Lottery logistics management method, device and equipment, medium and product
By collecting multi-dimensional data and utilizing sales forecasting models and logistics planning models, we optimize lottery logistics management, solving the problems of inventory backlogs and delivery delays in traditional management, achieving accurate inventory and delivery planning, and ensuring the timely supply and normal sales of lottery tickets.
Patent Information
- Application Number
- CN202510839170.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-23
- Publication Date
- 2025-10-03
AI Technical Summary
In traditional lottery logistics management, inventory management relies on manual operations, resulting in inventory backlogs or out-of-stocks, low distribution efficiency, and affecting lottery sales.
By collecting multi-dimensional data and using sales forecasting models to accurately estimate lottery sales quantities, we can generate detailed distribution plans based on logistics planning models to optimize inventory and distribution resource allocation.
It achieves precise inventory management and distribution planning, avoids inventory backlogs or stockouts, ensures timely lottery delivery, and improves the efficiency and accuracy of logistics management.
Smart Images
Figure CN120746447A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a lottery logistics management method, device, equipment, medium and product. Background Art
[0002] With the booming economy and the increasing diversification of people's entertainment needs, the lottery industry is experiencing rapid growth. The market continues to expand, sales outlets continue to increase, and lottery issuance and sales volumes continue to climb year by year, presenting unprecedented challenges to lottery logistics management.
[0003] Under traditional lottery logistics management models, inventory management relies primarily on manual processes, which make it difficult to update inventory data in real time. This prevents managers from keeping up to date with actual inventory conditions, leading to inventory overstocking and stockouts. Overstocking consumes significant capital and storage space, increasing operating costs; while stockouts impact lottery sales, leading to customer churn and damaging the interests of lottery issuers. Distribution is primarily based on experience, resulting in inefficient delivery, lengthy delivery times, and an inability to meet timely replenishment needs at sales outlets. Consequently, existing lottery logistics management methods are prone to delays in lottery delivery, impacting normal sales. Summary of the Invention
[0004] The purpose of this application is to provide a lottery logistics management method, device, equipment, medium and product that can avoid delays in lottery delivery so that lottery tickets can be sold normally.
[0005] To achieve the above objectives, this application provides the following solutions:
[0006] In a first aspect, the present application provides a lottery logistics management method, comprising:
[0007] Collecting lottery sales data, lottery inventory data, delivery demand information, and delivery vehicle load; wherein the delivery demand information includes target delivery requirements for multiple lottery sales points; and the lottery inventory data includes lottery inventory sub-data corresponding to various lottery types;
[0008] Inputting the lottery sales data into a pre-built sales forecast model to obtain a predicted number of lottery sales within a preset time period; wherein the starting time of the preset time period is the current time, the duration of the preset time period is a preset duration, and the predicted number of lottery sales includes sales forecast sub-numbers corresponding to various lottery types;
[0009] Determining lottery target inventory data and lottery replenishment inventory data based on the lottery sales forecast quantity and the lottery inventory data;
[0010] The lottery target inventory data, the distribution demand information, and the distribution vehicle load are input into a pre-built logistics planning model to obtain logistics planning information corresponding to the target distribution demand of each lottery sales point; wherein the logistics planning information at least includes the distribution lottery type, distribution lottery quantity, distribution vehicle, and distribution time corresponding to each lottery sales point.
[0011] Optionally, inputting the lottery sales data into a pre-built sales forecasting model to obtain a predicted number of lottery sales within a preset time period specifically includes:
[0012] Preprocessing the lottery sales data input into the pre-built sales forecasting model to obtain standardized lottery sales data;
[0013] performing feature extraction on the standardized lottery sales data to obtain lottery sales features;
[0014] The lottery sales characteristics are analyzed using the sales forecast model to obtain a predicted number of lottery sales within a preset time period.
[0015] Optionally, preprocessing the lottery sales data input into the pre-built sales forecasting model to obtain standardized lottery sales data specifically includes:
[0016] performing data cleaning on the lottery sales data input into the pre-built sales forecasting model to obtain lottery sales data to be processed;
[0017] The lottery sales data to be processed is normalized to obtain standardized lottery sales data.
[0018] Optionally, extracting features from the standardized lottery sales data to obtain lottery sales features specifically includes:
[0019] Extracting features from the standardized lottery sales data to obtain initial features; wherein the initial features include an initial feature set of multiple lottery sales data; wherein the initial feature set includes a time feature, a lottery type feature, a holiday feature, and a promotional activity feature of a lottery sales data;
[0020] The initial features are coded and converted to obtain lottery sales features of numerical value type corresponding to the initial features.
[0021] Optionally, determining lottery target inventory data and lottery supplementary inventory data based on the lottery sales forecast quantity and the lottery inventory data specifically includes:
[0022] Get the maximum lottery capacity of the lottery warehouse;
[0023] Get the storage cost of various lottery types;
[0024] Determining target lottery inventory data based on the lottery sales forecast, the lottery inventory data, the maximum lottery capacity, and the storage cost; wherein the target lottery inventory data includes target lottery inventory sub-data corresponding to each lottery type; the lottery storage cost corresponding to the target lottery inventory data within the preset time period is minimal; the target lottery inventory sub-data corresponding to any lottery type is greater than or equal to the lottery inventory sub-data corresponding to any lottery type; the target lottery inventory sub-data corresponding to any lottery type is less than or equal to the sales forecast sub-quantity corresponding to any lottery type; and the target lottery inventory data is equal to the maximum lottery capacity;
[0025] Lottery replenishment inventory data is determined based on the lottery target inventory data and the lottery inventory data; wherein the lottery replenishment inventory data includes lottery replenishment inventory sub-data corresponding to various lottery types.
[0026] Optionally, the lottery target inventory data, the delivery demand information, and the delivery vehicle load are input into a pre-built logistics planning model to obtain logistics planning information corresponding to the target delivery demand of each lottery sales point, specifically including:
[0027] Obtain map data containing each lottery sales point and business hours;
[0028] Obtaining traffic status data corresponding to the map data;
[0029] The map data, the business hours, the traffic status data, the lottery target inventory data, the delivery demand information and the delivery vehicle load are input into a pre-built logistics planning model to obtain logistics planning information corresponding to the target delivery demand of each lottery sales point.
[0030] In a second aspect, the present application provides a lottery logistics management device, comprising:
[0031] A collection unit is configured to collect lottery sales data, lottery inventory data, delivery demand information, and delivery vehicle load; wherein the delivery demand information includes target delivery requirements for multiple lottery sales points; and the lottery inventory data includes lottery inventory sub-data corresponding to various lottery types;
[0032] a first input unit, configured to input the lottery sales data into a pre-established sales forecast model to obtain a predicted number of lottery sales within a preset time period; wherein the starting time of the preset time period is the current time, the duration of the preset time period is a preset duration, and the predicted number of lottery sales includes sales forecast sub-numbers corresponding to various lottery types;
[0033] a determining unit, configured to determine target lottery inventory data and lottery supplementary inventory data based on the lottery sales forecast quantity and the lottery inventory data;
[0034] The second input unit is used to input the lottery target inventory data, the distribution demand information and the distribution vehicle load into a pre-built logistics planning model to obtain logistics planning information corresponding to the target distribution demand of each lottery sales point; wherein the logistics planning information at least includes the distribution lottery type, distribution lottery quantity, distribution vehicle and distribution time corresponding to each lottery sales point.
[0035] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the lottery logistics management methods described above.
[0036] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the lottery logistics management methods described above.
[0037] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the lottery logistics management methods described above.
[0038] In a sixth aspect, the present application provides a chip, which includes a processor and a communication interface, the communication interface is coupled to the processor, the processor is used to run a program or instruction, and when the processor executes the program or instruction, it implements the steps of any one of the lottery logistics management methods described above.
[0039] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0040] This application provides a lottery logistics management method, device, equipment, medium and product. By collecting multi-dimensional data, the sales forecast model is used to accurately estimate the sales forecast quantity of various types of lottery tickets in a preset time period. Based on this, reasonable target inventory and replenishment inventory data are determined. Then, combined with distribution demand information and vehicle load, detailed logistics planning information is generated with the help of a logistics planning model to clarify the type, quantity, vehicle and time of distribution of lottery tickets at each sales point. This scientific and comprehensive management method can accurately plan distribution arrangements in advance, effectively coordinate inventory and distribution resources, and avoid lottery delivery delays caused by unreasonable planning, insufficient inventory or chaotic distribution arrangements, so that lottery tickets can be sold normally. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0042] Figure 1 This is a flowchart of a lottery logistics management method in one embodiment of the present application;
[0043] Figure 2 A schematic diagram of the functional modules of a lottery logistics management device provided in one embodiment of the present application;
[0044] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0045] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0046] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0047] The application scenarios of the embodiments of the present application may include the following: servers, workstations, printers, RFID tags, and PDAs (Personal Digital Assistants). Specifically:
[0048] Server: As the data processing center of the entire system, it is responsible for storing, processing and analyzing data from various workstations and sensors, as well as making reasonable planning routes for logistics delivery.
[0049] Workstation: For operators to use, input instructions, query data through the interface, and exchange data with the server.
[0050] Printer: used to print paper documents such as inventory reports and delivery orders, and is connected to the workstation via a data cable.
[0051] RFID tag: Attached to the goods, it stores the unique identification, quantity, location and other information of the goods, and communicates with the RFID reader through radio frequency lines.
[0052] PDA: For operators to use to scan barcodes or RFID tags on goods, transmit data to inventory management terminals, and view real-time route planning.
[0053] The structures and their functions:
[0054] Server: It is the core of the entire system, responsible for data storage, processing and analysis, and providing data support for other devices.
[0055] Workstation: It is the interface for operators to interact with the system, used for inputting instructions, querying data, etc.
[0056] Printer: Outputs the data generated by the system in paper form for operators to view and record.
[0057] RFID tags: enable automatic identification and information collection of goods, improving the accuracy and efficiency of inventory management.
[0058] PDA: Used for inventory counting and cargo tracking, improving the accuracy and flexibility of inventory management. Real-time view of route planning during delivery.
[0059] In an exemplary embodiment, Figure 1 As shown, a lottery logistics management method is provided, which is executed by a computer device. Specifically, it can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In an embodiment of the present application, the method includes the following steps 101 to 104.
[0060] in:
[0061] Step 101: Collect lottery sales data, lottery inventory data, delivery demand information, and delivery vehicle load.
[0062] In this embodiment of the present application, step 101 may be performed by a workstation at lottery points of sale, warehouses, and logistics nodes, collecting lottery sales data, inventory information, and delivery requirements in real time and inputting them into the server. Each point of sale, warehouse, and logistics node uses dedicated software or an API to upload relevant data to the server, ensuring data accuracy and timeliness.
[0063] In the embodiment of the present application, the distribution demand information includes target distribution demands of multiple lottery sales points; the lottery inventory data includes lottery inventory sub-data corresponding to various lottery types.
[0064] Step 102: Input the lottery sales data into a pre-built sales forecasting model to obtain a predicted number of lottery sales within a preset time period.
[0065] In this embodiment of the present application, the starting time of the preset time period is the current time, the duration of the preset time period is a preset duration, and the lottery sales forecast includes sales forecast sub-quantities corresponding to each lottery type. Based on the collected data, lottery inventory is monitored and analyzed in real time to predict future inventory demand. Advanced algorithms are used to learn from historical sales data to predict sales of each lottery type over a period of time, thereby determining an appropriate inventory level. The impact of factors such as holidays and promotional activities on inventory is also considered.
[0066] In the embodiments of the present application, the pre-built sales forecast model can be constructed using a time series forecasting model (such as ARIMA, LSTM) or a regression model (such as linear regression, random forest regression). The pre-processed data and extracted features can be input into the model for training. During the training process, the model parameters are adjusted through methods such as cross-validation to improve the prediction accuracy.
[0067] Use the trained model to predict sales of various lottery products over the next period of time. Based on the forecast results, combined with factors such as inventory costs and sales demand, determine the optimal inventory level. Monitor inventory status in real time and automatically trigger replenishment or allocation instructions when inventory approaches critical levels.
[0068] Time series analysis: Use time series forecasting models such as ARIMA (Autoregressive Integrated Moving Average) model or LSTM (Long Short-Term Memory Network) to learn from historical sales data.
[0069] The ARIMA model builds a forecasting model by identifying the autoregressive, differencing, and moving average components in a time series.
[0070] The LSTM model uses the memory capacity of neural networks to capture long-term dependencies in time series.
[0071] Feature Engineering: Quantify factors like holidays and promotions. For example, set holidays as dummy variables and quantify the impact of promotions as specific values. Use these quantified features as part of the model input to improve prediction accuracy.
[0072] Model prediction: Input the preprocessed data and extracted features into the trained model to predict sales. For ARIMA models, the predicted value can be obtained by solving the model parameters. For LSTM models, the prediction result is obtained through the forward propagation algorithm.
[0073] The accuracy of the prediction results can also be evaluated using indicators such as mean square error (MSE) and root mean square error (RMSE). Based on the evaluation results, the model can be adjusted and optimized to improve the prediction performance.
[0074] Among them, the prediction formula of the ARIMA (p, d, q) model is:
[0075]
[0076] in, and is the model parameter, y t-i is the historical sales data, ∈ t-i is the residual term.
[0077] The prediction process of the LSTM model is relatively complex, involving the forward propagation and backpropagation algorithms of the neural network. The prediction results are usually obtained through the output layer of the neural network and can be expressed as:
[0078]
[0079] Among them, f() is the activation function, W h and W x is the weight matrix, h t-i is the hidden state of the previous moment, x t is the input feature at the current moment, and b is the bias term.
[0080] As an optional implementation, step 102 inputs the lottery sales data into a pre-built sales forecast model to obtain the predicted number of lottery sales within a preset time period. The method may include:
[0081] Preprocessing the lottery sales data input into the pre-built sales forecasting model to obtain standardized lottery sales data;
[0082] performing feature extraction on the standardized lottery sales data to obtain lottery sales features;
[0083] The lottery sales characteristics are analyzed using the sales forecast model to obtain a predicted number of lottery sales within a preset time period.
[0084] This implementation method preprocesses the lottery sales data input into the sales forecasting model to generate standardized data. This eliminates interference caused by differences in data dimensions and formats, making the data more consistent and comparable, and laying a solid foundation for subsequent analysis. Feature extraction is performed on the standardized data to accurately capture key characteristics of lottery sales, effectively filtering out irrelevant or redundant information, and improving data quality and usability. Finally, the sales forecasting model analyzes the extracted lottery sales characteristics, more accurately and deeply exploring the inherent patterns and trends in the data, thereby deriving a more accurate and reliable lottery sales forecast for a preset time period. This series of operations ensures the accuracy of sales forecasts, providing a reliable basis for subsequent inventory management and logistics planning. It effectively avoids inventory backlogs or stockouts caused by forecast deviations, thereby preventing lottery delivery delays, ensuring normal lottery sales, and improving the efficiency and accuracy of the entire lottery logistics management.
[0085] Specifically, the method of preprocessing the lottery sales data input into the pre-built sales forecasting model to obtain standardized lottery sales data may include:
[0086] performing data cleaning on the lottery sales data input into the pre-built sales forecasting model to obtain lottery sales data to be processed;
[0087] The lottery sales data to be processed is normalized to obtain standardized lottery sales data.
[0088] Among them, implementing this implementation method and performing data cleaning on the lottery sales data input into the sales forecasting model can effectively eliminate abnormal information such as errors, duplications, and omissions in the original data, ensuring that the data used for subsequent analysis is complete and accurate, avoiding deviations in the forecast results due to interference from dirty data, and providing a high-quality data foundation for accurate forecasting. The data to be processed is then normalized, and data of different dimensions and value ranges are uniformly converted to a specific interval, eliminating the impact caused by differences in data scales. This makes different features equally important in model analysis, helping the sales forecasting model to learn data features and patterns more efficiently and stably. Obtaining standardized lottery sales data through these two steps of preprocessing can significantly improve the performance and accuracy of the sales forecasting model, thereby providing a reliable basis for inventory management and logistics planning, effectively avoiding inventory imbalances and delivery delays caused by inaccurate forecasts, ensuring normal lottery sales, and improving the overall efficiency and benefits of lottery logistics management.
[0089] In the embodiment of the present application, data cleaning of lottery sales data may specifically include removing outliers, missing values, etc., so as to ensure data quality.
[0090] The standardization of the lottery sales data to be processed may specifically include smoothing and normalizing the time series, etc., to facilitate subsequent analysis.
[0091] In addition, the method of extracting features from the standardized lottery sales data to obtain lottery sales features may include:
[0092] Extracting features from the standardized lottery sales data to obtain initial features; wherein the initial features include an initial feature set of multiple lottery sales data; wherein the initial feature set includes a time feature, a lottery type feature, a holiday feature, and a promotional activity feature of a lottery sales data;
[0093] The initial features are coded and converted to obtain lottery sales features of numerical value type corresponding to the initial features.
[0094] This implementation extracts initial features from standardized lottery sales data, encompassing multiple dimensions such as time, lottery type, holidays, and promotional events. This allows for a comprehensive and detailed characterization of the complex patterns and influencing factors of lottery sales. Temporal features help capture temporal patterns in sales, such as fluctuations in sales across time periods and seasons; lottery type features distinguish the sales characteristics of different lottery types; and holiday and promotional event features account for the stimulating effects of external factors on sales. Encoding these rich initial features into numerical data allows machine learning algorithms to directly process and analyze them, fully exploring the underlying correlations and trends within the data. Based on these precisely extracted and converted features, sales forecasting models can generate more accurate and reliable forecasts, providing strong support for inventory management and logistics planning. This effectively avoids inventory overstocks or stockouts caused by forecasting bias, ensures timely delivery of lottery tickets to all points of sale, and safeguards sales, ultimately improving the scientific and efficient nature of lottery logistics management.
[0095] In the embodiments of this application, after extracting key features, these features can be encoded and converted so that they can be processed by machine learning algorithms. Since machine learning algorithms can usually only process numerical data, we need to convert non-numerical features (such as text, categories, etc.) into numerical form. Common encoding and conversion methods include:
[0096] One-Hot Encoding: Used to process categorical features, such as lottery types and holidays. It creates a new binary feature (0 or 1) for each category to indicate whether the category exists.
[0097] Label Encoding: Converts categorical features into integer labels. This method works well for ordered categories, but may introduce unnecessary order relationships in unordered categories.
[0098] Time feature extraction: For sales time features, finer-grained time features such as year, month, day, hour, and day of the week can be extracted to better capture time-related sales patterns.
[0099] Standardization and normalization: Standardize or normalize numerical features (such as sales volume) to eliminate dimensional differences and improve the convergence speed and performance of the model.
[0100] Handling missing values: For missing values in the data, you can use methods such as interpolation, deletion, or use model prediction to ensure the completeness and accuracy of the data.
[0101] By properly encoding and transforming these key features, we can construct a dataset suitable for processing by machine learning algorithms, and then train a model that can accurately predict sales trends and demand, providing strong support for the optimization of lottery logistics management systems.
[0102] Step 103: Determine lottery target inventory data and lottery supplementary inventory data based on the lottery sales forecast quantity and the lottery inventory data.
[0103] As an optional implementation, step 103 may include determining the target lottery inventory data and the lottery replenishment inventory data based on the lottery sales forecast quantity and the lottery inventory data:
[0104] Get the maximum lottery capacity of the lottery warehouse;
[0105] Get the storage cost of various lottery types;
[0106] Determining target lottery inventory data based on the lottery sales forecast, the lottery inventory data, the maximum lottery capacity, and the storage cost; wherein the target lottery inventory data includes target lottery inventory sub-data corresponding to each lottery type; the lottery storage cost corresponding to the target lottery inventory data within the preset time period is minimal; the target lottery inventory sub-data corresponding to any lottery type is greater than or equal to the lottery inventory sub-data corresponding to any lottery type; the target lottery inventory sub-data corresponding to any lottery type is less than or equal to the sales forecast sub-quantity corresponding to any lottery type; and the target lottery inventory data is equal to the maximum lottery capacity;
[0107] Lottery replenishment inventory data is determined based on the lottery target inventory data and the lottery inventory data; wherein the lottery replenishment inventory data includes lottery replenishment inventory sub-data corresponding to various lottery types.
[0108] This implementation method, which scientifically considers multiple factors, determines target inventory data by determining the maximum lottery warehouse capacity and the storage costs of various lottery types, combining lottery sales forecasts with existing lottery inventory data. Minimizing lottery storage costs within a preset time period helps reduce economic expenditures in lottery warehousing and improve the economic benefits of lottery operations. Furthermore, it ensures that the target inventory sub-data for each lottery type is neither less than the current inventory sub-data (to avoid stockouts impacting sales) nor greater than the sales forecast sub-data (to prevent excessive inventory and resource occupancy), and that the total target inventory data is equal to the warehouse's maximum capacity. This fully utilizes warehouse space and avoids space waste or management challenges caused by overstocking. Further calculation of replenishment inventory data based on the determined target inventory data accurately defines the replenishment quantities for each lottery type, making inventory replenishment more targeted and planned. This series of operations effectively coordinates inventory costs, sales demand, and warehouse capacity, avoiding delivery delays caused by inappropriate inventory, ensuring timely and sufficient lottery supply to various points of sale, and ensuring normal lottery sales. This improves the refinement of lottery logistics management and overall operational efficiency.
[0109] Step 104 : Input the lottery target inventory data, the distribution demand information, and the distribution vehicle load into a pre-built logistics planning model to obtain logistics planning information corresponding to the target distribution demand of each lottery sales point.
[0110] In the embodiment of the present application, the logistics planning information includes at least the type of delivery lottery tickets, the quantity of delivery lottery tickets, the delivery vehicle, and the delivery time corresponding to each lottery sales point.
[0111] In this embodiment, the optimal delivery route is automatically planned based on the predicted lottery sales volume and lottery inventory data, reducing delivery time and costs. A route optimization algorithm is used to automatically generate the optimal delivery plan, taking into account factors such as traffic conditions, delivery vehicle load, and delivery time windows. Furthermore, the system supports real-time updates of delivery information to address emergencies.
[0112] Implementation of path optimization algorithm:
[0113] Traffic condition data: obtain real-time map information through third-party data providers;
[0114] Delivery vehicle load information: the current load, maximum load, and number of lottery tickets that can be delivered for each delivery vehicle;
[0115] Delivery time window requirements: Specific delivery time requirements for each sales point or warehouse, including the earliest and latest acceptable delivery time periods;
[0116] Multi-objective optimization: The algorithm comprehensively considers multiple objectives, such as minimizing delivery time, minimizing delivery costs (including fuel costs, labor costs, etc.), maximizing customer satisfaction (meeting time window requirements), etc.
[0117] Constraints: In addition to the aforementioned load and time window constraints, these may also include vehicle quantity restrictions, driver work hour restrictions, etc.
[0118] Solution: An ant colony algorithm is used to determine the optimal delivery plan, including detailed information such as each vehicle's delivery route, delivery sequence, and estimated arrival time. The optimal plan's delivery cost and time are quantitatively evaluated for comparison with alternative plans.
[0119] As an optional implementation, step 104 inputs the lottery target inventory data, the delivery demand information, and the delivery vehicle load into a pre-built logistics planning model. A method for obtaining logistics planning information corresponding to the target delivery demand of each lottery sales point may include:
[0120] Obtain map data containing each lottery sales point and business hours;
[0121] Obtaining traffic status data corresponding to the map data;
[0122] The map data, the business hours, the traffic status data, the lottery target inventory data, the delivery demand information and the delivery vehicle load are input into a pre-built logistics planning model to obtain logistics planning information corresponding to the target delivery demand of each lottery sales point.
[0123] This implementation method, by acquiring map data and business hours for each lottery point of sale, and further acquiring traffic status data corresponding to the map data, feeds this rich and practical information into the logistics planning model, along with lottery target inventory data, delivery demand information, and delivery vehicle loads. This significantly improves the scientific and rational nature of logistics planning. The map data provides fundamental geographic information for delivery route planning. The business hours ensure that delivery schedules align with the actual operating conditions of the point of sale, preventing lottery tickets from being delayed due to inappropriate delivery times. Traffic status data provides real-time information on dynamic conditions such as road congestion and accidents, enabling the logistics planning model to flexibly adjust delivery routes and times based on real-time traffic conditions, thereby improving delivery efficiency. By integrating this multi-dimensional information, the logistics planning model generates more accurate and realistic logistics planning information, clearly defining the type, quantity, vehicle, and time of delivery for each point of sale. This effectively avoids lottery delivery delays caused by factors such as inappropriate route planning, delivery time conflicts, or traffic congestion, ensuring that lottery tickets are delivered promptly according to point of sale needs. This effectively ensures the normal sales of lottery tickets and enhances the intelligent level of lottery logistics management and overall operational efficiency.
[0124] Implementing steps 101 to 104 above can avoid delays in lottery ticket delivery, allowing for normal lottery ticket sales. Furthermore, the present application can effectively avoid inventory backlogs or stockouts caused by forecasting bias, thereby avoiding delays in lottery ticket delivery, ensuring normal lottery ticket sales, and improving the efficiency and accuracy of the entire lottery logistics management. Furthermore, the present application can also improve the performance and accuracy of the sales forecast model, thereby providing a reliable basis for inventory management and logistics planning, effectively avoiding inventory imbalances and delivery delays caused by inaccurate forecasts, ensuring normal lottery ticket sales, and improving the overall efficiency and effectiveness of lottery logistics management. Furthermore, the present application can also effectively avoid inventory backlogs or stockouts caused by forecasting bias, ensuring that lottery tickets are delivered to various sales points in a timely manner, ensuring normal lottery ticket sales, and improving the scientific nature and efficiency of the entire lottery logistics management. Furthermore, the present application can also effectively coordinate the relationship between inventory costs, sales demand, and warehouse capacity, avoiding delivery delays caused by unreasonable inventory, ensuring that lottery tickets can be supplied to various sales points in a timely and sufficient manner, ensuring normal lottery ticket sales, and improving the refinement level of lottery logistics management and overall operational efficiency. In addition, this application can also generate more accurate and practical logistics planning information, clarify the type, quantity, vehicle and time of lottery delivery corresponding to each sales point, effectively avoid lottery delivery delays caused by unreasonable route planning, delivery time conflicts or traffic congestion, and ensure that lottery tickets can be delivered in time according to the needs of the sales point, effectively guaranteeing the normal sales of lottery tickets and improving the intelligence level of lottery logistics management and overall operational efficiency.
[0125] In addition, this application can also improve inventory accuracy: through real-time monitoring and early warning mechanisms, inventory errors can be effectively reduced and inventory accuracy can be improved. This system significantly reduces distribution costs and time by automatically planning the optimal distribution routes and times, thereby improving overall operational efficiency. At the same time, real-time monitoring of inventory status also ensures the timeliness and effectiveness of inventory management, avoiding operational interruptions or waste of resources due to insufficient or excessive inventory. This advantage mainly comes from the technological innovation of the distribution optimization module. Through advanced algorithms and data analysis, the system can automatically generate the optimal distribution plan, reducing unnecessary transportation time and costs. In addition, the real-time monitoring function of the inventory management module also ensures the timeliness and accuracy of inventory adjustments, thereby improving overall operational efficiency.
[0126] This application can also optimize delivery efficiency: automatically plan the optimal delivery route and time, reduce delivery costs and time, and improve delivery efficiency. This system effectively reduces inventory errors and improves inventory accuracy through real-time monitoring and early warning mechanisms. This high-precision inventory management helps companies better understand inventory status and avoid overstocking or out-of-stock situations. This advantage mainly comes from the technological innovation of the inventory management module. The real-time monitoring function can track inventory changes in real time to ensure the accuracy and timeliness of data. At the same time, the early warning mechanism can notify relevant personnel in advance when the inventory approaches the critical value, so that necessary adjustment measures can be taken to avoid operational risks caused by inventory errors.
[0127] This application has a high degree of data integration: it enables data exchange with other systems, improving data integration and information sharing. This system enables data exchange with other systems, improving data integration and information sharing. This high degree of data integration helps enterprises better integrate resources from all parties, optimize business processes, and improve decision-making efficiency. This advantage mainly comes from the optimized design of the data interface module. Through standardized data interfaces and protocols, the system can seamlessly connect with other systems to achieve real-time data sharing and exchange. This data integration not only improves the flexibility and scalability of the system, but also provides enterprises with more comprehensive data support, helping to optimize decision-making and business processes.
[0128] Based on the same inventive concept, embodiments of the present application also provide a lottery logistics management device for implementing the aforementioned lottery logistics management method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more lottery logistics management device embodiments provided below can be found in the above-described limitations of the lottery logistics management method and will not be further elaborated here.
[0129] In an exemplary embodiment, Figure 2 As shown, a lottery logistics management device is provided, comprising:
[0130] The collection unit 201 is configured to collect lottery sales data, lottery inventory data, delivery demand information, and delivery vehicle load; wherein the delivery demand information includes target delivery requirements for multiple lottery sales points; and the lottery inventory data includes lottery inventory sub-data corresponding to various lottery types.
[0131] A first input unit 202 is configured to input the lottery sales data into a pre-established sales forecast model to obtain a predicted number of lottery sales within a preset time period; wherein the starting time of the preset time period is the current time, the duration of the preset time period is a preset duration, and the predicted number of lottery sales includes sales forecast sub-numbers corresponding to various lottery types;
[0132] A determination unit 203 is configured to determine target lottery inventory data and supplementary lottery inventory data based on the lottery sales forecast quantity and the lottery inventory data;
[0133] The second input unit 204 is used to input the lottery target inventory data, the distribution demand information and the distribution vehicle load into a pre-built logistics planning model to obtain logistics planning information corresponding to the target distribution demand of each lottery sales point; wherein the logistics planning information at least includes the distribution lottery type, distribution lottery quantity, distribution vehicle and distribution time corresponding to each lottery sales point.
[0134] As an optional implementation, the first input unit 202 inputs the lottery sales data into a pre-built sales forecast model to obtain the predicted lottery sales quantity within a preset time period in the following manner:
[0135] Preprocessing the lottery sales data input into the pre-built sales forecasting model to obtain standardized lottery sales data;
[0136] performing feature extraction on the standardized lottery sales data to obtain lottery sales features;
[0137] The lottery sales characteristics are analyzed using the sales forecast model to obtain a predicted number of lottery sales within a preset time period.
[0138] This implementation method preprocesses the lottery sales data input into the sales forecasting model to generate standardized data. This eliminates interference caused by differences in data dimensions and formats, making the data more consistent and comparable, and laying a solid foundation for subsequent analysis. Feature extraction is performed on the standardized data to accurately capture key characteristics of lottery sales, effectively filtering out irrelevant or redundant information, and improving data quality and usability. Finally, the sales forecasting model analyzes the extracted lottery sales characteristics, more accurately and deeply exploring the inherent patterns and trends in the data, thereby deriving a more accurate and reliable lottery sales forecast for a preset time period. This series of operations ensures the accuracy of sales forecasts, providing a reliable basis for subsequent inventory management and logistics planning. It effectively avoids inventory backlogs or stockouts caused by forecast deviations, thereby preventing lottery delivery delays, ensuring normal lottery sales, and improving the efficiency and accuracy of the entire lottery logistics management.
[0139] As an optional implementation, the first input unit 202 pre-processes the lottery sales data input to the pre-built sales forecast model to obtain standardized lottery sales data in the following manner:
[0140] performing data cleaning on the lottery sales data input into the pre-built sales forecasting model to obtain lottery sales data to be processed;
[0141] The lottery sales data to be processed is normalized to obtain standardized lottery sales data.
[0142] Among them, implementing this implementation method and performing data cleaning on the lottery sales data input into the sales forecasting model can effectively eliminate abnormal information such as errors, duplications, and omissions in the original data, ensuring that the data used for subsequent analysis is complete and accurate, avoiding deviations in the forecast results due to interference from dirty data, and providing a high-quality data foundation for accurate forecasting. The data to be processed is then normalized, and data of different dimensions and value ranges are uniformly converted to a specific interval, eliminating the impact caused by differences in data scales. This makes different features equally important in model analysis, helping the sales forecasting model to learn data features and patterns more efficiently and stably. Obtaining standardized lottery sales data through these two steps of preprocessing can significantly improve the performance and accuracy of the sales forecasting model, thereby providing a reliable basis for inventory management and logistics planning, effectively avoiding inventory imbalances and delivery delays caused by inaccurate forecasts, ensuring normal lottery sales, and improving the overall efficiency and benefits of lottery logistics management.
[0143] As an optional implementation, the first input unit 202 extracts features from the standardized lottery sales data to obtain lottery sales features in the following manner:
[0144] Extracting features from the standardized lottery sales data to obtain initial features; wherein the initial features include an initial feature set of multiple lottery sales data; wherein the initial feature set includes a time feature, a lottery type feature, a holiday feature, and a promotional activity feature of a lottery sales data;
[0145] The initial features are coded and converted to obtain lottery sales features of numerical value type corresponding to the initial features.
[0146] This implementation extracts initial features from standardized lottery sales data, encompassing multiple dimensions such as time, lottery type, holidays, and promotional events. This allows for a comprehensive and detailed characterization of the complex patterns and influencing factors of lottery sales. Temporal features help capture temporal patterns in sales, such as fluctuations in sales across time periods and seasons; lottery type features distinguish the sales characteristics of different lottery types; and holiday and promotional event features account for the stimulating effects of external factors on sales. Encoding these rich initial features into numerical data allows machine learning algorithms to directly process and analyze them, fully exploring the underlying correlations and trends within the data. Based on these precisely extracted and converted features, sales forecasting models can generate more accurate and reliable forecasts, providing strong support for inventory management and logistics planning. This effectively avoids inventory overstocks or stockouts caused by forecasting bias, ensures timely delivery of lottery tickets to all points of sale, and safeguards sales, ultimately improving the scientific and efficient nature of lottery logistics management.
[0147] As an optional implementation manner, the determination unit 203 may determine the target lottery inventory data and the lottery replenishment inventory data according to the lottery sales forecast quantity and the lottery inventory data in the following manner:
[0148] Get the maximum lottery capacity of the lottery warehouse;
[0149] Get the storage cost of various lottery types;
[0150] Determining target lottery inventory data based on the lottery sales forecast, the lottery inventory data, the maximum lottery capacity, and the storage cost; wherein the target lottery inventory data includes target lottery inventory sub-data corresponding to each lottery type; the lottery storage cost corresponding to the target lottery inventory data within the preset time period is minimal; the target lottery inventory sub-data corresponding to any lottery type is greater than or equal to the lottery inventory sub-data corresponding to any lottery type; the target lottery inventory sub-data corresponding to any lottery type is less than or equal to the sales forecast sub-quantity corresponding to any lottery type; and the target lottery inventory data is equal to the maximum lottery capacity;
[0151] Lottery replenishment inventory data is determined based on the lottery target inventory data and the lottery inventory data; wherein the lottery replenishment inventory data includes lottery replenishment inventory sub-data corresponding to various lottery types.
[0152] This implementation method, which scientifically considers multiple factors, determines target inventory data by determining the maximum lottery warehouse capacity and the storage costs of various lottery types, combining lottery sales forecasts with existing lottery inventory data. Minimizing lottery storage costs within a preset time period helps reduce economic expenditures in lottery warehousing and improve the economic benefits of lottery operations. Furthermore, it ensures that the target inventory sub-data for each lottery type is neither less than the current inventory sub-data (to avoid stockouts impacting sales) nor greater than the sales forecast sub-data (to prevent excessive inventory and resource occupancy), and that the total target inventory data is equal to the warehouse's maximum capacity. This fully utilizes warehouse space and avoids space waste or management challenges caused by overstocking. Further calculation of replenishment inventory data based on the determined target inventory data accurately defines the replenishment quantities for each lottery type, making inventory replenishment more targeted and planned. This series of operations effectively coordinates inventory costs, sales demand, and warehouse capacity, avoiding delivery delays caused by inappropriate inventory, ensuring timely and sufficient lottery supply to various points of sale, and ensuring normal lottery sales. This improves the refinement of lottery logistics management and overall operational efficiency.
[0153] As an optional implementation, the second input unit 204 inputs the lottery target inventory data, the delivery demand information, and the delivery vehicle load into a pre-built logistics planning model to obtain logistics planning information corresponding to the target delivery demand of each lottery sales point in a specific manner:
[0154] Obtain map data containing each lottery sales point and business hours;
[0155] Obtaining traffic status data corresponding to the map data;
[0156] The map data, the business hours, the traffic status data, the lottery target inventory data, the delivery demand information and the delivery vehicle load are input into a pre-built logistics planning model to obtain logistics planning information corresponding to the target delivery demand of each lottery sales point.
[0157] This implementation method, by acquiring map data and business hours for each lottery point of sale, and further acquiring traffic status data corresponding to the map data, feeds this rich and practical information into the logistics planning model, along with lottery target inventory data, delivery demand information, and delivery vehicle loads. This significantly improves the scientific and rational nature of logistics planning. The map data provides fundamental geographic information for delivery route planning. The business hours ensure that delivery schedules align with the actual operating conditions of the point of sale, preventing lottery tickets from being delayed due to inappropriate delivery times. Traffic status data provides real-time information on dynamic conditions such as road congestion and accidents, enabling the logistics planning model to flexibly adjust delivery routes and times based on real-time traffic conditions, thereby improving delivery efficiency. By integrating this multi-dimensional information, the logistics planning model generates more accurate and realistic logistics planning information, clearly defining the type, quantity, vehicle, and time of delivery for each point of sale. This effectively avoids lottery delivery delays caused by factors such as inappropriate route planning, delivery time conflicts, or traffic congestion, ensuring that lottery tickets are delivered promptly according to point of sale needs. This effectively ensures the normal sales of lottery tickets and enhances the intelligent level of lottery logistics management and overall operational efficiency.
[0158] Implementing the above-mentioned embodiment can avoid situations that cause lottery ticket delivery delays, thereby ensuring that lottery tickets can be sold normally. In addition, this application can also effectively avoid inventory backlogs or stockouts caused by forecast deviations, thereby avoiding lottery ticket delivery delays, ensuring normal lottery ticket sales, and improving the efficiency and accuracy of the entire lottery logistics management. In addition, this application can also improve the performance and accuracy of the sales forecast model, thereby providing a reliable basis for inventory management and logistics planning, effectively avoiding inventory imbalances and delivery delays caused by inaccurate forecasts, ensuring normal lottery ticket sales, and improving the overall efficiency and benefits of lottery logistics management. In addition, this application can also effectively avoid inventory backlogs or stockouts caused by forecast deviations, ensuring that lottery tickets are delivered to each sales point in a timely manner, ensuring normal lottery ticket sales, and improving the scientific nature and efficiency of the entire lottery logistics management. In addition, this application can also effectively coordinate the relationship between inventory costs, sales demand, and warehouse capacity, avoiding delivery delays caused by unreasonable inventory, ensuring that lottery tickets can be supplied to each sales point in a timely and sufficient manner, ensuring normal lottery ticket sales, and improving the refinement level of lottery logistics management and overall operational efficiency. In addition, this application can also generate more accurate and practical logistics planning information, clarify the type, quantity, vehicle and time of lottery delivery corresponding to each sales point, effectively avoid lottery delivery delays caused by unreasonable route planning, delivery time conflicts or traffic congestion, and ensure that lottery tickets can be delivered in time according to the needs of the sales point, effectively guaranteeing the normal sales of lottery tickets and improving the intelligence level of lottery logistics management and overall operational efficiency.
[0159] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store lottery logistics management data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a lottery logistics management method is implemented.
[0160] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.
[0161] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.
[0162] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.
[0163] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0164] In an exemplary embodiment, a chip is provided, which includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps in the above-mentioned method embodiments and achieve the same technical effects. To avoid repetition, they are not described here.
[0165] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.
[0166] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0167] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0168] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.
[0169] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0170] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A lottery logistics management method, characterized in that: The lottery logistics management method includes: Collecting lottery sales data, lottery inventory data, delivery demand information, and delivery vehicle load; wherein the delivery demand information includes target delivery requirements for multiple lottery sales points; the lottery inventory data includes lottery inventory sub-data corresponding to various lottery types; Inputting the lottery sales data into a pre-built sales forecast model to obtain a predicted number of lottery sales within a preset time period; wherein the starting time of the preset time period is the current time, the duration of the preset time period is a preset duration, and the predicted number of lottery sales includes sales forecast sub-numbers corresponding to various lottery types; Determining lottery target inventory data and lottery replenishment inventory data based on the lottery sales forecast quantity and the lottery inventory data; The lottery target inventory data, the distribution demand information, and the distribution vehicle load are input into a pre-built logistics planning model to obtain logistics planning information corresponding to the target distribution demand of each lottery sales point; wherein the logistics planning information at least includes the distribution lottery type, distribution lottery quantity, distribution vehicle, and distribution time corresponding to each lottery sales point.
2. The lottery logistics management method according to claim 1, characterized in that: Inputting the lottery sales data into a pre-built sales forecasting model to obtain a predicted number of lottery sales within a preset time period specifically includes: Preprocessing the lottery sales data input into the pre-built sales forecasting model to obtain standardized lottery sales data; performing feature extraction on the standardized lottery sales data to obtain lottery sales features; The lottery sales characteristics are analyzed using the sales forecast model to obtain a predicted number of lottery sales within a preset time period.
3. The lottery logistics management method according to claim 2, characterized in that: The preprocessing of the lottery sales data input into the pre-built sales forecasting model to obtain standardized lottery sales data specifically includes: performing data cleaning on the lottery sales data input into the pre-built sales forecasting model to obtain lottery sales data to be processed; The lottery sales data to be processed is normalized to obtain standardized lottery sales data.
4. The lottery logistics management method according to claim 2, characterized in that: The feature extraction of the standardized lottery sales data to obtain lottery sales features specifically includes: Extracting features from the standardized lottery sales data to obtain initial features; wherein the initial features include an initial feature set of multiple lottery sales data; wherein an initial feature set includes a time feature, a lottery type feature, a holiday feature, and a promotion activity feature of a lottery sales data item; The initial features are coded and converted to obtain lottery sales features of numerical value type corresponding to the initial features.
5. The lottery logistics management method according to claim 1, characterized in that: The step of determining target lottery inventory data and supplementary lottery inventory data based on the lottery sales forecast quantity and the lottery inventory data specifically includes: Get the maximum lottery capacity of the lottery warehouse; Get the storage cost of various lottery types; Determining target lottery inventory data based on the lottery sales forecast, the lottery inventory data, the maximum lottery capacity, and the storage cost; wherein the target lottery inventory data includes target lottery inventory sub-data corresponding to each lottery type; the lottery storage cost corresponding to the target lottery inventory data within the preset time period is minimal; the target lottery inventory sub-data corresponding to any lottery type is greater than or equal to the lottery inventory sub-data corresponding to any lottery type; the target lottery inventory sub-data corresponding to any lottery type is less than or equal to the sales forecast sub-quantity corresponding to any lottery type; and the target lottery inventory data is equal to the maximum lottery capacity; Lottery replenishment inventory data is determined based on the lottery target inventory data and the lottery inventory data; wherein the lottery replenishment inventory data includes lottery replenishment inventory sub-data corresponding to various lottery types.
6. The lottery logistics management method according to claim 1, characterized in that: The lottery target inventory data, the distribution demand information, and the distribution vehicle load are input into a pre-built logistics planning model to obtain logistics planning information corresponding to the target distribution demand of each lottery sales point, specifically including: Obtain map data containing each lottery sales point and business hours; Obtaining traffic status data corresponding to the map data; The map data, the business hours, the traffic status data, the lottery target inventory data, the delivery demand information and the delivery vehicle load are input into a pre-built logistics planning model to obtain logistics planning information corresponding to the target delivery demand of each lottery sales point.
7. A lottery logistics management device, characterized in that: The lottery logistics management device includes: A collection unit is configured to collect lottery sales data, lottery inventory data, delivery demand information, and delivery vehicle load; wherein the delivery demand information includes target delivery requirements for multiple lottery sales points; and the lottery inventory data includes lottery inventory sub-data corresponding to various lottery types; a first input unit, configured to input the lottery sales data into a pre-established sales forecast model to obtain a predicted number of lottery sales within a preset time period; wherein the starting time of the preset time period is the current time, the duration of the preset time period is a preset duration, and the predicted number of lottery sales includes sales forecast sub-numbers corresponding to various lottery types; a determining unit, configured to determine target lottery inventory data and supplementary lottery inventory data based on the lottery sales forecast quantity and the lottery inventory data; The second input unit is used to input the lottery target inventory data, the distribution demand information and the distribution vehicle load into a pre-built logistics planning model to obtain logistics planning information corresponding to the target distribution demand of each lottery sales point; wherein the logistics planning information at least includes the distribution lottery type, distribution lottery quantity, distribution vehicle and distribution time corresponding to each lottery sales point.
8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the lottery logistics management method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the lottery logistics management method according to any one of claims 1 to 6 are implemented.
10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the lottery logistics management method according to any one of claims 1 to 6 are implemented.