Automobile renting management system and method

Abstract

The embodiment of the invention provides an automobile renting management system which comprises a cloud platform, and the cloud platform comprises at least one server which is configured to be in communication connection with a vehicle-mounted communication unit of an automobile, a service system of a dispatching point and a mobile terminal of a user. The cloud platform is configured to obtain vehicle operation information of the vehicle through the vehicle-mounted communication unit; obtaining scheduling point information of the scheduling point through a service system of the scheduling point; reading the operation data from the database; determining a target scheduling vehicle based on the vehicle operation information and the operation data of the vehicle; generating a scheduling strategy based on the vehicle operation information of the target scheduling vehicle and the scheduling point information; and based on the scheduling strategy, generating recommended scheduling information of the target scheduling vehicle, and sending the recommended scheduling information to a display device of the vehicle, and / or sending the recommended scheduling information to a mobile terminal of the user.

Description

Technical Field

[0001] The present invention relates to the field of vehicle rental technology, and in particular to a vehicle rental management system and method. Background Art

[0002] With the development of the transportation industry, it is becoming more and more common for people to rent cars from rental companies, which is also a common way for people to travel. Car rental companies have a large number of cars and rental points for rent. Car rental companies need to coordinate and manage all cars, including rented and unrented cars, to grasp all vehicle information in real time and reasonably dispatch vehicles when necessary, so as to improve the car experience of all car rental users.

[0003] Therefore, it is hoped that a car rental management system and method can be provided to improve the car rental experience of car rental users. Summary of the invention

[0004] One or more embodiments of the present specification provide a car rental management system. The system includes a cloud platform, the cloud platform includes at least one server, and the cloud platform is configured to communicate with the vehicle's on-board communication unit, the service system of the dispatch point, and the user's mobile terminal. The cloud platform is configured to: obtain the vehicle operation information of the vehicle through the on-board communication unit; obtain the dispatch point information of the dispatch point through the service system of the dispatch point; read the operation data from the database; determine the target dispatch vehicle based on the vehicle operation information and the operation data of the vehicle; generate a dispatch strategy based on the vehicle operation information and the dispatch point information of the target dispatch vehicle; and generate recommended dispatch information of the target dispatch vehicle based on the dispatch strategy, and send the recommended dispatch information to the display device of the vehicle and / or to the mobile terminal of the user.

[0005] One or more embodiments of the present specification provide a car rental management method. The method includes: obtaining vehicle operation information of a vehicle and dispatch point information and operation data of a dispatch point; determining a target dispatch vehicle based on the vehicle operation information and operation data of the vehicle; generating a dispatch strategy based on the vehicle operation information and dispatch point information of the target dispatch vehicle; and generating recommended dispatch information of the target dispatch vehicle based on the dispatch strategy, and sending the recommended dispatch information to a display device of the vehicle and / or to a mobile terminal of the user.

[0006] One or more embodiments of the present specification provide a car rental management device, including a processor, wherein the processor is used to execute a car rental management method.

[0007] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a car rental management method. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0009] Figure 1 is a schematic diagram of a module of a car rental management system according to some embodiments of this specification;

[0010] Figure 2 is an exemplary flow chart of a car rental management method according to some embodiments of this specification;

[0011] Figure 3 is a schematic diagram of determining a scheduling strategy according to some embodiments of this specification;

[0012] Figure 4 This is an exemplary flowchart for determining a scheduling strategy according to some embodiments of this specification. DETAILED DESCRIPTION

[0013] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0014] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0015] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0016] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 It is a module diagram of a car rental management system according to some embodiments of this specification.

[0018] Car rental companies have a large number of vehicles and rental points for rental. Car rental companies need to coordinate the management of all vehicles, including those that are rented out and those that are not. Car rental companies can obtain the operating status (speed, power, location) and order information of all vehicles in real time, so they can combine the information of all vehicles and dispatch the vehicles. For example, in the same area, there are multiple vehicles that need to be replaced, but the number of battery replacement stations and batteries is limited, and the locations are not the same. Car rental companies need to recommend a suitable battery replacement station for each vehicle, or recommend certain vehicles to go directly to the rental point to replace the vehicle, so as to achieve intelligent scheduling of vehicles and intelligent recommendation of battery replacement stations, thereby ensuring the car experience of all car rental users.

[0019] In some embodiments, the car rental company can implement the above vehicle management based on the car rental management system 100. Figure 1 As mentioned above, in some embodiments, the car rental management system 100 may include a cloud platform 110 .

[0020] The cloud platform 110 may also be referred to as a cloud computing platform, which provides computing, network, and storage capabilities based on services of hardware resources and software resources. Cloud computing platforms may include storage-type cloud platforms that focus on data storage, computing-type cloud platforms that focus on data processing, and comprehensive cloud computing platforms that take both computing and data storage processing into account. In some embodiments, the cloud platform 110 may include at least one server 111. In some embodiments, the cloud platform 110 may also include a database ( Figure 1 Not shown in the figure), used to store operational data in the process of car rental management.

[0021] In some embodiments, the cloud platform 110 may be connected to a number of external systems or modules in a communication manner, and the communication connection may be wireless or wired. For example, the cloud platform 110 may be configured to be connected to a vehicle-mounted communication unit 210, a service system 220 at a dispatch point, and a user's mobile terminal 230 in a communication manner.

[0022] In some embodiments, the cloud platform 110 can be configured to obtain the vehicle operation information of the vehicle through the vehicle-mounted communication unit 210 of the vehicle; obtain the dispatch point information of the dispatch point through the service system 220 of the dispatch point; read the operation data from the database; determine the target dispatch vehicle based on the vehicle operation information and operation data of the vehicle; generate a dispatch strategy based on the vehicle operation information and dispatch point information of the target dispatch vehicle; and generate recommended dispatch information of the target dispatch vehicle based on the dispatch strategy, and send the recommended dispatch information to the display device of the vehicle and / or to the mobile terminal of the user. The dispatch strategy may include dispatch parameters for each target dispatch vehicle. For more information about the dispatch strategy, please refer to Figure 2 Related description.

[0023] The vehicle-mounted communication unit 210 may be used for communication between the vehicle and the cloud platform 110. For example, the cloud platform 110 may be connected to the vehicle-mounted communication unit 210 for communication, thereby obtaining vehicle operation information of the vehicle.

[0024] The service system 220 of the dispatching point may be a device with computing function such as a computer of the dispatching point. The cloud platform 110 may be connected to the service system 220 for communication, so as to obtain the dispatching point information of the dispatching point.

[0025] The mobile terminal 230 may refer to one or more terminal devices or software used by the user. In some embodiments, the mobile terminal 230 may be a mobile device, a tablet computer, a laptop computer, etc. or any combination thereof. In some embodiments, the cloud platform 110 may be connected to the mobile terminal 230 in communication. The cloud platform 110 may send the recommended dispatch information of the target dispatch vehicle to the mobile terminal 230 of the user.

[0026] In some embodiments, the car rental management system 100 may further include a traffic data acquisition module 120 .

[0027] In some embodiments, the traffic data acquisition module 120 may be configured to acquire traffic data of a target area. In some embodiments, the traffic data acquisition module 120 may be in communication with the cloud platform 110 to process the traffic data.

[0028] Some embodiments of the present specification also provide a car rental management device. In some embodiments, the car rental management device may include at least one processor and at least one memory, the memory may be used to store computer instructions, and the at least one processor may be used to execute at least part of the computer instructions to implement the car rental management method.

[0029] Some embodiments of the present specification also provide a computer-readable storage medium. In some embodiments, the storage medium can store computer instructions, and when the computer instructions are executed by a processor, a car rental management method can be implemented.

[0030] It should be understood that Figure 1 The system and its modules shown can be implemented in various ways. For example, in some embodiments, Figure 1 The illustrated system and its modules may be executed jointly or independently by a computing device utilizing two different CPUs and / or processors.

[0031] It should be noted that the above description of the car rental management system and its modules is only for convenience of description and does not limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, Figure 1 The cloud platform 110 and the traffic data acquisition module 120 disclosed in the specification can be different modules in a system, or a module can realize the functions of two or more modules mentioned above. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

[0032] Figure 2 FIG. 1 is an exemplary flow chart of a car rental management method according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 may be executed by the cloud platform 110 .

[0033] Step 210, obtaining vehicle operation information of the vehicle and dispatch point information and operation data of the dispatch point.

[0034] Vehicle operation information may refer to information related to vehicle operation. In some embodiments, the vehicle operation information may include vehicle speed, vehicle position, vehicle load, vehicle battery type, vehicle battery health, vehicle battery information (e.g., remaining power, battery temperature, etc.), vehicle equipment working status (e.g., whether air conditioning, audio equipment, etc. are turned on, the gear is turned on, etc.), and other information.

[0035] The health of the vehicle battery can usually be obtained through the battery management system (BMS) and transmitted to the cloud platform 110 based on the vehicle communication unit 210. For example, the BMS can monitor the status of the vehicle battery, including but not limited to voltage, current, temperature, state of charge (SOC), etc., and can estimate the battery health state (SOH) based on these data. SOH can be expressed as a percentage based on the new battery capacity.

[0036] For details about the working status of the vehicle equipment, please refer to the description of step 420.

[0037] In some embodiments, the vehicle may refer to a vehicle in the target area, and the vehicle may include an electric vehicle, a hybrid electric vehicle, etc. The target area may be the entire dispatch area, or a part of the dispatch area, such as a designated area, etc.

[0038] A dispatch point is a location where a user can replace a vehicle battery or change a vehicle. For example, a dispatch point may include a battery swap station or a car rental point.

[0039] The dispatch point information refers to information related to the dispatch point. In some embodiments, the dispatch point information may include battery swap station information and car rental point information. The battery swap station information refers to information related to the battery swap station, and the car rental point information refers to information related to the car rental point.

[0040] The information of the battery swap station may include the location data of the battery swap station, the number of available batteries, the number of vehicles in the queue, the battery type, the consumption time, and the reservation information, etc. The information of the car rental point may include the number of available vehicles at the car rental point, the consumption time, the model information of the available vehicles (such as power, vehicle type), and the reservation information, etc. Among them, the consumption time refers to the time taken by the target dispatch vehicle to enter the battery swap station or the car rental point and leave the battery swap station or the car rental point. The reservation information refers to the information on the user's appointment on the mobile terminal to replace the battery at the battery swap station or the car rental point.

[0041] The operation data may include user information and order information. In some embodiments, the operation data may be obtained from a database configured on the cloud platform.

[0042] User information may include credit rating, rental frequency, preferred car models, etc.

[0043] Order information may include the status of the car rental order (e.g., reservation, pick-up, return), rental duration, return location, order rating, etc.

[0044] In some embodiments, the cloud platform 110 can obtain vehicle operation information of the vehicle and dispatch point information and operation data of the dispatch point.

[0045] In some embodiments, the cloud platform 110 can obtain vehicle operation information of the vehicle through the vehicle-mounted communication unit 210 of the vehicle, obtain dispatch point information of the dispatch point through the service system 220 of the dispatch point, and obtain operation data from the database. For more descriptions of the cloud platform 110, the vehicle-mounted communication unit 210, the service system 220, and the database, see Figure 1 Related description.

[0046] Step 220, determining a target dispatch vehicle based on the vehicle operation information and operation data of the vehicle.

[0047] The target dispatch vehicle refers to a vehicle that needs to replace the vehicle battery or replace the vehicle. For example, a vehicle whose battery level is less than the battery threshold and whose return time is greater than the time threshold.

[0048] In some embodiments, the cloud platform 110 may determine a target dispatch vehicle based on the vehicle operation information and operational data of the vehicle.

[0049] In some embodiments, the cloud platform 110 can determine candidate scheduling vehicles based on the vehicle operation information and operation data of the vehicle, predict the predicted power consumption rate of the candidate scheduling vehicles based on the historical vehicle operation information and environmental data of the candidate scheduling vehicles, and determine the target scheduling vehicle based on the predicted power consumption rate corresponding to the candidate scheduling vehicles.

[0050] The candidate dispatch vehicle may refer to a vehicle that is preliminarily judged to have a replacement requirement or a battery replacement requirement.

[0051] The historical vehicle operation information of the candidate dispatch vehicle refers to the operation information of the candidate dispatch vehicle at a historical time. For example, historical vehicle speed, historical vehicle position, historical vehicle load, historical vehicle battery type, historical vehicle battery health, historical vehicle battery information, historical vehicle equipment working status, etc. The historical time can be a preset time or a preset time length, for example, a historical week or a historical 72 hours, etc.

[0052] In some embodiments, the cloud platform 110 may determine candidate dispatch vehicles based on vehicle operation information and operation data of the vehicle. For example, a vehicle whose battery level is less than a battery level threshold and whose time to return the vehicle is greater than a time threshold may be determined as a candidate dispatch vehicle. The battery level threshold may be negatively correlated to the load level of the dispatch point in the target area.

[0053] The load level of the dispatching point can be determined based on the relevant information of the dispatching point. For example, the load level of the dispatching point is related to the number of available batteries, the number of available vehicles, the number of reserved vehicles, the number of vehicles in the queue, etc. As an example only, if the number of available batteries, the number of available vehicles, the number of reserved vehicles, and the number of vehicles in the queue (the slower the battery replacement / vehicle speed) in the dispatching point is, the greater the load level of the dispatching point is. The specific correspondence between the load level of the dispatching point and the relevant information of the dispatching point can be determined by looking up a table.

[0054] In some embodiments, the load level of the scheduling point in the target area can be determined based on the statistical value of the load level of each scheduling point in the target area. For example, the mean, extreme value, weighted value, etc. of the load level of each scheduling point can be used as the load level of the scheduling point in the target area.

[0055] In some embodiments, the cloud platform 110 can determine the charge confidence of the vehicle battery based on the historical charge and discharge records of the vehicle battery, the historical battery temperature, the health of the vehicle battery, and the battery type; and determine candidate dispatch vehicles based on the charge confidence.

[0056] Historical charging and discharging records refer to the data related to the historical charging and discharging of the vehicle at historical times, such as the amount of electricity, voltage, current, and time of each charging / discharging.

[0057] The historical battery temperature refers to the historical data of the battery temperature.

[0058] Battery health is data that reflects the health status of the battery.

[0059] Battery type refers to different types of batteries, for example, ternary lithium battery and lithium iron phosphate battery.

[0060] In some embodiments, historical charging and discharging records, historical battery temperature, battery health, and battery type may be stored in a database, and the cloud platform 110 may read these data by reading the battery serial number.

[0061] The power confidence level can reflect the probability that the battery has false power. For example, if the vehicle displays a remaining power of 30%, but the actual power level of the battery is only 20%, then the battery has false power. The higher the power confidence level, the lower the probability that the battery has false power, that is, the higher the reliability of the current vehicle power level (i.e., displayed power level). When the battery has false power, the current vehicle power level can be corrected based on the power confidence level to obtain the corrected vehicle power level, that is, the actual vehicle power level. For example, the corrected vehicle power level = current vehicle power level * power confidence level.

[0062] The power confidence can be determined based on historical charging and discharging records, historical battery temperature, battery health, and battery type. For example, a virtual power feature vector (a0, b0, c0, t0) can be constructed based on historical charging and discharging records, historical battery temperature, battery health, and battery type, and then based on the virtual power feature vector, the power confidence is determined through the first vector database.

[0063] The first vector database stores a number of historical vectors and the power confidence levels corresponding to each historical vector. The historical vectors can be constructed based on the historical charging and discharging records of historical vehicles, historical battery temperatures, battery health, and battery types. The power confidence levels corresponding to the historical vectors refer to the power confidence levels of the power levels displayed by the historical vehicles under the circumstances corresponding to the historical vectors. The power confidence levels can be determined based on the actual power levels of the historical vehicles. For example, the actual power levels of the historical vehicles can be obtained based on a variety of power testing methods, and the power confidence levels of the displayed power levels can be obtained based on the ratio of the actual power levels to the displayed power levels.

[0064] The cloud platform 110 may use a historical vector having the smallest vector distance to the virtual electricity feature vector or a distance threshold value as a reference vector, and use the power confidence of the reference vector as the power confidence of the virtual electricity feature vector.

[0065] In some embodiments, the cloud platform 110 may determine the corrected vehicle power based on the power confidence level, and then determine vehicles whose corrected vehicle power is less than a power threshold and whose time to return is greater than a time threshold as candidate dispatch vehicles.

[0066] Environmental data refers to data related to the environment in which the candidate dispatch vehicle is located. Environmental data may include data such as temperature and humidity. Environmental data may be determined based on corresponding sensors on the vehicle or by obtaining user input.

[0067] The predicted power consumption rate may be the power consumed per kilometer traveled by the predicted candidate dispatch vehicle.

[0068] In some embodiments, the cloud platform 110 may determine the predicted power consumption rate based on a variety of methods. For example, the predicted power consumption rate may be the average power consumption rate of the candidate dispatch vehicle history.

[0069] In some embodiments, the predicted power consumption rate can also be determined based on historical vehicle operation information, environmental data, and the working state of vehicle equipment. In some embodiments, the cloud platform 110 can construct a power consumption feature vector (a1, b1, c1) based on the historical vehicle battery information, environmental data, and working state of vehicle equipment of the candidate scheduling vehicle, and determine the predicted power consumption rate of the candidate scheduling vehicle by querying the second vector database based on the power consumption feature vector. Among them, the working state of the vehicle equipment can be the working state of the equipment on the vehicle that will consume power, such as vehicle air conditioning, vehicle monitoring and other equipment, and the working state of the vehicle equipment can include parameters such as on, off, and working power.

[0070] The method of determining the predicted power consumption rate based on vector retrieval is similar to the method of determining the power confidence based on vector retrieval, wherein the predicted power consumption rate corresponding to the historical vector in the second vector database is the actual power consumption rate of the historical vehicle under the conditions corresponding to the historical vector (i.e., the historical vehicle under the historical vehicle battery information, historical environmental data, and historical vehicle equipment working conditions), which can be obtained based on a variety of battery performance testing methods.

[0071] In some embodiments, the predicted power consumption rate is also related to traffic data. In some embodiments, the cloud platform 110 can adjust the predicted power consumption rate based on traffic data.

[0072] Traffic data may include the current location and number of vehicles on each road, the location and status of traffic lights (eg, red light / green light), and whether there are any construction or other conditions that hinder traffic.

[0073] Traffic data can be obtained by connecting the traffic data module to the real-time traffic information interface provided by the city traffic management center, such as the intelligent transportation system (ITS) equipped by the city. Traffic data can also be obtained from a third party, such as a third-party map APP.

[0074] In some embodiments, the cloud platform 110 can determine the power consumption increase based on the traffic data, and adjust the predicted power consumption rate based on the power consumption increase. The power consumption increase refers to the increase in the power consumption rate. For example, when the congestion level in the area where the vehicle is located (for example, a radius of 3 km around the vehicle) is relatively high (such as greater than a preset congestion level), the vehicle may start and stop frequently, resulting in increased power consumption, that is, an increase in the power consumption rate.

[0075] In some embodiments, the increase in power consumption may be predicted by a prediction function / algorithm, wherein the prediction function / algorithm may be obtained by fitting based on historical data, and the historical data may include historical data of the vehicle itself or historical data of multiple other vehicles.

[0076] For example, a power consumption increase prediction function f1 is set: y=a*x1+b*x2+c*x3, where a, b, and c are coefficient parameters respectively; x1 is the traffic congestion level in the area where the candidate dispatch vehicle is located, x2 is the battery health level, x3 represents the vehicle load, and y is the power consumption increase; a fitting algorithm is used to fit the above-mentioned large amount of historical data to obtain coefficients a, b, and c, thereby obtaining a power consumption increase prediction function; based on the current traffic congestion level, battery health level, and vehicle load corresponding to the candidate dispatch vehicle, the power consumption increase is determined through this function.

[0077] The degree of traffic congestion can be obtained based on traffic data. For example, if the number of vehicles on a road / area is greater, the distance between vehicles is closer, the average speed of vehicles is slower, and the number of traffic lights is greater, the degree of traffic congestion is greater. Battery health and vehicle load can be determined based on the vehicle information.

[0078] In some embodiments, the predicted power consumption rate may be adjusted based on the following method: adjusted predicted power consumption rate=predicted power consumption rate+predicted power consumption rate*power consumption increase rate.

[0079] In some embodiments, the cloud platform may determine the target dispatch vehicle in a variety of ways based on the predicted power consumption rate or adjusted predicted power consumption rate corresponding to the candidate dispatch vehicle. For example, the cloud platform may determine the candidate dispatch vehicle whose predicted power consumption rate or adjusted predicted power consumption rate is greater than the consumption threshold as the target dispatch vehicle.

[0080] The consumption threshold is positively correlated to the number of dispatch points in the area where the candidate dispatch vehicle is located (eg, within a radius of 3 km centered on the vehicle).

[0081] The car rental management method provided in some embodiments of the present specification predicts the predicted power consumption rate of candidate scheduling vehicles based on the historical vehicle operation information and environmental data of the candidate scheduling vehicles, thereby accurately identifying vehicles that need to replace batteries or vehicles, and recommending suitable scheduling points to users, thereby improving users' car rental experience.

[0082] Step 230, generating a dispatch strategy based on the vehicle operation information and dispatch point information of the target dispatch vehicle.

[0083] The dispatch strategy can be composed of the dispatch parameters of each target dispatch vehicle. The dispatch parameters refer to the dispatch point corresponding to each target dispatch vehicle, that is, the point where each target dispatch vehicle performs battery or vehicle replacement. For example, the dispatch strategy can be expressed as [(target dispatch vehicle 1, dispatch point 3), (target dispatch vehicle 2, dispatch point 5)].

[0084] In some embodiments, the cloud platform 110 can generate a dispatch strategy based on the vehicle operation information and dispatch point information of the target dispatch vehicle. For example, for each target dispatch vehicle, the cloud platform 110 can find the dispatch point closest to the target dispatch vehicle among the currently available dispatch points, and use it as the dispatch point corresponding to the target dispatch vehicle determined in the dispatch strategy. Among them, the existence of an available dispatch point means that there are available batteries or available vehicles, and the number of queued vehicles + the number of reserved vehicles < the number of vehicles corresponding to the available batteries + the number of available vehicles.

[0085] In some embodiments, the cloud platform 110 can also determine the priority of each target scheduling vehicle when determining the scheduling point based on the ratio of the current vehicle power level to the power confidence level of the target scheduling vehicle. For example, the smaller the ratio of the current vehicle power level to the power confidence level of the target scheduling vehicle, the higher the priority of the cloud platform 110 in determining its corresponding scheduling point.

[0086] In some embodiments, the cloud platform 110 can determine candidate scheduling strategies based on scheduling point information and vehicle operation information; determine user satisfaction and battery loss of the candidate scheduling strategies based on the candidate scheduling strategies; and determine the scheduling strategy based on user satisfaction, battery loss and the candidate scheduling strategies. For details, see Figure 3 and its related description.

[0087] Step 240 : generating recommended scheduling information of the target scheduling vehicle based on the scheduling strategy, and sending the recommended scheduling information to a display device of the vehicle and / or to a mobile terminal of the user.

[0088] Recommended dispatch information refers to information related to the dispatch strategy recommended for the target dispatch vehicle, for example, the dispatch point corresponding to the target dispatch vehicle.

[0089] In some embodiments, the cloud platform 110 may generate recommended scheduling information of a target scheduling vehicle based on a scheduling strategy, and send the recommended scheduling information to a display device of the vehicle and / or to a mobile terminal 230 of a user.

[0090] In some embodiments, the cloud platform 110 sends the recommended scheduling information to the user's mobile terminal 230 so that the user can make an appointment based on the recommended scheduling information.

[0091] The car rental management method provided in some embodiments of this specification determines the target dispatch vehicle and combines the vehicle operation information and dispatch point information of the target dispatch vehicle to recommend a suitable dispatch point to the user, thereby ensuring the car rental user's car experience.

[0092] Figure 3 It is a schematic diagram of determining a scheduling strategy according to some embodiments of this specification. Figure 4 is an exemplary flow chart of determining a scheduling strategy according to some embodiments of this specification. Figure 4 As shown, the process 400 includes the following steps: In some embodiments, the process 400 may be executed by the cloud platform 110 .

[0093] Step 410, determining candidate scheduling strategies based on scheduling point information and vehicle operation information.

[0094] The candidate dispatching strategy refers to the dispatching strategy formed by randomly assigning the target dispatching vehicles to various candidate dispatching points. The candidate dispatching point refers to an idle dispatching point, for example, there are available batteries or available vehicles at the candidate dispatching point, and the number of queued vehicles at the dispatching point is less than the sum of the number of vehicles corresponding to the available batteries and the number of available vehicles.

[0095] In some embodiments, the cloud platform 110 may determine a candidate scheduling strategy 303 based on the scheduling point information 301 and the vehicle operation information 302 .

[0096] In some embodiments, the candidate dispatch strategy may be formed by randomly assigning the target dispatch vehicle to various candidate dispatch points.

[0097] Among them, the distance between the candidate dispatching point allocated to the target dispatching vehicle and the target dispatching vehicle is less than the maximum driving distance of the target dispatching vehicle. The maximum driving distance refers to the maximum distance that the target dispatching vehicle can still travel, which can be obtained by the remaining power / predicted power consumption rate of the target dispatching vehicle. For example, with the target dispatching vehicle as the center and the maximum driving distance of the target dispatching vehicle as the radius, all the candidate dispatching points within the circle can participate in the allocation.

[0098] When assigning a target dispatch vehicle to an alternative dispatch point, you should also consider whether the alternative dispatch point is overloaded. If the assignment will not overload the alternative dispatch point, you can continue to assign. If the assignment will overload the alternative dispatch point, you need to find another alternative dispatch point for assignment. The overload of an alternative dispatch point means that the sum of the number of assigned vehicles, the number of queued vehicles, and the number of reserved vehicles at the alternative dispatch point is greater than the sum of the number of available batteries and the number of available vehicles at the alternative dispatch point.

[0099] When the candidate dispatch point is a battery swap station or a car rental point, the target dispatch vehicle will be assigned to the candidate dispatch point that is a battery swap station first. If an alternative dispatch point can be used as both a battery swap point and a car rental point, it will be assigned as a battery swap point first.

[0100] In some embodiments, the cloud platform 110 can predict the predicted load data of the scheduling point at a future time point based on historical scheduling point information, holiday information, traffic data, and future weather data; determine the preliminary scheduling point based on the predicted load data and load threshold parameters; and determine the candidate scheduling strategy based on the preliminary scheduling point, the scheduling point information of the preliminary scheduling point, and the vehicle operation information.

[0101] The predicted load data includes multiple predicted future time points, the number of queued vehicles at the dispatch point, reserved vehicle data, available battery quantity and available vehicle data. Multiple future time points can be preset, for example, the next 24 hours, the next 3 days, etc.

[0102] In some embodiments, the predicted load data may be obtained through a load prediction model. The load prediction model may be a model for determining the predicted load data of a dispatch point, and may be a machine learning model or a custom model.

[0103] In some embodiments, the input of the load prediction model may include historical dispatch point information, holiday information, traffic data, and future weather data, and the output of the load prediction model may be predicted load data of the dispatch point.

[0104] Future weather data refers to weather data at a future time point, such as future temperature, future humidity, precipitation, weather type (such as foggy day, thunderstorm), etc.

[0105] The holiday information includes whether the current time point is a holiday and the type of holiday.

[0106] The parameters of the load prediction model can be obtained through training. In some embodiments, the load prediction model can be obtained through training based on multiple first training samples with first labels. For example, multiple first training samples with first labels can be input into the initial load prediction model, and a loss function can be constructed based on the first label and the prediction result of the initial load prediction model. The parameters of the initial load prediction model are updated based on the iteration of the loss function. When the loss function of the initial load prediction model meets the preset conditions, the model training is completed, wherein the preset conditions can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0107] In some embodiments, the first training sample may include sample historical dispatch point information, sample holiday information, sample traffic data, and sample weather data at a first historical time point, and the first label may be dispatch point load data of the dispatch point actually collected at the second historical time point. In some embodiments, the first training sample may be obtained based on historical data, and the first label may be obtained based on manual annotation. The first historical time point is before the second historical time point.

[0108] The load threshold parameter refers to the maximum value of the target dispatching vehicles that the dispatching point can receive in the future. It can be determined by preset. The load threshold parameter can include a first load threshold and a second load threshold. The first load threshold is greater than the second load threshold.

[0109] In some embodiments, the load threshold parameter may also be related to the degree of traffic congestion. For example, the greater the degree of traffic congestion, the smaller the load threshold parameter (first threshold parameter, second threshold parameter). The load threshold parameter can be obtained by looking up a table. For more information on calculating the degree of traffic congestion, see Figure 2 Related description.

[0110] A preparatory dispatching point refers to a dispatching point where there are available batteries or replaceable vehicles when the target dispatching vehicle arrives at a future time. For example, the dispatching points can be classified into busy dispatching points, general dispatching points, and idle dispatching points based on the predicted load data of the dispatching points. Among them, the general dispatching points and the idle dispatching points are determined as preparatory dispatching points.

[0111] The classification method of dispatching points includes: calculating the load level of each dispatching point at multiple future time points based on the predicted load data; if the future average load level of a dispatching point (i.e., the average of the load levels at multiple future time points) is greater than the first load threshold, the dispatching point is considered to be a busy dispatching point; if the future average load level of a dispatching point is lower than the second load threshold, the dispatching point is considered to be an idle dispatching point; if the future average load level of a dispatching point is between the first load threshold and the second load threshold, the dispatching point is considered to be a general dispatching point. For more information on calculating the load level, please refer to Figure 2 Related description.

[0112] In some embodiments, the candidate dispatch strategies may be generated by randomly assigning target dispatch vehicles to various preliminary dispatch points.

[0113] The car rental management method provided in some embodiments of the present specification takes into account the predicted load data of the dispatch point at a future time point when determining the candidate dispatch strategy, thereby avoiding the situation where there is no available battery / vehicle to use when the target dispatch vehicle arrives at the dispatch point at a future time point, thereby recommending a suitable dispatch point for the target dispatch vehicle.

[0114] Step 420 , based on the candidate scheduling strategies, determine the user satisfaction and battery loss of the candidate scheduling strategies.

[0115] User satisfaction refers to the satisfaction of the user of each target dispatch vehicle with the candidate dispatch strategy. For example, if the estimated time to reach the dispatch point is shorter and the driving distance is shorter, the user satisfaction is higher. User satisfaction can be determined in many ways, such as by looking up a table or obtaining user feedback.

[0116] The battery loss refers to the additional loss of the battery caused by the target dispatch vehicle running at an unhealthy power level. The unhealthy power level can be a preset value such as 20% of the total power.

[0117] In some embodiments, the cloud platform 110 may determine user satisfaction and battery consumption of the candidate scheduling strategies based on the candidate scheduling strategies.

[0118] In some embodiments, the cloud platform 110 can determine the predicted arrival time series 309 and the predicted vehicle battery information series 311 based on the candidate scheduling strategy 303, the target area map information 304, the current environmental data 305, the future weather data 306, and the vehicle equipment working status 307 through the arrival prediction layer 308-1 of the prediction model 308; based on the driving distance series 312, the predicted arrival time series 309, the predicted vehicle battery information series 311, and the current battery health 313 corresponding to the candidate scheduling strategy, determine the vehicle battery loss series 314 through the health prediction layer 308-2 of the prediction model 308.

[0119] The target area map information refers to a map of the target area obtained in real time, for example, an electronic map of the target area obtained in real time.

[0120] Current environment data refers to the relevant information of the current environment of the target dispatch vehicle.

[0121] Future weather data refers to weather data at a future time point.

[0122] The vehicle equipment working status refers to the working status of the equipment in the target dispatched vehicle, for example, whether the air conditioner is turned on, whether the audio system is turned on, and the corresponding gear position of the turned-on equipment (i.e., the turned-on power of the equipment).

[0123] The prediction model 308 may be a machine learning model for determining information such as a predicted arrival time series, a predicted vehicle battery information series, a vehicle battery loss series, etc., or it may be other custom models.

[0124] In some embodiments, the prediction model may include an attainment prediction layer 308 - 1 and a health prediction layer 308 - 2 .

[0125] The arrival prediction layer 308-1 may be a model for extracting a predicted arrival time series and a predicted vehicle battery information series. For example, the arrival prediction layer may be a recurrent neural network (RNN) model.

[0126] The health prediction layer 308-2 may be a model for extracting a vehicle battery depletion degree sequence. For example, the health prediction layer may be a deep neural network (DNN) model.

[0127] In some embodiments, the input to the prediction layer 308-1 may include candidate scheduling strategies 303, target area map information 304, current environmental data 305, future weather data 306, and vehicle equipment operating status 307, and the output to the prediction layer 308-1 may be a predicted arrival time series 309 and a predicted vehicle battery information sequence 311.

[0128] The predicted arrival time sequence refers to a sequence of predicted arrival times of each target dispatch vehicle under the candidate dispatch strategy 303. The arrival time refers to the time when the target dispatch vehicle arrives at its corresponding dispatch point.

[0129] The predicted vehicle battery information sequence refers to a sequence of vehicle battery information of each target dispatched vehicle at a future time point. The vehicle battery information may include remaining power, battery temperature, etc.

[0130] In some embodiments, the input of the health prediction layer 308-2 may include a driving distance sequence 312 corresponding to the candidate scheduling strategy, a predicted arrival time sequence 309, a predicted vehicle battery information sequence 311, and a current battery health level 313, and the output of the health prediction layer 308-2 may be a vehicle battery loss level sequence 314.

[0131] The driving distance sequence refers to the sequence consisting of the driving distances between each target dispatch vehicle and its corresponding dispatch point.

[0132] For more information about battery health, see Figure 2 and its related description.

[0133] The vehicle battery loss degree sequence refers to the sequence of predicted vehicle battery loss degrees of each target dispatch vehicle after driving (such as after changing or returning the vehicle). The vehicle battery loss degree can be obtained by subtracting the battery health degree collected after driving from the sample battery health degree. The sample battery health degree can be the average battery health degree of the vehicle or the initial preset value.

[0134] The prediction model 308 can be obtained by separately training the reach prediction layer 308 - 1 and the health prediction layer 308 - 2 .

[0135] The parameters of the arrival prediction layer 308-1 can be obtained through training. In some embodiments, the arrival prediction layer 308-1 can be obtained through training based on the second training sample and the second label. The specific content of the training method can be referred to the training of the load forecasting model. In some embodiments, the second training sample can include a sample scheduling strategy, sample target area map information, sample environmental data at a first historical time point, and sample weather data at a second historical time point. The second label can be an actually recorded arrival time series and a vehicle battery information series.

[0136] The parameters of the health prediction layer 308-2 can be obtained through training. In some embodiments, the health prediction layer 308-1 can be obtained through training based on the third training sample and the third label. The specific content of the training method can be referred to the training of the load forecasting model. In some embodiments, the third sample can include a sample driving distance sequence, a sample arrival time sequence, a sample vehicle battery information sequence, and a sample battery health level. The third label can be the vehicle battery loss level collected after the driving is completed.

[0137] In some embodiments, the time when the target dispatch vehicle arrives at the dispatch point and the power consumption rate of the target dispatch vehicle may also be related to traffic data.

[0138] In some embodiments, the input to the prediction layer may also include current traffic data and historical traffic data. The historical traffic data may be traffic data at a preset historical time point or time period. For more information about the traffic data, see the relevant description of step 220.

[0139] In some embodiments, the cloud platform 110 can also determine or adjust user satisfaction based on the user's historical order information and scheduling point information. For example, the user can determine the satisfaction correction value by looking up a table based on the average historical order score and the time consumed at the scheduling point. The historical order score can be obtained through the historical order information. If the user's historical order score average is lower and the time consumed at the scheduling point is longer, the satisfaction correction value will be greater.

[0140] In some embodiments, user satisfaction can be adjusted based on the satisfaction correction value. For example, adjusted user satisfaction = user satisfaction - satisfaction correction value.

[0141] Step 430, determining a scheduling strategy based on user satisfaction, battery depletion, and candidate scheduling strategies.

[0142] In some embodiments, the cloud platform 110 may determine a scheduling strategy 316 based on the user satisfaction 315 , the battery depletion degree, and the candidate scheduling strategies 303 .

[0143] In some embodiments, the cloud platform 110 may determine the dispatch strategy for the target dispatch vehicle by calculating the dispatch effect score of each candidate dispatch strategy. For example, a candidate dispatch strategy with the highest dispatch effect score is selected as the dispatch strategy.

[0144] The scheduling effect score can also be called a comprehensive score, which can be determined in a variety of ways. For example, the cloud platform can perform a weighted summation on the mean user satisfaction and the mean battery loss corresponding to the candidate scheduling strategies to obtain the scheduling effect score of the candidate scheduling strategy. As an example only, the scheduling effect score of the candidate scheduling strategy = α*mean user satisfaction - β*mean battery loss, where α and β represent the weight coefficients of the importance of user satisfaction and battery loss, respectively.

[0145] In some embodiments, the determination of the dispatch strategy may also be related to the adaptation value between the dispatch point and the target dispatch vehicle.

[0146] In some embodiments, the adaptation value may be determined based on the available battery quality at the dispatch point and the predicted power consumption rate of the target dispatch vehicle.

[0147] In some embodiments, when considering the adaptation value between the dispatch point and the target dispatch vehicle, the dispatch effect score of the candidate dispatch strategy can be calculated by the following formula: dispatch effect score = α*mean of user satisfaction - β*mean of loss degree + δ*total adaptation value; wherein δ is the weight coefficient of the importance of the total adaptation value.

[0148] The available battery quality of the dispatching point can be determined by the battery power confidence, for example, the higher the power confidence, the higher the available battery quality. The average available battery quality of the dispatching point is obtained by averaging all the available battery qualities of the dispatching point.

[0149] In some embodiments, the adaptation value can be determined based on the average available battery quality of the dispatch point and the predicted power consumption rate of the target dispatch vehicle. For example, the higher the predicted power consumption rate of the target dispatch vehicle, the higher the adaptation value with the dispatch point with high average available battery quality; conversely, the higher the adaptation value with the dispatch point with low average available battery quality. The adaptation value can be determined by looking up a table.

[0150] The total adaptation value can be obtained by averaging the adaptation values ​​of each target dispatch vehicle and its corresponding dispatch point.

[0151] In some embodiments, the determination of the dispatch strategy may also be related to the usage ratio of the idle dispatch points in the candidate dispatch strategy. For example, the greater the usage ratio of the idle dispatch points in the candidate dispatch strategy, the lower the probability of insufficient available batteries or replaceable vehicles at the dispatch point, and the shorter the queue time of the target dispatch vehicle. Therefore, under the same other conditions, the target dispatch vehicle is preferentially assigned to the idle dispatch point.

[0152] The usage ratio of idle dispatch points refers to the ratio of idle dispatch points used by target dispatch vehicles in the candidate dispatch strategy to the total dispatch points in the candidate dispatch strategy. For example, the usage ratio of idle dispatch points = the number of vehicles assigned to idle dispatch points / the number of all target dispatch vehicles.

[0153] In some embodiments, when considering the usage ratio of idle scheduling points in the candidate scheduling strategies, the scheduling effect score of the candidate scheduling strategies can be calculated by the following formula: Scheduling effect score = α*mean of user satisfaction - β*mean of loss degree + γ*usage ratio of idle scheduling points; wherein γ is the weight coefficient of the importance of the usage ratio of idle scheduling points.

[0154] The car rental management method provided in some embodiments of this specification can more accurately recommend a better scheduling strategy for the target scheduling vehicle by determining the user satisfaction with the candidate scheduling strategy and the battery loss of the target scheduling vehicle. It also takes the user satisfaction into consideration, which can further improve the user's car rental experience.

[0155] It should be noted that the above description of process 400 is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 400 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0156] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0157] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0158] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0159] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0160] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0161] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0162] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A car rental management system, comprising: The cloud platform includes at least one server configured to be communicatively connected with the vehicle-mounted communication unit of the vehicle, the service system of the dispatching point, and the mobile terminal of the user; The cloud platform is configured as follows: Obtaining vehicle operation information of the vehicle through the vehicle-mounted communication unit; Obtaining dispatch point information of the dispatch point through a service system of the dispatch point; Read operational data from the database; Determining a target dispatch vehicle based on the vehicle operation information and the operation data of the vehicle; Generate a dispatch strategy based on the vehicle operation information and the dispatch point information of the target dispatch vehicle; as well as Based on the scheduling strategy, recommended scheduling information of the target scheduling vehicle is generated, and the recommended scheduling information is sent to a display device of the vehicle and / or to a mobile terminal of the user.

2. The system according to claim 1, characterized in that The cloud platform is further configured as follows: Determining a candidate dispatch vehicle based on the vehicle operation information and the operation data of the vehicle; Predicting a predicted power consumption rate of the candidate dispatch vehicle based on historical vehicle operation information and environmental data of the candidate dispatch vehicle; as well as The target dispatching vehicle is determined based on the predicted power consumption rate corresponding to the candidate dispatching vehicles.

3. The system according to claim 1, characterized in that The cloud platform is further configured as follows: Determining a candidate scheduling strategy based on the scheduling point information and the vehicle operation information; Based on the candidate scheduling strategy, determining user satisfaction and battery loss of the candidate scheduling strategy; as well as The scheduling strategy is determined based on the user satisfaction, the battery depletion degree, and the candidate scheduling strategies.

4. The system according to claim 3, characterized in that The cloud platform is further configured as follows: Based on historical dispatch point information, holiday information, and traffic data, predict the predicted load data of the dispatch point at a future time point; Determining a preliminary dispatching point based on the predicted load data and load threshold parameters; The candidate scheduling strategy is determined based on the preliminary scheduling point, the scheduling point information of the preliminary scheduling point, and the vehicle operation information.

5. A car rental management method, comprising: Obtain vehicle operation information of the vehicle and dispatch point information and operation data of the dispatch point; Determining a target dispatch vehicle based on the vehicle operation information and the operation data of the vehicle; Generate a dispatch strategy based on the vehicle operation information and the dispatch point information of the target dispatch vehicle; as well as Based on the scheduling strategy, recommended scheduling information of the target scheduling vehicle is generated, and the recommended scheduling information is sent to a display device of the vehicle and / or to a mobile terminal of the user.

6. The method according to claim 5, wherein determining the target dispatch vehicle based on the vehicle operation information and the operation data of the vehicle comprises: Determining a candidate dispatch vehicle based on the vehicle operation information and the operation data of the vehicle; Predicting a predicted power consumption rate of the candidate dispatch vehicle based on historical vehicle operation information and environmental data of the candidate dispatch vehicle; as well as The target dispatching vehicle is determined based on the predicted power consumption rate corresponding to the candidate dispatching vehicles.

7. The method according to claim 5, wherein generating a dispatch strategy based on the vehicle operation information and the dispatch point information of the target dispatch vehicle comprises: Determining a candidate scheduling strategy based on the scheduling point information and the vehicle operation information; Based on the candidate scheduling strategy, determining user satisfaction and battery loss of the candidate scheduling strategy; as well as The scheduling strategy is determined based on the user satisfaction, the battery depletion degree, and the candidate scheduling strategies.

8. The method according to claim 7, wherein determining a candidate scheduling strategy based on the scheduling point information and the vehicle operation information comprises: Based on historical dispatch point information, holiday information, and traffic data, predict the predicted load data of the dispatch point at a future time point; Determining a preliminary dispatching point based on the predicted load data and load threshold parameters; The candidate scheduling strategy is determined based on the preliminary scheduling point, the scheduling point information of the preliminary scheduling point, and the vehicle operation information.

9. A car rental management device, comprising a processor, characterized in that: The processor is used to execute the car rental management method as described in any one of claims 5-8.

10. A computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the car rental management method according to any one of claims 5 to 8.

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