Vehicle carbon emission determination method and device, storage medium and electronic equipment

Abstract

The invention discloses a vehicle carbon emission determination method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a plurality of trajectory segments of a current vehicle in a target area, and determining energy consumption values corresponding to the plurality of trajectory segments based on an energy consumption prediction model; determining the carbon emission of the current vehicle in the target area based on the energy consumption values corresponding to the plurality of track segments; and obtaining the total carbon emission amount of the vehicles in the target area according to the carbon emission amount of each vehicle in the target area. According to the invention, the technical problem of low accuracy of vehicle carbon emission determination in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle carbon emission determination, and in particular to a vehicle carbon emission determination method and device, a storage medium and an electronic device. Background Art

[0002] In the current context of global climate change and increasingly prominent environmental issues, reducing carbon emissions from automobiles has become an urgent task. As one of the main sources of greenhouse gas emissions, the carbon emissions assessment of automobiles throughout their life cycle has important public knowledge and research significance. The algorithms for calculating the carbon emissions of gasoline vehicles, diesel vehicles and other vehicles in the existing technology are too subjective, lack a baseline, and are too coarse. The accuracy of determining vehicle carbon emissions is low, making it difficult to scientifically manage and optimize vehicle operations. Summary of the invention

[0003] The embodiments of the present application provide a method and device for determining vehicle carbon emissions, a storage medium, and an electronic device to at least solve the technical problem of low accuracy in determining vehicle carbon emissions in related technologies.

[0004] According to one aspect of an embodiment of the present application, a method for determining vehicle carbon emissions is provided, including: obtaining multiple trajectory segments of a current vehicle in a target area, and determining energy consumption values ​​corresponding to each of the multiple trajectory segments based on an energy consumption prediction model; determining the carbon emissions of the current vehicle in the target area based on the energy consumption values ​​corresponding to each of the multiple trajectory segments; and obtaining the total carbon emissions of vehicles in the target area based on the carbon emissions of each vehicle in the target area.

[0005] According to another aspect of an embodiment of the present application, a vehicle carbon emission determination device is also provided, including: a first acquisition unit, used to acquire multiple trajectory segments of the current vehicle in a target area, and determine the energy consumption values ​​corresponding to each of the multiple trajectory segments based on an energy consumption prediction model; a first determination unit, used to determine the carbon emissions of the current vehicle in the target area based on the energy consumption values ​​corresponding to each of the multiple trajectory segments; and a second acquisition unit, used to acquire the total carbon emissions of vehicles in the target area based on the carbon emissions of each vehicle in the target area.

[0006] According to another aspect of an embodiment of the present application, there is also provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the vehicle carbon emission determination method through the computer program.

[0007] According to another aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned vehicle carbon emission determination method when running.

[0008] In an embodiment of the present application, a method is adopted in which multiple trajectory segments of the current vehicle in a target area are obtained, and the energy consumption values ​​corresponding to each of the multiple trajectory segments are determined based on an energy consumption prediction model; the carbon emissions of the current vehicle in the target area are determined based on the energy consumption values ​​corresponding to each of the multiple trajectory segments; and the total carbon emissions of vehicles in the target area are obtained according to the carbon emissions of each vehicle in the target area. In the above method, the present application can accurately determine the energy consumption values ​​of multiple trajectory segments of the vehicle in the target area based on the trained energy consumption prediction model, and the carbon emissions of the vehicle in the target area can be accurately obtained according to the energy consumption values. Based on this, the present application can not only obtain the total carbon emissions of vehicles in the target area in real time and objectively, but also can improve the accuracy of determining the carbon emissions of vehicles, thereby solving the technical problem of low accuracy in determining the carbon emissions of vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0010] Figure 1 is a schematic diagram of an application environment of an optional vehicle carbon emission determination method according to an embodiment of the present application;

[0011] Figure 2 is a schematic diagram of an application environment of another optional vehicle carbon emission determination method according to an embodiment of the present application;

[0012] Figure 3 is a flow chart of an optional method for determining vehicle carbon emissions according to an embodiment of the present application;

[0013] Figure 4 is an optional schematic diagram of a trajectory segment and state of a vehicle in a target area according to an embodiment of the present application;

[0014] Figure 5 is a schematic diagram showing the types of points of interest of an optional industry knowledge base according to an embodiment of the present application;

[0015] Figure 6 is a schematic structural diagram of an optional vehicle carbon emission determination device according to an embodiment of the present application;

[0016] Figure 7 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] According to one aspect of an embodiment of the present application, a vehicle carbon emission determination method is provided. Optionally, as an optional implementation, the vehicle carbon emission determination method can be but is not limited to being applied to Figure 1 In the application environment shown. The application environment includes: a terminal device 102 for human-computer interaction with a user, a network 104, and a server 106. A user 108 can perform human-computer interaction with the terminal device 102, and a vehicle carbon emission determination application client is running in the terminal device 102. The above-mentioned terminal device 102 includes a human-computer interaction screen 1022, a processor 1024 and a memory 1026. The human-computer interaction screen 1022 is used to display vehicle carbon emission information; the processor 1024 is used to obtain multiple trajectory segments of the vehicle in the target area, the vehicle's driving information and attribute information. The memory 1026 is used to store the above-mentioned multiple trajectory segments, the vehicle's driving information and attribute information.

[0020] In addition, the server 106 includes a database 1062 and a processing engine 1064. The database 1062 is used to store multiple track segments, vehicle driving information and attribute information. The processing engine 1064 is used to obtain multiple track segments of the current vehicle in the target area, determine the energy consumption values ​​corresponding to each of the multiple track segments based on the energy consumption prediction model; determine the carbon emissions of the current vehicle in the target area based on the energy consumption values ​​corresponding to each of the multiple track segments; obtain the total carbon emissions of the vehicles in the target area based on the carbon emissions of each vehicle in the target area; and send the above carbon emissions to the client of the above terminal device 102.

[0021] In one or more embodiments, the vehicle carbon emission determination method described above in the present application can be applied to Figure 2 In the application environment shown. Figure 2 As shown, human-computer interaction can be performed between user 202 and user device 204. User device 204 includes memory 206 and processor 208. In this embodiment, user device 204 can refer to but not be limited to executing the operations performed by the above terminal device 102 to display the total carbon emissions corresponding to the target area.

[0022] Optionally, the terminal device 102 and the user device 204 include but are not limited to mobile phones, tablet computers, laptop computers, PCs, vehicle-mounted electronic devices, wearable devices and other terminals, and the network 104 may include but is not limited to wireless networks or wired networks. Among them, the wireless network includes: WIFI and other networks that realize wireless communication. The wired network may include but is not limited to: wide area network, metropolitan area network, local area network. The server 106 may include but is not limited to any hardware device that can perform computing. The server may be a single server, or a server cluster composed of multiple servers, or a cloud server. The above is only an example, and no limitation is made to this in this embodiment.

[0023] As an optional implementation, Figure 3 As shown, the embodiment of the present application provides a method for determining vehicle carbon emissions, comprising the following steps:

[0024] S302, obtaining multiple trajectory segments of the current vehicle in the target area, and determining energy consumption values ​​corresponding to each of the multiple trajectory segments based on an energy consumption prediction model.

[0025] Specifically, in the embodiments of the present application, Figure 4 As shown, the target area is Figure 4For example, the trajectory of the current vehicle in the target area and the target time period is from the starting point A to the exit point F, and the driving trajectory includes the trajectory segment AB, the trajectory segment BC, the trajectory segment CD, the trajectory segment DE, and the trajectory segment EF. It should be noted that the above energy consumption value includes the fuel consumption value or the gas consumption value. The fuel consumption value can be, for example, the fuel consumption per 100 kilometers, and the gas consumption value can be the natural gas per 100 kilometers. The energy consumption value of each of the above trajectory segments can be determined according to the energy consumption prediction model.

[0026] S304: Determine the carbon emission of the current vehicle in the target area based on the energy consumption values ​​corresponding to each of the multiple trajectory segments.

[0027] Specifically, in the implementation of this application, the carbon emissions of a vehicle = (mileage × energy consumption per 100 kilometers) ÷ 100 × emission conversion coefficient, and different types of fuel correspond to different emission conversion coefficients. Among them, the conversion coefficients are: 1 liter of gasoline = 2.179 kilograms; 1 liter of diesel = 2.718 kilograms; 1 kilogram of liquefied natural gas = 3.014 kilograms.

[0028] Assume that the current vehicle's fuel type is gasoline, such as Figure 4 As shown, the current driving trajectory of the vehicle entering the target area includes trajectory segment AB, trajectory segment BC, trajectory segment CD, trajectory segment DE, and trajectory segment EF.

[0029] The mileage of trajectory segment AB is 50KM, and the corresponding fuel consumption per 100 kilometers is 10L. The carbon emission of trajectory segment AB = 0.5*10*2.179 = 10.895.

[0030] The mileage of trajectory segment BC is 60KM, and the corresponding fuel consumption per 100 kilometers is 5L. The carbon emission of trajectory segment BC = 0.6*5*2.179 = 6.537.

[0031] The mileage of trajectory segment CD is 20KM, the corresponding fuel consumption per 100 kilometers is 12L, and the carbon emission of trajectory segment BC = 5.2296.

[0032] The mileage of trajectory segment DE is 50KM, the corresponding fuel consumption per 100 kilometers is 10L, and the carbon emission of trajectory segment BC = 10.895.

[0033] The mileage of trajectory segment EF is 50KM, the corresponding fuel consumption per 100 kilometers is 8L, and the carbon emission of trajectory segment BC = 8.716.

[0034] Then the carbon emissions of the vehicle in the target area are 10.895+6.537+5.2296+10.895+8.716=42.2726.

[0035] S306: Obtain the total carbon emissions of vehicles in the target area according to the carbon emissions of each vehicle in the target area.

[0036] Specifically, in an embodiment of the present application, for example, there are 100 vehicles traveling in the target area during the target period, the carbon emissions corresponding to the 100 vehicles are obtained respectively, and then all the carbon emissions are added together to obtain the total carbon emissions of the vehicles in the target area.

[0037] In an embodiment of the present application, a method is adopted in which multiple trajectory segments of the current vehicle in a target area are obtained, and the energy consumption values ​​corresponding to each of the multiple trajectory segments are determined based on an energy consumption prediction model; the carbon emissions of the current vehicle in the target area are determined based on the energy consumption values ​​corresponding to each of the multiple trajectory segments; and the total carbon emissions of vehicles in the target area are obtained according to the carbon emissions of each vehicle in the target area. In the above method, the present application can accurately determine the energy consumption values ​​corresponding to each of the multiple trajectory segments of the vehicle in the target area based on the energy consumption prediction model, and the carbon emissions of the vehicle in the target area can be accurately obtained according to the energy consumption values. Based on this, the present application can not only obtain the total carbon emissions of vehicles in the target area in real time and objectively, but also can improve the accuracy of determining the carbon emissions of vehicles, thereby solving the technical problem of low accuracy in determining the carbon emissions of vehicles.

[0038] In one or more embodiments, the vehicle carbon emissions determination method further includes:

[0039] Acquire training data, wherein the training data includes a sample set and a test set, wherein the sample set and the test set both include driving record data corresponding to a plurality of vehicles, attribute data of the vehicles, and actual energy consumption values ​​corresponding to the plurality of vehicles in each trajectory segment;

[0040] A prediction model for determining the vehicle energy consumption value is trained based on the training data until a convergence condition is reached to obtain the energy consumption prediction model.

[0041] Specifically, in an embodiment of the present application, the training data includes a sample set and a test set. The data corresponding to the sample set is used to train the model. The model learns how to derive a prediction of the vehicle's energy consumption value through the data in the sample set. The test set is a data set for evaluating the performance of the model. As shown in Table (1), the driving record data includes data such as the vehicle's heavy-load mileage or empty-load mileage corresponding to each trajectory segment, the slope value corresponding to each trajectory segment, and the vehicle's average speed corresponding to each trajectory segment; the vehicle's attribute data includes factory energy consumption values, vehicle age, vehicle model, vehicle brand, vehicle emission standards, cargo type, and other data. In Table (1), VID represents the trajectory identifier of the trajectory segment corresponding to the vehicle, and the fuel consumption per 100 kilometers corresponding to the V1 trajectory segment is 35L. The energy consumption prediction model used to determine the vehicle energy consumption type is trained based on the above training data until the convergence condition is reached, and an energy consumption prediction model can be obtained.

[0042] In one or more embodiments, the training of the energy consumption prediction model for determining the vehicle energy consumption value based on the training sample data until a convergence condition is reached to obtain the energy consumption prediction model includes:

[0043] Acquire driving record data and attribute data corresponding to each trajectory segment in the training data, and convert the driving record data and attribute data into a feature vector;

[0044] Based on the sample set, an initial energy consumption prediction model is trained using a preset classification algorithm;

[0045] sequentially calculating the Euclidean distance between the feature vector of each trajectory segment in the sample set and the feature vector of each trajectory segment in the test set;

[0046] The energy consumption value of the second trajectory segment having the closest Euclidean distance to the first trajectory segment is used as the predicted energy consumption value of the first trajectory segment; the first trajectory segment is any trajectory segment in the test set, and the second trajectory segment is any trajectory segment in the sample set; when the difference between the actual energy consumption value of the first trajectory segment and the predicted energy consumption value corresponding to the first trajectory segment is less than a preset value, it is determined that the first trajectory segment reaches the target accuracy;

[0047] When the ratio of the first number to the second number is greater than or equal to a preset threshold, it is determined that the convergence condition is met and the energy consumption prediction model is obtained; the first number is the number of the first trajectory segments that achieve the target accuracy, and the second number is the total number of the first trajectory segments in the test set.

[0048] Specifically, in the embodiment of the present application, the driving record data and attribute data corresponding to each trajectory segment contained in the sample set and the test set are first obtained, and then the driving record data and attribute data are converted into feature vectors; the above-mentioned preset classification algorithms include but are not limited to various clustering algorithms, such as KNN clustering algorithm, hierarchical clustering algorithm, DBSCAN clustering algorithm, etc. Using the above-mentioned classification algorithm and the feature vectors corresponding to each trajectory segment of the sample set, the initial energy consumption prediction model can be trained to obtain the predicted energy consumption value corresponding to each trajectory segment in the sample set. Assume that the feature vector corresponding to the trajectory segment P1 in the test set is (x 11 , x 12 , ..., x 1n ), the feature vector corresponding to the trajectory segment P2 in the sample set is (x 21 , x 22 , ..., x 2n ). Then the Euclidean distance between the two According to the above calculation formula, when the Euclidean distance between the trajectory segment P1 and the trajectory segment P2 is the shortest, the energy consumption value of the trajectory segment P2 is used as the predicted energy consumption value of the trajectory segment P1; when the difference between the actual energy consumption value of the trajectory segment P1 and the predicted energy consumption value corresponding to the trajectory segment P1 is less than a preset value, it is determined that the trajectory segment P1 has reached the target accuracy;

[0049] When the predicted energy consumption values ​​corresponding to the trajectory segments in the sample set reach the first number of target accuracy and the ratio of the predicted energy consumption values ​​to the second number of trajectory segments in the test set is greater than or equal to the preset threshold, it is determined that the convergence condition is met and the energy consumption prediction model is obtained. In one example, the value range of the preset threshold can be, for example, [90%, 95%].

[0050] Table (1)

[0051]

[0052] In one or more embodiments, the driving record data includes at least one of the following: the vehicle's heavy-load mileage and unloaded mileage corresponding to each track segment, the slope value corresponding to each track segment, and the vehicle's average speed corresponding to each track segment;

[0053] The attribute data of the vehicle includes at least one of the following: factory energy consumption value, vehicle age, vehicle model, vehicle brand, vehicle emission standard, and cargo type.

[0054] Specifically, in the embodiment of the present application, when the vehicle is climbing a slope, greater power is required to overcome gravity, which causes the engine to consume more fuel and thus generate more carbon emissions. The slope between the two tracks can be calculated by the altitude between the two tracks.

[0055] The age of the vehicle has a certain impact on the vehicle's carbon emissions. As the vehicle is used for a longer period of time, the vehicle's engine, transmission system, tires and other components will wear and age, resulting in reduced vehicle performance, increased fuel consumption, and thus increased carbon emissions. This application calculates the age of the vehicle through the vehicle's VIN. The VIN is a 17-bit string. Take the 10th bit, and then a mapping relationship can be established. For example, the tenth character of the 2001 VIN is 1, the tenth character of the 2002 VIN is 2, and so on. The tenth character of the 2030 VIN is Y.

[0056] Different types of trucks (such as heavy trucks, light trucks, tractors, etc.) and different types of engines and vehicle configurations, such as power systems, emission control systems, etc. Here, the types of trucks may include, for example, flatbed vans, closed trucks, tank trucks, flatbed trucks, container trucks, and dump trucks. Different types of trucks have different corresponding carbon emissions.

[0057] The brand's technical level and R&D capabilities have a great impact on carbon emissions. Some brands may have advanced engine technology and emission control technology that can reduce vehicle carbon emissions, while other brands may have relatively backward technology, resulting in higher carbon emissions.

[0058] The type of fuel a vehicle uses also affects its carbon emissions. For example, diesel engines are generally more efficient than gasoline engines and produce lower carbon emissions. Another example is the National VI standard, which is stricter than the National V standard and has lower carbon emissions.

[0059] The type and weight of cargo a truck carries also affects its carbon emissions. For example, transporting heavy goods may require a larger engine and longer driving distances, which results in more carbon emissions.

[0060] To obtain the factory energy consumption value of the vehicle, you can obtain the factory fuel consumption or factory gas consumption corresponding to each engine model from the Ministry of Industry and Information Technology's announcement database. For example, if the engine model is general cargo, the factory gas consumption is N1kg / 100km, if the engine model is dump / mixing, the factory gas consumption is M2kg / 100km, and if the engine model is semi-trailer tractor / tractor, the factory gas consumption is K1kg / 100km.

[0061] In one or more embodiments, the vehicle carbon emissions determination method further includes:

[0062] Obtaining the starting trajectory point and the ending trajectory point of the vehicle passing through the target area;

[0063] Based on the starting trajectory point, the ending trajectory point and the vehicle stop point, dividing the driving trajectory of the vehicle in the target area into a plurality of trajectory segments;

[0064] According to the types of points of interest corresponding to the starting track point, the ending track point and the vehicle stop point of each track segment, it is determined whether each track segment corresponds to a heavy-load mileage or an unloaded mileage.

[0065] Specifically, in the embodiments of the present application, Figure 4 As shown, the target area is Figure 4 For example, the trajectory of the current vehicle in the target area target time period is from the starting trajectory point A to the ending trajectory point F, and the vehicle's stop points include four stop points B, C, D, and E. The vehicle's driving trajectory in the target area is divided into multiple trajectory segments, including trajectory segment AB, corresponding to segment 1; trajectory segment BC, corresponding to segment 2; trajectory segment CD, corresponding to segment 3; trajectory segment DE, corresponding to segment 4; trajectory segment EF, corresponding to segment 5.

[0066] According to the types of points of interest corresponding to the starting track point, the ending track point and the vehicle stop point of each track segment, it is determined that each track segment corresponds to a heavy-load mileage or an empty-load mileage. Figure 5 As shown, taking the points of interest (POI) in the concrete industry as an example, assuming that the POI type corresponding to the current stop point C is a sand and gravel yard or a waste yard, the POI type corresponding to the stop point D is a mixing station, and the POI type corresponding to the stop point E is a construction site or a road and bridge site, it can be determined that the trajectory segments CD and DE both correspond to heavy-load mileage.

[0067] In one or more embodiments, the vehicle carbon emissions determination method further includes:

[0068] For any of the trajectory segments of the vehicle, perform the following operations;

[0069] Obtaining position information of multiple coordinate positions of any track segment and the time corresponding to each coordinate position;

[0070] Based on the distance and time difference between two adjacent coordinate positions, sequentially obtaining the average driving speed of the vehicle between two adjacent coordinate positions;

[0071] The average value of the obtained average driving speeds is determined as the average speed corresponding to any one trajectory segment.

[0072] Specifically, under the same driving conditions, the faster the truck travels, the higher its carbon emissions. This is mainly because when the vehicle is traveling at high speed, the engine needs to generate more power, resulting in increased fuel consumption, thereby generating more carbon emissions. In an embodiment of the present application, for example, the positioning system of the truck will report the coordinate position and time information of the truck at a preset frequency, such as longitude and latitude information and time point, and based on the distance and time difference between two adjacent coordinate positions, the average driving speed of the vehicle between two adjacent coordinate positions is obtained in sequence; the average value of the obtained average driving speeds is determined as the average speed corresponding to any one of the trajectory segments.

[0073] In one or more embodiments, determining the carbon emission of the current vehicle in the target area according to the energy consumption values ​​corresponding to each of the plurality of trajectory segments includes:

[0074] The carbon emission of the current vehicle in the target area is determined according to the mileage of the multiple trajectory segments corresponding to the current vehicle, the energy consumption values ​​corresponding to each of the multiple trajectory segments, and the emission conversion coefficient of the current vehicle.

[0075] In the implementation of this application, the carbon emission of a vehicle = (mileage × energy consumption per 100 kilometers) ÷ 100 × emission conversion coefficient, and different types of fuel correspond to different emission conversion coefficients. Among them, the emission conversion coefficient is: 1 liter of gasoline = 2.179 kilograms; 1 liter of diesel = 2.718 kilograms; 1 kilogram of liquefied natural gas = 3.014 kilograms.

[0076] Assume that the current vehicle's fuel type is gasoline, such as Figure 4 As shown, the current driving trajectory of the vehicle entering the target area includes trajectory segment AB, trajectory segment BC, trajectory segment CD, trajectory segment DE, and trajectory segment EF.

[0077] The mileage of trajectory segment AB is 50KM, and the corresponding energy consumption value is: 10L per 100km, and the carbon emission of trajectory segment AB = 0.5*10*2.179 = 10.895.

[0078] The mileage of trajectory segment BC is 60KM, and the corresponding energy consumption value is: 5L per 100 kilometers, and the carbon emission of trajectory segment BC = 0.6*5*2.179 = 6.537.

[0079] The mileage of trajectory segment CD is 20KM, the corresponding fuel consumption per 100 kilometers is 12L, and the carbon emission of trajectory segment BC = 5.2296.

[0080] The mileage of trajectory segment DE is 50KM, and the corresponding energy consumption value is: 10L per 100 kilometers, and the carbon emission of trajectory segment BC = 10.895.

[0081] The mileage of trajectory segment EF is 50KM, and the corresponding energy consumption value is: 8L per 100km, and the carbon emission of trajectory segment BC = 8.716.

[0082] Then the carbon emissions of the vehicle in the target area are 10.895+6.537+5.2296+10.895+8.716=42.2726.

[0083] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0084] According to another aspect of the embodiment of the present application, a vehicle carbon emission determination device for implementing the above vehicle carbon emission determination method is also provided. Figure 6 As shown, the device comprises:

[0085] A first acquisition unit 602 is used to acquire multiple trajectory segments of the current vehicle in the target area, and determine energy consumption values ​​corresponding to each of the multiple trajectory segments based on the energy consumption prediction model;

[0086] A first determining unit 604 is used to determine the carbon emission of the current vehicle in the target area based on the energy consumption values ​​corresponding to each of the plurality of trajectory segments;

[0087] The second acquisition unit 606 is used to acquire the total carbon emission of vehicles in the target area according to the carbon emission of each vehicle in the target area.

[0088] In an embodiment of the present application, a method is adopted in which multiple trajectory segments of the current vehicle in a target area are obtained, and the energy consumption values ​​corresponding to each of the multiple trajectory segments are determined based on an energy consumption prediction model; the carbon emissions of the current vehicle in the target area are determined based on the energy consumption values ​​corresponding to each of the multiple trajectory segments; and the total carbon emissions of vehicles in the target area are obtained according to the carbon emissions of each vehicle in the target area. In the above method, the present application can accurately determine the energy consumption values ​​of multiple trajectory segments of the vehicle in the target area based on the trained energy consumption prediction model, and the carbon emissions of the vehicle in the target area can be accurately obtained according to the energy consumption values. Based on this, the present application can not only obtain the total carbon emissions of vehicles in the target area in real time and objectively, but also can improve the accuracy of determining the carbon emissions of vehicles, thereby solving the technical problem of low accuracy in determining the carbon emissions of vehicles.

[0089] In one or more embodiments, the vehicle carbon emission determination device further comprises:

[0090] a third acquisition unit, configured to acquire training data, wherein the training data includes a sample set and a test set, wherein the sample set and the test set both include driving record data corresponding to a plurality of vehicles, attribute data of the vehicles, and actual energy consumption values ​​corresponding to the plurality of vehicles in each trajectory segment;

[0091] A training unit is used to train a prediction model for determining a vehicle energy consumption value based on the training data until the energy consumption prediction model is obtained when a convergence condition is reached.

[0092] In one or more embodiments, the training unit comprises:

[0093] An acquisition conversion module is used to obtain the driving record data and attribute data corresponding to each trajectory segment in the training data, and convert the driving record data and attribute data into a feature vector;

[0094] A training module, used for training an initial energy consumption prediction model based on the sample set using a preset classification algorithm;

[0095] A calculation module, used for sequentially calculating the Euclidean distance between the feature vector of each trajectory segment in the sample set and the feature vector of each trajectory segment in the test set;

[0096] A first determination module is configured to use the energy consumption value of the second trajectory segment that is closest to the first trajectory segment in Euclidean distance as the predicted energy consumption value of the first trajectory segment; the first trajectory segment is any trajectory segment in the test set, and the second trajectory segment is any trajectory segment in the sample set; when the difference between the actual energy consumption value of the first trajectory segment and the predicted energy consumption value corresponding to the first trajectory segment is less than a preset value, determine that the first trajectory segment reaches the target accuracy;

[0097] The second determination module is used to determine that the convergence condition is met and obtain the energy consumption prediction model when the ratio of the first number to the second number is greater than or equal to a preset threshold; the first number is the number of the first trajectory segments that achieve the target accuracy, and the second number is the total number of the first trajectory segments in the test set.

[0098] In one or more embodiments, the driving record data includes at least one of the following: the vehicle's heavy-load mileage and unloaded mileage corresponding to each track segment, the slope value corresponding to each track segment, and the vehicle's average speed corresponding to each track segment;

[0099] The attribute data of the vehicle includes at least one of the following: factory energy consumption value, vehicle age, vehicle model, vehicle brand, vehicle emission standard, and cargo type.

[0100] In one or more embodiments, the vehicle carbon emission determination device further comprises:

[0101] A fourth acquisition unit, used to acquire a starting trajectory point and an ending trajectory point of the vehicle passing through the target area;

[0102] A second division unit, configured to divide the driving trajectory of the vehicle in the target area into a plurality of trajectory segments based on the starting trajectory point, the ending trajectory point and the vehicle stop point;

[0103] The second determination unit is used to determine whether each trajectory segment corresponds to a heavy-load mileage or an unloaded mileage according to the types of points of interest corresponding to the starting trajectory point, the ending trajectory point and the vehicle stop point of each trajectory segment.

[0104] In one or more embodiments, the vehicle carbon emission determination device further comprises:

[0105] For any of the trajectory segments of the vehicle, perform the following operations;

[0106] A fifth acquisition unit, used to acquire position information of a plurality of coordinate positions of any track segment and a time corresponding to each coordinate position;

[0107] a sixth acquisition unit, configured to sequentially acquire an average driving speed of the vehicle between two adjacent coordinate positions based on a distance and a time difference between the two adjacent coordinate positions;

[0108] The third determining unit is used to determine the average value of the acquired average driving speeds as the average speed corresponding to any one trajectory segment.

[0109] In one or more embodiments, the first determining unit 604 includes:

[0110] A third determination module is used to determine the energy consumption value corresponding to each trajectory segment according to the energy consumption type to which the current vehicle belongs according to the multiple trajectory segments;

[0111] The fourth determination module is used to determine the carbon emissions of the current vehicle in the target area according to the mileage of the multiple trajectory segments corresponding to the current vehicle, the energy consumption value corresponding to each trajectory segment and the emission conversion coefficient of the current vehicle.

[0112] According to another aspect of the embodiment of the present application, an electronic device for implementing the above-mentioned vehicle carbon emission determination method is also provided. The electronic device may be Figure 7 The terminal device or server shown in the figure. This embodiment is described by taking the electronic device as a server as an example. Figure 7 As shown, the electronic device includes a memory 702 and a processor 704. The memory 702 stores a computer program, and the processor 704 is configured to execute the steps in any of the above method embodiments through the computer program.

[0113] Optionally, in this embodiment, the electronic device may be located in at least one network device among a plurality of network devices of a computer network.

[0114] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0115] S1, obtaining multiple trajectory segments of the current vehicle in the target area, and determining energy consumption values ​​corresponding to each of the multiple trajectory segments based on an energy consumption prediction model;

[0116] S2, determining the carbon emission of the current vehicle in the target area based on the energy consumption values ​​corresponding to each of the plurality of trajectory segments;

[0117] S3, acquiring the total carbon emission of vehicles in the target area according to the carbon emission of each vehicle in the target area.

[0118] Alternatively, a person skilled in the art may understand that: Figure 7The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (MID), a PAD, and other terminal devices. Figure 7 The electronic device and the electronic equipment described above are not limited in structure. Figure 7 More or fewer components (such as network interfaces, etc.) as shown in, or with Figure 7 Different configurations are shown.

[0119] Among them, the memory 702 can be used to store software programs and modules, such as the program instructions / modules corresponding to the vehicle carbon emission determination method and device in the embodiment of the present application. The processor 704 executes various functional applications and data processing by running the software programs and modules stored in the memory 702, that is, to implement the above-mentioned vehicle carbon emission determination method. The memory 702 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 702 may further include a memory remotely located relative to the processor 704, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Among them, the memory 702 can be specifically, but not limited to, used to store the above-mentioned multiple trajectory segments, vehicle driving information, and attribute information. As an example, such as Figure 7 As shown, the memory 702 may include, but is not limited to, the first acquisition unit 602, the first determination unit 604, and the second acquisition unit 606 in the vehicle carbon emission determination device. In addition, it may also include, but is not limited to, other module units in the vehicle carbon emission determination device, which will not be repeated in this example.

[0120] Optionally, the transmission device 706 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wired network and a wireless network. In one example, the transmission device 707 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 706 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0121] In addition, the electronic device further includes: a display 708 for displaying the multiple trajectory segments, the vehicle's driving information and attribute information, and the vehicle's carbon emissions; and a connection bus 710 for connecting the various module components in the electronic device.

[0122] In other embodiments, the terminal device or server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes through network communication. Among them, the nodes may form a peer-to-peer (P2P, Peer To Peer) network, and any form of computing device, such as a server, terminal and other electronic devices, may become a node in the blockchain system by joining the peer-to-peer network.

[0123] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above-mentioned vehicle carbon emission determination method, wherein the computer program is configured to execute the steps of any one of the above-mentioned method embodiments when it is run.

[0124] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0125] S1, obtaining multiple trajectory segments of the current vehicle in the target area, and determining energy consumption values ​​corresponding to each of the multiple trajectory segments based on an energy consumption prediction model;

[0126] S2, determining the carbon emission of the current vehicle in the target area based on the energy consumption values ​​corresponding to each of the plurality of trajectory segments;

[0127] S3, acquiring the total carbon emission of vehicles in the target area according to the carbon emission of each vehicle in the target area.

[0128] Optionally, in this embodiment, a person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing hardware related to the terminal device through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0129] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0130] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which can be personal computers, servers or network devices, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention.

[0131] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in the present application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0135] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining vehicle carbon emissions, characterized in that: include: Acquire multiple trajectory segments of the current vehicle in the target area, and determine energy consumption values ​​corresponding to each of the multiple trajectory segments based on the energy consumption prediction model; Determining the carbon emission of the current vehicle in the target area based on the energy consumption values ​​corresponding to each of the plurality of trajectory segments; The total carbon emission of the vehicles in the target area is obtained according to the carbon emission of each vehicle in the target area.

2. The method according to claim 1, characterized in that The method further comprises: Acquire training data, wherein the training data includes a sample set and a test set, wherein the sample set and the test set both include driving record data corresponding to a plurality of vehicles, attribute data of the vehicles, and actual energy consumption values ​​corresponding to the plurality of vehicles in each trajectory segment; A prediction model for determining the vehicle energy consumption value is trained based on the training data until a convergence condition is reached to obtain the energy consumption prediction model.

3. The method according to claim 2, characterized in that The step of training the prediction model for determining the vehicle energy consumption value based on the training data until a convergence condition is reached to obtain the energy consumption prediction model comprises: Acquire driving record data and attribute data corresponding to each trajectory segment in the training data, and convert the driving record data and attribute data into a feature vector; Based on the sample set, an initial energy consumption prediction model is trained using a preset classification algorithm; sequentially calculating the Euclidean distance between the feature vector of each trajectory segment in the sample set and the feature vector of each trajectory segment in the test set; The energy consumption value of the second trajectory segment having the closest Euclidean distance to the first trajectory segment is used as the predicted energy consumption value of the first trajectory segment; the first trajectory segment is any trajectory segment in the test set, and the second trajectory segment is any trajectory segment in the sample set; when the difference between the actual energy consumption value of the first trajectory segment and the predicted energy consumption value corresponding to the first trajectory segment is less than a preset value, it is determined that the first trajectory segment reaches the target accuracy; When the ratio of the first number to the second number is greater than or equal to a preset threshold, it is determined that the convergence condition is met and the energy consumption prediction model is obtained; the first number is the number of the first trajectory segments that achieve the target accuracy, and the second number is the total number of the first trajectory segments in the test set.

4. The method according to any one of claims 2 to 3, characterized in that The driving record data includes at least one of the following: the vehicle's loaded mileage or unloaded mileage corresponding to each track segment, the slope value corresponding to each track segment, and the vehicle's average speed corresponding to each track segment; The attribute data of the vehicle includes at least one of the following: factory energy consumption value, vehicle age, vehicle model, vehicle brand, vehicle emission standard, and cargo type.

5. The method according to claim 4, characterized in that The method further comprises: Obtaining the starting trajectory point and the ending trajectory point of the vehicle passing through the target area; Based on the starting trajectory point, the ending trajectory point and the vehicle stop point, dividing the driving trajectory of the vehicle in the target area into a plurality of trajectory segments; According to the types of points of interest corresponding to the starting track point, the ending track point and the vehicle stop point of each track segment, it is determined whether each track segment corresponds to a heavy-load mileage or an unloaded mileage.

6. The method according to claim 4, characterized in that The method further comprises: For any of the trajectory segments of the vehicle, perform the following operations; Obtaining position information of multiple coordinate positions of any track segment and the time corresponding to each coordinate position; Based on the distance and time difference between two adjacent coordinate positions, sequentially obtaining the average driving speed of the vehicle between two adjacent coordinate positions; The average value of the obtained average driving speeds is determined as the average speed corresponding to any one trajectory segment.

7. The method according to any one of claims 1 to 3, characterized in that: The determining, according to the energy consumption values ​​corresponding to the plurality of trajectory segments, the carbon emission of the current vehicle in the target area includes: The carbon emission of the current vehicle in the target area is determined according to the mileage of the multiple trajectory segments corresponding to the current vehicle, the energy consumption values ​​corresponding to each of the multiple trajectory segments, and the emission conversion coefficient of the current vehicle.

8. A vehicle carbon emission determination device, characterized in that: include: A first acquisition unit, configured to acquire a plurality of trajectory segments of the current vehicle in a target area, and determine energy consumption values ​​corresponding to each of the plurality of trajectory segments based on an energy consumption prediction model; A first determination unit, configured to determine the carbon emission of the current vehicle in the target area based on the energy consumption values ​​corresponding to each of the plurality of trajectory segments; The second acquisition unit is used to acquire the total carbon emission of vehicles in the target area according to the carbon emission of each vehicle in the target area.

9. An electronic device, comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.

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