A food material demand prediction method, device, medium and equipment

Abstract

The present specification provides a food material demand prediction method, device, medium and equipment, the method comprises: obtaining cooking record information of a cooking device in a preset area at a current location; based on the cooking record information, counting the cooking frequency information of the cooking recipe in a preset time period, and predicting the expected cooking frequency information of the cooking recipe in the next preset time period according to the statistical result; based on the expected cooking frequency information, determining the food material quantity information of the cooking recipe. Through the above method, the food material quantity information can be determined to guide the merchants to realize accurate preparation.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of data prediction, and in particular to a food material demand prediction method, device, medium and equipment. BACKGROUND

[0002] Nowadays, many people choose to cook at home, so people need to buy food materials for cooking. In the prior art, the merchant usually prepares a certain amount of food materials in advance for users to pick up.

[0003] However, considering that the shelf life of food materials (especially fresh food materials such as vegetables and meat) is short, if the merchant prepares too much food material, it will cause waste; if the merchant prepares too little food material, it will be difficult to meet the pick-up demand of people, so a solution is needed to help the merchant determine the amount of food materials to be prepared, which is beneficial to guide the merchant to prepare the amount of food materials. SUMMARY

[0004] To overcome the problems in the related art, the present specification provides a food material demand prediction method, device, medium and equipment.

[0005] According to a first aspect of an embodiment of the present specification, a food material demand prediction method is provided, comprising:

[0006] obtaining cooking record information of a cooking device in a preset area at a current location, wherein the cooking record information includes a cooking recipe of the cooking device;

[0007] based on the cooking record information, counting cooking frequency information of the cooking recipe in a preset time period, and predicting predicted cooking frequency information of the cooking recipe in a next preset time period according to the counting result;

[0008] determining food material quantity information of the cooking recipe based on the predicted cooking frequency information.

[0009] According to a second aspect of an embodiment of the present specification, a food material demand prediction device is provided, the device comprising:

[0010] an obtaining unit configured to obtain cooking record information of a cooking device in a preset area at a current location, wherein the cooking record information includes a cooking recipe of the cooking device;

[0011] a prediction unit configured to count cooking frequency information of the cooking recipe in a preset time period based on the cooking record information, and predict predicted cooking frequency information of the cooking recipe in a next preset time period according to the counting result;

[0012] a determination unit configured to determine food material quantity information of the cooking recipe based on the predicted cooking frequency information.

[0013] According to a third aspect of the embodiments of the present specification, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the food material demand prediction method according to any one of the embodiments of the first aspect.

[0014] According to a fourth aspect of the embodiments of the present specification, a computer device is provided, and the computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor; and the processor is configured to execute the steps of the food material demand prediction method according to any one of the embodiments of the first aspect.

[0015] The technical solutions provided by the embodiments of the present specification can include the following beneficial effects:

[0016] In the embodiments of the present specification, by determining the cooking record information of the cooking device in the preset area of the current position, the cooking times information of each cooking recipe in the past preset time period (several historical periods) can be counted according to the cooking recipe recorded in the cooking record information, so as to predict the predicted cooking times information of the cooking recipe in the next preset time period according to the statistical result. After the predicted cooking times information is determined, the food material quantity information of the cooking recipe can be determined according to the predicted cooking times information.

[0017] By the above method, the food material quantity information of the food material required for executing the cooking recipe under the predicted cooking times can be predicted. Compared with the scheme in the prior art that the merchant prepares goods according to his own experience, the embodiments of the present specification help the merchant to realize precise preparation, and thus solve the food waste problem caused by excessive preparation in the prior art.

[0018] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present specification. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings, which are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present specification, and together with the specification serve to explain the principles of the present specification.

[0020] Figure 1 A flowchart of a food material demand prediction method provided by an embodiment of the present specification is shown;

[0021] Figure 2 A flowchart of another food material demand prediction method provided by an embodiment of the present specification is shown;

[0022] Figure 3 A structural schematic diagram of a food material demand prediction device provided by an embodiment of the present specification is shown;

[0023] Figure 4 FIG. 1 is a hardware structure diagram of a computer device in which a food material demand prediction device according to an exemplary embodiment of the present specification is implemented. DETAILED DESCRIPTION

[0024] The exemplary embodiments will be described in detail with reference to the accompanying drawings. In the following description, the same drawings refer to the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present specification. Rather, they are merely examples of devices and methods consistent with some aspects of the present specification, as detailed in the appended claims.

[0025] The terms used in the present specification are merely used to describe particular embodiments, and are not intended to limit the present specification. As used in the present specification and the appended claims, singular forms "a," "an" and "the" are intended to include plural forms, unless the context clearly indicates otherwise. It will be further understood that the term "and / or" used herein includes any or all possible combinations of one or more associated listed items.

[0026] It should be understood that although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to limit the information. These terms are used only to distinguish one piece of information from another piece of information of the same type. For example, without departing from the scope of the present specification, first information can also be referred to as second information, and similarly, second information can also be referred to as first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "upon" or "in response to determining."

[0027] As the number of cooking devices (e.g., a wok, a microwave oven, an electric rice cooker, a soybean milk machine, etc.) for processing and cooking food materials increases, more people choose to use cooking devices at home for cooking. Therefore, people need to obtain food materials required for cooking.

[0028] Generally, most of the food materials required for cooking are fresh food materials (e.g., live fish, shrimp, etc.), and in order to ensure the freshness of the food materials during cooking and prevent the fresh food materials from spoiling, users usually purchase them before use, such as directly going to a supply site (e.g., a market, a fresh food supermarket, etc.) near the cooking device or placing an order on a purchase website (a webpage, an application software) and then having a supply site deliver food materials to a preset area (a community, an office building, a village, etc.) in a point-to-point manner, so as to achieve same-day delivery (even within two hours, within one hour, etc.) and ensure the freshness of the food materials obtained by the user.

[0029] However, since the preparation site does not know how many users are expected to purchase or pick up the food materials in the next preset time period (next week, next three days, next day), the preparation quantity of each food material needs to be adjusted by the preparation site based on its own experience. However, since fresh food materials are prone to spoilage, if the preparation site prepares too much food material and the number of users who purchase or pick up the food material is too small, the food material will spoil and be wasted; if the preparation site prepares too little food material and the number of users who purchase or pick up the food material is too much, it will affect the user's cooking use. Therefore, a solution is needed to predict the user's food material demand.

[0030] The present specification can be applied to a server or a terminal in a current location (hereinafter referred to as a preparation site). Each preparation site has a corresponding preset area after it is established, for example, the preset area is near the preparation site (within a range of 5 km or 10 km radius with the preparation site as the center), or the preset area is far away from the preparation site, but the preparation site delivers goods to the preset area to make it convenient for users to pick up goods in the preset area.

[0031] Considering that more and more people start to use cooking equipment for cooking, and the cooking equipment records the cooking record information of each recipe, if the preparation site can obtain the cooking record information in each cooking equipment in the preset area, it can determine the recipes that the user often cooks, and thus predict that the user may cook these recipes again, thereby increasing the preparation quantity of the food materials required for the recipes that are often cooked, to prevent the problem that the user cannot obtain the required food materials in time when he wants to cook; similarly, if the cooking frequency of a recipe in the cooking record information of the recipe is too small, the probability of the user cooking the recipe again is small, so the preparation quantity of the food materials required for the recipe can be reduced.

[0032] The embodiments of the present specification are described in detail below.

[0033] Figure 1 A flowchart of a food material demand prediction method provided by an embodiment of the present specification is shown in FIG. 1. Figure 1 As shown in FIG. 1, the method comprises the following steps:

[0034] Step 101, obtaining cooking record information of a cooking equipment in a preset area of a current location, wherein the cooking record information comprises a cooking recipe of the cooking equipment.

[0035] Specifically, each cooking recipe requires at least one food material, which can be an original food material or a prepared food material (such as prepared dishes, prepared rice, etc.) obtained by processing (slicing, washing, slicing, blanching, drying, heating, etc.) the food material.

[0036] The cooking record information records information such as a cooking recipe cooked by the cooking device, and a time of the cooking recipe.

[0037] In step 102, based on the cooking record information, the cooking times of the cooking recipe in a preset time period are counted, and the predicted cooking times of the cooking recipe in a next preset time period are predicted according to the counting result.

[0038] Specifically, the length of the preset time period is preset (manually set or automatically set), for example, one week, one day, or two days, or can be automatically determined. The cooking device is a device for cooking food into edible dishes, such as a rice cooker, a microwave oven, an oven, a frying machine, a soybean milk machine, etc. In the embodiments of the present application, the cooking device in the preset area needs to meet the following conditions: the actual start-up times of the cooking device in the preset time period reach the preset start-up times, and the difference between the start-up frequency in each sub-period of the preset time period and the average start-up frequency in the preset time period is less than a preset difference.

[0039] The preset start-up times are preset, for example, 2 times per week or 1 time per week. When the actual start-up times of the cooking device meet the preset start-up times, it means that the start-up activity of the cooking device in the last preset time period meets the requirements. If the difference between the start-up frequency in each sub-period of the preset time period and the average start-up frequency in the preset time period meets the preset difference requirement, it means that the start-up times of the cooking device are relatively stable and regular, and the cooking record information of the cooking device can be used as effective data, which helps to improve the prediction accuracy. If the start-up frequency of the cooking device does not meet the above difference requirement, it means that the start-up times of the cooking device are not stable and regular, and the information predicted according to the cooking record information of the cooking device is not accurate. The above method helps to improve the prediction accuracy.

[0040] An example is given to illustrate how to determine whether the cooking device meets the start-up frequency condition.

[0041] Suppose the preset time period is three weeks, and the start-up times of the cooking device in the past three weeks are 15 times, of which the start-up times in each sub-period (one week) of the preset time period are 1 time (first week), 10 times (second week), and 4 times (third week), and the preset difference is 1.5.

[0042] The average start-up times of the cooking device in the preset time period is (1+10+5) ÷ 3 = 5 times / week, and the difference between the start-up frequency of the cooking device in the first week and the average start-up frequency is |1-5| = 4, the difference between the start-up frequency of the cooking device in the second week and the average start-up frequency is |10-5| = 5, and the difference between the start-up times of the cooking device in the third week and the average start-up times is |4-5| = 1. Therefore, the difference in the first week and the second week is greater than the preset difference value, and the cooking device does not satisfy that the difference between the start-up frequency in each sub-period of the preset time period and the average start-up frequency in the preset time period is less than the preset difference value.

[0043] Specifically, after the cooking times information of each cooking recipe in the preset time period is counted, the predicted cooking times of the cooking recipes in the next preset time period can be predicted.

[0044] For example, the predicted cooking times of each cooking recipe in the next preset time period can be predicted according to the cooking times, cooking frequency, taste, etc. of the executed cooking recipes in the cooking record information, and then the predicted cooking times information is determined.

[0045] The predicted cooking times of each cooking recipe can be determined based on the cooking record information by using a prediction model, a prediction formula, etc.

[0046] It should be noted that, in order to improve the accuracy of the predicted cooking times, the number of all cooking devices in the preset area and the number of all cooking devices in the last preset time period can be referred to to determine the activity and active number of the cooking devices, the predicted number of cooking devices in the next preset time period is calculated, and the cooking recipes and predicted cooking times are set for the predicted growing cooking devices. The average cooking times and total cooking times of each recipe in the preset time period can also be referred to.

[0047] In step 103, the food material quantity information of the cooking recipe is determined based on the predicted cooking times information.

[0048] Specifically, the predicted cooking times of each cooking device performing a cooking recipe are aggregated to obtain the total predicted cooking times of all cooking devices for the same cooking recipe. For example, there are two cooking devices: device one and device two. The predicted cooking time of device one for cooking recipe one is 2, and the predicted cooking time of device two for cooking recipe one is 3. Then the predicted cooking time of cooking recipe one is: 2+3=5 times. The quantity value (5 times) corresponding to the predicted cooking time of cooking recipe one is assigned to the food material associated with the cooking recipe one. Wherein, one cooking recipe needs one food material (including at least one food material), and when the food material is a prepared dish, one cooking recipe corresponds to one prepared dish (for example, a prepared Gongbao chicken dish package). Then the total quantity value of food material one associated with cooking recipe one is 5 portions.

[0049] Based on the predicted cooking time information, the food material quantity information of the cooking recipe is determined, specifically including:

[0050] For each cooking recipe, the predicted cooking time of the cooking recipe is determined as the quantity of the prepared dish corresponding to the cooking recipe, and the sum of the quantities is determined as the food material quantity.

[0051] For example, the cooking recipes include cooking recipe one and cooking recipe two, the predicted cooking time of cooking recipe one is 5 portions, and the predicted cooking time of cooking recipe two is 10 portions. Then the food material quantity is 15 portions.

[0052] It should be noted that when determining the food material quantity, the purchase possibility of the user can also be considered to adjust the value of the food material quantity.

[0053] Specifically, the cooking record information includes the actual demand quantity (assumed to be 300) of each food material by the cooking device within a preset time period. If the actual purchase (pick-up) quantity of each food material by the user within the last preset time period is 350, the value of the food material quantity can be appropriately increased to accurately determine the actual stock quantity of the food material.

[0054] In the embodiments of the present specification, by determining the cooking record information of the cooking device in the preset area of the current position, the cooking recipe recorded in the cooking record information can be used to count the cooking times of each cooking recipe in the past preset time period (several historical periods), so as to predict the predicted cooking time information of the cooking recipe in the next preset time period according to the statistical result. After the predicted cooking time information is determined, the food material quantity information of the cooking recipe can be determined according to the predicted cooking time information.

[0055] By the above method, the food material amount information of the food material required for performing the cooking recipe in the predicted cooking times can be predicted. Compared with the scheme in the prior art in which the merchant prepares goods according to his own experience, the embodiments of the present specification help the merchant to realize precise preparation of goods, thereby solving the food waste problem caused by excessive preparation of goods in the prior art.

[0056] It should be noted that when determining the food material amount, the transportation loss and storage loss of the food material can also be considered, and the food material amount can be appropriately increased.

[0057] It should be further noted that after the food material amount is determined, recommended preparation purchase information including the food material amount of each food material can be generated, and the recommended preparation purchase information is sent to the terminal of the preparation site, so that the staff of the preparation site can purchase each food material. The recommended preparation purchase information can be a purchase page, a purchase link, store information selling each food material, etc. When the recommended preparation purchase information is a purchase link, a supply link, or a supply page, the staff can also modify the food material amount.

[0058] It should be further noted that the cooking recipe that the user can perform in the next preset time period can be a cooking recipe (referred to as an old recipe) that has been performed by the cooking device in the previous preset time period, or a cooking recipe (referred to as a new recipe) that has not been performed by the cooking device in the previous preset time period. Thus, a new cooking recipe can be recommended for the user, and the types of recipes that can be cooked by the cooking device are expanded.

[0059] Therefore, the predicted recipe that the user can perform in the next preset time period can also be predicted, and the predicted recipe includes:

[0060] at least one recipe in the recipes recorded in the recipe execution record that meets the target requirement; and / or, a recipe recommended for the second cooking device based on a preference recipe associated with the user; wherein the user has a binding relationship with the second cooking device, and the preference recipe is set for the user in advance; and / or, at least one recipe reserved in advance in the second cooking device.

[0061] In the embodiments of the present specification, the target requirement can be that the cooking frequency of the old recipe reaches a specified frequency, and / or the cooking frequency of the old recipe reaches a specified frequency. Alternatively, the target requirement can also be that the similarity between the old recipe and the preferred recipe reaches a target threshold (for example, 70%, 80%, 90%, etc.). The information of the user bound to the cooking device can be obtained first, and then the corresponding judgment is made according to the user's preferred recipe, which is selected by the user or determined according to the cooking record information in at least one cooking device bound to the user. The similarity to the preferred recipe includes but is not limited to the similarity of the cooking method, the similarity of the cooking procedure, the taste similarity of the dish, and the similarity of the ingredients.

[0062] In the judgment of whether the user will let the cooking device execute the new recipe in the future preset time period, the new recipe is recommended to the cooking device according to the user's preferred recipe, and the new recipe to be recommended to the cooking device is determined as the cooking recipe. Further, the probability of recommending the new recipe in the next preset time period can be predicted according to the probability of the user accepting each new recipe in a plurality of preset time periods, and the new recipe is determined as the cooking recipe when the probability reaches a specified probability (for example, 80%, 90%).

[0063] In addition, the cooking recipe can also be a recipe that the user has pre-ordered in the cooking device for execution in the next preset time period.

[0064] In a feasible implementation, when step 102 is executed, if the preset time period is automatically determined, the determining step includes:

[0065] Step 1021, for each ingredient corresponding to the cooking recipe, obtaining the shelf life of the ingredient, and obtaining the estimated time of the ingredient reaching the current location.

[0066] Specifically, when the ingredient is a prepared dish, the shelf life is set for the prepared dish, and when the ingredient is bulk food composed of at least one ingredient, the shelf life can be determined according to the shelf life of the most perishable ingredient in the ingredient.

[0067] The ingredients are usually delivered to the storage location in batches after being ordered from the headquarters or suppliers, so the estimated time of the ingredients reaching the storage location can be determined according to the delivery time and the transportation time.

[0068] Step 1022, determining the estimated storage duration of each ingredient based on the shelf life, the estimated time, and the storage environment of the current location.

[0069] Specifically, the predicted storage duration refers to that during the predicted storage duration, the food material is still in line with the quality standard (still does not exceed the shelf life, or the freshness of the food material exceeds a certain value) when the food material is sold out. After the storage environment at the stocking location (for example, normal temperature storage, cold storage, refrigeration storage, etc.) is determined, the predicted storage duration of the food material at the stocking location can be determined according to the shelf life of the food material and the predicted time of arrival at the stocking location.

[0070] For example, the food material arrives at the stocking location on March 1, 2022, and the shelf life of the food material expires on March 3, 2022, so the storage time is from March 1, 2022 to March 3, 2022, and the predicted storage duration is 2 days.

[0071] Step 1023, determining the preset time period based on the predicted storage duration.

[0072] Specifically, when the predicted storage duration is 2 days, if the next preset time period is 5 days, the food material quantity is predicted once every 5 days, which may have the problems of untimeliness and inaccuracy, so the length of the next preset time period is adjusted accordingly, for example, the next preset time period is adjusted to 3 days, 2 days, or 1 day.

[0073] By the above method, the length of the next preset time period can be dynamically adjusted, thereby ensuring that the food material quantity is provided to the stocking location in time and the stocking location is guided for stocking.

[0074] In a feasible implementation, Figure 2 A flowchart of another method for predicting food material demand provided by an embodiment of the present specification is shown in FIG. 6. Figure 2 As shown in FIG. 6, the storage environment at the stocking location includes a target storage environment, and the average temperature in the target storage environment is lower than the average temperature in the region where the current location is located, which indicates that the current location where the stocking location is located has a refrigeration storage environment. At this time, the method further includes the following steps:

[0075] Step 201, obtaining a target storage capacity of the target storage environment.

[0076] Specifically, the stocking location has a low-temperature storage device (for example, a refrigerator, a refrigeration cabinet, a freezing cabinet, etc.) or a low-temperature storage room (a refrigeration room, a cold storage, etc.), and the low-temperature storage device or the low-temperature storage room constitutes the target storage environment of the stocking location. The average temperature in the target storage environment is lower than the average temperature in the region where the current location is located (if the region where the current location is located is an outdoor region, the average temperature in the region where the current location is located depends on the outdoor average temperature in the region where the current location is located; if the region where the current location is located is an indoor region, the average temperature in the region where the current location is located depends on the indoor average temperature), and the target storage capacity is the idle capacity in the target storage environment constituted by the low-temperature storage device or the low-temperature storage room (for example, a refrigerator, a cold storage) of the stocking location.

[0077] In step 202, a target food material with a cold storage requirement is determined. When it is determined that the target storage capacity is less than the total storage capacity required by the target food material and the target food material is multiple, the ratio between the expected cooking times of each target food material corresponding to the cooking recipe is determined according to the expected cooking times of each target food material corresponding to the cooking recipe.

[0078] Specifically, in order to prevent the problem of premature deterioration of food materials (especially fresh food materials) due to not being placed in the storage area provided in the target storage environment, it is necessary to store the food materials in the most suitable storage environment, so it is necessary to determine the target food material with a cold storage requirement. The cold storage requirement can be pre-marked and set for each target food material, automatically calculated according to the production number, production information, production category, and production manufacturer of the food material, or uniformly entered.

[0079] The total storage capacity is used to store all target food materials with a cold storage requirement. When the total storage capacity is greater than the target storage capacity, it means that the inventory location cannot store all target food materials purchased according to the quantity of food materials. Further, the expected cooking times of the cooking recipe associated with each target food material are determined.

[0080] Suppose there are two target food materials: target food material one and target food material two. The expected cooking times of the cooking recipe associated with the target food material one is 3, and the expected cooking times of the cooking recipe associated with the target food material two is 7. The ratio between the expected cooking times of the target food material one and the target food material two is 3:7.

[0081] In step 203, the storage quantity of each target food material in the target storage environment is adjusted based on the ratio between the expected cooking times of each target food material.

[0082] After obtaining the ratio between the expected cooking times (for example, 3:7 as described above), the storage quantity of the target food material one in the target storage environment is reduced, and the storage quantity of the target food material two in the target storage environment is increased. The specific adjustment value depends on the ratio and the volume of each target food material, which is not limited in the embodiments of the present specification. The above method is advantageous for placing food materials of cooking recipes with more expected cooking times in the target storage environment for storage, which is advantageous for ensuring the freshness of food materials when the user picks up the food materials.

[0083] Figure 3 FIG. 1 shows a structural schematic diagram of a food material demand prediction device provided by an embodiment of the present specification. Figure 3 As shown in FIG. 1, the device comprises:

[0084] The acquisition unit 301 is configured to acquire cooking record information of a cooking device in a preset area at a current location, wherein the cooking record information comprises a cooking recipe of the cooking device.

[0085] The prediction unit 302 is configured to count cooking frequency information of the cooking recipe in a preset time period based on the cooking record information, and predict predicted cooking frequency information of the cooking recipe in a next preset time period according to a statistical result.

[0086] The determination unit 303 is configured to determine ingredient quantity information of the cooking recipe based on the predicted cooking frequency information.

[0087] In the embodiments of the present specification, by determining the cooking record information of the cooking device in the preset area at the current location, the cooking frequency information of each cooking recipe in the past preset time period (several historical periods) can be counted according to the cooking recipe recorded in the cooking record information, so as to predict the predicted cooking frequency information of the cooking recipe in the next preset time period according to the statistical result. After the predicted cooking frequency information is determined, the ingredient quantity information of the cooking recipe can be determined according to the predicted cooking frequency information.

[0088] By using the above device, the ingredient quantity information of the ingredient required for executing the cooking recipe under the predicted cooking frequency can be predicted. Compared with the scheme in the prior art in which the merchant prepares goods according to his own experience, the embodiments of the present specification help the merchant to realize precise preparation, and thus solve the food waste problem caused by excessive preparation in the prior art.

[0089] In a feasible implementation, the step of determining the preset time period comprises:

[0090] For each ingredient corresponding to the cooking recipe, the shelf life of the ingredient is acquired, and the predicted time of the ingredient reaching the current location is acquired.

[0091] Based on the shelf life, the predicted time, and the storage environment at the current location, the predicted storage duration of each ingredient is determined.

[0092] Based on the predicted storage duration, the preset time period is determined.

[0093] In a feasible implementation, the device further comprises:

[0094] The capacity acquisition unit is configured to acquire a target storage capacity of a target storage environment when the storage environment comprises the target storage environment, and an average temperature in the target storage environment is lower than an average temperature in a region where the current location is located.

[0095] The ratio determination unit is configured to determine a ratio between the expected cooking times of each target food material when the target storage capacity is determined to be less than the total storage capacity required by the target food materials and the target food materials are multiple.

[0096] The storage quantity adjustment unit is configured to adjust the storage quantity of each target food material in the target storage environment based on the ratio between the expected cooking times of each target food material.

[0097] In an implementation, the prediction unit is configured to, when predicting the expected cooking time information of the cooking recipes in the next preset time period based on the statistical result, specifically:

[0098] For each cooking recipe, the expected cooking time information of the cooking recipe in the next preset time period is determined based on the cooking frequency and / or the cooking times of the cooking recipe in the statistical result.

[0099] In an implementation, the food material corresponding to the cooking recipe is a prepared dish.

[0100] In an implementation, the determination unit is configured to, when determining the food material quantity information of the cooking recipe based on the expected cooking time information, specifically:

[0101] For each cooking recipe, the expected cooking time of the cooking recipe is determined as the quantity of the prepared dish corresponding to the cooking recipe, and the sum of the quantities is determined as the food material quantity.

[0102] In an implementation, the actual start-up times of the cooking device in the preset time period reach a preset start-up times, and the difference between the start-up frequency in each sub-period of the preset time period and the average start-up frequency in the preset time period is less than a preset difference.

[0103] The functions and effects of each module in the above device are specifically described in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0104] For the device embodiment, since it basically corresponds to the method embodiment, the relevant part can be seen in the part of the method embodiment. The device embodiment described above is only illustrative, wherein the modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place or distributed on multiple network modules. Part or all of the modules can be selected to achieve the purpose of the scheme of the present specification according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0105] Figure 4 Fig. 1 is a hardware structure diagram of a computer device according to an exemplary embodiment of the present specification, which can include a processor 401, a memory 402, an input / output interface 403, a communication interface 404 and a bus 405. The processor 401, the memory 402, the input / output interface 403 and the communication interface 404 are connected to each other through the bus 405 for internal communication connection. Figure 4

[0106] The processor 401 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing related programs to implement the food material demand prediction method provided by the embodiments of the present specification.

[0107] The memory 402 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 402 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 402 and called and executed by the processor 401.

[0108] The input / output interface 403 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. The input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0109] ​The communication interface 404 is configured to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize the communication through a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0110] The bus 405 includes a path for transmitting information between various components (for example, the processor 401, the memory 402, the input / output interface 403 and the communication interface 404) of the device.

[0111] It should be noted that, although the above device only shows the processor 401, the memory 402, the input / output interface 403, the communication interface 404 and the bus 405, in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only contain the components necessary for the implementation of the embodiments of the present specification, and does not have to contain all the components shown in the figure.

[0112] The embodiments of the present specification also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the steps of any one of the food material demand prediction methods provided by the embodiments of the present specification.

[0113] The computer readable medium includes permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be 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 disc (DVD) or other optical storage, magnetic cassette, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device. According to the definition in this paper, the computer readable medium does not include transitory computer readable media, such as modulated data signals and carriers.

[0114] It is also to be noted that the term "comprising" or "including" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0115] The above description of certain implementations has been presented for the purposes of clarity and explanation. It is not intended to be exhaustive or to limit the implementations to the precise forms described. Many modifications and variations are possible and will be appreciated by those of ordinary skill in the art. Certain embodiments were chosen and described in order to best explain the principles of the implementations and its practical applications. Others were provided to illustrate advantageous alternatives and modifications. It will be appreciated that some implementations described herein can be utilized in other systems and environments.

Claims

1. A method of predicting demand for food ingredients, characterized by, The method comprises: acquiring cooking record information of a cooking device in a preset area at a current location, wherein the cooking record information comprises a cooking recipe of the cooking device; the cooking recipe requires at least one ingredient; and the cooking record information comprises actual required amounts of each ingredient by the cooking device in a preset time period; based on the cooking record information, counting cooking frequency information of the cooking recipe in the preset time period, and predicting predicted cooking frequency information of the cooking recipe in a next preset time period according to a statistical result; determining ingredient amount information of the cooking recipe based on the predicted cooking frequency information.

2. The method of claim 1, wherein, The step of determining the preset time period comprises: for each ingredient corresponding to the cooking recipe, acquiring a shelf life of the ingredient, and acquiring a predicted time of arrival of the ingredient at the current location; based on the shelf life, the predicted time, and a storage environment at the current location, determining a predicted storage duration of each ingredient; based on the predicted storage duration, determining the preset time period.

3. The method of claim 2, wherein, The storage environment comprises a target storage environment, and an average temperature in the target storage environment is lower than an average temperature in a region where the current location is located; The method further comprises: acquiring a target storage capacity of the target storage environment; determining a target ingredient provided with a refrigeration storage requirement; when it is determined that the target storage capacity is less than a total storage capacity required by the target ingredient and the target ingredient is multiple, determining a ratio between predicted cooking frequencies of each target ingredient corresponding to a cooking recipe of the target ingredient according to the predicted cooking frequency information of the cooking recipe; based on the ratio between the predicted cooking frequencies of each target ingredient, adjusting a storage quantity of each target ingredient in the target storage environment.

4. The method of claim 1, wherein, The method further comprises: for each cooking recipe, determining predicted cooking frequency information of the cooking recipe in the next preset time period based on a cooking frequency and / or a cooking frequency of the cooking recipe in the statistical result.

5. The method of claim 1, wherein, The ingredient corresponding to the cooking recipe is a prepared dish.

6. The method of claim 5, wherein, The method further comprises: for each cooking recipe, determining a predicted cooking frequency of the cooking recipe as a quantity of a prepared dish corresponding to the cooking recipe, and determining a sum of the quantities as the ingredient amount.

7. The method of claim 1, wherein, The actual start-up times of the cooking device in the preset time period reach a preset start-up time, and a difference between a start-up frequency in each sub-period of the preset time period and an average start-up frequency in the preset time period is less than a preset difference.

8. A food material demand prediction device characterized by comprising: The apparatus comprises: an acquisition unit, configured to acquire cooking record information of a cooking device in a preset area at a current location, wherein the cooking record information comprises a cooking recipe of the cooking device; the cooking recipe requires at least one ingredient; and the cooking record information comprises actual required amounts of each ingredient by the cooking device in a preset time period; a prediction unit configured to count cooking frequency information of the cooking recipe in a preset time period based on the cooking record information, and predict predicted cooking frequency information of the cooking recipe in a next preset time period according to a counting result; a determination unit configured to determine ingredient quantity information of the cooking recipe based on the predicted cooking frequency information.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the steps of the method of any one of claims 1-7.

10. A computer device, comprising: A computer program product, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the method of any one of claims 1-7 when executing the computer program. A computer program product, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the method of any one of claims 1-7 when executing the computer program.

Images