method for lottery production and processing

Abstract

The invention relates to the technical field of logistics, and discloses an automatic stacking method for lottery production and processing, which comprises the following steps: step 1, collecting real-time sales data; 2, demand prediction and inventory management; step 3, real-time demand prediction; step 4, an inventory preparation strategy; 5, a dynamic scheduling strategy; and step 6, predicting the arrival time of the demander. According to the invention, through real-time data acquisition and demand prediction, the inventory level is optimized, inventory overstock and stockout risks are reduced, safe inventory and reorder points are scientifically calculated, capital occupation and storage cost increase caused by excessive inventory are avoided, through a dynamic scheduling strategy, high-priority lottery tickets are preferentially processed, and through scientific inventory management and production scheduling, the production efficiency is improved. And unnecessary inventory and production cost is reduced, the situations of stockout and delayed delivery are reduced through accurate demand prediction and inventory management, and the beneficial effect that the stock of a demander is calculated according to real-time sales data in the stacking process and the stock is prepared in advance is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics, and in particular to an automated palletizing method for lottery production and processing. Background Art

[0002] With the rapid development of technology, the introduction of automated palletizing systems at lottery point-of-sale (POS) has become a trend. This system not only improves work efficiency but also significantly enhances the accuracy and reliability of the palletizing process. An automated palletizing system typically consists of the following core components: sensors and cameras for identifying and locating the position and status of lottery tickets; robotic arms and conveyor belts for grabbing, moving, and stacking lottery tickets; computer control systems for processing sensor data, controlling the robotic arms and conveyor belts, and executing palletizing strategies; and databases and software interfaces for storing and processing lottery-related information, such as batch, denomination, and number.

[0003] Artificial intelligence technology has further enhanced the intelligence level of automated palletizing systems, primarily through intelligent scheduling. AI intelligently schedules palletizing tasks based on real-time sales data and inventory status, ensuring that lottery tickets of all batches and denominations can be palletized on demand. At the same time, AI records and analyzes various data from the palletizing process in real time, providing powerful data support for lottery sales strategies and helping sales points better manage inventory and sales.

[0004] Although AI can perform intelligent scheduling based on real-time sales data, there is a time lag between the AI system receiving real-time information and scheduling, resulting in situations where the customer arrives but palletizing is not yet completed. Summary of the Invention

[0005] Technical issues solved:

[0006] In response to the shortcomings of the existing technology, the present invention provides an automated palletizing method for lottery ticket production and processing. The method has the advantages of calculating the demander's inventory based on real-time sales data during palletizing and preparing inventory in advance, and dynamically adjusting the palletizing sequence according to the demander's arrival time, thereby solving the problems of the above-mentioned technology.

[0007] Technical solution:

[0008] To achieve the above-mentioned object, the present invention provides the following technical solution: a lottery ticket production and processing automated stacking method, comprising the following steps:

[0009] Step 1: Real-time sales data collection: Obtain real-time sales data from the POS system, such as lottery ticket sales data, including type, denomination, and sales volume information, through the API interface. The collected sales data is transmitted to the central control system in real time via a high-speed data transmission channel;

[0010] Step 2: Demand Forecasting and Inventory Management: The central control system uses historical sales data to build a demand forecasting model through machine learning and statistical analysis methods;

[0011] Step 3: Real-time demand forecasting: Combine current sales data with the forecasting model to calculate demand in real time over a period of time.

[0012] Step 4: Inventory preparation strategy: Automatically adjust inventory levels based on forecasted demand, preparing sufficient lottery inventory in advance to cope with unexpected demand spikes;

[0013] Step 5: Dynamic Scheduling Strategy: Based on demand forecast results and inventory status, calculate the demand priority of each batch and denomination of lottery tickets. Adjust the stacking order in real time based on the demand priority, giving priority to high-priority lottery tickets.

[0014] Step 6. Predict the arrival time of the buyer: Combine logistics and sales data to predict the arrival time of the buyer and ensure that palletizing is completed before the buyer arrives.

[0015] Preferably, the total sales volume in step 1 is calculated as:

[0016]

[0017] Where: S i represents the sales volume at the i-th time point.

[0018] Preferably, the model for predicting future lottery demand in step 2 is:

[0019]

[0020] in: represents the predicted demand at the hth time point after time t; ∈ t_k+1 represents the error term at the past k time points, used to capture white noise; β0, β j ,γ k ,δ i Represents the coefficient of the model, which needs to be estimated through training data; n represents the number of other influencing factors.

[0021] Preferably, the demand in step 3 is calculated as:

[0022]

[0023] Where: y t―j+1 represents the actual demand at the past j time points, which is used to capture the autoregressive part; j and θ k The coefficients representing the autoregressive and moving average parts need to be estimated using training data.

[0024] Preferably, the four lottery inventory preparation calculations in step 4 are as follows:

[0025]

[0026] Where: SS represents the safety stock; σ represents the standard deviation of demand, represents the square root of the order lead time L; z is the quantile of the standard normal distribution;

[0027] The preparation strategy in step 4 also includes a reorder point, which is calculated as:

[0028] ROP=D·L+SS

[0029] Where: ROP represents the reorder point; L represents the order lead time; D represents the average demand rate.

[0030] Preferably, the preparation strategy in step 4 further includes calculating the average inventory level, and the average inventory level is calculated as:

[0031]

[0032] Among them: Q represents the order quantity; Average Inventory represents the average inventory.

[0033] Preferably, the demand priority in step 5 is calculated as follows:

[0034]

[0035] Where: P i represents the demand priority of the lottery ticket of the i-th batch and denomination; U i Represents the unit value of the lottery ticket of batch i and denomination: par value; Represents the sum of the demand priorities of lottery tickets across all lots and denominations.

[0036] Preferably, the prediction model in step 6 is:

[0037] T=T0+ΔT

[0038] Where: T represents the predicted arrival time of the customer; ΔT represents the predicted time increment, that is, the expected time difference from the current time to the customer's arrival time.

[0039] Preferably, the predicted time increment ΔT in step 6 is calculated by performing regression analysis or time series analysis on historical data:

[0040] ΔT=α+β·X

[0041] Where: ΔT represents the predicted time increment; β represents the regression coefficient; α is the intercept, which represents the value of ΔT when X=0.

[0042] Preferably, in step 6, a multiple linear regression model is used to predict the time increment ΔT by combining logistics and sales data:

[0043] ΔT=α+β1·D+β2·S

[0044] Where: α represents the intercept term.

[0045] Compared with the prior art, the present invention provides an automated palletizing method for lottery ticket production and processing, which has the following beneficial effects:

[0046] 1. The present invention optimizes inventory levels through real-time data collection and demand forecasting, reduces inventory backlogs and stock-out risks, scientifically calculates safety stocks and reorder points, avoids capital occupation and increased storage costs caused by excessive inventory, and prioritizes high-priority lottery tickets through dynamic scheduling strategies to improve production efficiency. Through scientific inventory management and production scheduling, it reduces unnecessary inventory and production costs. Through accurate demand forecasting and inventory management, it ensures the timely supply of lottery tickets, reduces stock-outs and delayed shipments, and improves customer satisfaction. It achieves the beneficial effect of calculating the demander's inventory based on real-time sales data during palletizing and preparing inventory in advance.

[0047] 2. The present invention reduces waiting time by accurately predicting the arrival time of the demander, ensuring that palletizing is completed before the demander arrives, thereby improving logistics efficiency and customer satisfaction. Through dynamic scheduling strategies and demand priority calculations, high-demand, high-value lottery tickets are given priority, optimizing the production sequence, improving production efficiency, and reducing inventory backlogs. Through scientific calculation of safety stocks and reorder points, inventory levels are optimized, which can not only meet market demand but also avoid capital occupation and increased warehousing costs caused by excessive inventory. Through real-time demand forecasting and inventory preparation strategies, timely satisfaction of demand is ensured, unnecessary waste of resources is reduced, and overall operational efficiency is improved, achieving the beneficial effect of dynamically adjusting the palletizing sequence according to the arrival time of the demander. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the process of the present invention; DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] See also Figure 1, a lottery production and processing automated stacking method, comprising the following steps:

[0051] Step 1: Real-time sales data collection: Obtain real-time sales data from the POS system, such as lottery ticket sales data, including type, denomination, and sales volume information, through the API interface. The collected sales data is transmitted to the central control system in real time via a high-speed data transmission channel;

[0052] Step 2: Demand Forecasting and Inventory Management: The central control system uses historical sales data to build a demand forecasting model through machine learning and statistical analysis methods;

[0053] Step 3: Real-time demand forecasting: Combine current sales data with the forecasting model to calculate demand in real time over a period of time.

[0054] Step 4: Inventory preparation strategy: Automatically adjust inventory levels based on forecasted demand, preparing sufficient lottery inventory in advance to cope with unexpected demand spikes;

[0055] Step 5: Dynamic Scheduling Strategy: Based on demand forecast results and inventory status, calculate the demand priority of each batch and denomination of lottery tickets. Adjust the stacking order in real time based on the demand priority, giving priority to high-priority lottery tickets.

[0056] Step 6. Predict the arrival time of the buyer: Combine logistics and sales data to predict the arrival time of the buyer and ensure that palletizing is completed before the buyer arrives.

[0057] Data accuracy: Obtain real-time sales data through the POS system to ensure the timeliness and accuracy of the data.

[0058] High-speed transmission: Data is transmitted to the central control system in real time through high-speed data transmission channels to ensure timely updating and processing of information.

[0059] Scientific forecasting: Using historical sales data and machine learning and statistical analysis methods to establish a demand forecasting model, the accuracy of demand forecasting is improved.

[0060] Inventory optimization: Optimize inventory management strategies based on forecasting models to reduce inventory overstock and stock-out risks.

[0061] Dynamic Adjustment: Combining current sales data with forecasting models, future demand is calculated in real time to ensure that inventory preparation strategies can respond to market changes in a timely manner.

[0062] Flexible response: Real-time updates of demand forecasts ensure flexible response during peak demand periods or unexpected events.

[0063] Advance preparation: Automatically adjust inventory levels based on predicted demand and prepare sufficient lottery inventory in advance to cope with unexpected demand peaks.

[0064] Reduce risks: Scientifically calculate safety stock and reorder points, optimize inventory levels, and reduce inventory backlogs and stock-out risks.

[0065] Prioritize high-demand tickets: Calculate the demand priority for each lot and denomination based on demand forecast results and inventory status, and prioritize high-demand, high-value tickets.

[0066] Optimize production sequence: Through dynamic scheduling strategies, the stacking sequence can be adjusted in real time to optimize the production sequence and improve production efficiency.

[0067] Accurate forecasting: Combining logistics and sales data, we can predict the arrival time of the buyer and ensure that palletizing is completed before the buyer arrives.

[0068] Reduce waiting time: Through accurate prediction models, reduce waiting time, improve logistics efficiency and customer satisfaction.

[0069] Automated management: Through automated palletizing methods, manual intervention is reduced and production efficiency is improved.

[0070] Optimized scheduling: Dynamic scheduling strategies and accurate demander arrival time predictions optimize production sequences and logistics processes, improving overall operational efficiency.

[0071] Scientific inventory management: By scientifically calculating safety stock and reorder points, we optimize inventory levels, reduce inventory backlogs and out-of-stock risks, and lower storage costs and capital occupation.

[0072] Reduce resource waste: By optimizing production sequence and inventory management, unnecessary resource waste is reduced and overall operating costs are lowered.

[0073] Timely supply: Through accurate demand forecasting and inventory management, we ensure the timely supply of lottery tickets, reduce out-of-stock and delayed shipments, and improve customer satisfaction.

[0074] Reduce waiting time: Accurate prediction of demander arrival time reduces waiting time, improves logistics efficiency and customer satisfaction.

[0075] Real-time demand forecasting: Combining current sales data with forecasting models, future demand is calculated in real time to ensure that inventory preparation strategies can respond to market changes in a timely manner and flexibly respond to demand peaks or emergencies.

[0076] Specifically, the total sales volume in step 1 is calculated as:

[0077]

[0078] Where: S i represents the sales volume at the i-th time point.

[0079] Real-time data collection: By obtaining sales data in real time, we ensure that the calculation of total sales volume can reflect the current market situation.

[0080] Data accuracy: Obtaining sales data through the API interface reduces human intervention, avoids errors that may be caused by manual data entry, and improves data accuracy.

[0081] Fast calculation: Utilize high-speed data transmission channels to transmit sales data to the central control system in real time, and perform fast cumulative calculations to ensure efficient calculation of total sales volume.

[0082] Timely updates: Real-time calculation of total sales volume can be quickly reflected in subsequent demand forecasting and inventory management steps, improving the responsiveness of the entire process.

[0083] Comprehensive coverage: By accumulating sales at all time points, the calculation of total sales volume ensures that all sales data is included, avoiding data omissions.

[0084] Global perspective: The calculation of total sales volume provides a comprehensive understanding of the overall market sales situation, providing a solid foundation for subsequent decision-making.

[0085] Dynamic adjustment: Total sales volume calculated in real time can be dynamically adjusted based on market changes, allowing demand forecasting and inventory management to adapt to market fluctuations in a timely manner.

[0086] Supports multiple analyses: The calculation method for total sales volume is simple and universal, and supports multiple subsequent analyses, such as segmented analysis by region, time period, product type, etc.

[0087] Traceability: Sales volume at each point in time can be traced back to specific sales records, ensuring that the calculation process of total sales volume is transparent and traceable.

[0088] Data monitoring: By monitoring the changes in total sales volume in real time, abnormal situations such as sudden increases or decreases in sales can be discovered in a timely manner, so that corresponding countermeasures can be taken.

[0089] Specifically, the model for predicting future lottery demand in step 2 is:

[0090]

[0091] in: represents the predicted demand at the hth time point after time t; ∈ t_k+1 represents the error term at the past k time points, used to capture white noise; β0, β j ,γk ,δ i Represents the coefficient of the model, which needs to be estimated through training data; n represents the number of other influencing factors.

[0092] Through comprehensive consideration of historical data and error terms, the model can better fit the changing trend of actual demand and improve the accuracy of prediction. The introduction of error terms enables the model to correct past prediction errors and reduce the deviation of future predictions.

[0093] Specifically, the demand in step 3 is calculated as:

[0094]

[0095] Where: y t―j+1 represents the actual demand at the past j time points, which is used to capture the autoregressive part; j and θ k The coefficients representing the autoregressive and moving average parts need to be estimated using training data.

[0096] Specifically, the four steps of lottery inventory preparation calculation are as follows:

[0097]

[0098] Where: SS represents the safety stock; σ represents the standard deviation of demand, represents the square root of the order lead time L; z is the quantile of the standard normal distribution;

[0099] The preparatory strategy in step 4 also includes the reorder point, which is calculated as:

[0100] ROP=D·L+SS

[0101] Where: ROP represents the reorder point; L represents the order lead time; D represents the average demand rate.

[0102] Specifically, the preparatory strategy in step 4 also includes the calculation of the average inventory level, which is calculated as:

[0103]

[0104] Among them: Q represents the order quantity; Average Inventory represents the average inventory.

[0105] Specifically, the demand priority in step 5 is calculated as:

[0106]

[0107] Where: P i represents the demand priority of the lottery ticket of the i-th batch and denomination; U iRepresents the unit value of the lottery ticket of batch i and denomination: par value; Represents the sum of the demand priorities of lottery tickets across all lots and denominations.

[0108] Specifically, the prediction model in step six is:

[0109] T=T0+ΔT

[0110] Where: T represents the predicted arrival time of the customer; ΔT represents the predicted time increment, that is, the expected time difference from the current time to the customer's arrival time.

[0111] Specifically, the predicted time increment ΔT in step 6 is calculated by performing regression analysis or time series analysis on historical data:

[0112] ΔT=α+β·X

[0113] Where: ΔT represents the predicted time increment; β represents the regression coefficient; α is the intercept, which represents the value of ΔT when X=0.

[0114] Specifically, in step 6, the logistics and sales data are combined to use a multiple linear regression model to predict the time increment ΔT:

[0115] ΔT=α+β1·D+β2·S

[0116] Where: α represents the intercept term.

[0117] Through real-time data collection and demand forecasting, inventory levels are optimized, inventory backlogs and out-of-stock risks are reduced, safety stock and reorder points are scientifically calculated, and capital occupation and increased storage costs caused by excessive inventory are avoided. Through dynamic scheduling strategies, high-priority lottery tickets are prioritized to improve production efficiency. Through scientific inventory management and production scheduling, unnecessary inventory and production costs are reduced. Through accurate demand forecasting and inventory management, timely supply of lottery tickets is ensured, out-of-stock and delayed delivery are reduced, and customer satisfaction is improved. Accurate prediction of the arrival time of the demander reduces waiting time and ensures that palletizing is completed before the arrival of the demander, improving logistics efficiency and customer satisfaction. Through dynamic scheduling strategies and demand priority calculation, high-demand and high-value lottery tickets are prioritized, production sequence is optimized, production efficiency is improved, and inventory backlogs are reduced. Through scientific calculation of safety stock and reorder points, inventory levels are optimized, which can both meet market demand and avoid capital occupation and increased storage costs caused by excessive inventory. Through real-time demand forecasting and inventory preparation strategies, timely satisfaction of demand is ensured, unnecessary waste of resources is reduced, and overall operational efficiency is improved.

[0118] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A lottery ticket production and processing automated stacking method, characterized in that: The following steps are involved: Step 1: Real-time sales data collection: Obtain real-time sales data from the POS system, such as lottery ticket sales data, including type, denomination, and sales volume information, through the API interface. The collected sales data is transmitted to the central control system in real time via a high-speed data transmission channel; Step 2: Demand Forecasting and Inventory Management: The central control system uses historical sales data to build a demand forecasting model through machine learning and statistical analysis methods; Step 3: Real-time demand forecasting: Combine current sales data with the forecasting model to calculate demand in real time over a period of time. Step 4: Inventory preparation strategy: Automatically adjust inventory levels based on forecasted demand, preparing sufficient lottery inventory in advance to cope with unexpected demand spikes; Step 5: Dynamic Scheduling Strategy: Based on demand forecast results and inventory status, calculate the demand priority of each batch and denomination of lottery tickets. Adjust the stacking order in real time based on the demand priority, giving priority to high-priority lottery tickets. Step 6. Predict the arrival time of the buyer: Combine logistics and sales data to predict the arrival time of the buyer and ensure that palletizing is completed before the buyer arrives.

2. The lottery ticket production and processing automated stacking method according to claim 1, characterized in that: The total sales volume in step 1 is calculated as: Where: S i represents the sales volume at the i-th time point.

3. The lottery ticket production and processing automated stacking method according to claim 1, characterized in that: The model for predicting future lottery demand in step 2 is: in: represents the predicted demand at the hth time point after time t; ∈ t_k+1 represents the error term at the past k time points, used to capture white noise; β0, β j ,γ k ,δ i Represents the coefficient of the model, which needs to be estimated through training data; n represents the number of other influencing factors.

4. The lottery ticket production and processing automated stacking method according to claim 1, characterized in that: The demand in step 3 is calculated as: Where: y t―j+1 represents the actual demand at the past j time points, which is used to capture the autoregressive part; j and θ k The coefficients representing the autoregressive and moving average parts need to be estimated using training data.

5. The lottery ticket production and processing automated stacking method according to claim 1, characterized in that: The four lottery inventory preparation calculations in the steps are: Where: SS represents the safety stock; σ represents the standard deviation of demand, represents the square root of the order lead time L; z is the quantile of the standard normal distribution; The preparation strategy in step 4 also includes a reorder point, which is calculated as: ROP=D·L+SS Where: ROP represents the reorder point; L represents the order lead time; D represents the average demand rate.

6. The lottery ticket production and processing automated stacking method according to claim 1, characterized in that: The preparation strategy in step 4 also includes an average inventory level calculation, which is calculated as: Among them: Q represents the order quantity; Average Inventory represents the average inventory.

7. The lottery ticket production and processing automated stacking method according to claim 1, characterized in that: The demand priority in step 5 is calculated as: Where: P i represents the demand priority of the lottery ticket of the i-th batch and denomination; U i Represents the unit value of the lottery ticket of batch i and denomination: par value; Represents the sum of the demand priorities of lottery tickets across all lots and denominations.

8. The lottery ticket production and processing automated stacking method according to claim 1, characterized in that: The prediction model in step 6 is: T=T0+ΔT Where: T represents the predicted arrival time of the customer; ΔT represents the predicted time increment, that is, the expected time difference from the current time to the customer's arrival time.

9. The lottery ticket production and processing automated stacking method according to claim 1, characterized in that: The predicted time increment ΔT in step 6 is calculated by performing regression analysis or time series analysis on historical data: ΔT=α+β·X Where: ΔT represents the predicted time increment; β represents the regression coefficient; α is the intercept, which represents the value of ΔT when X=0.

10. The lottery ticket production and processing automated stacking method according to claim 1, characterized in that: In step 6, the logistics and sales data are combined to use a multiple linear regression model to predict the time increment ΔT: ΔT=α+β1·D+β2·S Where: α represents the intercept term.

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