Intelligent stereo garage scheduling method and system based on garage location energy efficiency and time length prediction

Abstract

The invention discloses an intelligent stereo garage scheduling method and system based on garage location energy efficiency and time length prediction, and the method comprises the steps: dividing all storage berths into three energy efficiency regions based on the storage time of a vehicle from an entrance to the storage berths, namely a high-efficiency region, a middle-efficiency region and a low-efficiency region; sorting the vehicle storage duration from small to large, and dividing the vehicle storage duration into three intervals which correspond to different energy efficiency areas for storing the vehicles respectively; and after a to-be-parked vehicle arrives at the entrance of the intelligent stereo garage, the predicted vehicle parking duration is obtained by using the trained XGBoost algorithm according to the historical parking data of the vehicle, and then the vehicle is automatically parked at the allocated storage berth. The invention provides an effective scheduling scheme, so that the full library rate is relatively balanced, the extreme full library rate condition is avoided, and the scheduling is more scientific and reasonable.

Description

Technical Field

[0001] The present invention relates to intelligent stereo garage scheduling, in particular to an intelligent stereo garage scheduling method and system based on storage space energy efficiency and duration prediction. Background Art

[0002] With the acceleration of urbanization and the increase in the number of cars, the parking problems faced by urban centers, schools, commercial complexes, tourist attractions, large hospitals and other places have become increasingly prominent, which not only brings inconvenience to people's lives, but also increases the traffic pressure in the city. Taking large hospitals as an example, large hospitals such as the top three in my country generally face parking difficulties. As a modern parking solution, stereo garages have gradually been widely used. However, the traditional manual parking method can no longer meet the growing parking demand. The development of stereo garages has begun to shift from traditional manual parking to a new development stage of automation and intelligence.

[0003] Traditional stereo garages generally face a series of problems in the operation and management process, especially the lack of intelligence and informationization, which leads to the failure of their operation efficiency and service quality to meet the expected goals. At present, most stereo garages still adopt the traditional management mode, relying on manual operation or simple nearby dispatching methods to store and retrieve vehicles. This method is not only inefficient, but also has obvious defects in user experience, energy consumption and safety performance. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide an efficient intelligent stereoscopic parking garage scheduling method and system based on storage space energy efficiency and duration prediction.

[0005] Technical solution: The intelligent stereo garage scheduling method based on storage space energy efficiency and duration prediction of the present invention, after the vehicle to be parked arrives at the entrance of the intelligent stereo garage, the elevator sends the vehicle to the designated floor, and the horizontal transporter moves the vehicle to the designated storage berth. The intelligent stereo garage scheduling method includes the following steps: The storage time of vehicles from the entrance to each storage berth is calculated, and all storage berths are divided into three energy efficiency areas according to the storage time from small to large, namely, high efficiency area, medium efficiency area and low efficiency area; Obtain the historical stay times of several vehicles and sort them from small to large, and standardize the historical stay times and divide them into three intervals, namely, a first interval, a second interval, and a third interval, which correspond to vehicles stored in a high-efficiency area, a medium-efficiency area, and a low-efficiency area, respectively; Re-acquire the historical parking data features of several vehicles, including the vehicle arrival date, arrival time, departure date and departure time, and use the historical parking data to train the XGBoost algorithm to predict the vehicle stay time; When the vehicle to be parked arrives at the entrance of the intelligent stereo garage, the trained XGBoost algorithm is used to predict the vehicle's stay time based on the vehicle's historical parking data; The predicted vehicle dwelling time is standardized, and allocated to storage berths corresponding to different energy efficiency areas for storage according to the interval of the standardized predicted vehicle dwelling time.

[0006] Furthermore, during the off-peak period of parking, the vehicles in the medium-efficiency area and the low-efficiency area are moved to the parking lot. The method is as follows: the remaining parking time of the vehicles in the medium-efficiency area and the low-efficiency area is calculated, the remaining parking time is standardized, and the vehicles are moved to the high-efficiency area or the medium-efficiency area according to the interval of the standardized remaining parking time; The low-peak period for entering the warehouse is a period when the average volume of vehicles entering the warehouse is not greater than a threshold value.

[0007] Furthermore, the warehousing time period classification method includes: When is the peak period of storage, When It is the low peak period for storage; in The first Average inbound traffic volume in the hourly period, is the average value of the average inbound vehicle flow within the statistical time range, is the constant term coefficient, is the standard deviation of the average inbound vehicle flow within the statistical time range.

[0008] Furthermore, the features of reacquiring historical parking data of several vehicles also include: whether the arrival date and departure date of the vehicle are weekdays or holidays, the number of times the vehicle enters the parking lot, and the province and city mentioned in the vehicle license plate number.

[0009] Furthermore, before using the historical parking data to train the XGBoost algorithm to predict the vehicle stay time, it also includes: preprocessing the historical parking data to eliminate abnormal data.

[0010] Furthermore, the method for removing abnormal data includes: calculating the interquartile range (IQR) of the box plot of historical parking data, and when the historical parking data is less than or greater than When , it is determined to be abnormal data, where Q1 is the lower quartile and Q3 is the upper quartile.

[0011] Further, the storage time of the vehicle from the entrance to each storage berth includes the time for the elevator to pick up the vehicle from the entrance, the time for the elevator to move vertically to the designated floor, and the time for the transverse conveyor to move the vehicle to the designated storage berth; The step of standardizing the predicted vehicle stay time and allocating the vehicle to storage spaces corresponding to different energy efficiency zones for storage according to the interval of the standardized predicted vehicle stay time includes: First, determine whether there are any vacant parking spaces in the intelligent three-dimensional parking garage. If there are vacant parking spaces, the driver parks the vehicle at the waiting point corresponding to the elevator and leaves, otherwise the driver drives the vehicle away; the elevator moves the vehicle to the floor where the storage parking spaces in the energy efficiency area are located, and the transverse conveyor removes the vehicle from the elevator and moves it to the storage parking spaces in the energy efficiency area; during this period, if there are no vacant elevators and / or transverse conveyors, the vehicle waits in place until the elevators and / or transverse conveyors are vacant.

[0012] The intelligent stereo garage scheduling system based on storage space energy efficiency and duration prediction of the present invention comprises: after the vehicle to be parked arrives at the entrance of the intelligent stereo garage, the elevator sends the vehicle to the designated floor, and the horizontal transporter moves the vehicle to the designated storage berth. The system comprises: The energy efficiency zone division unit is used to calculate the storage time of the vehicle from the entrance to each storage berth, and divide all the storage berths into three energy efficiency zones according to the storage time from small to large, namely, the high efficiency zone, the medium efficiency zone and the low efficiency zone; An energy efficiency area matching unit is used to obtain the historical stay time of a number of vehicles and sort them from small to large, and divide the historical stay time into three intervals after standardization, namely, a first interval, a second interval and a third interval, which correspond to the vehicles being stored in a high efficiency area, a medium efficiency area and a low efficiency area respectively; The vehicle stay time prediction unit is used to re-acquire the historical parking data features of several vehicles, including the vehicle arrival date, arrival time, departure date and departure time, and use the historical parking data to train the XGBoost algorithm to predict the vehicle stay time; after the parked vehicle arrives at the entrance of the intelligent stereo garage, the trained XGBoost algorithm is used to obtain the predicted vehicle stay time according to the historical parking data of the vehicle; The vehicle dispatching unit is used to standardize the predicted vehicle dwelling time and allocate the vehicle to storage berths corresponding to different energy efficiency areas for storage according to the interval of the standardized predicted vehicle dwelling time.

[0013] The electronic device described in the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the intelligent stereoscopic parking garage scheduling method based on storage location energy efficiency and duration prediction is implemented.

[0014] The computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the intelligent stereoscopic parking garage scheduling method based on storage location energy efficiency and duration prediction is implemented.

[0015] Beneficial effect: Compared with the prior art, the advantages of the present invention are: through the combination of warehouse management theory and machine learning, the present invention designs an intelligent stereoscopic parking garage scheduling method based on storage space energy efficiency and duration prediction, dynamically optimizes parking resource allocation, and provides an effective scheduling solution for the existing intelligent stereoscopic parking garage scheduling. It also provides a scientific solution for high-demand parking lots and provides new theoretical and practical references for research in related fields. Specifically, the present invention applies the ABC classification method of cargo storage to the division of stereoscopic parking garage berths, and divides the stereoscopic parking garage parking spaces into three categories: high efficiency area, medium efficiency area, and low efficiency area based on the storage time. In the duration prediction, the three aspects of parking duration, vehicle source, and historical warehousing conditions are comprehensively considered, and the parking duration of the vehicle is predicted based on the XGBoost algorithm. Then, the corresponding energy efficiency area is matched according to the parking duration, so that the fullness rate of each energy efficiency area is relatively balanced, avoiding extreme fullness rate conditions, while reducing the average waiting time of customers, and the scheduling is more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of an intelligent three-dimensional parking garage in an embodiment of the present invention.

[0017] Figure 2 The present invention is a flowchart of a storage scheduling method according to an embodiment of the present invention.

[0018] Figure 3 Schematic diagram of energy efficiency area according to an embodiment of the present invention.

[0019] Figure 4 The flowchart of the warehouse transfer scheduling method in the embodiment of the present invention is shown in FIG.

[0020] Figure 5 The figure is a flow chart of the method for discharging goods from a warehouse in an embodiment of the present invention.

[0021] Figure 6 This is a simulation diagram of the full inventory rate using the traditional nearest allocation method in an embodiment of the present invention.

[0022] Figure 7 This is a simulation diagram of the full inventory rate using the method described in the present invention in an embodiment of the present invention.

[0023] Figure 8 This is a comparison chart of the impact of different numbers of elevators and transverse conveyors on the average operation time in an embodiment of the present invention.

[0024] Fig. 9 This is a comparison chart of the impact of different numbers of elevators and transverse conveyors on the average waiting time in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0026] The management and scheduling of stereo garages is considered a complex resource optimization problem in the field of operations research, involving vehicle access methods, space allocation, energy consumption management, and other aspects. With the expansion of garage scale and the diversification of user needs, the complexity of solving this problem has further increased. However, the improvement of machine learning and simulation levels has provided new ideas and methods for solving these problems. How to effectively use these technologies to improve the throughput efficiency and customer satisfaction of stereo garages has become a common problem faced by managers of stereo garages.

[0027] Through in-depth research on multiple stereo garages, it was found that there is an imbalance between the speed of data accumulation and the depth of data application. Some garages have failed to fully utilize the accumulated operational data to optimize management and service processes. At the same time, managers of stereo garages rarely pay attention to the division of storage space energy efficiency, resulting in unbalanced and insufficient utilization of parking space resources. Based on this finding, we can consider using big data processing technology to analyze and mine a large amount of accumulated operational data, identify key and vulnerable links in the management and service process, and pay special attention to and strengthen these links in the design of vehicle scheduling methods. Therefore, it is necessary to combine warehouse management theory, the application of big data technology with the management and scheduling problems of stereo garages, and use the former to improve and enhance the scheduling methods of the latter, so as to obtain more scientific and efficient management and scheduling solutions.

[0028] Taking the F automatic stereo garage as an example, the F stereo garage has six floors, including a car lobby and five parking lots. The car lobby is located on the underground floor, and the vehicle entrance is located on the car lobby. Each parking floor has 30 parking spaces, with a total of 150 parking spaces. At present, the stereo garage adopts the nearest allocation scheduling method. After investigation, it was found that the garage failed to make full use of data resources. The current scheduling method will lead to uneven resource allocation, and some garage floors may be overcrowded or idle. At the same time, under this scheduling method, customers have a long waiting time for parking, low satisfaction, and are prone to cause emotional anxiety among customers.

[0029] Therefore, the present invention provides an intelligent scheduling method for a stereo garage system based on the warehouse management theory and the historical data accumulated from the operation of the stereo garage. Figure 1As shown in the figure, taking the intelligent stereo garage F as an example, a simulation model is established based on the actual operation process of the stereo garage, and the operation data provided by the intelligent stereo garage F is deeply analyzed and studied, so as to design a vehicle scheduling method that comprehensively considers the energy efficiency of the storage space and the duration prediction. The intelligent stereo garage F includes an elevator 1, a horizontal conveyor 2, a lifting channel 3, a handling channel 4, a storage berth 5, a first screen 6, a garage automatic door 7, a second screen 8, a waiting point 9, a third screen 10, a camera 11, a detection platform 12, a fourth screen 13, and a waiting area 14. Figure 2 As shown, the method of the present invention includes the following steps.

[0030] (1) Conduct field research and analyze the operation process of the stereo garage, and collect the operating parameters of hardware facilities such as elevators and horizontal conveyors; (2) Calculate the total storage time of the vehicle from the garage entrance to each berth and divide the energy efficiency area according to the ABC classification method of cargo storage; (3) Collect and organize the time characteristic data of customers’ parking, including the length of stay, vehicle source, historical parking conditions, etc.; (4) Establish a prediction model for vehicle parking time, including data preprocessing, data feature construction, machine learning model construction, etc.; (5) Allocate vehicles to different energy efficiency zones based on the predicted duration results; (6) During the off-peak period of parking, vehicles in the medium and low efficiency areas are moved to other areas, that is, the remaining parking time is re-predicted and the energy efficiency areas are allocated.

[0031] The details of each step are as follows.

[0032] Step 1: Energy efficiency division of storage spaces: Based on the ABC classification method for cargo storage, the F parking garage spaces are divided into different energy efficiency areas.

[0033] Step 1.1: Field research and analysis of the operation process of F multi-story parking garage (taking customer parking as an example), which is mainly divided into five stages: ① vehicle arrival at the intelligent multi-story parking garage, ② parking confirmation, ③ elevator handover, ④ horizontal car loading and unloading, and ⑤ storage at the designated storage berth.

[0034] Step 1.2: Calculate the relationship between the time consumption and motion parameters during the vehicle access process.

[0035] The entire parking process can be divided into the following three stages: 1) Lift Go to the ground floor car lobby to pick up Car; ; 2) Lift Vertical movement to parking level, lateral conveyor Move horizontally to the connection point of the corresponding lift, lift and lateral conveyor to transfer the vehicle; ; 3) The lateral conveyor moves the vehicle to the designated storage berth; ; Sum: .

[0036] The entire car collection process can also be divided into three stages: 1) The horizontal conveyor moves to the vehicle to be picked up storage berths; ; 2) Transport aircraft Horizontal movement to the lift Connection point, lift Move vertically to the corresponding parking level, horizontal conveyor and lifts Transfer the vehicle; ; 3) Machine Go to the ground car lobby floor to release the car; ; Sum: .

[0037] The meanings of related parameters are shown in the following table.

[0038] Table 1 Parameter meaning

[0039] Step 1.3: Calculate the total storage time of the vehicle from the entrance of the intelligent stereo garage to each berth, divide the energy efficiency area based on the ABC classification method of warehouse management, and put the vehicle with the shortest storage time in the front. The storage berths are divided into high-efficiency areas, with the longest storage time in the front The first 50 storage berths with the shortest storage time are divided into the high efficiency area, the first 50 storage berths with the longest storage time are divided into the low efficiency area, and the remaining 50 storage berths are divided into the medium efficiency area. Figure 3 The following is a schematic diagram of energy efficiency areas. Figure 3 The middle coordinate system represents the length, width and height of the intelligent stereo garage. The yellow points are high-efficiency areas, the green points are medium-efficiency areas, and the blue points are low-efficiency areas.

[0040] Step 2: Parking duration prediction. This embodiment mainly analyzes some factors that affect the parking duration of vehicles, including the three aspects of stay time, vehicle source, and historical parking conditions, and uses a machine learning algorithm to make predictions based on the historical parking data of these three aspects.

[0041] Step 2.1: Data analysis. Analyze the historical parking data of the garage to find out the factors that affect the vehicle's stay time, including stay time, parking reason, vehicle source, and medical stay.

[0042] Step 2.2: Data preprocessing. By calculating the interquartile range (IQR) of the box plot of historical parking time, outliers are removed. or greater than , it is determined to be an outlier, where Q1 is the lower quartile and Q3 is the upper quartile. Eliminating abnormal data can improve the accuracy of prediction.

[0043] Step 2.3: Data feature construction. Time data features include the car's arrival date, arrival time, departure date and departure time. The days of the week, weekdays, holidays and other factors that affect the number of times customers park in the garage and the length of time they park. Other data features include the city where the car's license plate number belongs, whether it is the local province or local city, the number of times the car has parked in the garage and other factors that will also lead to different parking times.

[0044] Step 2.4: Use the preprocessed data to predict parking duration based on the XGBoost algorithm.

[0045] 1) The original data set composed of data features is divided into training set and test set according to 7:3, and then the training set is divided into 20% validation set data by random sampling for cross-validation of the model; 2) The parameters of the XGBoost algorithm are set to be based on tree model iteration, the maximum search depth of the tree is 7, the learning rate is 0.01, the alpha is 0.01, the gamma is 0.1, and the loss function of the model convergence is RMSE; 3) Calculate the gradient of the loss function with respect to the current prediction value to guide the construction of the next tree, add the newly constructed decision tree to the model, and update the overall prediction result; 4) Repeat the above step 3), constructing a new decision tree each time until the predetermined number of trees or the predetermined number of iterations is reached; 5) Use five-fold cross validation to evaluate the performance of the model and select appropriate parameters. After training is completed, perform duration prediction and output the prediction results.

[0046] Step 3, Combine Figure 1The intelligent three-dimensional parking garage shown in the figure considers the storage space energy efficiency and parking time prediction to design the storage method, and performs scheduling based on the above storage space energy efficiency division and parking time prediction.

[0047] Step 3.1: The vehicle to be stored is located at the entrance of the intelligent three-dimensional garage. The customer uses the fourth screen 13 to determine whether there is an empty parking space in the garage; if there is, the vehicle will enter the garage, and if not, the vehicle will leave.

[0048] Step 3.2: After the vehicle enters the intelligent stereo garage, it is tested on the detection platform 12. If the weight and size of the vehicle meet the requirements, the vehicle enters the waiting area 14.

[0049] Step 3.3: Identify the vehicle through the camera 11, analyze the historical vehicle stay data, predict the vehicle stay time, and obtain the predicted stay time .

[0050] Step 3.4: Predict the length of stay Standardize and determine the interval in which the vehicle is located, allocate the vehicle to different energy efficiency areas, and allocate it to the nearest storage berth in the energy efficiency area according to the principle of proximity. The specific method is as follows.

[0051] 1) Collect the time T1 when M vehicles enter the smart stereo garage and the time T2 when they leave the smart stereo garage, calculate their parking time TT = T2-T1, and sort them in order of parking time TT from small to large; 2) Calculate the average parking time of M vehicles and standard deviation , standardize TT, ; 3) Delete Abnormal data; 4) Use the percentile method to quantify the remaining Divide into three equal parts and find the two quantiles of 33% and 67%. and , the standardized parking time is divided into three intervals. The first interval , corresponding to the vehicle storage in the high efficiency area; the second interval is , corresponding to the vehicle storage in the medium efficiency zone; the third interval is , corresponding to vehicles stored in low-efficiency areas.

[0052] 5) Predicted duration of stay Standardize to get , according to its corresponding interval, it is allocated to the corresponding energy efficiency area.

[0053] 6) If there are no free parking spaces in the energy efficiency zone to which the vehicle is assigned, the vehicle will be assigned to another energy efficiency zone with free parking spaces. The specific allocation plan is as follows: For vehicles assigned to the high-efficiency zone, if there are no free parking spaces in the high-efficiency zone, they will be assigned to the medium-efficiency zone first. If there are no free parking spaces in the medium-efficiency zone, they will be assigned to the low-efficiency zone; For vehicles assigned to the low-efficiency zone, if there are no free parking spaces in the low-efficiency zone, they will be assigned to the medium-efficiency zone first. If there are no free parking spaces in the medium-efficiency zone, they will be assigned to the high-efficiency zone; For vehicles assigned to the medium-efficiency zone, if there are no free parking spaces in the medium-efficiency zone, it depends on the vehicle's Value and , The distance between the values ​​is , it will be allocated to the low-efficiency area first, otherwise it will be allocated to the high-efficiency area.

[0054] Step 3.5: If there is an idle elevator 1, the driver will drive to the waiting point 9 of the corresponding idle elevator according to the instructions on the third screen 10.

[0055] Step 3.6: When the lift 1 rises to the car hall floor, the garage automatic door 7 opens, and the driver drives the car into the garage automatic door 7, parks the car and walks out of the garage. After the driver clicks the first screen 6 to confirm the storage operation, the vehicle officially enters the storage procedure.

[0056] Step 3.7: The vehicle moves vertically with the elevator 1 through the elevator channel 3 to the assigned parking floor and connects with the idle horizontal conveyor 2 on that floor.

[0057] Step 3.8: After the transverse conveyor 2 moves the vehicle out of the elevator 1 by its own device, it moves horizontally on the handling channel 4 to the designated storage berth 5. If there is no idle transverse conveyor 2 on this floor, the vehicle waits for the transverse conveyor in the elevator 1.

[0058] Step 4, such as Figure 4 As shown, during the off-peak period of warehousing, vehicles in the medium-efficiency area and the low-efficiency area are transferred to the warehouse.

[0059] Step 4.1: Count the traffic in and out of the warehouse every day and every hour over a period of time, and calculate the average traffic flow in each hour. , ,For example is the average inbound vehicle flow rate from 2 a.m. to 3 a.m.; in this embodiment, the average inbound vehicle flow rate per hour for 30 days is counted; Calculate the mean and standard deviation: ; ; when When is the peak period of storage, When It is the low peak period for storage; for Average inbound traffic volume in the hourly period, , is the average of the average inbound vehicle flow in 24 hours a day. is the constant term coefficient, It is the standard deviation of the average inbound vehicle flow rate in 24 hours a day.

[0060] In actual calculation, the statistical time range can be determined according to historical traffic conditions, decision maker needs and / or the opening hours of the intelligent stereoscopic parking garage, such as 6 am to 6 pm, 5 am to 8 pm, etc.

[0061] Step 4.2: Sort the remaining parking time of vehicles in the current medium and low efficiency areas from short to long, and determine whether the vehicle is assigned to a more efficient area. The remaining parking time of the vehicle is the predicted parking time minus the actual parking time. Judgment condition: Assume that the remaining parking time of the vehicle is ,Will To standardize the process, ,when , and the current vehicle is in the low efficiency area, it is assigned to the medium efficiency area. If the vehicle is not in the high efficiency area, it will be allocated to the high efficiency area.

[0062] Step 4.3: If the vehicle is assigned to a higher efficiency area, the garage system will determine whether there is an available parking space in the reallocated efficiency area; if there is an available parking space, the garage system will perform the transfer operation, otherwise the vehicle will wait in place.

[0063] Step 4.4: Repeat steps 4.2 to 4.3 until all vehicles that meet the conditions are moved to the high-efficiency area, or the high-efficiency area is full and no vehicles in the low-efficiency area meet the conditions for transfer.

[0064] Step 5, such as Figure 5 As shown, the driver picks up the vehicle and the vehicle leaves the warehouse.

[0065] Step 5.1: The driver clicks the pick-up button on the second screen 8, and the vehicle officially enters the outbound procedure after scanning the code and paying.

[0066] Step 5.2: The transverse conveyor 2 moves horizontally on the transport channel 4 to the storage berth 5 where the vehicle is currently parked, and then uses its own device to move the vehicle out of the current storage berth 5. The transverse conveyor 2 moves horizontally on the transport channel 4 and connects with the elevator 1. If there is no idle elevator 1, the vehicle waits on the transverse conveyor 2.

[0067] Step 5.3: The elevator 1 moves vertically through the elevator channel 3 to the parking floor where the vehicle to be unloaded is located, and the horizontal conveyor 2 moves the vehicle onto the elevator 1 through its own device. The elevator 1 moves vertically through the elevator channel 3 to the car hall floor.

[0068] Step 5.4: When the elevator rises to the car hall floor, the garage automatic door 7 opens. The driver enters the garage automatic door 7 to drive the vehicle out, and the vehicle leaves the garage.

[0069] The method of the present invention is verified by specific experiments below.

[0070] The improved XGBoost algorithm of the present invention is used to predict parking time, and the support vector regression SVR algorithm and the long short-term memory network LSTM algorithm are compared. The results are shown in Table 2. It can be seen that the improved XGBoost algorithm of the present invention has the best prediction effect. Compared with the SVR algorithm, the RMSE of the improved XGBoost is reduced by 84.48%, and the MAE is reduced by 83.80%; compared with the LSTR algorithm, the RMSE of the improved XGBoost is reduced by 88.09%, and the MAE is reduced by 46.23%.

[0071] Table 2 Comparison of prediction results of different algorithms

[0072] In order to observe the differences in vehicle dispatching results under different methods, this embodiment is aimed at Figure 1 The intelligent stereo garage F shown in the figure has 624 vehicle entry and exit tasks, of which 312 vehicles are used for parking and 312 vehicles are used for retrieval. The period from 6:00 a.m. to 18:00 p.m. is selected for analysis, of which 8:00-9:00 a.m. and 14:00-15:00 p.m. are the peak entry periods, 12:00-13:00 is the low entry period, and the rest of the period is the off-peak period. Figure 6 and Figure 7 They are schematic diagrams of simulation results according to the traditional allocation method and simulation results according to the method of the present invention. Table 3 shows the comparison of simulation results of the traditional allocation method and simulation results of the method of the present invention.

[0073] Table 3 Comparison of simulation results

[0074] The differences between the two methods in terms of average full warehouse rate, maximum full warehouse rate, average operation time, and average waiting time are mainly concentrated in high-efficiency areas and low-efficiency areas. Compared with the current nearest allocation method, the average full warehouse rate and the maximum full warehouse rate in the high-efficiency area under the method of the present invention decreased by 22.6% and 22.84% respectively, and the average full warehouse rate and the maximum full warehouse rate in the low-efficiency area increased by 353.82% and 280.17% respectively. Overall, the standard deviations of the average full warehouse rate and the maximum full warehouse rate of each energy efficiency area under the method of the present invention are 7.38 and 9.62 respectively, while the standard deviations of the average full warehouse rate and the maximum full warehouse rate of each energy efficiency area under the nearest allocation method are 13.29 and 34.43 respectively. In contrast, the nearest allocation scheduling method will lead to uneven resource utilization in each energy efficiency area, and the high-efficiency area may be full. The full warehouse rate of each layer under the method of the present invention is relatively balanced, avoiding extreme full warehouse conditions. In the long run, the vehicle scheduling of the present invention is more scientific and reasonable.

[0075] In addition, the average waiting time of customers under the method of the present invention is also reduced. The scale of the inbound vehicle flow is increased to 454 vehicles, and the average waiting time of customers is reduced from the original 462.33 seconds to 427.33 seconds, a decrease of 7.57%. Therefore, considering the comprehensive effect of the full warehouse rate and the average waiting time of customers, the scheduling method based on the energy efficiency of the storage space and the duration prediction proposed in the present invention is more suitable for the stereo garage system.

[0076] In order to explore the universality of the method proposed in the present invention, the present invention carried out simulation experiments on different vehicle flows and numbers of hardware facilities, and explored the service performance of the garage under different numbers of hardware facilities when the inbound vehicle flows were 312, 454, and 618, respectively.

[0077] Figure 8 The figure shows the comparison of the effect of different numbers of elevators and transverse conveyors on the average operation time under the same traffic volume. Figure 8 It can be seen that under the same traffic flow, the number of elevators and transverse conveyors has almost no effect on the average operation time. When the traffic flow increases, the average operation time of the garage also increases. Since the movement of elevators and transverse conveyors in the garage is almost unaffected by the traffic flow, it means that the increase in traffic flow will lead to a longer waiting time for coordination between elevators and transverse conveyors.

[0078] Fig. 9 The figure shows the comparison of the effect of different numbers of elevators and transverse conveyors on the average waiting time under the same traffic volume. Fig. 9It can be seen that 3 lifts and 10 transverse conveyors and 4 lifts and 10 transverse conveyors can both meet the parking needs of customers with a traffic volume of 312 and 454. When the traffic volume is 618, the average waiting time of 4 lifts and 10 transverse conveyors is 4031.59 seconds shorter than that of 4 lifts and 5 transverse conveyors, which is 86.85% shorter. There is no significant difference in the average waiting time between 3 lifts and 10 transverse conveyors and 4 lifts and 10 transverse conveyors, indicating that increasing the number of transverse conveyors can greatly save the waiting time for customers to park.

[0079] The intelligent stereo garage scheduling system based on storage space energy efficiency and duration prediction of the present invention comprises: after the vehicle to be parked arrives at the entrance of the intelligent stereo garage, the elevator sends the vehicle to the designated floor, and the horizontal transporter moves the vehicle to the designated storage berth. The system comprises: The energy efficiency zone division unit is used to calculate the storage time of the vehicle from the entrance to each storage berth, and divide all the storage berths into three energy efficiency zones according to the storage time from small to large, namely, the high efficiency zone, the medium efficiency zone and the low efficiency zone; An energy efficiency area matching unit is used to obtain the historical stay time of a number of vehicles and sort them from small to large, and divide the historical stay time into three intervals after standardization, namely, a first interval, a second interval and a third interval, which correspond to the vehicles being stored in a high efficiency area, a medium efficiency area and a low efficiency area respectively; The vehicle stay time prediction unit is used to re-acquire the historical parking data features of several vehicles, including the vehicle arrival date, arrival time, departure date and departure time, and use the historical parking data to train the XGBoost algorithm to predict the vehicle stay time; after the parked vehicle arrives at the entrance of the intelligent stereo garage, the trained XGBoost algorithm is used to obtain the predicted vehicle stay time according to the historical parking data of the vehicle; The vehicle dispatching unit is used to standardize the predicted vehicle dwelling time and allocate the vehicle to storage berths corresponding to different energy efficiency areas for storage according to the interval of the standardized predicted vehicle dwelling time.

[0080] The electronic device described in the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the intelligent stereoscopic parking garage scheduling method based on storage location energy efficiency and duration prediction is implemented.

[0081] The computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the intelligent stereoscopic parking garage scheduling method based on storage location energy efficiency and duration prediction is implemented.

[0082] The computer-readable storage media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory, or any other medium that can be used to store program code in the form of instructions or data structures and that can be accessed by a computer.

[0083] The processor is used to execute the computer program stored in the memory to implement each step of the method involved in the above embodiment.

Claims

1. An intelligent parking garage scheduling method based on storage space energy efficiency and duration prediction, characterized in that: After the vehicle to be parked arrives at the entrance of the intelligent stereo garage, the elevator sends the vehicle to the designated floor, and the horizontal transporter moves the vehicle to the designated storage berth. The intelligent stereo garage scheduling method includes the following steps: Calculate the storage time of vehicles from the entrance to each storage berth, and divide all storage berths into three energy efficiency areas according to the storage time from small to large, namely, high efficiency area, medium efficiency area and low efficiency area; Obtain the historical stay times of several vehicles and sort them from small to large, and standardize the historical stay times and divide them into three intervals, namely, a first interval, a second interval, and a third interval, which correspond to vehicles stored in a high-efficiency area, a medium-efficiency area, and a low-efficiency area, respectively; Re-acquire the historical parking data features of several vehicles, including the vehicle arrival date, arrival time, departure date and departure time, and use the historical parking data to train the XGBoost algorithm to predict the vehicle stay time; When the vehicle to be parked arrives at the entrance of the intelligent stereo garage, the trained XGBoost algorithm is used to predict the vehicle's stay time based on the vehicle's historical parking data; The predicted vehicle dwelling time is standardized, and allocated to storage berths corresponding to different energy efficiency areas for storage according to the interval of the standardized predicted vehicle dwelling time.

2. The intelligent stereoscopic parking garage scheduling method based on storage space energy efficiency and duration prediction according to claim 1 is characterized in that: During the off-peak period of parking, vehicles in the medium-efficiency area and the low-efficiency area are moved to the parking lot. The method is as follows: the remaining parking time of the vehicles in the medium-efficiency area and the low-efficiency area is calculated, the remaining parking time is standardized, and the vehicles are moved to the high-efficiency area or the medium-efficiency area according to the interval of the standardized remaining parking time. The low-peak period for entering the warehouse is a period when the average volume of vehicles entering the warehouse is not greater than a threshold value.

3. The intelligent stereoscopic parking garage scheduling method based on storage space energy efficiency and duration prediction according to claim 2 is characterized in that: The classification methods of storage time periods include: When is the peak period of storage, When It is the low peak period for storage; in The first Average inbound traffic volume in the hourly period, is the average value of the average inbound vehicle flow within the statistical time range, is the constant term coefficient, is the standard deviation of the average inbound vehicle flow within the statistical time range.

4. The intelligent stereoscopic parking garage scheduling method based on storage space energy efficiency and duration prediction according to claim 1 is characterized in that: The features of reacquiring historical parking data of several vehicles also include: whether the arrival date and departure date of the vehicle are weekdays or holidays, the number of times the vehicle enters the parking lot, and the province and city to which the vehicle license plate number belongs.

5. The intelligent stereoscopic parking garage scheduling method based on storage space energy efficiency and duration prediction according to claim 1 is characterized in that: Before using the historical parking data to train the XGBoost algorithm to predict the vehicle stay time, the method also includes: preprocessing the historical parking data to eliminate abnormal data.

6. The intelligent stereoscopic parking garage scheduling method based on storage space energy efficiency and duration prediction according to claim 5 is characterized in that: The method for removing abnormal data includes: calculating the interquartile range (IQR) of the box plot of historical parking data; when the historical parking data is less than or greater than When , it is determined to be abnormal data, where Q1 is the lower quartile and Q3 is the upper quartile.

7. The intelligent stereoscopic parking garage scheduling method based on storage space energy efficiency and duration prediction according to claim 1 is characterized in that: The storage time of the vehicle from the entrance to each storage berth includes the time for the elevator to pick up the vehicle from the entrance, the time for the elevator to move vertically to the designated floor, and the time for the horizontal conveyor to move the vehicle to the designated storage berth; The step of standardizing the predicted vehicle stay time and allocating the vehicle to storage spaces corresponding to different energy efficiency zones for storage according to the interval of the standardized predicted vehicle stay time includes: First, determine whether there are any vacant parking spaces in the intelligent three-dimensional parking garage. If there are vacant parking spaces, the driver parks the vehicle at the waiting point corresponding to the elevator and leaves, otherwise the driver drives the vehicle away; the elevator moves the vehicle to the floor where the storage parking spaces in the energy efficiency area are located, and the transverse conveyor removes the vehicle from the elevator and moves it to the storage parking spaces in the energy efficiency area; during this period, if there are no vacant elevators and / or transverse conveyors, the vehicle waits in place until the elevators and / or transverse conveyors are vacant.

8. An intelligent three-dimensional parking garage scheduling system based on storage space energy efficiency and duration prediction, characterized in that: After the vehicle to be parked arrives at the entrance of the intelligent three-dimensional parking garage, the elevator sends the vehicle to the designated floor, and the horizontal transporter moves the vehicle to the designated storage berth. The system includes: The energy efficiency zone division unit is used to calculate the storage time of the vehicle from the entrance to each storage berth, and divide all the storage berths into three energy efficiency zones according to the storage time from small to large, namely, the high efficiency zone, the medium efficiency zone and the low efficiency zone; An energy efficiency area matching unit is used to obtain the historical stay time of a number of vehicles and sort them from small to large, and divide the historical stay time into three intervals after standardization, namely, a first interval, a second interval and a third interval, which correspond to the vehicles being stored in a high efficiency area, a medium efficiency area and a low efficiency area respectively; The vehicle stay time prediction unit is used to re-acquire the historical parking data features of several vehicles, including the vehicle arrival date, arrival time, departure date and departure time, and use the historical parking data to train the XGBoost algorithm to predict the vehicle stay time; after the parked vehicle arrives at the entrance of the intelligent stereo garage, the trained XGBoost algorithm is used to obtain the predicted vehicle stay time according to the historical parking data of the vehicle; The vehicle dispatching unit is used to standardize the predicted vehicle dwelling time and allocate the vehicle to storage berths corresponding to different energy efficiency areas for storage according to the interval of the standardized predicted vehicle dwelling time.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into the processor, the intelligent stereoscopic parking garage scheduling method based on storage space energy efficiency and duration prediction according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the intelligent stereoscopic parking garage scheduling method based on storage space energy efficiency and duration prediction according to any one of claims 1 to 7 is implemented.

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