Store management system and information processing device

Abstract

To efficiently arrange staff of a restaurant.SOLUTION: A store management system 1 includes in-store cameras 30-1, 30-2, 30-3 installed within a restaurant, out-store cameras 40-1, 40-2, 40-3 installed along a drive-through lane outside the restaurant, an information processing device 10, and a display 20, the information processing device 10 monitors a congestion state in a prescribed area within the restaurant by using a store image being an image photographed by the in-store cameras 30-1, 30-2, 30-3, and monitors the progress of cars on the drive-through lane by using an out-store image being an image photographed by the out-store cameras 40-1, 40-2, 40-3, and the display 20 displays the congestion state and the advancing state.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a store management system and an information processing device. [Background technology]

[0002] Some restaurants, such as fast food restaurants, offer drive-through food and beverage services. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-063338 [Patent Document 2] Japanese Patent Application Laid-Open No. 2014-035633 [Patent Document 3] Japanese Patent Application Laid-Open No. 2005-242767 Summary of the Invention [Problem to be solved by the invention]

[0004] In restaurants that offer drive-through service, it is desirable to efficiently allocate staff (hereinafter referred to as "drive-through staff") who serve customers who purchase food and beverages through the drive-through (hereinafter referred to as "drive-through customers") and staff (hereinafter referred to as "in-store staff") who serve customers who purchase food and beverages inside the restaurant (hereinafter referred to as "in-store customers").

[0005] Therefore, the present disclosure proposes a technology that enables efficient allocation of restaurant staff. [Means for solving the problem]

[0006] The store management system disclosed herein includes an in-restaurant camera installed inside a restaurant, an exterior camera installed along a drive-through lane outside the restaurant, an information processing device, and a display. The information processing device monitors the congestion status in a predetermined area of ​​the restaurant using in-restaurant images taken by the in-restaurant camera, and monitors the progress of vehicles in the drive-through lane using exterior images taken by the exterior camera. The display displays the congestion status and the progress. [Effects of the Invention]

[0007] According to the present disclosure, restaurant staff can be allocated efficiently. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a store management system according to the present disclosure. [Figure 2] FIG. 1 shows an example of in-store camera installation according to the present disclosure. [Figure 3] FIG. 1 shows an example of in-store camera installation according to the present disclosure. [Figure 4] FIG. 1 shows an example of in-store camera installation according to the present disclosure. [Figure 5] FIG. 1 shows an example of installation of an outside camera according to the present disclosure. [Figure 6] FIG. 10 is a diagram showing an example of displaying the in-store congestion status and drive-through progress status of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. In the following embodiments, the same components or processes will be denoted by the same reference numerals, and redundant description may be omitted.

[0010] [Example] <Store management system configuration> FIG. 1 is a diagram illustrating an example configuration of a store management system according to the present disclosure. The store management system 1 shown in FIG. 1 is installed in a restaurant, such as a fast-food restaurant. In FIG. 1, the store management system 1 includes an information processing device 10, a display 20, a first in-store camera 30-1, a second in-store camera 30-2, a third in-store camera 30-3, a first outside camera 40-1, a second outside camera 40-2, and a third outside camera 40-3. The information processing device 10, the display 20, the first in-store camera 30-1, the second outside camera 30-2, and the third in-store camera 30-3 are installed inside the restaurant, while the first outside camera 40-1, the second outside camera 40-2, and the third outside camera 40-3 are installed along a drive-through lane outside the restaurant. The information processing device 10 is connected to the first in-store camera 30-1, the second in-store camera 30-2, the third in-store camera 30-3, the first outside-store camera 40-1, the second outside-store camera 40-2, and the third outside-store camera 40-3 via a network 50. An example of the information processing device 10 is a computer. An example of the display 20 is an LCD (Liquid Crystal Display). An example of the network 50 is a LAN (Local Area Network). Below, the first in-store camera 30-1, the second in-store camera 30-2, and the third in-store camera 30-3 may be collectively referred to as the "in-store cameras 30." Also below, the first outside-store camera 40-1, the second outside-store camera 40-2, and the third outside-store camera 40-3 may be collectively referred to as the "outside-store cameras 40."

[0011] <Configuration of information processing device> 1, an information processing device 10 has a processor 11, a communication module 12, and a memory unit 13. The communication module 12 is connected to a network 50, and the processor 11 can communicate with an in-store camera 30 and an outside-store camera 40 via the network 50 using the communication module 12. The memory unit 13 stores a trained model M1. Examples of the processor 11 include a CPU (Central Processing Unit) and a DSP (Digital Signal Processor). Examples of the memory unit 13 include storage and memory.

[0012] <In-store camera> 2, 3, and 4 are diagrams showing installation examples of in-store cameras according to the present disclosure. Fig. 2 shows an installation example of a first in-store camera 30-1, Fig. 3 shows an installation example of a second in-store camera 30-2, and Fig. 4 shows an installation example of a third in-store camera 30-3.

[0013] 2, the first in-store camera 30-1 is installed near the entrance of the restaurant, captures an image (hereinafter sometimes referred to as an "entrance image") of a predetermined area AR11 near the entrance (hereinafter sometimes referred to as an "entrance area"), and transmits the captured entrance image to the information processing device 10. In-store customers enter the restaurant by passing through the entrance area.

[0014] As shown in FIG. 3 , the second in-store camera 30-2 is installed near a counter CO1 used for serving customers in the restaurant (hereinafter, sometimes referred to as the “in-store customer counter”). The in-store customer counter CO1 is divided into a counter where customers order food and beverages (hereinafter, sometimes referred to as the “in-store order counter”) and a counter where customers who have already ordered food and beverages pick up their food and beverages (hereinafter, sometimes referred to as the “in-store pickup counter”). The second in-store camera 30-2 captures images of a predetermined area AR12 in front of the in-store order counter (hereinafter, sometimes referred to as the “in-store order area”) (hereinafter, sometimes referred to as the “in-store order area image”) and an image of a predetermined area AR13 in front of the in-store pickup counter (hereinafter, sometimes referred to as the “in-store pickup area”) (hereinafter, sometimes referred to as the “in-store pickup area image”), and transmits the captured in-store order area image and in-store pickup area image to the information processing device 10. When the restaurant is crowded, customers may linger in the in-store order area (i.e., waiting to order) or in the in-store pickup area (i.e., waiting to pick up).

[0015] 4, the third in-store camera 30-3 is installed near the customer seats in the restaurant, captures an image of the customer seating area AR14 (hereinafter sometimes referred to as a "customer seating image"), and transmits the captured customer seating image to the information processing device 10. Customers in the restaurant can eat the purchased food and drink at their seats or take it home.

[0016] Since the entrance images, in-store ordering area images, in-store pick-up area images, and customer seating images are all images taken inside a restaurant, in the following, the entrance images, in-store ordering area images, in-store pick-up area images, and customer seating images may be collectively referred to as "in-store images."

[0017] <Outside store camera> Fig. 5 is a diagram showing an example of the installation of the exterior cameras of the present disclosure. As shown in Fig. 5, a first exterior camera 40-1, a second exterior camera 40-2, and a third exterior camera 40-3 are installed along a drive-through lane arranged along the perimeter of a restaurant ST. The store ST has a counter CO2 (hereinafter sometimes referred to as an "exterior ordering counter") where drive-through customers order food and beverages, and a counter CO3 (hereinafter sometimes referred to as an "exterior pickup counter") where drive-through customers who have already ordered food and beverages pick up their food and beverages.

[0018] As shown in Figure 5, the drive-through lane starts at entrance line L1 and ends at pickup line L3. It is divided into an area AR21 from entrance line L1 to order line L2 (hereinafter referred to as the "off-site order waiting area") and an area AR22 from order line L2 to pickup line L3 (hereinafter referred to as the "off-site pickup waiting area"). For example, the off-site order waiting area AR21 can accommodate a maximum of six vehicles, CA5 to CA10, and the off-site pickup waiting area AR22 can accommodate a maximum of four vehicles, CA1 to CA4. In addition, in the drive-through lane, a drive-through product ordering area AR23 (hereinafter referred to as the "off-site order area") is set up in front of the off-site order counter CO2, and a drive-through product pickup area AR24 (hereinafter referred to as the "off-site pickup area") is set up in front of the off-site pickup counter CO3.

[0019] The entry line L1 is located at the start of the drive-through lane. The order line L2 is a line that serves as a landmark when a vehicle carrying a drive-through customer stops in front of the outside order counter CO2, and is located, for example, near the front end of vehicle CA5 that stops in front of the outside order counter CO2. The pickup line L3 is located at the end of the drive-through lane and is a line that serves as a landmark when a vehicle carrying a drive-through customer stops in front of the outside pickup counter CO3. The pickup line L3 is located, for example, near the front end of vehicle CA1 that stops in front of the outside pickup counter CO3.

[0020] As shown in FIG. 5, the first outside camera 40-1 is installed in a position that allows a panoramic view of the outside order waiting area AR21, captures an image of the outside order waiting area AR21 (hereinafter may be referred to as the "outside order waiting area image"), and transmits the captured outside order waiting area image to the information processing device 10. The second outside camera 40-2 is installed facing the outside order counter CO2, captures an image of the front of the outside order counter CO2 (hereinafter may be referred to as the "outside order counter image"), and transmits the captured outside order counter image to the information processing device 10. The third outside camera 40-3 is installed in a position that allows a panoramic view of the outside pickup waiting area AR22, captures an image of the outside pickup waiting area AR22 (hereinafter may be referred to as the "outside pickup waiting area image"), and transmits the captured outside pickup waiting area image to the information processing device 10.

[0021] Since the images of the outside order waiting area, the images of the outside order counter, and the images of the outside pickup waiting area are all images taken outside the restaurant, in the following, the images of the outside order waiting area, the images of the outside order counter, and the images of the outside pickup waiting area may be collectively referred to as "outside images."

[0022] <Operation of information processing device> In the information processing device 10, the processor 11 receives images inside the store from the in-store camera 30 and images outside the store from the outside camera 40 using the communication module 12.

[0023] Processor 11 recognizes the images of people (hereinafter referred to as "people images") contained in the received entrance image and counts the number of people images (hereinafter referred to as "number of people entering the store") contained in the entrance image.

[0024] Processor 11 also recognizes human images included in the received in-store ordering area image and counts the number of human images included in the in-store ordering area image (hereinafter, sometimes referred to as "number of people waiting to order in-store"). The number of people waiting to order in-store corresponds to the number of customers remaining in in-store ordering area AR12.

[0025] Processor 11 also recognizes people images included in the received in-store pickup area image and counts the number of people images included in the in-store pickup area image (hereinafter, sometimes referred to as "number of people waiting for pickup in-store"). The number of people waiting for pickup in-store corresponds to the number of customers remaining in in-store pickup area AR13.

[0026] The processor 11 also recognizes the human images included in the received seating image and counts the number of human images included in the seating image (hereinafter, sometimes referred to as the "number of seated customers"). The number of seated customers corresponds to the number of customers staying in the seating area AR14.

[0027] The processor 11 recognizes a human image using, for example, a trained model for human recognition (hereinafter, sometimes referred to as a “human recognition model”) generated by machine learning. The human recognition model is an example of the trained model M1 stored in the storage unit 13.

[0028] Processor 11 also recognizes the car images (hereinafter sometimes referred to as "car images") included in the received image of the outside-store order waiting area, and counts the number of car images (hereinafter sometimes referred to as "the number of cars waiting for orders outside the store"). The number of cars waiting for orders outside the store corresponds to the number of cars parked in outside-store order waiting area AR21.

[0029] In addition, processor 11 recognizes the car images contained in the received outside-store order counter image and counts the number of car images contained in the outside-store order counter image (hereinafter sometimes referred to as the "number of outside-store ordered cars").

[0030] Processor 11 also recognizes the car images included in the received outside-store pickup waiting area image and counts the number of car images included in the outside-store pickup waiting area image (hereinafter, sometimes referred to as the "number of cars waiting for outside-store pickup"). The number of cars waiting for outside-store pickup corresponds to the number of cars parked in outside-store pickup waiting area AR22.

[0031] The processor 11 recognizes car images using, for example, a trained model for car recognition (hereinafter, sometimes referred to as a “car recognition model”) generated by machine learning. The car recognition model is an example of the trained model M1 stored in the storage unit 13.

[0032] Processor 11 monitors the congestion status (hereinafter sometimes referred to as "in-store congestion status") in each of the predetermined areas of the restaurant, namely, entrance area AR11, in-store ordering area AR12, in-store pickup area AR13, and seating area AR14, by counting the number of people entering the restaurant, the number of people waiting to order inside the restaurant, the number of people waiting to pick up food inside the restaurant, and the number of people seated at the tables, and displays the monitored in-store congestion status on display 20. Processor 11 also monitors the progress of vehicles in the drive-through lane (hereinafter sometimes referred to as "drive-through progress status") by counting the number of vehicles waiting to order outside the restaurant, the number of vehicles ordering outside the restaurant, and the number of vehicles waiting to pick up food outside the restaurant, and displays the monitored drive-through progress status on display 20.

[0033] FIG. 6 is a diagram showing an example of displaying the in-store congestion status and drive-through progress status according to the present disclosure.

[0034] As shown in Figure 6, processor 11 indicates the congestion status of the store using the number of people waiting to order in the store ("3 people" in the example of Figure 6), the number of people waiting to pick up an order in the store ("8 people" in the example of Figure 6), and the number of customers seated at the tables ("9 people" in the example of Figure 6).

[0035] Processor 11 also calculates the average order waiting time per customer (hereinafter may be referred to as "average in-store order waiting time") based on multiple in-store order waiting numbers counted at regular intervals, and indicates the in-store congestion status using the calculated in-store average order waiting time ("2 minutes 32 seconds" in the example of FIG. 6). Processor 11 also calculates the average in-store pickup waiting time per customer (hereinafter may be referred to as "average in-store pickup waiting time") based on multiple in-store pickup waiting numbers counted at regular intervals, and indicates the in-store congestion status using the calculated average in-store pickup waiting time ("3 minutes 11 seconds" in the example of FIG. 6).

[0036] Furthermore, processor 11 calculates a target value for the in-store average order waiting time (hereinafter sometimes referred to as the "in-store order waiting time target value") and displays the comparison result between the calculated in-store order waiting time target value ("2 minutes 50 seconds" in the example of FIG. 6) and the in-store average order waiting time on display 20. If the in-store average order waiting time is equal to or less than the target value, processor 11 indicates on display 20 that the in-store average order waiting time has achieved the target value, and if the in-store average order waiting time exceeds the target value, indicates on display 20 that the target value has not been achieved or that the target value has been significantly below the target value, depending on the degree of exceedance.

[0037] Processor 11 also calculates a target value for the average in-store pickup wait time (hereinafter sometimes referred to as "target in-store pickup wait time") and displays the comparison result between the calculated target in-store pickup wait time ("3 minutes 00 seconds" in the example of FIG. 6) and the average in-store pickup wait time on display 20. If the average in-store pickup wait time is equal to or less than the target value, processor 11 indicates on display 20 that the average in-store pickup wait time has achieved the target value, and if the in-store pickup order wait time exceeds the target value, processor 11 indicates on display 20 that the target value has not been achieved or that the target value has been significantly below the target value, depending on the degree of exceedance.

[0038] Processor 11 also displays the result of comparing the target number of seated guests ("8 people" in the example of FIG. 6) with the number of seated guests on display 20. If the number of seated guests is equal to or less than the target number, processor 11 indicates on display 20 that the number of seated guests has achieved the target number, and if the number of seated guests exceeds the target number, processor 11 indicates on display 20 that the target number has not been achieved or that the target number has been significantly below the target number, depending on the degree of exceedance.

[0039] 6, processor 11 indicates the drive-through progress status using illustrations of cars corresponding to the number of cars waiting outside the store for orders ("4 cars" in the example of FIG. 6), the number of cars ordering outside the store ("1 car" in the example of FIG. 6), and the number of cars waiting outside the store for pickup ("4 cars" in the example of FIG. 6). Processor 11 may measure the elapsed time from the time each car passed entrance line L1 (hereinafter sometimes referred to as "drive-through elapsed time") for each car, taking the time each car passed entrance line L1 as the start point and the time each car passed pickup line L3 as the end point, and may add the measured drive-through elapsed time for each car to the illustration of each car.

[0040] Furthermore, processor 11 measures, for each vehicle, the time required for each vehicle to pass through entrance line L1 and reach order line L2, i.e., the time required for each vehicle to enter the drive-through lane and reach the outside ordering area AR23 (hereinafter, this may be referred to as the "outside order waiting time"). Processor 11 also measures, for each vehicle, the time required for each vehicle to pass through order line L2 and reach pick-up line L3, i.e., the time required for each vehicle to leave the outside ordering area AR23 and reach the outside pick-up area AR24 (hereinafter, this may be referred to as the "outside pick-up waiting time"). Processor 11 also measures, for each vehicle, the time required for each vehicle to pass through entrance line L1 and reach pick-up line L3, i.e., the time required for each vehicle to enter the drive-through lane and depart from the outside pick-up area AR24 (hereinafter, this may be referred to as the "drive-through residence time").

[0041] Processor 11 also calculates the average outside-store order waiting time per vehicle (hereinafter sometimes referred to as "average outside-store order waiting time") based on the outside-store order waiting times of multiple vehicles, and displays the drive-through progress status using the calculated average outside-store order waiting time ("1 minute 28 seconds" in the example of Figure 6). Processor 11 also calculates the average outside-store pickup waiting time per vehicle (hereinafter sometimes referred to as "average outside-store pickup waiting time") based on the outside-store pickup waiting times of multiple vehicles, and displays the drive-through progress status using the calculated average outside-store pickup waiting time ("4 minutes 18 seconds" in the example of Figure 6). Processor 11 also calculates the average drive-through residence time per vehicle (hereinafter sometimes referred to as "average drive-through residence time") based on the drive-through residence times of multiple vehicles, and displays the drive-through progress status using the calculated average drive-through residence time ("5 minutes 58 seconds" in the "total" in the example of Figure 6).

[0042] Furthermore, processor 11 divides one hour into unit times (for example, every 15 minutes), counts the number of cars that passed through the outside pick-up area AR24 in the unit time immediately before the current time (hereinafter referred to as the "number of cars used within the unit time"), and indicates the progress of the drive-through using the counted number of cars used within the unit time ("12 cars" in the example of Figure 6).

[0043] In addition, processor 11 counts the total number of cars that have passed through the off-site pickup area AR24 since the restaurant opened for business on that day (hereinafter referred to as the "total number of cars on that day"), starting from the restaurant's opening time, and indicates the drive-through progress using the counted total number of cars on that day ("125 cars" in the example of Figure 6).

[0044] In addition, processor 11 counts the number of vehicles that have passed through entrance line L1 since the restaurant opened for business on that day (i.e., the number of vehicles that have entered the drive-through lane) minus the number of vehicles that have passed through off-site ordering area AR23 since the restaurant opened for business on that day, i.e., the number of vehicles that entered the drive-through lane on the business day but left the drive-through lane without placing an order (hereinafter referred to as the "number of vehicles leaving").The processor 11 uses the counted number of vehicles leaving ("7 vehicles" in the example of Figure 6) to indicate the drive-through progress.

[0045] Furthermore, processor 11 calculates a target value for the average off-site order waiting time (hereinafter sometimes referred to as the "target off-site order waiting time value") and displays the result of comparing the calculated target off-site order waiting time value ("1 minute 35 seconds" in the example of FIG. 6) with the average off-site order waiting time on display 20. If the average off-site order waiting time is equal to or less than the target value, processor 11 indicates on display 20 that the average off-site order waiting time has achieved the target value, and if the average off-site order waiting time exceeds the target value, indicates on display 20 that the target value has not been achieved or that the target value has been significantly below the target value, depending on the degree of exceedance.

[0046] Processor 11 also calculates a target value for the average outside-store pickup waiting time (hereinafter sometimes referred to as "outside-store pickup waiting time target value") and displays the result of comparing the calculated outside-store pickup waiting time target value ("3 minutes 15 seconds" in the example of FIG. 6) with the average outside-store pickup waiting time on display 20. If the average outside-store pickup waiting time is equal to or less than the target value, processor 11 indicates on display 20 that the average outside-store pickup waiting time has achieved the target value, and if the outside-store pickup order waiting time exceeds the target value, processor 11 indicates on display 20 that the target value has not been achieved or that the target value has been significantly below the target value, depending on the degree of exceedance.

[0047] Processor 11 also calculates a target value for the average drive-through dwell time (hereinafter sometimes referred to as "target drive-through dwell time value") and displays the result of comparing the calculated target drive-through dwell time value ("3 minutes 50 seconds" in the example of FIG. 6) with the average drive-through dwell time on display 20. If the average drive-through dwell time is equal to or less than the target value, processor 11 indicates on display 20 that the average drive-through dwell time has achieved the target value, and if the average drive-through dwell time exceeds the target value, processor 11 indicates on display 20 that the target value has not been achieved or that the target value has been significantly below the target value, depending on the degree of exceedance.

[0048] Processor 11 also calculates a target value for the total number of vehicles on that day (hereinafter may be referred to as "total number target value") and displays the result of comparing the calculated total number target value ("120 vehicles" in the example of FIG. 6) with the total number of vehicles on that day on display 20. If the total number of vehicles on that day is equal to or greater than the target value, processor 11 indicates on display 20 that the total number of vehicles on that day has achieved the target value, and if the total number of vehicles on that day is less than the target value, processor 11 indicates on display 20 that the target value has not been achieved or that the target value has been significantly underachieved, depending on the degree of shortfall from the target value.

[0049] Processor 11 also displays the result of comparing the target value for the number of leaving vehicles ("5 vehicles" in the example of FIG. 6) with the number of leaving vehicles on display 20. If the number of leaving vehicles is less than the target value, processor 11 indicates on display 20 that the number of leaving vehicles has achieved the target value, and if the number of leaving vehicles exceeds the target value, processor 11 indicates on display 20 that the target value has not been achieved or that the target value has been significantly underachieved, depending on the degree of excess.

[0050] <Calculating target values> The processor 11 calculates the target in-store order waiting time according to the formulas (1) and (3), and calculates the target in-store pickup waiting time according to the formulas (2) and (3). In-store order waiting time target value = In-store productivity × number of people waiting to order in-store × target coefficient … (1) In-store pickup waiting time target = In-store productivity x number of people waiting for pickup in-store x target coefficient … (2) In-store productivity = Basic response time per customer x staff number coefficient x staff skill coefficient ... (3)

[0051] The processor 11 also calculates the target waiting time for an order outside the store according to equations (4) and (6), and calculates the target waiting time for a pickup outside the store according to equations (5) and (6). Target waiting time for orders outside the store = Productivity outside the store × Number of cars waiting for orders outside the store × Target coefficient … (4) Target waiting time for outside pickup = Outside productivity × Number of cars waiting for outside pickup × Target coefficient … (5) In-store productivity = Basic response time per vehicle x staff number coefficient x staff skill coefficient … (6)

[0052] The "target coefficients" in formulas (1), (2), (4), and (5) are set arbitrarily according to the sales targets of each store. The "staff number coefficient" in formulas (3) and (6) is a coefficient set according to the number of store staff, and is set to a larger value as the number of store staff increases. The "staff skill coefficient" in formulas (3) and (6) is a coefficient set according to the skills of the store staff, and is set to a larger value as the skills of the store staff increase.

[0053] Processor 11 also calculates the target drive-thru stay time value according to equation (7). Drive-thru dwell time target value = Target waiting time for ordering outside the store + Target waiting time for receiving outside the store … (7)

[0054] Processor 11 also calculates the target total number of units according to equation (8). The "target coefficient" in equation (8) is set arbitrarily according to the sales target of each store, etc. Total number target value = Total number forecast value for the day × Target coefficient ... (8)

[0055] Processor 11 calculates the "predicted total number of vehicles on the day" in equation (8) based on past performance data, day of the week (holiday information), specific day events, weather, humidity, store campaigns, and traffic volume prediction information. For example, the predicted total number of vehicles on the day is calculated using a trained model for calculating the predicted total number of vehicles on the day (hereinafter sometimes referred to as a "vehicle volume prediction model") that is generated by machine learning using past performance data, day of the week (holiday information), specific day events, weather, humidity, store campaigns, and traffic volume prediction information as training data. The vehicle volume prediction model is an example of trained model M1 stored in memory unit 13.

[0056] The above is a description of the embodiment.

[0057] As described above, the store management system (store management system 1 of the embodiment) of the present disclosure includes an in-store camera (in-store camera 30 of the embodiment) installed inside the restaurant, an outside camera (outside camera 40 of the embodiment) installed along the drive-through lane outside the restaurant, an information processing device (information processing device 10 of the embodiment), and a display (display 20 of the embodiment). The information processing device monitors the congestion status in a predetermined area inside the restaurant using in-store images taken by the in-store camera, and monitors the progress of vehicles in the drive-through lane using outside images taken by the outside camera, and the display shows the congestion status and progress.

[0058] This allows the restaurant to grasp the congestion situation inside the restaurant and the progress of the drive-through in real time, allowing the restaurant staff to be deployed efficiently according to the congestion situation inside the restaurant and the progress of the drive-through. [Explanation of symbols]

[0059] 1. Store management system 10. Information processing equipment 20 Display 30-1 First store camera 30-2 Second store camera 30-3 Third store camera 40-1 First store exterior camera 40-2 Second exterior camera 40-3 Third store exterior camera 11 processors 12 Communication Module 13 Storage section

Claims

1. In-store cameras are cameras installed inside restaurants; an exterior camera that is installed along a drive-through lane outside the restaurant; an information processing device that monitors the congestion status in a predetermined area of ​​the restaurant using an in-store image that is an image taken by the in-store camera, and monitors the progress of vehicles in the drive-through lane using an outside-store image that is an image taken by the outside-store camera; a display that displays the congestion status and the progress status; A store management system comprising:

2. a processor that monitors the congestion status in a predetermined area of ​​the restaurant using inside-restaurant images taken by an inside-restaurant camera that is a camera installed inside the restaurant, and monitors the progress of vehicles in the drive-through lane using outside-restaurant images taken by an outside-restaurant camera that is a camera installed along the drive-through lane outside the restaurant, and displays the congestion status and the progress on a display; An information processing device comprising:

3. The processor indicates the congestion status using a first number of customers, which is the number of customers staying in a first area, which is a predetermined area in front of the order counter, a second number of customers, which is the number of customers staying in a second area, which is a predetermined area in front of the pick-up counter, and a third number of customers, which is the number of customers staying at seats. The information processing device according to claim 2 .

4. The processor displays on the display a comparison result between the first number of people and a first target number of people value that is a target value for the first number of people, a comparison result between the second number of people and a second target number of people value that is a target value for the second number of people, and a comparison result between the third number of people and a third target number of people value that is a target value for the third number of people. The information processing device according to claim 3 .

5. the processor calculates the target number of customers based on the response time per customer, the number of customers, and a target coefficient, and calculates the target number of second customers based on the response time, the second number of customers, and the target coefficient; The information processing device according to claim 4 .

6. The processor indicates the progress status using a first time which is the time required for a vehicle to reach the ordering area after entering the drive-through lane, a second time which is the time required for a vehicle to reach the product receiving area after leaving the ordering area, a third time which is the time required for a vehicle to reach the drive-through lane after leaving the product receiving area, a first number which is the number of vehicles that have passed through the product receiving area, and a second number which is the number of vehicles that have entered the drive-through lane minus the number of vehicles that have passed through the ordering area. The information processing device according to claim 2 .

7. the processor causes the display to display a comparison result between the first time and a first time target value that is a target value for the first time, a comparison result between the second time and a second time target value that is a target value for the second time, a comparison result between the third time and a third time target value that is a target value for the third time, a comparison result between the first number and a first number target value that is a target value for the first number, and a comparison result between the second number and a second number target value that is a target value for the second number. The information processing device according to claim 6 .

8. the processor calculates the first time target value based on a response time per vehicle, the number of vehicles remaining between the start point of the drive-through lane and the order area, and a target coefficient, and calculates the second time target value based on the response time, the number of vehicles remaining between the order area and the product receiving area, and the target coefficient; The information processing device according to claim 7 .

Images