Battery swapping station battery swapping queue vehicle counting method, device, equipment and storage medium

Abstract

The present application relates to the technical field of vehicle battery replacement, in particular to a battery replacement station battery replacement queuing vehicle counting method, device, equipment and storage medium, the present application discloses a kind of battery replacement station battery replacement queuing vehicle counting method, the battery replacement station battery replacement queuing vehicle counting method includes: detecting new energy battery replacement vehicle in the preset queuing area of battery replacement station that meets queuing condition;The vehicle attribute and historical battery replacement data of each new energy battery replacement vehicle are obtained;Through the trained battery replacement prediction model, whether each new energy battery replacement vehicle exists battery replacement demand is predicted according to the vehicle attribute and the historical battery replacement data;The number of target new energy battery replacement vehicle that exists battery replacement demand is determined, by obtaining the vehicle attribute and historical battery replacement data of new energy battery replacement vehicle in the preset queuing area of battery replacement station that meets queuing condition, input to the trained battery replacement prediction model, to predict whether new energy battery replacement vehicle exists battery replacement demand, improve the precision of the vehicle battery replacement queuing number calculation of battery replacement station.

Description

Technical Field

[0001] This invention relates to the field of vehicle battery swapping technology, and more particularly to a method, apparatus, equipment, and storage medium for counting vehicles queuing at battery swapping stations. Background Technology

[0002] With the continuous development and popularization of new energy electric vehicles, users' demand for electricity is constantly expanding. How to meet users' battery swapping needs as quickly as possible without wasting battery resources and reduce users' queuing time is an urgent problem to be solved. However, the traditional method of counting vehicles in the queue at battery swapping stations only determines whether a vehicle is queuing for battery swapping by defining the queuing radius of the battery swapping station and judging whether the vehicle has entered the radius area. This method has a large error and cannot provide users with an accurate indication of the expected number of vehicles in the battery swapping queue.

[0003] The above content is only used to help understand 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 objective of this invention is to provide a method, apparatus, equipment, and storage medium for counting vehicles queuing at battery swapping stations, aiming to solve the technical problem of large errors in calculating the number of vehicles queuing at battery swapping stations in the prior art.

[0005] To achieve the above objectives, the present invention provides a method for counting vehicles queuing at a battery swapping station, the method comprising the following steps:

[0006] The system detects new energy battery swapping vehicles that meet the queuing conditions within the pre-set queuing area of ​​the battery swapping station.

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

[0008] The trained battery swapping prediction model is used to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and the historical battery swapping data.

[0009] Determine the number of target new energy battery swapping vehicles that have a battery swapping need.

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

[0011] The step of using a trained battery swapping prediction model to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and historical battery swapping data includes:

[0012] A list of vehicle queuing characteristics for each new energy battery swapping vehicle is constructed based on the current battery charge, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution.

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

[0014] The battery swapping probability set of the vehicles is weighted and calculated to obtain the battery swapping probability of the target vehicle;

[0015] Based on the target vehicle's battery swapping probability and a preset probability threshold, it is predicted whether each new energy battery swapping vehicle has a battery swapping need.

[0016] Optionally, before predicting whether each new energy battery-swapping vehicle has a battery-swapping need based on the vehicle attributes and historical battery-swapping data using the trained battery-swapping prediction model, the method further includes:

[0017] A battery swapping data sample was constructed based on the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle.

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

[0019] By associating the vehicle battery swapping feature list with each cluster of battery swapping data in the battery swapping data sample, a target battery swapping data sample set is obtained.

[0020] The target battery swapping features of the target battery swapping data sample set are used to train the model using 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 using 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 based on the target battery swapping data sample and the prediction results;

[0024] The importance of vehicle battery swapping features in each of the target battery swapping data samples is evaluated based on the model evaluation parameters to obtain battery swapping feature evaluation results.

[0025] Based on the evaluation results of the battery swapping characteristics, the model parameters of the initial random forest decision tree are adjusted to obtain a trained battery swapping prediction model.

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

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

[0028] A queue monitoring chart for battery swapping vehicles is constructed based on the real-time locations.

[0029] The battery swapping vehicle queue monitoring chart and the number of target new energy battery swapping vehicles with battery swapping needs are uploaded to the cloud server to update the battery swapping station queue data and the range of the battery swapping station queue area on the cloud server.

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

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

[0032] Based on the vehicle type, the current vehicle speed, and whether the change in vehicle speed meets the queuing conditions;

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

[0034] 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 is recorded in a preset battery swapping vehicle type database, and the vehicle speed is gradually decreasing.

[0035] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle counting device for battery swapping stations, the vehicle counting device for battery swapping stations comprising:

[0036] The detection module is used to detect new energy battery swapping vehicles that meet the queuing conditions within the preset queuing area of ​​the battery swapping station;

[0037] The acquisition module is used to acquire the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle.

[0038] The prediction module is used to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and the historical battery swapping data, using a trained battery swapping prediction model.

[0039] The counting module is used to determine the number of target new energy battery swapping vehicles that have a battery swapping need.

[0040] Furthermore, to achieve the above objectives, the present invention also proposes a battery swapping station queuing vehicle counting device, which includes: a memory, a processor, and a battery swapping station queuing vehicle counting program stored in the memory and executable on the processor. The battery swapping station queuing vehicle counting program is configured to implement the steps of the battery swapping station queuing vehicle counting method described above.

[0041] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a battery swapping station queuing vehicle counting program, wherein when the battery swapping station queuing vehicle counting program is executed by a processor, it implements the steps of the battery swapping station queuing vehicle counting method described above.

[0042] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the battery swapping station queuing vehicle counting method described above.

[0043] The present invention proposes one or more technical solutions, which 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 includes: detecting new energy battery swapping vehicles that meet the queuing conditions within a preset queuing area of ​​the battery swapping station; acquiring the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle; predicting whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and the historical battery swapping data using a trained battery swapping prediction model; determining the number of target new energy battery swapping vehicles with a battery swapping need. By acquiring the vehicle attributes and historical battery swapping data of new energy battery swapping vehicles that meet the queuing conditions within the preset queuing area of ​​the battery swapping station and inputting them into the trained battery swapping prediction model, the method predicts whether new energy battery swapping vehicles have a battery swapping need, thereby improving the accuracy of calculating the number of vehicles queuing for battery swapping at the battery swapping station and avoiding 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. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1This is a flowchart illustrating the first embodiment of the battery swapping station queuing vehicle counting method of the present invention;

[0047] Figure 2 This is a schematic diagram of a vehicle queue monitoring chart according to an embodiment of the battery swapping station vehicle counting method of the present invention;

[0048] Figure 3 This is a flowchart illustrating the second embodiment of the battery swapping station queuing vehicle counting method of the present invention;

[0049] Figure 4 This is a structural block diagram of the first embodiment of the battery swapping station vehicle counting device of the present invention;

[0050] Figure 5 This is a schematic diagram of the structure of the battery swapping station vehicle counting device in the hardware operating environment of the embodiment of the present invention.

[0051] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0053] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0054] Based on this, embodiments of the present invention provide a method for counting vehicles queuing at battery swapping stations, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of a method for counting vehicles queuing at a battery swapping station according to 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 battery swapping vehicles that meet the queuing conditions within the preset queuing area of ​​the battery swapping station.

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

[0058] The preset queuing area refers to an area designated around the battery swapping station. The queuing area can be adjusted adaptively for battery swapping stations with different traffic volumes. For example, for newly constructed battery swapping stations, the corresponding queuing area can be an initial fixed value of 30m, while the queuing area for battery swapping stations in urban areas with a longer construction time or a larger current consumption can be set to 50m. This embodiment does not impose specific restrictions on this.

[0059] In this embodiment, 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 is recorded in a preset battery swapping vehicle type database, and the vehicle speed is gradually decreasing.

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

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

[0062] Based on the vehicle type, the current vehicle speed, and whether the change in vehicle speed meets the queuing conditions;

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

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

[0065] In addition, the location of the battery swapping station can be a highway service area, urban area or parking lot. When determining whether a new energy vehicle has a battery swapping need, the current speed and speed change trend of the vehicle can be used to comprehensively judge whether the vehicle meets the queuing requirements. Generally speaking, new energy vehicles with low speed or gradually decreasing speed may have a higher probability of having a battery swapping need. The preset speed threshold can be set within 5-20 km / h. This embodiment does not impose specific restrictions on this.

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

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

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

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

[0070] Understandably, the trained battery swapping prediction model is a classification model based on the random forest algorithm. It is mainly used to calculate the probability of each new energy vehicle swapping at the local battery swapping station based on data such as the current battery charge, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution. This allows the model to predict whether a new energy vehicle has a need for battery swapping, realize the counting of vehicles swapping at the local battery swapping station, and improve the accuracy of vehicle battery swapping counting.

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

[0072] By using a trained battery swapping prediction model, the system identifies new energy battery swapping vehicles within the designated area of ​​the battery swapping station. It considers the historical battery swapping trends and current vehicle status of each new energy vehicle and predicts battery swapping demand from the user's perspective. Compared with existing battery swapping vehicle counting methods, this improves the accuracy of battery swapping prediction and counting. At the same time, when there are many battery swapping services, it can also provide battery swapping reference opinions for vehicles not in the queuing area, reducing the queuing time for each battery swapping user.

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

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

[0075] A queue monitoring chart for battery swapping vehicles is constructed based on the real-time locations.

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

[0077] In the specific implementation, refer to Figure 2 , Figure 2This is an example illustration of a battery swapping vehicle queue monitoring chart in this embodiment. After identifying the target new energy battery swapping vehicles with battery swapping needs, a battery swapping vehicle queue monitoring chart can be constructed based on the real-time location of each target new energy battery swapping vehicle. This battery swapping vehicle queue monitoring chart can be used by enterprises to manage the battery health of battery swapping stations, selectively increase the number of battery swapping staff or battery scheduling efficiency, and improve battery swapping revenue.

[0078] It can also be provided to drivers who need battery swapping, allowing them to plan their battery swapping in advance and reduce queuing time. This is especially helpful for drivers with operational needs or long-distance driving needs, who can plan their battery swapping locations in advance, saving on battery swapping costs without affecting their normal travel and improving the user's battery swapping experience.

[0079] This embodiment detects new energy battery swapping vehicles that meet the queuing conditions within a preset queuing area of ​​the battery swapping station; acquires the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle; uses a trained battery swapping prediction model to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and historical battery swapping data; determines the number of target new energy battery swapping vehicles with battery swapping needs; and improves the accuracy of calculating the number of vehicles queuing for battery swapping at the battery swapping station by acquiring the vehicle attributes and historical battery swapping data of new energy battery swapping vehicles that meet the queuing conditions within the preset queuing area of ​​the battery swapping station and inputting them into the trained battery swapping prediction model.

[0080] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S30 includes:

[0081] Step S301: Construct a vehicle queuing feature list for each new energy battery swapping vehicle based on its current battery charge, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution.

[0082] Step S302: Input the vehicle queuing feature list into the trained battery swapping prediction model to obtain the 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 need based on the target vehicle's 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 built on the random forest algorithm, when performing classification prediction, constructing a vehicle queuing feature list for each new energy battery swapping vehicle by taking the current battery charge, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution can reduce the model's secondary processing of data, conform to the model input format, and improve the model's decision-making efficiency.

[0086] The vehicle queuing feature list can be configured with a corresponding feature coefficient for each of the following six parameters: current battery charge, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution. These feature coefficients are used to preprocess each queuing feature to improve the credibility of various features in the battery swapping decision-making process.

[0087] The trained battery swapping prediction model outputs a corresponding probability value for each 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 reliability of the vehicle battery swapping probability. Finally, based on the preset probability threshold, it is comprehensively judged whether each new energy battery swapping vehicle has a battery swapping demand. The preset probability threshold can be set to 0.8, and this embodiment does not impose specific restrictions on it.

[0088] Optionally, before predicting whether each new energy battery-swapping vehicle has a battery-swapping need based on the vehicle attributes and historical battery-swapping data using the trained battery-swapping prediction model, the method further includes:

[0089] A battery swapping data sample was constructed based on the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle.

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

[0091] By associating the vehicle battery swapping feature list with each cluster of battery swapping data in the battery swapping data sample, a target battery swapping data sample set is obtained.

[0092] The target battery swapping features of the target battery swapping data sample set are used to train the model using a random forest model to obtain a trained battery swapping prediction model.

[0093] Since multiple parameters may exist simultaneously influencing the decision tree's decision-making results, when constructing a decision tree model, multiple candidate decision tree models are built based on different parameter combinations. By evaluating each candidate decision tree model, the decision tree model that theoretically yields the most accurate prediction result for battery swapping demand is obtained.

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

[0095] 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;

[0096] Calculate model evaluation parameters based on the target battery swapping data sample and the prediction results;

[0097] The importance of vehicle battery swapping features in each of the target battery swapping data samples is evaluated based on the model evaluation parameters to obtain battery swapping feature evaluation results.

[0098] Based on the evaluation results of the battery swapping characteristics, the model parameters of the initial random forest decision tree are adjusted to obtain a trained battery swapping prediction model.

[0099] In this embodiment, the model evaluation parameter can be at least one of model accuracy, model precision, model recall, and model false negative rate. This embodiment uses model accuracy and model precision as examples. The formula for calculating model accuracy is:

[0100]

[0101] The formula for calculating the model's accuracy is:

[0102]

[0103] Wherein, x11 represents the number of samples with existing battery swapping needs whose queuing features are predicted to indicate existing battery swapping needs, x10 represents the number of samples with existing battery swapping needs whose queuing features are predicted to indicate non-existent battery swapping needs, x01 represents the number of samples without existing battery swapping needs whose queuing features are predicted to indicate existing battery swapping needs, and x00 represents the number of samples without existing battery swapping needs whose queuing features are predicted to indicate non-existent battery swapping needs.

[0104] In practice, 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. Then, pruning and parameter optimization can be performed on the decision tree model to improve its accuracy.

[0105] This embodiment constructs a vehicle queuing feature list for each new energy battery swapping vehicle based on its current battery charge, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution. The vehicle queuing feature list is then input into a trained battery swapping prediction model to obtain a vehicle battery swapping probability set. The vehicle battery swapping probability set is then averaged to obtain the target vehicle battery swapping probability. Based on the target vehicle battery swapping probability and a preset probability threshold, the model predicts whether each new energy battery swapping vehicle has a battery swapping need, thereby improving the efficiency and accuracy of the model's vehicle battery swapping demand prediction.

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

[0107] The detection module 10 is used to detect new energy battery swapping vehicles that meet the queuing conditions within the preset queuing area of ​​the battery swapping station.

[0108] The acquisition module 20 is used to acquire the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle.

[0109] The prediction module 30 is used to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and the historical battery swapping data using a trained battery swapping prediction model.

[0110] The counting module 40 is used to determine the number of target new energy battery swapping vehicles that have a battery swapping need.

[0111] This embodiment detects new energy battery swapping vehicles that meet the queuing conditions within a preset queuing area of ​​the battery swapping station; acquires the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle; uses a trained battery swapping prediction model to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and historical battery swapping data; determines the number of target new energy battery swapping vehicles with battery swapping needs; and improves the accuracy of calculating the number of vehicles queuing for battery swapping at the battery swapping station by acquiring the vehicle attributes and historical battery swapping data of new energy battery swapping vehicles that meet the queuing conditions within the preset queuing area of ​​the battery swapping station and inputting them into the trained battery swapping prediction model.

[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 charge, 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; 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 the target vehicle battery swapping probability; and predict whether each new energy battery swapping vehicle has a battery swapping demand based on 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 based on 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 train the target battery swapping features of the target battery swapping data sample set using 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 the prediction result corresponding to each target battery swapping data sample; calculate model evaluation parameters based on the target battery swapping data samples and the prediction results; evaluate the importance of vehicle battery swapping features in each target battery swapping data sample based on the model evaluation parameters to obtain battery swapping feature evaluation results; and adjust the model parameters of the initial random forest decision tree based on the battery swapping feature evaluation results to obtain a trained battery swapping prediction model.

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

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

[0117] This application provides a battery swapping station vehicle queuing counting device, which 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 battery swapping station vehicle queuing counting method in Embodiment 1 above.

[0118] The following is for reference. Figure 5 This document illustrates a structural schematic diagram of a battery swapping station vehicle counting device suitable for implementing embodiments of this application. The battery swapping station vehicle counting device in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The battery swapping station vehicle counting device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0119] like Figure 5As shown, the vehicle counting device in the battery swapping station queue may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the vehicle counting device in the battery swapping station queue. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the battery swapping station queuing vehicle counting device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a battery swapping station queuing vehicle counting device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented alternatively.

[0120] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0121] The battery swapping station queuing vehicle counting device provided in this application adopts the battery swapping station queuing vehicle counting method in the above embodiments, which can solve the technical problem of battery swapping station queuing vehicle counting. Compared with the prior art, the beneficial effects of the battery swapping station queuing vehicle counting device provided in this application are the same as the beneficial effects of the battery swapping station queuing vehicle counting method provided in the above embodiments, and other technical features in the battery swapping station queuing vehicle counting device are the same as the features disclosed in the previous embodiment method, and will not be repeated here.

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

[0123] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0124] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the battery swapping station queuing vehicle counting method in the above embodiments.

[0125] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having 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 fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0126] The aforementioned computer-readable storage medium may be included in the battery swapping station's vehicle counting device for queuing vehicles; or it may exist independently and not be assembled into the battery swapping station's vehicle counting device for queuing vehicles.

[0127] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the battery swapping station's vehicle counting device, cause the battery swapping station's vehicle counting device to count the number of vehicles in the battery swapping station queue.

[0128] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and 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, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0130] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

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

[0132] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the battery swapping station queuing vehicle counting method described above.

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

[0134] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for counting vehicles queuing at a battery swapping station, characterized in that, The method for counting vehicles queuing for battery swapping at the battery swapping station includes: The system detects new energy battery swapping vehicles that meet the queuing conditions within the pre-set queuing area of ​​the battery swapping station. Obtain vehicle attributes and historical battery swapping data for each new energy battery swapping vehicle; The trained battery swapping prediction model is used to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and the historical battery swapping data. Determine the number of target new energy battery swapping vehicles that have a battery swapping need; The vehicle attributes include at least one of the following: current battery charge, current vehicle energy consumption, and current vehicle operating status; the historical battery swapping data includes at least one of the following: historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution. The step of using a trained battery swapping prediction model to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and historical battery swapping data includes: A list of vehicle queuing characteristics for each new energy battery swapping vehicle is constructed based on the current battery charge, current vehicle energy consumption, current vehicle operating status, historical battery swapping cycle, historical driving mileage distribution, and historical travel time distribution. Input the vehicle queuing feature list into the trained battery swapping prediction model to obtain the vehicle battery swapping probability set; The battery swapping probability set of the vehicles is weighted and calculated to obtain the battery swapping probability of the target vehicle; Based on the target vehicle's battery swapping probability and a preset probability threshold, it is predicted whether each new energy battery swapping vehicle has a battery swapping need.

2. The method for counting vehicles queuing at a battery swapping station as described in claim 1, characterized in that, Before the step of using a trained battery swapping prediction model to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and historical battery swapping data, the method further includes: A battery swapping data sample was constructed based on 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 the corresponding vehicle battery swapping feature list; By associating the vehicle battery swapping feature list with each cluster of battery swapping data in the battery swapping data sample, a target battery swapping data sample set is obtained. The target battery swapping features of the target battery swapping data sample set are used to train the model using a random forest model to obtain a trained battery swapping prediction model.

3. The method for counting vehicles queuing at a battery swapping station as described in claim 2, characterized in that, The step of training the target battery swapping prediction model by using a random forest model to train the target battery swapping features of the target battery swapping data sample set includes: 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; Calculate model evaluation parameters based on the target battery swapping data sample and the prediction results; The importance of vehicle battery swapping features in each of the target battery swapping data samples is evaluated based on the model evaluation parameters to obtain battery swapping feature evaluation results. Based on the evaluation results of the battery swapping characteristics, the model parameters of the initial random forest decision tree are adjusted to obtain a trained battery swapping prediction model.

4. The method for counting vehicles queuing for battery swapping at a battery swapping station as described in any one of claims 1-3, characterized in that, The method for counting vehicles queuing for battery swapping at the battery swapping station also includes: Obtain the real-time location of each target new energy battery swapping vehicle; A queue monitoring chart for battery swapping vehicles is constructed based on the real-time locations. The battery swapping vehicle queue monitoring chart and the number of target new energy battery swapping vehicles with battery swapping needs are uploaded to the cloud server to update the battery swapping station queue data and the range of the battery swapping station queue area on the cloud server.

5. The method for counting vehicles queuing for battery swapping at a battery swapping station as described in any one of claims 1-3, characterized in that, The new energy battery swapping vehicles that meet the queuing conditions within the preset queuing area of ​​the battery swapping station include: Obtain the vehicle type, current speed, and speed change of each new energy vehicle within the preset queuing area of ​​the battery swapping station; Based on the vehicle type, the current vehicle speed, and whether the change in vehicle speed meets 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 is recorded in a preset battery swapping vehicle type database, and the vehicle speed is gradually decreasing.

6. A vehicle counting device for battery swapping queuing at a battery swapping station, characterized in that, The battery swapping station queuing vehicle counting device is applied to the battery swapping station queuing vehicle counting method as described in any one of claims 1 to 5, and the device comprises: The detection module is used to detect new energy battery swapping vehicles that meet the queuing conditions within the preset queuing area of ​​the battery swapping station; The acquisition module is used to acquire the vehicle attributes and historical battery swapping data of each new energy battery swapping vehicle. The prediction module is used to predict whether each new energy battery swapping vehicle has a battery swapping need based on the vehicle attributes and the historical battery swapping data, using a trained battery swapping prediction model. The counting module is used to determine the number of target new energy battery swapping vehicles that have a battery swapping need.

7. A battery swapping station vehicle queuing counting device, characterized in that, The battery swapping station vehicle counting device includes: a memory, a processor, and a battery swapping station vehicle counting program stored in the memory and executable on the processor, wherein the battery swapping station vehicle counting program is configured to implement the battery swapping station vehicle counting method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a vehicle counting program for battery swapping stations, which, when executed by a processor, implements the vehicle counting method for battery swapping stations as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the battery swapping station queuing vehicle counting method as described in any one of claims 1 to 5.

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