Digital management system for group meals

Abstract

The invention relates to the technical field of data processing, in particular to a digital management system for group meals, which comprises a data acquisition module, a data integration module and a catering prediction module, wherein the data integration module is used for determining the meal selling quantity of the catering train in each meal selling time period so as to determine the meal selling high-point time period of the catering train and the corresponding expected meal selling quantity, and determining the average meal selling quantity and the meal selling fluctuation value of the train in the meal selling time period in the same time period; determining a plurality of meal selling floating time periods of the catering train and floating meal selling quantity corresponding to each meal selling floating time period; the catering prediction module determines the meal selling trend of the catering train, the meal preparation number of the catering train in the meal selling high-spot time period and the floating meal preparation number of each meal selling floating time period; the digital management system for group meals provided by the invention is helpful for improving meal selling efficiency, optimizing resource allocation, promoting digital transformation and improving customer experience.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a digital management system for group meals. Background Art

[0002] The digital management of group meals is extensive and in-depth, covering many aspects from order processing, supply chain management, inventory control, financial management to customer experience optimization; the background technology of the intelligent digital management system in the group meal industry mainly relies on the development of modern information technology, including key technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence. Through the integrated application of these technologies, it provides strong support for the intelligent management of the group meal industry and promotes the digital transformation of the group meal industry.

[0003] Chinese Patent Publication No. 113744091A discloses an intelligent digital management system for the group catering industry, which belongs to the field of digital management technology and includes an internal management system, an external management system and a data analysis system; the present invention realizes the complete, accurate and timely transmission and analysis of internal and external operational data through quantitative management of data at each internal process stage and digital collection and operation through external intelligent devices, thereby reducing labor waste, eliminating catering waste, and improving the operational efficiency of catering companies. At the same time, it can also improve customer experience and satisfaction, and can also realize raw material traceability, supervise and control food safety and hygiene, supervise and guide practitioners, continuously improve and innovate dietary products, and fully analyze and guide reasonable nutritional ratios. It can be seen that the invention has the following problems:

[0004] This invention does not consider how to adjust the meal preparation strategy for group meals on trains by analyzing historical meal sales data. Summary of the invention

[0005] To this end, the present invention provides a digital management system for group meals, so as to overcome the problem in the prior art that how to adjust the meal preparation strategy for train group meals by analyzing historical meal sales data is not considered.

[0006] To achieve the above object, the present invention provides a digital management system for group meals, comprising:

[0007] A data collection module is used to determine the schedule data of the catering train and collect the historical meal sales data of the catering train and the historical meal sales data of the trains in the same period. The schedule data includes the train number, departure time, arrival time and passing station information. The historical meal sales data includes the number of meals sold and the selling time of each meal item.

[0008] a data integration module, which is connected to the data acquisition module, and is used to determine the meal sales quantity of the catering train in each meal sales time period according to the historical meal sales data of the catering train, to determine the meal sales peak time period of the catering train and its corresponding expected meal sales quantity, to determine the average meal sales quantity and meal sales fluctuation value of the train in the same period in the meal sales time period according to the meal sales peak time period, and to determine several meal sales floating time periods of the catering train and the floating meal sales quantity corresponding to each meal sales floating time period according to the expected meal sales quantity;

[0009] a meal distribution prediction module, which is connected to the data acquisition module and the data integration module respectively, and is used to determine the meal sales trend of the meal distribution train according to the expected meal sales quantity and the average meal sales quantity, and determine the meal preparation quantity of the meal distribution train in the meal sales peak time period in combination with the meal sales trend and the meal sales fluctuation value, and determine the floating meal preparation quantity in a single meal sales floating time period according to the floating meal sales quantity in the meal sales peak time period and the expected meal sales quantity and the prepared meal quantity.

[0010] Furthermore, the data collection module determines the running time of the catering train according to the departure time and arrival time, and determines the trains in the same period according to the running time.

[0011] Furthermore, the data acquisition module determines that the overlap between the running time of the train in the same period and the running time of the catering train is greater than or equal to a preset overlap.

[0012] Furthermore, the data integration module determines a number of pre-sale meal time periods according to the number of passing stations, and determines whether the pre-sale meal time period is a meal selling time period according to the time period running time of a single pre-sale meal time period, including:

[0013] If the running time of a single pre-sale meal time period is less than or equal to the preset running time period, the pre-sale meal time period is determined to be a meal selling time period;

[0014] If the time period running time of a single pre-sale meal time period is greater than the preset running time period, it is determined that the pre-sale meal time period is not a meal selling time period, and the data integration module determines a number of meal selling time periods of the pre-sale meal time period according to the ratio of the time period running time period to the preset running time period;

[0015] Among them, the difference between the number of pre-sale meal time periods and the number of passing stations is 1.

[0016] Furthermore, the data integration module determines the quantity of meals sold in each time period according to the expected value of the historical meal distribution data of the meal distribution train in a single meal sales time period;

[0017] The data integration module determines the time period with the largest number of meals sold as the peak time period for meal sales, and the data integration module determines the number of meals sold in the peak time period for meal sales as the expected number of meals sold.

[0018] Furthermore, the data integration module determines the average meal sales quantity based on the average value of the historical meal sales data of the trains in the same period during the peak meal sales time period, and determines the meal sales fluctuation value based on the difference between the maximum meal sales quantity and the minimum meal sales quantity during the peak meal sales time period of the trains in the same period.

[0019] Furthermore, the data integration module determines the meal selling floating time period according to the expected meal selling quantity of the meal distribution train and the meal selling quantity in each meal selling time period, including:

[0020] If the ratio of the number of meals sold in a single meal selling time period to the expected number of meals sold is greater than or equal to the preset number ratio, the meal selling time period is determined to be a meal selling floating time period;

[0021] If the ratio of the number of meals sold in a single meal selling time period to the expected number of meals sold is less than the preset ratio, it is determined that the meal selling time period is not a meal selling floating time period;

[0022] The data integration module determines the meal sales quantity during the meal sales floating time period as the floating meal sales quantity corresponding to the meal sales floating time period.

[0023] Furthermore, the meal distribution prediction module determines the meal sales trend of the meal distribution train according to the expected meal sales quantity and the average meal sales quantity, including:

[0024] If the ratio of the expected meal sales quantity to the average meal sales quantity is within a preset range, it is determined that the meal sales trend of the catering train is a normal meal sales trend;

[0025] If the ratio of the expected meal sales quantity to the average meal sales quantity is not within a preset range, the meal sales trend of the catering train is determined to be an abnormal meal sales trend;

[0026] Wherein, the preset range is determined according to the meal sales fluctuation value.

[0027] Furthermore, the meal distribution prediction module determines the number of meals to be prepared by the meal distribution train during the meal sales peak period by combining the meal sales trend with the meal sales fluctuation value, including:

[0028] If the meal sales trend is a normal meal sales trend, the meal preparation quantity during the peak meal sales period is the expected meal sales quantity;

[0029] If the meal sales trend is an abnormal meal sales trend, the meal preparation quantity during the peak meal sales period is determined based on the expected meal sales quantity and the average meal sales quantity, where:

[0030] If the meal sales trend is an abnormal meal sales trend and the expected meal sales quantity is less than the average meal sales quantity, the meal preparation quantity during the peak meal sales period shall be the expected meal sales quantity;

[0031] If the meal sales trend is an abnormal meal sales trend and the expected meal sales quantity is greater than the average meal sales quantity, the meal preparation quantity during the meal sales peak period is the average meal sales quantity.

[0032] Further, the meal distribution prediction module determines the floating meal preparation quantity in the meal sales floating time period according to the floating meal sales quantity in the single meal sales floating time period, the expected meal sales quantity in the meal sales peak time period, and the meal preparation quantity;

[0033] Therein, the floating preparation quantity is positively correlated with the preparation quantity, and the floating preparation quantity is positively correlated with the ratio of the floating sale quantity to the expected sale quantity.

[0034] Compared with the prior art, the beneficial effect of the present invention is that the digital management system for group meals provided by the present invention helps to improve meal sales efficiency, optimize resource allocation, promote digital transformation and enhance customer experience.

[0035] Furthermore, the data collection module ensures that the running time of the selected train in the same period is highly consistent with that of the catering train by setting a preset overlap. This highly consistent running time similarity significantly improves the reference value of the historical meal sales data of the train in the same period, and thus achieves higher accuracy in predicting the meal sales trend of the catering train. It not only optimizes resource allocation and reduces operational risks, but also improves customer satisfaction. It is an important part of improving efficiency and accuracy in the digital management of group meals.

[0036] Furthermore, the data integration module determines the number of pre-sale meal time periods by intelligently analyzing the number of route stations, and divides the meal sale time periods by comparing the running time of each time period within the pre-sale meal time period with the preset running time. This not only ensures that the meals are sold within the best tasting time and avoids the quality degradation and secondary heating requirements caused by long-term storage, but also improves the analysis accuracy and feature recognition capabilities of meal sale data by reasonably setting the number of meal sale time periods. The optimized selection of preset running time balances the freshness of meals and the efficiency of meal sale data analysis, bringing higher flexibility and accuracy to group meal management, and significantly improving passengers' dining experience and overall operational efficiency.

[0037] Furthermore, the data integration module can accurately identify the peak meal sales time period and determine the expected meal sales quantity by deeply analyzing the historical meal distribution data of the meal distribution train in a single meal sales time period, and calculate the average meal sales quantity and meal sales fluctuation value by combining the historical meal sales data of the train in the same time period; this process not only strengthens the reliability of meal sales trend forecasting, but also provides strong support for formulating more accurate meal preparation plans by quantifying the range of meal sales fluctuations. The reduction in meal sales fluctuation value directly reflects the enhanced representativeness of the average meal sales quantity, thereby further improving the accuracy of meal sales trend forecasting, which helps to optimize resource allocation, reduce waste, and improve customer satisfaction and overall operational efficiency.

[0038] Furthermore, the meal distribution prediction module calculates the ratio of the expected number of meals sold to the average number of meals sold, and dynamically adjusts the preset range in combination with the meal sales fluctuation value, thereby achieving accurate judgment of the meal sales trend of the meal distribution train. This module ensures the accuracy and reliability of distinguishing normal meal sales trends from abnormal meal sales trends by reasonably setting the positive correlation between the preset range and the meal sales fluctuation value; in particular, by limiting the reasonable range of the deviation value (such as 0.05 to 0.3), it avoids the distortion of trend judgment caused by excessive preset range, thereby ensuring the scientificity and rationality of the number of prepared meals, reducing resource waste, and improving customer satisfaction and operational efficiency. This innovative prediction mechanism provides strong data support for group meal management. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a connection diagram of a digital management system for group meals according to an embodiment of the present invention;

[0040] Figure 2 This is a workflow diagram of a digital management system for group meals according to an embodiment of the present invention;

[0041] Figure 3 This is a relationship diagram between the pre-sale meal time period and the passing stations according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0043] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0044] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0045] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] See also Figure 1 and Figure 2 As shown, they are respectively a connection diagram of a digital management system for group meals according to an embodiment of the present invention and a workflow diagram of a digital management system for group meals according to an embodiment of the present invention. An embodiment of the present invention provides a digital management system for group meals, including:

[0047] A data collection module is used to determine the schedule data of the catering train and collect the historical meal sales data of the catering train and the historical meal sales data of the trains in the same period. The schedule data includes the train number, departure time, arrival time and passing station information. The historical meal sales data includes the number of meals sold and the selling time of each meal item.

[0048] a data integration module, which is connected to the data acquisition module, and is used to determine the meal sales quantity of the catering train in each meal sales time period according to the historical meal sales data of the catering train, to determine the meal sales peak time period of the catering train and its corresponding expected meal sales quantity, to determine the average meal sales quantity and meal sales fluctuation value of the train in the same period in the meal sales time period according to the meal sales peak time period, and to determine several meal sales floating time periods of the catering train and the floating meal sales quantity corresponding to each meal sales floating time period according to the expected meal sales quantity;

[0049] a meal distribution prediction module, which is connected to the data acquisition module and the data integration module respectively, and is used to determine the meal sales trend of the meal distribution train according to the expected meal sales quantity and the average meal sales quantity, and determine the meal preparation quantity of the meal distribution train in the meal sales peak time period in combination with the meal sales trend and the meal sales fluctuation value, and determine the floating meal preparation quantity in a single meal sales floating time period according to the floating meal sales quantity in the meal sales peak time period and the expected meal sales quantity and the prepared meal quantity.

[0050] It can be understood that the digital management system for group meals provided by the present invention is helpful to improve meal sales efficiency, optimize resource allocation, promote digital transformation and enhance customer experience: by accurately collecting historical meal sales data of catering trains and trains in the same period through the data acquisition module, the system can accurately identify the peak meal sales time periods and their corresponding meal sales demands, thereby ensuring that sufficient meals are provided during peak periods, avoiding waste caused by insufficient supply or excessive preparation, and significantly improving meal sales efficiency and customer satisfaction; the data integration module not only determines the peak meal sales time periods and the expected meal sales quantity based on intelligent analysis of historical data, but also takes into account the average meal sales of trains in the same period. The system can predict meal demand more scientifically and adjust meal distribution plans accordingly, which effectively reduces mismatches caused by human errors and optimizes allocation of food, manpower and other resources. The meal distribution forecast module combines multi-dimensional data to generate meal sales trend forecasts and meal preparation plans, providing strong data support for management, making meal preparation decisions more scientific and reasonable, helping to reduce operating costs and improve overall operating efficiency. Through accurate prediction and flexible adjustment, the system ensures that better-tasting meal options can be provided during train operation, meeting the diverse needs of passengers and improving customer experience and satisfaction.

[0051] Specifically, the data acquisition module determines the running time of the catering train according to its departure time and arrival time, and determines the trains in the same period according to the running time.

[0052] Specifically, the data acquisition module determines that the overlap between the running time of the train in the same period and the running time of the catering train is greater than or equal to a preset overlap.

[0053] It can be understood that the greater the preset overlap, the more similar the running time of the train in the same period is to the catering train, and the higher the reference value of the historical meal sales data of the train in the same period is, that is, the more accurate it is in determining the meal sales trend of the catering train. Generally, the preset overlap ∈ [80%, 100%], preferably, the preset overlap = 90%.

[0054] It is understandable that the data collection module ensures that the running time of the selected train in the same period is highly consistent with that of the catering train by setting a preset overlap. This highly consistent running time similarity significantly improves the reference value of the historical meal sales data of the train in the same period, and thus achieves higher accuracy in predicting the meal sales trend of the catering train. It not only optimizes resource allocation and reduces operational risks, but also improves customer satisfaction. It is an important part of improving efficiency and accuracy in the digital management of group meals.

[0055] like Figure 3Specifically, the data integration module determines a number of pre-sale meal time periods according to the number of passing stations, and determines whether the pre-sale meal time period is a meal selling time period according to the time period running time of each pre-sale meal time period, including:

[0056] If the running time of a single pre-sale meal time period is less than or equal to the preset running time period, the pre-sale meal time period is determined to be a meal selling time period;

[0057] If the time period running time of a single pre-sale meal time period is greater than the preset running time period, it is determined that the pre-sale meal time period is not a meal selling time period, and the data integration module determines a number of meal selling time periods of the pre-sale meal time period according to the ratio of the time period running time period to the preset running time period; in implementation, the number of meal selling time periods = time period running time ÷ preset running time period + 1 (the calculation result of the number of meal selling time periods is determined based on “rounding down”).

[0058] Among them, the difference between the number of pre-sale meal time periods and the number of passing stations is 1, that is, the number of pre-sale meal time periods = the number of passing stations + 1.

[0059] In implementation, the running time of a time period is the running time between two adjacent stations, e.g. Figure 2 As shown, the catering train has three passing stations, that is, the catering train has 4 pre-sale meal time periods, including pre-sale meal time period 1, pre-sale meal time period 2, pre-sale meal time period 3 and pre-sale meal time period 4;

[0060] It is understandable that the meals should be heated and prepared before the start of each meal selling time period so that the meals can be sold out within the preset running time, avoiding secondary heating and enhancing the taste. Therefore, the preset running time cannot be too long. At the same time, the longer the preset running time is, the fewer the number of meal selling time periods will be determined, which will reduce the analytical characteristics of the number of meals sold in each meal selling time period. Generally, the preset running time ∈ [20min, 45min], preferably the preset running time = 30min.

[0061] It is understandable that the data integration module determines the number of pre-sale meal time periods by intelligently analyzing the number of route stations, and divides the meal sale time periods by comparing the running time of each time period within the pre-sale meal time period with the preset running time. This not only ensures that the meals are sold within the best tasting time and avoids the quality degradation and secondary heating requirements caused by long-term storage, but also improves the analysis accuracy and feature recognition capabilities of meal sale data by reasonably setting the number of meal sale time periods. The optimized selection of preset running time balances the freshness of meals and the efficiency of meal sale data analysis, bringing higher flexibility and accuracy to group meal management, and significantly improving passengers' dining experience and overall operational efficiency.

[0062] Specifically, the data integration module determines the number of meals sold in each time period based on the expected value of the historical meal distribution data of the meal distribution train in a single meal distribution time period;

[0063] The data integration module determines the time period with the largest number of meals sold as the peak time period for meal sales, and the data integration module determines the number of meals sold in the peak time period for meal sales as the expected number of meals sold.

[0064] It is understandable that the method of calculating the expected value is a prior art, so it will not be described in detail.

[0065] Specifically, the data integration module determines the average meal sales quantity based on the average value of the historical meal sales data of the same period of trains during the peak meal sales time period, and determines the meal sales fluctuation value based on the difference between the maximum meal sales quantity and the minimum meal sales quantity of the same period of trains during the peak meal sales time period.

[0066] It is understandable that the method of finding the average value is an existing technology, so it will not be described in detail. In implementation, the difference between the maximum number of meals sold and the minimum number of meals sold is determined as the meal sales fluctuation value, which can reflect whether the meal sales fluctuation range during the peak meal sales period is large or not. The smaller the meal sales fluctuation value, the more representative the average meal sales number is, and the more accurate it is in determining the meal sales trend.

[0067] It is understandable that the data integration module can accurately identify the peak time period and determine the expected number of meals sold by deeply analyzing the historical meal distribution data of the meal distribution train in a single meal sales period, and at the same time, combine the historical meal sales data of the train in the same period to calculate the average number of meals sold and the meal sales fluctuation value; this process not only strengthens the reliability of the meal sales trend forecast, but also provides strong support for the formulation of a more accurate meal preparation plan by quantifying the range of meal sales fluctuations. The reduction in meal sales fluctuations directly reflects the enhanced representativeness of the average number of meals sold, thereby further improving the accuracy of meal sales trend forecasts, helping to optimize resource allocation, reduce waste, and improve customer satisfaction and overall operational efficiency.

[0068] Specifically, the data integration module determines the meal selling floating time period according to the expected meal selling quantity of the meal distribution train and the meal selling quantity in each meal selling time period, including:

[0069] If the ratio of the number of meals sold in a single meal selling time period to the expected number of meals sold is greater than or equal to the preset number ratio, the meal selling time period is determined to be a meal selling floating time period;

[0070] If the ratio of the number of meals sold in a single meal selling time period to the expected number of meals sold is less than the preset ratio, it is determined that the meal selling time period is not a meal selling floating time period;

[0071] The data integration module determines the meal sales quantity during the meal sales floating time period as the floating meal sales quantity corresponding to the meal sales floating time period.

[0072] In implementation, the preset quantity ratio ∈[0.2,0.5]. The smaller the preset quantity ratio is, the more floating meal selling time periods there are (meal preparation is required before each floating meal selling time period), and the more time points when meal preparation is required, which may result in a small amount of prepared meals in a certain time period and unsold meals, resulting in waste. In addition, if the prepared meals are not sold, they need to be reheated and resold, which will result in poor taste and reduced safety of the food that has been heated multiple times. Therefore, for time periods less than the preset quantity ratio, it is chosen not to pre-heat and prepare meals, and temporary meal preparation is sufficient when the passenger purchases them (the number of meals sold in this time period determined based on historical meal sales data is small, which can meet temporary meal sales requirements).

[0073] Specifically, the meal distribution prediction module determines the meal sales trend of the meal distribution train according to the expected meal sales quantity and the average meal sales quantity, including:

[0074] If the ratio of the expected number of meals sold to the average number of meals sold is within a preset range, it is determined that the meal sales trend of the catering train is a normal meal sales trend, that is, the meal sales trend of the catering train is the same as that of other trains in the same period, which is in line with the meal sales trend;

[0075] If the ratio of the expected number of meals sold to the average number of meals sold is not within the preset range, then the meal sales trend of the catering train is determined to be an abnormal meal sales trend, that is, the meal sales trend of the catering train is different from that of other trains in the same period and does not conform to the meal sales trend; it can be understood that the ratio of the expected number of meals sold to the average number of meals sold is not within the preset range, including two possibilities: (1) the ratio of the expected number of meals sold to the average number of meals sold is less than the minimum value of the preset range; (2) the ratio of the expected number of meals sold to the average number of meals sold is greater than the maximum value of the preset range;

[0076] Wherein, the preset range is determined according to the meal sales fluctuation value.

[0077] In practice, the preset range is positively correlated with the meal sales fluctuation value, that is, the larger the meal sales fluctuation value, the larger the preset range; in practice, the preset range ∈ [1-deviation value, 1+deviation value], deviation value = meal sales fluctuation value ÷ maximum meal sales quantity, deviation value ∈ [0.05, 0.3]; preferably, deviation value = 0.2, preset range [0.8, 1.2], if the calculated result of the deviation value is greater than 0.3, then 0.3 is taken; if the calculated result of the deviation value is less than 0.05, then 0.05 is taken. It is understandable that if the deviation value is too large, that is, the preset range is too large, the deviation between the meal distribution train and the train in the same period may be too large, which may lead to the judged meal sales trend being inaccurate, and thus leading to a large deviation in the number of prepared meals.

[0078] It is understandable that the meal distribution prediction module calculates the ratio of the expected number of meals sold to the average number of meals sold, and dynamically adjusts the preset range in combination with the meal sales fluctuation value, thereby achieving accurate judgment of the meal sales trend of the meal distribution train. This module ensures the accuracy and reliability of distinguishing normal meal sales trends from abnormal meal sales trends by reasonably setting the positive correlation between the preset range and the meal sales fluctuation value; in particular, by limiting the reasonable range of the deviation value (such as 0.05 to 0.3), it avoids the distortion of trend judgment caused by excessive preset range, thereby ensuring the scientificity and rationality of the number of prepared meals, reducing resource waste, and improving customer satisfaction and operational efficiency. This innovative prediction mechanism provides strong data support for group meal management.

[0079] Specifically, the meal distribution prediction module determines the number of meals to be prepared by the meal distribution train during the meal sales peak period in combination with the meal sales trend and the meal sales fluctuation value, including:

[0080] If the meal sales trend is a normal meal sales trend, the meal preparation quantity for the meal sales peak period is the expected meal sales quantity; it can be understood that when the meal sales trend is normal, meal preparation for the meal sales peak period is performed according to the expected meal sales quantity of the meal distribution train;

[0081] If the meal sales trend is an abnormal meal sales trend, the meal preparation quantity during the peak meal sales period is determined based on the expected meal sales quantity and the average meal sales quantity, where:

[0082] If the meal sales trend is an abnormal meal sales trend and the expected meal sales quantity is less than the average meal sales quantity, the meal preparation quantity during the peak meal sales period shall be the expected meal sales quantity;

[0083] If the meal sales trend is an abnormal meal sales trend and the expected meal sales quantity > the average meal sales quantity, then the meal preparation quantity during the peak meal sales period is the average meal sales quantity; it is understandable that when the meal sales trend is abnormal, a smaller quantity is prepared to avoid waste.

[0084] Specifically, the meal distribution prediction module determines the floating meal preparation quantity in the meal sales floating time period according to the floating meal sales quantity in the single meal sales floating time period, the expected meal sales quantity in the meal sales peak time period, and the meal preparation quantity;

[0085] Therein, the floating preparation quantity is positively correlated with the preparation quantity, and the floating preparation quantity is positively correlated with the ratio of the floating sale quantity to the expected sale quantity.

[0086] In implementation, the floating preparation quantity = floating sales quantity ÷ expected sales quantity × preparation quantity.

[0087] It can be understood that the meal distribution prediction module flexibly adjusts the number of prepared meals in peak meal sales time periods by comprehensively considering meal sales trends, meal sales fluctuation values, and floating meal sales quantities in each meal sales time period, thereby achieving precise meal preparation. When the meal sales trend is normal, the expected meal sales quantity is directly used as the meal preparation standard to ensure adequate meal supply. When the meal sales trend is abnormal, a conservative strategy is adopted to avoid waste of resources based on the comparison between the expected meal sales quantity and the average meal sales quantity. In addition, by calculating the floating meal preparation quantity, the meal preparation strategy is further refined, which not only meets the dining needs of passengers, but also effectively controls costs, thereby improving the overall efficiency and customer satisfaction of railway catering services.

[0088] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital management system for group meals, characterized in that: include: A data collection module is used to determine the schedule data of the catering train and collect the historical meal sales data of the catering train and the historical meal sales data of the trains in the same period. The schedule data includes the train number, departure time, arrival time and passing station information. The historical meal sales data includes the number of meals sold and the selling time of each meal item. a data integration module, which is connected to the data acquisition module, and is used to determine the meal sales quantity of the catering train in each meal sales time period according to the historical meal sales data of the catering train, to determine the meal sales peak time period of the catering train and its corresponding expected meal sales quantity, to determine the average meal sales quantity and meal sales fluctuation value of the train in the same period in the meal sales time period according to the meal sales peak time period, and to determine several meal sales floating time periods of the catering train and the floating meal sales quantity corresponding to each meal sales floating time period according to the expected meal sales quantity; a meal distribution prediction module, which is connected to the data acquisition module and the data integration module respectively, and is used to determine the meal sales trend of the meal distribution train according to the expected meal sales quantity and the average meal sales quantity, and determine the meal preparation quantity of the meal distribution train in the meal sales peak time period in combination with the meal sales trend and the meal sales fluctuation value, and determine the floating meal preparation quantity in a single meal sales floating time period according to the floating meal sales quantity in the meal sales peak time period and the expected meal sales quantity and the prepared meal quantity.

2. The digital management system for group meals according to claim 1 is characterized in that: The data acquisition module determines the running time of the catering train according to the departure time and arrival time, and determines the trains in the same period according to the running time.

3. The digital management system for group meals according to claim 2 is characterized in that: The data acquisition module determines that the overlap between the running time of the train in the same period and the running time of the catering train is greater than or equal to a preset overlap.

4. The digital management system for group meals according to claim 1, characterized in that: The data integration module determines a number of pre-sale meal time periods according to the number of passing stations, and determines whether the pre-sale meal time period is a meal selling time period according to the time period running time of a single pre-sale meal time period, including: If the running time of a single pre-sale meal time period is less than or equal to the preset running time period, the pre-sale meal time period is determined to be a meal selling time period; If the time period running time of a single pre-sale meal time period is greater than the preset running time period, it is determined that the pre-sale meal time period is not a meal selling time period, and the data integration module determines a number of meal selling time periods of the pre-sale meal time period according to the ratio of the time period running time period to the preset running time period; Among them, the difference between the number of pre-sale meal time periods and the number of passing stations is 1.

5. The digital management system for group meals according to claim 1 is characterized in that: The data integration module determines the quantity of meals sold in each time period according to the expected value of the historical meal distribution data of the meal distribution train in a single meal distribution time period; The data integration module determines the time period with the largest number of meals sold as the peak time period for meal sales, and the data integration module determines the number of meals sold in the peak time period for meal sales as the expected number of meals sold.

6. The digital management system for group meals according to claim 1, characterized in that: The data integration module determines the average meal sales quantity based on the average value of historical meal sales data of the same period of trains during the peak meal sales time period, and determines the meal sales fluctuation value based on the difference between the maximum meal sales quantity and the minimum meal sales quantity of the same period of trains during the peak meal sales time period.

7. The digital management system for group meals according to claim 1, characterized in that: The data integration module determines the meal selling floating time period according to the expected meal selling quantity of the meal distribution train and the meal selling quantity in each meal selling time period, including: If the ratio of the number of meals sold in a single meal selling time period to the expected number of meals sold is greater than or equal to the preset number ratio, the meal selling time period is determined to be a meal selling floating time period; If the ratio of the number of meals sold in a single meal selling time period to the expected number of meals sold is less than the preset ratio, it is determined that the meal selling time period is not a meal selling floating time period; The data integration module determines the meal sales quantity during the meal sales floating time period as the floating meal sales quantity corresponding to the meal sales floating time period.

8. The digital management system for group meals according to claim 1, characterized in that: The meal distribution prediction module determines the meal sales trend of the meal distribution train according to the expected meal sales quantity and the average meal sales quantity, including: If the ratio of the expected meal sales quantity to the average meal sales quantity is within a preset range, it is determined that the meal sales trend of the catering train is a normal meal sales trend; If the ratio of the expected meal sales quantity to the average meal sales quantity is not within a preset range, the meal sales trend of the catering train is determined to be an abnormal meal sales trend; Wherein, the preset range is determined according to the meal sales fluctuation value.

9. The digital management system for group meals according to claim 1, characterized in that: The meal distribution prediction module determines the number of meals to be prepared by the meal distribution train during the meal sales peak period in combination with the meal sales trend and the meal sales fluctuation value, including: If the meal sales trend is a normal meal sales trend, the meal preparation quantity during the peak meal sales period is the expected meal sales quantity; If the meal sales trend is an abnormal meal sales trend, the meal preparation quantity during the peak meal sales period is determined based on the expected meal sales quantity and the average meal sales quantity, where: If the meal sales trend is an abnormal meal sales trend and the expected meal sales quantity is less than the average meal sales quantity, the meal preparation quantity during the peak meal sales period shall be the expected meal sales quantity; If the meal sales trend is an abnormal meal sales trend and the expected meal sales quantity is greater than the average meal sales quantity, the meal preparation quantity during the meal sales peak period is the average meal sales quantity.

10. The digital management system for group meals according to claim 9, characterized in that: The meal distribution prediction module determines the floating meal preparation quantity in the meal sales floating time period according to the floating meal sales quantity in the single meal sales floating time period, the expected meal sales quantity in the meal sales peak time period, and the meal preparation quantity; Therein, the floating preparation quantity is positively correlated with the preparation quantity, and the floating preparation quantity is positively correlated with the ratio of the floating sale quantity to the expected sale quantity.

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