CN116629425A

CN116629425A - Method and device for calculating vehicle energy consumption, computer readable medium and electronic equipment

Info

Publication number: CN116629425A
Application number: CN202310601246.3A
Authority: CN
Inventors: 姜正申
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2023-05-25
Filing date: 2023-05-25
Publication date: 2023-08-22

Abstract

The embodiment of the application can be applied to the technical fields of maps, traffic and the like, and particularly provides a method and a device for calculating vehicle energy consumption, a computer readable medium and electronic equipment. The method for calculating the vehicle energy consumption comprises the following steps: acquiring a planned route of a vehicle terminal; extracting each road section contained in the planned route, and acquiring road condition data and road attribute data of each road section; estimating the passing speed of the vehicle terminal on each road section according to the road condition data and the road attribute data of each road section to obtain the estimated passing speed corresponding to each road section; and calculating the vehicle energy consumption corresponding to the planned route according to the estimated passing speed corresponding to each road section. The technical scheme of the embodiment of the application can improve the accuracy and rationality of vehicle energy consumption estimation.

Description

Method and device for calculating vehicle energy consumption, computer readable medium and electronic equipment

Technical Field

The present application relates to the field of computers and communication technologies, and in particular, to a method and apparatus for calculating vehicle energy consumption, a computer readable medium, and an electronic device.

Background

The running of the vehicle depends on energy sources, the traditional fuel oil vehicle uses gasoline, diesel oil and the like, and the new energy vehicle uses electric energy, hydrogen energy and the like. Although the types of energy sources are various, the consumption of energy can be measured uniformly in joules (or kilojoules, kilowatt-hours, etc.), or in percentages, for example, by running 100km to consume 10% of the energy of an automobile. Since the capacity of the fuel tank, battery, etc. of the vehicle is limited, it is necessary to supplement energy after traveling a certain distance. And the consumed energy sources are different for different planned routes, so that the energy consumption of the vehicle is estimated reasonably and accurately, and the energy consumption of the different planned routes is needed to be solved in order to prompt a driver to supplement the energy sources or provide the energy consumption of the different planned routes for the driver.

Disclosure of Invention

The embodiment of the application provides a method and a device for calculating vehicle energy consumption, a computer readable medium and electronic equipment, which can improve the accuracy and the rationality of vehicle energy consumption estimation.

Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application.

According to an aspect of the embodiment of the present application, there is provided a method for calculating energy consumption of a vehicle, including: acquiring a planned route of a vehicle terminal; extracting each road section contained in the planned route, and acquiring road condition data and road attribute data of each road section; estimating the passing speed of the vehicle terminal on each road section according to the road condition data and the road attribute data of each road section to obtain the estimated passing speed corresponding to each road section; and calculating the vehicle energy consumption corresponding to the planned route according to the estimated passing speed corresponding to each road section.

According to an aspect of an embodiment of the present application, there is provided a vehicle energy consumption calculation apparatus including: an acquisition unit configured to acquire a planned route of the vehicle terminal; the extraction unit is configured to extract each road section contained in the planned route and acquire road condition data and road attribute data of each road section; the estimating unit is configured to estimate the passing speed of the vehicle terminal on each road section according to the road condition data and the road attribute data of each road section to obtain the estimated passing speed corresponding to each road section; and the processing unit is configured to calculate the vehicle energy consumption corresponding to the planned route according to the estimated passing speed corresponding to each road section.

In some embodiments of the application, based on the foregoing, the processing unit is configured to: acquiring an energy consumption model corresponding to the vehicle terminal; calculating vehicle energy consumption corresponding to each road section through the energy consumption model based on the estimated passing speed corresponding to each road section; and calculating the vehicle energy consumption corresponding to the planned route according to the vehicle energy consumption corresponding to each road section.

In some embodiments of the present application, based on the foregoing solution, the processing unit calculates, based on the estimated traffic speed corresponding to each road segment, vehicle energy consumption corresponding to each road segment through the energy consumption model, including: generating energy consumption model input parameters corresponding to each road section based on the estimated passing speeds corresponding to each road section, wherein the energy consumption model input parameters at least comprise the estimated passing speeds; and inputting the energy consumption model input parameters corresponding to each road section into the energy consumption model to obtain the vehicle energy consumption corresponding to each road section output by the energy consumption model.

In some embodiments of the present application, based on the foregoing, the energy consumption model input parameter further comprises at least one of the following information:

the vehicle terminal obtains weather conditions when the vehicle approaches each road section, gradient of each road section, air pressure of each road section and altitude of each road section.

In some embodiments of the application, based on the foregoing, the processing unit calculates weather conditions for the vehicle terminal when routed to the respective road segments by: estimating the time of the vehicle terminal passing through each road section according to the estimated passing speed corresponding to each road section; and acquiring corresponding weather forecast data as weather conditions when the vehicle terminal approaches each road section according to the time when the vehicle terminal approaches each road section.

In some embodiments of the present application, based on the foregoing scheme, the estimating unit is configured to: generating input parameters of a speed estimation model according to the road condition data and the road attribute data of each road section; and inputting the input parameters of the speed estimation model into a pre-trained speed estimation model to obtain the estimated passing speeds corresponding to the road sections output by the speed estimation model.

In some embodiments of the present application, based on the foregoing, the vehicle energy consumption calculation apparatus further includes a training unit configured to train the speed estimation model by: generating training data of a speed estimation model according to a historical travel route of the vehicle terminal; inputting the training data into the speed estimation model to obtain estimated passing speeds corresponding to the road section samples output by the speed estimation model; calculating estimated passing time corresponding to the historical travel route according to the estimated passing speed corresponding to each road section sample; and calculating a loss value of the speed estimation model according to the actual passing time corresponding to the historical travel route and the estimated passing time corresponding to the historical travel route, and adjusting model parameters of the speed estimation model according to the loss value.

In some embodiments of the present application, based on the foregoing solution, the training unit generates training data of a speed estimation model according to a historical travel route of a vehicle terminal, including: acquiring a historical travel route of the vehicle terminal and a departure time of the vehicle terminal on the historical travel route; extracting road sections contained in the historical travel route to obtain a plurality of road section samples; acquiring road attribute data of each road section sample in the plurality of road section samples and road condition data of each road section sample at the departure time; and generating training data of the speed estimation model according to the road attribute data of each road section sample and the road condition data of each road section sample.

In some embodiments of the present application, based on the foregoing solution, the training unit calculates, according to the estimated traffic speeds corresponding to the road segment samples, estimated traffic times corresponding to the historical travel route, including: calculating estimated traffic time corresponding to each road section sample according to the estimated traffic speed corresponding to each road section sample and the mileage of each road section sample; and calculating the estimated transit time corresponding to the historical travel route according to the estimated transit time corresponding to each road section sample.

In some embodiments of the application, based on the foregoing, the training unit is further configured to: acquiring departure time and arrival time of the vehicle terminal on the historical travel route; and calculating the actual passing time corresponding to the historical travel route according to the arrival time and the departure time.

In some embodiments of the application, based on the foregoing, the processing unit is further configured to perform at least one of the following:

acquiring vehicle energy consumption corresponding to each of a plurality of planned routes, and recommending an energy-saving route to the vehicle terminal according to the vehicle energy consumption corresponding to each of the plurality of planned routes;

Recommending an energy supply station of the route of the planned route to the vehicle terminal according to the vehicle energy consumption corresponding to the planned route;

and acquiring the vehicle energy consumption corresponding to each of the plurality of road sections, and performing route planning processing according to the vehicle energy consumption corresponding to each of the plurality of road sections.

In some embodiments of the application, based on the foregoing, the road attribute data includes at least one of: road grade, lane number, speed limit data; the road condition data comprises at least one of the following: traffic flow, traffic flow speed, congestion conditions.

According to an aspect of the embodiments of the present application, there is provided a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements a method of calculating vehicle energy consumption as described in the above embodiments.

According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: one or more processors; and storage means for storing one or more computer programs which, when executed by the one or more processors, cause the electronic device to implement the method of calculating vehicle energy consumption as described in the above embodiments.

According to an aspect of an embodiment of the present application, there is provided a computer program product comprising a computer program stored in a computer readable storage medium. The processor of the electronic device reads and executes the computer program from the computer-readable storage medium, so that the electronic device performs the vehicle energy consumption calculation method provided in the above-described various alternative embodiments.

According to the technical scheme provided by the embodiments of the application, the road condition data and the road attribute data of each road section are obtained by extracting each road section included in the planned route, then the traffic speed of the vehicle terminal on each road section is estimated according to the road condition data and the road attribute data of each road section, the estimated traffic speed corresponding to each road section is obtained, and the vehicle energy consumption corresponding to the planned route is calculated according to the estimated traffic speed corresponding to each road section, so that the estimation of the traffic speed can be respectively carried out according to the actual conditions (namely the road condition data and the road attribute data) of each road section in the planned route, and the vehicle energy consumption corresponding to the planned route can be calculated according to the estimated traffic speed corresponding to each road section, thereby being beneficial to improving the accuracy and the rationality of the vehicle energy consumption estimation.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.

Drawings

FIG. 1 shows a schematic diagram of an exemplary system architecture to which the technical solution of an embodiment of the application may be applied;

FIG. 2 illustrates a flow chart of a method of calculating vehicle energy consumption according to one embodiment of the application;

FIG. 3 illustrates a flow chart of a training method of a velocity estimation model according to one embodiment of the application;

FIG. 4 illustrates a flow chart of a training method of a velocity estimation model according to one embodiment of the application;

FIG. 5 shows a schematic diagram of a system architecture implementing the technical solution of an embodiment of the present application, according to an embodiment of the present application;

FIG. 6 is a schematic diagram of an implementation process of the technical solution of the embodiment of the present application according to an embodiment of the present application;

FIG. 7 illustrates a training process diagram of a velocity estimation model according to one embodiment of the application;

FIG. 8 illustrates a block diagram of a computing device for vehicle energy consumption in accordance with one embodiment of the application;

fig. 9 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.

Detailed Description

Example embodiments are now described in a more complete manner with reference being made to the figures. However, the illustrated embodiments may be embodied in various forms and should not be construed as limited to only these examples; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.

Furthermore, the described features, structures, or characteristics of the application may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the application. However, it will be recognized by one skilled in the art that the present inventive arrangements may be practiced without all of the specific details of the embodiments, that one or more specific details may be omitted, or that other methods, elements, devices, steps, etc. may be used.

The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.

It should be noted that: references herein to "a plurality" means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

It will be appreciated that in the specific embodiment of the present application, related data such as planned routes, road condition data, road attribute data, etc. of the vehicle terminals are related, and when the above embodiments of the present application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data are required to comply with related laws and regulations and standards of related countries and regions.

In the running process of the vehicle, energy sources are needed to be relied on, for example, gasoline, diesel oil and the like are used by a fuel vehicle, and electric energy, hydrogen energy and the like are used by a new energy vehicle. Although the types of energy sources are various, the consumption of energy can be measured uniformly in joules (or kilojoules, kilowatt-hours, etc.), or in percentages, for example, by running 100km to consume 10% of the energy of an automobile. Since the capacity of the fuel tank, battery, etc. of the vehicle is limited, it is necessary to supplement energy after traveling a certain distance. And the consumed energy sources are different for different planned routes, so that it is very important to reasonably and accurately estimate the energy consumption of the vehicle so as to prompt a driver to supplement the energy sources in time or provide the energy consumption of different planned routes for the driver.

In a method for calculating the energy consumption of a vehicle, the global average speed of the vehicle in the route can be converted by using the global ETA (Estimated Time of Arrival) of the route and the energy consumption of the vehicle can be estimated according to an energy consumption model of the vehicle. The disadvantage of this solution is that it is too rough and the estimated vehicle energy consumption error is large for long distance or unstable vehicle speed in transit.

In another method for calculating the energy consumption of the vehicle, the entire ETA may be distributed to each road section in the route according to a certain rule, and a common distribution method includes the distribution according to mileage (such as the distribution according to the mileage of each road section and the entire mileage of the route in proportion), and the distribution according to real-time speed (such as the distribution according to the real-time speed obtained by statistics of each road section in proportion). The mileage allocation is equivalent to considering that the vehicle is running at a constant speed in a planned route, but the whole-course constant-speed running is difficult to occur in actual conditions, and further, the estimated error of the energy consumption of the vehicle is larger. The real-time speed distribution also causes lower estimated vehicle energy consumption accuracy, because the time for smooth road section distribution is possibly too short and the time for congestion road section distribution is possibly too long after the real-time speed distribution, thereby causing inaccurate estimation of the whole energy consumption of the vehicle.

Based on the above problems, the technical solution of the embodiment of the present application provides a new calculation solution for vehicle energy consumption, which can respectively estimate the traffic speed according to the actual conditions (such as road condition data and road attribute data) of each road section in the planned route, and further calculate the vehicle energy consumption corresponding to the planned route according to the estimated traffic speed corresponding to each road section, thereby being beneficial to improving the accuracy and rationality of vehicle energy consumption estimation.

Specifically, as shown in fig. 1, in the route planning scenario, after the vehicle terminal 101 selects the start point position and the end point position on the electronic map, the server 102 may plan a route (referred to as a planned route) from the start point position to the end point position according to the start point position and the end point position, specifically, the planned route including Link1, link2, link3, and Link4 as shown in fig. 1. Link (road segment) is the smallest data unit describing a road, is a set of structured data, and includes, but is not limited to, attributes of Link (Link) such as length, width, road class, etc., and each road segment has a length varying from several tens of meters to several kilometers, and is assigned a globally unique id. Thus, a route in a map is a sequence of all segments in the route.

After obtaining the planned route, server 102 may estimate the vehicle energy consumption corresponding to the planned route. Specifically, the server 102 may extract each road segment (such as Link1, link2, link3, and Link4 shown in fig. 1) included in the planned route, obtain road condition data (such as traffic flow, traffic flow speed, congestion status, etc.) and road attribute data (such as road class, number of lanes, speed limit data, etc.) of each road segment, and then estimate the traffic speed of the vehicle terminal on each road segment according to the road condition data and the road attribute data of each road segment, to obtain the estimated traffic speed corresponding to each road segment, and further calculate the vehicle energy consumption corresponding to the planned route according to the estimated traffic speed corresponding to each road segment. Therefore, the technical scheme of the embodiment of the application can divide the speed estimation into each road section, so that the more accurate estimated passing speed of each road section can be ensured, and the vehicle energy consumption corresponding to the planned route can be calculated through the estimated passing speed corresponding to each road section, thereby being beneficial to improving the accuracy and rationality of the vehicle energy consumption estimation.

Alternatively, the vehicle terminal 101 may have a similar function to the server 102, so that the vehicle energy consumption corresponding to the planned route may be estimated. For example, after obtaining a planned route (the planned route may be planned by the server 102 or may be planned by the vehicle terminal 101), the vehicle terminal 101 may extract each road segment (such as Link1, link2, link3, and Link4 shown in fig. 1) included in the planned route, obtain road condition data (such as traffic flow, traffic flow speed, congestion status, etc.) and road attribute data (such as road class, lane number, speed limit data, etc.) of each road segment, and then estimate a traffic speed of the vehicle terminal on each road segment according to the road condition data and the road attribute data of each road segment, obtain an estimated traffic speed corresponding to each road segment, and further calculate vehicle energy consumption corresponding to the planned route according to the estimated traffic speed corresponding to each road segment.

In some optional embodiments, when estimating the traffic speed of the vehicle terminal on each road section, input parameters of a speed estimation model can be generated according to road condition data and road attribute data of each road section, and then the input parameters of the speed estimation model are input into a pre-trained speed estimation model to obtain the estimated traffic speed corresponding to each road section output by the speed estimation model.

Optionally, the speed estimation model is a machine learning model trained by a machine learning algorithm in artificial intelligence (Artificial Intelligence, AI for short). Wherein. Artificial intelligence is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and expand human intelligence, sense the environment, acquire knowledge and use the knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning, automatic driving, intelligent traffic and other directions.

Machine Learning (ML) is a multi-domain interdisciplinary, and involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and the like. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. Machine learning is the core of artificial intelligence, a fundamental approach to letting computers have intelligence, which is applied throughout various areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.

It should be noted that, the server 102 may be an independent physical server, or may be a server cluster or a distributed system formed by at least two physical servers, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, a content distribution network (Content Delivery Network, CDN), and basic cloud computing services such as big data and an artificial intelligence platform. The vehicle terminal 101 may specifically refer to a smart phone, a smart speaker, a screen speaker, a smart watch, etc. with an in-vehicle function, but is not limited thereto, and for example, the vehicle terminal 101 may be replaced by a mobile terminal such as an aircraft. The respective vehicle terminals and servers may be directly or indirectly connected through wired or wireless communication, and the number of the vehicle terminals and servers may be one or at least two, which is not limited herein.

The implementation details of the technical scheme of the embodiment of the application are described in detail below:

fig. 2 shows a flowchart of a method for calculating vehicle energy consumption according to an embodiment of the present application, which may be performed by a server, by a terminal device (e.g., the vehicle terminal 101 shown in fig. 1), or by both the server and the terminal device. Referring to fig. 2, the method for calculating the vehicle energy consumption at least includes steps S210 to S240, and is described in detail as follows:

in step S210, a planned route of the vehicle terminal is acquired.

In some alternative embodiments, the planned route of the vehicle terminal may be a route from the start position to the end position planned according to the start position and the end position after the start position and the end position are selected on the electronic map. Alternatively, the planned route of the vehicle terminal may be a route selected on the electronic map. Alternatively, the obtained planned route of the vehicle end may be one or more.

In step S220, each road segment included in the planned route is extracted, and road condition data and road attribute data of each road segment are obtained.

In the embodiment of the present application, since the planned route is formed by each road segment, for example, in fig. 1, the planned route includes Link1, link2, link3, and Link4, and thus each road segment included in the planned route can be extracted.

In some alternative embodiments, the road attribute data for each road segment includes at least one of: road class, number of lanes, speed limit data, etc. Alternatively, the road grade may be, for example, expressways, national roads, county roads, rural roads, or the like; the number of lanes may be the number of lanes contained in the road section, such as 2 vehicles, 4 lanes, etc.; the speed limit data may be the highest speed limit, the lowest speed limit, etc., such as the highest speed limit 80Km/h, etc.

In some alternative embodiments, the road condition data for each road segment includes at least one of: traffic flow, traffic flow speed, congestion conditions. Alternatively, the vehicle flow rate may be the number of vehicles passing through each road section per unit time; the traffic speed may be an average speed of a vehicle passing through each road section, or the like; the congestion condition may refer to whether each road segment is in an unblocked state, a jogged state, a congested state, or the like.

In some alternative embodiments, road condition data of each Road section may be reported by a vehicle running on each Road section or may be collected by a Road Side Unit (RSU), a camera, etc. and then reported to a server, etc. The road attribute data for each road segment may be obtained from a road database.

In step S230, the traffic speed of the vehicle terminal on each road segment is estimated according to the road condition data and the road attribute data of each road segment, so as to obtain the estimated traffic speed corresponding to each road segment.

In some alternative embodiments, the process of estimating the traffic speed of the vehicle terminal on each road segment according to the road condition data and the road attribute data of each road segment may be: generating input parameters of a speed estimation model according to road condition data and road attribute data of each road section; and then inputting the input parameters of the speed estimation model into the pre-trained speed estimation model to obtain the estimated passing speed corresponding to each road section output by the speed estimation model.

In some alternative embodiments, the velocity estimation model may employ a CNN (Convolutional Neural Network ) model, RNN (Recurrent Neural Network, recurrent neural network) model, a transducer model, or the like. The training process of the velocity estimation model is shown in fig. 3, and will be described in detail below.

In step S240, the vehicle energy consumption corresponding to the planned route is calculated according to the estimated traffic speed corresponding to each road segment.

In some alternative embodiments, according to the estimated traffic speed corresponding to each road segment, the vehicle energy consumption corresponding to the planned route may be calculated as follows: and obtaining an energy consumption model corresponding to the vehicle terminal, and then calculating the vehicle energy consumption corresponding to each road section through the energy consumption model based on the estimated passing speed corresponding to each road section so as to calculate the vehicle energy consumption corresponding to the planned route according to the vehicle energy consumption corresponding to each road section.

Optionally, after the vehicle energy consumption corresponding to each road section is calculated, the vehicle energy consumption corresponding to each road section may be added to obtain the vehicle energy consumption corresponding to the planned route.

It should be noted that the energy consumption models of different vehicle types may be different. Taking a pure electric vehicle in a new energy automobile as an example, the vehicle runs for 1km on a flat road at a speed of 40km/h, and the electric energy consumed by vehicles of different brands and models is different. This is determined by the manufacturer's technical route, product design, and technology level. In general, for a fixed model of vehicle, an energy consumption model can be given, which is typically a function of vehicle speed, grade, temperature, etc., the unit of function result typically being kilowatt-hour/kilometer. Because the energy consumption models of different models of vehicles are different, the energy consumption models can be obtained from the manufacturer end of the vehicles.

In some optional embodiments, when the estimated traffic speed corresponding to each road section is based on the estimated traffic speed corresponding to each road section, the energy consumption time of the vehicle corresponding to each road section is calculated through the energy consumption model, and the energy consumption model input parameter corresponding to each road section can be generated based on the estimated traffic speed corresponding to each road section, wherein the energy consumption model input parameter at least comprises the estimated traffic speed, and then the energy consumption model input parameter corresponding to each road section is input into the energy consumption model to obtain the energy consumption of the vehicle corresponding to each road section output by the energy consumption model.

In some alternative embodiments, the energy consumption model input parameters further comprise at least one of the following information: the weather conditions when the vehicle terminal approaches each road section, the gradient of each road section, the air pressure of each road section and the elevation of each road section.

In some alternative embodiments, the weather conditions of the vehicle terminal as it approaches each road segment may be obtained by: and according to the estimated passing speed corresponding to each road section, estimating the time of the vehicle terminal passing through each road section, and then obtaining corresponding weather forecast data as the weather condition of the vehicle terminal passing through each road section according to the time of the vehicle terminal passing through each road section. And the gradient of each road segment, the air pressure of each road segment and the altitude of each road segment can be obtained from the geographic information system.

It should be noted that, the technical solution of the embodiment shown in fig. 2 may be applied in the route planning field, for example, after the driver sets the starting position and the ending position on the electronic map, a plurality of planned routes between the starting position and the ending position may be planned, then the vehicle energy consumption corresponding to the plurality of planned routes may be calculated by the technical solution of the embodiment of the present application, and then the energy-saving route may be recommended to the vehicle terminal according to the vehicle energy consumption corresponding to the plurality of planned routes. Alternatively, the recommended energy-saving route to the vehicle terminal may be the first three planned routes with the least energy consumption of the vehicle to the vehicle terminal of the driver, or the multiple planned routes are displayed to the driver through the vehicle terminal in order of low-to-high energy consumption of the vehicle, so as to facilitate comprehensive consideration of the driver.

In some alternative embodiments, the technical solution of the embodiment shown in fig. 2 may be applied to a recommended scenario of an energy supply station, for example, after a driver sets a start position and an end position on an electronic map, a planned route between the start position and the end position may be planned, and then, vehicle energy consumption of each road section in the planned route may be calculated by using the technical solution of the embodiment of the present application, and further, the energy supply station of the planned route path may be recommended to a vehicle terminal according to vehicle energy consumption corresponding to the planned route on the basis of referring to vehicle energy consumption of each road section. For example, in the embodiment shown in fig. 1, it is assumed that it is determined that the vehicle terminal cannot travel to the end position without supplementing energy according to the vehicle energy consumption corresponding to the planned route, but at least Link3 may be completed, then the energy replenishment site on Link4 may be recommended to the vehicle terminal, or the energy replenishment site on Link3 may be recommended to the vehicle terminal, or the like. Optionally, the energy supply station may recommend according to the energy type of the vehicle terminal, and if the energy type of the vehicle terminal is a fuel vehicle, the energy supply station is a gas station; if the energy type of the vehicle terminal is a pure electric vehicle, the energy supply station is a charging station; if the energy type of the vehicle terminal is a hybrid vehicle, the energy replenishment sites are gas stations and charging stations.

In some alternative embodiments, after the vehicle energy consumption corresponding to each of the plurality of road segments on the electronic map is calculated, the technical solution of the embodiment shown in fig. 2 may also perform route planning processing according to the vehicle energy consumption corresponding to each of the plurality of road segments. For example, a setting of route preference may be provided to the driver, and if the driver selects that the route preference is energy-saving, the most energy-saving route may be planned and recommended to the driver according to the energy consumption of the vehicle corresponding to each of the plurality of road segments on the electronic map.

In some alternative embodiments, the velocity estimation model in the foregoing embodiments may be trained by the flow shown in fig. 3, specifically including the following steps S310 to S340, which are described in detail below:

in step S310, training data of a speed estimation model is generated according to a historical travel route of the vehicle terminal.

In some alternative embodiments, the process of generating training data of the speed estimation model according to the historical travel route of the vehicle terminal may be: the method comprises the steps of obtaining a historical travel route of a vehicle terminal and a departure time of the vehicle terminal on the historical travel route, then extracting road sections contained in the historical travel route to obtain a plurality of road section samples, obtaining road attribute data of each road section sample in the plurality of road section samples and road condition data of each road section sample at the departure time, and further generating training data of a speed estimation model according to the road attribute data of each road section sample and the road condition data of each road section sample.

Alternatively, the historical travel route of the vehicle terminal may be a travel route that is historically planned by the vehicle terminal; the departure time of the vehicle terminal on the historical travel route can be obtained from the historical data record. The road attribute data of each road segment sample may also include at least one of: road class, number of lanes, speed limit data, etc. It should be noted that: the road attribute data used by the road segment sample should be consistent with the road attribute data used by the road segment when the vehicle energy consumption is estimated actually.

Optionally, the road condition data of each road section sample at the departure time may also include at least one of the following: traffic flow, traffic flow speed, congestion conditions. Similarly, the road condition data used by the road segment sample should be consistent with the road condition data used by the road segment when the vehicle energy consumption is estimated.

It should be noted that: the training data of the generated speed estimation model should be consistent with the input parameters of the speed estimation model when the vehicle energy consumption estimation is actually performed.

In step S320, training data is input to the speed estimation model, so as to obtain estimated traffic speeds corresponding to the road section samples output by the speed estimation model.

According to the embodiment of the application, the passing speed is estimated for each road section, so that the passing speed can be estimated accurately for each road section, and more accurate vehicle energy consumption can be ensured through an energy consumption model.

In step S330, according to the estimated traffic speed corresponding to each road segment sample, the estimated traffic time corresponding to the historical travel route is calculated.

In some optional embodiments, according to the estimated traffic speed corresponding to each road segment sample, the process of calculating the estimated traffic time corresponding to the historical travel route may be: and calculating the estimated transit time corresponding to each road section sample according to the estimated transit speed corresponding to each road section sample and the mileage of each road section sample, and then calculating the estimated transit time corresponding to the historical travel route according to the estimated transit time corresponding to each road section sample.

Optionally, when calculating the estimated transit time corresponding to each road section sample, the mileage of each road section sample may be divided by the estimated transit speed corresponding to each road section sample, and the obtained value is used as the estimated transit time corresponding to each road section sample. After obtaining the estimated transit time corresponding to each road section sample, the estimated transit time corresponding to each road section sample can be summed to obtain the estimated transit time corresponding to the historical travel route.

In step S340, a loss value of the speed estimation model is calculated according to the actual transit time corresponding to the historical travel route and the estimated transit time corresponding to the historical travel route, and model parameters of the speed estimation model are adjusted according to the loss value.

In some alternative embodiments, the actual transit time of the historical travel route may be calculated according to the departure time and the arrival time of the historical travel route, specifically, a time difference between the arrival time and the departure time may be calculated, and the obtained time difference is taken as the actual transit time corresponding to the historical travel route.

Optionally, when calculating the loss value of the speed estimation model according to the actual passing time corresponding to the historical travel route and the estimated passing time corresponding to the historical travel route, the loss value of the speed estimation model may be calculated in various manners, for example, an average absolute percentage error, a mean square error, an absolute value of a difference value, and the like of the actual passing time and the estimated passing time may be used to calculate the loss value of the speed estimation model.

Also shown in fig. 4 is a training process of the velocity estimation model, specifically comprising the steps of:

in step S410, a historical travel route of the vehicle terminal and a departure time of the vehicle terminal on the historical travel route are obtained.

Alternatively, the historical travel route of the vehicle terminal may be a travel route that is historically planned by the vehicle terminal; the departure time of the vehicle terminal on the historical travel route can be obtained from the historical data record.

In step S420, the road segments included in the historical travel route are extracted to obtain a plurality of road segment samples, and road attribute data of each road segment sample in the plurality of road segment samples and road condition data of each road segment sample at the departure time are obtained.

Optionally, the road attribute data of each road segment sample may also include at least one of the following: road class, number of lanes, speed limit data, etc. It should be noted that: the road attribute data used by the road segment sample should be consistent with the road attribute data used by the road segment when the vehicle energy consumption is estimated actually.

In step S430, training data of the speed estimation model is generated according to the road attribute data of each road segment sample and the road condition data of each road segment sample.

In step S440, the training data is input to the speed estimation model, so as to obtain estimated traffic speeds corresponding to the road segment samples output by the speed estimation model.

In step S450, according to the estimated traffic speed corresponding to each road segment sample, the estimated traffic time corresponding to the historical travel route is calculated.

In step S460, a loss value of the speed estimation model is calculated according to the actual passing time corresponding to the historical travel route and the estimated passing time corresponding to the historical travel route, and model parameters of the speed estimation model are adjusted according to the loss value.

In the above embodiment, the output target of the speed estimation model is the estimated traffic speed, so that the problem of road section level speed estimation can be solved end to end, the loss value of the speed estimation model is calculated through the actual traffic time and the estimated traffic time, and the model parameters of the speed estimation model are reversely adjusted accordingly, so that the training of the speed estimation model can be realized only through data (such as road attribute data, road condition data, departure time, arrival time and the like) which are easy to obtain without collecting the actual energy consumption data of the vehicle terminal, and the complexity of model training is reduced.

Therefore, the technical scheme of the embodiment of the application mainly provides a new calculation scheme of the vehicle energy consumption aiming at the problem of inaccurate estimation of the vehicle energy consumption, and the estimation accuracy of the vehicle energy consumption is improved by taking the road section level speed as a model target to perform end-to-end model learning. Specifically, as shown in fig. 5, the system implementing the technical solution of the embodiment of the present application mainly includes five main modules, namely, a road segment feature and ATA (Actual Time of Arrival, actual arrival time) extraction module 502, a road segment speed estimation model training module 504, a speed estimation model reasoning module 506, a weather and road attribute query module 508, and a vehicle energy consumption calculation module 510. The implementation process of the whole scheme is shown in fig. 6: relevant data (such as road attribute data, road condition data and the like) of each road section (road section 1, road section 2, … … and road section N) are input into a depth model (namely a speed pre-estimation model) from sequence to sequence, the pre-estimated passing speeds (namely speed 1 corresponding to the road section 1, speed 2 corresponding to the road section 2, … … corresponding to the road section N) corresponding to each road section output by the model are obtained, and then the vehicle energy consumption (namely energy consumption 1 corresponding to the road section 1, energy consumption 2 corresponding to the road section 2, … … corresponding to the road section N) corresponding to each road section is obtained by combining parameters such as weather, road attribute and pre-estimated passing speeds corresponding to each road section by combining an energy consumption model of the vehicle, and further the vehicle energy consumption corresponding to the whole route is obtained by combining the vehicle energy consumption corresponding to each road section.

The following describes the processing procedures of the above five modules respectively:

in one embodiment of the present application, the link feature and ATA extraction module 502 mainly extracts the road condition data such as the real-time road condition status, the real-time traffic speed, etc. of each link, and the road attributes based on the road class, the number of lanes, the speed limit, etc. of each link, which are included in the historical route (such as the historical planned route, the driving route, etc.), according to the historical data. In addition, the actual travel time ATA of the route needs to be calculated from the departure time and arrival time of the vehicle terminal for use in model training.

In one embodiment of the present application, the model used in the link speed estimation model training module 504 may be any deep learning model capable of performing sequence-to-sequence tasks, such as the CNN, RNN, transformer model, and the like. Taking the CNN model as an example, as shown in fig. 7, the speed estimation model performs convolution operation (the triangle symbol shown in fig. 7 represents the convolution process) on the characteristic data (i.e., the road condition data, the road attribute data, etc. in the foregoing embodiment) of each road segment (i.e., the road segment 1, the road segment 2, the road segment … …, the road segment N), and outputs an estimated traffic speed (i.e., the speed 1, the speed 2, the speed … …, the speed N) for each road segment after a plurality of layers of convolutions. After the speed estimation model outputs the estimated traffic speed corresponding to each road section, the estimated traffic time (namely ETA_1, ETA_2, … … and ETA_N) of each road section can be calculated according to the mileage of each road section, and then ETA of all road sections is accumulated to obtain the whole ETA. After the whole ETA is obtained, the real passing time ATA is compared with the real passing time ATA, a loss value is calculated according to a set loss function, and then the neural network is trained through back propagation.

Alternatively, the loss function may have a number of different choices, such as MAPE (Mean Absolute Percentage Error ), MSE (Mean Square Error), etc. Wherein, the liquid crystal display device comprises a liquid crystal display device,MSE＝(ETA-ATA) ² ETA in both formulas refers to global ETA; ATA refers to global ATA. Alternatively, in other embodiments of the present application, the loss function may be the absolute value of the difference between the full range ETA and the full range ATA.

In one embodiment of the present application, the speed estimation model inference module 506 deploys the speed estimation model to the application environment after training to obtain the speed estimation model, so as to estimate the speed of the road segment on the route. In the practical application environment, the input of the speed estimation model is consistent with the input in the training process, namely, each road section id contained in the route is input; real-time road condition state, real-time traffic speed and other road condition data of each road section at the departure time, and road attributes of the basis such as road grade, lane number, speed limit and the like of each road section. After these features are input into the trained speed estimation model, the estimated traffic speed of each road segment output by the speed estimation model can be obtained and then provided to the vehicle energy consumption calculation module 510.

In one embodiment of the present application, the weather and road attribute query module 508 queries the weather and road attribute data of the route and route region, because the energy consumption model of the vehicle often relies on information such as vehicle speed, weather, gradient, etc. as input, while the speed estimation model in the embodiment of the present application provides vehicle speed information, other information such as weather, gradient, etc. needs to be additionally acquired. Specifically, the time of passing through each road section can be calculated according to the estimated vehicle speed information of the road section level, and then the weather condition of passing through the road section can be queried through the disclosed weather forecast data. And grade information is typically contained in underlying road attributes that may be derived from road network data.

In one embodiment of the present application, the vehicle energy consumption calculation module 510 calculates the energy consumption of the vehicle according to the energy consumption model. In particular, different energy consumption models exist in different factories and different vehicle types, the simplest energy consumption model is only related to the speed of a vehicle, and a complex model can relate to various factors such as the speed of the vehicle, weather, gradient, even air pressure and altitude. In the embodiment of the application, an energy consumption model corresponding to a vehicle model is required to be acquired from a vehicle manufacturer and is marked as E=f (x). Wherein, different vehicle types correspond to different f functions, and x represents the estimated vehicle speed, weather, gradient and other information obtained in the embodiment. The energy consumption model and the input information are provided, so that the energy consumption of each road section can be calculated, and the whole-course energy consumption of the route can be calculated according to the energy consumption of each road section.

The technical scheme of the embodiment of the application has various application scenes, for example, when a driver initiates navigation, a plurality of candidate routes can be firstly planned according to the starting position and the ending position selected by the driver, then the estimated energy consumption of each candidate route is calculated by utilizing the technical scheme of the embodiment of the application, and then one or more routes with relatively energy conservation are selected from the estimated energy consumption and provided for the driver. For another example, when a route is planned, a gas station, a charging station or a power exchange station and the like in the midway can be planned for a driver according to the total energy consumption of the vehicle reaching each road section, so that the travel planning of the user is facilitated. For another example, in the driving scene, the technical scheme of the embodiment of the application can be utilized to calculate the energy consumption of each route and the energy consumption from the destination to the nearest charging station, thereby more reasonably dispatching the order to the driver and preventing the problem of stopping order receiving in advance due to insufficient electric quantity and other conditions. Also, for example, the impact weight of each road segment on energy consumption (such as the energy consumption situation of each road segment) may be given for use by upstream services, e.g. to avoid high energy consumption road segments, to explain optimal routes, etc.

According to the technical scheme, the real energy consumption data of the vehicle are not required to be collected, only the energy consumption model of the vehicle is required to be used, and the energy consumption model can be provided by a vehicle factory or can be set manually according to experience; the technical scheme of the embodiment of the application can be quickly adapted to different vehicle types with low cost, and only different energy consumption models are required to be configured for the different vehicle types, and a depth model of a bottom layer is not required to be modified; meanwhile, the technical scheme of the embodiment of the application has lower requirements on data, only comprises the road section id, the departure time and the arrival time in the route, has no mandatory requirements on the route track, and depends on other information including real-time road conditions, historical road conditions, road foundation attributes and the like, which belong to the data which are easy to acquire; in addition, the technical scheme of the embodiment of the application can dynamically adjust the energy consumption estimation according to the information such as the air temperature, the road condition state, the road gradient and the like, and ensure that more accurate vehicle energy consumption is obtained.

The following describes an embodiment of the apparatus of the present application, which may be used to perform the method of calculating the vehicle energy consumption in the above-described embodiment of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method for calculating vehicle energy consumption described above.

Fig. 8 shows a block diagram of a vehicle energy consumption calculation apparatus according to an embodiment of the present application, which may be applied to a server or a terminal device (e.g., the vehicle terminal 101 shown in fig. 1).

Referring to fig. 8, a vehicle energy consumption calculating apparatus 800 according to an embodiment of the present application includes: an acquisition unit 802, an extraction unit 804, an estimation unit 806 and a processing unit 808.

Wherein the obtaining unit 802 is configured to obtain a planned route of the vehicle terminal; the extracting unit 804 is configured to extract each road segment included in the planned route, and obtain road condition data and road attribute data of each road segment; the estimating unit 806 is configured to estimate a traffic speed of the vehicle terminal on each road section according to the road condition data and the road attribute data of each road section, so as to obtain an estimated traffic speed corresponding to each road section; the processing unit 808 is configured to calculate the vehicle energy consumption corresponding to the planned route according to the estimated traffic speed corresponding to each road segment.

In some embodiments of the present application, based on the foregoing scheme, the processing unit 808 is configured to: acquiring an energy consumption model corresponding to the vehicle terminal; calculating vehicle energy consumption corresponding to each road section through the energy consumption model based on the estimated passing speed corresponding to each road section; and calculating the vehicle energy consumption corresponding to the planned route according to the vehicle energy consumption corresponding to each road section.

In some embodiments of the present application, based on the foregoing solution, the processing unit 808 calculates, based on the estimated traffic speeds corresponding to the respective road segments, vehicle energy consumption corresponding to the respective road segments through the energy consumption model, including: generating energy consumption model input parameters corresponding to each road section based on the estimated passing speeds corresponding to each road section, wherein the energy consumption model input parameters at least comprise the estimated passing speeds; and inputting the energy consumption model input parameters corresponding to each road section into the energy consumption model to obtain the vehicle energy consumption corresponding to each road section output by the energy consumption model.

In some embodiments of the present application, based on the foregoing, the processing unit 808 calculates weather conditions when the vehicle terminal approaches the respective road segments by: estimating the time of the vehicle terminal passing through each road section according to the estimated passing speed corresponding to each road section; and acquiring corresponding weather forecast data as weather conditions when the vehicle terminal approaches each road section according to the time when the vehicle terminal approaches each road section.

In some embodiments of the present application, based on the foregoing scheme, the estimating unit 806 is configured to: generating input parameters of a speed estimation model according to the road condition data and the road attribute data of each road section; and inputting the input parameters of the speed estimation model into a pre-trained speed estimation model to obtain the estimated passing speeds corresponding to the road sections output by the speed estimation model.

In some embodiments of the present application, based on the foregoing, the vehicle energy consumption computing apparatus 800 further includes a training unit configured to train the speed estimation model by: generating training data of a speed estimation model according to a historical travel route of the vehicle terminal; inputting the training data into the speed estimation model to obtain estimated passing speeds corresponding to the road section samples output by the speed estimation model; calculating estimated passing time corresponding to the historical travel route according to the estimated passing speed corresponding to each road section sample; and calculating a loss value of the speed estimation model according to the actual passing time corresponding to the historical travel route and the estimated passing time corresponding to the historical travel route, and adjusting model parameters of the speed estimation model according to the loss value.

In some embodiments of the application, based on the foregoing, the processing unit 808 is further configured to perform at least one of the following:

It should be noted that, the computer system 900 of the electronic device shown in fig. 9 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.

As shown in fig. 9, the computer system 900 may include a central processing unit (Central Processing Unit, CPU) 901 which may perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 902 or a program loaded from a storage section 908 into a random access Memory (Random Access Memory, RAM) 903, for example, performing the methods described in the above embodiments. In the RAM 903, various programs and data required for system operation are also stored. The CPU 901, ROM 902, and RAM 903 are connected to each other through a bus 904. An Input/Output (I/O) interface 905 is also connected to bus 904.

The following components may be connected to the I/O interface 905: an input section 906 including a keyboard, a mouse, and the like; an output section 907 including a speaker and the like, such as a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and the like; a storage portion 908 including a hard disk or the like; and a communication section 909 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as needed. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 910 so that a computer program read out therefrom is installed into the storage section 908 as needed.

In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909 and/or installed from the removable medium 911. When the computer program is executed by a Central Processing Unit (CPU) 901, various functions defined in the system of the present application are performed.

It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having 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 (Erasable Programmable Read Only Memory, EPROM), 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. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer programs.

The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more computer programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause an electronic device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.

Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.

It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A method of calculating vehicle energy consumption, comprising:

acquiring a planned route of a vehicle terminal;

extracting each road section contained in the planned route, and acquiring road condition data and road attribute data of each road section;

estimating the passing speed of the vehicle terminal on each road section according to the road condition data and the road attribute data of each road section to obtain the estimated passing speed corresponding to each road section;

and calculating the vehicle energy consumption corresponding to the planned route according to the estimated passing speed corresponding to each road section.

2. The method according to claim 1, wherein calculating the vehicle energy consumption corresponding to the planned route according to the estimated traffic speed corresponding to each road section, comprises:

acquiring an energy consumption model corresponding to the vehicle terminal;

calculating vehicle energy consumption corresponding to each road section through the energy consumption model based on the estimated passing speed corresponding to each road section;

and calculating the vehicle energy consumption corresponding to the planned route according to the vehicle energy consumption corresponding to each road section.

3. The method according to claim 2, wherein calculating the vehicle energy consumption corresponding to the respective road segments by the energy consumption model based on the estimated traffic speeds corresponding to the respective road segments, comprises:

generating energy consumption model input parameters corresponding to each road section based on the estimated passing speeds corresponding to each road section, wherein the energy consumption model input parameters at least comprise the estimated passing speeds;

and inputting the energy consumption model input parameters corresponding to each road section into the energy consumption model to obtain the vehicle energy consumption corresponding to each road section output by the energy consumption model.

4. A method of calculating vehicle energy consumption according to claim 3, wherein the energy consumption model input parameters further comprise at least one of the following information:

5. The method for calculating the vehicle energy consumption according to claim 4, wherein the weather condition at the time of the vehicle terminal route to the respective road segments is calculated by:

estimating the time of the vehicle terminal passing through each road section according to the estimated passing speed corresponding to each road section;

and acquiring corresponding weather forecast data as weather conditions when the vehicle terminal approaches each road section according to the time when the vehicle terminal approaches each road section.

6. The method according to claim 1, wherein estimating the traffic speed of the vehicle terminal on each road section based on the road condition data and the road attribute data of the each road section comprises:

generating input parameters of a speed estimation model according to the road condition data and the road attribute data of each road section;

and inputting the input parameters of the speed estimation model into a pre-trained speed estimation model to obtain the estimated passing speeds corresponding to the road sections output by the speed estimation model.

7. The method of calculating vehicle energy consumption according to claim 6, wherein the speed estimation model is trained by:

generating training data of a speed estimation model according to a historical travel route of the vehicle terminal;

inputting the training data into the speed estimation model to obtain estimated passing speeds corresponding to the road section samples output by the speed estimation model;

calculating estimated passing time corresponding to the historical travel route according to the estimated passing speed corresponding to each road section sample;

and calculating a loss value of the speed estimation model according to the actual passing time corresponding to the historical travel route and the estimated passing time corresponding to the historical travel route, and adjusting model parameters of the speed estimation model according to the loss value.

8. The method of calculating vehicle energy consumption according to claim 7, wherein generating training data of a speed estimation model from a historical travel route of the vehicle terminal includes:

acquiring a historical travel route of the vehicle terminal and a departure time of the vehicle terminal on the historical travel route;

extracting road sections contained in the historical travel route to obtain a plurality of road section samples;

Acquiring road attribute data of each road section sample in the plurality of road section samples and road condition data of each road section sample at the departure time;

and generating training data of the speed estimation model according to the road attribute data of each road section sample and the road condition data of each road section sample.

9. The method according to claim 7, wherein calculating the estimated travel time corresponding to the historical travel route according to the estimated travel speed corresponding to the each road segment sample comprises:

calculating estimated traffic time corresponding to each road section sample according to the estimated traffic speed corresponding to each road section sample and the mileage of each road section sample;

and calculating the estimated transit time corresponding to the historical travel route according to the estimated transit time corresponding to each road section sample.

10. The method for calculating vehicle energy consumption according to claim 7, characterized in that the method for calculating vehicle energy consumption further comprises:

acquiring departure time and arrival time of the vehicle terminal on the historical travel route;

and calculating the actual passing time corresponding to the historical travel route according to the arrival time and the departure time.

11. The method for calculating vehicle energy consumption according to claim 1, characterized in that the method further comprises at least one of the following steps:

12. The method of calculating vehicle energy consumption according to any one of claims 1 to 11, characterized in that the road attribute data includes at least one of: road grade, lane number, speed limit data; the road condition data comprises at least one of the following: traffic flow, traffic flow speed, congestion conditions.

13. A vehicle energy consumption computing device, comprising:

an acquisition unit configured to acquire a planned route of the vehicle terminal;

the extraction unit is configured to extract each road section contained in the planned route and acquire road condition data and road attribute data of each road section;

The estimating unit is configured to estimate the passing speed of the vehicle terminal on each road section according to the road condition data and the road attribute data of each road section to obtain the estimated passing speed corresponding to each road section;

and the processing unit is configured to calculate the vehicle energy consumption corresponding to the planned route according to the estimated passing speed corresponding to each road section.

14. A computer readable medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of calculating the vehicle energy consumption according to any one of claims 1 to 12.

15. An electronic device, comprising:

one or more processors;

a memory for storing one or more computer programs that, when executed by the one or more processors, cause the electronic device to implement the method of computing vehicle energy consumption of any of claims 1-12.

16. A computer program product, characterized in that the computer program product comprises a computer program stored in a computer readable storage medium, from which computer readable storage medium a processor of an electronic device reads and executes the computer program, causing the electronic device to execute the method of calculating the vehicle energy consumption according to any one of claims 1 to 12.