Method, device and equipment for counting battery replacement queuing vehicles in battery replacement station and storage medium

Abstract

The invention relates to the technical field of vehicle battery replacement, in particular to a battery replacement station battery replacement queuing vehicle counting method, device and equipment and a storage medium. The charging station charging queuing vehicle counting method comprises the following steps: detecting new energy charging vehicles meeting a queuing condition in a preset queuing area of a charging station; obtaining vehicle attributes and historical battery replacement data of each new energy battery replacement vehicle; through a trained battery replacement prediction model, predicting whether each new energy battery replacement vehicle has a battery replacement demand or not according to the vehicle attributes and the historical battery replacement data; and determining the number of target new energy battery replacement vehicles having battery replacement demands, obtaining vehicle attributes and historical battery replacement data of new energy battery replacement vehicles meeting queuing conditions in a preset queuing area of the battery replacement station, and inputting the vehicle attributes and historical battery replacement data to a trained battery replacement prediction model to predict whether the new energy battery replacement vehicles have the battery replacement demands or not. And the calculation precision of the vehicle battery replacement queuing number of the battery replacement station is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle battery swapping, and particularly to a method, device, equipment and storage medium for counting battery swapping queuing vehicles in a battery swapping station. Background Art

[0002] With the continuous development and popularization of new energy electric vehicles, users' demand for electricity is constantly expanding. How to avoid wasting battery resources and meet users' battery swapping needs as soon as possible, and reduce users' queuing time is an urgent problem to be solved. For the traditional counting of queuing vehicles in a battery swapping station, only by demarcating the queuing radius of the battery swapping station and judging whether a vehicle is queuing for battery swapping according to whether the vehicle enters the radius area, there is a large error in this way, and it cannot provide users with an accurate expected indication of the number of vehicles queuing for battery swapping.

[0003] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method, device, equipment and storage medium for counting battery swapping queuing vehicles in a battery swapping station, aiming to solve the technical problem of large error in calculating the number of vehicles queuing for battery swapping in the prior art.

[0005] To achieve the above purpose, the present invention provides a method for counting battery swapping queuing vehicles in a battery swapping station, and the method includes the following steps:

[0006] Detect new energy battery swapping vehicles that meet the queuing conditions in the preset queuing area of the battery swapping station;

[0007] Obtain the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle;

[0008] Through a trained battery swapping prediction model, predict whether each new energy battery swapping vehicle has a battery swapping demand according to the vehicle attributes and the historical battery swapping data;

[0009] Determine the number of target new energy battery swapping vehicles with battery swapping demands.

[0010] Optionally, the vehicle attributes include at least one of the current battery power, current vehicle energy consumption, and current vehicle operating state; the historical battery swapping data includes at least one of the historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution;

[0011] The step of predicting whether each new energy battery swapping vehicle has a battery swapping demand according to the vehicle attributes and the historical battery swapping data through a trained battery swapping prediction model includes:

[0012] Construct a vehicle queuing feature list corresponding to each new energy battery swapping vehicle based on the current battery power, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycles, historical driving mileage distribution, and historical travel time distribution of each new energy battery swapping vehicle;

[0013] Input the vehicle queuing feature list into the trained battery swapping prediction model to obtain a vehicle battery swapping probability set;

[0014] Calculate the weights of the vehicle battery swapping probability set to obtain the target vehicle battery swapping probability;

[0015] Predict whether there is a battery swapping demand for each new energy battery swapping vehicle according to the target vehicle battery swapping probability and a preset probability threshold.

[0016] Optionally, before predicting whether there is a battery swapping demand for each new energy battery swapping vehicle through the trained battery swapping prediction model according to the vehicle attributes and the historical battery swapping data, it further includes:

[0017] Construct a battery swapping data sample according to the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle;

[0018] Extract the queuing battery swapping characteristics of each cluster of battery swapping data in the battery swapping data sample and generate a corresponding vehicle battery swapping feature list;

[0019] Associate the vehicle battery swapping feature list with each cluster of battery swapping data in the battery swapping data sample to obtain a target battery swapping data sample set;

[0020] Train the target battery swapping features of the target battery swapping data sample set through a random forest model to obtain a trained battery swapping prediction model.

[0021] Optionally, the step of training the target battery swapping features of the target battery swapping data sample set through a random forest model to obtain a trained battery swapping prediction model includes:

[0022] Input the target battery swapping data sample set into the initial random forest decision tree to obtain the prediction results corresponding to each target battery swapping data sample;

[0023] Calculate model evaluation parameters according to the target battery swapping data sample and the prediction results;

[0024] Evaluate the importance of the vehicle battery swapping characteristics in each target battery swapping data sample according to the model evaluation parameters to obtain a battery swapping feature evaluation result;

[0025] Adjust the model parameters of the initial random forest decision tree according to the battery swapping feature evaluation result to obtain a trained battery swapping prediction model.

[0026] Optionally, the method for counting the queuing vehicles for battery swapping at the battery swapping station further includes:

[0027] Obtain the real-time positions of each target new energy vehicle for battery swapping;

[0028] Construct a monitoring chart for the queuing of vehicles for battery swapping based on the real-time positions;

[0029] Upload the monitoring chart for the queuing of vehicles for battery swapping and the number of target new energy vehicles for battery swapping with battery swapping requirements to the cloud server to update the queuing battery swapping data and the scope of the queuing area of the battery swapping station in the cloud server.

[0030] Optionally, the detection of new energy vehicles for battery swapping that meet the queuing conditions within the preset queuing area of the battery swapping station includes:

[0031] Obtain the vehicle type, current vehicle speed, and vehicle speed change of each new energy vehicle within the preset queuing area of the battery swapping station;

[0032] Determine whether the vehicle type, the current vehicle speed, and the vehicle speed change meet the queuing conditions;

[0033] If so, mark the new energy vehicles for battery swapping that meet the queuing conditions;

[0034] The queuing conditions include at least one of: the current vehicle speed is less than the preset vehicle speed threshold, the vehicle type has a record in the preset database of vehicle types for battery swapping, and the vehicle speed change is gradually decreasing.

[0035] In addition, to achieve the above object, the present invention also proposes a device for counting the queuing vehicles for battery swapping at the battery swapping station, and the device for counting the queuing vehicles for battery swapping at the battery swapping station includes:

[0036] A detection module, configured to detect new energy vehicles for battery swapping that meet the queuing conditions within the preset queuing area of the battery swapping station;

[0037] An acquisition module, configured to acquire the vehicle attributes and historical battery swapping data of each new energy vehicle for battery swapping;

[0038] A prediction module, configured to predict whether each new energy vehicle for battery swapping has a battery swapping requirement according to the vehicle attributes and the historical battery swapping data through a trained battery swapping prediction model;

[0039] A counting module, configured to determine the number of target new energy vehicles for battery swapping with battery swapping requirements.

[0040] In addition, to achieve the above object, the present invention further provides a counting device for vehicles queuing for battery swapping at a battery swapping station. The counting device for vehicles queuing for battery swapping at the battery swapping station includes: a memory, a processor, and a program for counting vehicles queuing for battery swapping at the battery swapping station stored on the memory and executable on the processor. The program for counting vehicles queuing for battery swapping at the battery swapping station is configured to implement the steps of the method for counting vehicles queuing for battery swapping at the battery swapping station as described above.

[0041] In addition, to achieve the above object, the present invention further provides a storage medium. A program for counting vehicles queuing for battery swapping at the battery swapping station is stored on the storage medium. When the program for counting vehicles queuing for battery swapping at the battery swapping station is executed by a processor, the steps of the method for counting vehicles queuing for battery swapping at the battery swapping station as described above are implemented.

[0042] In addition, to achieve the above object, the present application further provides a computer program product. The computer program product includes a computer program. When the computer program is executed by a processor, the steps of the method for counting vehicles queuing for battery swapping at the battery swapping station as described above are implemented.

[0043] One or more technical solutions proposed by the present invention have at least the following technical effects: The present invention discloses a method for counting vehicles queuing for battery swapping at a battery swapping station. The method for counting vehicles queuing for battery swapping at the battery swapping station includes: detecting new energy battery swapping vehicles that meet the queuing conditions in a preset queuing area of the battery swapping station; obtaining the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle; through a trained battery swapping prediction model, predicting whether each new energy battery swapping vehicle has a battery swapping demand according to the vehicle attributes and the historical battery swapping data; determining the number of target new energy battery swapping vehicles with a battery swapping demand. By obtaining the vehicle attributes and historical battery swapping data of new energy battery swapping vehicles that meet the queuing conditions in a preset queuing area of the battery swapping station and inputting them into the trained battery swapping prediction model to predict whether new energy battery swapping vehicles have a battery swapping demand, the accuracy of calculating the number of vehicles queuing for battery swapping at the battery swapping station is improved, and the technical problem of large errors in calculating the number of vehicles queuing for battery swapping at the battery swapping station in the prior art is avoided. Description of the Drawings

[0044] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0045] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1Schematic flowchart of the first embodiment of the method for counting vehicles queuing for battery swapping at the battery swapping station of the present invention;

[0047] Figure 2 Schematic diagram of the vehicle queuing monitoring chart of an embodiment of the method for counting vehicles queuing for battery swapping at the battery swapping station of the present invention;

[0048] Figure 3 Schematic flowchart of the second embodiment of the method for counting vehicles queuing for battery swapping at the battery swapping station of the present invention;

[0049] Figure 4 Block diagram of the first embodiment of the device for counting vehicles queuing for battery swapping at the battery swapping station of the present invention;

[0050] Figure 5 Schematic diagram of the structure of the equipment for counting vehicles queuing for battery swapping at the battery swapping station, which is the hardware operating environment involved in the solution of the embodiment of the present invention.

[0051] The implementation, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0052] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0053] For a better understanding of the technical solutions of the present application, the following will be described in detail with reference to the accompanying drawings of the specification and specific embodiments.

[0054] Based on this, the embodiment of the present invention provides a method for counting vehicles queuing for battery swapping at a battery swapping station. Referring to Figure 1 , Figure 1 Schematic flowchart of the first embodiment of a method for counting vehicles queuing for battery swapping at a battery swapping station of the present invention.

[0055] In this embodiment, the method for counting vehicles queuing for battery swapping at the battery swapping station includes:

[0056] Step S10: Detect new energy vehicles for battery swapping that meet the queuing conditions in the preset queuing area of the battery swapping station.

[0057] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a battery swapping station control chip or a terminal computer, etc., or an electronic device capable of realizing the above functions. The following takes the control chip of the battery swapping station as an example to illustrate this embodiment and the following embodiments.

[0058] The preset queuing area refers to an area demarcated with the swapping station as the center. For swapping stations with different traffic volumes, the queuing area can be adjusted adaptively. For example, for newly built swapping stations, the corresponding queuing area can be an initial fixed value of 30 m, while for swapping stations in urban areas with a longer construction time or a larger swapping flow, the queuing area can be set to 50 m. This embodiment does not make specific limitations on this.

[0059] In this embodiment, the queuing conditions include at least one of the following: the current vehicle speed is less than the preset vehicle speed threshold, the vehicle type has a record in the preset swapping vehicle type database, and the vehicle speed change is gradually decreasing.

[0060] Optionally, detecting new energy swapping vehicles that meet the queuing conditions within the preset queuing area of the swapping station includes:

[0061] Obtaining the vehicle type, current vehicle speed, and vehicle speed change of each new energy vehicle within the preset queuing area of the swapping station;

[0062] Determining whether the vehicle type, the current vehicle speed, and the vehicle speed change meet the queuing conditions;

[0063] If so, marking the new energy swapping vehicles that meet the queuing conditions.

[0064] Specifically, not all new energy vehicles are swapping models, and due to the existence of enterprise swapping agreements, some swapping models cannot be swapped at some swapping stations. Therefore, in order to improve the accuracy of counting the queuing vehicles for swapping in the swapping station, this embodiment can verify the vehicle types of each new energy vehicle within the preset queuing area of the swapping station through the preset swapping vehicle type database, thereby improving the counting accuracy.

[0065] In addition, the set position of the swapping station can be areas such as highway service areas, urban areas, or parking lots. When determining whether a new energy vehicle has a swapping demand, it is also possible to comprehensively judge whether the vehicle meets the queuing demand based on the current vehicle speed and the vehicle speed change trend of the vehicle. Generally speaking, new energy vehicles with a lower vehicle speed or a gradually decreasing vehicle speed may have a greater probability of having a swapping demand. The preset vehicle speed threshold can be set within 5 - 20 yards. This embodiment does not make specific limitations on this.

[0066] Step S20: Obtain the vehicle attributes and historical swapping data of each new energy swapping vehicle.

[0067] In this embodiment, the vehicle attributes include at least one of the current battery power, the current vehicle energy consumption, and the current vehicle operating state; the historical swapping data includes at least one of the historical swapping cycle, the historical driving mileage distribution, and the historical travel time distribution.

[0068] Among them, since the vehicle attributes and historical battery replacement data of the vehicle are stored on the vehicle side or in the cloud associated with the vehicle, if the battery replacement station wants to obtain the vehicle attributes and historical battery replacement data of each vehicle, it can first request to establish a communication connection with each new energy battery replacement vehicle. In addition, since the license plate number of a vehicle is a unique identifier, the vehicle attributes or historical battery replacement data can also be exchanged with the battery replacement station through vehicle registration or driver scanning of a QR code, so that the battery replacement station can obtain the vehicle attributes and historical battery replacement data of each new energy battery replacement vehicle. This embodiment does not impose specific restrictions on this.

[0069] Step S30: Using the trained battery swap prediction model, predict whether each new energy battery swap vehicle has a battery swap demand based on the vehicle attributes and the historical battery swap data.

[0070] It can be understood that the trained battery swap prediction model is a classification model based on the random forest algorithm, which is mainly used to calculate the battery swap probability of each new energy battery swap vehicle at this battery swap station based on data such as the current battery power of each new energy battery swap vehicle, current vehicle energy consumption, current vehicle operating status, historical battery swap cycles, historical driving mileage distribution, and historical travel time distribution, so as to predict whether there is a demand for battery swapping for new energy battery swapping vehicles, realize the counting of battery swapping vehicles at local battery swapping stations, and improve the accuracy of vehicle battery swap counting.

[0071] Step S40: Determine the number of target new energy battery-swapping vehicles that have battery-swapping needs.

[0072] Through the trained battery swap prediction model, the new energy battery swap vehicles within the designated range of the battery swap station are judged, and the historical battery swap trends and current vehicle status of each new energy vehicle are taken into consideration. The battery swap demand is predicted from the user's perspective. Compared with the existing battery swap vehicle counting method, the accuracy of battery swap prediction and battery swap counting is improved. At the same time, when there are a large number of battery swap businesses, battery swap reference opinions can be provided to vehicles that are not in the queue area, thereby reducing the waiting time of each battery swap user.

[0073] Optionally, the method for counting vehicles queuing for battery swapping at a battery swapping station further includes:

[0074] Obtain the real-time location of each target new energy battery swap vehicle;

[0075] Constructing a battery swap vehicle queue monitoring chart based on the real-time location;

[0076] The battery swap vehicle queue monitoring chart and the number of target new energy battery swap vehicles with battery swap needs are uploaded to the cloud server to update the battery swap station queue data and the range of the battery swap station queue area on the cloud server.

[0077] In the specific implementation, refer to Figure 2 , Figure 2This is an example schematic diagram of the queuing monitoring chart for battery swapping vehicles in this embodiment. After determining the target new energy battery swapping vehicles with battery swapping requirements, a queuing monitoring chart for battery swapping vehicles can be constructed based on the real-time positions of the target new energy battery swapping vehicles. This queuing monitoring chart for battery swapping vehicles can be used by enterprises to control the battery health of battery swapping stations, so as to selectively increase the number of battery swapping staff or the efficiency of battery scheduling, and improve the battery swapping revenue.

[0078] It can also be provided to drivers with battery swapping requirements for early battery swapping planning, reducing the battery swapping queuing time. Especially for drivers with operating requirements or long-distance driving requirements, planning the battery swapping location in advance can not only save the battery swapping cost, but also not affect the normal travel, improving the user's battery swapping experience.

[0079] In this embodiment, new energy battery swapping vehicles that meet the queuing conditions in the preset queuing area of the battery swapping station are detected; the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle are obtained; through a trained battery swapping prediction model, according to the vehicle attributes and the historical battery swapping data, it is predicted whether each new energy battery swapping vehicle has a battery swapping requirement; the number of target new energy battery swapping vehicles with battery swapping requirements is determined. By obtaining the vehicle attributes and historical battery swapping data of new energy battery swapping vehicles that meet the queuing conditions in the preset queuing area of the battery swapping station and inputting them into the trained battery swapping prediction model, it is possible to predict whether new energy battery swapping vehicles have a battery swapping requirement, improving the accuracy of calculating the number of vehicles queuing for battery swapping at the battery swapping station.

[0080] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as in the above-mentioned first embodiment can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 3 , step S30 includes:

[0081] Step S301: Construct a vehicle queuing feature list corresponding to each new energy battery swapping vehicle based on the current battery level, current vehicle energy consumption, current vehicle operating state, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution of each new energy battery swapping vehicle.

[0082] Step S302: Input the vehicle queuing feature list into the trained battery swapping prediction model to obtain a vehicle battery swapping probability set.

[0083] Step S303: Calculate the weights of the vehicle battery swapping probability set to obtain the target vehicle battery swapping probability.

[0084] Step S304: Predict whether each new energy battery swapping vehicle has a battery swapping requirement according to the target vehicle battery swapping probability and a preset probability threshold.

[0085] It should be noted that since the trained battery swapping prediction model is a decision tree model constructed based on the random forest algorithm, when performing classification prediction, constructing a vehicle queuing feature list corresponding to each new energy battery swapping vehicle based on the current battery power, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution of each new energy battery swapping vehicle can reduce the secondary processing of data by the model to conform to the model input format and improve the model decision-making efficiency.

[0086] In the vehicle queuing feature list, corresponding feature coefficients can be set respectively according to six parameters such as the current battery power, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution, and the queuing features are preprocessed with the feature coefficients to improve the credibility of various features in the battery swapping decision-making process.

[0087] The trained battery swapping prediction model will output a corresponding probability value for each type of feature in the vehicle queuing feature list. When determining the final battery swapping probability of the target vehicle, weight calculation can be performed based on the feature coefficients corresponding to each feature to improve the credibility of the vehicle battery swapping probability. Finally, based on a preset probability threshold, it is comprehensively judged whether each new energy battery swapping vehicle has a battery swapping demand. Among them, the preset probability threshold can be set to 0.8, and this embodiment does not make specific limitations on this.

[0088] Optionally, before predicting whether each new energy battery swapping vehicle has a battery swapping demand according to the vehicle attributes and the historical battery swapping data through the trained battery swapping prediction model, it further includes:

[0089] Construct a battery swapping data sample according to the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle;

[0090] Extract the queuing and battery swapping features of each cluster of battery swapping data in the battery swapping data sample and generate a corresponding vehicle battery swapping feature list;

[0091] Associate the vehicle battery swapping feature list with each cluster of battery swapping data in the battery swapping data sample to obtain a target battery swapping data sample set;

[0092] Train the target battery swapping features of the target battery swapping data sample set through a random forest model to obtain a trained battery swapping prediction model.

[0093] Since there may be multiple parameters that affect the decision result of the decision tree at the same time, when constructing the decision tree model, multiple candidate decision tree models will be constructed according to different parameter combinations, and by evaluating each candidate decision tree model, a decision tree model with the most accurate theoretically predicted result of battery swapping demand can be obtained.

[0094] Optionally, training the target battery swapping features of the target battery swapping data sample set through a random forest model to obtain a trained battery swapping prediction model includes:

[0095] Inputting the target battery swapping data sample set into an initial random forest decision tree to obtain prediction results corresponding to each target battery swapping data sample;

[0096] Calculating model evaluation parameters according to the target battery swapping data sample and the prediction results;

[0097] Evaluating the importance of the vehicle battery swapping features in each of the target battery swapping data samples according to the model evaluation parameters to obtain a battery swapping feature evaluation result;

[0098] Adjusting the model parameters of the initial random forest decision tree according to the battery swapping feature evaluation result to obtain a trained battery swapping prediction model.

[0099] In this embodiment, the model evaluation parameters can be at least one of model accuracy, model precision, model recall, and model false negative rate. In this embodiment, model accuracy and model precision are taken as examples for illustration. Among them, the calculation formula for model accuracy is:

[0100]

[0101] The formula for calculating model precision is:

[0102]

[0103] Among them, x11 is the number of queuing battery swapping features of samples with battery swapping requirements predicted as having battery swapping requirements, x10 is the number of queuing battery swapping features of samples with battery swapping requirements predicted as not having battery swapping requirements, x01 is the number of queuing battery swapping features of samples without battery swapping requirements predicted as having battery swapping requirements, and x00 is the number of queuing battery swapping features of samples without battery swapping requirements predicted as not having battery swapping requirements.

[0104] In specific implementation, parameters such as model accuracy, model precision, model recall, and model false negative rate can be used to determine whether the decision tree model is overfitting, and then pruning, parameter optimization, etc. can be performed on the decision tree model to improve the accuracy of the decision tree model.

[0105] In this embodiment, a vehicle queuing feature list corresponding to each new energy battery swapping vehicle is constructed based on the current battery power, current vehicle energy consumption, current vehicle operating state, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution of each new energy battery swapping vehicle; the vehicle queuing feature list is input into a trained battery swapping prediction model to obtain a vehicle battery swapping probability set; the vehicle battery swapping probability set is averaged to obtain the target vehicle battery swapping probability; whether each new energy battery swapping vehicle has a battery swapping demand is predicted based on the target vehicle battery swapping probability and a preset probability threshold, improving the efficiency and accuracy of the model's prediction of the vehicle battery swapping demand.

[0106] This application also provides a counting device for battery swapping queuing vehicles at a battery swapping station. Please refer to Figure 4 , the counting device for battery swapping queuing vehicles at the battery swapping station includes:

[0107] A detection module 10, configured to detect new energy battery swapping vehicles that meet the queuing conditions in a preset queuing area of the battery swapping station.

[0108] An acquisition module 20, configured to acquire the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle.

[0109] A prediction module 30, configured to predict whether each new energy battery swapping vehicle has a battery swapping demand through a trained battery swapping prediction model according to the vehicle attributes and the historical battery swapping data.

[0110] A counting module 40, configured to determine the number of target new energy battery swapping vehicles that have a battery swapping demand.

[0111] In this embodiment, new energy battery swapping vehicles that meet the queuing conditions in a preset queuing area of the battery swapping station are detected; the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle are acquired; whether each new energy battery swapping vehicle has a battery swapping demand is predicted through a trained battery swapping prediction model according to the vehicle attributes and the historical battery swapping data; the number of target new energy battery swapping vehicles that have a battery swapping demand is determined. By acquiring the vehicle attributes and historical battery swapping data of new energy battery swapping vehicles that meet the queuing conditions in a preset queuing area of the battery swapping station and inputting them into a trained battery swapping prediction model, whether new energy battery swapping vehicles have a battery swapping demand is predicted, improving the accuracy of calculating the number of queuing vehicles for battery swapping at the battery swapping station.

[0112] In one embodiment, the prediction module 30 is further configured to construct a vehicle queuing feature list corresponding to each new energy battery swapping vehicle based on the current battery power, current vehicle energy consumption, current vehicle operating state, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution of each new energy battery swapping vehicle; input the vehicle queuing feature list into a trained battery swapping prediction model to obtain a vehicle battery swapping probability set; perform mean processing on the vehicle battery swapping probability set to obtain a target vehicle battery swapping probability; and predict whether there is a battery swapping demand for each new energy battery swapping vehicle according to the target vehicle battery swapping probability and a preset probability threshold.

[0113] In one embodiment, the prediction module 30 is further configured to construct a battery swapping data sample according to the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle; extract the queuing battery swapping features of each cluster of battery swapping data in the battery swapping data sample, and generate a corresponding vehicle battery swapping feature list; associate the vehicle battery swapping feature list with each cluster of battery swapping data in the battery swapping data sample to obtain a target battery swapping data sample set; and perform model training on the target battery swapping features of the target battery swapping data sample set through a random forest model to obtain a trained battery swapping prediction model.

[0114] In one embodiment, the prediction module 30 is further configured to input the target battery swapping data sample set into an initial random forest decision tree to obtain a prediction result corresponding to each target battery swapping data sample; calculate a model evaluation parameter according to the target battery swapping data sample and the prediction result; perform importance evaluation on the vehicle battery swapping features in each of the target battery swapping data samples according to the model evaluation parameter to obtain a battery swapping feature evaluation result; and adjust the model parameters of the initial random forest decision tree where it is located according to the battery swapping feature evaluation result to obtain a trained battery swapping prediction model.

[0115] In one embodiment, the counting module 40 is further configured to obtain the real-time position of each target new energy battery swapping vehicle; construct a battery swapping vehicle queuing monitoring chart according to the real-time position; and upload the battery swapping vehicle queuing monitoring chart and the number of target new energy battery swapping vehicles with a battery swapping demand to a cloud server to update the battery swapping queuing data of the cloud server and the range of the battery swapping queuing area of the cloud server.

[0116] In one embodiment, the detection module 10 is further configured to obtain the vehicle type, current vehicle speed, and vehicle speed change of each new energy vehicle within a preset queuing area of the battery swapping station; determine whether the vehicle type, the current vehicle speed, and the vehicle speed change meet the queuing conditions; if so, mark the new energy battery swapping vehicles that meet the queuing conditions; the queuing conditions include at least one of the following: the current vehicle speed is less than a preset vehicle speed threshold, the vehicle type has a record in a preset battery swapping database, and the vehicle speed change is gradually decreasing.

[0117] The present application provides a vehicle counting device for queuing for battery swapping at a battery swapping station. The vehicle counting device for queuing for battery swapping at a battery swapping station includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the vehicle counting method for queuing for battery swapping at a battery swapping station in the first embodiment above.

[0118] Reference is made below Figure 5 , which shows a schematic structural diagram of a vehicle counting device for queuing for battery swapping at a battery swapping station suitable for implementing the embodiments of the present application. The vehicle counting device for queuing for battery swapping at a battery swapping station in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions: tablet computers), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The vehicle counting device for queuing for battery swapping at a battery swapping station shown is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present application.

[0119] As Figure 5As shown, the vehicle counting device for queuing to swap batteries at a battery swapping station may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the vehicle counting device for queuing to swap batteries at the battery swapping station are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the vehicle counting device for queuing to swap batteries at the battery swapping station to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a vehicle counting device for queuing to swap batteries at a battery swapping station having various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems can be alternatively implemented or had.

[0120] Particularly, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiments disclosed in the present application are executed.

[0121] The vehicle counting device for queuing to swap batteries at a battery swapping station provided by the present application adopts the vehicle counting method for queuing to swap batteries at a battery swapping station in the above-mentioned embodiment, and can solve the technical problem of counting the vehicles queuing to swap batteries at a battery swapping station. Compared with the prior art, the beneficial effects of the vehicle counting device for queuing to swap batteries at a battery swapping station provided by the present application are the same as those of the vehicle counting method for queuing to swap batteries at a battery swapping station provided by the above-mentioned embodiment, and other technical features in the vehicle counting device for queuing to swap batteries at a battery swapping station are the same as the features disclosed in the method of the previous embodiment, and will not be elaborated here.

[0122] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0123] As described above, the above are only specific embodiments of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0124] This application provides a computer-readable storage medium with computer-readable program instructions (i.e., computer programs) stored thereon. The computer-readable program instructions are used to execute the method for counting queuing vehicles for battery swapping in the battery swapping station in the above embodiments.

[0125] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, device, or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0126] The above computer-readable storage medium can be included in the device for counting queuing vehicles for battery swapping in the battery swapping station; it can also exist alone without being assembled into the device for counting queuing vehicles for battery swapping in the battery swapping station.

[0127] The above computer-readable storage medium carries one or more programs, which, when executed by the battery swapping queuing vehicle counting device of the battery swapping station, cause the battery swapping queuing vehicle counting device of the battery swapping station to count the vehicles queuing for battery swapping at the battery swapping station.

[0128] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutively represented blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0130] The modules involved in the embodiments described in this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0131] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for counting vehicles queuing for battery swapping at a battery swapping station, and can solve the technical problem of counting vehicles queuing for battery swapping at a battery swapping station. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the method for counting vehicles queuing for battery swapping at a battery swapping station provided in the above embodiments, and will not be elaborated here.

[0132] This application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it realizes the steps of the method for counting vehicles queuing for battery swapping at a battery swapping station as described above.

[0133] The computer program product provided by this application can solve the technical problem of counting vehicles queuing for battery swapping at a battery swapping station. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as those of the method for counting vehicles queuing for battery swapping at a battery swapping station provided in the above embodiments, and will not be elaborated here.

[0134] The above are only some embodiments of this application, and do not limit the patent scope of this application accordingly. Any equivalent structural transformation made under the technical concept of this application by using the content of the specification and drawings of this application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of this application.

Claims

1. A method for counting vehicles in a battery swapping queue at a battery swapping station, characterized in that: The method for counting vehicles queuing for battery swapping at a battery swapping station comprises: Detect new energy battery swapping vehicles that meet the queuing conditions in the preset queuing area of ​​the battery swapping station; Obtain vehicle attributes and historical battery swap data for each new energy battery swap vehicle; By using the trained battery swap prediction model, predict whether each new energy battery swap vehicle has a battery swap demand according to the vehicle attributes and the historical battery swap data; Determine the number of target new energy battery-swap vehicles that have battery-swap needs.

2. The method for counting vehicles in the battery swapping queue at a battery swapping station according to claim 1, characterized in that: The vehicle attributes include: at least one of the current battery power, the current vehicle energy consumption, and the current vehicle operating state; the historical battery replacement data includes: at least one of the historical battery replacement cycle, the historical driving mileage distribution, and the historical travel time distribution; The trained battery swap prediction model predicts whether each new energy battery swap vehicle has a battery swap demand according to the vehicle attributes and the historical battery swap data, including: A vehicle queuing feature list corresponding to each new energy battery swapping vehicle is constructed based on the current battery power, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution of each new energy battery swapping vehicle; Inputting the vehicle queuing feature list into the trained battery swap prediction model to obtain a vehicle battery swap probability set; Performing weight calculation on the vehicle battery replacement probability set to obtain the target vehicle battery replacement probability; Based on the battery replacement probability of the target vehicle and the preset probability threshold, it is predicted whether each new energy battery replacement vehicle has a battery replacement demand.

3. The method for counting vehicles in the battery swapping queue at a battery swapping station according to claim 2, characterized in that: Before predicting whether each new energy battery-swap vehicle has a battery-swap demand based on the vehicle attributes and the historical battery-swap data using the trained battery-swap prediction model, the method further includes: Construct a battery swap data sample based on the vehicle attributes and historical battery swap data of each new energy battery swap vehicle; Extracting the queue battery swapping features of each cluster of battery swapping data in the battery swapping data sample, and generating a corresponding vehicle battery swapping feature list; Associating the vehicle battery swap feature list with each cluster of battery swap data in the battery swap data sample to obtain a target battery swap data sample set; The target battery swap features of the target battery swap data sample set are trained through a random forest model to obtain a trained battery swap prediction model.

4. The method for counting vehicles in the battery swapping queue at a battery swapping station as claimed in claim 3, characterized in that: The target battery swapping features of the target battery swapping data sample set are trained by a random forest model to obtain a trained battery swapping prediction model, including: Input the target battery swap data sample set into the initial random forest decision tree to obtain the prediction results corresponding to each target battery swap data sample; Calculate model evaluation parameters according to the target battery replacement data sample and the prediction result; Performing an importance evaluation on the vehicle battery swapping characteristics in each of the target battery swapping data samples according to the model evaluation parameters to obtain a battery swapping characteristics evaluation result; The model parameters of the initial random forest decision tree are adjusted according to the battery swap feature evaluation results to obtain a trained battery swap prediction model.

5. The method for counting vehicles in the battery swapping queue at a battery swapping station according to any one of claims 1 to 4, characterized in that: The method for counting vehicles queuing for battery swapping at a battery swapping station further includes: Obtain the real-time location of each target new energy battery swap vehicle; Constructing a battery swap vehicle queue monitoring chart based on the real-time location; The battery swap vehicle queue monitoring chart and the number of target new energy battery swap vehicles with battery swap needs are uploaded to the cloud server to update the battery swap station queue data and the range of the battery swap station queue area on the cloud server.

6. The method for counting vehicles in the battery swapping queue at a battery swapping station according to any one of claims 1 to 4, characterized in that: The detection of new energy battery swapping vehicles that meet the queuing conditions in the preset queuing area of ​​the battery swapping station includes: Obtain the vehicle type, current speed, and speed change of each new energy vehicle in the preset queuing area of ​​the battery swap station; According to the vehicle type, the current vehicle speed and whether the vehicle speed change meets the queuing condition; If so, mark the new energy battery swap vehicles that meet the queuing conditions; The queuing conditions include: the current vehicle speed is less than a preset vehicle speed threshold, the vehicle type is recorded in a preset battery-swapping vehicle type database, and the vehicle speed changes to gradually decrease.

7. A device for counting vehicles in a battery swapping queue at a battery swapping station, characterized in that: The battery swapping queuing vehicle counting device of the battery swapping station comprises: A detection module is used to detect new energy battery swapping vehicles that meet the queuing conditions in the preset queuing area of ​​the battery swapping station; An acquisition module is used to obtain vehicle attributes and historical battery swap data of each new energy battery swap vehicle; A prediction module, used to predict whether each new energy battery-swap vehicle has a battery-swap demand based on the vehicle attributes and the historical battery-swap data by using a trained battery-swap prediction model; The counting module is used to determine the number of target new energy battery-swapping vehicles that have battery-swapping needs.

8. A device for counting vehicles in the battery swapping queue at a battery swapping station, characterized in that: The battery swap station battery swap queue vehicle counting device comprises: a memory, a processor and a battery swap station battery swap queue vehicle counting program stored in the memory and executable on the processor, wherein the battery swap station battery swap queue vehicle counting program is configured to implement the battery swap station battery swap queue vehicle counting method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a vehicle counting program for battery swapping queues at battery swapping stations, and when the vehicle counting program for battery swapping queues at battery swapping stations is executed by a processor, a vehicle counting method for battery swapping queues at battery swapping stations as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the steps of the method for counting vehicles in the battery swapping queue at a battery swapping station according to any one of claims 1 to 6.

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