Meal preparation method and device for central kitchen and storage medium

Abstract

The invention discloses a meal preparation method and device for a central kitchen and a storage medium, and the method comprises the steps: obtaining first historical overtime meal data of a meal ordering person in a historical time period, the first historical overtime meal data comprises daily first overtime meal data of non-production employees in a historical time period and daily second overtime meal data of production employees in the historical time period; determining at least one overtime meal influence parameter corresponding to a future date according to the first historical overtime meal data; and according to the at least one overtime meal influence parameter, predicting overtime meal preparation data of all the meal ordering personnel in the future date. According to the scheme, multi-dimensional information can be considered, so that the overtime meal preparation data of the future date can be comprehensively and accurately predicted, a central kitchen can reasonably arrange food material purchasing, inventory management and meal making, the operation cost of an enterprise is reduced, and economic benefits are improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a meal preparation method, device, and storage medium for a central kitchen. Background Art

[0002] Currently, the employee dining mode will adopt the method of unified production and distribution by the central kitchen, and the overall positioning of the central kitchen is to operate and manage in the mode of independent operation of the company. In terms of the existing dining mode, the subsidiary company counts the number of people having overtime dinners on the same day to the central kitchen every day. The central kitchen, based on personal experience, produces and makes food with an appropriate increase on the basis of the total number of reported meals. Due to the special nature, seasonality, periodicity, etc. of the industry, the meal preparation link for workers' overtime dinners has always troubled the managers of the central kitchen. The managers of the central kitchen can only prepare relatively more ingredients every day based on past experience to ensure the supply of overtime meals for workers, resulting in a large amount of waste. Summary of the Invention

[0003] The purpose of the embodiments of this application is to provide a meal preparation method, device, and storage medium for a central kitchen, so as to solve the technical problem of meal waste in the existing central kitchen.

[0004] To achieve the above purpose, the first aspect of this application provides a meal preparation method for a central kitchen, and the method includes:

[0005] Obtain the first historical overtime meal data of the meal ordering personnel in a historical time period, where the first historical overtime meal data includes the first overtime meal data of non-production employees every day in the historical time period and the second overtime meal data of production employees every day in the historical time period;

[0006] Determine at least one overtime meal influence parameter corresponding to a future date according to the first historical overtime meal data, where the at least one overtime meal influence parameter includes at least one of an overtime meal outlier coefficient and a production scheduling influence coefficient corresponding to non-production employees, and the overtime meal ordering probability of production employees;

[0007] Predict the overtime meal preparation data of all meal ordering personnel on the future date according to the at least one overtime meal influence parameter.

[0008] In an embodiment of the present application, determining at least one overtime meal impact parameter corresponding to a future date based on first historical overtime meal data includes: determining outlier data among all first historical overtime meal data in a historical time period; determining a first historical date corresponding to the outlier data; in a case where a time interval between the first historical date and the future date is not equal to a preset time interval, determining an overtime meal outlier coefficient as a first value; in a case where the time interval is equal to the preset time interval, obtaining second historical overtime meal data of a second historical date, where the second historical date is in a historical time period earlier than the historical time period and a time interval between the second historical date and the first historical date is an integer multiple of the preset time interval; determining a first overtime meal statistical value corresponding to the first historical date based on all first historical overtime meal data; determining a second overtime meal statistical value corresponding to the first historical date based on second historical overtime meal data of all second historical dates; and determining a ratio of the second overtime meal statistical value to the first overtime meal statistical value as an overtime meal outlier coefficient corresponding to non-production employees.

[0009] In an embodiment of the present application, determining outlier data among all first historical overtime meal data in a historical time period includes: determining a first quartile, a third quartile, and an interquartile range among all first historical overtime meal data in the historical time period; determining first historical overtime meal data greater than a sum of the third quartile and the interquartile range among all first historical overtime meal data as outlier data among all first historical overtime meal data; and determining first historical overtime meal data less than a difference between the first quartile and the interquartile range among all first historical overtime meal data as outlier data among all first historical overtime meal data.

[0010] In an embodiment of the present application, determining at least one overtime meal impact parameter corresponding to a future date based on historical overtime meal data includes: obtaining a production schedule for a historical time period; in a case where the production schedule is greater than a preset production floor number, using a regression model to fit the production schedule and the first historical overtime meal data to determine a production impact coefficient.

[0011] In an embodiment of the present application, determining at least one overtime meal impact parameter corresponding to a future date based on historical overtime meal data includes: obtaining the number of overtime times of each production employee in a historical time period; determining the number of meal ordering times for which each production employee applied for overtime meals in the historical time period based on all second historical overtime meal data in the historical time period; and determining an overtime meal ordering probability for each production employee based on the number of overtime times and the number of meal ordering times of each production employee.

[0012] In an embodiment of the present application, determining the overtime meal preparation data of all meal orderers for a future date according to at least one overtime meal impact parameter includes: fitting all first historical overtime meal data in a historical time period to predict the overtime meal fitting data of non-production employees for a future date; determining the overtime meal preparation data of all meal orderers for a future date according to at least one overtime meal impact parameter and the overtime meal fitting data.

[0013] In an embodiment of the present application, it further includes: removing the outlier data in all historical overtime meal data in the historical time period to obtain updated historical overtime meal data; determining the standard deviation corresponding to the updated historical overtime meal data; obtaining the standard score corresponding to a preset confidence interval; updating the overtime meal preparation data according to the standard deviation and the standard score.

[0014] A second aspect of the present application provides a meal preparation device for a central kitchen, including:

[0015] A memory configured to store instructions;

[0016] A processor configured to call instructions from the memory and capable of implementing the meal preparation method for a central kitchen according to the above.

[0017] A third aspect of the present application provides a machine-readable storage medium, characterized in that instructions are stored on the machine-readable storage medium, and the instructions are used to cause the machine to execute the meal preparation method for a central kitchen according to the above.

[0018] A fourth aspect of the present application provides a computer program product, including a computer program, and the computer program implements the meal preparation method for a central kitchen for identifying network traffic types according to the above when executed by a processor.

[0019] Through the above technical solutions, the first historical overtime meal data of meal orderers in a historical time period is obtained. The first historical overtime meal data includes the first overtime meal data of non-production employees every day in the historical time period and the second overtime meal data of production employees every day in the historical time period; at least one overtime meal impact parameter corresponding to a future date is determined according to the first historical overtime meal data. The at least one overtime meal impact parameter includes at least one of an overtime meal outlier coefficient corresponding to non-production employees, a production impact coefficient, and the overtime meal ordering probability of production employees; the overtime meal preparation data of all meal orderers for a future date is predicted according to at least one overtime meal impact parameter. The above solutions can take into account multi-dimensional information, so as to comprehensively and accurately predict the overtime meal preparation data for a future date, facilitate the central kitchen to reasonably arrange food material procurement, inventory management, and meal preparation, reduce the operating costs of the enterprise, and improve economic benefits.

[0020] Other features and advantages of the embodiments of the present application will be described in detail in the following specific implementation section. Brief Description of the Drawings

[0021] The drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the drawings:

[0022] Figure 1 Schematically shows a flowchart of a meal preparation method for a central kitchen according to an embodiment of the present application;

[0023] Figure 2 Schematically shows a structural block diagram of a meal preparation device for a central kitchen according to an embodiment of the present application;

[0024] Figure 3 Schematically shows a structural diagram of a computer device according to an embodiment of the present application. Detailed Description of the Embodiments

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiments of the present application, and is not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0026] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present application, the directional indications are only used to explain the relative position relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0027] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0028] Figure 1 Schematically shown is a flowchart of a meal preparation method for a central kitchen according to an embodiment of the present application. As Figure 1 shown, an embodiment of the present application provides a meal preparation method for a central kitchen, and the method may include the following steps.

[0029] S102, obtaining first historical overtime meal data of meal ordering personnel in a historical time period, where the first historical overtime meal data includes first overtime meal data of non-production employees every day in the historical time period and second overtime meal data of production employees every day in the historical time period;

[0030] S104, determining at least one overtime meal influence parameter corresponding to a future date according to the first historical overtime meal data, where the at least one overtime meal influence parameter includes at least one of an overtime meal outlier coefficient and a production scheduling influence coefficient corresponding to non-production employees, and an overtime meal ordering probability of production employees;

[0031] S106, predicting overtime meal preparation data of all meal ordering personnel on the future date according to the at least one overtime meal influence parameter.

[0032] It can be understood that the historical time period can be in years, months, weeks, etc. For example, the historical time period can be the past year or the past month. The meal ordering personnel include production employees and non-production employees. Production employees include manufacturing workers, production line workers, etc. Their main responsibilities are to complete specific production tasks, such as assembly, processing, etc., and directly participate in the product manufacturing process. Non-production employees include management personnel, engineering and technical personnel, service personnel, etc., and do not directly participate in the product production process. Their responsibilities are to support the production process, such as planning, organizing, coordinating, serving, etc. The overtime meal data includes the meal ordering quantity of the meal ordering personnel during overtime. The first historical overtime meal data refers to the overtime meal data of the meal ordering personnel in the historical time period. The first overtime meal data refers to the daily overtime meal data of non-production employees in the historical time period. The second overtime meal data refers to the daily overtime meal data of production employees in the historical time period. The future date refers to a certain future date. The overtime meal impact parameter refers to the parameter that affects the prediction result when predicting the overtime meal data of a certain future date. The overtime meal impact parameter includes at least one of the overtime meal outlier coefficient corresponding to non-production employees, the production scheduling impact coefficient, and the overtime meal ordering probability of production employees. Among them, the overtime meal outlier coefficient is an impact parameter set for the object that significantly deviates from other data points in the data set. For example, September 28th of each year is the anniversary of a certain enterprise. Then, the overtime meals on the anniversary day every day will be greatly reduced. If the future date for predicting the overtime meal preparation data is the anniversary day, the overtime meal outlier coefficient should be considered to participate in the prediction. The production scheduling impact coefficient is a parameter set for the production scheduling plan of the manufacturing workshop affecting the overtime meal data. For example, in the case of a large production quantity planned in the production scheduling plan, it is a high-probability event for non-production employees to work overtime. At this time, the probability of non-production employees ordering meals during overtime will increase accordingly. The overtime meal ordering probability of production employees refers to the probability that production employees apply for meal ordering during overtime. For example, although production employees work overtime, they still choose to go home for meals. Then, it is not possible to determine the meal ordering situation only based on the overtime situation of this employee, but the overtime meal ordering probability needs to be considered. Further, based on at least one overtime meal impact parameter, predict the overtime meal preparation data of all meal ordering personnel on the future date.

[0033] Through the above technical solution, multi-dimensional information such as historical data, production plans, workers' overtime habits, holidays, and external factors can be taken into account, and the overtime meal preparation data for future dates can be predicted more comprehensively and accurately, so that the central kitchen can reasonably arrange food ingredient procurement, inventory management, and meal preparation, avoid food ingredient waste and excessive costs, and can be effectively controlled within a certain reasonable range value. This helps to reduce the operating costs of the enterprise and improve economic benefits.

[0034] In an embodiment of the present application, determining the overtime meal preparation data of all meal orderers for a future date according to at least one overtime meal influence parameter includes: fitting all first historical overtime meal data in a historical time period to predict the overtime meal fitting data of non-production employees for a future date; determining the overtime meal preparation data of all meal orderers for a future date according to at least one overtime meal influence parameter and the overtime meal fitting data.

[0035] It can be understood that the overtime meal fitting data may refer to the number of prepared overtime meals for non-production employees on a future date. Specifically, the first historical overtime meal data of non-production employees every day in the past year can be counted, and the ARIMA model (Autoregressive Integrated Moving Average Model) is used to fit all the first historical overtime meal data to predict the overtime meal fitting data every day in the next week. Further, according to at least one set of overtime meal influence parameters, weighted calculation is performed on the overtime meal fitting data, so as to determine the overtime meal preparation data of all meal orderers for a future date. Specifically, perform a d-order difference operation on all the first historical overtime meal data to convert it into a stationary time series. Further, the autocorrelation coefficient ACF and the partial autocorrelation coefficient PACF of the stationary time series are obtained respectively. Through the analysis of the autocorrelation graph and the partial autocorrelation graph, the optimal order p and order q are obtained. The ARIMA model is obtained from the above d, q, and p, and then the obtained model is subjected to model testing. After the testing is completed, the overtime meal fitting data every day in the next week is fitted according to the obtained ARIMA model.

[0036] In an embodiment of the present application, determining at least one overtime meal influence parameter corresponding to a future date according to the first historical overtime meal data includes: determining the outlier data in all the first historical overtime meal data in a historical time period; determining the first historical date corresponding to the outlier data; in the case where the time interval between the first historical date and the future date is not equal to the preset time interval, determining the overtime meal outlier coefficient as the first value; in the case where the time interval is equal to the preset time interval, obtaining the second historical overtime meal data of the second historical date, where the second historical date is located in a historical time period earlier than the historical time period and the time interval between it and the first historical date is an integer multiple of the preset time interval; determining the first overtime meal statistical value corresponding to the first historical date according to all the first historical overtime meal data; determining the second overtime meal statistical value corresponding to the first historical date according to all the second historical overtime meal data of the second historical date; and determining the ratio of the second overtime meal statistical value to the first overtime meal statistical value as the overtime meal outlier coefficient corresponding to the non-production employee.

[0037] It can be understood that outlier data refers to data with one or several values that are significantly different from other values among all the first historical overtime meal data in a historical time period. After determining the outlier data among all the first historical overtime meal data in the historical time period, the date corresponding to the outlier data is determined, which is the first historical date. When the time interval between the first historical date and the future date is not equal to the preset time interval, the overtime meal outlier coefficient is determined as the first value. The preset time interval is a time interval preset by technicians, such as one month, three months, one year, etc. The first value can be a value preset by technicians and can be 1. For example, September 28th of each year is the anniversary of a certain enterprise, then the overtime meals on the anniversary day every day will be greatly reduced. If the future date for predicting the overtime meal preparation data is not the anniversary day, it can be considered that the overtime meal preparation data for this future date is not affected by the outlier data, and the overtime meal outlier coefficient can be set to 1.

[0038] In one embodiment, after fitting all the first historical overtime meal data and predicting the overtime meal fitting data for the future time, the overtime meal preparation data for this future date can be obtained by multiplying the overtime meal fitting data by the overtime meal outlier coefficient.

[0039] If the time interval is equal to the preset time interval, it can be considered that this future date conforms to the law of outlier data. Then, the second historical overtime meal data of the second historical date can be obtained first, where the second historical date is in a historical time period earlier than the historical time period and the time interval between it and the first historical date is an integer multiple of the preset time interval. For example, when the first historical date is September 28, 2023 and the preset time interval is one year, the second historical date can be September 28, 2022, September 28, 2021, September 28, 2020, and so on. Determine the first overtime meal statistical value corresponding to the first historical date according to all the first historical overtime meal data. The first overtime meal statistical value can be the average of all the first historical overtime meal data. Determine the second overtime meal statistical value corresponding to the first historical date according to the second historical overtime meal data of all the second historical dates. The second historical overtime meal data is relative to the first historical overtime meal data, and the second historical overtime meal data refers to the overtime meal data of the second historical date. The second overtime meal statistical value can be the average of all the second historical overtime meal data. Then, the ratio of the second overtime meal statistical value to the first overtime meal statistical value can be determined as the outlier coefficient of the corresponding overtime meal for non-production employees.

[0040] In an embodiment of the present application, determining the outlier data among all the first historical overtime meal data for a historical time period includes: determining the first quartile, the third quartile, and the interquartile range among all the first historical overtime meal data for the historical time period; determining the first historical overtime meal data that is greater than the sum of the third quartile and the interquartile range among all the first historical overtime meal data as the outlier data among all the first historical overtime meal data; and determining the first historical overtime meal data that is less than the difference between the first quartile and the interquartile range among all the first historical overtime meal data as the outlier data among all the first historical overtime meal data.

[0041] In an embodiment of the present application, determining at least one overtime meal impact parameter corresponding to a future date based on historical overtime meal data includes: obtaining the production scheduling plan for a historical time period; and in the case where the production scheduling plan is greater than a preset production scheduling base number, using a regression model to fit the production scheduling plan and the first historical overtime meal data to determine a production scheduling impact coefficient.

[0042] It can be understood that the production scheduling plan refers to a detailed production plan formulated based on the production plan and production capacity, which includes the production time, required resources, and production personnel for each process. For non-production personnel, they need to cooperate with the production scheduling plan to carry out their work and also need to work overtime when the production volume planned in the production scheduling plan is large. That is, the production scheduling plan is also strongly correlated with the overtime meal situation of non-production personnel. Then, the production scheduling plans of each manufacturing workshop in the historical time period can be obtained, and a regression model can be used to fit the relationship between the production scheduling plan and all the first historical overtime meal data of non-production employees to determine the production scheduling impact coefficient corresponding to the production scheduling plan and the overtime meal data. Among them, the preset production scheduling base number refers to the critical value that affects the number of overtime meals for non-production employees. The production scheduling plan needs to satisfy being greater than the preset production scheduling base number before it can be considered that it has an impact on the number of overtime meals for non-production employees. When the production scheduling plan is less than or equal to the preset production scheduling base number, it can be considered that the production scheduling plan has no impact on the number of overtime meals for non-production employees. For example, on May 1, 2024: the production scheduling plan PL is 1000 pieces of equipment, and the number of overtime meals OV is 100; on May 2, 2024: the production scheduling plan PL is 1500 pieces of equipment, and the number of overtime meals OV is 130; on May 3, 2024: the production scheduling plan PL is 1800 pieces of equipment, and the number of overtime meals OV is 140. Using a regression model to fit and calculate the production scheduling impact coefficient. Then, in the coming week, if the average production scheduling plan PL is 1000 pieces of equipment, the average daily prepared number of overtime meals for non-production employees is 100.

[0043] In an embodiment of the present application, determining at least one overtime meal impact parameter corresponding to a future date based on historical overtime meal data includes: obtaining the number of overtime times of each production employee in a historical time period; determining the number of meal ordering times for overtime meal applications of each production employee in the historical time period according to all the second historical overtime meal data in the historical time period; and determining the overtime meal ordering probability of each production employee based on the number of overtime times and the number of meal ordering times of each production employee. For example, if non-production employee a has worked overtime 10 times and ordered overtime meals 8 times, then the overtime meal ordering probability of this non-production employee is 0.8.

[0044] Specifically, in one embodiment, all the first historical overtime meal data is fitted to predict the overtime meal fitting data A for a future time 1 . Then, based on the overtime meal ordering probabilities of all production employees, the prepared meal data A for production employees' overtime meals on a future date for all production employees at a future time is predicted 2 . The sum of the overtime meal fitting data A 1 and the prepared meal data A for production employees' overtime meals 2 is determined as the prepared meal data A for overtime meals for all meal ordering personnel on the future date 3 .

[0045] In one embodiment, after all the first historical overtime meal data is fitted to predict the overtime meal fitting data A for a future time 1 , the overtime meal fitting data A 1 can be multiplied by the overtime meal outlier coefficient to obtain the prepared meal data A for non-production employees' overtime meals on this future date 4 . Then, the sum of the prepared meal data A for non-production employees' overtime meals 4 and the prepared meal data A for production employees' overtime meals 2 is determined as the prepared meal data A for overtime meals for all meal ordering personnel on the future date 3 .

[0046] In one embodiment, after all the first historical overtime meal data is fitted to predict the overtime meal fitting data A for a future time 1 , the overtime meal fitting data A 1 can be multiplied by the production scheduling impact coefficient to obtain the prepared meal data A for non-production employees' overtime meals under the influence of the production scheduling plan on this future date 5 Then, the sum of the prepared meal data A for non-production employees' overtime meals under the influence of the production scheduling plan 5 and the prepared meal data A for production employees' overtime meals 2 is determined as the prepared meal data A for overtime meals for all meal ordering personnel on the future date 3 .

[0047] In one embodiment, after all the first historical overtime meal data is fitted to predict the overtime meal fitting data A for a future time 1, the overtime meal fitting data A can be multiplied by the overtime meal outlier coefficient to obtain the non-production employee overtime meal preparation data A for the future date. 1 And, the overtime meal fitting data A can be multiplied by the production schedule impact coefficient to obtain the non-production employee overtime meal preparation data A under the influence of the production schedule for the future date. 4 . Also, the overtime meal fitting data A 1 is multiplied by the production schedule impact coefficient to obtain the non-production employee overtime meal preparation data A under the influence of the production schedule for the future date. 5 . Take the average of A 4 and A 5 , and determine the sum of this average and the production employee overtime meal preparation data A 2 as the overtime meal preparation data A for all meal orderers on the future date. 3 .

[0048] In the embodiments of the present application, it further includes: removing the outlier data from all historical overtime meal data in the historical time period to obtain the updated historical overtime meal data; determining the standard deviation corresponding to the updated historical overtime meal data; obtaining the standard score corresponding to the preset confidence interval; and updating the overtime meal preparation data according to the standard deviation and the standard score.

[0049] Specifically, the preset confidence interval is a linear interval preset by the technical personnel. For example, a 95% confidence interval can be taken. Calculate the updated overtime meal preparation data A according to the following formula (1) 6 :

[0050] A 6 = A 3 + z×SD (1)

[0051] where, A 6 refers to the updated overtime meal preparation data for the future date, A 3 refers to the overtime meal preparation data for the future date, z refers to the standard score, and SD refers to the standard deviation corresponding to the updated historical overtime meal data.

[0052] Through the above technical solutions, multi-dimensional information such as historical data, production plans, workers' overtime habits, holidays, and external factors can be considered. Compared with prediction methods that rely solely on experience or single factors, this comprehensive analysis can reduce errors, improve prediction accuracy, and avoid over-reliance on the errors of individual experience values. In this way, it is possible to more comprehensively and accurately predict the prepared meal data for overtime meals on future dates, so that the central kitchen can reasonably arrange food procurement, inventory management, and meal preparation, avoid food waste and excessive costs, and effectively control within a reasonable range value. This helps to reduce the operating costs of the enterprise and improve economic efficiency. On the other hand, the above prediction method can be adjusted and optimized according to the actual situation of the company to adapt to different production environments, workers' needs, and market changes. At the same time, historical data can be updated in real time to timely reflect the latest situation, thereby maintaining the timeliness and accuracy of the prediction. At the same time, the taste preferences and eating habits of employees can also be understood, and the enterprise can more accurately formulate meal plans, improve the dining experience of employees, and enhance employees' sense of belonging and satisfaction.

[0053] Figure 1 It is a schematic flowchart of a meal preparation method for a central kitchen in an embodiment. It should be understood that although each step in the Figure 1 flowchart is shown in sequence according to the indication of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, Figure 1 at least a part of the steps in

[0054] Figure 2 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps. Figure 2 As shown in

[0055] a memory configured to store instructions;

[0056] a processor configured to call instructions from the memory and be able to implement the above meal preparation method for a central kitchen when executing the instructions.

[0057] Specifically, in the embodiment of the present application, the processor can be configured to:

[0058] Obtain the first historical overtime meal data of meal orderers in a historical time period, where the first historical overtime meal data includes the first overtime meal data of non-production employees every day in the historical time period and the second overtime meal data of production employees every day in the historical time period;

[0059] Determine at least one overtime meal impact parameter corresponding to a future date according to the first historical overtime meal data, where the at least one overtime meal impact parameter includes at least one of an overtime meal outlier coefficient corresponding to non-production employees, a production impact coefficient, and the overtime meal ordering probability of production employees;

[0060] Predict the overtime meal preparation data of all meal orderers on the future date according to the at least one overtime meal impact parameter.

[0061] In an embodiment of the present application, the processor may further be configured to:

[0062] Determining at least one overtime meal impact parameter corresponding to a future date according to the first historical overtime meal data includes: determining the outlier data among all the first historical overtime meal data in the historical time period; determining the first historical date corresponding to the outlier data; in the case that the time interval between the first historical date and the future date is not equal to the preset time interval, determining the overtime meal outlier coefficient as the first value; in the case that the time interval is equal to the preset time interval, obtaining the second historical overtime meal data of the second historical date, where the second historical date is in a historical time period earlier than the historical time period and the time interval between the second historical date and the first historical date is an integer multiple of the preset time interval; determining the first overtime meal statistical value corresponding to the first historical date according to all the first historical overtime meal data; determining the second overtime meal statistical value corresponding to the first historical date according to all the second historical overtime meal data of the second historical dates; and determining the ratio of the second overtime meal statistical value to the first overtime meal statistical value as the overtime meal outlier coefficient corresponding to non-production employees.

[0063] In an embodiment of the present application, the processor may further be configured to:

[0064] Determining the outlier data among all the first historical overtime meal data in the historical time period includes: determining the first quartile, the third quartile, and the interquartile range among all the first historical overtime meal data in the historical time period; determining the first historical overtime meal data greater than the sum of the third quartile and the interquartile range among all the first historical overtime meal data as the outlier data among all the first historical overtime meal data; and determining the first historical overtime meal data less than the difference between the first quartile and the interquartile range among all the first historical overtime meal data as the outlier data among all the first historical overtime meal data.

[0065] In an embodiment of the present application, the processor may further be configured to:

[0066] Determining at least one overtime meal impact parameter corresponding to a future date based on historical overtime meal data includes: obtaining a production schedule for a historical time period; when the production schedule is greater than a preset production base number, using a regression model to fit the production schedule and first historical overtime meal data to determine a production impact coefficient.

[0067] In an embodiment of the present application, the processor may also be configured to:

[0068] Determining at least one overtime meal impact parameter corresponding to a future date based on historical overtime meal data includes: obtaining the number of overtime times of each production employee in a historical time period; determining the number of meal orders for overtime meals applied by each production employee in the historical time period according to all second historical overtime meal data in the historical time period; determining the overtime meal ordering probability of each production employee according to the number of overtime times and the number of meal orders of each production employee.

[0069] In an embodiment of the present application, the processor may also be configured to:

[0070] Determining the overtime meal preparation data of all meal orderers on a future date according to at least one overtime meal impact parameter includes: fitting all first historical overtime meal data in a historical time period to predict the overtime meal fitting data of non-production employees on the future date; determining the overtime meal preparation data of all meal orderers on the future date according to at least one overtime meal impact parameter and the overtime meal fitting data.

[0071] In an embodiment of the present application, the processor may also be configured to:

[0072] Removing the outlier data in all historical overtime meal data in a historical time period to obtain updated historical overtime meal data; determining the standard deviation corresponding to the updated historical overtime meal data; obtaining the standard score corresponding to a preset confidence interval; updating the overtime meal preparation data according to the standard deviation and the standard score.

[0073] An embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the above-mentioned meal preparation method for a central kitchen.

[0074] In one embodiment, a computer program product is provided, including a computer program, and the computer program, when executed by a processor, implements the meal preparation method for a central kitchen according to the above-mentioned method for identifying network traffic types.

[0075] In one embodiment, a computer device is provided, and the computer device may be a server, and its internal structure diagram may be as Figure 3As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure), and a database (not shown in the figure) connected by a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data for the meal preparation method in the central kitchen. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, it implements a meal preparation method for the central kitchen.

[0076] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0077] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0078] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0079] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device that implements the functions specified in one or more of the acts Figure 1 or acts and / or boxes Figure 1 or boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing steps for implementing the functions specified in one or more of the acts Figure 1 or acts and / or boxes Figure 1 or boxes.

[0081] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0082] The memory may include non-permanent memory in the computer-readable medium, random access memory (RAM) and / or non-volatile memory such as read only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0083] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0084] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0085] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A meal preparation method for a central kitchen, characterized in that: The method comprises: Acquire first historical overtime meal data of meal ordering personnel in a historical time period, wherein the first historical overtime meal data includes first overtime meal data of non-production employees every day in the historical time period and second overtime meal data of production employees every day in the historical time period; Determine at least one overtime meal impact parameter corresponding to a future date according to the first historical overtime meal data, wherein the at least one overtime meal impact parameter includes at least one of an overtime meal outlier coefficient and a production scheduling impact coefficient corresponding to the non-production employee, and a probability of the production employee ordering overtime meals; The overtime meal preparation data of all meal ordering personnel on the future date is predicted based on the at least one overtime meal impact parameter.

2. The method according to claim 1, characterized in that The determining, according to the first historical overtime meal data, at least one overtime meal impact parameter corresponding to a future date comprises: Determine outlier data in all first historical overtime meal data for the historical time period; determining a first historical date corresponding to the outlier data; When the time interval between the first historical date and the future date is not equal to the preset time interval, determining the overtime meal outlier coefficient as a first value; When the time interval is equal to the preset time interval, obtaining second historical overtime meal data of a second historical date, wherein the second historical date is in a historical time period earlier than the historical time period and the time interval between the second historical date and the first historical date is equal to an integer multiple of the preset time interval; Determine a first overtime meal statistical value corresponding to the first historical date according to all first historical overtime meal data; Determine a second overtime meal statistical value corresponding to the first historical date according to the second historical overtime meal data of all second historical dates; The ratio of the second overtime meal statistics value to the first overtime meal statistics value is determined as the overtime meal outlier coefficient corresponding to the non-production employee.

3. The method according to claim 1, characterized in that The determining of outlier data in all first historical overtime meal data in the historical time period includes: Determine the first quartile, the third quartile, and the interquartile range of all first historical overtime meal data in the historical time period; Determine the first historical overtime meal data whose value is greater than the sum of the third quartile and the interquartile range among all the first historical overtime meal data as outlier data among all the first historical overtime meal data; The first historical overtime meal data whose value is smaller than the difference between the first quartile and the interquartile range among all the first historical overtime meal data are determined as outlier data among all the first historical overtime meal data.

4. The method according to claim 1, characterized in that: Determining at least one overtime meal impact parameter corresponding to a future date according to the historical overtime meal data includes: Obtaining the production schedule for the historical time period; When the production schedule is greater than the preset production schedule base number, a regression model is used to fit the production schedule and the first historical overtime meal data to determine the production schedule impact coefficient.

5. The method according to claim 1, characterized in that Determining at least one overtime meal impact parameter corresponding to a future date according to the historical overtime meal data includes: Get the number of overtimes worked by each production employee in the historical period; Determine the number of times each production employee has ordered overtime meals during the historical time period according to all second historical overtime meal data during the historical time period; The overtime ordering probability of each production employee is determined according to the overtime times and the meal ordering times of each production employee.

6. The method according to claim 1, characterized in that The determining of the overtime meal preparation data of all meal ordering personnel on the future date according to the at least one overtime meal impact parameter includes: Fitting all first historical overtime meal data of the historical time period to predict overtime meal fitting data of the non-production employee on the future date; The overtime meal preparation data of all meal ordering personnel on the future date is determined according to the at least one overtime meal impact parameter and the overtime meal fitting data.

7. The method according to claim 6, characterized in that Also includes: Removing outlier data from all historical overtime meal data in the historical time period to obtain updated historical overtime meal data; Determine the standard deviation corresponding to the updated historical overtime meal data; Obtain the standard score corresponding to the preset confidence interval; The overtime meal preparation data is updated according to the standard deviation and the standard score.

8. A meal preparation device for a central kitchen, characterized in that: include: a memory configured to store instructions; A processor is configured to call the instructions from the memory and implement the meal preparation method for a central kitchen according to any one of claims 1 to 7 when executing the instructions.

9. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for enabling the machine to execute the meal preparation method for a central kitchen according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the meal preparation method for a central kitchen according to any one of claims 1 to 7.

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