Parking lot management program and management device

Abstract

To provide a management program and a management device of a parking lot for setting a parameter value of a parking fee to estimate sales.SOLUTION: At a step 1201, an operator sets parameter values P, Q, R, S of a parking fee for a future period V0 of a weekday. At a step 1202, sales W1 per one day are estimated and extracted by using a learning control model for the weekday, by using the parameter values P, Q, R, S (=PV0, QV0, RV0, SV0) of the parking fee set at the step 1201 as input data. At a step 1203, the optimum sales per one day, for example, the greatest sales per one day among the sales W1, W2, ... per one day of the future periods V0, V1, ... are selected. At a step 1204, the parameter value of the parking fee with respect to the sales per one day selected at the step 1203 is set as a new parking fee for the weekday.SELECTED DRAWING: Figure 12A

Description

[Technical Field]

[0001] The present invention relates to a parking lot management program and management device for setting parameter values ​​for parking fees and estimating sales. [Background technology]

[0002] Generally, parking fees at a parking lot have two or more parameters, for example, four parameters: maximum time, maximum fee, daytime fee, and nighttime fee, and the values ​​of each parameter are set, for example, 12 hours, 1,200 yen, 20 minutes / 100 yen, and 60 minutes / 100 yen.

[0003] Conventionally, parameter values ​​for parking fees at parking lots have been set based on the market price of land for the parking lot, the accessibility of the parking lot, and the parameter values ​​for parking fees at surrounding parking lots.

[0004] The method for authenticating the vehicle number of a vehicle is publicly known (see Patent Document 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2022-54632 A (Patent No. 7277879) Summary of the Invention [Problem to be solved by the invention]

[0006] However, the parameter values ​​set for parking fees at parking lots have a significant impact on revenue. That is, if the parameter values ​​set for parking fees are too high, fewer customers will use the parking lot, and parking lot revenue will decline. On the other hand, if the parameter values ​​set for parking fees are too low, the number of customers will increase, but parking lot revenue will not increase. Therefore, while the parameter values ​​for parking fees in the past are set based on the operator's experience, they cannot be set quickly, especially when the parking lot has a large capacity. As a result, parking lot revenue cannot be quickly predicted and cannot be optimized, which is a problem. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems, the parking lot management program according to the present invention comprises: A parking lot management program having at least one camera capable of capturing an image of the entire parking lot at once, comprising a management order recording procedure for extracting a vehicle number from an image captured by the camera, and further extracting the parking space number of the space in which the vehicle is located, and recording a management order consisting of the vehicle number and the parking space number together with the entry time, exit time and settlement amount obtained by extracting both the vehicle number and the parking space number; a past period linking procedure for linking a parameter value of parking fees for each past period to a management order belonging to each past period; a past period parking fee parameter value acquisition procedure for acquiring a parameter value of parking fees for each past period; a past period daily sales calculation procedure for calculating daily sales for each past period of the parking lot using the management orders linked to each past period; The computer executes a sales estimation procedure using a trained learning control model with only the parameter values ​​of parking fees for a past period as input training data and the parking lot's daily sales for each past period as output training data, and with only the parameter values ​​of parking fees for a future period as input data to estimate the parking lot's daily sales for the future period.

[0008] In addition, the parking lot management device according to the present invention includes: A parking lot management device having at least one camera capable of capturing an image of the entire parking lot at once, comprising: a management order recording means for extracting a vehicle number from an image captured by the camera, and further extracting the parking space number of the space in which the vehicle is located, and recording a management order consisting of the vehicle number and the parking space number together with the entry time, exit time and settlement amount obtained by extracting both the vehicle number and the parking space number; a past period linking means for linking a parameter value of parking fees for each past period and a management order belonging to each past period to each past period of the parking lot; a past period parking fee parameter value acquisition means for acquiring the parameter value of parking fees for each past period; a past period daily sales calculation means for calculating daily sales for each past period of the parking lot using the management order linked to each past period; The system is equipped with a sales estimation means that uses a trained learning control model with only parameter values ​​of parking fees for past periods as input training data and the parking lot's daily sales for each past period as output training data, and estimates the parking lot's daily sales for a future period using only parameter values ​​of parking fees for a future period as input data. [Effects of the Invention]

[0009] According to the present invention, parameter values ​​for future parking fees can be set quickly to quickly estimate sales, which contributes to optimal sales for parking lots. [Brief explanation of the drawings]

[0010] [Figure 1]This figure is for explaining the principle of the present invention, where (A) shows an example of parameter values ​​for parking fees, (B) is a graph showing an example of sales distribution against usage time, and (C) is a graph showing an example of sales distribution against entry time. [Figure 2] 1 is a diagram showing a parking lot including an embodiment of a management device according to the present invention; [Figure 3] FIG. 3 is a detailed block circuit diagram of the management device of FIG. 2. [Figure 4] 4 is a flowchart for explaining a parameter value change routine for parking fees in the central processing unit of FIG. 3. [Figure 5] 4 is a flowchart for explaining a vehicle front image learning operation of the central processing unit of FIG. 3. [Figure 6] 4 is a flowchart for explaining a vehicle rear image learning operation of the central processing unit of FIG. 3. [Figure 7] 4 is a flowchart for explaining a main operation of the central processing unit of FIG. 3. [Figure 8] 10 is a table showing parameter values ​​and management orders for parking fees for past periods on weekdays. [Figure 9] 10 is a table showing parameter values ​​and management orders for parking fees for past holiday periods. [Figure 10A] 4 is a flowchart for explaining a weekday sales learning operation of the central processing unit of FIG. 3. [Figure 10B] 10B is a table showing the relationship between past periods and daily sales for the past periods used in the weekday sales learning operation of FIG. 10A. [Figure 11A] 4 is a flowchart for explaining a holiday sales learning operation of the central processing unit of FIG. 3. [Figure 11B] 11B is a table showing the relationship between past periods and daily sales for the past periods used in the holiday sales learning operation of FIG. 11A. [Figure 12A] 10 is a flowchart for explaining the weekday sales estimation / parking fee setting for a future period by the central processing unit of FIG. 3; [Figure 12B]12B is a table showing the relationship between future periods and estimated daily sales for the future periods used in the weekday sales estimation operation of FIG. 12A. [Figure 13A] 10 is a flowchart for explaining the future holiday sales estimation / parking fee setting by the central processing unit of FIG. 3; [Figure 13B] 13B is a table showing the relationship between future periods and estimated daily sales for the future periods used in the holiday sales estimation operation of FIG. 13A. [Figure 14] 4 is a block diagram showing another example of the parking lot management device of FIG. 3. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0011] First, the principle of the present invention will be explained with reference to Fig. 1. That is, the setting of parameter values ​​for parking fees and their correlation with total daily sales will be explained.

[0012] Parking fees consist of multiple parameters, for example, four parameters: maximum time P after entry, maximum fee Q within maximum time P, fee R per unit time during the day (8:00-22:00), and fee S per unit time during the night (22:00-8:00).

[0013] Figure 1(A) shows an example of parameter settings for weekday (Monday to Friday) parking fees for a 100-car parking lot. Specifically, the maximum time P is 12 hours, the maximum fee Q is 1,200 yen, the daytime (8:00 AM to 10:00 PM) fee R is 100 yen / 20 minutes (= 5 yen / minute), and the nighttime (10:00 PM to 8:00 AM) fee S is 100 yen / 60 minutes (= 5 / 3 yen / minute). In this case, the sales distribution for usage time is shown in Figure 1(B), and the sales distribution for entry time is shown in Figure 1(C). Total daily sales are approximately 100,000 yen. Varying the maximum time P, maximum fee Q, daytime fee R per unit hour, and nighttime fee S per unit hour is expected to change the sales distribution for usage time shown in Figure 1(B) and the sales distribution for entry time shown in Figure 1(C). For example, if the maximum time P is reduced, it is predicted that there will be no impact based on the sales distribution for usage time shown in Figure 1(B), but sales will increase based on the sales distribution for entry time shown in Figure 1(C). Also, if the daytime rate per unit time R is increased, it is predicted that there will be no impact based on the sales distribution for entry time shown in Figure 1(C), but sales will decrease based on the sales distribution for usage time shown in Figure 1(B). In this way, changing the parameter values ​​for parking fees will lead to fluctuations in customers, and sales trends will become more complex, especially when the parking capacity is large.

[0014] The present invention aims to learn daily sales based on parameter values ​​of parking fees for past periods, estimate (predict) daily sales for parameter values ​​of parking fees for future periods, and further achieve optimal daily sales.

[0015] FIG. 2 is a diagram showing a parking lot including an embodiment of a management device according to the present invention.

[0016] In FIG. 2, one side of a parking lot 10 has, for example, six parking spaces 10-1, 10-2, ..., 10-6, and the other side of the parking lot 10 has, for example, six parking spaces 10-7, 10-8, ..., 10-12. The parking lot 10 also has an unregulated entrance / exit 10-3 and a parking fee sign 10-14. A management device 1 is also provided at one end of the parking lot 10. At least one camera (e.g., a wide-angle monocular camera) 2 is fixed to a pole (not shown) at a height of, for example, 3 m, so that the entire parking lot 10 can be imaged at once. In other words, it can image both vehicles in the parking lot 10 and parking spaces 10-1 to 10-12 at once. In particular, it can capture images of the front of vehicles parked in parking spaces 10-1 to 10-6 and images of the rear of vehicles parked in parking spaces 10-7 to 10-12. The camera 2 is connected to the management device 1. The management device 1 is connected to an adjustment machine 1a located in the parking lot 10 or in close proximity to the parking lot 10. Furthermore, the management device 1 is connected to an information terminal 4 and the like via the Internet 3 as needed. An operator can operate the management device 1 via the information terminal 4 or directly.

[0017] FIG. 3 is a detailed block circuit diagram of the management device 1 of FIG.

[0018] In FIG. 3, the management device 1 is configured as a computer, specifically including a central processing unit (CPU) 11, a read-only memory (ROM) 12 for storing programs, a flash memory 13, a random access memory (RAM) 14 for storing temporary data, a USB (registered trademark) terminal, an input / output (I / O) interface 15 having interrupt terminals INT0, INT1, INT1', INT2, INT2', INT3, and INT3', an image interface 16 connected to a camera 2, a communication interface 17 connected to the Internet 3, a vehicle front image learning control model 18, a vehicle rear image learning control model 18', a weekday learning control model 19, and a holiday learning control model 19'. Each learning control model 18, 18', 19, and 19' is a deep learning neural network consisting of an input layer, multiple intermediate layers, and an output layer. In the neural network, learning is performed by adjusting the weights and biases of a multilayer perceptron using the backpropagation algorithm.

[0019] Normal management operations of the management device 1 of FIG. 3 will be described with reference to FIGS. 4, 5, 6 and 7. FIG.

[0020] Fig. 4 is a flowchart showing a parking fee change routine executed by CPU 11 in response to an interrupt from the operator via terminal INT0 in Fig. 3. That is, when the operator changes the parking fee, he or she redraws the parking fee sign 10-14 and the parking fee parameter value change routine in Fig. 4 is executed.

[0021] First, in step 401, the contents of the parking fee sign 10-14 are input as parameter values ​​of the new parking fee into the flash memory 13. For example, the parameter values ​​of the weekday parking fee are as follows: P: Maximum time after receipt Q: Maximum charge within maximum time R: Daytime (8:00~22:00) hourly rate S: Nighttime (22:00-8:00) hourly rate and as the parameter value for parking fees on holidays (Saturdays, Sundays, and public holidays), P': Maximum time after receipt Q': Maximum charge within maximum time R': Daytime (8:00~22:00) hourly rate S': Nighttime (22:00-8:00) hourly rate In this case, if no maximum fee is adopted, enter P=P'=0 and Q=Q'=0. If only a maximum fee is adopted, enter R=R'=0 and S=S'=0. Most parking fees can be expressed by the four parameter values ​​P, Q, R, and S for weekdays and the four parameter values ​​P', Q', R', and S' for holidays. Holiday fees can be set higher, for example, for parking lots close to event venues. If holiday fees are not required, delete P', Q', R', and S'. This establishes the new parking fees.

[0022] Next, in step 402, the end date is entered into the old parking fee in the flash memory 13 to end the use of the old parking fee.

[0023] Next, in step 403, the start date is entered into the new parking fee in the flash memory 13 to start using the new parking fee.

[0024] Then, in step 404, the routine of FIG.

[0025] 5 shows a vehicle front image learning operation routine executed by the CPU 11 in response to an interrupt from the operator via the terminal INT1. This learning operation routine is stored in the ROM 12 or the flash memory 13.

[0026] First, in step 501, a plurality of learning image data for the front of the vehicle recognized by the operator is acquired from a camera for the front of the vehicle (not shown) or a USB memory previously acquired, etc. The greater the number of recognized learning image data for the front of the vehicle, the greater the learning effect in step 502.

[0027] In step 502, a vehicle front image learning process is performed. That is, a vehicle front image learning control model 18 that extracts feature amounts using a large amount of recognized learning vehicle front image data as input data is trained by deep learning.

[0028] Then, the routine of FIG. 5 ends at step 503.

[0029] 6 shows a vehicle rear image learning operation routine executed by the CPU 11 in response to an interrupt from the operator via the terminal INT1'. This learning operation routine is stored in the ROM 12 or the flash memory 13.

[0030] First, in step 601, a plurality of learning rear-of-vehicle image data recognized by the operator is acquired from a vehicle rear camera (not shown) or a previously acquired USB memory, etc. The more recognized learning rear-of-vehicle image data there are, the greater the learning effect in step 602.

[0031] In step 602, a vehicle rear image learning process is performed. That is, a vehicle rear image learning control model 18′ that extracts features using a large amount of recognized learning vehicle rear image data as input data is trained by deep learning.

[0032] Then, the routine of FIG. 6 ends at step 603.

[0033] 5 and 6 can be executed by the operator at any time thereafter, which will increase the learning effect. Alternatively, the learning operations can be executed on a cloud connected to the Internet 3, and the learning results can be introduced into the learning control models 18 and 18' via the Internet 3.

[0034] 7 shows the main routine executed by the CPU 11. This main routine is stored in the ROM 12 or the flash memory 13.

[0035] First, in step 701, the camera 2 captures an image of the entire parking lot 10, and in particular, an image of the vehicles present in the parking lot 10.

[0036] Next, in step 702, the trained vehicle front image learning control model 18, which extracts features using the recognized learning vehicle front image data as input data, inputs image data from the camera 2 and determines whether vehicle front image data has been extracted. In this case, the vehicle front image data is extracted together with the vehicle number whether the vehicle is inside or outside the vehicle. If vehicle front image data has been extracted, the process proceeds to step 703; if vehicle front image data has not been extracted, the process proceeds to step 704.

[0037] Next, in step 703, the vehicle number is recognized from the image data of the vehicle ahead of the vehicle extracted in step 702. That is, the vehicle number recognition process includes a black and white binary data conversion procedure for converting the image data of the vehicle ahead of the vehicle extracted in step 702 into black and white binary data, a rectangular outline area extraction procedure for extracting connected outlines of connected black data or white data of the black and white binary data as a plurality of rectangular outline areas, a number and dot area extraction procedure for discriminating and extracting a plurality of number areas and dot areas from the plurality of rectangular outline areas, and a vehicle number extraction procedure for extracting the vehicle number of the vehicle from the number areas and dot areas, and the number and dot area extraction procedure includes a Boolean image conversion procedure for converting an image within each rectangular outline area into a Boolean image, and a Boolean number template comparison procedure for comparing the Boolean image with a Boolean number template by pixel-by-pixel Boolean operation, and determining that each rectangular outline area is a Boolean image when the similarity between the Boolean image and the Boolean number template is equal to or greater than a first threshold or equal to or less than a second threshold that is smaller than the first threshold. The method includes a number / dot discrimination and extraction procedure that discriminates and extracts the contour area as a number / dot area, and the vehicle number extraction processing procedure includes an average value calculation procedure that calculates the average height of all the number / dot areas, a first selection procedure that selects one of the number / dot areas as a first area, a second selection procedure that selects a number / dot area within a range of a predetermined multiple of the average height to the right of the first area as a second area, a third selection procedure that selects a number / dot area between the first and second areas as a third area, a vehicle number candidate selection procedure that sets the first, second, and third areas as vehicle number candidates when there is a predetermined number of third areas, and a vehicle number selection procedure that selects the vehicle number candidate as the vehicle number of the vehicle if there is one vehicle number candidate, and selects the vehicle number candidate that is most likely to satisfy predetermined conditions as the vehicle number of the vehicle if there are multiple vehicle number candidates (see Patent Document 1).

[0038] Next, in step 704, the trained vehicle rear image learning control model 18', which extracts features using the recognized learning vehicle rear image data as input data, inputs image data from camera 2 and determines whether vehicle rear image data has been extracted. If the vehicle is outside the vehicle cabin, the image data is extracted along with the vehicle number. However, if the vehicle is completely inside the vehicle cabin, it is difficult to extract the vehicle number. Even in this case, when the vehicle begins to enter the vehicle cabin, a vehicle rear image including the vehicle number is extracted. If vehicle rear image data has been extracted, the process proceeds to step 705; if vehicle rear image data has not been extracted, the process proceeds directly to step 706.

[0039] Next, in step 705, the vehicle number is recognized from the vehicle rear image data extracted in step 704. That is, the vehicle number recognition process includes a black and white binary data conversion procedure for converting the vehicle rear image data extracted in step 704 into black and white binary data, a rectangular outline area extraction procedure for extracting connected outlines of connected black data or white data of the black and white binary data as a plurality of rectangular outline areas, a number and dot area extraction procedure for discriminating and extracting a plurality of number areas and dot areas from the plurality of rectangular outline areas, and a vehicle number extraction procedure for extracting the vehicle number of the vehicle from the number areas and dot areas, and the number and dot area extraction procedure includes a Boolean image conversion procedure for converting an image within each rectangular outline area into a Boolean image, and a Boolean number template comparison procedure for comparing the Boolean image with a Boolean number template by pixel-by-pixel Boolean operation, and determining that each rectangular outline area is a Boolean image when the similarity between the Boolean image and the Boolean number template is equal to or greater than a first threshold or equal to or less than a second threshold that is smaller than the first threshold. The method includes a number / dot discrimination and extraction procedure that discriminates and extracts the contour area as a number / dot area, and the vehicle number extraction processing procedure includes an average value calculation procedure that calculates the average height of all the number / dot areas, a first selection procedure that selects one of the number / dot areas as a first area, a second selection procedure that selects a number / dot area within a range of a predetermined multiple of the average height to the right of the first area as a second area, a third selection procedure that selects a number / dot area between the first and second areas as a third area, a vehicle number candidate selection procedure that sets the first, second, and third areas as vehicle number candidates when there is a predetermined number of third areas, and a vehicle number selection procedure that selects the vehicle number candidate as the vehicle number of the vehicle if there is one vehicle number candidate, and selects the vehicle number candidate that is most likely to satisfy predetermined conditions as the vehicle number of the vehicle if there are multiple vehicle number candidates (see Patent Document 1).

[0040] Next, in step 706, it is determined whether the vehicle whose vehicle number was recognized in steps 703 and 705 is located in any vehicle compartment. For example, the vehicle compartment is determined to be full by the overlap rate between the vehicle's projection data and the vehicle compartment area data calculated using Boolean operations. Here, if the vehicle compartment area data is 100%, the vehicle compartment is determined to be full if the overlap rate of the vehicle's projection data exceeds 30%, and on the other hand, if the overlap rate is less than 30%, the vehicle compartment is determined to be vacant. Note that the vehicle projection data is preferably, but not limited to, vertical projection data.

[0041] Finally, in step 707, the management order is started or updated in the flash memory 13 or RAM 14. For example, the management order is composed of, for each management order number, the time of entry, vehicle number, vehicle compartment number, vehicle front image data, vehicle rear image data, and departure time (last time).

[0042] In addition, the settlement amount of the parking fee is recorded in each management order. That is, settlement is carried out by each user operating the vehicle number of the vehicle at the payment machine 1a, and as a result, the settlement amount is recorded in each management order. Therefore, the validity period, parking fee parameter value, and management order (management order number, entry time, settlement amount, ...) are linked by the past periods T0, T1, ..., T13; T0', T1', ..., T13', as shown in Figures 8 and 9. The validity period to which a management order belongs is determined by the entry time.

[0043] Next, learning of parking lot sales over the past period will be described.

[0044] 10A shows a weekday sales learning operation routine executed by the CPU 11 in response to an interrupt from the operator via the terminal INT2. This learning operation routine is stored in the ROM 12 or the flash memory 13.

[0045] First, in step 1001, parameter values ​​P, Q, R, and S of one parking fee for the past weekday periods T0, T1, . . . , T13 shown in FIG. 8 are obtained.

[0046] Next, in step 1002, the management orders belonging to the parking fee parameter values ​​P, Q, R, and S acquired in step 1001 are used to calculate the sales per day.

[0047] Next, in step 1003, weekday sales learning processing is performed. That is, the parameter values ​​P, Q, R, and S of the parking fee acquired in step 1001 are used as input training data, and the daily sales acquired in step 1002 are used as output training data to train the weekday learning control model 19 by deep learning.

[0048] Then, in step 1004, the process shown in FIG. A The routine ends.

[0049] In this way, when the routine of FIG. 10A is executed for each past period, the daily sales U for the past periods T0, T1, ..., T13 are calculated as shown in FIG. 10B. 0 , U 1 , ..., U13 is obtained, and learning control is performed based on this.

[0050] 11A shows a holiday sales learning operation routine that is executed by the CPU 11 in response to an interrupt from the operator via the terminal INT2'. This learning operation routine is stored in the ROM 12 or the flash memory 13.

[0051] First, in step 1101, parameter values ​​P', Q', R', and S' of one parking fee for the past holiday periods T0', T1', . . . , T13' shown in FIG. 9 are obtained.

[0052] Next, in step 1102, the management orders belonging to the parking fee parameter values ​​P', Q', R', and S' obtained in step 1101 are used to calculate the sales per day.

[0053] Next, in step 1103, holiday sales learning processing is performed. That is, the parking fee parameter values ​​P', Q', R', and S' obtained in step 1101 are used as input training data, and the daily sales obtained in step 1102 are used as output training data to train holiday learning control model 19' by deep learning.

[0054] Then, the routine of FIG. 11 ends at step 1104.

[0055] In this way, when the routine of FIG. 11A is executed for each past period, the daily sales for the past periods T0′, T1′, ..., T13′ are obtained as shown in FIG. 11B. Ge U 0 ', U 1 ', ..., U13' is obtained, and learning control is performed based on this.

[0056] 10A and 11A can be executed by the operator at any time thereafter, and the learning effect will be greater. In particular, the learning effect will be greater if daily sales based on management orders with different parking fee parameter values ​​are used. Alternatively, the learning operations may be executed on a cloud connected to the Internet 3, and the learning results may be introduced into the learning control models 18 and 18' via the Internet 3.

[0057] Next, we will explain the estimation (forecasting) of future parking lot sales / parking fee setting.

[0058] 12A shows a weekday sales estimation / parking fee setting routine executed by the CPU 11 in response to an interrupt from the operator via the terminal INT3. This estimation / setting routine is stored in the ROM 12 or the flash memory 13.

[0059] First, in step 1201, the operator sets the parameter values ​​P, Q, R, and S of the parking fee for a future period V0 on weekdays to, for example, PV0, QV0, RV0, and SV0. However, these parameter values ​​of the parking fee may be automatically changed to the parameter values ​​of the parking fee for the current weekday.

[0060] Next, in step 1202, the parking fee parameter values ​​P, Q, R, S (=PV0, QV0, RV0, SV0) set in step 1201 are used as input data to estimate and extract daily sales W1 using the weekday learning control model 19.

[0061] In this way, the parking lot's daily sales can be predicted by setting the parameter values ​​P, Q, R, and S for the future weekday parking fees. Therefore, as shown in Figure 12B, the parameter values ​​for the parking fees for multiple future periods V0, V1, ... can be predicted as (PV0, QV0, RV0, SV0), (PV1, QV1, RV1, SV1), ...

[0062] Next, in step 1203, the daily sales W for future periods V0, V1, ... are calculated. 0 , W 1 , ...and select the best daily sales, for example the maximum daily sales.

[0063] Next, in step 1204, the parameter value of the parking fee for the daily sales selected in step 1203 is set as the new parking fee for weekdays. In this case, the parking fee sign 10-14 in FIG. 2 is also redrawn.

[0064] Then, the routine of FIG. 12A ends at step 1205.

[0065] 13A shows a holiday sales estimation / parking fee setting routine executed by the CPU 11 in response to an interrupt from the operator via the terminal INT3. This estimation / setting routine is stored in the ROM 12 or the flash memory 13.

[0066] First, in step 1301, the operator sets the parking fee parameter values ​​P', Q', R', and S' for the future holiday period V0' to, for example, PV0', QV0', RV0', and SV0'. However, these parking fee parameter values ​​may be automatically changed to the parameter values ​​for the current holiday parking fee.

[0067] Next, in step 1302, the parking fee parameter values ​​P', Q', R', S' (= PV0', QV0', RV0', SV0') set in step 1301 are used as input data to estimate and extract daily sales W1' using the holiday learning control model 19'.

[0068] In this way, by setting the parameter values ​​P', Q', R', and S' for the holiday parking fees for future periods, the parking lot's daily sales can be predicted. Therefore, as shown in Figure 13B, the parameter values ​​for parking fees for multiple future periods V0', V1', ... can be predicted as (PV0', QV0', RV0', SV0'), (PV1', QV1', RV1', SV1'), ...

[0069] Next, in step 1303, the daily sales W for the future periods V0', V1', ... are calculated. 0 ', W 1 ', ...select the best daily sales, for example the maximum daily sales.

[0070] Next, in step 1304, the parameter value of the parking fee for the daily sales selected in step 1303 is set as the new parking fee for the holiday. In this case, the parking fee sign 10-14 in FIG. 2 is also redrawn.

[0071] Then, the routine of FIG. 13A ends at step 1305.

[0072] In this way, in the present invention, the daily sales for the future period are quickly estimated and provided based on the daily sales based on the parameter values ​​of the parking fees for the past period. hundred It can also accommodate parking lots that can accommodate cars.

[0073] The management device of FIG. 3 can also be configured with hardware shown in FIG.

[0074] In Figure 14, the parking lot management device has a sales estimation means 1402 that uses a trained learning control model 1401 with parameter values ​​P, Q, R, and S of the parking lot's parking fees for multiple past periods as input training data and the parking lot's daily sales for each of the past periods as output training data, and that uses parameter values ​​P, Q, R, and S of the parking lot's parking fees for a future period as input data to estimate the parking lot's daily sales for the future period.

[0075] In the above embodiment, the parking lot has an unregulated entrance and exit and recognizes vehicle numbers, but the present invention is not limited to this. For example, the parking lot may have a gate with a bar at the entrance and exit, or a camera. Furthermore, the parameters for the parking fee are maximum time P, maximum fee Q, daytime fee R, and nighttime fee S, but are not limited to these. Furthermore, while weekday parking fees and holiday parking fees are set, parking fees that are not separated for weekdays and holidays may also be used.

[0076] Furthermore, the present invention can be applied to any modifications within the obvious scope of the above-described embodiments. [Industrial Applicability]

[0077] The vehicle entry / exit management program and device for a parking lot according to the present invention can be used in a parking lot charging system, etc. [Explanation of symbols]

[0078] 1: Management device 1a:Payment machine 2: Camera 3: Internet 4: Information terminal 10: Parking lot 10-1, 10-2,…, 10-12: Vehicle interior 10-13: Unregulated entrance / exit gate 10-14: Parking fee sign 11:CPU 12:ROM 13: Flash memory 14:RAM 15: I / O interface 16: Image interface 17: Communication interface 18: Vehicle front image learning control model 18': Vehicle rear image learning control model 19: Weekday learning control model 19': Holiday learning control model

Claims

1. A parking lot management program having at least one camera capable of capturing images of the entire parking lot at once, a management order recording procedure for extracting a vehicle number from the image of the camera, further extracting the compartment number of the compartment where the vehicle is located, and recording a management order consisting of the vehicle number and the compartment number together with the entry time, exit time, and settlement amount obtained by extracting both the vehicle number and the compartment number; a past period linking procedure for linking a parameter value of a parking fee for each past period with the management order belonging to each past period of the parking lot; a parameter value acquisition step for acquiring parameter values ​​of the parking fees for past periods; a past period daily sales calculation procedure for calculating daily sales for each past period of the parking lot using the management order linked to each past period; a sales estimation procedure for estimating the daily sales for the parking lot for a future period using only the parameter values ​​of the parking fees for each past period as input training data and the daily sales for each past period as output training data, using a trained learning control model and only the parameter values ​​of the parking fees for a future period of the parking lot as input data; A parking lot management program for causing a computer to execute the above.

2. A parking lot management program having at least one camera capable of capturing images of the entire parking lot at once, a management order recording procedure for extracting a vehicle number from the image of the camera, further extracting the compartment number of the compartment where the vehicle is located, and recording a management order consisting of the vehicle number and the compartment number together with the entry time, exit time, and settlement amount obtained by extracting both the vehicle number and the compartment number; a past period linking procedure for linking a parameter value of a parking fee for each past period with the management order belonging to each past period of the parking lot; a parameter value acquisition step for acquiring parameter values ​​of the parking fees for each past period; a past period daily sales calculation procedure for calculating daily sales for each past period of the parking lot using the management order linked to each past period; a sales estimation procedure for estimating daily sales for each future period of the parking lot using a trained learning control model with only the parameter values ​​of the parking fees for each past period as input training data and the daily sales for each past period as output training data, and using only the parameter values ​​of the parking fees for a plurality of future periods of the parking lot as input data; an optimal sales selection procedure for selecting an optimal daily sales from the estimated daily sales for each future period; a parking fee setting step of setting a parameter value of the parking fee for the future period when the selected daily sales are estimated as a new parking fee; A parking lot management program for causing a computer to execute the above.

3. 3. The parking lot management program executed by a computer according to claim 1, wherein the past period and the future period are weekday periods.

4. 3. The parking lot management program executed by a computer according to claim 1, wherein the past period and the future period are holiday periods.

5. A parking lot management program to be executed by a computer as described in claim 1 or 2, wherein the parameter values ​​of the parking fee for the past period and the parking fee for the future period are a maximum time, a maximum fee up to the maximum time, a daytime fee, and a nighttime fee.

6. A parking lot management device having at least one camera capable of capturing images of the entire parking lot at once, a management order recording means for extracting a vehicle number from the image of the camera, and further extracting the compartment number of the compartment where the vehicle is located, and recording a management order consisting of the vehicle number and the compartment number together with the entry time, exit time, and settlement amount obtained by extracting both the vehicle number and the compartment number; a past period linking means for linking a parameter value of a parking fee for each past period with the management order belonging to each past period of the parking lot; a parameter value acquisition means for acquiring parameter values ​​of the parking fees for each past period; a past period daily sales calculation means for calculating daily sales for each past period of the parking lot using the management orders linked to each past period; A parking lot management device equipped with a sales estimation means that uses a trained learning control model with only the parameter values ​​of the parking fees for each past period as input training data and the daily sales for each past period as output training data, and estimates the daily sales for the parking lot for a future period using only the parameter values ​​of the parking fees for the parking lot for a future period as input data.

7. A parking lot management device having at least one camera capable of capturing images of the entire parking lot at once, a management order recording means for extracting a vehicle number from the image of the camera, and further extracting the compartment number of the compartment where the vehicle is located, and recording a management order consisting of the vehicle number and the compartment number together with the entry time, exit time, and settlement amount obtained by extracting both the vehicle number and the compartment number; a past period linking means for linking a parameter value of a parking fee for each past period with the management order belonging to each past period of the parking lot; a parameter value acquisition means for acquiring parameter values ​​of the parking fees for each past period; a past period daily sales calculation means for calculating daily sales for each past period of the parking lot using the management orders linked to each past period; a sales estimation means for estimating the daily sales for each future period of the parking lot using a trained learning control model with only the parameter values ​​of the parking fees for each past period as input training data and the daily sales for each past period as output training data, and using only the parameter values ​​of the parking fees for a plurality of future periods of the parking lot as input data; an optimal sales selection means for selecting an optimal daily sales from the estimated daily sales for each future period; a parking fee setting means for setting a parameter value of the parking fee for the future period when the selected daily sales are estimated as a new parking fee; A parking lot management device comprising:

8. 8. The parking lot management device according to claim 6, wherein the past period and the future period are weekday periods.

9. 8. The parking lot management device according to claim 6, wherein the past period and the future period are holiday periods.

10. 8. The parking lot management device according to claim 6, wherein the parameter values ​​of the past period parking fee and the future period parking fee are a maximum time, a maximum fee up to the maximum time, a daytime fee, and a nighttime fee.

Images