Intelligent deduction method, device and background server for meal expenses

Abstract

This invention relates to the field of food service fee deduction technology, and discloses an intelligent deduction method, device, and backend server for food service fees. The method includes: when the food service equipment detects a decrease in the weight of food in the food area, determining the target weight parameter of the food with the decreased weight, and obtaining the food type parameter; determining the pending payment fee for the food based on the target weight parameter and the type parameter; obtaining the personnel identifier of the person serving the food, and deducting the pending payment fee from the person serving the food's payment account based on the personnel identifier. Therefore, implementing this invention can automatically determine the pending payment fee for food based on the target weight parameter and type parameter of the food with decreased weight, and deduct the pending payment fee from the person serving the food's payment account. This not only meets customers' personalized food service needs but also improves the efficiency and accuracy of food service fee deduction, thereby improving overall food service efficiency and facilitating the smooth operation of the restaurant.

Description

Technical Field

[0001] This invention relates to the field of food payment deduction technology, and in particular to an intelligent deduction method, device and back-end server for food payment. Background Technology

[0002] In the food service industry, food dispensing equipment is a core facility for meeting customers' self-service food collection needs. Early traditional food dispensing equipment had a single function, focusing solely on food serving and dispensing, providing customers with a basic way to collect their food. With technological advancements, some dispensing equipment has incorporated simple automation elements such as electronic scales, which can roughly weigh food to assist operations, but its overall functionality remains limited. For example, most restaurants currently use a traditional payment process: customers must first select their food and calculate the price at the cashier, then pay with cash, card, or mobile payment, and finally collect their food from the dispensing equipment. This separate process easily causes congestion in restaurants during peak hours, significantly increasing customer waiting time. Furthermore, existing payment methods lack flexible and targeted billing mechanisms, failing to meet the growing personalized needs of customers and impacting their dining experience. Therefore, providing an efficient and personalized intelligent deduction technology solution for food dispensing fees is particularly important. Summary of the Invention

[0003] This invention provides an intelligent deduction method, device, and back-end server for meal ordering fees. It can not only meet customers' personalized meal ordering needs, but also improve the efficiency and accuracy of meal ordering fee deduction, thereby improving the overall efficiency of meal ordering and facilitating the smooth operation of the restaurant.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a method for intelligently deducting meal fees, the method comprising: When the food dispensing equipment detects a decrease in the weight of food in the food area, it determines the target weight parameter of the food whose weight has decreased and obtains the type parameter of the food. The amount to be paid for the food is determined based on the target weight parameter and the type parameter. Obtain the personnel identifier of the person serving the food, and deduct the unpaid fee from the payment account of the person serving the food based on the personnel identifier.

[0005] As an optional implementation, in the first aspect of the invention, before determining the unpaid cost of the food based on the target weight parameter and the type parameter, the method further includes: The food is captured as a cooked image, and the cooked food parameters are determined based on the cooked image. The cooked food parameters include at least one of the following: cooked food appearance parameters, cooked food degree of doneness parameters, and current food temperature parameters. Based on the parameters of the cooked dish, determine the impact of the food on the food server; the impact includes physiological and / or psychological effects. Determine the baseline level of food impact corresponding to the aforementioned food impact situation, and determine whether the baseline level of food impact is greater than or equal to a preset baseline level of impact threshold. When it is determined that the basic impact of consumption is less than the basic impact threshold, the operation of determining the payment amount of the food based on the target weight parameter and the type parameter is triggered.

[0006] As an optional implementation, in the first aspect of the invention, determining the unpaid cost of the food based on the target weight parameter and the type parameter includes: Obtain the weight gain parameter of the food in the serving tray of the food server, and calculate the weight difference between the target weight parameter and the weight gain parameter; Obtain the target proportion parameters of the food in the serving tray and the expected proportion parameters of the food of the serving staff, and calculate the proportion difference between the target proportion parameters and the expected proportion parameters of the food; the target proportion parameters include ingredient proportion parameters and / or solid-liquid proportion parameters, and the expected proportion parameters of the food include ingredient proportion parameters and / or solid-liquid proportion parameters. The unpaid cost of the food is determined based on the weight difference, the weight gain parameter, and the ratio difference.

[0007] As an optional implementation, in a first aspect of the invention, determining the unpaid cost of the food based on the weight difference, the weight gain parameter, and the proportion difference includes: The sensory parameters of the food and the eating habits of the person serving the food are obtained; the sensory parameters include at least one of the following: texture parameters, temperature parameters, and taste parameters; the eating habits parameters include at least one of the following: texture preference parameters, temperature preference parameters, and taste preference parameters. Based on the taste sensation parameters and the eating habit parameters, determine the difference parameters of the food sensation of the serving staff; The unpaid cost of the food is determined based on the weight difference, the weight gain parameter, the ratio difference, and the taste difference parameter.

[0008] As an optional implementation, in a first aspect of the invention, determining the unpaid cost of the food based on the weight difference, the weight gain parameter, the proportion difference, and the palatability difference parameter includes: The food serving situation of the food serving personnel is obtained; the food serving situation includes at least one of the following: food serving rate, food serving time, and food serving behavior. The system obtains the food queuing information of the people around the food-serving personnel, and determines the impact of the food-serving personnel on the food-serving situation of the people around them based on the food serving situation and the food queuing situation. The unpaid cost of the food is determined based on the weight difference, the weight gain parameter, the ratio difference, the taste difference parameter, and the impact of serving.

[0009] As an optional implementation, in the first aspect of the present invention, the method further includes: When it is detected that the person serving the food has finished eating and returned the serving tray, the target dining situation of the person serving the food is obtained; the target dining situation includes dining parameters and / or post-dining behavior, the dining parameters include dining duration parameters and / or food remaining amount parameters, and the post-dining behavior includes food waste classification and / or the return of the serving tray; Based on the target dining situation, determine whether the target dining situation meets the preset conditions for good dining behavior; When it is determined that the target dining situation meets the good dining behavior conditions, the refund amount for the deducted fees for the person who ordered the food is determined according to the target dining situation, and the refund amount is returned to the payment account of the person who ordered the food according to the person's identification.

[0010] As an optional implementation, in the first aspect of the invention, the personnel identification of the person receiving the food includes: When the food serving equipment integrates an image acquisition device, the image acquisition device captures a target image of the person serving the food, which serves as the person's identification; or... When the food serving equipment is integrated with a code reader, the code reader identifies the code on the food serving tray of the food serving personnel, and determines the personnel identifier of the food serving personnel that matches the code based on the pre-determined tray identifier-person identifier binding relationship and the code.

[0011] A second aspect of the present invention discloses an intelligent deduction processing device for meal payment fees, the device comprising: The determination module is used to determine the target weight parameter of the food whose weight has decreased when the food dispensing equipment detects a decrease in the weight of the food in the food area. The acquisition module is used to acquire the type parameter of the food; The determining module is further configured to determine the unpaid cost of the food based on the target weight parameter and the type parameter; The acquisition module is also used to acquire the personnel identifier of the person serving the food; The deduction module is used to deduct the unpaid fees from the payment account of the person who ordered the food, based on the person's identifier.

[0012] As an optional implementation, in a second aspect of the invention, the acquisition module is further configured to acquire a cooked image of the food before the determining module determines the amount to be paid for the food based on the target weight parameter and the type parameter; The determining module is further configured to determine the post-cooking dish parameters of the food based on the post-cooking image; the post-cooking dish parameters include at least one of the following: post-cooking dish appearance parameters, post-cooking dish doneness parameters, and current dish temperature parameters; determine the impact of the food on the food server based on the post-cooking dish parameters; the impact on the food server includes physiological impact and / or psychological impact; and determine the basic degree of impact on the food server corresponding to the impact on the food server. The device further includes: The first judgment module is used to determine whether the basic edible impact level is greater than or equal to a preset basic impact level threshold; when it is determined that the basic edible impact level is less than the basic impact level threshold, the determination module is triggered to perform the operation of determining the payment amount of the food based on the target weight parameter and the type parameter.

[0013] As an optional implementation, in a second aspect of the invention, the method by which the determining module determines the unpaid cost of the food based on the target weight parameter and the type parameter specifically includes: Obtain the weight gain parameter of the food in the serving tray of the food server, and calculate the weight difference between the target weight parameter and the weight gain parameter; Obtain the target proportion parameters of the food in the serving tray and the expected proportion parameters of the food of the serving staff, and calculate the proportion difference between the target proportion parameters and the expected proportion parameters of the food; the target proportion parameters include ingredient proportion parameters and / or solid-liquid proportion parameters, and the expected proportion parameters of the food include ingredient proportion parameters and / or solid-liquid proportion parameters. The unpaid cost of the food is determined based on the weight difference, the weight gain parameter, and the ratio difference.

[0014] As an optional implementation, in a second aspect of the invention, the method by which the determining module determines the unpaid cost of the food based on the weight difference, the weight gain parameter, and the proportion difference specifically includes: The sensory parameters of the food and the eating habits of the person serving the food are obtained; the sensory parameters include at least one of the following: texture parameters, temperature parameters, and taste parameters; the eating habits parameters include at least one of the following: texture preference parameters, temperature preference parameters, and taste preference parameters. Based on the taste sensation parameters and the eating habit parameters, determine the difference parameters of the food sensation of the serving staff; The unpaid cost of the food is determined based on the weight difference, the weight gain parameter, the ratio difference, and the taste difference parameter.

[0015] As an optional implementation, in a second aspect of the invention, the method by which the determining module determines the unpaid cost of the food based on the weight difference, the weight gain parameter, the proportion difference, and the palatability difference parameter specifically includes: The food serving situation of the food serving personnel is obtained; the food serving situation includes at least one of the following: food serving rate, food serving time, and food serving behavior. The system obtains the food queuing information of the people around the food-serving personnel, and determines the impact of the food-serving personnel on the food-serving situation of the people around them based on the food serving situation and the food queuing situation. The unpaid cost of the food is determined based on the weight difference, the weight gain parameter, the ratio difference, the taste difference parameter, and the impact of serving.

[0016] As an optional implementation, in a second aspect of the invention, the acquisition module is further configured to: When it is detected that the person serving the food has finished eating and returned the serving tray, the target dining situation of the person serving the food is obtained; the target dining situation includes dining parameters and / or post-dining behavior, the dining parameters include dining duration parameters and / or food remaining amount parameters, and the post-dining behavior includes food waste classification and / or the return of the serving tray; The device further includes: The second judgment module is used to determine whether the target dining situation meets the preset conditions for good dining behavior based on the target dining situation. The determining module is further configured to, when the second determining module determines that the target dining situation meets the good dining behavior conditions, determine the refund of the deducted fees to the person serving the food based on the target dining situation; The refund module is used to refund the refund amount to the payment account of the person who ordered the meal, based on the person's identifier.

[0017] As an optional implementation, in the second aspect of the present invention, the method by which the acquisition module acquires the personnel identifier of the person serving the food specifically includes: When the food serving equipment integrates an image acquisition device, the image acquisition device captures a target image of the person serving the food, which serves as the person's identification; or... When the food serving equipment is integrated with a code reader, the code reader identifies the code on the food serving tray of the food serving personnel, and determines the personnel identifier of the food serving personnel that matches the code based on the pre-determined tray identifier-person identifier binding relationship and the code.

[0018] A third aspect of the present invention discloses a backend server, the backend server comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent deduction method for meal fees disclosed in the first aspect of the present invention.

[0019] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the intelligent deduction processing method for meal service fees disclosed in the first aspect of the present invention.

[0020] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In this embodiment of the invention, when the food dispensing equipment detects a decrease in the weight of food in the food area, it determines the target weight parameter of the food that has decreased in weight and obtains the food type parameter; based on the target weight parameter and type parameter, it determines the amount due for the food; it obtains the personnel identifier of the person dispensing the food, and deducts the amount due from the dispensing personnel's payment account based on the personnel identifier. Therefore, implementing this invention can automatically determine the amount due for the food based on the target weight parameter and type parameter of the food that has decreased in weight, and deduct the amount due from the dispensing personnel's payment account. This not only meets customers' personalized food dispensing needs but also improves the efficiency and accuracy of food dispensing fee deduction, thereby improving overall food dispensing efficiency and facilitating the smooth operation of the restaurant. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an intelligent deduction method for meal service fees disclosed in an embodiment of the present invention. Figure 2 This is a flowchart illustrating another intelligent deduction method for meal service fees disclosed in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an intelligent deduction processing device for meal payment disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of another intelligent deduction processing device for meal service fees disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a backend server disclosed in an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0026] This invention discloses an intelligent deduction method, device, and back-end server for meal ordering fees. It can not only meet customers' personalized meal ordering needs, but also improve the efficiency and accuracy of meal ordering fee deduction, thereby improving the overall efficiency of meal ordering and facilitating the smooth operation of the restaurant.

[0027] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for intelligent deduction of meal fees disclosed in an embodiment of the present invention. Figure 1 The described intelligent deduction method for meal service fees can be applied to meal service equipment already installed in various scenarios such as schools, office buildings, and factories; this embodiment of the invention is not limited to this. Optionally, the method can be implemented by a meal service fee deduction device, which can be integrated into the meal service equipment or can be a local server or cloud server used to process the intelligent deduction process for meal service fees; this embodiment of the invention is not limited to this. Figure 1 As shown, the intelligent deduction method for meal fees can include the following operations: 101. When the food dispensing equipment detects a decrease in the weight of food in the food area, it determines the target weight parameter of the food that has decreased in weight and obtains the type parameter of the food.

[0028] In this embodiment of the invention, when the food serving device detects a decrease in the weight of a certain food in the food area containing dishes, it means that the food serving personnel are performing a food serving operation for that food. Then, by obtaining the type parameters of the food (such as scrambled eggs with tomatoes, stir-fried pork with peppers, etc.) and the decrease in its weight in the food area, the amount of food that the food serving personnel are preparing to eat can be determined.

[0029] 102. Determine the amount to be paid for the food based on the target weight and type parameters.

[0030] In this embodiment of the invention, the unpaid fee for the food is calculated as: target weight parameter * unit weight price for that food type. Furthermore, the unpaid fee for the food can be adjusted based on post-cooking parameters such as the current temperature and appearance of the food, reflecting a flexible and targeted fee collection process for the merchant.

[0031] 103. Identify the food handlers and deduct the outstanding amount from their payment accounts based on the identification.

[0032] In this embodiment of the invention, the personnel identification of the food handler further includes: When the food serving equipment integrates an image acquisition device, the device captures a target image of the person serving the food, which serves as their identification; or... When the food serving equipment is equipped with a barcode scanner, the scanner identifies the barcode on the food serving tray of the food serving personnel, and determines the personnel identification of the food serving personnel that matches the barcode based on the pre-determined tray identification-person identification binding relationship and the barcode.

[0033] In this optional embodiment, in addition to obtaining the payment account and deducting the pending payment through the face image of the person serving the food, the payment identification image, etc., the payment account can also be obtained and the pending payment can be deducted through the identification code on the serving tray used by the person serving the food.

[0034] Optionally, the target image may include a facial image of the person serving the food and / or a payment identifier image.

[0035] As can be seen, implementing this embodiment of the invention can automatically determine the unpaid amount for food based on the target weight and type parameters of the food being reduced in weight, and then deduct the unpaid amount from the payment account of the food server. This not only meets customers' personalized food ordering needs but also improves the efficiency and accuracy of food ordering fee deduction, thereby enhancing overall food ordering efficiency and facilitating the smooth operation of the restaurant.

[0036] In an optional embodiment, before determining the amount to be paid for the food based on the target weight parameter and the type parameter in step 102 above, the method further includes: Acquire images of the food after cooking, and determine the post-cooking dish parameters based on the images; Based on the parameters of the cooked dishes, determine the impact of the food on the food service personnel. Determine the baseline level of food impact corresponding to each food impact scenario, and determine whether the baseline level of food impact is greater than or equal to a preset baseline level of impact threshold. When it is determined that the basic impact of consumption is less than the basic impact threshold, the operation of determining the payment amount of the food based on the target weight parameter and type parameter is triggered.

[0037] In this optional embodiment, the parameters of the dish after cooking may include at least one of the following: the appearance parameters of the dish after cooking, the degree of rawness or cookedness of the dish after cooking, and the current temperature parameters of the dish.

[0038] Further optional, the impact of consumption may include physiological impact and / or psychological impact.

[0039] Furthermore, the method also includes: when it is determined that the basic impact of food consumption is greater than or equal to the basic impact threshold, the current food payment operation can be suspended, and the food processing personnel can be informed that the food may be inedible, and further food processing operations can be carried out.

[0040] Furthermore, the calculation process for this basic impact on edibility can be carried out in the following way: First, establish quantitative scoring rules for each parameter of the cooked dish (such as appearance, degree of rawness / cooking, and temperature). For example, no charring / discoloration in appearance earns 0 points, slight charring (area <10%) earns 2 points, moderate charring (10%-30%) earns 5 points, and severe charring (>30%) earns 10 points; Degree of rawness / cooking: fully cooked and with a suitable taste earns 0 points, slightly overcooked (slightly hard texture) earns 3 points, severely overcooked (difficult to chew) earns 8 points, and undercooked (posing a safety hazard) earns 10 points; Temperature: meets safety standards (such as meat ≥70℃) earns 0 points, too low a temperature (may breed bacteria) earns 5 points, and too high a temperature (produces harmful substances) earns 8 points. Then, weights are assigned based on the importance of the parameters to edibility. For example, the weights for physiological impact are: appearance 30%, degree of ripeness 50%, and temperature 20%; and the weights for psychological impact are: appearance 70%, degree of ripeness 20%, and temperature 10%. Each parameter's score is then multiplied by its corresponding weight to obtain the sub-scores for each parameter's physiological and psychological impact. Finally, the sub-scores for each parameter's physiological and psychological impact are summed to obtain the basic degree of edibility impact.

[0041] For example, in a smart food dispensing scenario in a school cafeteria, when serving meals to students, the dispensing equipment first acquires the target weight and type parameters of the food, and simultaneously uses a camera to capture images of the cooked food (such as color images, infrared images, etc.). It then analyzes these images to derive the food's post-cooking parameters, including appearance, doneness, and temperature. Based on these parameters, the system can determine the food's impact on students' consumption, including physiological and psychological aspects (e.g., overcooked food can affect a student's appetite and mood, and is detrimental to their health), and determine the basic level of impact. If this level is greater than or equal to a preset threshold of 5 points (e.g., braised pork with a burnt or overcooked state has an impact level of 7 points), the system will suspend payment and report the problem to the food handlers for further processing. If the level is less than the preset threshold (e.g., stir-fried vegetables have an impact level of 2 points), the system will determine the amount to be paid based on the target weight and type parameters. This solution not only ensures the food safety of food handlers and reduces the amount of food with consumption problems, but also allows for reasonable charging, making food dispensing management more scientific and efficient for businesses.

[0042] As can be seen, this optional embodiment can determine the post-cooking parameters of food based on images of the food, thereby determining the basic impact of the food on the food handlers' consumption. Only when the impact is minimal will the food handling fee be charged. This improves the reliability and accuracy of the analysis of the basic impact of food consumption, thus ensuring the smooth and accurate collection of food handling fees. It also helps ensure the safety of food handling personnel, thereby improving the efficiency and scientific nature of the business's food handling management.

[0043] In another optional embodiment, determining the amount to be paid for the food based on the target weight parameter and the type parameter in step 102 above includes: Obtain the weight gain parameters of the food in the serving trays of the food server, and calculate the weight difference between the target weight parameter and the weight gain parameter; Obtain the target proportion parameters of food in the serving tray and the expected proportion parameters of food for the serving staff, and calculate the proportion difference between the target proportion parameters and the expected proportion parameters. The unpaid cost of the food is determined based on the weight difference, weight gain parameters, and proportion difference.

[0044] In this optional embodiment, the target ratio parameter may include the ingredient ratio parameter and / or the solid-liquid ratio parameter, and the expected food ratio parameter may include the expected ingredient ratio parameter and / or the expected solid-liquid ratio parameter.

[0045] Furthermore, based on the weight difference, weight gain parameters, and proportion difference, the unpaid cost of the food is determined, including: Obtain food taste parameters and the eating habits of the staff serving the food; Based on taste perception parameters and eating habit parameters, determine the differences in taste perception of food among food service personnel; The cost to be paid for the food is determined based on the weight difference, weight gain parameters, proportion difference, and taste difference parameters.

[0046] Optionally, the taste sensation parameters (which can be determined according to the chef's cooking process) include at least one of the following: the texture parameter when eating, the temperature parameter when eating, and the taste parameter when eating; the eating habit parameters include at least one of the following: the texture habit parameter when eating, the temperature habit parameter when eating, and the taste habit parameter when eating.

[0047] Furthermore, based on the weight difference, weight gain parameters, proportion difference, and taste difference parameters, the unpaid cost of the food is determined, including: Obtain information on the food distribution by the staff serving meals; Obtain information on the food queuing situation of people around the food serving staff, and determine the impact of the food serving staff on the food serving situation of people around them based on the food serving situation and the food queuing situation; The unpaid cost of the food is determined based on the weight difference, weight gain parameters, proportion difference, taste difference parameters, and the impact of serving.

[0048] In this optional embodiment, the food serving situation may optionally include at least one of the following: food serving rate, food serving duration, and food serving behavior.

[0049] For example, when a server is serving food A, the serving equipment records a weight gain of 280 grams (20 grams lower than the target weight). Simultaneously, image recognition technology analyzes the actual ingredient ratio as "lean meat:fat = 6:4," a 10% deviation from the target ratio. The system then retrieves the server's historical eating habits data (e.g., preference for medium firmness, temperature above 60℃, and slightly sweet taste), combining this with the chef's recorded parameters for the current batch of food A (e.g., firmer texture, temperature 40℃, moderate sweetness), calculating the difference in palatability parameters as "firmness deviation +2, temperature deviation -2, taste deviation 0." Furthermore, due to peak dining hours and long queues, the system detects that Xiao Li's repeated selection significantly extended the serving time, and his multiple grabbing actions prolonged the waiting time for others behind him. Therefore, the overall impact of the serving is assessed as "mild delay." Finally, the system can adjust the basic unpaid fee based on the pre-set comprehensive weight and the corresponding score of the relevant parameters. For example, assuming the basic unpaid fee is 15 yuan (based on the target weight of 300 grams × unit price of 0.05 yuan / gram), the system presets the following weights and scoring methods (wherein, the weights and scoring methods can be adjusted by the merchant according to the needs of operation and management, customer type, food queuing situation, etc., such as increasing the corresponding weight when the food queuing is more serious). First, regarding weight difference, since each gram deviation is ±0.2 points, the weight difference score is -4 points; regarding proportion difference, since each 1% deviation is ±1 point, the proportion difference score is -10 points; regarding texture difference, due to deviations in hardness and temperature, the texture difference score is -6 points; and regarding food serving, due to delays by the serving staff, the score is 5 points. Combining the weight difference (30%), proportion difference (20%), texture difference (30%), and food serving impact (20%), the total deviation score can be calculated as: (-4 points × 30%) + (-10 points × 20%) + (-6 points × 30%) + 5 points × 20% = -4 points. Finally, the adjustment ratio is calculated as: 1 + total deviation score / 100 = 1 - 0.04 = 0.96, and the final amount to be paid is: 15 * 0.96 = 14.4 yuan.

[0050] As can be seen, this optional embodiment can dynamically determine the final payment amount of food by analyzing the adjustment ratio of the basic pre-payment fee from multiple dimensions, including the difference between the food's weight gain and target weight, the difference between the actual and expected proportions of the food, the difference in taste sensation and eating habits, and the impact of food-serving behavior on other customers' queuing. This approach helps ensure cost control for businesses while also considering the customer's dining experience and fairness. Furthermore, adapting dynamic weights to different scenario needs improves the efficiency of food-serving management for businesses and reduces the negative impact of customers' queuing on others.

[0051] Example 2 Please see Figure 2 , Figure 2 This is a flowchart illustrating another intelligent deduction method for meal fees disclosed in an embodiment of the present invention. Figure 2 The described intelligent deduction method for meal service fees can be applied to meal service equipment already installed in various scenarios such as schools, office buildings, and factories; this embodiment of the invention is not limited to this. Optionally, the method can be implemented by a meal service fee deduction device, which can be integrated into the meal service equipment or can be a local server or cloud server used to process the intelligent deduction process for meal service fees; this embodiment of the invention is not limited to this. Figure 2 As shown, the intelligent deduction method for meal fees can include the following operations: 201. When the food dispensing equipment detects a decrease in the weight of food in the food area, it determines the target weight parameter of the food whose weight has decreased and obtains the type parameter of the food.

[0052] 202. Determine the amount to be paid for the food based on the target weight and type parameters.

[0053] 203. Identify the personnel who receive the food and deduct the amount due from their payment account based on the identification.

[0054] 204. When it is detected that the person serving the meal has finished eating and returned the serving tray, obtain the target eating information of the person serving the meal.

[0055] In this embodiment of the invention, optionally, the target dining situation includes dining parameters and / or post-dinning behavior. Further optionally, dining parameters include dining duration parameters and / or food residue parameters, and post-dinning behavior includes food waste sorting and / or returning of plates.

[0056] 205. Based on the target dining situation, determine whether the target dining situation meets the preset conditions for good dining behavior.

[0057] In this embodiment of the invention, it can be understood that if it is determined that the meal time of the person serving the food is less than or equal to a preset meal time threshold, and / or, the food waste parameter is less than or equal to a preset waste threshold, and / or, the food waste classification is a preset target classification, and / or, the return of the serving tray is a preset target return, then the target meal situation is determined to meet the preset good meal behavior conditions.

[0058] 206. When it is determined that the target dining situation meets the conditions for good dining behavior, the refund amount for the deducted fees for the person who took the meal is determined according to the target dining situation, and the refund amount is returned to the payment account of the person who took the meal according to the person's identification.

[0059] In this embodiment of the invention, it can be understood that the system determines the return ratio or fixed amount corresponding to the target dining situation according to the preset return calculation method. The return calculation process may include the following dimensions: (1) Dining time: If the dining time is ≤ preset threshold (e.g., 15 minutes), the return fee = the amount to be paid × preset first ratio to encourage efficient dining; (2) Food leftovers: If the leftovers are ≤ preset threshold (e.g., 50 grams), the return fee = the amount to be paid × preset second ratio to serve as a reward for reducing waste; (3) Kitchen waste classification: If the classification accuracy is ≥ 90% (e.g., dry and wet separation is correct), the return fee = fixed amount x yuan to incentivize environmentally friendly behavior; (4) Returning plates: If the accuracy of returning tableware and plates is ≥ 90% and there is no damage, the return fee = fixed amount y yuan to incentivize the maintenance of public facilities, etc.

[0060] In this embodiment of the invention, for other descriptions of steps 201-203, please refer to the detailed description of steps 101-103 in Embodiment 1. This embodiment of the invention will not repeat them.

[0061] As can be seen, implementing this embodiment of the invention can determine whether the dining behavior of the person serving food is good based on their target dining situation. If their behavior is good, the refund amount, which has already been deducted from the food service fee, can be determined based on the target dining situation and returned to the person's payment account. This incentivizes civilized dining behavior among food service personnel, thereby facilitating the smooth and efficient operation of the business and improving the dining experience for both the food service personnel and the business's user experience with the food service equipment.

[0062] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an intelligent deduction processing device for meal fees disclosed in an embodiment of the present invention. Figure 3 As shown, the intelligent deduction processing device for meal fees may include: The determination module 301 is used to determine the target weight parameter of the food that has lost weight when the food dispensing equipment detects a decrease in the weight of the food in the food area. Module 302 is used to obtain the type parameter of the food; The determination module 301 is also used to determine the amount to be paid for the food based on the target weight parameters and type parameters; The acquisition module 302 is also used to acquire the personnel identification of the food serving personnel; The deduction module 303 is used to deduct the amount to be paid from the payment account of the person who ordered the meal, based on the person's identification.

[0063] In this optional embodiment, the method by which the acquisition module 302 acquires the personnel identifier of the food server specifically includes: When the food serving equipment integrates an image acquisition device, the device captures a target image of the person serving the food, which serves as their identification; or... When the food serving equipment is equipped with a barcode scanner, the scanner identifies the barcode on the food serving tray of the food serving personnel, and determines the personnel identification of the food serving personnel that matches the barcode based on the pre-determined tray identification-person identification binding relationship and the barcode.

[0064] It is evident that implementation Figure 3 The described intelligent deduction device for food service fees can automatically determine the amount due for the food based on the target weight and type parameters of the food, and then deduct the amount from the payment account of the food server. This not only meets customers' personalized food service needs but also improves the efficiency and accuracy of fee deduction, thereby enhancing overall food service efficiency and facilitating the smooth operation of the restaurant.

[0065] In an optional embodiment, the acquisition module 302 is further configured to acquire a cooked image of the food before the determination module 301 determines the amount to be paid for the food based on the target weight parameter and the type parameter. The determination module 301 is also used to determine the post-cooking dish parameters of the food based on the post-cooking image; determine the impact of the food on the food server based on the post-cooking dish parameters; and determine the basic degree of impact on food corresponding to the impact on food. The device also includes: The first judgment module 304 is used to determine whether the basic edible impact level is greater than or equal to the preset basic impact level threshold. When it is determined that the basic edible impact level is less than the basic impact level threshold, the determination module 301 is triggered to perform the operation of determining the food payment fee based on the target weight parameter and type parameter.

[0066] In this optional embodiment, the parameters of the dish after cooking include at least one of the following: the appearance parameters of the dish after cooking, the degree of rawness or cookedness of the dish after cooking, and the current temperature parameters of the dish; the impact on consumption includes the physiological impact on consumption and / or the psychological impact on consumption.

[0067] It is evident that implementation Figure 4The intelligent deduction device for meal service fees described herein can determine the post-cooking parameters of the food based on images of the food, thereby assessing the basic impact of the food on the service provider's consumption. Only when the impact is minimal will the meal service fee be charged. This improves the reliability and accuracy of the analysis of the basic impact of the food on consumption, ensuring smooth and accurate collection of meal service fees. It also helps ensure the safety of the service provider's food, thus enhancing the efficiency and scientific nature of the business's meal service management.

[0068] In another optional embodiment, the method by which the determining module 301 determines the amount to be paid for the food based on the target weight parameter and the type parameter specifically includes: Obtain the weight gain parameters of the food in the serving trays of the food server, and calculate the weight difference between the target weight parameter and the weight gain parameter; Obtain the target proportion parameters of food in the serving tray and the expected proportion parameters of food for the serving staff, and calculate the proportion difference between the target proportion parameters and the expected proportion parameters. The unpaid cost of the food is determined based on the weight difference, weight gain parameters, and proportion difference.

[0069] In this optional embodiment, the target ratio parameter may include the ingredient ratio parameter and / or the solid-liquid ratio parameter, and the expected food ratio parameter may include the expected ingredient ratio parameter and / or the expected solid-liquid ratio parameter.

[0070] Furthermore, the method by which module 301 determines the unpaid cost of the food based on the weight difference, weight gain parameters, and proportion difference specifically includes: Obtain food taste parameters and the eating habits of the staff serving the food; Based on taste perception parameters and eating habit parameters, determine the differences in taste perception of food among food service personnel; The cost to be paid for the food is determined based on the weight difference, weight gain parameters, proportion difference, and taste difference parameters.

[0071] Optionally, the taste sensation parameters include at least one of the following: texture parameters, temperature parameters, and flavor parameters during consumption; the eating habit parameters include at least one of the following: texture habit parameters, temperature habit parameters, and flavor habit parameters.

[0072] Furthermore, the method by which module 301 determines the amount payable for the food based on the weight difference, weight gain parameters, proportion difference, and palatability difference parameters specifically includes: Obtain information on the food distribution by the staff serving meals; Obtain information on the food queuing situation of people around the food serving staff, and determine the impact of the food serving staff on the food serving situation of people around them based on the food serving situation and the food queuing situation; The unpaid cost of the food is determined based on the weight difference, weight gain parameters, proportion difference, taste difference parameters, and the impact of serving.

[0073] In this optional embodiment, the food serving situation may optionally include at least one of the following: food serving rate, food serving duration, and food serving behavior.

[0074] It is evident that implementation Figure 4 The intelligent deduction device for food service fees described herein can dynamically determine the final payment amount by analyzing multiple dimensions, including the difference between the weight gain and target weight of the food, the difference between the actual and expected weight ratio, the difference in taste between the perceived and consuming habits, and the impact of food service behavior on other customers' queues. This approach helps ensure cost control for businesses while also considering customer experience and fairness. Furthermore, by dynamically adjusting weights to adapt to different scenarios, it improves the efficiency of food service management for businesses and reduces the negative impact of customers' queueing on others.

[0075] In yet another optional embodiment, the acquisition module 302 is further configured to: When the monitoring system detects that the person serving the food has finished eating and returned the food tray, the target eating situation of the person serving the food is obtained. The device also includes: The second judgment module 305 is used to determine whether the target dining situation meets the preset good dining behavior conditions based on the target dining situation. The determining module 301 is also used to determine the refund of the deducted fees to the person serving the meal based on the target dining situation when the second determining module 305 determines that the target dining situation meets the conditions for good dining behavior. The return module 306 is used to return the refund fee to the payment account of the person who ordered the meal, based on the person's identification.

[0076] In this optional embodiment, the target dining situation includes dining parameters and / or post-dining behavior. Dining parameters include dining duration parameters and / or food leftover parameters. Post-dining behavior includes food waste sorting and / or dish return.

[0077] It is evident that implementation Figure 4 The described intelligent deduction processing device for meal fees is capable of Example 4 Please see Figure 5 , Figure 5This is a schematic diagram of the structure of a backend server disclosed in an embodiment of the present invention. Figure 5 As shown, the backend server may include: Memory 401 storing executable program code; Processor 402 coupled to memory 401; The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the intelligent deduction method for meal service fees described in Embodiment 1 or Embodiment 2 of the present invention.

[0078] Example 5 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the intelligent deduction method for meal service fees described in Embodiment 1 or Embodiment 2 of this invention.

[0079] Example 6 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the intelligent deduction processing method for meal service fees described in Embodiment 1 or Embodiment 2.

[0080] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0081] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0082] Finally, it should be noted that the intelligent deduction method, device, and backend server for meal payment disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligently deducting meal fees, characterized in that, The method includes: When the food dispensing equipment detects a decrease in the weight of food in the food area, it determines the target weight parameter of the food whose weight has decreased and obtains the type parameter of the food. The amount to be paid for the food is determined based on the target weight parameter and the type parameter. Obtain the personnel identifier of the person serving the food, and deduct the unpaid fee from the payment account of the person serving the food based on the personnel identifier.

2. The intelligent deduction method for meal service fees according to claim 1, characterized in that, Before determining the amount to be paid for the food based on the target weight parameter and the type parameter, the method further includes: The food is captured as a cooked image, and the cooked food parameters are determined based on the cooked image. The cooked food parameters include at least one of the following: cooked food appearance parameters, cooked food degree of doneness parameters, and current food temperature parameters. Based on the parameters of the cooked dish, determine the impact of the food on the food server; the impact includes physiological and / or psychological effects. Determine the baseline level of food impact corresponding to the aforementioned food impact situation, and determine whether the baseline level of food impact is greater than or equal to a preset baseline level of impact threshold. When it is determined that the basic impact of consumption is less than the basic impact threshold, the operation of determining the payment amount of the food based on the target weight parameter and the type parameter is triggered.

3. The intelligent deduction method for meal service fees according to claim 2, characterized in that, The step of determining the unpaid cost of the food based on the target weight parameter and the type parameter includes: Obtain the weight gain parameter of the food in the serving tray of the food server, and calculate the weight difference between the target weight parameter and the weight gain parameter; Obtain the target proportion parameters of the food in the serving tray and the expected proportion parameters of the food of the serving staff, and calculate the proportion difference between the target proportion parameters and the expected proportion parameters of the food; the target proportion parameters include ingredient proportion parameters and / or solid-liquid proportion parameters, and the expected proportion parameters of the food include ingredient proportion parameters and / or solid-liquid proportion parameters. The unpaid cost of the food is determined based on the weight difference, the weight gain parameter, and the ratio difference.

4. The intelligent deduction method for meal service fees according to claim 3, characterized in that, The step of determining the unpaid cost of the food based on the weight difference, the weight gain parameter, and the proportion difference includes: The sensory parameters of the food and the eating habits of the person serving the food are obtained; the sensory parameters include at least one of the following: texture parameters, temperature parameters, and taste parameters; the eating habits parameters include at least one of the following: texture preference parameters, temperature preference parameters, and taste preference parameters. Based on the taste sensation parameters and the eating habit parameters, determine the difference parameters of the food sensation of the serving staff; The unpaid cost of the food is determined based on the weight difference, the weight gain parameter, the ratio difference, and the taste difference parameter.

5. The intelligent deduction method for meal payment fees according to claim 4, characterized in that, The step of determining the unpaid cost of the food based on the weight difference, the weight gain parameter, the proportion difference, and the taste difference parameter includes: The food serving situation of the food serving personnel is obtained; the food serving situation includes at least one of the following: food serving rate, food serving time, and food serving behavior. The system obtains the food queuing information of the people around the food-serving personnel, and determines the impact of the food-serving personnel on the food-serving situation of the people around them based on the food serving situation and the food queuing situation. The unpaid cost of the food is determined based on the weight difference, the weight gain parameter, the ratio difference, the taste difference parameter, and the impact of serving.

6. The intelligent deduction method for meal service fees according to any one of claims 1-5, characterized in that, The method further includes: When it is detected that the person serving the food has finished eating and returned the serving tray, the target dining situation of the person serving the food is obtained; the target dining situation includes dining parameters and / or post-dining behavior, the dining parameters include dining duration parameters and / or food remaining amount parameters, and the post-dining behavior includes food waste classification and / or the return of the serving tray; Based on the target dining situation, determine whether the target dining situation meets the preset conditions for good dining behavior; When it is determined that the target dining situation meets the good dining behavior conditions, the refund amount for the deducted fees for the person who ordered the food is determined according to the target dining situation, and the refund amount is returned to the payment account of the person who ordered the food according to the person's identification.

7. The intelligent deduction method for meal payment fees according to claim 6, characterized in that, The personnel identification of the person receiving the food includes: When the food serving equipment integrates an image acquisition device, the image acquisition device captures a target image of the person serving the food, which serves as the person's identification; or... When the food serving equipment is integrated with a code reader, the code reader identifies the code on the food serving tray of the food serving personnel, and determines the personnel identifier of the food serving personnel that matches the code based on the pre-determined tray identifier-person identifier binding relationship and the code.

8. A smart deduction processing device for meal payment, characterized in that, The device includes: The determination module is used to determine the target weight parameter of the food whose weight has decreased when the food dispensing equipment detects a decrease in the weight of the food in the food area. The acquisition module is used to acquire the type parameter of the food; The determining module is further configured to determine the unpaid cost of the food based on the target weight parameter and the type parameter; The acquisition module is also used to acquire the personnel identifier of the person serving the food; The deduction module is used to deduct the unpaid fees from the payment account of the person who ordered the food, based on the person's identifier.

9. A backend server, characterized in that, The backend server includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent deduction processing method for meal service fees as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the intelligent deduction method for meal service fees as described in any one of claims 1-7.

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