Shared vehicle control method, system and device and storage medium

Abstract

The embodiment of the invention provides a shared vehicle control method, system and device and a storage medium. The method comprises the steps that environment data, state data and driving data of a shared vehicle are periodically obtained at a first interval time; the environment data, the state data and the driving data are input into a trained electric quantity prediction model, an electric quantity consumption mode of the shared vehicle is determined based on the electric quantity prediction model, and the electric quantity prediction model is a machine learning model; the power consumption mode comprises a first power consumption mode and a second power consumption mode, and the power consumption of the first power consumption mode is greater than that of the second power consumption mode; judging whether to switch the power consumption mode or not based on the determined power consumption mode and the current power consumption mode of the shared vehicle; and in response to the need of switching the power consumption mode, switching the power consumption mode of the shared vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of shared vehicles, and in particular to a control method, system, device and storage medium for a shared vehicle. Background Art

[0002] As a new green and healthy travel mode, shared vehicles are increasingly favored by users. At present, in winter, measures are taken to uniformly stockpile shared vehicles to reduce the problem of low battery and loss of connection of shared vehicles; or shared vehicles are put on the road normally, and when the battery is low, the operation and maintenance master will arrange for the vehicle to return to the warehouse for battery replacement. The above measures will increase the stockpile cost or operating cost of shared vehicles.

[0003] Therefore, it is necessary to provide a control method, system, device and storage medium for shared vehicles, so as to minimize power consumption of shared vehicles when power replenishment is insufficient in winter, so as to reduce operating costs and increase the number of available vehicles in the city. Summary of the invention

[0004] One of the embodiments of this specification provides a control method for a shared vehicle. The control method for a shared vehicle includes: periodically acquiring environmental data, status data, and driving data of a shared vehicle at a first interval; inputting the environmental data, status data, and driving data into a trained power consumption model, and determining a power consumption mode of the shared vehicle based on the power consumption model, wherein the power consumption model is a machine learning model; the power consumption mode includes a first power consumption mode and a second power consumption mode, and the power consumption of the first power consumption mode is greater than the power consumption of the second power consumption mode; judging whether to switch the power consumption mode based on the determined power consumption mode and the current power consumption mode of the shared vehicle; and issuing a switching mode instruction in response to the need to switch the power consumption mode.

[0005] In some embodiments, the environmental data includes the ambient temperature, and the status data includes the battery power of the shared vehicle and the number of times the battery is charged and discharged; in response to the ambient temperature being lower than a preset temperature threshold, the control method of the shared vehicle is enabled.

[0006] In some embodiments, in response to the power consumption mode being the first power consumption mode, the communication module of the shared vehicle reports vehicle information at a second interval, the vehicle information including the status data and the driving data; in response to the power consumption mode being the second power consumption mode, the communication module is turned off; in response to satisfying a preset condition, the power consumption mode is switched from the second power consumption mode to the first power consumption mode.

[0007] In some embodiments, the power consumption model is obtained through a training process, and the training process includes: obtaining multiple training samples and their labels, the multiple training samples include historical environmental data, historical status data and historical driving data of the sample vehicle, and the label is whether the battery power of the sample vehicle is less than a preset power threshold after a third interval time; and training an initial power consumption model based on the multiple training samples and their labels to obtain the power consumption model.

[0008] In some embodiments, the method further includes: dividing the operating area of ​​the shared vehicle into multiple grids, determining characteristic information of the multiple grids, the characteristic information including the average usage frequency and average parking time of vehicles in the grids; clustering the multiple grids based on the characteristic information of the multiple networks; and issuing the same switching mode instructions to vehicles in grids of the same clustering category.

[0009] In some embodiments, inputting the environmental data, the status data and the driving data into a trained power consumption model, and determining the power consumption mode of the shared vehicle based on the power consumption model includes: inputting the environmental data, the status data and the driving data into a trained power consumption model, and determining whether the battery power of the shared vehicle is less than a preset power threshold after a third interval time based on the power consumption model, wherein the preset power threshold is related to the usage of the shared vehicle within the third interval time period; in response to the battery power of the shared vehicle being not less than the preset power threshold after the third interval time, determining that the power consumption mode of the shared vehicle is the first power consumption mode; and in response to the battery power of the shared vehicle being less than the preset power threshold after the third interval time, determining that the power consumption mode of the shared vehicle is the second power consumption mode.

[0010] One of the embodiments of the present specification provides a control system for a shared vehicle, including: an acquisition module, used to periodically acquire environmental data, status data and driving data of the shared vehicle at a first interval; a determination module, used to input the environmental data, the status data and the driving data into a trained power consumption model, and determine a power consumption mode of the shared vehicle based on the power consumption model, wherein the power consumption model is a machine learning model; the power consumption mode includes a first power consumption mode and a second power consumption mode, the power consumption of the first power consumption mode is greater than the power consumption of the second power consumption mode; a judgment module, used to judge whether to switch the power consumption mode based on the determined power consumption mode and the current power consumption mode of the shared vehicle; and a switching module, used to issue a mode switching instruction in response to the need to switch the power consumption mode.

[0011] In some embodiments, the environmental data includes the ambient temperature, and the status data includes the battery power of the shared vehicle and the number of times the battery is charged and discharged; in response to the ambient temperature being lower than a preset temperature threshold, the control method of the shared vehicle is enabled.

[0012] One of the embodiments of the present specification provides a control device for a shared vehicle, including a processor, wherein the processor is used to execute a control method for a shared vehicle.

[0013] One of the embodiments of the present specification provides 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 method for controlling a shared vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] 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:

[0015] Figure 1 is a schematic diagram of an application scenario of a control system for a shared vehicle according to some embodiments of this specification;

[0016] Figure 2 is an exemplary module diagram of a server for sharing vehicle control according to some embodiments of the present specification;

[0017] Figure 3 is an exemplary flow chart of controlling a shared vehicle according to some embodiments of the present specification;

[0018] Figure 4 is a schematic diagram of a flow chart for determining a power consumption mode of a shared vehicle according to some embodiments of this specification;

[0019] Figure 5 is an exemplary flow chart of controlling a shared vehicle according to some further embodiments of the present specification. DETAILED DESCRIPTION

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] In winter, due to weather reasons (for example, insufficient sunlight intensity), the batteries of shared vehicles cannot be recharged well; and in winter, due to the inherent characteristics of lithium batteries, the battery capacity also decays, resulting in low power in shared vehicles, affecting normal use by users, and even the inability to effectively locate and report shared vehicles, resulting in the loss of connection with shared vehicles. Therefore, it is necessary to provide a control method, system, device and storage medium for shared vehicles, so as to minimize power consumption when the power of shared vehicles is insufficiently replenished in winter, and avoid the problem of inability to use shared vehicles normally or even loss of connection due to low power.

[0025] Figure 1 1 is a schematic diagram of an application scenario of a shared vehicle control system according to some embodiments of this specification. In some embodiments, the application scenario 100 of the shared vehicle control system may include a server 110, a network 120, and a shared vehicle 130. In some embodiments, the application scenario 100 of the shared vehicle control system may also include a user terminal, Bluetooth, and / or a storage device (not shown in the figure).

[0026] In some embodiments, the server 110 may be used to process information and / or data related to the control system of the shared vehicle. In some embodiments, the server 110 may be used to periodically obtain environmental data, state data, and driving data of the shared vehicle at a first interval. Further, the environmental data, state data, and driving data are input into the trained power consumption model, and the power consumption mode of the shared vehicle is determined based on the power consumption model. Then, based on the determined power consumption mode and the current power consumption mode of the shared vehicle, it is determined whether to switch the power consumption mode; in response to the need to switch the power consumption mode, the power consumption mode of the shared vehicle is switched. The server 110 may be an independent server or a server group. The server group may be centralized or distributed. In some embodiments, the server 110 may be regional or remote. For example, the server 110 may communicate with the communication module 131-2 via the network 120. In some embodiments, the server 110 may be executed on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, etc.

[0027] In some embodiments, the network 120 may include any suitable wired or wireless network that can facilitate information and / or data exchange. For example, the central control 131 may obtain the power consumption pattern in the server 110 through the network 120. In some embodiments, one or more components of the shared vehicle 130, the server 110, the user terminal and / or the storage device, etc. may transmit information and / or data to one or more other components of the control system based on the shared vehicle via the network 120.

[0028] The shared vehicle 130 may be any type of shared vehicle, including a bicycle, an electric motorcycle, a car, etc. In some embodiments, a central control 131 may be integrated into the shared vehicle 130 .

[0029] The central control 131 refers to the central control system of the shared vehicle 130. In some embodiments, the central control 131 may have functions such as 4G Bluetooth vehicle borrowing and returning, battery compartment lock, helmet monitoring, controller communication, BMS communication, headlight control, voice prompt, positioning and orientation, mileage calculation, status monitoring, abnormal warning, OTA upgrade, etc.

[0030] In some embodiments, the central control 131 may be integrated with the shared vehicle 110. Figure 1 As shown, the central control 131 can be integrated and installed below the front panel of the shared vehicle 130.

[0031] In some embodiments, the central control 131 may include a processing device 131 - 1 , a communication module 131 - 2 , and a battery 131 - 3 .

[0032] The processing device 131-1 can be used to control the communication module 131-2. For example, in response to the power consumption mode being the first power consumption mode, the processing device 131-1 can control the communication module 131-2 to report vehicle information at a second interval. The vehicle information may include status data and driving data. For another example, in response to the power consumption mode being the second power consumption mode, the processing device 131-1 can turn off the communication module 131-2. In addition, in response to meeting a preset condition, the processing device 131-1 controls the power consumption mode to switch from the second power consumption mode to the first power consumption mode, that is, turns on the communication module 131-2, and controls the communication module 131-2 to report vehicle information at a second interval.

[0033] In some embodiments, the processing device 131-1 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, etc., or any combination of the above.

[0034] The communication module 131-2 can be used to connect to the server 110 and upload vehicle information. For example, the communication module 131-2 can connect to the server 110 through a 4G network and upload vehicle information. In some embodiments, in response to the power consumption mode being the first power consumption mode, the communication module 131-2 can report the vehicle information at a second interval.

[0035] The battery 131-3 can be used to provide power for the central control 131. In some embodiments, the battery 131-3 can be charged by solar energy, and the battery 131-3 can also be charged by manual power replacement.

[0036] In some embodiments, the application scenario 100 of the control system of a shared vehicle may also include some or more other devices, such as a user terminal, Bluetooth and / or a storage device.

[0037] The user terminal may include a mobile device, a tablet computer, a laptop computer, etc., or any combination thereof. In some embodiments, the user may input a return instruction through the user terminal. In some embodiments, the user may scan and unlock the vehicle through the user terminal. In some embodiments, the operation and maintenance personnel may check the status of the vehicle through the user terminal to determine whether to transfer the vehicle to the hot spot area.

[0038] In some embodiments, in response to the power consumption mode being the second power consumption mode, the communication module 131 - 2 is turned off, and only Bluetooth is left on, which can ensure the user's normal switch lock operation.

[0039] The storage device may store data, instructions, and / or any other information. In some embodiments, the storage device may store data and / or instructions related to the control system of the shared vehicle. For example, the storage device may store a power consumption pattern. For another example, the storage device may store environmental data, status data, and driving data.

[0040] In some embodiments, the storage device may be connected to the network 120 to communicate with one or more other components in the application scenario 100 of the control system of the shared vehicle (e.g., the server 110, the network 120, the shared vehicle 130, the central control 131, the processing device 131-1, the communication module 131-2 and / or the battery 131-3, etc.). One or more components of the application scenario 100 of the control system of the shared vehicle may access the data or instructions stored in the storage device through the network. In some embodiments, the storage device may be part of the server 110.

[0041] Figure 2 is an exemplary module diagram of a server for controlling a shared vehicle according to some embodiments of this specification. The server 200 may be Figure 1 The specific implementation of the server 110 is shown in FIG.

[0042] In some embodiments, the server 200 may include an acquisition module 210 , a determination module 220 , a judgment module 230 , and a switching module 240 .

[0043] The acquisition module 210 may be configured to periodically acquire the environment data, status data, and driving data of the shared vehicle at a first interval.

[0044] In some embodiments, the environmental data includes the ambient temperature, and the status data includes the battery power of the shared vehicle and the number of times the battery is charged and discharged; in response to the ambient temperature being lower than a preset temperature threshold, the control method of the shared vehicle is enabled.

[0045] The determination module 220 can be used to input environmental data, status data and driving data into a trained power consumption model, and determine the power consumption mode of the shared vehicle based on the power consumption model, where the power consumption model is a machine learning model; the power consumption mode includes a first power consumption mode and a second power consumption mode, and the power consumption of the first power consumption mode is greater than the power consumption of the second power consumption mode.

[0046] In some embodiments, the determination module 220 can be further used to input environmental data, status data and driving data into a trained power consumption model, and determine whether the battery power of the shared vehicle is less than a preset power threshold after the third interval time based on the power consumption model, and the preset power threshold is related to the usage of the shared vehicle during the third interval time period; in response to the battery power of the shared vehicle being not less than the preset power threshold after the third interval time, determining that the power consumption mode of the shared vehicle is the first power consumption mode; and in response to the battery power of the shared vehicle being less than the preset power threshold after the third interval time, determining that the power consumption mode of the shared vehicle is the second power consumption mode.

[0047] In some embodiments, the determination module 220 can be further used to obtain multiple training samples and their labels, the multiple training samples include historical environmental data, historical status data and historical driving data of the sample vehicle, and the label is whether the battery power of the sample vehicle is less than a preset power threshold after a third interval time; and based on the multiple training samples and their labels, train an initial power consumption model to obtain a power consumption model.

[0048] In some embodiments, in response to the power consumption mode being the first power consumption mode, the communication module of the shared vehicle reports vehicle information at a second interval, and the vehicle information includes status data and driving data; in response to the power consumption mode being the second power consumption mode, the communication module is turned off; in response to meeting a preset condition, the power consumption mode is switched from the second power consumption mode to the first power consumption mode.

[0049] The switching module 240 may be configured to determine whether to switch the power consumption mode based on the determined power consumption mode and the current power consumption mode of the shared vehicle.

[0050] The switching module 240 may be configured to issue a switching mode instruction in response to a need to switch the power consumption mode.

[0051] In some embodiments, the switching module 240 can be further used to divide the operating area of ​​the shared vehicle into multiple grids, determine the characteristic information of the multiple grids, the characteristic information including the average usage frequency and average parking time of the vehicles in the grids; cluster the multiple grids based on the characteristic information of multiple networks; and issue the same switching mode instructions to the vehicles in the grids of the same clustering category.

[0052] It should be understood that Figure 2The system and its modules shown can be implemented in various ways. It should be noted that the above description of the control system and its modules of the shared vehicle is only for the convenience of description and cannot 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, they can arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, Figure 2 The acquisition module, determination module, judgment module and switching module disclosed in the specification can be different modules in a system, or one module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the protection scope of this specification.

[0053] Figure 3 FIG. 1 is an exemplary flow chart of controlling a shared vehicle according to some embodiments of the present specification. Figure 3 As shown, process 300 includes the following steps. In some embodiments, Figure 3 One or more operations of the process 300 shown may be performed in Figure 1 The control system of the shared vehicle is implemented in the application scenario 100 shown. For example, Figure 3 The process 300 shown may be stored in the storage device 150 in the form of instructions, and called and / or executed by the processing device 110 .

[0054] Step 310 , periodically acquiring environmental data, state data, and driving data of the shared vehicle at a first interval. In some embodiments, step 310 may be executed by the acquisition module 210 .

[0055] The first interval time refers to the time interval between two acquisitions of the environment data, state data, and driving data of the shared vehicle. For example, the first interval time may be 0.5h, 1h, 2h, etc. In some embodiments, the first interval time may be pre-set.

[0056] Environmental data refers to relevant data that can reflect the conditions of the environment in which the shared vehicle is located. For example, environmental data may include ambient temperature, light intensity, etc. When the ambient temperature is low (for example, less than 0°C), the lithium battery is limited by its own characteristics, and the battery capacity will decay, which can easily lead to low power. When the light intensity is insufficient (for example, the solar panel charging current value is less than 100mA), the vehicle cannot be recharged well, which can easily lead to low power. In some embodiments, the server can obtain environmental data through the network based on a third-party platform. For example, the server can query the China Meteorological Network through the network for data such as the ambient temperature and light intensity of the city where the shared vehicle is located.

[0057] Status data refers to relevant data that can reflect the status of the shared vehicle. For example, the status data may include vehicle health, vehicle location information, etc.

[0058] Vehicle health refers to data used to describe the overall status and performance of a shared vehicle at a certain point in time or over a period of time. For example, vehicle health may include battery status (e.g., the battery power of a shared vehicle, the number of times the battery is charged and discharged, battery aging, etc.), the degree of wear of mechanical parts, the stability of the software system, and whether the vehicle requires repair or maintenance. The better the battery status of a shared vehicle (e.g., the higher the battery power of a shared vehicle, the fewer times the battery is charged and discharged, and the less aging the battery), the less wear of mechanical parts, the better the stability of the software system, and the less repair or maintenance is required for the vehicle, the less likely it is to run low on power.

[0059] Vehicle location information refers to data that reflects the location of a shared vehicle. For example, the latitude and longitude information of a shared vehicle. When a shared vehicle is located in a non-hotspot area, there may be a long period of time when no user rides it, and there is no need to upload vehicle information in real time. Among them, vehicle information may include status data and driving data. It should be noted that a hotspot area refers to an area in which the average parking time of all shared vehicles is not higher than the time threshold; a non-hotspot area refers to an area in which the average riding frequency of all shared vehicles is higher than the time threshold. Among them, the time threshold can be set in advance.

[0060] In some embodiments, the server may retrieve data in a storage device to obtain status data. The data in the storage device is uploaded by the shared vehicle through the communication module.

[0061] Driving data refers to relevant data that can reflect the driving status of a shared vehicle. For example, driving data may include driving location, driving frequency, driving time, driving distance, driving speed, etc. When it is judged as an inactive vehicle, there may be a situation where no user rides for a long time. If the solar panel cannot be charged at this time, it is easy to cause low power. When it is judged as an active vehicle, although there may not be a situation where no user rides for a long time, if the power is less than or equal to the reference power threshold, and the solar panel charging current value is less than the first current threshold (for example, 90mA, 100mA, 110mA, etc.), it is also easy to cause low power. Among them, the reference power threshold and the first current threshold can be pre-set. It should be noted that an inactive vehicle refers to a shared vehicle whose driving frequency is lower than the frequency threshold; an active vehicle refers to a shared vehicle whose driving frequency is not lower than the frequency threshold. Among them, the frequency threshold can be pre-set.

[0062] In some embodiments, the server may retrieve data in a storage device to obtain driving data. The data in the storage device is uploaded by the shared vehicle through the communication module.

[0063] Step 320 , input the environmental data, the state data, and the driving data into the trained power consumption model, and determine the power consumption mode of the shared vehicle based on the power consumption model. In some embodiments, step 320 may be performed by the determination module 220 .

[0064] The power consumption mode refers to the way in which power is consumed. In some embodiments, the power consumption mode may include a first power consumption mode and a second power consumption mode.

[0065] In some embodiments, in response to the power consumption mode being the first power consumption mode, the communication module of the shared vehicle may report vehicle information to the server at a second interval.

[0066] The communication module is a component that can upload vehicle information to the server. For more information about the communication module, see Figure 1 and its related description.

[0067] The first interval time refers to the interval time between two consecutive times when the communication module uploads the vehicle information to the server, for example, 3s, 5s, 10s, etc. In some embodiments, the first interval time can be preset.

[0068] Vehicle information refers to information that can reflect the vehicle's condition. In some embodiments, the vehicle information may include status data and driving data. In some embodiments, the processing device may obtain the vehicle information based on the corresponding processing module. For example, the central control may also include a positioning module, and the processing device may control the positioning module to obtain the vehicle location information of the shared vehicle.

[0069] In some embodiments, in response to the power consumption mode being the second power consumption mode, the processing device may turn off the communication module. After the communication module is turned off, the shared vehicle will stop reporting vehicle information to the server, and only Bluetooth will be turned on to ensure normal lock and unlock operations by the user.

[0070] In some embodiments, in response to satisfying a preset condition, the power consumption mode may be switched from the second power consumption mode to the first power consumption mode.

[0071] The preset condition refers to the condition that needs to be met for the power consumption mode to switch from the second power consumption mode to the first power consumption mode. For example, the preset condition may be that the user turns on the shared vehicle. For another example, the preset condition may be that the communication module is restarted. The communication module restart cycle may be 1 day, 2 days, 3 days, etc.

[0072] In some embodiments, when the preset condition is that the user turns on the vehicle, the processing device can switch the power consumption mode from the second power consumption mode to the first power consumption mode to ensure that the driving data generated by the user during riding can be uploaded to the server in a timely manner. When the ride is over, the server can periodically obtain the environmental data, status data and driving data of the shared vehicle at a first interval to continue to determine whether to switch the power consumption mode.

[0073] In some embodiments, when the preset condition is that the communication module restarts, the communication module can be restarted for a short time (for example, 0.5min, 1min, 2min, etc.), that is, the power consumption mode is switched from the second power consumption mode to the first power consumption mode in a short time. The communication module can report the vehicle information to the server once to ensure that the vehicle is not lost. After reporting the vehicle information, the processing device can switch the power consumption mode from the first power consumption mode to the second power consumption mode to minimize the useless power consumption data generated by the connection with the server and reduce power consumption.

[0074] The power consumption of the first power consumption mode is greater than the power consumption of the second power consumption mode. The first power consumption mode can be called a long connection mode, which is a high power consumption mode and can upload vehicle information to the server in a timely manner. The second power consumption mode can be called a short connection mode, which is a low power consumption mode and reduces power consumption by turning off the communication module.

[0075] In some embodiments of the present specification, by setting a first power consumption mode and a second power consumption mode, a suitable power consumption mode can be selected in different application scenarios, thereby reducing power consumption while ensuring timely data upload.

[0076] In some embodiments, the server may input environmental data, state data, and driving data into a trained power consumption model, and determine the power consumption mode of the shared vehicle based on the power consumption model, which is a machine learning model. For example, when it is winter, the shared vehicle is an inactive vehicle, and is in a non-rechargeable area, the server may determine that the power consumption mode at this time is the second power consumption mode through the power consumption model. When a user is riding, the processing device may switch the power consumption mode to the first power switching mode. For another example, when it is winter, the shared vehicle is an active vehicle, and the battery power is less than the reference power threshold, and the solar panel charging current value is less than the first current threshold, the server may determine that the power consumption mode at this time is the second power consumption mode through the power consumption model. When the battery power of the shared vehicle returns to the second current threshold, the processing device may switch the power consumption mode to the first power switching mode. Among them, the second current threshold is greater than the first current threshold, and the second current threshold can be pre-set.

[0077] For more information on determining the power consumption pattern of shared vehicles through power consumption models, see Figure 4 and its related description.

[0078] Step 330 , based on the determined power consumption mode and the current power consumption mode of the shared vehicle, determine whether to switch the power consumption mode. In some embodiments, step 330 may be performed by the determination module 230 .

[0079] In some embodiments, the server may retrieve data in the memory to obtain the current power consumption mode of the shared vehicle. Then, the server may determine whether the power consumption mode determined in step 320 is consistent with the previous power consumption mode of the shared vehicle. In response to the consistency of the two, the server may determine that the power consumption mode does not need to be switched; in response to the inconsistency of the two, the server may determine that the power consumption mode needs to be switched.

[0080] Step 340 : In response to the need to switch the power consumption mode, issue a mode switching instruction. In some embodiments, step 340 may be performed by the switching module 240 .

[0081] In some embodiments, in response to the need to switch the power consumption mode, the server may send a switching mode instruction to the central control, thereby switching the power consumption mode of the shared vehicle. The switching mode instruction refers to the code for the electronic computer to implement the switching of the power consumption mode. In some embodiments, the server may poll and send the switching mode instruction at a preset interval (for example, 5 minutes, 20 minutes, 30 minutes, etc.), and confirm it. That is, the server may send the switching mode instruction at a preset interval, and query the power consumption mode of all current vehicles based on the storage device. When it is found that there are vehicles that have not switched successfully, the switching mode instruction continues to be sent until all vehicles are switched successfully.

[0082] In some embodiments of the present specification, by periodically acquiring environmental data, status data and driving data, the power consumption pattern of a shared vehicle can be determined, and the power consumption pattern of the shared vehicle can be dynamically adjusted to make the power consumption pattern of the shared vehicle more reasonable, thereby reducing power consumption, reducing operating costs and increasing the number of available vehicles in the city while ensuring timely data upload.

[0083] It should be noted that the above description of the control of the process sharing vehicle 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 the control of the process sharing vehicle under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0084] In some embodiments, in response to the ambient temperature being lower than a preset temperature threshold, the server may enable the control method of the shared vehicle, wherein the preset temperature threshold may be -3°C, 0°C, 2°C, etc. The preset threshold may be pre-set.

[0085] In some embodiments of this specification, the lithium battery's own characteristics, such as the attenuation of battery capacity, will only be affected when the ambient temperature is lower than a certain temperature. Therefore, in response to the ambient temperature being lower than a preset temperature threshold, the server activates the control method of the shared vehicle, thereby avoiding unnecessary information processing and reducing the server's computing power.

[0086] In some embodiments, the server may divide the operation area of ​​the shared vehicle into multiple grids, determine the characteristic information of the multiple grids, cluster the multiple grids based on the characteristic information of the multiple networks, and issue the same switching mode instruction to the vehicles in the grids of the same clustering category. The characteristic information may include the average frequency of use and average parking time of the vehicles in the grids.

[0087] For more information on controlling shared vehicles by dividing the operating area into multiple grids, see Figure 5 and its related description.

[0088] Figure 4 FIG. 1 is a flow chart of determining the power consumption mode of a shared vehicle according to some embodiments of this specification. Figure 4 As shown, process 400 includes the following steps. In some embodiments, Figure 4 One or more operations of the process 400 shown may be performed in Figure 1 The control system of the shared vehicle is implemented in the application scenario 100 shown. For example, Figure 4 The process 400 shown may be stored in the storage device 150 in the form of instructions, and called and / or executed by the processing device 110. In some embodiments, the process 400 may be executed by the determination module 220.

[0089] Step 410: Input the environmental data, the state data and the driving data into the trained power prediction model, and determine the predicted battery power of the shared vehicle after the third interval based on the power prediction model.

[0090] In some embodiments, the power prediction model may be a deep learning neural network model. Exemplary deep learning neural network models may include convolutional neural networks (CNN), deep neural networks (DNN), recurrent neural networks (RNN), etc. or a combination thereof.

[0091] In some embodiments, Figure 4 As shown, the input of the power prediction model can be environmental data, state data and driving data. For more details about environmental data, state data and driving data, please refer to Figure 3 and its related description.

[0092] In some embodiments, Figure 4 As shown, the output of the power prediction model is the predicted battery power of the shared vehicle after the third interval time.

[0093] The third interval time refers to the time interval for judging whether the shared vehicle will be low on power in the future. For example, the third time interval can be 0.5h, 1h, 2h, etc. If the third interval time is too short (for example, 0.1h), then in response to the battery power of the shared vehicle being less than the preset power threshold after the third interval time, the buffer time is short, and it may quickly enter a low-power state, which is not reasonable. If the third interval time is too long (for example, 72h), some changes may occur during the third interval time period (for example, the ambient temperature rises, the shared vehicle is ridden to a hot spot by the user, etc.). At this time, it is unreasonable to determine the power consumption mode of the shared vehicle based on whether the predicted battery power of the shared vehicle after the third interval time is less than the preset power threshold. In some embodiments, the third interval time can be set in advance.

[0094] The preset power threshold refers to a preset power critical value for determining whether the power consumption mode of the shared vehicle is the first power consumption mode or the second power consumption mode. For example, the preset power threshold may be 15%, 20%, 25%, etc.

[0095] In some embodiments, the preset power threshold is related to the usage of the shared vehicle in the third interval time period. Among them, the usage of the shared vehicle in the third interval time period refers to the predicted probability of the shared vehicle being used in the third interval time period. The predicted probability of being used is inversely proportional to the preset power threshold. That is, the higher the predicted probability of being used, the lower the preset power threshold, for example, 15%; conversely, the lower the predicted probability of being used, the higher the preset power threshold, for example, 25%. Because the higher the predicted probability of being used, the higher the probability that the shared vehicle moves from a non-hotspot area to a hotspot area; and after moving, the location that was originally difficult to charge with solar energy may be able to be charged, then the preset power threshold can be reduced accordingly. Conversely, the lower the predicted probability of being used, the more likely the shared vehicle will not be used for a long time, so to ensure that the power can meet the long-term safety requirements (that is, it will not lose connection), the preset power threshold can be increased accordingly.

[0096] In some embodiments, the server may obtain the preset power threshold by looking up a table. For example, the server may organize the used prediction probability and the preset power threshold into a data comparison table, and determine the preset power threshold based on the data comparison table. The preset power threshold corresponding to the used prediction probability may be preset.

[0097] In some embodiments, the server may determine the predicted probability to be used based on the vehicle location information, the surrounding vehicle information, the current time and the third interval time through a predicted probability model. The surrounding vehicle information may include the total number of surrounding vehicles, the number of damaged surrounding vehicles, etc. For example, the server may input the vehicle location information, the surrounding vehicle information, the current time and the third interval time into the predicted probability model, and the predicted probability model outputs the predicted probability to be used. The predicted probability model may be a CNN model or a DNN model, etc. The server may use historical vehicle location information, historical surrounding vehicle information, historical time and historical third interval time as training data to train the predicted probability model, so that the predicted probability model can output its corresponding predicted probability to be used based on the vehicle location information, the surrounding vehicle information, the current time and the third interval time. The label corresponding to the training data can be determined by historical data.

[0098] In some embodiments, the server may obtain multiple training samples and their labels, and train an initial power prediction model based on the multiple training samples and their labels to obtain the power prediction model. The multiple training samples include historical environmental data, historical state data, and historical driving data of the sample vehicle, and the label is the predicted battery power of the sample vehicle after the third interval time.

[0099] In some embodiments, the power prediction model can be formed by using a plurality of positive samples and negative samples to form training samples for training the initial power prediction model. Among them, the positive samples may include sample vehicles whose predicted battery power of the shared vehicle after the third interval is less than the preset power threshold, and the corresponding historical environment data, historical status data, and historical driving data; the negative samples may include sample vehicles whose predicted battery power of the shared vehicle after the third interval is not less than the preset power threshold, and the corresponding historical environment data, historical status data, and historical driving data.

[0100] The tag may be the historical battery power of the sample vehicle after the third interval. In some embodiments, the tag may be determined by historical data.

[0101] The training of the initial power prediction model may include one or more iterations. As an example only, in the current iteration, for each training sample, the server may use the intermediate model to determine the historical environmental data, historical state data, and historical driving data of the sample vehicle, and the corresponding predicted battery power of the sample vehicle after the third interval. If the current iteration is the first iteration, the intermediate model may be the initial power prediction model. If the current iteration is another iteration, the intermediate model may be the model generated in the previous iteration. The server may determine the value of the loss function based on the difference between the predicted battery power and the historical battery power, and update the intermediate model based on the value of the loss function.

[0102] In some embodiments, the parameters of the initial power prediction model can be iteratively updated based on multiple training samples so that the loss function of the intermediate model meets the preset conditions. For example, the loss function converges, or the loss function value is less than the preset value. When the loss function meets the preset conditions, the model training is completed, and the trained initial power prediction model can be used as the power prediction model.

[0103] Step 420, determining whether the predicted battery power is less than a preset power threshold.

[0104] In some embodiments, in response to the battery power of the shared vehicle being not less than the preset power threshold after the third interval, the server may execute step 430. In some embodiments, in response to the battery power of the shared vehicle being less than the preset power threshold after the third interval, the server may execute step 440.

[0105] Step 430: determine that the power consumption mode of the shared vehicle is the first power consumption mode.

[0106] In some embodiments, in response to the battery power of the shared vehicle being not less than a preset power threshold after the third interval time, the server may determine that the power consumption mode of the shared vehicle is the first power consumption mode.

[0107] Step 440: determine that the power consumption mode of the shared vehicle is the second power consumption mode.

[0108] In some embodiments, in response to the battery power of the shared vehicle being less than a preset power threshold after the third interval time, the server may determine that the power consumption mode of the shared vehicle is the second power consumption mode.

[0109] In some embodiments of the present specification, the battery power can be quickly and accurately determined through a power prediction model, thereby quickly and accurately determining the power consumption pattern of the shared vehicle.

[0110] It should be noted that the above description of the process for determining the power consumption mode of a shared vehicle 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 the process for determining the power consumption mode of a shared vehicle under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0111] Figure 5 FIG. 1 is an exemplary flow chart of controlling a shared vehicle according to some other embodiments of this specification. Figure 5 As shown, process 500 includes the following steps. In some embodiments, Figure 5 One or more operations of the process 500 shown may be performed in Figure 1 The control system of the shared vehicle is implemented in the application scenario 100 shown. For example, Figure 5 The process 500 shown may be stored in the storage device 150 in the form of instructions, and called and / or executed by the processing device 110. In some embodiments, the process 500 may be executed by the switching module 240.

[0112] Step 510: Divide the operation area of ​​the shared vehicle into a plurality of grids, and determine characteristic information of the plurality of grids.

[0113] The operating area refers to the area within which shared vehicles can be ridden.

[0114] A grid is an area divided from an operating area.

[0115] In some embodiments, the server may divide the operation area of ​​the shared vehicle into a plurality of grids based on a preset rule. For example, the server may evenly divide the operation area into a preset number (e.g., 500, 1000, 1500, etc.) of grids by area. The preset number may be pre-set.

[0116] Characteristic information refers to data that can reflect the commonalities of vehicles in a grid. For example, characteristic information may include the average frequency of use and average parking duration of vehicles in a grid. For another example, characteristic information may also include average battery power and average light intensity. For a grid with a relatively high average frequency of use, a relatively low average parking duration, a relatively high average battery power, and / or a relatively high average light intensity, the power consumption mode of the shared vehicle may be determined as the first power consumption mode.

[0117] In some embodiments, for one of the multiple grids, the server can obtain the average value of the light intensity in the area where the grid is located through the network based on a third-party platform, and determine the average value as the average light intensity. In some embodiments, for one of the multiple grids, the server can retrieve data from a storage device to obtain the usage frequency, parking duration, and battery power of all vehicles in the grid, and obtain the average parking duration, average battery power, and average light intensity by calculation. The data in the storage device is uploaded by the shared vehicle through the communication module.

[0118] Step 520: clustering the multiple grids based on the feature information of the multiple grids.

[0119] The characteristic information of the grid can be represented by a grid feature vector. In some embodiments, the grid feature vector is constructed based on the average frequency of use and the average parking duration. For example, the grid feature vector can be (a, b), where a represents the average frequency of use and b represents the average parking duration. In some embodiments, the grid feature vector is constructed based on the average frequency of use, the average parking duration, the average battery charge and the average light intensity. For example, the grid feature vector can be (a, b, c, d), where a represents the average frequency of use, b represents the average parking duration, c represents the average battery charge, and d represents the average light intensity.

[0120] In some embodiments, the server may cluster the multiple grids based on the grid feature vectors corresponding to the feature information of the multiple grids through a clustering algorithm. There may be multiple types of clustering algorithms, for example, the clustering algorithm may include K-Means clustering, density-based clustering method (DBSCAN), etc. After clustering the multiple grids, the multiple grids may be divided into one or more cluster categories.

[0121] Step 530 : Send the same switching mode instruction to the shared vehicles in the grids of the same clustering category.

[0122] In some embodiments, the server may determine the mode switching instructions for the same clustering category, and send the same switching mode instructions to the shared vehicles in the grid of the same clustering category. For example, the server randomly selects at least one shared vehicle from the shared vehicles in the grid of the same clustering category, determines the average environmental data, average state data, and average driving data of at least one shared vehicle, and then determines the power consumption mode by looking up a table or a power prediction model. Then, it is determined whether to switch the power consumption mode, and in response to the need to switch the power consumption mode, the same switching mode instruction is sent to the shared vehicles in the grid of the same clustering category.

[0123] In some embodiments of this specification, the operation area of ​​shared vehicles is divided into multiple grids, and the multiple grids are clustered to issue the same switching mode instruction to the vehicles in the grids of the same clustering category. In the grids of the same clustering category, only one power consumption mode needs to be determined, and there is no need to determine a power consumption mode for each shared vehicle, thereby improving efficiency and reducing costs.

[0124] It should be noted that the above description of the control of the process sharing vehicle 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 the control of the process sharing vehicle under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0125] In some embodiments, the control device of the shared vehicle includes a processor, and the processor is used to execute the control method of the shared vehicle.

[0126] In some embodiments, a computer-readable storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the control method of the shared vehicle.

[0127] The beneficial effects that may be brought about by the embodiments of this specification include but are not limited to: (1) By setting the first power consumption mode and the second power consumption mode, it is possible to select a suitable power consumption mode in different application scenarios, thereby reducing power consumption while ensuring timely data upload. (2) By periodically acquiring environmental data, status data and driving data to determine the power consumption mode of a shared vehicle, the power consumption mode of the shared vehicle can be dynamically adjusted to make the power consumption mode of the shared vehicle more reasonable, thereby reducing power consumption, reducing operating costs and increasing the number of available vehicles in the city while ensuring timely data upload. (3) Since the inherent characteristics of lithium batteries will only be affected when the ambient temperature is lower than a certain temperature, such as the attenuation of battery capacity, the server will only enable the control method of the shared vehicle in response to the ambient temperature being lower than the preset temperature threshold, thereby avoiding unnecessary information processing and reducing the computing power of the server. (4) The power prediction model can be used to quickly and accurately determine the predicted battery power, thereby quickly and accurately determining the power consumption mode of the shared vehicle. (5) By dividing the operation area of ​​shared vehicles into multiple grids and clustering the multiple grids, the same switching mode instruction can be issued to the vehicles in the grids of the same clustering category. In the grids of the same clustering category, only one power consumption mode needs to be determined, and there is no need to determine a power consumption mode for each shared vehicle, thereby improving efficiency and reducing costs.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 method for controlling a shared vehicle, characterized in that: The method comprises: Periodically acquiring environmental data, status data, and driving data of the shared vehicle at a first interval; Inputting the environmental data, the state data and the driving data into a trained power prediction model, and determining a power consumption mode of the shared vehicle based on the power prediction model, wherein the power prediction model is a machine learning model; the power consumption mode includes a first power consumption mode and a second power consumption mode, and the power consumption of the first power consumption mode is greater than the power consumption of the second power consumption mode; Based on the determined power consumption mode and the current power consumption mode of the shared vehicle, determining whether to switch the power consumption mode; and In response to the need to switch the power consumption mode, a mode switching instruction is issued.

2. The method according to claim 1, characterized in that The environmental data includes the ambient temperature, and the status data includes the battery power of the shared vehicle and the number of times the battery is charged and discharged; In response to the ambient temperature being lower than a preset temperature threshold, a control method for the shared vehicle is enabled.

3. The method according to claim 1, characterized in that In response to the power consumption mode being the first power consumption mode, the communication module of the shared vehicle reports vehicle information at a second interval, the vehicle information including the state data and the driving data; In response to the power consumption mode being the second power consumption mode, shutting down the communication module; In response to satisfying a preset condition, the power consumption mode is switched from the second power consumption mode to the first power consumption mode.

4. The method according to claim 1, characterized in that The power prediction model is obtained through a training process, and the training process includes: Acquire multiple training samples and labels thereof, wherein the multiple training samples include historical environment data, historical state data, and historical driving data of the sample vehicle, and the label is a historical battery power of the sample vehicle after a third interval; and Based on the plurality of training samples and their labels, an initial power prediction model is trained to obtain the power prediction model.

5. The method according to claim 1, characterized in that The method further comprises: Dividing the operation area of ​​the shared vehicle into a plurality of grids, and determining characteristic information of the plurality of grids, wherein the characteristic information includes an average usage frequency and an average parking duration of vehicles in the grids; Clustering the plurality of grids based on feature information of the plurality of grids; and The same switching mode instruction is issued to the shared vehicles in the grids of the same clustering category.

6. The method according to claim 1, characterized in that The step of inputting the environmental data, the state data, and the driving data into a trained power prediction model, and determining the power consumption mode of the shared vehicle based on the power prediction model includes: Inputting the environmental data, the state data and the driving data into a trained power prediction model, and determining a predicted battery power of the shared vehicle after a third interval based on the power prediction model; Determining whether the predicted battery power is less than a preset power threshold, where the preset power threshold is related to the usage of the shared vehicle within a third interval time period; In response to the predicted battery power being not less than the preset power threshold, determining that the power consumption mode of the shared vehicle is the first power consumption mode; and In response to the predicted battery power being less than the preset power threshold, determining the power consumption mode of the shared vehicle to be the second power consumption mode.

7. A control system for a shared vehicle, characterized in that: include: An acquisition module, used for periodically acquiring environmental data, state data and driving data of a shared vehicle at a first interval; a determination module, configured to input the environmental data, the state data, and the driving data into a trained power prediction model, and determine a power consumption mode of the shared vehicle based on the power prediction model, wherein the power prediction model is a machine learning model; the power consumption mode includes a first power consumption mode and a second power consumption mode, and the power consumption of the first power consumption mode is greater than the power consumption of the second power consumption mode; A determination module, configured to determine whether to switch the power consumption mode based on the determined power consumption mode and the current power consumption mode of the shared vehicle; as well as The switching module is used to issue a switching mode instruction in response to the need to switch the power consumption mode.

8. The system according to claim 7, characterized in that The environmental data includes the ambient temperature, and the status data includes the battery power of the shared vehicle and the number of times the battery is charged and discharged; In response to the ambient temperature being lower than a preset temperature threshold, a control method for the shared vehicle is enabled.

9. A control device for a shared vehicle, characterized in that: It comprises a processor, wherein the processor is used to execute the control method of the shared vehicle according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the control method of the shared vehicle according to any one of claims 1 to 6.

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