Service optimization system based on big data

Abstract

The invention discloses a big data-based service optimization system, which comprises a data acquisition module, a dining monitoring module and an output module, and is characterized in that the dining monitoring module is used for analyzing the dining condition of a guest on a dining table; according to the system, the dish taking data statistics sub-module analyzes the condition that a customer takes dishes from a seat, the dish with the maximum dish taking target probability is obtained from the perspective of dish taking of the customer, the time when the dish is taken is recorded through the timing unit, and the real-time consumption condition of the dish is accurately expressed; the dining consumption analysis sub-module is combined with different dining speeds and dish consumption of guests in different time periods to help a waiter to accurately obtain the serving time of dishes and control the serving speed, meanwhile, the problem that dishes are pushed and stacked on a dining table is prevented, the dining process is effectively matched, the dish outlet speed is controlled, and the table top is kept clean and tidy. The system has the characteristics of high service monitoring accuracy and high human resource utilization rate.

Description

Technical Field

[0001] The present invention relates to the technical field of catering management, and particularly to a service optimization system based on big data. Background Art

[0002] As a necessary way for people to have dinner together, although the main criteria for people to evaluate the quality of a restaurant during a dinner are mainly determined by the quality of the dishes in the restaurant, the importance of the hotel service level cannot be ignored.

[0003] In the prior art, after the next dish is prepared and placed in the waiting area for serving, the waiter regularly observes the consumption situation of the dishes on the table, and when it is found that a certain dish is almost finished, the next dish is taken from the waiting area and placed on the table. On the one hand, it is somewhat subjective and inaccurate for the waiter to judge whether the dish is finished by visual inspection, and the waiter's frequent observation of the table situation is also likely to cause dissatisfaction among the guests; on the other hand, since the waiter needs to take into account the dining situations in each private room, running around between each private room makes the service pressure of the waiter too high, and since the dining speeds of the guests are different during different time periods of the meal, the waiter cannot grasp the time for serving and clearing the dishes, resulting in low service efficiency. Therefore, it is very necessary to design a service optimization system based on big data with high service monitoring accuracy and high human resource utilization rate. Summary of the Invention

[0004] The purpose of the present invention is to provide a service optimization system based on big data to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: A service optimization system based on big data, including a data acquisition module, a dining monitoring module, and an output module. The data acquisition module is used to obtain relevant data of the restaurant private rooms; the dining monitoring module is used to analyze the dining situation of the guests at the table; the output module is used to prompt the waiter to serve meals in a timely manner.

[0006] According to the above technical solution, the data acquisition module includes a dish plate information acquisition module and a seating information acquisition module. The dish plate information acquisition module is used to obtain the shape characteristics and area of the dish plate; the seating information acquisition module is used to obtain the seating situation of the guests.

[0007] According to the above technical solution, the dining monitoring module includes a rotation analysis module, a dish-taking monitoring module, a camera module, and a timing unit. The rotation analysis module is used to analyze the situation when the rotating plate rotates; the dish-taking monitoring module is used to monitor the dining of the guests in real time; the camera module is used to take pictures of the guests dining at the table; the timing unit is used to time the process of the guests dining.

[0008] According to the above technical solution, the dish-taking monitoring module further includes a dish-taking data statistics sub-module and a dining consumption analysis sub-module. The dish-taking data statistics sub-module is used to count the time and frequency of guests taking dishes; the dining consumption analysis sub-module is used to analyze whether the dining consumption of the dishes on the table by guests reaches the expected value within the interval time.

[0009] According to the above technical solution, the output module includes a dish-serving prompt module and a cleared-plate prompt module. The dish-serving prompt module is used to prompt the waiter to serve the prepared dishes on the table; the cleared-plate prompt module is used to prompt the waiter to ask the guests whether they want to take away and clean up the dishes that are not needed on the table.

[0010] According to the above technical solution, the operation method of the service optimization system mainly includes the following steps:

[0011] Step S1: When the guests are seated, the dining monitoring module collects the seating situation of the guests around the table through the seating information collection module, and modifies the top view taken by the camera module into a black-and-white analysis diagram;

[0012] Step S2: The waiter serves the dishes normally, and the dish-taking monitoring module monitors the dish-taking situation of the guests. The dish-taking data statistics sub-module obtains the dish-taking time and total dish-taking times of each dish in the black-and-white analysis diagram by the guests;

[0013] Step S3: When the camera module monitors that the white part starts to move around the center of the table, the rotation analysis module determines that the table is rotated by an external force and continues to monitor the white part inside the table. If after T seconds, there is a disappearance or addition of white part inside the table, the rotation analysis module determines that there is an increase or decrease in the dishes on the table, and checks whether the dishes on the table match the increase or decrease information of the dishes in the output module through the output module. The timing unit starts timing for the dishes corresponding to the newly added white part and stops timing for the dishes corresponding to the reduced white part;

[0014] Step S4: When the amount of dishes on the table reaches or reaches the maximum number P1 of dish plates that can be loaded on the table, the dining consumption analysis sub-module judges whether the dining consumption of the dishes on the table by guests reaches the standard value at regular intervals, and transmits the result to the output module, where P is the total number of dishes recorded in the guests' order menu;

[0015] Step S5: The temperature sensor obtains the temperatures of H dishes in the to-be-served area as C1, C2... C H, and obtain the temperature change of the dishes in real time. When the temperature change of the dishes on the table exceeds the rated edible temperature of F degrees Celsius, the dish serving reminder module marks it as a "compulsory dish serving item". The camera module counts the number of dishes on the current table. If the number of dishes on the current table exceeds the maximum placement quantity, the dining consumption analysis sub-module obtains the dishes corresponding to Max{η1, η2......η L}, and the dish clearing reminder module prompts the waiter to negotiate with the guests, adjust the corresponding dishes, make room and place the "compulsory dish serving item";

[0016] Step S6: When the output module receives the dish serving signal, the reminder of the dish serving reminder module prompts the waiter to serve the dishes; when the output module receives the dish clearing signal, the reminder of the dish clearing reminder module prompts the waiter to ask the guests whether to take away the dish plates.

[0017] According to the above technical solution, in step S1, the dining monitoring module takes pictures of the guests' dining situation through a camera set directly above the table, obtains the top view of the dining taken, takes the guests outside the table as the external monitoring targets within the top view, obtains the shape characteristics of the guests in the top view through the database and sets the area occupied by the guests in the top view as black; takes the dish plates inside the table as the internal monitoring targets within the top view, obtains the characteristics of the dish plates through the database and sets the dish plates in the top view as white, obtains the area of each white part on the current table and marks each white part according to the different shape distributions of the white parts.

[0018] According to the above technical solution, in step S2, when the camera module monitors that the black part starts to move in the picture, it focuses on the guest corresponding to the black part, and obtains that the number of coincidences between the black part and the white part is X. When X = 1, it is determined that the guest is grabbing the dish in front of him / her, and the timing unit records the time when the guest eats the dish corresponding to the current white part, and the dish taking data statistics sub-module adds 1 to the eating times of this dish; when X > 1, it is determined that the guest crosses the dish in front of him / her to grab other dishes. When the black part stops, the dish taking data statistics sub-module obtains the maximum value point of the central offset distance of the black part inside compared with the original position when the black part stops and obtains the maximum value M of the offset distance, screens the two nearest dishes at the offset maximum value point, obtains the serving times of the two dishes and screens out the dish with the shortest serving time, the timing unit records the time when the guest eats the target dish, and the dish taking data statistics sub-module adds 1 to the eating times of the screened target dish.

[0019] According to the above technical solution, step S4 further includes:

[0020] Step S41: After the target dish is served, the meal consumption analysis submodule obtains the time intervals between the last time the dish was consumed on the table and S1, S2, ..., S3 every five minutes through the timing unit. L , and by the formula Calculate the proportion of dishes consumed by guests as η1, η2, ... η L , where K is the number of times the target dish has been eaten, and K1 is the average number of times the dish has been eaten in history obtained through big data. Then it is determined that the current table can continue to be served, and the newly served dish is set as the new target dish, where A is the standard value of the uneaten consumption of all dishes on the table by the guests during the dining monitoring interval, L is the number of dishes on the current table, λ is the unit conversion parameter, and the unit of S is seconds;

[0021] Step S42: If After the next five minutes, continue to monitor the time intervals between the last consumption of the dishes in the previous ten minutes and S L+1 , S L+2 ……S 2L ,like It is determined that the current table can continue to serve dishes, and the newly served dish is set as the new target dish, and the process returns to step S1 to perform a timed detection on the target dish;

[0022] Step S43: If Repeat step S42 until the uneaten food consumption during all time after the target dish is served exceeds the standard value, and then it is determined that the current table can continue to serve food, and the new dish is set as the new target dish, and return to step S1 to perform regular detection on the target dish;

[0023] Step S44: the dish serving prompt module prompts the waiter to prepare to serve the dishes at the target table location, the dining consumption analysis submodule selects dishes with a proportion of more than 90% of the edible dishes and marks these dishes, and the plate clearing prompt module prompts the waiter to ask whether the marked dishes need to be cleared after serving.

[0024] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, through the dish-taking data statistics submodule, analyzes the situation of guests taking dishes from their seats, obtains the dishes with the highest probability of being taken from the perspective of guests taking dishes, records the time when the dishes are eaten through a timing unit, and accurately expresses the real-time consumption of the dishes; the dining consumption analysis submodule combines the different dining speeds and dish consumption of guests in different time periods to help waiters accurately obtain the serving time of dishes and control the serving speed, while preventing the problem of pushing and stacking plates on the dining table, effectively cooperating with the dining process, controlling the speed of dish delivery, and keeping the table clean. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0026] Figure 1 It is a schematic diagram of the system module composition of the present invention. Detailed implementation manners

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] Please refer to Figure 1 , the present invention provides a technical solution: a service optimization system based on big data, including:

[0029] A data collection module, a dining monitoring module, and an output module. The data collection module is used to obtain relevant data of the restaurant private rooms; the dining monitoring module is used to analyze the dining situation of guests at the table; the output module is used to prompt the waiter to serve food in a timely manner.

[0030] Through the dish-taking data statistics sub-module of the present invention, by analyzing the situation of guests taking dishes from their seats, the dish with the highest probability of being the dish-taking target is obtained from the perspective of guests taking dishes. The consumed time of the dish is recorded by the timing unit, accurately expressing the real-time consumption situation of the dish; the dining consumption analysis sub-module combines the different dining speeds and dish consumption amounts of guests in different time periods to help the waiter accurately obtain the serving time of the dishes, control the serving speed, and at the same time prevent the problem of stacked dishes on the table, effectively coordinating the dinner process, controlling the speed of dish preparation, and maintaining the cleanliness of the tabletop.

[0031] The data collection module includes a dish plate information collection module and a seating information collection module. The dish plate information collection module is used to obtain the shape characteristics and area of the dish plate; the seating information collection module is used to obtain the seating situation of guests.

[0032] The dining monitoring module includes a rotation analysis module, a dish-taking monitoring module, a camera module, and a timing unit. The rotation analysis module is used to analyze the situation when the rotating plate rotates; the dish-taking monitoring module is used to monitor the dining of guests in real time; the camera module is used to capture the images of guests dining at the table; the timing unit is used to time the process of guests dining.

[0033] The dish-taking monitoring module further includes a dish-taking data statistics sub-module and a dining consumption analysis sub-module. The dish-taking data statistics sub-module is used to count the time and frequency of guests taking dishes; the dining consumption analysis sub-module is used to analyze whether the dining consumption of the dishes on the table by guests reaches the expected value within the interval time.

[0034] The output module includes a serving reminder module and a clearing reminder module. The serving reminder module is used to remind the waiter to serve the prepared dishes onto the table; the clearing reminder module is used to remind the waiter to ask the guests whether they want to take away and clear the unnecessary dishes on the table.

[0035] In a preferred embodiment, the operation method of the service optimization system mainly includes the following steps:

[0036] Step S1: When the guests are seated, the dining monitoring module collects the seating situation of the guests around the table through the seating information collection module, and modifies the top view taken by the camera module into a black-and-white analysis diagram;

[0037] Step S2: The waiter serves the dishes normally, and the dish-taking monitoring module monitors the dish-taking situation of the guests. The dish-taking data statistics sub-module obtains the dish-taking time and total dish-taking frequency of each dish in the black-and-white analysis diagram by the guests;

[0038] Step S3: When the camera module monitors that the white part starts to move around the center of the table, the rotation analysis module determines that the table is rotated by an external force and continues to monitor the white part inside the table. If there is a disappearance or addition of the white part inside the table after T seconds, the rotation analysis module determines that there is an increase or decrease in the dishes on the table, and checks whether the dishes on the table match the increase and decrease information of the dishes in the output module through the output module. The timing unit starts timing for the dishes corresponding to the newly added white part and stops timing for the dishes corresponding to the reduced white part;

[0039] Step S4: When the amount of dishes on the table reaches or reaches the maximum number of dish plates P1 that can be loaded on the table, the dining consumption analysis sub-module determines every once in a while whether the dining consumption of the dishes on the table by the guests reaches the standard value, and transmits the result to the output module, where P is the total number of dishes recorded in the guests' order menu;

[0040] When the number of dish plates has not reached the rated value, since there is still space on the table to accommodate dishes, other factors do not need to be considered; but when the amount of dishes reaches the rated value, it is necessary to start appropriately clearing the eaten dish plates on the table. Therefore, the increase and decrease of the dishes need to be comprehensively evaluated according to the guests' consumption of the dishes.

[0041] Step S5: The temperature sensor obtains the temperatures of H dishes in the to-be-served area as C1, C2... C H, and obtain the temperature change of the dishes in real time. When the temperature change of the dishes on the table exceeds the rated edible temperature of F degrees Celsius, the dish serving reminder module marks it as a "compulsory dish to be served". The camera module counts the number of dishes on the current table. If the number of dishes on the current table exceeds the maximum placement quantity, the dining consumption analysis sub-module obtains the dishes corresponding to Max{η1, η2......η L}, and the dish clearing reminder module prompts the waiter to negotiate with the guests, adjust the corresponding dishes, make room and place the "compulsory dish to be served";

[0042] In order to ensure that guests can eat the dishes at the most suitable time for eating, by monitoring the temperature change of the dishes, while trying to ensure that the serving frequency is consistent with the guests, the dishes are served in a timely manner after the temperature reaches the rated value, improving the degree of humanization and the reputation of the hotel.

[0043] Step S6: When the output module receives a dish serving signal, the reminder of the dish serving reminder module prompts the waiter to serve the dishes; when the output module receives a dish clearing signal, the reminder of the dish clearing reminder module prompts the waiter to ask the guests whether to take away the dish plates.

[0044] In step S1 of this embodiment, the dining monitoring module takes pictures of the guests' dining situations through a camera set directly above the table, obtains the top view of the dining taken, takes the guests outside the table as the external monitoring targets within the top view, obtains the shape characteristics of the guests' top view through the database and sets the area occupied by the guests in the top view as black; takes the dish plates inside the table as the internal monitoring targets within the top view, obtains the characteristics of the dish plates through the database and sets the dish plates in the top view as white, obtains the area of each white part on the current table and marks each white part according to the different shape distributions of the white parts.

[0045] In step S2 of this embodiment, when the camera module monitors that the black part starts to move in the picture, it focuses on the guests corresponding to the black part, and obtains that the number of overlaps between the black part and the white part is X. When X = 1, it is determined that the guests are grabbing the dishes in front of them, and the timing unit records the time when the guests eat the dishes corresponding to the current white part, and the dish taking data statistics sub-module adds 1 to the number of times the dish is eaten; when X > 1, it is determined that the guests cross the dishes in front of them to grab other dishes. When the black part stops, the dish taking data statistics sub-module obtains the maximum value point of the center offset distance of the black part inside compared with the original position when the black part stops and obtains the maximum value M of the offset distance, screens the two nearest dishes at the offset maximum value point, obtains the serving times of the two dishes and screens out the dish with the shortest serving time, the timing unit records the time when the guests eat the target dish, and the dish taking data statistics sub-module adds 1 to the number of times the screened target dish is eaten.

[0046] The dish-taking data statistics sub-module analyzes the situation of guests taking dishes from their seats. Since the dish-taking situation is complex, directly analyzing the dishes to obtain the consumption situation would be cumbersome and inaccurate. Instead, by obtaining the dishes with the highest probability of being the dish-taking target from the perspective of guests taking dishes, and using the timing unit to record the consumption time of the dishes, the real-time consumption situation of the dishes can be accurately expressed.

[0047] In this embodiment, step S4 further includes:

[0048] Step S41: Every five minutes after the target dish is served, the dining consumption analysis sub-module obtains, through the timing unit, the time intervals S1, S2,..., S between the times when the dishes on the table are consumed and the last time they were consumed. L , and through the formula calculate the proportions η1, η2,..., η of the dishes consumed by the guests. L , where K is the number of times the target dish has been consumed, K1 is the average value of the historical consumption times of this dish obtained through big data. If then it is determined that the current table can continue to be served, and the newly served dish is set as the new target dish, where A is the standard value of the unconsumed amount of all dishes on the table by the guests within the dining monitoring interval, L is the number of dishes on the current table, λ is the unit conversion parameter, and the unit of S is seconds;

[0049] Step S42: If Continue to monitor the time intervals S L+1 , S L+2 ... S 2L between the times when the dishes were consumed and the last time they were consumed within the first ten minutes in the next five minutes. If then it is determined that the current table can continue to be served, and at the same time, the newly served dish is set as the new target dish, and return to step S1 to perform timed detection on the target dish;

[0050] Step S43: If Repeat the steps of step S42 until it is determined that the current table can continue to be served after the unconsumed amount exceeds the standard value within all the time after the target dish is served, and at the same time, set the newly served dish as the new target dish, and return to step S1 to perform timed detection on the target dish;

[0051] It is calculated from (300 - S1) + (300 - S2) +... + (300 - S L ), and (300 - S) represents the time when the guests at the table did not consume the target dish during the interval time.

[0052] The dining consumption amount A of the dishes on the table within the dining monitoring interval can quantify the consumption situation of the dishes on the table by the guests within five minutes. If This means that the consumption of dishes on the table within five minutes is greater than the standard value, and the waiter needs to add dishes in time to meet the needs of the guests; if This means that within five minutes, the amount of food consumed on the table was less than the standard value due to factors such as guests chatting and drinking, and the waiter does not need to add more dishes for the time being.

[0053] Step S44: the dish serving prompt module prompts the waiter to prepare to serve the dishes at the target table location, the dining consumption analysis submodule selects dishes with a proportion of more than 90% of the edible dishes and marks these dishes, and the plate clearing prompt module prompts the waiter to ask whether the marked dishes need to be cleared after serving.

[0054] The dining consumption analysis submodule combines the different dining speeds and food consumption of guests in different time periods to help waiters accurately obtain the serving time of dishes and control the serving speed. If the guests eat quickly, the dishes will be served quickly to avoid being out of sync and leaving guests waiting in vain; if the guests eat slowly, the dishes will be served slowly to prevent the serving speed from being too fast, causing plates to be pushed and stacked on the table, effectively coordinating the dining process, controlling the speed of serving dishes, and keeping the table clean.

[0055] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0056] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A service optimization system based on big data, characterized in that: It includes a data acquisition module, a dining monitoring module, and an output module. The data acquisition module is used to obtain relevant data of the restaurant private room; the dining monitoring module is used to analyze the dining situation of guests at the table; the output module is used to prompt the waiter to serve food in a timely manner.

2. The service optimization system based on big data according to claim 1, wherein: The data acquisition module includes a dish plate information acquisition module and a seating information acquisition module. The dish plate information acquisition module is used to obtain the shape characteristics and area of the dish plate. The seating information acquisition module is used to obtain the seating situation of guests.

3. The service optimization system based on big data according to claim 2, characterized in that: The dining monitoring module includes a rotation analysis module, a dish-taking monitoring module, a camera module, and a timing unit. The rotation analysis module is used to analyze the situation when the rotating plate rotates; the dish-taking monitoring module is used to monitor the dining of guests in real time; the camera module is used to take pictures of the guests dining at the table; the timing unit is used to time the process of guests dining.

4. The service optimization system based on big data according to claim 3, wherein: The dish-taking monitoring module further includes a dish-taking data statistics sub-module and a dining consumption analysis sub-module. The dish-taking data statistics sub-module is used to count the time and number of times guests take dishes; the dining consumption analysis sub-module is used to analyze whether the dining consumption of dishes on the table by guests reaches the expected value within the interval time.

5. The service optimization system based on big data according to claim 4, characterized in that: The output module includes a serving prompt module and a clearing prompt module. The serving prompt module is used to prompt the waiter to bring the prepared dishes to the table; the clearing prompt module is used to prompt the waiter to ask the guests whether they want to take away and clean up the dishes that are not needed on the table.

6. The service optimization system based on big data according to claim 5, characterized in that: The operation method of the service optimization system mainly includes the following steps: Step S1: When the guests are seated, the dining monitoring module collects the seating situation of the guests around the table through the seating information acquisition module, and modifies the top view taken by the camera module into a black-and-white analysis diagram. Step S2: The waiter serves the dishes normally. The dish-taking monitoring module monitors the dish-taking situation of the guests. The dish-taking data statistics sub-module obtains the dish-taking time and total number of times of each dish in the black-and-white analysis diagram by the guests. Step S3: When the camera module monitors that the white part starts to move around the center of the table, the rotation analysis module determines that the table is rotated by an external force and continues to monitor the white part inside the table. If after T seconds, there is a disappearance or addition of white part inside the table, the rotation analysis module determines that there is an increase or decrease in the dishes on the table, and checks whether the dishes on the table match the increase or decrease information of the dishes in the output module through the output module. The timing unit starts timing for the dishes corresponding to the newly added white part and stops timing for the dishes corresponding to the reduced white part. Step S4: When the amount of dishes on the table reaches or reaches the maximum number of dish plates P1 that can be loaded on the table, the dining consumption analysis sub-module determines every once in a while whether the dining consumption of the guests for the dishes on the table reaches the standard value, and transmits the result to the output module, where P is the total number of dishes recorded in the guests' order menu; Step S5: The temperature sensor obtains the temperatures of H dishes in the to-be-served area as C1, C2... C H , and obtains the temperature changes of the dishes in real time. When the temperature change of the dishes on the table exceeds the rated edible temperature by F degrees Celsius, the serving reminder module marks it as a "compulsory serving dish". The camera module counts the number of dishes on the current table. If the number of dishes on the current table exceeds the maximum placement quantity, the dining consumption analysis sub-module obtains the dish corresponding to Max{η1, η2...... η L}, and the clearing reminder module prompts the waiter to negotiate with the guests to adjust the corresponding dishes, make room and place the "compulsory serving dish"; Step S6: When the output module receives the serving signal, it prompts the waiter to serve the dishes through the prompt device of the serving prompt module; when the output module receives the clearing signal, it prompts the waiter to ask the guests whether they want to take away the dish plates through the prompt device of the clearing prompt module.

7. The service optimization system based on big data according to claim 6, characterized in that: In step S1, the dining monitoring module takes pictures of the guests' dining situation through a camera set directly above the dining table, obtains the top view of the dining taken, takes the guests outside the dining table as external monitoring targets within the top view, obtains the shape characteristics of the guests' top view through the database and sets the area occupied by the guests in the top view as black; takes the food plates inside the dining table as internal monitoring targets within the top view, obtains the characteristics of the food plates through the database and sets the food plates in the top view as white, obtains the area of each white part on the current dining table and marks each white part according to the different shape distributions of the white parts.

8. A service optimization system based on big data according to claim 7, characterized in that: In step S2, when the camera module monitors that the black part starts to move in the picture, it focuses on the guest corresponding to the black part, obtains the number of coincidences between the black part and the white part as X. When X = 1, it is determined that the guest is grabbing the food in front of him / her, and the timing unit records the time when the guest eats the food corresponding to the current white part, and the dish-taking data statistics sub-module adds 1 to the number of times the dish is eaten; when X > 1, it is determined that the guest has crossed the food in front of him / her to grab other food. When the black part stops, the dish-taking data statistics sub-module obtains the maximum value point of the central offset distance inside the black part compared to the original position when the black part stops and obtains the maximum value M of the offset distance, selects the two closest dishes at the offset maximum value point, obtains the serving times of the two dishes and selects the dish with the shortest serving time. The timing unit records the time when the guest eats the target dish, and the dish-taking data statistics sub-module adds 1 to the number of times the selected target dish is eaten.

9. A service optimization system based on big data according to claim 8, characterized in that: Step S4 further includes: Step S41: Every five minutes after the target dish is served, the dining consumption analysis sub-module obtains, through the timing unit, the time intervals S1, S2, …, S between the times when the dishes on the table were last consumed L , and calculates, through the formula , the proportions η1, η2, …, η of the dishes consumed by the guests L , where K is the number of times the target dish has been consumed, K1 is the average value of the historical consumption times of this dish obtained through big data. If , it is determined that new dishes can be served at the current table, and the newly served dishes are set as the new target dishes, where A is the standard value of the unconsumed amount of all the dishes on the table by the guests within the dining monitoring interval, L is the number of dishes on the current table, λ is the unit conversion parameter, and the unit of S is seconds; Step S42: If After the next five minutes, continue to monitor the time intervals between the dishes in the previous ten minutes and the last time they were eaten, which are S L+1 , S L+2 ……S 2L , if It is determined that the current table can continue to be served, and the newly served dish is set as the new target dish, and return to step S1 to perform timed detection on the target dish; Step S43: If Repeat the steps of Step S42 until it is determined that the current table can continue to serve dishes after the uneaten consumption during all the time after the target dish is served exceeds the standard value. At the same time, set the newly served dish as the new target dish and return to Step S1 to perform timing detection on the target dish; Step S44: The serving prompt module prompts the waiter to get ready to serve at the target dining table position. The dining consumption analysis sub-module selects the dishes with a consumption ratio exceeding 90% among the eaten dishes and marks these dishes, and through the cleared plate prompt module, prompts the waiter to ask whether the marked dishes need to be cleared after serving.

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