CN113379304A - Method and device for determining electricity consumption - Google Patents
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Abstract
The application discloses a method and a device for determining power consumption, and relates to the technical field of deep learning and industrial big data. The method comprises the following steps: acquiring a target trip prediction model, wherein the target trip prediction model comprises a mapping relation between a trip time period of a vehicle and a trip path of the vehicle; determining at least one target travel path of the vehicle in a target time period by adopting a target travel prediction model; the method comprises the steps of obtaining power consumption parameters of a vehicle, and determining expected power consumption of the vehicle in a target time period based on the power consumption parameters and at least one target trip path. By adopting the method, the accuracy of determining the expected electricity consumption of the new energy vehicle can be improved.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to the technical field of deep learning and industrial big data, and particularly relates to a method and a device for determining power consumption.
Background
With the development of new energy vehicles, the market occupancy of new energy vehicles is higher and higher, and the electric quantity demand of new energy vehicles is also higher and higher. The charging requirement of the new energy automobile in an expected time period is determined, and the power supply can be reasonably distributed to meet the charging requirement of the new energy automobile while the stability of a power grid is ensured. The existing method for determining the electric quantity of the new energy automobile of the user comprises the following steps: the predicted travel distance of the vehicle in the future time period is predicted based on the average travel distance of the vehicle in the historical time period, and the expected electricity consumption of the vehicle is determined based on the predicted travel distance.
However, the existing method for determining the electric quantity of the new energy automobile has the problem of inaccurate determination.
Disclosure of Invention
The present disclosure provides a method, an apparatus, an electronic device, and a computer-readable storage medium for determining a power usage amount.
According to a first aspect, there is provided a method for determining a power usage, the method comprising: acquiring a target trip prediction model, wherein the target trip prediction model comprises a mapping relation between a trip time period of a vehicle and a trip path of the vehicle; determining at least one target travel path of the vehicle in a target time period by adopting a target travel prediction model; the method comprises the steps of obtaining power consumption parameters of a vehicle, and determining expected power consumption of the vehicle in a target time period based on the power consumption parameters and at least one target trip path.
According to a second aspect, there is provided a method for training a model, the method comprising: acquiring travel characteristics of the vehicle, wherein the travel characteristics comprise each travel path of the vehicle traveling in each time period; and training an initial travel prediction model by adopting a time period and a travel path, and obtaining a target travel prediction model.
According to a third aspect, there is provided an apparatus for determining a used amount of electricity, the apparatus comprising: a first obtaining unit configured to obtain a target trip prediction model, wherein the target trip prediction model includes a mapping relationship between a trip time period of a vehicle and a trip path of the vehicle; the second prediction unit is configured to determine at least one target travel path of the vehicle in a target time period by adopting a target travel prediction model; the determining unit is configured to acquire the power consumption parameters of the vehicle and determine the expected power consumption of the vehicle in the target time period based on the power consumption parameters and the at least one target trip path.
According to a fourth aspect, there is provided an apparatus for training a model, the apparatus comprising: the third acquisition unit is configured to acquire travel characteristics of the vehicle, wherein the travel characteristics comprise various travel paths of the vehicle traveling in various time periods; and the first training unit is configured to train an initial trip prediction model by adopting the time period and the trip path, and obtain a target trip prediction model.
According to a fifth aspect, embodiments of the present disclosure provide an electronic device, comprising: one or more processors: a storage device for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement the method for determining power usage as provided in the first aspect, or the method for training a model as provided in the second aspect.
According to a sixth aspect, embodiments of the present disclosure provide a computer readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the method for determining power usage provided by the first aspect or implements the method for training a model as provided by the second aspect.
According to the method and the device for determining the power consumption, a target trip prediction model is obtained, wherein the target trip prediction model comprises a mapping relation between a trip time period of a vehicle and a trip path of the vehicle; determining at least one target travel path of the vehicle in a target time period by adopting a target travel prediction model; the power consumption parameters of the vehicle are obtained, the expected power consumption of the vehicle in the target time period is determined based on the power consumption parameters and at least one target trip path, and the accuracy of estimating the power consumption of the vehicle can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is an exemplary system architecture diagram in which embodiments of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for determining a power usage in accordance with the present application;
FIG. 3 is a flow diagram of another embodiment of a method for determining a power usage in accordance with the present application;
FIG. 4 is a flow diagram of one embodiment of a method for training a model according to the present application;
FIG. 5 is a block diagram illustrating one embodiment of an apparatus for determining a power usage in accordance with the present application;
FIG. 6 is a schematic block diagram of one embodiment of an apparatus for training models according to the present application;
FIG. 7 is a block diagram of an electronic device used to implement the method for determining power usage of embodiments of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the present method for authenticating a system or apparatus for authenticating a system may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various emulation class processes or processes for testing the system can be installed on the terminal devices 101, 102, 103. Various client applications, such as a new energy vehicle charging application, a map application, a video application, a play application, an audio application, a search application, a shopping application, a financial application, etc., may also be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting receiving server messages, including but not limited to smartphones, tablets, e-book readers, electronic players, laptop portable computers, desktop computers, and the like.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various hardware modules to be verified or electronic devices, and when the terminal devices 101, 102, 103 are software, they may be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules (e.g., multiple software modules to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may obtain a target trip prediction model through the terminal devices 101, 102, and 103, where the target trip prediction model includes a mapping relationship between a trip time period of the vehicle and a trip path of the vehicle; determining at least one target travel path of the vehicle in a target time period by adopting a target travel prediction model; and acquiring the power consumption parameters of the vehicle, and determining the expected power consumption of the vehicle in the target time period based on the power consumption parameters of the vehicle and the at least one target trip path.
It should be noted that the method for authenticating the system provided by the embodiment of the present disclosure is generally performed by the server 105, and accordingly, the apparatus for authenticating the system is generally disposed in the server 105.
It should be understood that the number of devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of devices, networks, and servers, as desired for an implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for determining a power usage in accordance with the present disclosure is shown. A method for determining a power usage, comprising the steps of:
In the present embodiment, an executing subject (for example, a server shown in fig. 1) of the method for determining the power consumption may obtain a target trip prediction model from a local storage, a cloud storage, or a terminal device, wherein the target trip prediction model includes a mapping relationship between a trip time period of a vehicle and a trip path of the vehicle. The target trip prediction model may be trained using the method in the embodiment described in fig. 4.
In this embodiment, at least one target travel path of the vehicle in the target time period may be determined by using the target travel prediction model. The target time period is an arbitrary time period, for example, any time period such as the second day of the current day, the sunday of each week, the second week of 2 months, 9 am to 11 am of each day. The target travel path refers to a place where the user drives his vehicle to reach.
In this embodiment, the power consumption parameter of the vehicle may be acquired, and the expected power consumption of the vehicle in the target time period may be determined based on the power consumption parameter of the vehicle and at least one target travel route traveled by the vehicle in the target time period. The power consumption parameter may be a parameter that can be used to calculate power consumption/electricity consumption of the vehicle, such as battery capacity of the vehicle, fuel consumption per hundred kilometers of the vehicle, fuel consumption per hour of the vehicle, and remaining power of a battery of the vehicle.
For example, the fuel consumption per hundred kilometers of the vehicle may be acquired, and the fuel consumption required for the vehicle to travel the target trip path may be determined based on the distance between the departure point and the destination of the target small trip path of the vehicle. For another example, the hourly fuel consumption of the vehicle may be obtained, and the fuel consumption required for the vehicle to travel the target travel route may be determined based on the vehicle history/elapsed time of the travel target travel route, or the elapsed time of travel target travel routes of a plurality of other vehicles obtained through the internet.
The method for determining the power consumption provided by the embodiment obtains the target trip prediction model, determines at least one target trip path of the vehicle in the target time period by adopting the target trip prediction model, obtains the power consumption parameter of the vehicle, and determines the expected power consumption of the vehicle in the target time period based on the power consumption parameter and the at least one target trip path, so that the accuracy of estimating the power consumption of the vehicle can be improved.
Optionally, determining an expected power consumption of the vehicle in the target time period based on the power consumption parameter and the at least one target trip path includes: determining a target travel distance of the vehicle for traveling within a target time period based on at least one target travel path; and determining the expected power consumption by adopting the power consumption parameters and the target trip distance.
In this embodiment, a target travel distance of the vehicle in the target time period may be determined based on at least one target travel path of the vehicle in the target time period, and the expected power consumption of the vehicle in the target time period may be calculated by using the power consumption parameter of the vehicle and the target travel distance of the vehicle in the target time period.
For example, if the target trip prediction model predicts that the vehicle will respectively visit the point a and the point B from the current point in the target time period, it may be determined that the distance from the current point to the point a is "0-a", the distance from the point a to the current point is "a-0", the distance from the current point to the point B is "0-B", and finally, it may be determined that the target trip distance of the vehicle in the target time period is the sum of three items, that is, "0-a", and "0-B".
For another example, if the target trip prediction model predicts that the vehicle will sequentially visit the location C and the location D from the current location within the target time period, the distance "0-C" from the current location to the location C and the distance "C-D" from the location C to the location D may be determined, and finally the target trip distance of the vehicle within the target time period may be determined to be the sum of the two terms "0-C" and "C-D".
The embodiment can determine the distance to be traveled by the vehicle in the target time period based on all travel paths to be reached by the vehicle in the target time period and the sequence of reaching each travel path, and determine the expected power consumption of the vehicle in the target time period according to the power consumption parameter of the vehicle and the distance to be traveled by the vehicle in the target time period, so that the accuracy of power consumption prediction of the vehicle can be improved.
With further reference to FIG. 3, a flow 300 of another embodiment of a method for determining a power usage is illustrated. The flow 300 of the method for determining a power usage includes the steps of:
In this embodiment, the descriptions of step 301 and step 302 are the same as the descriptions of step 201 and step 202, and are not repeated here.
In this embodiment, a location attribute prediction model may be obtained, where the location prediction model includes a mapping relationship between feature information of a location and attribute information of the location. The feature information of the location may be an area feature of the location, a time length feature of the vehicle staying at the location, passenger flow information of the location obtained based on the navigation platform, and the like. The attribute information of the place may be information for characterizing a relationship between the place and a user to which the vehicle belongs (e.g., the place is the user's home, the place is the user's work place, etc.).
The location attribute prediction model may be trained based on the following method: acquiring characteristic information of a place indicated by the sample travel path and attribute information of the place indicated by the sample travel path; and training an initial location attribute prediction model by adopting the characteristic information and the attribute information of the location indicated by the sample travel path, and obtaining a trained location attribute prediction model.
And step 304, determining attribute information of the location indicated by the target travel path by using the characteristic information of the location indicated by the target travel path and the location attribute prediction model.
In this embodiment, the attribute information of the location indicated by the target travel path may be determined by using the feature information of the location indicated by the target travel path and the location attribute prediction model. That is, points (such as a departure point, a passing point, and a destination) indicated by the travel route are input to the point attribute prediction model, and the attribute information of each point output by the point attribute prediction model is acquired.
In this embodiment, for the attribute information of the location indicated by each of the at least one target travel path predicted by the target travel prediction model, the travel distance of the vehicle traveling the target travel path within the target time period may be determined.
For example, if the attribute information of the location indicated by the target travel path indicates that the target travel path is a travel path of a regular vehicle (for example, the attribute information of the location is a work location of the user or a home of the user), and the target time period is an early-peak time period of a working day, a historical driving record of the vehicle or a distance from the home to the work location in the setting of a map/navigation system of the user may be determined as a travel distance of the vehicle driving the target travel path in the target time period.
For another example, if the attribute information of the location indicated by the target travel path indicates that the destination in the target travel path is an educational location (if the attribute information indicates that the destination is a school), and the target time period is a late peak period of a weekday, the distance between the company of the user and the educational location may be determined as the travel distance of the vehicle to the target travel path in the target time period.
And step 306, determining the target travel distance by adopting the travel distance of each target travel path traveled by the vehicle.
In this embodiment, the sum of the travel distances of each target travel path traveled by the vehicle in the target time period may be determined as the target travel distance.
And 307, acquiring power consumption parameters of the vehicle, and determining expected power consumption by adopting the power consumption parameters and the target trip distance.
In this embodiment, the power consumption parameter of the vehicle may be obtained, and the power consumption of the vehicle in the target time period may be determined by using the power consumption parameter and the target trip distance of the vehicle in the target time period. It will be appreciated that since the amount of power used in the historical period of time is a known, determined value, the target period of time is typically a future period of time that has not yet arrived, the target trip distance is the distance the vehicle will travel in the future predicted based on the target trip prediction model, and therefore, the amount of power determined using the power consumption parameters and the target trip distance may be referred to as the expected amount of power used by the vehicle.
Compared with the embodiment described in fig. 2, the embodiment adds the steps of predicting the attribute information of the location indicated by the target travel path by using the location attribute prediction model, and determining the target travel distance corresponding to at least one target travel path based on the attribute information of the location, so that the efficiency and the accuracy of determining the target travel distance can be improved.
Optionally, determining a travel distance of the vehicle traveling along the target travel path based on the attribute information of the location indicated by the target travel path includes: in response to the fact that the attribute information representation place of the place indicated by the target travel path belongs to the first type of place, obtaining travel distance information of the vehicle travel target travel path; and determining the row distance based on the travel distance information.
In this embodiment, if attribute information of a location indicated by the target travel path is determined, the location indicated by the characteristic target travel path belongs to a first class of location, travel distance information of the vehicle traveling the target travel path may be acquired, and the travel distance of the target travel path is determined based on the travel distance information. The first type of location may be a location where vehicles regularly go and go, such as a work location of the user, a home of the user, a school of children and girls of the user, and the like. The travel distance information may be geographical positioning of a first type of location set in vehicle navigation of the user terminal device/vehicle, or time length information, historical average traveling speed information, and the like used when the vehicle travels to and fro the location regularly recorded in the vehicle navigation of the user terminal device/vehicle.
In this embodiment, if it is determined that the location indicated by the target travel path is represented by the attribute information of the location indicated by the target travel path and belongs to the first class of location, the travel distance of the vehicle traveling on the target travel path may be determined based on the travel distance information of the user traveling on the target travel path, and the accuracy of determining the travel distance of the vehicle is improved.
Optionally, determining a travel distance of the vehicle traveling along the target travel path based on the attribute information of the location indicated by the target travel path includes: in response to the fact that the attribute information representation place of the place indicated by the target travel path belongs to the second type place, obtaining travel distance probability distribution information of the vehicle travel target travel path; and determining the row distance based on the travel distance probability distribution information.
In this embodiment, if the attribute information of the location indicated by the target travel path is determined, and the location is represented to belong to the second type of location, the travel distance probability distribution information of the vehicle having traveled the target travel path in the historical time period may be obtained, and the travel distance traveled by the vehicle in the target time period may be determined based on the travel probability distribution information. The second type of location may be a location where the vehicle does not travel regularly, such as a mall, a supermarket, a restaurant, a tourist attraction, and the like.
The travel distance probability distribution information of the target travel path once traveled by the vehicle in the historical time period may be determined according to the probability of the vehicle traveling each path in the historical time period (e.g., eight nights each day, the morning of each weekday, or the last day of each month in the past year).
When determining the probability of the vehicle traveling along each path within the target time period based on the traveling probability distribution information, determining the path corresponding to the maximum probability as the target traveling path of the vehicle traveling within the target time period, and acquiring the traveling distance corresponding to the target traveling path; alternatively, the probabilities of the respective paths traveled by the vehicle in the target time period may be sequentially ranked from large to small, the paths corresponding to a preset number of probabilities are determined as the most likely target travel paths traveled by the vehicle in the target time period, and the average (or median, weighted average using the respective probabilities as weights, etc.) of the travel distances corresponding to the plurality of most likely target travel paths is determined as the travel distance traveled by the vehicle in the target time period.
In this embodiment, if it is determined that the location indicated by the target travel path is represented by the attribute information of the location indicated by the target travel path and belongs to the second type of location, the travel distance of the vehicle travel target travel path may be determined based on the travel distance probability distribution information of the vehicle historical travel target travel path, and the accuracy of determining the travel distance of the vehicle is improved.
With further reference to FIG. 4, a flow 400 of one embodiment of a method for training a model is shown. The process 400 of the method for training a model includes the steps of:
In the present embodiment, an executive (e.g., a server shown in fig. 1) of the method for training a model may acquire travel characteristics of a vehicle. The travel characteristics may include respective travel routes traveled by the user driving his vehicle at respective time periods, and may include a time distribution of departure time, a time distribution of arrival time, a time distribution of departure time, a time distribution of stay time at a destination, a frequency of traveling the travel route, or whether the user has performed a charging operation on the vehicle while traveling the travel route, a place where the charging operation occurred (such as a departure point, a transit point, a destination, etc.), a charging time or a charging amount, etc. when the user travels the travel route.
In this embodiment, the initial travel prediction model may be trained by using the travel paths of the vehicles in each time period and each time period to obtain a trained target travel model, so that the target travel model may predict the travel path of the vehicle to be traveled based on the known time period. The initial travel prediction model may be any type of deep learning model obtained based on local storage, cloud storage, or a user terminal.
The travel characteristics for training the initial travel prediction model may further include a travel time point of the vehicle at each travel route, and a departure point and an arrival point, so that the trained target travel prediction model may predict whether the vehicle will be used at the time point, and a departure point and a destination to be traveled, based on a given future time point.
The trip characteristics for training the initial trip prediction model may further include whether a charging operation may occur when the vehicle trips each trip path, so that the trained target trip model may predict a place where the charging operation is to occur of the vehicle based on the trip path to which the vehicle is about to travel, so as to facilitate planning of power supply, and determine a charging pile layout strategy based on statistics of the charging operations of a large number of vehicles.
The travel characteristics used for training the initial travel prediction model may further include a time length for the vehicle to stay at the destination when traveling each travel path, so that the trained target travel prediction model may predict whether the vehicle will leave from the destination based on a given future time point, so that the navigation platform or the traffic guidance center may predict a predicted pressure of a road network in a certain area, so as to perform path optimization and management and control in advance.
According to the method for training the model, the travel characteristics of the vehicle are obtained, wherein the travel characteristics include travel paths of the vehicle in various time periods, the time periods and the travel paths are adopted to train the initial travel prediction model, and the target travel prediction model is obtained, so that the travel path of the vehicle in a future time period can be predicted by the trained target travel prediction model based on the house type characteristics of the vehicle, and prediction of expected power consumption of the vehicle is facilitated.
Optionally, the method for training a model further comprises: acquiring characteristic information of a place indicated by a travel path and attribute information of the place indicated by the travel path; and training an initial location attribute prediction model by using the characteristic information and the attribute information, and obtaining a trained location attribute prediction model.
In this embodiment, the feature information of the location indicated by the travel path and the attribute information of the location indicated by the travel path may be obtained, and an initial location attribute prediction model may be trained by using the feature information of the location and the attribute information of the location to obtain a trained location attribute prediction model, which may predict the attribute information of the location based on the feature information of the location. The initial location attribute prediction model may be any type of deep learning model based on local storage, cloud storage, or user terminal acquisition.
The characteristic information of the location indicated by the travel route may be an area characteristic of the location, a time length characteristic of the vehicle staying at the location, passenger flow information of the location obtained based on the navigation platform, and the like.
The attribute information of the location indicated by the travel path may be information for characterizing a relationship between the location and a user to which the vehicle belongs, and the attribute information may be determined based on a label of the user or may be determined based on information acquired from a user navigation system. For example, the user may label a place as "home" or "company", or may extract a name of each user's commonly used place, which is acquired from the user navigation system, according to the geographical location of the place, as well as the name, as attribute information of the place.
It is understood that the feature information of the location is associated with the attribute information of the location, for example, if the vehicle stays at the location E for 8 pm to 8 pm every day, the attribute information of the location E is the home of the user. For another example, if the vehicle stays at the location F for 1 hour to 2 hours each time, the attribute information of the location F is a supermarket or a restaurant. Therefore, the location attribute prediction model trained based on the feature information of the location and the attribute information of the location can accurately predict the attribute information of the location to be predicted based on the feature information of the location to be predicted.
Optionally, obtaining travel characteristics of the vehicle includes: acquiring each travel path of a vehicle traveling in each time period in a preset historical time period; the method comprises the following steps of training an initial trip prediction model by adopting a time period and a trip path, and obtaining a target trip prediction model, wherein the method comprises the following steps: and establishing a travel path probability distribution model of the vehicle by adopting each time period and each travel path, and determining the travel path probability distribution model of the vehicle as a target travel prediction model.
In this embodiment, each travel path of the vehicle traveling in each time period within the preset historical time period may be acquired, and a travel path probability distribution model of the vehicle is established by using each time period and each travel path, where the travel path probability distribution model includes probability distribution of each travel path traveled by the vehicle in each time period. And then, determining the probability distribution model as a target trip prediction model.
For example, if the preset historical time period is within one year before the current time point, each time period is a time period in each hour per day (i.e., 6 o 'clock to 7 o' clock, 7 o 'clock to 8 o' clock, 8 o 'clock to 9 o' clock, etc.). According to the travel route of the vehicle in each time interval in each day in the past year, a travel route probability distribution model of the vehicle is established, the travel route probability distribution model comprises the probability distribution of each travel route of the vehicle in each time interval (for example, in the time interval from 8 to 9, the probability of the travel route of the vehicle from the home of the user to the work place of the user is 80%, the probability of the travel route of the vehicle from the home of the user to the market is 15%, and the like), and then the probability distribution model is determined as a target travel prediction model.
With further reference to fig. 5, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of an apparatus for determining a used amount of electricity, which corresponds to the method embodiments shown in fig. 2 and 3, and which may be applied in various electronic devices in particular.
As shown in fig. 5, the apparatus 500 for determining a used amount of electricity of the present embodiment includes: a first acquisition unit 501, a first prediction unit 502, and a determination unit 503. The method comprises the steps that a first obtaining unit is configured to obtain a target trip prediction model, wherein the target trip prediction model comprises a mapping relation between a trip time period of a vehicle and a trip path of the vehicle; the first prediction unit is configured to determine at least one target travel path of the vehicle in a target time period by adopting a target travel prediction model; the determining unit is configured to acquire the power consumption parameters of the vehicle and determine the expected power consumption of the vehicle in the target time period based on the power consumption parameters and the at least one target trip path.
In some embodiments, the determining unit comprises: the first determination module is configured to determine a target travel distance of the vehicle traveling within a target time period based on at least one target travel path; and the second determination module is configured to determine the expected electricity consumption by adopting the electricity consumption parameters and the target trip distance.
In some embodiments, the means for determining a power usage further comprises: a second acquisition unit configured to acquire a place attribute prediction model, wherein the place attribute prediction model includes a mapping relationship between feature information of a place and attribute information of the place; and the second prediction unit is configured to determine the attribute information of the place indicated by the target travel path by adopting the characteristic information of the place indicated by the target travel path and the place attribute prediction model. A first determination module comprising: a first determining sub-module configured to determine, for a location indicated by each of the at least one target travel path, a travel distance at which the vehicle travels the target travel path based on attribute information of the location indicated by the target travel path; and the second determining submodule is configured to determine the target travel distance by adopting the travel distance of each target travel path traveled by the vehicle.
In some embodiments, the first determination submodule includes: the first judgment module is configured to respond to the fact that the attribute information representation place of the place indicated by the target travel path belongs to a first type of place, and obtain travel distance information of the vehicle travel target travel path; a first distance determination module configured to determine a travel distance based on the travel distance information.
In some embodiments, the first determination submodule includes: the second judgment module is configured to respond to the fact that the attribute information representation place of the place indicated by the target travel path belongs to a second type of place, and obtain travel distance probability distribution information of the vehicle travel target travel path; a second distance determination module configured to determine a travel distance based on the travel distance probability distribution information.
The units in the apparatus 500 described above correspond to the steps in the method described with reference to fig. 2 and 3. Thus, the operations, features, and technical effects that may be achieved as described above with respect to the method for determining a power usage amount are equally applicable to the apparatus 500 and the units included therein, and will not be described in detail herein.
With further reference to fig. 6, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of an apparatus for training a model, which corresponds to the method embodiment shown in fig. 4, and which can be applied in various electronic devices.
As shown in fig. 6, the apparatus 600 for determining a used amount of electricity of the present embodiment includes: third acquisition unit 601, first training unit 602. The third obtaining unit is configured to obtain travel characteristics of the vehicle, wherein the travel characteristics include travel routes of the vehicle in various time periods; and the first training unit is configured to train an initial trip prediction model by adopting the time period and the trip path, and obtain a target trip prediction model.
In some embodiments, the means for training the model further comprises: a fourth acquisition unit configured to acquire feature information of a place indicated by the travel path and attribute information of the place indicated by the travel path; and the second training unit is configured to train the initial location attribute prediction model by adopting the characteristic information and the attribute information, and obtain a trained location attribute prediction model.
In some embodiments, the third obtaining unit includes: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is configured to acquire each travel path of a vehicle travelling in each time period in a preset historical time period; a first training unit comprising: the system comprises an establishing module and a model determining module, wherein the establishing module is configured to adopt each time period and each travel path to establish a travel path probability distribution model of the vehicle, and the model determining module is configured to determine the travel path probability distribution model of the vehicle as a target travel prediction model.
The units in the apparatus 600 described above correspond to the steps in the method described with reference to fig. 4. Thus, the operations, features, and technical effects that can be achieved as described above with respect to the method for determining a used amount of power are also applicable to the apparatus 600 and the units included therein, and will not be described again here.
There is also provided, in accordance with an embodiment of the present application, an electronic device, a readable storage medium, and a computer program product.
FIG. 7 illustrates a schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 7, the apparatus 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 707 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 707 such as a magnetic disk, an optical disk, or the like; and a communication unit 705 such as a network card, modem, wireless communication transceiver, etc. The communication unit 705 allows the device 700 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, may be executed sequentially, or may be executed in different orders, as long as the desired data of the technical solution disclosed in the present application can be realized, and the present disclosure is not limited thereto.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (19)
1. A method for determining a power usage, comprising:
acquiring a target trip prediction model, wherein the target trip prediction model comprises a mapping relation between a trip time period of a vehicle and a trip path of the vehicle;
determining at least one target travel path of the vehicle in a target time period by adopting the target travel prediction model;
acquiring a power consumption parameter of the vehicle, and determining expected power consumption of the vehicle in the target time period based on the power consumption parameter and the at least one target travel path.
2. The method of claim 1, wherein the determining an expected power usage of the vehicle over the target time period based on the power consumption parameter and the at least one target travel path comprises:
determining a target travel distance of the vehicle for traveling within the target time period based on the at least one target travel path;
and determining the expected electricity consumption by adopting the electricity consumption parameters and the target trip distance.
3. The method of claim 2, wherein the method further comprises:
acquiring a place attribute prediction model, wherein the place attribute prediction model comprises a mapping relation between feature information of a place and attribute information of the place;
and determining attribute information of the place indicated by the target travel path by adopting the characteristic information of the place indicated by the target travel path and the place attribute prediction model.
The determining a target travel distance of the vehicle within the target time period based on the at least one target travel path includes:
for a place indicated by each target travel path in the at least one target travel path, determining a travel distance of the vehicle driving the target travel path based on attribute information of the place indicated by the target travel path;
and determining the target travel distance by adopting the travel distance of each target travel path traveled by the vehicle.
4. The method of claim 3, wherein the determining the travel distance of the vehicle traveling the target travel path based on the attribute information of the location indicated by the target travel path comprises:
responding to attribute information of a place indicated by the target travel path, wherein the attribute information represents that the place belongs to a first class of places, and acquiring travel distance information of the vehicle for traveling the target travel path;
determining the travel distance based on the travel distance information.
5. The method of claim 3, wherein the determining the travel distance of the vehicle traveling the target travel path based on the attribute information of the location indicated by the target travel path comprises:
responding to attribute information of a place indicated by the target travel path, wherein the attribute information represents that the place belongs to a second type of place, and acquiring travel distance probability distribution information of the vehicle for traveling the target travel path;
and determining the travel distance based on the travel distance probability distribution information.
6. A method for training a model, comprising:
acquiring travel characteristics of a vehicle, wherein the travel characteristics comprise travel paths of the vehicle in various time periods;
and training an initial travel prediction model by adopting the time period and the travel path, and obtaining a target travel prediction model.
7. The method of claim 6, wherein the method further comprises:
acquiring feature information of a place indicated by the travel path and attribute information of the place indicated by the travel path;
and training an initial location attribute prediction model by adopting the characteristic information and the attribute information, and obtaining a trained location attribute prediction model.
8. The method of claim 6, wherein the obtaining travel characteristics of the vehicle comprises:
acquiring each travel path of the vehicle traveling in each time period in a preset historical time period;
the training of the initial travel prediction model by adopting the time period and the travel path to obtain the target travel prediction model comprises the following steps:
establishing a travel path probability distribution model of the vehicle by adopting the time periods and the travel paths,
and determining a travel path probability distribution model of the vehicle as the target travel prediction model.
9. An apparatus for determining a power usage, comprising:
a first obtaining unit configured to obtain a target trip prediction model, wherein the target trip prediction model includes a mapping relationship between a trip time period of a vehicle and a trip path of the vehicle;
a first prediction unit configured to determine at least one target travel path of the vehicle within a target time period by using the target travel prediction model;
the determining unit is configured to acquire a power consumption parameter of the vehicle and determine an expected power consumption of the vehicle in the target time period based on the power consumption parameter and the at least one target trip path.
10. The apparatus of claim 9, wherein the determining unit comprises:
a first determination module configured to determine a target travel distance for the vehicle to travel within the target time period based on the at least one target travel path;
a second determination module configured to determine the expected power usage using the power consumption parameter and the target trip distance.
11. The apparatus of claim 10, wherein the apparatus further comprises:
a second acquisition unit configured to acquire a place attribute prediction model, wherein the place attribute prediction model includes a mapping relationship between feature information of a place and attribute information of the place;
a second prediction unit configured to determine attribute information of the location indicated by the target travel path by using the feature information of the location indicated by the target travel path and the location attribute prediction model.
The first determining module includes:
a first determining sub-module, configured to determine, for a location indicated by each of the at least one target travel path, a travel distance for the vehicle to travel the target travel path based on attribute information of the location indicated by the target travel path;
a second determining submodule configured to determine the target travel distance by using the travel distance of the vehicle traveling on each target travel path.
12. The apparatus of claim 11, wherein the first determination submodule comprises:
the first judging module is configured to respond to the fact that the attribute information of the place indicated by the target travel path indicates that the place belongs to a first class of places, and obtain travel distance information of the vehicle for traveling the target travel path;
a first distance determination module configured to determine the travel distance based on the travel distance information.
13. The apparatus of claim 11, wherein the first determination submodule comprises:
the second judging module is configured to respond to the fact that the attribute information of the place indicated by the target travel path indicates that the place belongs to a second type of place, and obtain travel distance probability distribution information of the vehicle for traveling the target travel path;
a second distance determination module configured to determine the travel distance based on the travel distance probability distribution information.
14. An apparatus for training a model, comprising:
a third obtaining unit, configured to obtain travel characteristics of a vehicle, where the travel characteristics include respective travel routes traveled by the vehicle in respective time periods;
and the first training unit is configured to train an initial trip prediction model by adopting the time period and the trip path, and obtain a target trip prediction model.
15. The apparatus of claim 14, wherein the apparatus further comprises:
a fourth acquisition unit configured to acquire feature information of a place indicated by the travel path and attribute information of the place indicated by the travel path;
and the second training unit is configured to train an initial location attribute prediction model by adopting the characteristic information and the attribute information, and obtain a trained location attribute prediction model.
16. The apparatus of claim 14, wherein the third obtaining unit comprises:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is configured to acquire each travel path of the vehicle travelling in each time period in a preset historical time period;
the first training unit includes:
a building module configured to build a travel path probability distribution model of the vehicle using the respective time periods and the respective travel paths,
a model determination module configured to determine a travel path probability distribution model of the vehicle as the target travel prediction model.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5 or claims 6-8.
18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of claims 1-5 or claims 6-8.
19. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5 or claims 6-8.
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